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added Lakeshore calibration curves

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steppke_a
2025-07-04 16:54:18 +02:00
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devices/LakeShore372/2023_12_08_FP_CMN_calibration_stabilised_at_Tc.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fixed point device calibration of the CMN thermometer\n",
"08.12.2023 Jakub - copied from the DilSc calibration run in 2021, code mainly written by Lise Hanson\n",
"\n",
"This script is for calibrating the CMN (cerium magnesium nitrate) thermometer by using the fix-point device called SRD (superconductive reference device). The temperature in the Dilution fridge was stabilised around at each transition temperature of superconductors, T_c, in the fix-point device for around one hour. The voltages of the CMN at T=T_c is then read out as an average over the stable time windows. All the individual time windows has to be specified manually (in the \"TransitionTimes\" array). Two sets of data is made: \n",
"\n",
"- H, When the fridge is heating up and \n",
"- C, when the fridge is cooling down. \n",
"\n",
"This is done in order to ensure that the timesteps were sufficiently long to equilibriate the temperature between the bottom plate and the thermometers. \n",
"\n",
"The CMN can be used for calibration of thermometers between 8 mK and 2 K.\n",
"\n",
"\n",
"#TO DO:\n",
"add more poits around 15.12.2023 - there should be two more fixed points\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import datetime as dt\n",
"from scipy.optimize import curve_fit\n",
"from lmfit import Model\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib widget\n",
"\n",
"# Function for reading the SEA files\n",
"def readseai(path):\n",
" with open(path) as fil:\n",
" line = fil.readline()\n",
" if bool(line.startswith(\"# SEA\")) :\n",
" content = fil.read()\n",
" headerlist = []\n",
" for linenr, line in enumerate(content.split('\\n')):\n",
" try:\n",
" name,value = line.split(': ')\n",
" value = value.replace(\"\\t\", \"\")\n",
" headerlist.append(value.split()[0])\n",
" except:\n",
" #print(linenr, '/'+line+'/')\n",
" table = pd.read_csv(path, header=linenr, names=headerlist, delim_whitespace = True,)\n",
" \n",
" #Get the time of the data points:\n",
" COL1=content.split('\\n')[0].split('.')\n",
" Date = int(COL1[1])\n",
" Year = int(COL1[3].split(' ')[0])+2000 #+2000 since the information is just 21 in year 2021 eg.\n",
" MonthDict = {'Jan':1,'Feb':2,'Mar':3,'Apr':4,'May':5,'Jun':6,'Jul':7,'Aug':8,'Sep':9,'Oct':10,'Nov':11,'Dec':12} #Continue!\n",
" Month = MonthDict.get(COL1[2])\n",
" Hour = 0 #This should be default in the SEA data file\n",
" Minutes = 0 #This should be default in the SEA data file\n",
" Start_date=dt.datetime(Year,Month,Date,Hour,Minutes)\n",
" \n",
" rec_date=Start_date + pd.to_timedelta(table.iloc[:,0],unit='sec')\n",
" table.insert(1,'REC_Date',rec_date)\n",
" \n",
" return table \n",
" elif bool(line.startswith(\"# col\")) :\n",
" content = line + fil.read() \n",
" headerlist = []\n",
" for linenr, line in enumerate(content.split('\\n')):\n",
" try:\n",
" name,value = line.split(': ')\n",
" value = value.replace(\"\\t\", \"\")\n",
" headerlist.append(value.split()[0])\n",
" \n",
" except:\n",
" #print(linenr, '/'+line+'/')\n",
" table = pd.read_csv(path, header=linenr-1, names=headerlist, delim_whitespace = True,)\n",
" return table \n",
" else:\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['Time/1', 'REC_Date', 't_mix', 't_pt2head', 't_pt2plate', 't_still',\n",
" 'htr_still', 't_coldpl', 't_mixcx', 't_pt1head', 't_pt1plate',\n",
" 't_pucksensor', 't_pucksensor.target', 'htr_pucksensor', 't_magnet',\n",
" 'action', 'p_dump', 'p_cond', 'p_still', 'p_fore', 'p_back', 'p_ovc',\n",
" 'v1', 'v2', 'v4', 'v5', 'v9', 'mfx', 'mfx.target', 'mfy', 'mfy.target',\n",
" 'mfz', 'mfz.target', 'mf', 'mf.target', 'res1.rx1', 'res1.set.power',\n",
" 'res1.set.reg', 'res2.rx8', 'res2.set.power', 'res2.set.reg', 'cmn.u1',\n",
" 'cmn.u2', 'cmn.temp', 'softloop.reg', 'softloop.target', 'res1.rx1.raw',\n",
" 'res1.rx2.raw', 'res1.rx3.raw', 'res1.rx4.raw', 'res1.rx5.raw',\n",
" 'res1.rx6.raw', 'res2.rx7.raw', 'res2.rx8.raw', 'res2.rx9.raw',\n",
" 'res2.rx10.raw', 'res2.rx11.raw', 'res2.rx12.raw', 'res2.rx13.raw'],\n",
" dtype='object')\n"
]
}
],
"source": [
"#Read the data\n",
"d=readseai('2023_12_08.dat')\n",
"print(d.columns)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['Time/1', 'REC_Date', 't_mix', 'htr_still', 'htr_pucksensor', 'action',\n",
" 'p_dump', 'p_cond', 'p_still', 'p_fore', 'p_back', 'p_ovc', 'v1', 'v2',\n",
" 'v4', 'v5', 'v9', 'mfx', 'mfx.target', 'mfy', 'mfy.target', 'mfz',\n",
" 'mfz.target', 'mf', 'mf.target', 'res1.set.power', 'res2.set.power',\n",
" 'cmn.u1', 'cmn.u2', 'res1.rx1.raw', 'res1.rx2.raw', 'res1.rx3.raw',\n",
" 'res1.rx4.raw', 'res1.rx5.raw', 'res1.rx6.raw', 'res2.rx7.raw',\n",
" 'res2.rx8.raw', 'res2.rx9.raw', 'res2.rx10.raw', 'res2.rx11.raw',\n",
" 'res2.rx12.raw', 'res2.rx13.raw'],\n",
" dtype='object')\n"
]
}
],
"source": [
"#Read the data\n",
"d2=readseai('2023_12_16-CMN_cooldown_v2.dat')\n",
"print(d2.columns)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['Time/1', 'REC_Date', 't_mix', 't_pt2head', 't_pt2plate', 't_still',\n",
" 'htr_still', 't_coldpl', 't_mixcx', 't_pt1head', 't_pt1plate',\n",
" 't_pucksensor', 't_pucksensor.target', 'htr_pucksensor', 't_magnet',\n",
" 'action', 'p_dump', 'p_cond', 'p_still', 'p_fore', 'p_back', 'p_ovc',\n",
" 'v1', 'v2', 'v4', 'v5', 'v9', 'mfx', 'mfx.target', 'mfy', 'mfy.target',\n",
" 'mfz', 'mfz.target', 'mf', 'mf.target', 'res1.rx1', 'res1.set.power',\n",
" 'res1.set.reg', 'res2.rx8', 'res2.set.power', 'res2.set.reg', 'cmn.u1',\n",
" 'cmn.u2', 'cmn.temp', 'softloop.reg', 'softloop.target', 'res1.rx1.raw',\n",
" 'res1.rx2.raw', 'res1.rx3.raw', 'res1.rx4.raw', 'res1.rx5.raw',\n",
" 'res1.rx6.raw', 'res2.rx7.raw', 'res2.rx8.raw', 'res2.rx9.raw',\n",
" 'res2.rx10.raw', 'res2.rx11.raw', 'res2.rx12.raw', 'res2.rx13.raw'],\n",
" dtype='object')\n"
]
}
],
"source": [
"#Read the data\n",
"d3=readseai('2024_01_10-CMN_cooldown_v3.dat')\n",
"print(d3.columns)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/l_samenv/py3_kernel/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
"of pandas will change to not sort by default.\n",
"\n",
"To accept the future behavior, pass 'sort=False'.\n",
"\n",
"To retain the current behavior and silence the warning, pass 'sort=True'.\n",
"\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
}
],
"source": [
"d = pd.concat([d,d2,d3],ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "71e1bc07dcde491e9209b323777247ec",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot raw data between the indecies \"Start\" and \"End\"\n",
"%matplotlib widget\n",
"Start = 0 #Crop the data set to be plotted\n",
"End = len(d['REC_Date'])-0\n",
"\n",
"fig, axs = plt.subplots(3, 1, sharex=True)\n",
"axs[0].plot(d['REC_Date'][Start:End],d['cmn.u1'][Start:End]*1000,color='blue',label='CMN voltage')\n",
"axs[0].set_ylabel('mV')\n",
"axs[1].plot(d['REC_Date'][Start:End],d['cmn.u2'][Start:End]*1000,color='red',label='Fix point')\n",
"axs[1].set_ylabel('mV')\n",
"axs[2].plot(d['REC_Date'][Start:End],d['softloop.target'][Start:End],color='k',label='target')\n",
"axs[2].plot(d['REC_Date'][Start:End],d['cmn.temp'][Start:End],color='coral',label='CMN temp',linewidth=1)\n",
"axs[2].set_ylabel('Temp')\n",
"axs[2].set_xlabel('Time')\n",
"fig.autofmt_xdate()\n",
"for i in range(len(axs)):\n",
" axs[i].minorticks_on()\n",
" axs[i].grid(b=True, which='major', color='silver', linestyle='-')\n",
" axs[i].grid(b=True, which='minor', color='gainsboro', linestyle='--')\n",
" axs[i].legend()\n",
"fig.subplots_adjust(hspace=0)\n",
"plt.tight_layout(pad=0)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5c941d1e807c4b26b4307ed51ce2df3c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No handles with labels found to put in legend.\n"
]
}
],
"source": [
"# Plot raw data between the indecies \"Start\" and \"End\"\n",
"\n",
"Start = 0 #Crop the data set to be plotted\n",
"End = len(d2['REC_Date'])-0\n",
"\n",
"fig, axs = plt.subplots(3, 1, sharex=True)\n",
"axs[0].plot(d2['REC_Date'][Start:End],d2['cmn.u1'][Start:End]*1000,color='blue',label='CMN voltage')\n",
"axs[0].set_ylabel('mV')\n",
"axs[1].plot(d2['REC_Date'][Start:End],d2['cmn.u2'][Start:End]*1000,color='red',label='Fix point')\n",
"axs[1].set_ylabel('mV')\n",
"#axs[2].plot(d2['REC_Date'][Start:End],d2['softloop.target'][Start:End],color='k',label='target')\n",
"#axs[2].plot(d2['REC_Date'][Start:End],d2['cmn.temp'][Start:End],color='coral',label='CMN temp',linewidth=1)\n",
"axs[2].set_ylabel('Temp')\n",
"axs[2].set_xlabel('Time')\n",
"fig.autofmt_xdate()\n",
"for i in range(len(axs)):\n",
" axs[i].minorticks_on()\n",
" axs[i].grid(b=True, which='major', color='silver', linestyle='-')\n",
" axs[i].grid(b=True, which='minor', color='gainsboro', linestyle='--')\n",
" axs[i].legend()\n",
"fig.subplots_adjust(hspace=0)\n",
