def create_instrument(test_data_file, calibrated, pixel_number):
"""
Create instruments: PyRS and Mantid
:param calibrated:
:param pixel_number:
:return:
"""
# instrument
instrument = calibration_file_io.import_instrument_setup(
xray_2k_instrument_file)
# 2theta
two_theta = 35.
arm_length_shift = 0.
center_shift_x = 0.
center_shift_y = 0.
rot_x_flip = 0.
rot_y_flip = 0.
rot_z_spin = 0.
if False:
center_shift_x = 1.0 * (random.random() - 0.5) * 2.0
center_shift_y = 1.0 * (random.random() - 0.5) * 2.0
arm_length_shift = (random.random() -
0.5) * 2.0 # 0.416 + (random.random() - 0.5) * 2.0
# calibration
rot_x_flip = 2.0 * (random.random() - 0.5) * 2.0
rot_y_flip = 2.0 * (random.random() - 0.5) * 2.0
rot_z_spin = 2.0 * (random.random() - 0.5) * 2.0
# END-IF: arbitrary calibration
test_calibration = AnglerCameraDetectorShift()
test_calibration.center_shift_x = center_shift_x
test_calibration.center_shift_y = center_shift_y
test_calibration.center_shift_z = arm_length_shift
test_calibration.rotation_x = rot_x_flip
test_calibration.rotation_y = rot_y_flip
test_calibration.rotation_z = rot_z_spin
# reduction engine
engine = reduction_manager.HB2BReductionManager()
test_data_id, two_the_tmp = engine.load_data(data_file_name=test_data_file,
target_dimension=pixel_number,
load_to_workspace=True)
# load instrument
pyrs_reducer = reduce_hb2b_pyrs.PyHB2BReduction(instrument)
pyrs_reducer.build_instrument(two_theta, arm_length_shift, center_shift_x,
center_shift_y, rot_x_flip, rot_y_flip,
rot_z_spin)
mantid_reducer = None
# mantid_reducer = reduce_hb2b_mtd.MantidHB2BReduction()
# data_ws_name = engine.get_raw_data(test_data_id, is_workspace=True)
# mantid_reducer.set_workspace(data_ws_name)
# mantid_reducer.load_instrument(two_theta, xray_idf_name, test_calibration)
return engine, pyrs_reducer, mantid_reducer
def peaks_alignment_score(x, engine, hb2b_setup, two_theta, roi_vec_set, plot=False):
""" Cost function for peaks alignment
:param x:
:param engine:
:param hb2b_setup:
:param two_theta:
:param roi_vec_set: list/array of ROI/mask vector
:param plot:
:return:
"""
peak_centers = '17.5,24.5,30.25,35.2,39.4,43.2,53.5'
fit_windows = '16,19,23,26,29,32,33,37,38,41,42,44.5,51.5,55'
# check
assert isinstance(roi_vec_set, list), 'must be list'
if len(roi_vec_set) < 2:
raise RuntimeError('User must specify more than 1 ROI/MASK vector')
else:
num_reduced_set = len(roi_vec_set)
# convert the input X array (to be refined) to geometry calibration values
geom_calibration = AnglerCameraDetectorShift()
geom_calibration.center_shift_x = x[0]
geom_calibration.center_shift_y = x[1]
geom_calibration.center_shift_z = x[2]
geom_calibration.rotation_x = x[3]
geom_calibration.rotation_y = x[4]
geom_calibration.rotation_z = x[5]
# reduce data
reduced_data_set = [None] * num_reduced_set
for i_roi in range(num_reduced_set):
ws_name_i = 'reduced_data_{:02}'.format(i_roi)
out_peak_pos_ws = 'peaks_positions_{:02}'.format(i_roi)
fitted_ws = 'fitted_peaks_{:02}'.format(i_roi)
# reduce
reduced_i = convert_to_2theta(engine, two_theta, hb2b_setup, roi_vec_set[i_roi], geom_calibration,
ws_name_i)
# fit peaks
FitPeaks(InputWorkspace=ws_name_i, OutputWorkspace=out_peak_pos_ws,
StartWorkspaceIndex=0, StopWorkspaceIndex=0,
PeakCenters=peak_centers,
FitWindowBoundaryList=fit_windows,
FittedPeaksWorkspace=fitted_ws,
OutputPeakParametersWorkspace='hb2b_rotate_p30deg_reduced_FITS', # FIXME - need to give a good name too
