yuanpeng / texture_corr_steps.md
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Steps for Texture Correction
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First, we need to prepare the grouping file, which will divide detectors into small groups according to the polar and azimuthal angles. The
MantidTotalScattering(MTS) reduction will then take the grouping file for reducing data into those small groups.-
Go to
/SNS/NOM/shared/scripts/textureand run thetexture_group_gen.pyscript like this,mantidpython texture_group_gen.py
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yuanpeng / output_group.json
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| 1 | { |
| 2 | "bank_1": [0, 35], |
| 3 | "bank_2": [35, 55], |
| 4 | "bank_3": [55, 75], |
| 5 | "bank_4": [75, 105], |
| 6 | "bank_5": [105, 135], |
| 7 | "bank_6": [135, 180] |
| 8 | } |
yuanpeng / texture_proc_real_1step_not_aligned_2step_aligned.py
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| 1 | import json |
| 2 | import numpy as np |
| 3 | import os |
| 4 | import scipy |
| 5 | from scipy.optimize import minimize |
| 6 | from scipy.optimize import curve_fit |
| 7 | from scipy.signal import argrelextrema |
| 8 | from pystog import Pre_Proc |
| 9 | import matplotlib.pyplot as plt |
| 10 | import random |
yuanpeng / ttheta_group_params_new_new_new.json
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| 1 | { |
| 2 | "Group-1": { |
| 3 | "QRange": { |
| 4 | "12": { |
| 5 | "LeftBound": 0.5, |
| 6 | "RightBound": 7.8 |
| 7 | }, |
| 8 | "17": { |
| 9 | "LeftBound": 0.75, |
| 10 | "RightBound": 7.8 |
yuanpeng / NOMAD_Invalid_groups_exported_1.csv
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| 1 | 2Theta,Group ID |
| 2 | 2,1 |
| 3 | 2,2 |
| 4 | 2,3 |
| 5 | 2,4 |
| 6 | 2,5 |
| 7 | 2,6 |
| 8 | 2,7 |
| 9 | 2,8 |
| 10 | 2,9 |
yuanpeng / remove_invalid_banks.py
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| 1 | import csv |
| 2 | import numpy as np |
| 3 | import os |
| 4 | |
| 5 | data = [] |
| 6 | |
| 7 | csv_file = 'NOMAD_Invalid_groups_exported_1.csv' |
| 8 | |
| 9 | with open(csv_file, 'r') as file: |
| 10 | csv_reader = csv.reader(file) |
yuanpeng / wksp2data.py
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| 1 | # import mantid algorithms, numpy and matplotlib |
| 2 | from mantid.simpleapi import * |
| 3 | import matplotlib.pyplot as plt |
| 4 | import numpy as np |
| 5 | import os |
| 6 | from pathlib import Path |
| 7 | |
| 8 | nxs_file = "./SofQ/NOM_Si_640e.nxs" |
| 9 | out_dir = "./texture_proc" |
yuanpeng / silicon.json
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| 1 | { |
| 2 | "Facility": "SNS", |
| 3 | "Instrument": "NOM", |
| 4 | "Title": "NOM_Si_640e", |
| 5 | "Sample": { |
| 6 | "Runs": "200047, 200048", |
| 7 | "Background": { |
| 8 | "Runs": "200044", |
| 9 | "Background": { |
| 10 | "Runs": "200046" |
yuanpeng / powgen_mts.json
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| 1 | { |
| 2 | "Facility": "SNS", |
| 3 | "Instrument": "PG3", |
| 4 | "Title": "pg3_test", |
| 5 | "Sample": { |
| 6 | "Runs": "53601", |
| 7 | "Background": { |
| 8 | "Runs": "51877", |
| 9 | "Background": { |
| 10 | "Runs": "51909" |
yuanpeng / uniform_move_snippet
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| 1 | ! For the polar angle part, since projection is involved and therefore |
| 2 | ! we need to guarantee that the projection is uniform. We don't need |
| 3 | ! to do this for the phi angle since only rotation is involved in |
| 4 | ! that case. |
| 5 | polar_ang = dacos(2.0d0*(rmca_ran(1)-0.5d0)) |
| 6 | azim_ang = rmca_ran(1) * 2.0d0 * pi |
| 7 | rand_x = delta(move_type) * dsin(polar_ang) * dcos(azim_ang) |
| 8 | rand_y = delta(move_type) * dsin(polar_ang) * dsin(azim_ang) |
| 9 | rand_z = delta(move_type) * dcos(polar_ang) |
| 10 | ! We go from the Cartesian coordinates to fractional with respect |
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