"plt.tight_layout(pad=0)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'd4' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-16-d925ba3f579c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mStart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;31m#Crop the data set to be plotted\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mEnd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md4\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'REC_Date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msharex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'd4' is not defined"
]
}
],
"source": [
"# Plot raw data between the indecies \"Start\" and \"End\"\n",
"\n",
"Start = 0 #Crop the data set to be plotted\n",
"End = len(d4['REC_Date'])-0\n",
"\n",
"fig, axs = plt.subplots(3, 1, sharex=True)\n",
"axs[0].plot(d4['REC_Date'][Start:End],d4['cmn.u1'][Start:End]*1000,color='blue',label='CMN voltage')\n",
"axs[0].set_ylabel('mV')\n",
"axs[1].plot(d4['REC_Date'][Start:End],d4['cmn.u2'][Start:End]*1000,color='red',label='Fix point')\n",
"axs[1].set_ylabel('mV')\n",
"#axs[2].plot(d2['REC_Date'][Start:End],d2['softloop.target'][Start:End],color='k',label='target')\n",
"#axs[2].plot(d2['REC_Date'][Start:End],d2['cmn.temp'][Start:End],color='coral',label='CMN temp',linewidth=1)\n",
"axs[2].set_ylabel('Temp')\n",
"axs[2].set_xlabel('Time')\n",
"fig.autofmt_xdate()\n",
"for i in range(len(axs)):\n",
" axs[i].minorticks_on()\n",
" axs[i].grid(b=True, which='major', color='silver', linestyle='-')\n",
" axs[i].grid(b=True, which='minor', color='gainsboro', linestyle='--')\n",
" axs[i].legend()\n",
"fig.subplots_adjust(hspace=0)\n",
"plt.tight_layout(pad=0)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fe37590b86af4b048261cc0ecbda7472",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Time arrays for when a stabilization around a superconducting transition starts and ends \n",
"#Heating up = H\n",
"TransitionTimeRampedHeating08Dec_Start = np.array([\n",
" dt.datetime(2023,12,8,12,23,0),\n",
" dt.datetime(2023,12,8,13,30,0),\n",
" dt.datetime(2023,12,8,14,12,0),\n",
" dt.datetime(2023,12,8,14,53,0),\n",
" dt.datetime(2023,12,8,16,26,0),\n",
" dt.datetime(2023,12,8,17,11,0),\n",
" dt.datetime(2023,12,8,18,0,0),\n",
" dt.datetime(2023,12,15,0,40,0),\n",
" dt.datetime(2023,12,15,1,55,0),\n",
" dt.datetime(2023,12,14,23,25,0),\n",
"])\n",
"TransitionTimeRampedHeating08Dec_End = np.array([\n",
" dt.datetime(2023,12,8,12,53,0),\n",
" dt.datetime(2023,12,8,13,58,0),\n",
" dt.datetime(2023,12,8,14,34,0),\n",
" dt.datetime(2023,12,8,15,20,0),\n",
" dt.datetime(2023,12,8,16,55,0),\n",
" dt.datetime(2023,12,8,17,37,0),\n",
" dt.datetime(2023,12,8,18,35,0),\n",
" dt.datetime(2023,12,15,1,15,0),\n",
" dt.datetime(2023,12,15,2,30,0),\n",
" dt.datetime(2023,12,15,0,3,0),\n",
"])\n",
"TransitionTimeH = np.array([TransitionTimeRampedHeating08Dec_Start,TransitionTimeRampedHeating08Dec_End])\n",
"\n",
"\n",
"#Cooling down = C\n",
"TransitionTimeRampedCooling07Dec_Start = np.array([\n",
" dt.datetime(2023,12,7,22,15,0),\n",
" dt.datetime(2023,12,7,23,5,0),\n",
" dt.datetime(2023,12,7,23,59,0),\n",
" dt.datetime(2023,12,8,8,52,0),\n",
" dt.datetime(2023,12,8,9,38,0),\n",
" dt.datetime(2023,12,8,10,38,0),\n",
" dt.datetime(2023,12,8,11,31,0),\n",
" dt.datetime(2023,12,15,6,50,0),\n",
" dt.datetime(2023,12,15,5,32,0),\n",
" dt.datetime(2023,12,15,4,15,0),\n",
"])\n",
"TransitionTimeRampedCooling07Dec_End = np.array([\n",
" dt.datetime(2023,12,7,22,46,0),\n",
" dt.datetime(2023,12,7,23,32,0),\n",
" dt.datetime(2023,12,8,0,21,0),\n",
" dt.datetime(2023,12,8,9,26,0),\n",
" dt.datetime(2023,12,8,10,9,0),\n",
" dt.datetime(2023,12,8,11,6,0),\n",
" dt.datetime(2023,12,8,12,3,0),\n",
" dt.datetime(2023,12,15,7,40,0),\n",
" dt.datetime(2023,12,15,6,16,0),\n",
" dt.datetime(2023,12,15,4,55,0),\n",
"])\n",
"TransitionTimeC = np.array([TransitionTimeRampedCooling07Dec_Start,TransitionTimeRampedCooling07Dec_End])\n",
"\n",
"#Tc values of all the 10 Superconductors --------------------------------------------------------------------\n",
"Tc_W = 0.01429\n",
"Tc_Be = 0.02059\n",
"Tc_Ir80Rh20 = 0.03530\n",
"Tc_Ir92Rh08 = 0.06536\n",
"Tc_Ir = 0.09308\n",
"Tc_AuAl2 = 0.15539\n",
"Tc_AuIn2 = 0.20773\n",
"Tc_Cd = 0.51558\n",
"Tc_Zn = 0.84880\n",
"Tc_Al = 1.182\n",
"\n",
"#Temperature arrays with the superconducting transitions that were observed in the data. Both for Heating up and cooling down.\n",
"TcValuesH = np.array([ Tc_Ir92Rh08,Tc_Ir,Tc_AuAl2,Tc_AuIn2,Tc_Cd,Tc_Zn,Tc_Al,Tc_Ir80Rh20,Tc_Ir92Rh08,Tc_Be])\n",
"TcValuesC = np.array([ Tc_Al,Tc_Zn,Tc_Cd,Tc_AuIn2,Tc_AuAl2,Tc_Ir,Tc_Ir92Rh08,Tc_Be,Tc_Ir80Rh20,Tc_Ir92Rh08])\n",
"\n",
"\n",
"#Caluclating average voltages at each stabilization around a transition temperatuere-------------------------- \n",
"def VcValuesFunc(TransitionTime):\n",
" VcValues=np.zeros(TransitionTime.shape[1])\n",
" for i in range(TransitionTime.shape[1]):\n",
" StepData=d[(d['REC_Date'] >= TransitionTime[0,i]) & (d['REC_Date'] <= TransitionTime[1,i])]['cmn.u1'].to_numpy()\n",
" VcValues[i] = np.mean(StepData)\n",
" return VcValues\n",
"\n",
"VcValuesH = VcValuesFunc(TransitionTimeH)\n",
"VcValuesC = VcValuesFunc(TransitionTimeC)\n",
"\n",
"# Manually adding the last fixed point from \n",
"\n",
"#Plot of the mean voltages at each stabilized transition as a function of the superconducting transition temperatures.\n",
"plt.figure()\n",
"plt.plot(TcValuesH*10**(3),VcValuesH*10**(3),'o',color='red',markersize=4)\n",
"plt.plot(TcValuesC*10**(3),VcValuesC*10**(3),'o',color='blue',markersize=3)\n",
"plt.xlabel('T [mK]')\n",
"plt.ylabel('V [mV]')\n",
"plt.xscale('log')\n",
"plt.minorticks_on()\n",
"plt.grid(b=True, which='major', color='silver', linestyle='-')\n",
"plt.grid(b=True, which='minor', color='gainsboro', linestyle='--')\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6d5270bde11043a0bbed601e1d7eb5e2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plot to visually inspect wether the correct time windows were selected when calculating the average voltages.\n",
"#Black lines = start of averaging time\n",
"#Grey lines = end of averaging time\n",
"#Dots = readings of the voltage at the end and beginning of the time window.\n",
"Start_index = 20\n",
"End_index = len(d['REC_Date'])\n",
"col = np.array(['k','grey'])\n",
"\n",
"plt.figure()\n",
"plt.plot(d['REC_Date'][Start_index:End_index],d['cmn.u1'][Start_index:End_index]*1000,color='blue',label='CMN')\n",
"plt.plot(d['REC_Date'][Start_index:End_index],d['cmn.u2'][Start_index:End_index]*1000,color='red',label='Fix point')\n",
"for i in range(2):\n",
" plt.plot(TransitionTimeH[i],VcValuesH*1000,'o',color=col[i],markersize = 3)\n",
" plt.plot(TransitionTimeC[i],VcValuesC*1000,'o',color=col[i],markersize = 3)\n",
" plt.vlines(TransitionTimeH[i],min(d['cmn.u1'])*1000,max(d['cmn.u1'])*1000,color=col[i],linewidth=1)\n",
" plt.vlines(TransitionTimeC[i],min(d['cmn.u1'])*1000,max(d['cmn.u1'])*1000,color=col[i],linewidth=1)\n",
"plt.xlabel('Time')\n",
"plt.ylabel('Voltage [mV]')\n",
"plt.xticks(rotation=45)\n",
"plt.minorticks_on()\n",
"plt.grid(b=True, which='major', color='silver', linestyle='-')\n",
"plt.grid(b=True, which='minor', color='gainsboro', linestyle='--')\n",
"plt.legend()\n",
"plt.tight_layout()\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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},
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"output_type": "display_data"
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{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f395ac60a20>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def Function(V,intercept,B,A):\n",
" \"\"\"Hyperbolic function used to fit the Temperature as a function of the voltage on the CMN. \n",
" This fit is used as the calibration of the CMN. This function is adviced in the manual from hdleiden.\"\"\"\n",
" return intercept + B/(V-A)\n",
"\n",
"#Fitting the hyperbolic function\n",
"mod= Model(Function)\n",
"pars = mod.make_params(intercept=0.00085,B=16,A=0.06)\n",
" \n",
"#Heating\n",
"resultH = mod.fit(TcValuesH, pars, V=VcValuesH)\n",
"compsH = resultH.eval_components()\n",
"#Cooling\n",
"resultC = mod.fit(TcValuesC, pars, V=VcValuesC)\n",
"compsC = resultC.eval_components()\n",
"\n",
"\n",
"V = np.linspace(np.min(VcValuesC),np.max(VcValuesC),1000)#Voltage array for plotting the fit smoothly\n",
"\n",
"plt.figure()\n",
"plt.plot(1/mod.eval(resultH.params,V=V)*1000,V*1000,color='red', label='H fit',linewidth=2)\n",
"plt.plot(1/mod.eval(resultC.params,V=V)*1000,V*1000,color='blue', label='C fit',linewidth=1)\n",
"plt.plot(1/TcValuesH*1000,VcValuesH*1000,'o',color='lightcoral',markersize=6,label = 'H data')\n",
"plt.plot(1/TcValuesC*1000,VcValuesC*1000,'o',mfc='none', color='skyblue',markersize=9,label = 'C data')\n",
"plt.xlabel('1/T [mK]')\n",
"plt.ylabel('V [mV]')\n",
"plt.minorticks_on()\n",
"plt.grid(b=True, which='major', color='silver', linestyle='-')\n",
"plt.grid(b=True, which='minor', color='gainsboro', linestyle='--')\n",
"plt.legend()\n",
"#plt.xscale('log')\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5a414f6478a749c4bd83415abf9528cb",
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plot of the relative deviation in voltages between the cooling and the heating data.\n",