OutputParameterFitErrorsWorkspace='hb2b_rotate_p30deg_reduced_Errors')
reduced_data_set[i_roi] = reduced_i, ws_name_i, out_peak_pos_ws, fitted_ws
# END-FOR
# calculate the quality of peak alignment for each pair of ROI
residual = None
for roi_i, roi_j in list(itertools.combinations(range(num_reduced_set), 2)):
# get data
r_t_i = reduced_data_set[roi_i]
r_t_j = reduced_data_set[roi_j]
# calculate residual/cost
num_peaks = len(mtd[r_t_i[2]].readY(0))
residual_sq = np.zeros(shape=(num_peaks,), dtype='float')
for p_index in range(num_peaks):
pos_pos_i = mtd[r_t_i[2]].readY(0)[p_index]
neg_pos_i = mtd[r_t_j[2]].readY(0)[p_index]
if pos_pos_i < 0. and neg_pos_i < 0.:
# both failed to fit
residual_sq[p_index] = 20 ** 2
elif pos_pos_i * neg_pos_i < 0.:
# 1 failed to fit
residual_sq[p_index] = 10 ** 2
else:
residual_sq[p_index] = (pos_pos_i - neg_pos_i) ** 2
# END-FOR
residual_ij = np.sqrt(residual_sq)
if residual is None:
residual = residual_ij
else:
residual = np.concatenate([residual, residual_ij])
# END-IF-ELSE
c_n_2 = math.factorial(num_reduced_set) / (math.factorial(2) * math.factorial(num_reduced_set - 2))
norm_cost = residual.sum() / c_n_2
# plot
num_rows = 1 + num_reduced_set / 2 + num_reduced_set % 2
ax1 = plt.subplot(num_rows, 1, num_rows)
ax1.margins(0.05) # Default margin is 0.05, value 0 means fit
colors = ['black', 'red', 'blue', 'green', 'yellow']
for roi_i in range(num_reduced_set):
r_t_i = reduced_data_set[roi_i]
ax1.plot(r_t_i[0][0], r_t_i[0][1], color=colors[roi_i % 5])
ax1.set_title('Normalized Cost = {}'.format(norm_cost))
for roi_i in range(num_reduced_set):
index_i = roi_i + 1
print('subplot: {}, {}, {}'.format(num_rows, 2, index_i))
ax2 = plt.subplot(num_rows, 2, index_i)
ax2.plot(reduced_data_set[roi_i][0][0], reduced_data_set[roi_i][0][1], color='black')
ax2.plot(mtd[reduced_data_set[roi_i][3]].readX(0), mtd[reduced_data_set[roi_i][3]].readY(0), color='red')
ax2.set_title('{}'.format(roi_i))
if plot:
plt.show()
else:
plt.savefig('Round{:010}.png'.format(GlobalParameter.global_curr_sequence))
GlobalParameter.global_curr_sequence += 1
# print ('Parameters: {}'.format(x))
# print ('Fitted Peaks +: {}'.format(mtd[P30_Fit].readY(0)))
# print ('Fitted Peaks -: {}'.format(mtd[N30_Fit].readY(0)))
print('Residual = {}'.format(norm_cost))
return residual
def create_instrument_load_data(instrument, calibrated, pixel_number):
""" Create instruments: PyRS and Mantid and load data
:param instrument: name of instrument
:param calibrated:
:param pixel_number:
:return:
"""
# instrument
if instrument == 'XRay-XRayMock':
hydra_idf = xray_2k_instrument_file
mantid_idf = xray_idf_name
two_theta = xray_2theta
test_data_file = test_xray_data
elif instrument == 'HBZ':
hydra_idf = hbz_instrument_file
mantid_idf = hbz_idf
two_theta = hbz_2theta
test_data_file = test_hbz_data
print('Loaded {} and {} to compare @ 2theta = {} degree'
''.format(hydra_idf, mantid_idf, two_theta))
else:
raise RuntimeError('Instrument {} does not have test data and IDF'.format(instrument))
print('----------- IDF in Test: {} vs {} ---------------'.format(hydra_idf, mantid_idf))
instrument = calibration_file_io.import_instrument_setup(hydra_idf)
# instrument geometry calibration
if calibrated:
# Note: README/TODO-ING: ONLY shift Y
center_shift_x = int(1000. * (random.random() - 0.5) * 2.0) / 1000.
