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 |
yuanpeng / dify_docker_compose.yml
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| 1 | services: |
| 2 | api: |
| 3 | image: langgenius/dify-api:0.14.0 |
| 4 | restart: always |
| 5 | environment: |
| 6 | MODE: api |
| 7 | LOG_LEVEL: INFO |
| 8 | SECRET_KEY: O/PzhwXRXX+8rv8CK144R8EPdFkOFODB/1QwI8iR4FApqah50KJD9gcU |
| 9 | CONSOLE_WEB_URL: '' |
| 10 | INIT_PASSWORD: '' |
yuanpeng / HaoL_stog_notes_12122024.txt
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| 1 | 首先，stog是有两个不同的package，一个是在RMCProfile里边打包的，直接在RMCProfile的命令行里就可以执行的，命令是'stog_new'，具体的使用说明在这里，https://rmcprofile.ornl.gov/data-pre-processing-for-rmcprofile/ |
| 2 | |
| 3 | 你在文档里提到是stog的Python版本，功能上跟上面的版本是一样的，可能还会更丰富些，包括data rebinning这些都可以在pystog里边做。 |
| 4 | |
| 5 | 针对data rescaling的需求，两个版本需要的参数基本是一对一对应的，这些参数的说明在上面提到的链接里都有，有可能名字是不一样的，比如你提到的rho，也叫做number density，这个在连接里也有提到。另外你在文档里提到的pystog的参考文档的使用方式是可以，但是其实它有一种更简便的办法，可以参照这里的文档，https://powder.ornl.gov/total_scattering/data_reduction/ts_pp.html，在这里你就能看到，它的输入和RMCProfile版本的stog是基本一对一对应的。参数说明上面的两个链接里都有介绍，应该是比较清楚的，有问题的话可以找我哈。值得一提的是你提到的<b_coh>^2，这个叫做Faber-Ziman系数，是由composition决定的，可以在这里说如composition进行计算，https://addie.ornl.gov/helpsheet，在结果的显示页里，关于它的定义也是有介绍的。 |
| 6 | |
| 7 | 在上面提到的第二个链接里，有一个Google Colab Notebook的链接，点进去直接运行所有的cells一步一步可以跟着做，主要就是利用pystog的一些基础功能做一些rebin，傅里叶变换，数据格式转换这些。'stog_new'和里边提到的'pystog_cli'（它的输入文件是一个json文件）就是把这些基础攒在一起直接take in reciprocal space data，做rescaling，傅里叶变换等等这些，in a single shot。 |
| 8 | |
| 9 | 我之前分享给你的数据都是经历过scaling的步骤的，就是都用‘pystog_cli’处理过，如果需要rescale的话，就需要用到原始的reciprocal space的数据，它不在你文档里show的那张图里边，你的数据的话可以去这里找，/SNS/NOM/IPTS-33670/shared/autoreduce/multi_banks_summed/SofQ_merged，里边所有以merged.sq结尾的数据都是原始的，没有经过任何rescaling的reciprocal space的数据。 |