"plt.figure(figsize=(7,3))\n",
"plt.plot(TcValuesC,(VcValuesH[::-1]-VcValuesC)/VcValuesC*100,'-o',label=r'$(V_{c,H}-V_{c,C})/V_{c,C}$')\n",
"plt.xlabel('Tc')\n",
"plt.ylabel('%')\n",
"plt.minorticks_on()\n",
"plt.grid(b=True, which='major', color='silver', linestyle='-')\n",
"plt.grid(b=True, which='minor', color='gainsboro', linestyle='--')\n",
"plt.legend()\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4235d0702bd4466db6dcbb4d2042e072",
"version_major": 2,
"version_minor": 0
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
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],
"source": [
"#Linear plots of 1/(T-intercept). OBS! This plot is only used to see that the fit to \n",
"#\"Function()\"=intercept+B/(V-A) went well and no systematical deviations appears. \n",
"\n",
"def line(x,a,b):\n",
" return a+b*x\n",
"modLine = Model(line)\n",
"parsLine = modLine.make_params(a=1.,b=1.)\n",
"\n",
"InterceptH = resultH.params['intercept'].value\n",
"resultLineH = modLine.fit(VcValuesH, parsLine, x=1/(TcValuesH-InterceptH))\n",
"compsLineH = resultLineH.eval_components()\n",
"\n",
"InterceptC = resultC.params['intercept'].value\n",
"resultLineC = modLine.fit(VcValuesC, parsLine, x=1/(TcValuesC-InterceptC))\n",
"compsLineC = resultLineC.eval_components()\n",
"\n",
"aH = np.round(resultLineH.params['a'].value,4)\n",
"bH = np.round(resultLineH.params['b'].value,4)\n",
"\n",
"aC = np.round(resultLineC.params['a'].value,4)\n",
"bC = np.round(resultLineC.params['b'].value,4)\n",
"\n",
"#plot\n",
"plt.figure()\n",
"plt.plot(1/(TcValuesH-InterceptH),VcValuesH*1000,'o',color='lightcoral',label = 'H data')\n",
"plt.plot(1/(TcValuesC-InterceptC),VcValuesC*1000,'o',color='skyblue', label = 'C data')\n",
"plt.plot(1/(TcValuesH-InterceptH),compsLineH['line']*1000,'r--',label=f'H fit: {aH}+{bH}/(T-{np.round(InterceptH,4)})')\n",
"plt.plot(1/(TcValuesC-InterceptC),compsLineC['line']*1000,'b',label=f'C fit: {aC}+{bC}/(T-{np.round(InterceptC,4)})')\n",
"# plt.plot(1/(TcValues-Intercept),A+B*1/(TcValues-Intercept),color=red )\n",
"plt.xlabel('1/(T-intersept) [1/K]')\n",
"plt.ylabel('V [mV]')\n",
"plt.minorticks_on()\n",
"plt.grid(b=True, which='major', color='silver', linestyle='-')\n",
"plt.grid(b=True, which='minor', color='gainsboro', linestyle='--')\n",
"plt.legend()\n",
"plt.tight_layout()\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"def T_calib(Volt,fit_result):\n",
" \"\"\"Function that takes a voltage from the CMN and returns the calibrated temperature at this voltage.\n",
" The calibration is based on the hyperbolic fit wich has shown to be better than the linear 1/T fit \"\"\"\n",
" A=fit_result.params['A'].value\n",
" B=fit_result.params['B'].value\n",
" inter=fit_result.params['intercept'].value\n",
" return inter + B/(Volt-A)\n",
"\n",
"def T_calib_Line(Volt,fit_result,intercept):\n",
" \"\"\"This function is not used in the following code. It takes a voltage from the CMN and returns\n",
" the calibrated temperature at this voltage. The calibration is based on the linear fit wich has\n",
" shown to be worse that the hyperbolic fit \"\"\"\n",
" a=fit_result.params['a'].value\n",
" b=fit_result.params['b'].value\n",
" inter=intercept\n",
" return inter + b/(Volt-a)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
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"application/vnd.jupyter.widget-view+json": {
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plot of the calibrated CMN temperature as a function of time. The CMN temperature is calculated from \n",
"#the CMN voltage that was recorded when stabilizing and ramping the temperatures around the transitions.\n",
"\n",
"Start = 500\n",
"End = len(d['REC_Date'])-10\n",
"\n",
"plt.figure('Calibrated CMN')\n",
"plt.plot(d['REC_Date'][Start:End],T_calib(d['cmn.u1'][Start:End],resultH)*1000,color='red',label='CMN Temp New H',linewidth=2)\n",
"plt.plot(d['REC_Date'][Start:End],T_calib(d['cmn.u1'][Start:End],resultC)*1000,color='blue',label='CMN Temp New C',linewidth=1)\n",
"#plt.plot(d['REC_Date'][Start:End],d['tcoldpl'][Start:End]*1000,color='red',label='tcoldpl')\n",
"#plt.plot(d['REC_Date'][Start:End],d['tmix'][Start:End]*1000,color='darkred',label='tmix')\n",
"for i,Tc in enumerate(TcValuesH):\n",
" EndTime = d['REC_Date'][End]\n",
" StartTime = d['REC_Date'][Start]\n",
" if i==0:\n",
" plt.hlines(Tc*1000, StartTime, EndTime, label='Tc values',color='green',linewidth=1)\n",
" else:\n",
" plt.hlines(Tc*1000, StartTime, EndTime,color='green',linewidth=1)\n",
"\n",
"plt.plot(d['REC_Date'][Start:End],d['cmn.temp'][Start:End]*1000,color='coral',label='CMN temp old calibration',linewidth=1)\n",
"plt.ylabel('T [mK]')\n",
"plt.xlabel('Time')\n",
"plt.xticks(rotation=30)\n",
"plt.minorticks_on()\n",
"plt.grid(b=True, which='major', color='silver', linestyle='-')\n",
"plt.grid(b=True, which='minor', color='gainsboro', linestyle='--')\n",
"plt.legend()\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"#Save the calibration data in a txt file. The txt also includes the chi value of the fit \n",
"#and errors on the variables\n",
"with open('CMN_Calibration_result.txt', 'w+') as fh:\n",
" fh.write(resultH.fit_report())\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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Comment: Dec 2024, FP and CMN & 300mK calibrated Cernox, 10mK--300K
Sensor Model: RX-1000-BF0.007
Serial Number: QT2-PS1_RuO
Data Format: 4 (Log Ohms/Kelvin)
SetPoint Limit: 300 (Kelvin)
Temperature coefficient: 1 (Negative)
Number of Breakpoints: 198
No. Units Temperature (K)
1 3.010868022700612 298.49688513816693
2 3.0119668131003245 283.5825934342938
3 3.0130806575402858 269.4134890610202
4 3.0142102427354818 255.95233899590562
5 3.0153558905707984 243.16377055135902
6 3.0165181282323474 231.01417842366283
7 3.017697535637202 219.47163638626043
8 3.018894679065738 208.50581339525684
9 3.020110005008432 198.08789388668083
10 3.0213445310686287 188.19050205606183
11 3.0225978963528872 178.7876299213554
12 3.023871754285988 169.85456898017722
13 3.0251660638129017 161.3678452817595
14 3.026482094954098 153.30515774301503
15 3.0278204044409827 145.64531954661564
16 3.029182035480534 138.36820246709738
17 3.0305670104499303 131.4546839786898
18 3.0319765717854192 124.88659700588568
19 3.033412273255269 118.64668218470553
20 3.0348742406101574 112.71854250921002
21 3.036363273700224 107.08660024408532
22 3.0378807078174224 101.73605599007405
23 3.0394278151934553 96.65284979468888
24 3.0410054155162367 91.82362420601538
25 3.042615400558714 87.23568917251785
26 3.0442566605414667 82.87698869661477
27 3.045932675976197 78.73606915439669
28 3.047644219218378 74.80204919823733
29 3.049391761761297 71.06459116321105
30 3.0511772061071047 67.51387390217839
31 3.0530016437796146 64.14056697815646
32 3.054867662891665 60.93580614615907
33 3.056772841672294 57.89117006007804
34 3.0587234275394914 54.998658143396405
35 3.0607168291336087 52.250669565584225
36 3.062755965937101 49.63998326893135
37 3.0648396423659165 47.159738993331885
38 3.0669721804753527 44.80341924915941
39 3.0691532221944633 42.564832190860415
40 3.0713820436315244 40.43809534626304
41 3.073659762609317 38.4176201588452
42 3.0759888010665555 36.49809730234244
43 3.0783639002356002 34.674482729106586
44 3.0807877967510198 32.941984415553286
45 3.0832578540283104 31.296049769868215
46 3.0857735015168917 29.732353668883153
47 3.088331758768205 28.246787092685306
48 3.090929664942162 26.8354463270941
49 3.093563418231694 25.4946227056327
50 3.0962291184117308 24.22079286403828
51 3.0989211986021665 23.01060948170203
52 3.101634577751591 21.86089248571008
53 3.104363256965815 20.76862069437142
54 3.1071011473862353 19.730923878273778
55 3.109841550588085 18.745075218006274
56 3.1125779171512864 17.808484138729273
57 3.11530408883608 16.918689502762266
58 3.118014154849895 16.073353142301936
59 3.1207076088481642 15.270253715275466
60 3.1233860535650377 14.507280868184118
61 3.126050909661218 13.78242969059827
62 3.128703417260741 13.093795446731537
63 3.131345060602488 12.43956857025005
64 3.133976920760799 11.81802990916433
65 3.136600559950213 11.227546208308349
66 3.1392168136191114 10.666565817535048
67 3.1418269212247285 10.13361461435034
68 3.144432041699499 9.627292130271185
69 3.147033280394613 9.146267870728904
70 3.1496317301498324 8.689277818847232
71 3.152228425693628 8.255121113907778
72 3.154824498606876 7.842656895774877
73 3.1574210068030233 7.450801306987635
74 3.160018941030277 7.078524644641312
75 3.162619584545642 6.724848654574061
76 3.1652239144290943 6.38884396074857
77 3.167832920568131 6.0696276230738055
78 3.1704477385101058 5.766360817249336
79 3.1730699940597993 5.478246630535362
80 3.1756998497589723 5.204527967656371
81 3.1783410922696174 4.944485561335578
82 3.1809882049278153 4.697436082232267
83 3.1836527130829855 4.46273034331549
84 3.1863303014095328 4.239751593955603
85 3.189019177076423 4.02791389925087
86 3.1917298721119356 3.8266606003304986
87 3.194453515407478 3.635462851588069
88 3.197221890737846 3.4538182310015606
89 3.2000501929236584 3.2812494198882787
90 3.2028784951094704 3.117302948625356
91 3.205706797295283 2.961548005039863
92 3.2085350994810953 2.8135753023373127