center_shift_y = int(1000. * (random.random() - 0.5) * 2.0) / 1000.
arm_length_shift = int(1000. * (random.random() - 0.5) * 2.0) / 1000. # 0.416 + (random.random() - 0.5) * 2.0
# calibration FIXME - Disable rotation calibration to find out the source of difference: 10-17 vs 10-7
rot_x_flip = int(1000. * 2.0 * (random.random() - 0.5) * 5.0) / 1000.
rot_y_flip = int(1000. * 2.0 * (random.random() - 0.5) * 5.0) / 1000.
rot_z_spin = int(1000. * 2.0 * (random.random() - 0.5) * 5.0) / 1000.
print('[(Random) Calibration Setup]\n Shift Linear (X, Y, Z) = {}, {}, {}\n Shift Rotation '
'(X, Y, Z) = {}, {}, {}'
''.format(center_shift_x, center_shift_y, arm_length_shift, rot_x_flip, rot_y_flip,
rot_z_spin))
else:
arm_length_shift = center_shift_x = center_shift_y = 0.
rot_x_flip = rot_y_flip = rot_z_spin = 0.
# END-IF: arbitrary calibration
test_calibration = AnglerCameraDetectorShift()
test_calibration.center_shift_x = center_shift_x
test_calibration.center_shift_y = center_shift_y
test_calibration.center_shift_z = arm_length_shift
test_calibration.rotation_x = rot_x_flip
test_calibration.rotation_y = rot_y_flip
test_calibration.rotation_z = rot_z_spin
# reduction engine
engine = reduction_manager.HB2BReductionManager()
test_data_id, two_theta_tmp = engine.load_data(data_file_name=test_data_file, target_dimension=pixel_number,
load_to_workspace=True)
# load instrument
pyrs_reducer = reduce_hb2b_pyrs.PyHB2BReduction(instrument)
pyrs_reducer.build_instrument(two_theta, arm_length_shift, center_shift_x, center_shift_y,
rot_x_flip, rot_y_flip, rot_z_spin)
mantid_reducer = reduce_hb2b_mtd.MantidHB2BReduction()
data_ws_name = engine.get_raw_data(test_data_id, is_workspace=True)
mantid_reducer.set_workspace(data_ws_name)
mantid_reducer.build_instrument(two_theta, mantid_idf, test_calibration)
return engine, pyrs_reducer, mantid_reducer
def MinDifference(x, engine, hb2b_setup, positive_roi_vec, negative_roi_vec):
""" Cost function to align the peaks!
:param x:
:return:
"""
def convert_to_2theta(two_theta, instrument_setup, roi_vec, geometry_shift,
ws_name):
# load instrument: as it changes
pyrs_reducer = reduce_hb2b_pyrs.PyHB2BReduction(
instrument_setup, 1.239)
pyrs_reducer.build_instrument(two_theta, geometry_shift.center_shift_z,
geometry_shift.center_shift_x,
geometry_shift.center_shift_y,
geometry_shift.rotation_x,
geometry_shift.rotation_y,
geometry_shift.rotation_z)
# reduce data
min_2theta = 8.
max_2theta = 64.
