yuanpeng / rmc_lammps_build_notes.md

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Build LAMMPS with RMCProfile

We first need to compile LAMMPS separately to obtain the library file liblammps.so.0, together with a soft link file liblammps.so pointing to the library file. Once we have the compiled liblammps.so.0 library file, creating the soft link is as simple as running,
```
ln -s liblammps.so.0 liblammps.so
```
- The LAMMPS source codes are hosted on GitHub and the building instructions can be found here

yuanpeng / Filter_by_time_stamp_ZYP.py

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1	#
2	# Filter_by_time_stamp_ZYP.py
3	#
4	# ---------------------------------------------------------------------------
5	# Script for filtering events based on time stamp
6	# ---------------------------------------------------------------------------
7	#
8	# ----------------------------
9	# Yuanpeng Zhang @ Nov-10-2024
10	# ----------------------------

yuanpeng / Filter_combined_ZYP.py

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1	#
2	# Filter_combined_ZYP.py
3	#
4	# ---------------------------------------------------------------------------
5	# This is a combined script for event filter by either sample log or time.
6	# The two ways of conducting event filter is not compatible with each other
7	# and users can choose which one to use by setting the `filter_by_time`
8	# variable in the 'Input section' below to either `True` (to enable filter by
9	# time) or `False` (to enable filter by sample log).
10	# ---------------------------------------------------------------------------

yuanpeng / rmc2cif_win_linux

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1	data2config -noannotate -order [Ga P O] -supercell [1 1 1] -cif gapo4_xray.rmc6f

yuanpeng / rmc2cif

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1	data2config -noannotate -order "[Ga P O]" -supercell "[1 1 1]" -cif gapo4_xray.rmc6f

yuanpeng / slurm_example.sh

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1	#!/bin/bash
2	#
3	#SBATCH --job-name=MY_JOB_NAME
4	#SBATCH --output=MY_OUTPUT_NAME.out
5	#
6	#SBATCH --ntasks=1
7	#SBATCH --cpus-per-task=4
8	#SBATCH --time=1000000:00
9	#SBATCH --mem-per-cpu=256

yuanpeng / snippet.py

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1	if "-" in str(energy_theory[0]):
2	energy_theory = energy_theory[1:]
3	Percentage_theory = Percentage_theory[1:]

yuanpeng / rebin_example.py

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1	from mantid.simpleapi import *
2	import matplotlib.pyplot as plt
3	import numpy as np
4
5
6	def rebin_data(datax, datay, xmin, xmax, xbin):
7	"""Rebin input data with specified binning params
8
9	Parameters:
10	datax (numpy.ndarray): Input data X array

yuanpeng / submit.slurm

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Run Jobs on NERSC

First, download the RMCProfile package on Linux platform here and untar it somewhere in our home directory. Once logged onto NERSC from terminal, we can do this,
```
cd SOMEWHERE
wget https://yr.iris-home.net/rmclinux -O RMCProfile_V6.7.9_Linux_64.tgz
tar xzvf RMCProfile_V6.7.9_Linux_64.tgz
```

yuanpeng / si_mts.json

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1	{
2	"Facility": "SNS",
3	"Instrument": "NOM",
4	"Title": "NOM_Si_640d_sgb_12_by_12",
5	"Sample": {
6	"Runs": "197077",
7	"Background": {
8	"Runs": "197075",
9	"Background": {
10	"Runs": "197073"