93 3.2113634016669073 2.6729960035937204
94 3.2141917038527197 2.539440699985007
95 3.2171545880720274 2.412558440068855
96 3.220164893794879 2.2920158075681116
97 3.223175199517731 2.1774960452324486
98 3.226185505240583 2.068698222475962
99 3.2292902369657788 1.965336444603446
100 3.2324734554224186 1.8671391015474186
101 3.235656673879058 1.7738481541417364
102 3.238839892335698 1.685218456056277
103 3.2421201702646463 1.6010171096109382
104 3.245486149050878 1.521022853776154
105 3.2488521278371096 1.445025482751749
106 3.2522181066233413 1.3728252935962966
107 3.255739930089029 1.3042325614554824
108 3.259298002192172 1.2390670410104954
109 3.262856074295315 1.1771574928363793
110 3.266469650380578 1.1183412334257172
111 3.2702386389072626 1.0624637076952241
112 3.2740076274339476 1.0093780828519028
113 3.277776615960632 0.9589448625515277
114 3.2816949375856646 0.911031520335566
115 3.285687640738601 0.865512151383297
116 3.289680343891537 0.8222671416640099
117 3.2937760821824233 0.7811828536198984
118 3.297995556301073 0.7421513275536951
119 3.3022150304197226 0.7050699979363734
120 3.306538759255905 0.6698414238894298
121 3.3110002189760657 0.6363730331335257
122 3.315461678696226 0.6045768787306465
123 3.3200715240208525 0.5743694079805515
124 3.3249098228800262 0.5456712428642305
125 3.3297481217392004 0.5184069714574288
126 3.3345864205983746 0.49250494976611864
127 3.339424719457549 0.46789711346319374
128 3.344263018316723 0.4445187990316713
129 3.3494866347695753 0.42230857384441417
130 3.354725420928567 0.4012080747338569
131 3.360011547071489 0.3811618546275393
132 3.3655304381832907 0.3621172368464428
133 3.3710493292950923 0.3440241766832577
134 3.3767205954988166 0.32683512989684405
135 3.3825152830231686 0.31050492777731986
136 3.388309970547521 0.29499065845347977
137 3.3943501312522857 0.2802515541306451
138 3.4004093799285355 0.26624888396263513
139 3.4065687869992 0.25294585227635386
140 3.4128647339774987 0.24030750188154823
141 3.419162645867923 0.22830062221166025
142 3.4256263209433717 0.21689366205439
143 3.432152915963469 0.20605664664264445
144 3.4387693046477503 0.1957610988880081
145 3.445477436629284 0.18597996454975532
146 3.4522663147660433 0.17668754114276716
147 3.45916074606306 0.1678594103975386
148 3.4661355658599398 0.1594723740947974
149 3.4732088546250304 0.15150439310612474
150 3.480372073804251 0.14393452948038865
151 3.4876241371465957 0.13674289142380883
152 3.4949791927178513 0.1299105810290733
153 3.5024169984830844 0.1234196446161511
154 3.509956955153247 0.11725302555430878
155 3.51759106442651 0.1113945194413584
156 3.525320266549544 0.10582873152235844
157 3.5331419940370172 0.1005410362358743
158 3.5410551800692263 0.09551753878149592
159 3.5490555496413037 0.0907450386076204
160 3.55714947275824 0.08621099472355512
161 3.5653303870194684 0.08190349274478918
162 3.573589795831886 0.07781121358483618
163 3.581932729576683 0.07392340371137829
164 3.5903547179890243 0.07022984688855147
165 3.598851141869654 0.06672083733111725
166 3.6074170851727825 0.06338715419997731
167 3.6160504098906623 0.060220037372010336
168 3.6247431133916734 0.057211164420560436
169 3.6334891167359786 0.05435262874608763
170 3.642284433173201 0.05163691879951241
171 3.6511219832749586 0.0490568983436586
172 3.659997028168835 0.04660578770092646
173 3.668901599183208 0.04427714593791899
174 3.6778291438508455 0.0420648539402072
175 3.686771387338376 0.03996309833275872
176 3.6957212960348156 0.037966356203776606
177 3.704672066152627 0.0360693805918061
178 3.7136159377275586 0.03426718669797303
179 3.7225453818930587 0.03255503878712273
180 3.731453618955497 0.030928437743439154
181 3.7403330859660993 0.02938310924784296
182 3.7491774197163528 0.027914992546101684
183 3.7579795234258473 0.026520229778137512
184 3.766733497737004 0.02519515584049227
185 3.7754333971333143 0.023936288755310797
186 3.784072816538262 0.022740320520534776
187 3.792648807765258 0.02160410841726332
188 3.801155431980935 0.020524666751438186
189 3.809587953705807 0.019499159008152907
190 3.8179429903536506 0.018524890397968993
191 3.826217228777992 0.01759930077565286
192 3.8344079862337828 0.016719957912725552

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Comment: 08.04.2025, T_chip calibrated from T_plato 34mK <--> 1.75K
Sensor Model: RX-1000-BF0.007
Serial Number: T_chip_2025-04-08
Data Format: 4 (Log Ohms/Kelvin)
SetPoint Limit: 300 (Kelvin)
Temperature coefficient: 1 (Negative)
Number of Breakpoints: 198
No. Units Temperature (K)
1 3.2624961002030286 1.5375657300791132
2 3.266111381762495 1.50720490075646
3 3.269800877666054 1.4774435774836137
4 3.273751881524704 1.4482699223921183
5 3.2777028853833543 1.4196723313645028
6 3.281653889242005 1.3916394294186236
7 3.285604893100655 1.3641600661831537
8 3.2895558969593055 1.3372233114624068
9 3.2935069008179556 1.3108184508887424
10 3.2976292416906823 1.2849349816608149
11 3.301899824098095 1.2595626083659806
12 3.3061704065055078 1.2346912388851923
13 3.3104409889129203 1.210310980378754
14 3.3147115713203332 1.1864121353513464
15 3.3189821537277457 1.1629851977947494
16 3.3233284263932408 1.1400208494067279
17 3.327706200238759 1.1175099558845862
18 3.3320839740842767 1.0954435632919055
19 3.3364617479297944 1.0738128944970213
20 3.340839521775312 1.0526093456818355
21 3.3452379089962987 1.0318244829195578
22 3.3496786627051236 1.0114500388200236
23 3.354119416413949 0.9914779092412598
24 3.358560170122774 0.9719001500659781
25 3.363000923831599 0.9527089740417202
26 3.367486942511239 0.9338967476834017
27 3.3720798276443644 0.9154559882370146
28 3.37667271277749 0.8973793607032864
29 3.3812655979106157 0.8796596749201082
30 3.3858584830437413 0.8622898827025771
31 3.390492226252673 0.8452630750395079
32 3.3951458844386484 0.8285724793453025
33 3.399799542624624 0.8122114567660879
34 3.4044532008106 0.796173499539043
35 3.4091401717316807 0.7804522284038694
36 3.413897806812087 0.7650413900653772
37 3.4186554418924935 0.7499348547061733
38 3.4234130769729 0.7351266135484623
39 3.428195868564213 0.7206107764639968
40 3.433052830556706 0.7063815696312179
41 3.4379097925491986 0.6924333332386586
42 3.442766754541691 0.6787605192336966
43 3.447641143110771 0.6653576891157609
44 3.45253777535622 0.6522195117731121
45 3.4574344076016694 0.6393407613623382
46 3.4623310398471183 0.6267163152297238
47 3.4673323639813547 0.6143411518736618
48 3.4723580885419705 0.602210348947299
49 3.4773838131025863 0.5903190813006247
50 3.4824152262578405 0.5786626190612175
51 3.4874546638162705 0.5672363257528894
52 3.492494101374701 0.5560356564514822
53 3.4975495595953956 0.5450561559770769
54 3.5026916861211634 0.5342934571218999
55 3.5078338126469313 0.5237432789132231
56 3.5129944862873863 0.5134014249105634
57 3.5183724252813486 0.5032637815365042
58 3.523750364275311 0.49332631644048064
59 3.529129396228362 0.4835850768948695
60 3.534511895260631 0.47403618822275284
61 3.539894394292901 0.46467585225672425
62 3.545293865746656 0.45550034582813126
63 3.5507103462924428 0.446506019286146
64 3.5561268268382293 0.43768929504607934
65 3.5616490958858695 0.4290466661663611
66 3.567187751174305 0.4205746949536182
67 3.5727697402376557 0.4122700115952963
68 3.5784247901275146 0.4041293128192841
69 3.584080297605383 0.3961493605800025
70 3.5898212787514217 0.38832698077043876
71 3.59556225989746 0.3806590619596142
72 3.601364288311148 0.3731425541549802
73 3.6071873779026937 0.36577446758925314
74 3.61305662128231 0.35855187153120327
75 3.618959520070723 0.3514718931199278
76 3.6248999978796927 0.3445317162221409
77 3.6308776592356593 0.3377285803120271
78 3.6368961720385986 0.3310597793732159
79 3.642954061439128 0.32452266082243464
80 3.6490609372826137 0.3181146244544178
81 3.6552012021626474 0.3118331214076481
82 3.6614001677199934 0.3056756531505213
83 3.667618184862338 0.2996397704875303
84 3.6739116335939714 0.2937230725850718
85 3.6802286840109546 0.2879232060164914
86 3.6866037693056546 0.2822378638259835
87 3.6930293416034483 0.2766647846109763
88 3.6994875518899897 0.27120175162263677
89 3.7060145854727833 0.2658465918841351
90 3.7125721146250465 0.2605971753263219
91 3.7191598855989 0.2554514139404714
92 3.7258003636509622 0.25040726094775473
93 3.732477480842017 0.2454627099851127
94 3.7391928992563055 0.24061579430720498
95 3.745961854965175 0.2358645860041167
96 3.752790064130333 0.23120719523451305
97 3.759666412138439 0.22664176947393544
98 3.7665927058785424 0.22216649277794118
99 3.7735695144909185 0.2177795850597934
100 3.780603167041398 0.2134793013824129
101 3.787681023354388 0.20926393126431247
102 3.794806200818474 0.2051317979992348
103 3.801983884089396 0.20108125798922596
104 3.809219124234974 0.1971107000908776
105 3.816516181438231 0.19321854497447813
106 3.823878028029037 0.18940324449581838
107 3.8313057709485503 0.18566328108040167
108 3.8387980060235805 0.18199716711981287
109 3.846350129972724 0.17840344438000721
110 3.8539536527565423 0.17488068342128305
111 3.861595571359397 0.17142748302970728
112 3.869258035102862 0.16804246965976877
113 3.87692544658918 0.16472429688803633
114 3.884596470911434 0.16147164487760518