num_bins = 1800
# reduce PyRS (pure python)
curr_id = engine.current_data_id
vec_2theta, vec_hist = pyrs_reducer.reduce_to_2theta_histogram(
counts_array=engine.get_counts(curr_id),
mask=roi_vec,
x_range=(min_2theta, max_2theta),
num_bins=num_bins,
is_point_data=True,
use_mantid_histogram=False)
CreateWorkspace(DataX=vec_2theta,
DataY=vec_hist,
DataE=np.sqrt(vec_hist),
NSpec=1,
OutputWorkspace=ws_name)
return vec_2theta, vec_hist
if False:
WS_p30deg_Rot = test_rotate_2theta(idf_name,
WS_p30deg,
'hb2b_rotate_p30deg_Rot',
DetDistance=0.0,
DetTTH=35.0,
DetTTH_Shift=0.0,
Beam_Center_X=-0.002,
Beam_Center_Y=-0.007,
DetFlit=x[0],
DetSpin=0.0)
WS_n30deg_Rot = test_rotate_2theta(idf_name,
WS_n30deg,
'hb2b_rotate_n30deg_Rot',
DetDistance=0.0,
DetTTH=35.0,
DetTTH_Shift=0.0,
Beam_Center_X=-0.002,
Beam_Center_Y=-0.007,
DetFlit=x[0],
DetSpin=0.0)
convert_to_2theta(WS_p30deg_Rot, vanadium_P30)
convert_to_2theta(WS_n30deg_Rot, vanadium_N30)
else:
# TODO - TONIGHT 0 - From here!
geom_calibration = AnglerCameraDetectorShift()
geom_calibration.center_shift_x = x[0]
geom_calibration.center_shift_y = x[1]
geom_calibration.center_shift_z = x[2]
geom_calibration.rotation_x = x[3]
geom_calibration.rotation_y = x[4]
geom_calibration.rotation_z = x[5]
positive_roi = convert_to_2theta(two_theta, hb2b_setup,
positive_roi_vec, geom_calibration,
'positive_roi_ws')
negative_roi = convert_to_2theta(two_theta, hb2b_setup,
negative_roi_vec, geom_calibration,
'negative_roi_ws')
# plt.plot(positive_roi[0], positive_roi[1], color='red')
# plt.plot(negative_roi[0], negative_roi[1], color='green')
# plt.show()
N30_Fit = 'Fit_N30'
P30_Fit = 'Fit_P30'
FitPeaks(
InputWorkspace='positive_roi_ws',
OutputWorkspace=N30_Fit,
StartWorkspaceIndex=0,
StopWorkspaceIndex=0,
PeakCenters='17.5,24.5,30.25,35.2,39.4,43.2,53.5',
FitWindowBoundaryList='16,19,23,26,29,32,33,37,38,41,42,44.5,51.5,55',
FittedPeaksWorkspace='hb2b_rotate_n30deg_reduced_Output',
OutputPeakParametersWorkspace='hb2b_rotate_n30deg_reduced_FITS',
OutputParameterFitErrorsWorkspace='hb2b_rotate_n30deg_reduced_Errors')
FitPeaks(
InputWorkspace='negative_roi_ws',
OutputWorkspace=P30_Fit,
StartWorkspaceIndex=0,
StopWorkspaceIndex=0,
PeakCenters='17.5,24.5,30.25,35.2,39.4,43.2,53.5',
FitWindowBoundaryList='16,19,23,26,29,32,33,37,38,41,42,44.5,51.5,55',
FittedPeaksWorkspace='hb2b_rotate_p30deg_reduced_Output',
OutputPeakParametersWorkspace='hb2b_rotate_p30deg_reduced_FITS',
OutputParameterFitErrorsWorkspace='hb2b_rotate_p23deg_reduced_Errors')
Error3 = (mtd[N30_Fit].extractY()[0] - mtd[P30_Fit].extractY()[0])
print('Parameters: {}'.format(x))
print('Fitted Peaks +: {}'.format(mtd[P30_Fit].readY(0)))
print('Fitted Peaks -: {}'.format(mtd[N30_Fit].readY(0)))
print('Diff**2 = {}'.format(Error3 * Error3))
return (Error3 * Error3) * 1e8
def peaks_alignment_score(x,
engine,
hb2b_setup,
two_theta,
roi_vec_set,
plot=False,
ScalarReturn=False):
""" Cost function for peaks alignment
:param x:
:param engine:
:param hb2b_setup:
:param two_theta:
:param roi_vec_set: list/array of ROI/mask vector
:param plot:
:return:
"""
