new claib (d7bf0976) · Commits · sns-hfir-scse / diffraction / pg3_calibration

2025-2_11A_CAL/PG3_char_2025_11_17-MAG_He3.txt

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		version=1
		freq wl van van_back mt_env mt_instr PAC03 PAC06 PAC08 PAC10 N/A
		60 0.8 0 0 0 0 0 0 0 0 0
		60 1.5 0 0 0 0 0 0 0 0 0
		60 2.665 61794 61758 0 0 0 61795 0 0 0
		60 4.797 0 0 0 0 0 0 0 0 0

2025-2_11A_CAL/PG3_char_2025_11_6-MAG_newVRod.txt

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Original line number	Diff line number	Diff line
		version=1
		freq wl van van_back mt_env mt_instr PAC03 PAC06 PAC08 PAC10 N/A
		60 0.8 61626 61619 0 0 0 61623 0 0 0
		60 1.5 0 0 0 0 0 0 0 0 0
		60 2.665 61794 61758 0 0 0 61622 0 0 0
		60 4.797 55483 55436 0 0 0 61624 0 0 0

2025-2_11A_CAL/PG3_char_2025_2-HighRes_PAC.txt

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		version=1
		freq wl van van_back mt_env mt_instr PAC03 PAC06 PAC08 PAC10 PAC10withInsert N/A VanadiumSampleCan
		60 0.800 60637 60634 0 0 0 60641 60644 60640 60666 0 0
		60 1.500 60638 60635 0 0 0 60642 60645 0 60667 0 0
		60 1.500 60638 60635 0 0 0 60642 60645 61868 60667 0 0
		60 2.665 60639 60636 0 0 0 60643 60665 0 60668 0 0
		60 4.797 0 0 0 0 0 0 0 0 0 0 0

Scripts/ContourPlots/thyang_share/gen_ascii.py

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		"""
		This script saves sliced diffraction data from POWGEN
		and all sample temperature values sorted by slices for errobar of sample temperatures.
		Author: Tsung-Han Yang @ ORNL
		Date: March 2024
		"""
		# import mantid algorithms, numpy and matplotlib
		from mantid.simpleapi import *
		import matplotlib.pyplot as plt
		import numpy as np
		import csv
		from mantid.api import AnalysisDataService as ADS

		# Use this script in Mantidworkbench after you sliced your data
		#################################################################################
		########## parameters for your scans
		#################################################################################
		scan_num = 45610 # scan number
		savepath = '/SNS/users/tt9/data/SNS/PG3/IPTS-24773/shared/example/' # save path
		slice_num = 28 # number of slices
		#################################################################################
		#################################################################################

		def read_log_values(ws,scan_num,ii,savepath,LogName='SampleTemp',save=True) :
		sample_temp_log = ws.getRun().getLogData(LogName)
		# Extract the time and value data
		time_data = sample_temp_log.times # This gives you the time data
		temp_data = sample_temp_log.value # This gives you the temperature data
		# If you want to save the data to a CSV file:
		if save==True :
		with open(savepath + 'PG3_{}_{:03d}_{}.csv'.format(scan_num,ii,LogName), 'w', newline='') as file:
		writer = csv.writer(file)
		writer.writerow(["Time (s)", LogName])
		writer.writerows(zip(time_data, temp_data))
		#file.close()

		for ii in np.arange(slice_num) :
		ConvertUnits('PG3_{}_{}'.format(scan_num,ii),'dSpacing',OutputWorkspace='PG3_{}_{}'.format(scan_num,ii))
		#Minus('PG3_{}_0_split_{}'.format(scan_num,ii), 'bkg_scaled',OutputWorkspace='PG3_{}_0_split_{}'.format(scan_num,ii)) # background
		SaveAscii('PG3_{}_{}'.format(scan_num,ii), savepath+'PG3_{}_{:03d}.ascii'.format(scan_num,ii))
		temp_ws = ADS.retrieve('PG3_{}_{}'.format(scan_num,ii))
		read_log_values(temp_ws,scan_num,ii,savepath)

Scripts/ContourPlots/thyang_share/simple_cmap.py

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		"""
		Example: read and plot sliced data from POWGEN
		Author: Tsung-Han Yang @ ORNL
		Date: March 2024
		"""
		#/usr/bin/env python3
		import matplotlib as plt
		import numpy as np
		import sys, glob
		import csv
		from scipy.optimize import curve_fit
		#import matplotlib.font_manager

		from matplotlib import rc
		plt.rcParams['font.family'] = 'Dejavu Sans'
		plt.rcParams['mathtext.fontset'] = 'dejavusans'
		plt.rcParams['lines.linewidth']= 1
		plt.rcParams['axes.facecolor'] = 'w'

		# The path of your saved .ascii and .csv files
		fpath = '/Users/tt9/Research/Mn3Ga/data/PG3_45610/10K_step/'
		temp_unit = 'K' # 'C' if sample env. uses C as temperature unit

		fnames = glob.glob(fpath + '*.ascii')
		fnames.sort()
		fnames.reverse()