115 3.8922774465016245 0.15828321985311866
116 3.8999763261411626 0.15515775358615563
117 3.907697947700646 0.15209400289078
118 3.915447855925582 0.1490907491290507
119 3.923231219838567 0.14614679772629616
120 3.931060701258755 0.14326097769596016
121 3.93893389097798 0.14043214117383
122 3.9468543902920805 0.13765916296146172
123 3.954834270546749 0.13494094007862129
124 3.9628641237053217 0.1322763913245627
125 3.970951090223476 0.12966445684796962
126 3.9790858753940896 0.12710409772538875
127 3.987265845409717 0.12459429554798726
128 3.99547536489277 0.12213405201647068
129 4.0037009685438 0.11972238854399893
130 4.011925546071123 0.11735834586694355
131 4.020149307626737 0.11504098366333103
132 4.028376742606027 0.11276938017881989
133 4.036610977026227 0.11054263186006384
134 4.044854379938178 0.108359852995314
135 4.0531083598680535 0.10622017536211824
136 4.061373106869784 0.10412274788197683
137 4.069646631287045 0.10206673628181731
138 4.077924671489499 0.1000513227621539
139 4.086200667981979 0.0980757056717995
140 4.09446512035844 0.09613909918900083
141 4.102705267951826 0.0942407330088696
142 4.110905224308763 0.0923798520369863
143 4.119056239800178 0.09055571608905347
144 4.127160935567507 0.08876759959647984
145 4.13522494353849 0.08701479131777831
146 4.143252744266391 0.0852965940556623
147 4.1512483326618685 0.08361232437972822
148 4.15921527811664 0.08196131235461455
149 4.167155705226355 0.08034290127352789
150 4.175072871437949 0.07875644739703123
151 4.182968954104629 0.0772013196969902
152 4.190844040635403 0.07567689960557511
153 4.19869890961708 0.07418258076921924
154 4.206532422364478 0.07271776880743575
155 4.214342030453433 0.07128188107639669
156 4.222124376205662 0.0698743464371804
157 4.229874014522166 0.0684946050285955
158 4.237583351769237 0.06714210804449017
159 4.245244878257252 0.06581631751545887
160 4.252845345022618 0.06451670609485988
161 4.2603712954852 0.0632427568490575
162 4.267797901814624 0.0619939630518066
163 4.275091104427148 0.06076982798269711
164 4.282219904664355 0.059569864729578315
165 4.289158399893226 0.05839359599488477
166 4.295873354037459 0.05724055390578628
167 4.3023460726254585 0.05611027982808663
168 4.30855979726476 0.05500232418379737
169 4.314502261088947 0.053916246272313485
170 4.320164637956229 0.052851614095120215
171 4.325554164312285 0.051808004183961386
172 4.330671912642743 0.05078500143240056
173 4.3355229454100055 0.04978219893070815
174 4.340115931161327 0.048799197804009084
175 4.344462674881419 0.04783560705362622
176 4.348577600935354 0.04689104340155655
177 4.352477224320379 0.04596513113801867
178 4.356179640865825 0.0450575019720103
179 4.359673651496368 0.04416779488481672
180 4.3630015534050575 0.04329565598641209
181 4.366149839912545 0.04244073837469598
182 4.3691543573238825 0.04160270199750952
183 4.371999835962292 0.0407812135173763
184 4.374710960961869 0.03997594617891394
185 4.377298923390189 0.039186579678863784
186 4.379769488075658 0.03841280003868713
187 4.382126517109294 0.03765429947967701
188 4.384372948196973 0.03691077630053609
189 4.386522812856844 0.0361819347573719
190 4.388581785956868 0.03546748494606175
191 4.390555498683307 0.034767142686940415
192 4.392449534557004 0.03408062941176469

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Comment: Dec 2024, FP and CMN & 300mK calibrated Cernox, 10mK--300K
Sensor Model: RX-1000-BF0.007
Serial Number: U07012
Data Format: 4 (Log Ohms/Kelvin)
SetPoint Limit: 300 (Kelvin)
Temperature coefficient: 1 (Negative)
Number of Breakpoints: 192
No. Units Temperature (K)
1 3.011246820953167 405.27872576048435
2 3.0118991903884478 384.3863158245219
3 3.0125636692549396 364.5709246541343
4 3.0132417218702128 345.7770311569748
5 3.0139327518192696 327.95197639295867
6 3.014638450142462 311.04581602825186
7 3.01535916147551 295.0111803953696
8 3.016095688228611 279.80314176727046
9 3.0168482958011844 265.37908847357056
10 3.017618642746138 251.69860550615573
11 3.018407292515184 238.72336127966142
12 3.019215053527205 226.41700022953086
13 3.0200433392037715 214.74504094671897
14 3.0208932337460213 203.67477956362976
15 3.0217659348142827 193.1751981205738
16 3.022662750674006 183.21687765600305
17 3.023586237491318 173.77191577700597
18 3.024535775004802 164.8138484791031
19 3.0255158162041584 156.31757599628833
20 3.0265252534614824 148.25929247355415
21 3.027569665152745 140.6164192648483
22 3.0286488366215987 133.36754166956965
23 3.0297666753874655 126.49234893034128
24 3.0309268009295747 119.97157732394486
25 3.032128635139149 113.78695618595503
26 3.033379237667072 107.92115671784448
27 3.034685217324887 102.3577434331191
28 3.0360422817048325 97.08112810643993
29 3.0374560480501636 92.07652609670082
30 3.038937186923469 87.32991492168328
31 3.0404875727762146 82.82799496821696
32 3.042110591529588 78.55815222776056
33 3.0438129052164076 74.50842295298958
34 3.0455955789897726 70.66746013636272
35 3.047461137832211 67.02450171674242
36 3.0494138738517544 63.569340424986464
37 3.0514468692840357 60.29229518402193
38 3.053560786865093 57.1841839832651
39 3.0557510939689414 54.2362981513847
40 3.0580184078334876 51.44037795532319
41 3.060359098580304 48.788589457205475
42 3.0627670514560226 46.27350256429091
43 3.0652320203376977 43.88807021046608
44 3.0677452180034614 41.625608610946415
45 3.070290726467877 39.479778534862476
46 3.072860099454214 37.444567543257634
47 3.0754517950580764 35.51427314272967
48 3.078063685834555 33.683486807514655
49 3.0806941837151687 31.947078825243516
50 3.083341165923261 30.30018392391097
51 3.0860025789363874 28.73818763978448
52 3.0886759137996087 27.256713388057232
53 3.091359004949285 25.85161020001851
54 3.0940492940751643 24.51894109238151
55 3.0967440245944955 23.254972036180693
56 3.099440851304478 22.056161494330627
57 3.1021370798199244 20.919150498530968
58 3.104830144154759 19.840753237714235
59 3.107517421817848 18.817948131665887
60 3.1101964024535147 17.84786936480559
61 3.1128646689199075 16.927798856408376
62 3.115519832126881 16.055158644766465
63 3.118159590976822 15.227503663953065
64 3.1207820820288803 14.442514892949344
65 3.1233851916773365 13.69799285793864
66 3.1259691946515162 12.991851469562208
67 3.1285357383629906 12.322112177868823
68 3.1310860289672386 11.686898428580896
69 3.133621567980842 11.084430405144186
70 3.136143702804028 10.513020041828506
71 3.1386534012785314 9.971066293906695
72 3.1411521533571114 9.457050651659362
73 3.143640700098038 8.969532885635797
74 3.146120497821419 8.507147011249812
75 3.1485925576271536 8.068597461403554
76 3.1510580426484993 7.65265545641528
77 3.153517318344381 7.258155561080046
78 3.1559721265697265 6.883992419216328
79 3.1584227419710422 6.529117656549108
80 3.160870539328334 6.1925369432516355
81 3.1633164953552924 5.8733072079152056
82 3.1657613208122775 5.570533995140829
83 3.1682060393436897 5.283368959348954
84 3.170651618757752 5.01100748778508
85 3.1730984198440457 4.75268644606118
86 3.1755490993631965 4.5076820399160535
87 3.178002914146345 4.2753077872034675
88 3.1804651643590165 4.054912594425805
89 3.1829348803933257 3.8458789324237492
90 3.18541928886898 3.6476211061104964
91 3.187920113877965 3.4595836134024145
92 3.1905023714894183 3.28123958874799
93 3.193084629100871 3.1120893268940137
94 3.1956668867123246 2.951658882752646
95 3.1982491443237775 2.799498743446351
96 3.200831401935231 2.655182568809889
97 3.203413659546684 2.518305996820314
98 3.2060669966825937 2.3884855106079277
99 3.2088655239432127 2.265357363873629
100 3.2116640512038317 2.148576561701768
101 3.2144625784644507 2.0378158939128475
102 3.2173011954546666 1.9327650182475713
103 3.2203248309108625 1.8331295908134146
104 3.2233484663670584 1.7386304413573166
105 3.2263721018232543 1.64900279105364
106 3.2294842691929837 1.5639955106157344
107 3.232736965197784 1.4833704166523776
108 3.2359896612025842 1.406901604297553
109 3.239242357207385 1.334374814243641
110 3.2426668287842233 1.2655868324045019
111 3.246141387829702 1.2003449205263608
112 3.24961594687518 1.138466276151112
113 3.2531963235745733 1.0797775204189037
114 3.2568795244546087 1.0241142122748665
115 3.260562725334644 0.9713203877188338
116 3.264282693839344 0.9212481228070729
117 3.2681468895473262 0.8737571191815925
118 3.272011085255309 0.8287143109657087
119 3.275906956380357 0.7859934919244289
120 3.2799078031576268 0.7454749618449878
121 3.283908649934897 0.7070451911467204
122 3.2879353564217335 0.6705965027805363
123 3.292023932690906 0.6360267705267056
124 3.296112508960079 0.6032391328456115
125 3.3002612804693623 0.572141721479703
126 3.304565415538248 0.5426474040462105
127 3.3088695506071333 0.5146735398993928
128 3.3131736856760186 0.48814174857826176
129 3.317477820744904 0.4629776901909934
130 3.3217819558137895 0.4391108571206791
131 3.3266714655568825 0.4164743764687962
132 3.3316980860504675 0.3950048226828599
133 3.3370111117108943 0.37464203984325517
134 3.3428253163101536 0.3553289731113089
135 3.3486399549469743 0.3370115088663357
136 3.354457469345249 0.3196383230837346
137 3.3602749837435235 0.303160737529304
138 3.365938743610714 0.28753258336684234
139 3.371570144620834 0.27271007179687523