peak_centers = '17.5,24.5,30.25,35.2,39.4,43.2,53.5'
fit_windows = '16,19,23,26,29,32,33,37,38,41,42,44.5,51.5,55'
peak_pos = [17.5, 24.5, 30.25, 35.2, 39.4, 43.2, 53.5]
peak_pos = [25.5, 30.25, 35.2, 39.4, 43.2, 50.7, 54., 57., 59]
# check
#assert isinstance(roi_vec_set, list), 'must be list'
if len(roi_vec_set) < 2:
raise RuntimeError('User must specify more than 1 ROI/MASK vector')
else:
num_reduced_set = len(roi_vec_set)
num_peaks = len(peak_pos)
# convert the input X array (to be refined) to geometry calibration values
geom_calibration = AnglerCameraDetectorShift()
geom_calibration.center_shift_x = x[0]
geom_calibration.center_shift_y = x[1]
geom_calibration.center_shift_z = x[2]
geom_calibration.rotation_x = x[3]
geom_calibration.rotation_y = x[4]
geom_calibration.rotation_z = x[5]
# load instrument: as it changes
pyrs_reducer = reduce_hb2b_pyrs.PyHB2BReduction(hb2b_setup, 1.239)
pyrs_reducer.build_instrument(two_theta, geom_calibration.center_shift_z,
geom_calibration.center_shift_x,
geom_calibration.center_shift_y,
geom_calibration.rotation_x,
geom_calibration.rotation_y,
geom_calibration.rotation_z)
Eta_val = pyrs_reducer.get_eta_value()
# reduce data
reduced_data_set = [None] * num_reduced_set
if plot:
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
for i_roi in range(num_reduced_set):
# Define Mask
Mask = np.zeros_like(Eta_val)
if abs(roi_vec_set[i_roi]) == roi_vec_set[i_roi]:
index = np.where((Eta_val < (roi_vec_set[i_roi] + 5)) == (
Eta_val > (roi_vec_set[i_roi] - 5)))[0]
else:
index = np.where((Eta_val > (roi_vec_set[i_roi] - 5)) == (
Eta_val < (roi_vec_set[i_roi] + 5)))[0]
Mask[index] = 1.
#x_vec, y_vec = convert_to_2theta(engine, pyrs_reducer, Mask, 18, 63, 1800 )
vec_x, vec_y = convert_to_2theta(engine, pyrs_reducer, Mask, 18,
63, 1800)
ax.plot(vec_x.T, vec_y.T, label=roi_vec_set[i_roi])
ax.set_ylabel('Int (cts.)')
ax.set_ylabel('2theta (deg.)')
handles, labels = ax.get_legend_handles_labels()
fig.legend(handles, labels, loc='upper center')
plt.show()
return
for i_roi in range(num_reduced_set):
Peaks = [None] * num_peaks
# Define Mask
Mask = np.zeros_like(Eta_val)
if abs(roi_vec_set[i_roi]) == roi_vec_set[i_roi]:
index = np.where((Eta_val < (roi_vec_set[i_roi] + 5)) == (
Eta_val > (roi_vec_set[i_roi] - 5)))[0]
else:
index = np.where((Eta_val > (roi_vec_set[i_roi] - 5)) == (
Eta_val < (roi_vec_set[i_roi] + 5)))[0]
Mask[index] = 1.
for i_peak in range(num_peaks):
# reduce
Peaks[i_peak] = convert_to_2theta(engine, pyrs_reducer, Mask,
peak_pos[i_peak] - 1,
peak_pos[i_peak] + 1, 60)[1]
reduced_data_set[i_roi] = Peaks
# END-FOR
# calculate the quality of peak alignment for each pair of ROI
residual = None
for i_roi in range(num_reduced_set):
for peak_i, peak_j in list(itertools.combinations(range(num_peaks),
2)):
# get data
#residual_sq = 1. / np.min( np.corrcoef( reduced_data_set[i_roi][peak_i], reduced_data_set[i_roi][peak_j] ) )
temp_p1 = reduced_data_set[i_roi][peak_i]
temp_p2 = reduced_data_set[i_roi][peak_j]
residual_sq = (
1. / np.correlate(temp_p1 / np.linalg.norm(temp_p1),
temp_p2 / np.linalg.norm(temp_p2))) - 1.