		SampleTemps = glob.glob(fpath + '*.csv')
		SampleTemps.sort()
		SampleTemps.reverse()

		#fit_data = False

		def read_temp(fname) :
		try:
		# Open the CSV file
		with open(fname, mode='r') as file:
		csv_reader = csv.reader(file)

		# Create lists to store the columns
		column1 = []
		column2 = []

		# Read each row
		for row in csv_reader:
		if len(row) != 2:
		raise ValueError("Each row in the CSV file must have exactly two columns.")
		# Append the values to respective lists
		column1.append(row[0])
		column2.append(row[1])

		# Display the content
		print("Column 1:", column1)
		print("Column 2:", column2)

		# Return the columns as lists for further use
		return np.mean(np.float64(column2[1:])), np.std(np.float64(column2[1:]))
		except Exception as e:
		print(f"An error occurred: {e}")
		'''
		def gauss(x,a,b,c,bk) :
		#c = 2.211
		#bk = 13
		#bk = 12.7532
		#b = 3*2.83960843e-03
		result = anp.exp(-(x-c)2/b*2) + bk
		return result

		def gauss3(x,a1,b1,c1,a2,b2,c2,a3,b3,c3,bk) :
		#c = 2.211
		#bk = 13
		#bk = 12.7532
		#b = 3*2.83960843e-03
		g1 = a1np.exp(-(x-c1)2/b1*2)
		g2 = a2np.exp(-(x-c2)2/b2*2)
		g3 = a3np.exp(-(x-c3)2/b3*2)
		result = g1 + g2 + g3 + bk
		return result

		def bkgrnd(x,bk) :
		result = bk
		return result
		'''

		def d2q(xdata) :
		xdata = np.array(xdata)
		xdata = 2*np.pi / xdata
		return xdata

		T_list = []
		T_list_err = []
		axis = []
		axis_Q = []
		data = []
		errs = []

		for jj in np.arange(len(fnames)) :
		f = open(fnames[jj],'r')
		lines = f.readlines()
		x = []
		y = []
		E = []
		for ii in np.arange(2,len(lines)) :
		ln = lines[ii].split(',')
		t1 = np.float64(ln[0])
		t2 = np.float64(ln[1])
		e1 = np.float64(ln[2])
		x.append(t1)
		y.append(t2)
		E.append(e1)
		T, T_err = read_temp(SampleTemps[jj])
		T_list.append(T)
		T_list_err.append(T_err)
		axis.append(x)
		axis_Q.append(d2q(x))
		data.append(y)
		errs.append(E)

		axis = np.array(axis)
		axis_Q = np.array(axis_Q)
		data = np.array(data)
		errs = np.array(errs)
		T_list = np.array(T_list)
		T_list_err = np.array(T_list_err)

		if temp_unit=='C' :
		T_list = T_list + 273


		# colormap for neutron diffraction
		figz = plt.figure(figsize=(3.375,3.375*9/16))
		zx = figz.add_subplot(111)

		pcolorplot = zx.pcolormesh(axis_Q,T_list,data,vmin=0,vmax=8,cmap='viridis')
		#pcolorplot = zx.pcolormesh(axis_Q,T_list,data,vmin=0,vmax=280,cmap='viridis')
		zx.set_xlim([d2q(1.8),d2q(1.2)])
		zx.xaxis.set_major_locator(MultipleLocator(0.5))
		zx.xaxis.set_minor_locator(MultipleLocator(0.25))
		zx.yaxis.set_major_locator(MultipleLocator(50))
		zx.yaxis.set_minor_locator(MultipleLocator(10))
		#fig.subplots_adjust(left = 0.15,right = 0.99, bottom = 0.22, top = 0.99,wspace =0.02,hspace = 0.30) # APS
		figz.subplots_adjust(left = 0.15,right = 0.99, bottom = 0.22, top = 0.98,wspace =0.02,hspace = 0.30) # ACS
		#figz.subplots_adjust(left = 0.32,right = 0.97, bottom = 0.18, top = 0.98,wspace =0.02,hspace = 0.30) # SciAdv

		### plot params
		zx.tick_params(axis='both',which='major',labelsize=8)
		zx.spines['left'].set_linewidth(0.5)
		zx.spines['right'].set_linewidth(0.5)
		zx.spines['bottom'].set_linewidth(0.5)
		zx.spines['top'].set_linewidth(0.5)

		zx.tick_params(which='both', labelsize=9,
		labelbottom=True, labeltop=False, labelleft=True, labelright=False,
		bottom=True, top=True, left=True, right=True, direction='in')
		zx.set_xlabel(r'Q ($\mathrm{\AA^{-1}}$)',fontsize=10)
		zx.set_ylabel(r'T (K)',fontsize=10)


		show()