140 3.377202211262505 0.25865167136404993
141 3.382834971817711 0.24531799158942197
142 3.3884778512054514 0.23267167260158017
143 3.3942177715891746 0.22067728045736718
144 3.400011858799879 0.20930120785889228
145 3.405876486542055 0.1985115799886538
146 3.4118107047028023 0.18827816519892812
147 3.4178083781922064 0.17857229030518515
148 3.4238842207859546 0.16936676024618944
149 3.4300215571359387 0.16063578188568095
150 3.43623916831596 0.15235489174213346
151 3.442520798881782 0.14450088744409642
152 3.4488728082393645 0.1370517627190631
153 3.4553001133367087 0.1299866457337094
154 3.461799374468806 0.12328574061273757
155 3.468371519772139 0.11693027197246586
156 3.475016210881847 0.11090243231375137
157 3.4817350549719883 0.10518533212684555
158 3.4885268014435136 0.09976295256838053
159 3.4953929904723506 0.09462010057789046
160 3.50233397248146 0.08974236630810903
161 3.5093517884040843 0.08511608274976512
162 3.5164504359780127 0.08072828743774979
163 3.523629491719269 0.07656668613135789
164 3.530891224885235 0.07261961836683904
165 3.538233002270702 0.06887602478573954
166 3.5456591665110695 0.06532541614749188
167 3.5531712894322984 0.06195784393942753
168 3.560770135979373 0.05876387250186458
169 3.5684535370552104 0.055734552590167206
170 3.5762189277390894 0.052861396299700096
171 3.5840583882426396 0.05013635328341951
172 3.59195887017521 0.04755178819546453
173 3.59991658102435 0.045100459297547434
174 3.6079143533089733 0.04277549816820008
175 3.6159473419917476 0.040570390458023346
176 3.6240020017128 0.03847895763701708
177 3.632066216665988 0.03649533968284844
178 3.6401282003250515 0.03461397866155248
179 3.648175995773244 0.03282960315465949
180 3.6561991128786486 0.031137213489114947
181 3.6641853960022255 0.029532067728607843
182 3.67212363440122 0.02800966838705612
183 3.680001495311217 0.0265657498270215
184 3.6878157148263955 0.02519626630774503
185 3.695552417740189 0.023897380649315018
186 3.703205785876385 0.022665453481205307
187 3.7107741107478365 0.02149703304505914
188 3.7182428855395773 0.020388845523146796
189 3.725610947763068 0.019337785865398225
190 3.7328743333979877 0.018340909089308757
191 3.740029951216142 0.01739542202834095
192 3.7470776066526144 0.016498675505702227

209
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Comment: 04.03.2024, FP and CMN & 300mK calibrated Cernox, 10mK--300K
Sensor Model: RX-1000-BF0.007
Serial Number: U08127
Data Format: 4 (Log Ohms/Kelvin)
SetPoint Limit: 20 (Kelvin)
Temperature coefficient: 1 (Negative)
Number of Breakpoints: 198
No. Units Temperature (K)
1 3.0041751510270878 300.14122991735803
2 3.014456700465595 152.63771605820907
3 3.024500461622686 103.05461623914978
4 3.0343171853033546 77.72374814760329
5 3.0439169093082183 62.395119007475564
6 3.053309020121808 51.13184432093144
7 3.062502308070355 41.95324350785817
8 3.071505016761529 34.54751984961631
9 3.0803248875030143 28.64289287677919
10 3.088969199299713 23.975659705842062
11 3.0974448049474077 20.297251416592655
12 3.1057581636712976 17.321486468174502
13 3.1139153706988627 14.826428320957545
14 3.121922184106239 12.671890917457272
15 3.129784049234295 10.7749117385729
16 3.137506120933729 9.122102420127735
17 3.1450932838667818 7.714317190278185
18 3.1525501710658173 6.535985893109262
19 3.159881180925329 5.562844757336096
20 3.1670904927834416 4.767342772090756
21 3.1741820812311095 4.117461385933856
22 3.1811597292716836 3.582313591970864
23 3.188027040439932 3.1381615915549395
24 3.1947874499777105 2.7667829237228774
25 3.201444235153055 2.454064948585019
26 3.2080005248002865 2.188985997223348
27 3.214459308150652 1.962869654338626
28 3.2208234430158846 1.7688339158383042
29 3.227095663380766 1.6013807185352544
30 3.2332785864551683 1.4560875070018866
31 3.239374719231116 1.329373619113681
32 3.245386464585979 1.2183219898282371
33 3.251316126968973 1.1205420813344817
34 3.2571659177046652 1.0340637718282581
35 3.262937959944006 0.9572546621816017
36 3.2686342932906527 0.8887552204075527
37 3.274256878127789 0.8274276045709429
38 3.279807599668426 0.7723150421127573
39 3.285288271750112 0.722609406427747
40 3.290700640393174 0.6776251964906768
41 3.2960463871399477 0.6367785465235264
42 3.3013271321909876 0.5995702087442316
43 3.306544437352874 0.5655716908623856
44 3.311699808811056 0.5344139112350523
45 3.3167946997400315 0.5057786499025764
46 3.3218305127621948 0.47939970598453746
47 3.3268086022657446 0.4550507050199406
48 3.331730276591256 0.43253215913642384
49 3.3365968000957276 0.4116677008384455
50 3.341409395102266 0.39230103361091834
51 3.34616924374292 0.37429333012161936
52 3.350877489701615 0.35752100608095994
53 3.355535239863626 0.34187381032689274
54 3.3601435658775274 0.3272531818806946
55 3.364703505635139 0.3135708330282369
56 3.3692160646745863 0.30074752428648227
57 3.3736822175112082 0.2887120027061562
58 3.378102908900729 0.2774000795693808
59 3.3824790550387847 0.2667538273499527
60 3.386811544700616 0.2567208789614958
61 3.391101240324471 0.24725381494377904
62 3.395348979042024 0.23830962642578937
63 3.3995555736588905 0.22984924353335198
64 3.4037218135881067 0.22183712044202075
65 3.407848465739259 0.2142408857416558
66 3.4119362753657683 0.20703173376289563
67 3.4159859668726664 0.20018425956012226
68 3.4199982445870614 0.19367512534357278
69 3.423973793493333 0.18748279523007014
70 3.4279132799349843 0.181587374945554
71 3.4318173522849476 0.17597046756490736
72 3.4356866415860243 0.17061504351537796
73 3.439521762163053 0.16550532328395345
74 3.4433233122082774 0.16062667145525258
75 3.4470918743413206 0.15596550086861363
76 3.450828016145069 0.15150918582454725
77 3.4545322906787033 0.1472459833942867
78 3.458205236969035 0.1431649619943395
79 3.4618473804812373 0.13925593648270887
80 3.4654592335700034 0.13550940911663273
81 3.4690412959120986 0.1319165157847933
82 3.472594054921216 0.12846897699128942
83 3.476117986146005 0.12515905312535527
84 3.47961355365208 0.12197950360086121
85 3.4830812103887725 0.1189235494938422
86 3.486521398541368 0.1159848393454153
87 3.4899345498694894 0.11315741783212702
88 3.493321086032294 0.11043569703648969
89 3.496681418901088 0.10781443007779157
90 3.500015950859938 0.10528868688752957
91 3.50332507509483 0.10285383193542984
92 3.5066091758718962 0.10050550373128267
93 3.509868628805199 0.09823959594500482
94 3.5131038011145446 0.0960522400026891
95 3.5163150518737574 0.09393978903013726
96 3.5195027322498524 0.09189880302764594
97 3.522667185733486 0.08992603517084467
98 3.525808748361074 0.08801841733738257
99 3.5289277489289352 0.08617294185813036
100 3.532024509199798 0.08438660762841536
101 3.5350993441019973 0.08265658014937569
102 3.538152561921667 0.08098019464037312
103 3.541184464488229 0.07935494426767435
104 3.544195347353446 0.07777846930106215
105 3.547185499964314 0.07624854711655116
106 3.550155205830043 0.07476308297132794
107 3.5531047426833657 0.07332010148414522
108 3.5560343826364083 0.07191773876075322
109 3.5589443923313344 0.07055423510965997
110 3.5618350330859823 0.06922792829862155
111 3.5647065610346793 0.06793724730686924
112 3.567559227264438 0.0666807065321984
113 3.5703932779467067 0.06545690041577405
114 3.573208954464845 0.06426449845085444
115 3.5760064935375007 0.06310224054465019
116 3.5787861273380304 0.06196893270527076
117 3.5815480836101306 0.06086344302816629
118 3.5842925857798087 0.059784697958706015
119 3.587019853063843 0.05873167880954455
120 3.5897301005748528 0.05770341851326026
121 3.5924235394231165 0.05669899859239748
122 3.5951003768152483 0.05571754633055359
123 3.597760816149852 0.054758232129513765
124 3.6004050571102657 0.05382026703867446
125 3.6030332957544955 0.05290290044412864
126 3.6056457246024496 0.05200541790580869
127 3.6082425327205607 0.0511271391320223
128 3.610823905803894 0.05026741608156775
129 3.6133900262558307 0.049425631184392874
130 3.61594107326541 0.048601195672476435
131 3.6184772228824134 0.04779354801325561
132 3.620998648090268 0.047002152438522085
133 3.6235055188768484 0.046226497562249086
134 3.6259980023032448 0.045466095081313336
135 3.6284762625705675 0.04472047855353038
136 3.6309404610848612 0.043989202247838574
137 3.633390756520183 0.04327184006185711
138 3.6358273048799115 0.04256798450238985
139 3.6382502595563504 0.04187724572477339
140 3.640659771388672 0.04119925062726871
141 3.643055988719267 0.04053364199696499
142 3.6454390574485465 0.03988007770392062
143 3.6478091210882475 0.03923822994049847
144 3.6501663208132946 0.038607784503064645
145 3.65251079551226 0.03798844011342123
146 3.654842681836467 0.037379907777524896
147 3.6571621142477873 0.03678191017920927
148 3.659469225065164 0.03619418110678995
149 3.6617641445099065 0.035616464910571445
150 3.664047000749797 0.03504851598940836
151 3.666317919942043 0.0344900983045998
152 3.6685770262751074 0.03394098491950748
153 3.670824442009467 0.03340095756339411
154 3.673060287517312 0.03286980621807832
155 3.675284681321233 0.032347328726093236
156 3.677497740131922 0.03183333041911785
157 3.679699578884918 0.031327623765532754
158 3.681890310776424 0.030830028036022257
159 3.6840700472982277 0.03034036898621258
160 3.686238898271748 0.029858478555401444