# residual_sq = np.correlate( reduced_data_set[i_roi][peak_i], reduced_data_set[i_roi][peak_j] )
if not np.isfinite(residual_sq):
residual_sq = np.array([1000.])
if residual is None:
residual = residual_sq
else:
residual = np.concatenate([residual, residual_sq])
# END-IF-ELSE
c_n_2 = math.factorial(num_reduced_set) / (
math.factorial(2) * math.factorial(num_reduced_set - 2))
norm_cost = residual.sum() / c_n_2
print('Residual = {}'.format(norm_cost))
if ScalarReturn:
return np.sum(residual)
else:
return residual
def CostFunction(x,
engine,
hb2b_setup,
two_theta,
positive_roi_vec,
negative_roi_vec,
plot=False):
""" Cost function to align the peaks!
:param x:
:return:
"""
# Reduce
geom_calibration = AnglerCameraDetectorShift()
geom_calibration.center_shift_x = x[0]
geom_calibration.center_shift_y = x[1]
geom_calibration.center_shift_z = x[2]
geom_calibration.rotation_x = x[3]
geom_calibration.rotation_y = x[4]
geom_calibration.rotation_z = x[5]
positive_roi = convert_to_2theta(engine, two_theta, hb2b_setup,
positive_roi_vec, geom_calibration,
'positive_roi_ws')
negative_roi = convert_to_2theta(engine, two_theta, hb2b_setup,
negative_roi_vec, geom_calibration,
'negative_roi_ws')
# Fit peaks
N30_Fit = 'Fit_N30'
P30_Fit = 'Fit_P30'
p30_fitted = 'hb2b_rotate_p30deg_reduced_Output'
n30_fitted = 'hb2b_rotate_n30deg_reduced_Output'
FitPeaks(
InputWorkspace='positive_roi_ws',
OutputWorkspace=P30_Fit,
StartWorkspaceIndex=0,
StopWorkspaceIndex=0,
PeakCenters='17.5,24.5,30.25,35.2,39.4,43.2,53.5',
FitWindowBoundaryList='16,19,23,26,29,32,33,37,38,41,42,44.5,51.5,55',
FittedPeaksWorkspace=p30_fitted,
OutputPeakParametersWorkspace='hb2b_rotate_p30deg_reduced_FITS',
OutputParameterFitErrorsWorkspace='hb2b_rotate_p30deg_reduced_Errors')
FitPeaks(
InputWorkspace='negative_roi_ws',
OutputWorkspace=N30_Fit,
StartWorkspaceIndex=0,
StopWorkspaceIndex=0,
PeakCenters='17.5,24.5,30.25,35.2,39.4,43.2,53.5',
FitWindowBoundaryList='16,19,23,26,29,32,33,37,38,41,42,44.5,51.5,55',
FittedPeaksWorkspace=n30_fitted,
OutputPeakParametersWorkspace='hb2b_rotate_n30deg_reduced_FITS',
OutputParameterFitErrorsWorkspace='hb2b_rotate_n30deg_reduced_Errors')
assert len(mtd[P30_Fit].readY(0)) == len(
mtd[N30_Fit].readY(0)), 'Number of peaks fitted must be equal'
# calculate residual/cost
num_peaks = len(mtd[N30_Fit].readY(0))
residual_sq = np.zeros(shape=(num_peaks, ), dtype='float')
for p_index in range(len(mtd[P30_Fit].readY(0))):
pos_pos_i = mtd[P30_Fit].readY(0)[p_index]
neg_pos_i = mtd[N30_Fit].readY(0)[p_index]
if pos_pos_i < 0. and neg_pos_i < 0.:
# both failed to fit
residual_sq[p_index] = 20**2
elif pos_pos_i * neg_pos_i < 0.:
# 1 failed to fit
residual_sq[p_index] = 10**2
else:
residual_sq[p_index] = (pos_pos_i - neg_pos_i)**2
# END-FOR
residual = np.sqrt(residual_sq)
# plot
ax1 = plt.subplot(212)
ax1.margins(0.05) # Default margin is 0.05, value 0 means fit
ax1.plot(r[0], positive_roi[1], color='red')
ax1.plot(negative_roi[0], negative_roi[1], color='green')
ax1.set_title('Cost = {}'.format(residual))
ax2 = plt.subplot(221)
# ax2.margins(2, 2) # Values >0.0 zoom out
ax2.plot(positive_roi[0], positive_roi[1], color='black')
ax2.plot(mtd[p30_fitted].readX(0), mtd[p30_fitted].readY(0), color='red')
ax2.set_title('Positive')
ax3 = plt.subplot(222)
# ax3.margins(x=0, y=-0.25) # Values in (-0.5, 0.0) zooms in to center
ax3.plot(negative_roi[0], negative_roi[1], color='black')
ax3.plot(mtd[n30_fitted].readX(0), mtd[n30_fitted].readY(0), color='red')
ax3.set_title('Negative')
if plot:
plt.show()
else:
plt.savefig('Round{:010}.png'.format(
GlobalParameter.global_curr_sequence))
GlobalParameter.global_curr_sequence += 1
print('Parameters: {}'.format(x))
print('Fitted Peaks +: {}'.format(mtd[P30_Fit].readY(0)))
print('Fitted Peaks -: {}'.format(mtd[N30_Fit].readY(0)))
print('Residual = {}'.format(residual.sum()))
return residual
def do_reduce_data(self):
""" reduce data
:return:
"""
try:
cal_shift_x = gui_helper.parse_line_edit(self.ui.lineEdit_centerX,
float,
False,
'Center X',
default=0.)
cal_shift_y = gui_helper.parse_line_edit(self.ui.lineEdit_centerY,
float,
False,
'Center Y',
default=0.)
cal_shift_z = gui_helper.parse_line_edit(self.ui.lineEdit_centerZ,
float,
False,
'Center Z',
default=0.)
cal_rot_x = gui_helper.parse_line_edit(self.ui.lineEdit_rotationX,
float,
False,
'Rotation X',
default=0.)
cal_rot_y = gui_helper.parse_line_edit(self.ui.lineEdit_rotationY,
float,
False,
'Rotation Y',
default=0.)
cal_rot_z = gui_helper.parse_line_edit(self.ui.lineEdit_rotationZ,
float,
False,
'Rotation Z',
default=0.)
cal_wave_length = gui_helper.parse_line_edit(
self.ui.lineEdit_wavelength,
float,
False,
'Rotation Z',
default=1.)
except RuntimeError as run_err:
gui_helper.pop_message(self, 'Unable to parse calibration value',
str(run_err), 'error')
return
# get data file
try:
two_theta = gui_helper.parse_line_edit(self.ui.lineEdit_2theta,
float,
True,
'Two theta',
default=None)
except RuntimeError as run_err:
gui_helper.pop_message(self, '2-theta error', str(run_err),
'error')
return
# load instrument
# self._core.reduction_engine.set_instrument(instrument)
#
# self._core.reduction_engine.load_instrument(two_theta, cal_shift_x, cal_shift_y, cal_shift_z,
# cal_rot_x, cal_rot_y, cal_rot_z,
# cal_wave_length)
# reduce masks
geom_calibration = AnglerCameraDetectorShift()
geom_calibration.center_shift_x = cal_shift_x
geom_calibration.center_shift_y = cal_shift_y
geom_calibration.center_shift_z = cal_shift_z
geom_calibration.rotation_x = cal_rot_x
geom_calibration.rotation_y = cal_rot_y
geom_calibration.rotation_z = cal_rot_z
self._core.reduction_service.set_geometry_calibration(geom_calibration)
for mask_id in self._core.reduction_service.get_mask_ids():
# mask_vec = self._core.reduction_engine.get_mask_vector(mask_id)
self._core.reduction_service.reduce_to_2theta_histogram(
data_id=self._curr_data_id,
output_name=None,
use_mantid_engine=False,
mask=mask_id,
two_theta=two_theta)
"""
data_id, output_name, use_mantid_engine, mask, two_theta,
min_2theta=None, max_2theta=None, resolution_2theta=None
"""
vec_x, vec_y = self._core.reduction_service.get_reduced_data()
self.ui.graphicsView_calibration.plot_data(
vec_x, vec_y, self._mask_subplot_dict[mask_id])
if cal_wave_length:
# TODO - Need to implement how to calibrate wave length
raise NotImplementedError('Implement ASAP')
return