161 3.6883969718812377 0.02938419458048864
162 3.690544374706162 0.028917360524277774
163 3.692681211752785 0.0284578252173641
164 3.694807586484979 0.02800544261287524
165 3.696923600854286 0.027560071553374938
166 3.699029355329247 0.027121575549278604
167 3.7011249489240274 0.026689822568172466
168 3.7032104792263545 0.026264684834459612
169 3.7052860424247838 0.025846038638793607
170 3.7073517333353214 0.025433764156788694
171 3.7094076454274143 0.025027745276527326
172 3.7114538708493265 0.02462786943441207
173 3.7134905004529246 0.024234027458934295
174 3.7155176238178806 0.02384611342195757
175 3.7175353292753153 0.023464024497135038
176 3.719543703930892 0.02308766082510168
177 3.7215428336873835 0.02271692538510184
178 3.7235328032667177 0.022351723872731523
179 3.72551369623152 0.021991964583493038
180 3.7274855950061725 0.02163664656919779
181 3.7294485808973907 0.02127793428704172
182 3.7314027341143396 0.020905563560174253
183 3.7333481337883017 0.020510303758750324
184 3.735284857991901 0.020084027115167952
185 3.7372129837579076 0.019619789819440937
186 3.739132587097619 0.01911193261602585
187 3.7410437430188423 0.018556189009702757
188 3.7429465255434806 0.017949788979602484
189 3.7448410077247365 0.017291546163894802
190 3.7467272616639398 0.01658191698900895
191 3.7486053585270147 0.015823021312078953
192 3.7504753685605907 0.01501861590523027
193 3.752337361107766 0.01417401455055061
194 3.75419140462354 0.01329595157488315
195 3.7560375666899124 0.012392389195261371
196 3.7578759140306643 0.011472272856632524
197 3.7597065125258298 0.010545247253855637
198 3.7615294272258613 0.009624316970408492

132
devices/LakeShore372/curves/U08497.340 Normal file
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Sensor Model: XRX-102B-RS-0.01B-19180
Serial Number: U08497
Data Format: 4 (Log Ohms/Kelvin)
SetPoint Limit: 40.0 (Kelvin)
Temperature coefficient: 1 (Negative)
Number of Breakpoints: 123
No. Units Temperature (K)
1 3.05924 45.098
2 3.06481 40.000
3 3.06737 37.900
4 3.06997 35.900
5 3.07260 34.000
6 3.07540 32.100
7 3.07839 30.200
8 3.08141 28.400
9 3.08446 26.700
10 3.08773 25.000
11 3.09123 23.300
12 3.09480 21.700
13 3.09839 20.200
14 3.09929 19.850
15 3.10044 19.400
16 3.10215 18.750
17 3.10392 18.100
18 3.10576 17.450
19 3.10767 16.800
20 3.10965 16.150
21 3.11170 15.500
22 3.11384 14.850
23 3.11589 14.250
24 3.11801 13.650
25 3.12022 13.050
26 3.12252 12.450
27 3.12492 11.850
28 3.12721 11.300
29 3.12959 10.750
30 3.13208 10.200
31 3.13468 9.650
32 3.13715 9.150
33 3.13974 8.650
34 3.14246 8.150
35 3.14504 7.700
36 3.14775 7.250
37 3.15061 6.800
38 3.15331 6.400
39 3.15582 6.050
40 3.15848 5.700
41 3.16140 5.340
42 3.16437 5.000
43 3.16737 4.680
44 3.17040 4.380
45 3.17368 4.080
46 3.17641 3.850
47 3.17881 3.660
48 3.18122 3.480
49 3.18380 3.300
50 3.18655 3.120
51 3.18934 2.950
52 3.19216 2.790
53 3.19519 2.630
54 3.19827 2.480
55 3.20158 2.330
56 3.20495 2.190
57 3.20861 2.050
58 3.21232 1.920
59 3.21637 1.790
60 3.22049 1.670
61 3.22501 1.550
62 3.23003 1.430
63 3.23516 1.320
64 3.24089 1.210
65 3.24150 1.200
66 3.24377 1.160
67 3.24647 1.115
68 3.24932 1.070
69 3.25232 1.025
70 3.25550 0.980
71 3.25850 0.940
72 3.26166 0.900
73 3.26503 0.860
74 3.26862 0.820
75 3.27247 0.780
76 3.27608 0.745
77 3.27990 0.710
78 3.28400 0.675
79 3.28839 0.640
80 3.29311 0.605
81 3.29820 0.570
82 3.30291 0.540
83 3.30796 0.510
84 3.31230 0.486
85 3.31650 0.464
86 3.32055 0.444
87 3.32483 0.424
88 3.32938 0.404
89 3.33420 0.384
90 3.33935 0.364
91 3.34430 0.346
92 3.34957 0.328
93 3.35521 0.310
94 3.36128 0.292
95 3.36745 0.275
96 3.37373 0.259
97 3.38007 0.244
98 3.38693 0.229
99 3.39388 0.215
100 3.40143 0.201
101 3.40968 0.187
102 3.41808 0.174
103 3.42728 0.161
104 3.43744 0.148
105 3.44779 0.136
106 3.45919 0.124
107 3.47187 0.112
108 3.48485 0.101
109 3.49804 0.091
110 3.51218 0.082
111 3.52752 0.073
112 3.54433 0.064
113 3.56393 0.056
114 3.58268 0.049
115 3.60490 0.042
116 3.62972 0.035
117 3.65758 0.029
118 3.68904 0.024
119 3.72490 0.019
120 3.76673 0.014
121 3.81795 0.010
122 3.89842 0.006
123 3.90507 0.005

132
devices/LakeShore372/curves/U08830.340 Normal file
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Sensor Model: RX-102B-RS-0.01B
Serial Number: U08830
Data Format: 4 (Log Ohms/Kelvin)
SetPoint Limit: 40.0 (Kelvin)
Temperature coefficient: 1 (Negative)
Number of Breakpoints: 123
No. Units Temperature (K)
1 3.06613 44.986
2 3.07185 40.000
3 3.07453 37.900
4 3.07724 35.900
5 3.07999 34.000
6 3.08291 32.100
7 3.08602 30.200
8 3.08918 28.400
9 3.09236 26.700
10 3.09576 25.000
11 3.09942 23.300
12 3.10312 21.700
13 3.10710 20.100
14 3.10739 20.000
15 3.10897 19.400
16 3.11089 18.700
17 3.11273 18.050
18 3.11464 17.400
19 3.11662 16.750
20 3.11867 16.100
21 3.12080 15.450
22 3.12302 14.800
23 3.12514 14.200
24 3.12735 13.600
25 3.12964 13.000
26 3.13202 12.400
27 3.13451 11.800
28 3.13710 11.200
29 3.13958 10.650
30 3.14216 10.100
31 3.14486 9.550
32 3.14743 9.050
33 3.15012 8.550
34 3.15295 8.050
35 3.15564 7.600
36 3.15816 7.200
37 3.16082 6.800
38 3.16364 6.400
39 3.16680 5.980
40 3.16970 5.620
41 3.17264 5.280
42 3.17560 4.960
43 3.17880 4.640
44 3.18204 4.340
45 3.18532 4.060
46 3.18810 3.840
47 3.19066 3.650
48 3.19325 3.470
49 3.19600 3.290
50 3.19879 3.120
51 3.20178 2.950
52 3.20481 2.790
53 3.20807 2.630
54 3.21138 2.480
55 3.21496 2.330
56 3.21860 2.190
57 3.22256 2.050
58 3.22658 1.920
59 3.23099 1.790
60 3.23548 1.670
61 3.24041 1.550
62 3.24544 1.440
63 3.25100 1.330
64 3.25722 1.220
65 3.26005 1.175
66 3.26228 1.140
67 3.26531 1.095
68 3.26850 1.050
69 3.27188 1.005
70 3.27545 0.960
71 3.27882 0.920
72 3.28238 0.880
73 3.28618 0.840
74 3.29024 0.800
75 3.29404 0.765
76 3.29808 0.730
77 3.30240 0.695
78 3.30702 0.660
79 3.31200 0.625
80 3.31737 0.590
81 3.32235 0.560
82 3.32769 0.530
83 3.33248 0.505
84 3.33674 0.484
85 3.34104 0.464
86 3.34559 0.444
87 3.35042 0.424
88 3.35556 0.404
89 3.36104 0.384
90 3.36630 0.366
91 3.37189 0.348
92 3.37788 0.330
93 3.38428 0.312
94 3.39117 0.294
95 3.39776 0.278
96 3.40483 0.262
97 3.41245 0.246
98 3.42019 0.231
99 3.42856 0.216
100 3.43705 0.202
101 3.44628 0.188
102 3.45566 0.175
103 3.46589 0.162
104 3.47629 0.150
105 3.48772 0.138
106 3.49934 0.127
107 3.51222 0.116
108 3.52534 0.106
109 3.54005 0.096
110 3.55590 0.087
111 3.57396 0.077
112 3.59453 0.068
113 3.61555 0.059
114 3.63714 0.052
115 3.66080 0.045
116 3.68696 0.038
117 3.71848 0.032
118 3.75708 0.025
119 3.80056 0.019
120 3.84900 0.014
121 3.89943 0.009
122 3.94834 0.006
123 3.96770 0.004

135
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Sensor Model: RX-102B-RS-0.01B
Serial Number: U08833
Data Format: 4 (Log Ohms/Kelvin)
SetPoint Limit: 40.0 (Kelvin)
Temperature coefficient: 1 (Negative)
Number of Breakpoints: 126
No. Units Temperature (K)
1 3.06903 44.985
2 3.07478 40.000
3 3.07748 37.900
4 3.08020 35.900
5 3.08295 34.000
6 3.08588 32.100
7 3.08900 30.200
8 3.09217 28.400
9 3.09536 26.700
10 3.09877 25.000
11 3.10243 23.300
12 3.10615 21.700
13 3.11015 20.100
14 3.11044 20.000
15 3.11202 19.400
16 3.11395 18.700
17 3.11580 18.050
18 3.11772 17.400
19 3.11971 16.750
20 3.12177 16.100
21 3.12391 15.450
22 3.12614 14.800
23 3.12827 14.200
24 3.13049 13.600
25 3.13280 13.000
26 3.13519 12.400
27 3.13769 11.800
28 3.14008 11.250
29 3.14257 10.700
30 3.14516 10.150
31 3.14788 9.600
32 3.15047 9.100
33 3.15318 8.600
34 3.15574 8.150
35 3.15843 7.700
36 3.16127 7.250
37 3.16394 6.850
38 3.16677 6.450
39 3.16940 6.100
40 3.17212 5.760
41 3.17503 5.420
42 3.17795 5.100
43 3.18109 4.780
44 3.18427 4.480
45 3.18747 4.200
46 3.19044 3.960
47 3.19295 3.770
48 3.19547 3.590
49 3.19816 3.410
50 3.20104 3.230
51 3.20396 3.060
52 3.20691 2.900
53 3.21008 2.740
54 3.21328 2.590
55 3.21675 2.440
56 3.22051 2.290
57 3.22434 2.150
58 3.22821 2.020
59 3.23243 1.890
60 3.23707 1.760
61 3.24179 1.640
62 3.24700 1.520
63 3.25232 1.410
64 3.25822 1.300
65 3.26362 1.210
66 3.26525 1.185
67 3.26754 1.150
68 3.27064 1.105
69 3.27392 1.060
70 3.27737 1.015
71 3.28102 0.970
72 3.28445 0.930
73 3.28807 0.890
74 3.29194 0.850
75 3.29555 0.815
76 3.29938 0.780
77 3.30345 0.745
78 3.30780 0.710
79 3.31245 0.675
80 3.31745 0.640
81 3.32284 0.605
82 3.32783 0.575
83 3.33318 0.545
84 3.33895 0.515
85 3.34371 0.492
86 3.34809 0.472
87 3.35273 0.452
88 3.35765 0.432
89 3.36289 0.412
90 3.36848 0.392
91 3.37385 0.374
92 3.37956 0.356
93 3.38566 0.338
94 3.39219 0.320
95 3.39922 0.302
96 3.40636 0.285
97 3.41360 0.269
98 3.42139 0.253
99 3.42929 0.238
100 3.43781 0.223
101 3.44707 0.208
102 3.45649 0.194
103 3.46674 0.180
104 3.47717 0.167
105 3.48859 0.154
106 3.50022 0.142
107 3.51195 0.131
108 3.52491 0.120
109 3.53806 0.110
110 3.55355 0.100
111 3.56958 0.090
112 3.58695 0.081
113 3.60688 0.072
114 3.62831 0.064
115 3.65151 0.056
116 3.67717 0.048
117 3.70559 0.041
118 3.73759 0.035
119 3.77415 0.029
120 3.81256 0.024
121 3.85607 0.019
122 3.90609 0.015
123 3.97300 0.011
124 4.05817 0.008
125 4.12467 0.006
126 4.17331 0.004

203
devices/LakeShore372/curves/U08905.340 Normal file
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Comment: Using U08910 curve. 04.03.2024, FP and CMN & 300mK calibrated Cernox, 10mK--300K.
Sensor Model: RX-1000-BF0.007
Serial Number: U08905
Data Format: 4 (Log Ohms/Kelvin)
SetPoint Limit: 300 (Kelvin)
Temperature coefficient: 1 (Negative)
Number of Breakpoints: 198
No. Units Temperature (K)
1 3.2956295902328776 302.29637503738195
2 3.296971553758573 286.59226140605614
3 3.2983135172842686 271.70396696844415
4 3.299655480809964 257.58911041144177
5 3.300997444335659 244.20751210549773
6 3.302339407861355 231.52107972848407
7 3.3036818765611495 219.49369983134258
8 3.3050323435963356 208.09113503682508
9 3.3063934527709518 197.28092657869934
10 3.3077654039497144 187.03230190398392
11 3.3091483996009528 177.3160870751909
12 3.3105426698485854 168.10462372321913
13 3.3119485429113964 159.3716903144932
14 3.313365832918274 151.09242750822975
15 3.314795228931611 143.2432673913455
16 3.3162368461502933 135.8018663895707
17 3.3176906985072994 128.74704166378885
18 3.319157426347004 122.05871081054872
19 3.320636699020502 115.71783469509784
20 3.3221294031199156 109.70636325420453
21 3.323635646989159 104.00718411449277
22 3.325155697312089 98.60407388002093
23 3.3266895030606904 93.4816519504428
24 3.328238010142075 88.62533673828631
25 3.3298008788239555 84.02130416071917
26 3.3313788446867956 79.65644828764113
27 3.3329727432588303 75.51834403408456
28 3.3345811872805866 71.59521179072186
29 3.336206204307136 67.87588389179719
30 3.3378478744654845 64.34977282502827
31 3.3395059840391 61.00684109298458
32 3.3411815292834635 57.837572640149205
33 3.342875053133617 54.832945764327
34 3.3445861403197856 51.984407435288716
35 3.346315698908105 49.28384894754648
36 3.3480641173233012 46.72358283795236
37 3.3498322407191785 44.29632100241391
38 3.3516204680550605 41.995153949432975
39 3.3534286930796613 39.81353113141089
40 3.3552582600565795 37.74524229773012
41 3.3571092858062257 35.784399816532066
42 3.3589825915944305 33.9254219148681
43 3.3608785913010175 32.16301678951433
44 3.3627980289823687 30.49216754321982
45 3.364741620223096 28.90811790350802
46 3.3667098700936138 27.406358683377427
47 3.3687039478285716 25.98261494536069
48 3.3707235773374595 24.632833832403346
49 3.37277133503211 23.353173030920722
50 3.3748455474506427 22.13998983319222
51 3.3769492037738775 20.98983076795747
52 3.379082130210903 19.89942176969694
53 3.3812468912200466 18.86565885861225
54 3.3834415713505166 17.885599304775926
55 3.3856685392929724 16.956453251298342
56 3.387929826792408 16.07557577266593
57 3.3902261503658764 15.240459345643886
58 3.3925578041399542 14.448726711311119
59 3.3949270835254253 13.698124107907942
60 3.397334638561418 12.986514855233365
61 3.3997820025545176 12.311873272328953
62 3.4022709453035063 11.67227891113548
63 3.4048025551410936 11.065911089707491
64 3.407378355422513 10.49104370942407
65 3.4100015348859105 9.94604034144247
66 3.4126709440454133 9.429349568407416
67 3.4153898146397594 8.939500568155754
68 3.418160887344365 8.475098926845106
69 3.4209904043122297 8.03482266958783
70 3.423867471449978 7.617418497291169
71 3.426804146772174 7.221698218991336
72 3.4298048270181756 6.846535369525613
73 3.432870807504802 6.490862002914374
74 3.436004010114819 6.153665652324934
75 3.4392020513422925 5.833986447963495
76 3.4424697766282844 5.5309143846908135
77 3.4458369896176633 5.243586731583568
78 3.4492607571427865 4.971185576067502
79 3.452776776304858 4.71293549563132
80 3.4563978335001964 4.46810135049362
81 3.460121839605028 4.2359861909395065
82 3.4639172272350875 4.015929273369738
83 3.467833038329369 3.807304179414953
84 3.4718732525135834 3.6095170327607455
85 3.4760360318685564 3.4220048086076407
86 3.480312983322039 3.244233730953563
87 3.4847516614737923 3.075697753136513
88 3.489322824785482 2.9159171173121625
89 3.4940628250494328 2.7644369887656834
90 3.4992781908553496 2.6208261611702555
91 3.504493556661267 2.4846758291066324
92 3.509708922467184 2.3555984243495516
93 3.514924288273101 2.2332265126083577
94 3.520913958670413 2.1172117475813073
95 3.527026477483079 2.007223879346092
96 3.5331389962957447 1.9029498142638925
97 3.5393943873586804 1.8040927237208286
98 3.5465319139534577 1.710371199169777
99 3.5536694405482345 1.6215184510672331
100 3.560806967143012 1.5372815494249217
101 3.5686905153641195 1.4574207038143048
102 3.5768508955582234 1.381708580774404
103 3.5850112757523274 1.3099296566798764
104 3.593698085472017 1.2418796042272113
105 3.602631619392163 1.1773647107925866
106 3.611565153312309 1.1162013270056885
107 3.620882639856867 1.058215343969781
108 3.6302995177307116 1.003241697639888
109 3.6397163956045557 0.9511238989482141
110 3.649533300856781 0.9017135883392788
111 3.659436352438514 0.8548701134466694
112 3.6693479003538143 0.8104601287092384
113 3.6797714440274105 0.7683572157869951
114 3.690194987701007 0.728441523696159
115 3.7007681829294663 0.6905994276389837
116 3.711756789517688 0.6547232055571611
117 3.72274539610591 0.6207107314880822
118 3.7340844685272794 0.5884651848510551
119 3.745680147471145 0.5578947748359261
120 3.7573196563629345 0.5289124791095453
121 3.7695684590443044 0.5014357960962763
122 3.781859577422382 0.47538651012737754
123 3.79443201382344 0.4506904687907373
124 3.8074509225413267 0.4272773718471503
125 3.8204789400748815 0.4050805711122684
126 3.8340535211284785 0.3840368807345631
127 3.8480390185041493 0.36408639732923054
128 3.8622289734274187 0.3451723294560343
129 3.877045131102691 0.3272408359556686
130 3.891861288777964 0.3102408726844505
131 3.9074050969632546 0.2941240472110529
132 3.9232569930343835 0.27884448106165194
133 3.939249533723833 0.26435867912135835
134 3.9562141364028824 0.2506254058201632
135 3.973178739081932 0.23760556775094957
136 3.990811505670929 0.2252621023854283
137 4.008898357507565 0.2135598725712115
138 4.027525306166772 0.20246556650970152
139 4.046669062715386 0.19194760293006582
140 4.066269293060244 0.18197604118936814
141 4.086464635020096 0.17252249604294276
142 4.10708267496966 0.16356005684239572
143 4.128212304273615 0.15506321093122177
144 4.1497964988101135 0.14700777101997242
145 4.1718545620357785 0.13937080633423948
146 4.1944731546272775 0.13213057733946004
147 4.2175931299504645 0.1252664738567274
148 4.241320472954545 0.11875895639344891
149 4.265650972000818 0.11258950052184033
150 4.290637706203435 0.10674054414692336
151 4.3162693569021275 0.10119543751391945
152 4.342571536323674 0.09593839581272874
153 4.369631083366805 0.09095445424457764
154 4.397461209645033 0.08622942542292727
155 4.426096322389766 0.08174985898737834
156 4.455594816299184 0.07750300331560955
157 4.485969952485805 0.07347676922435774
158 4.517286983891768 0.0696596955561098
159 4.549560141125718 0.06604091655354509
160 4.5828445692629565 0.06261013092885631
161 4.617178068253681 0.05935757253989995
162 4.652615771955327 0.05627398258970298
163 4.68912614277281 0.05335058327018861
164 4.72676568020895 0.05057905277509439
165 4.765573053660718 0.04795150161095398
166 4.805367540464258 0.04546045013870923
167 4.846329596238018 0.04309880728202191
168 4.888307477100742 0.0408598503416756
169 4.931236929634964 0.03873720585860732
170 4.975023573391239 0.036724831471093074
171 5.019510673835353 0.034816998714441556
172 5.06456090421599 0.033008276714233335
173 5.109977140496435 0.03129351672668646
174 5.155565201987605 0.029667837482140764
175 5.201123341544657 0.02812661128993907
176 5.246436209367498 0.026665450865151596
177 5.2913068994535655 0.025280196839644054
178 5.335601353712727 0.023966905921938023
179 5.379134719679635 0.022721839672159586
180 5.421779127089443 0.021541453860122534
181 5.463440548802624 0.02042238837625263
182 5.504046641622135 0.019361457666633382
183 5.543560197902048 0.018355641664945254
184 5.581962708369557 0.01740207719548528
185 5.619232597122327 0.016498049822794465
186 5.65538878178095 0.015640986124692228
187 5.690449928333749 0.014828446366721992
188 5.724435993781677 0.014058117557154814
189 5.757380641974088 0.013327806862781469
190 5.789364194551921 0.012635435366750132
191 5.82032460391876 0.011979032150680542
192 5.850468560762334 0.011356728684208848

203
devices/LakeShore372/curves/U08905_not_well_thermalised.340 Normal file
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Comment: 04.03.2024, FP and CMN & 300mK calibrated Cernox, 10mK--300K
Sensor Model: RX-1000-BF0.007
Serial Number: U08905
Data Format: 4 (Log Ohms/Kelvin)
SetPoint Limit: 300 (Kelvin)
Temperature coefficient: 1 (Negative)
Number of Breakpoints: 198
No. Units Temperature (K)
1 3.26998236978141 886.7698434959185
2 3.271538184581481 835.5437549709238
3 3.273087100416986 787.2768470775411
4 3.2746098292531554 741.7981766447679
5 3.2761294149393088 698.946675386359
6 3.2776392531442577 658.570579457746
7 3.279143786130827 620.5268919657786
8 3.280642355197603 584.6808765276984
9 3.2821359635787966 550.9055800857224
10 3.2836250997407066 519.0813832872274
11 3.285110294911474 489.095576838149
12 3.286592022550685 460.8419623292004
13 3.288070800340279 434.2204761211881
14 3.2895470933171693 409.1368349573671
15 3.291021373120509 385.50220204773024
16 3.2924941104250154 363.2328724426259
17 3.2939657566500906 342.24997658141854
18 3.2954367737797114 322.4792009662712
19 3.296907614524145 303.85052497178384
20 3.2983787329919068 286.2979728583647
21 3.299850585099957 269.75938011106115
22 3.301323625531506 254.1761732763099
23 3.3027983141433643 239.49316251687088
24 3.3042751167126 225.6583461502534
25 3.305754514357418 212.6227264783831
26 3.307236980457365 200.34013625624786
27 3.3087230056525643 188.76707518494038
28 3.3102130921716304 177.86255585001717
29 3.3117077553399272 167.5879585595459
30 3.3132075251638553 157.90689456773242
31 3.314712949858649 148.7850771997167
32 3.3162246158608486 140.19020042111077
33 3.31774309521965 132.09182442221876
34 3.3192689961384914 124.46126781171787
35 3.3208029767032508 117.27150603799618
36 3.3223457205724434 110.49707567839016
37 3.323897908409214 104.11398425735189
38 3.3254604656168665 98.09962527415593
39 3.327033750306444 92.43269813920556
40 3.3286190955651938 87.09313273538402
41 3.3302171212947065 82.06201833727422
42 3.331828854407386 77.32153663650664
43 3.3334555808021227 72.85489863603595
44 3.335097650113738 68.64628518985006
45 3.3367570025299935 64.6807909775252
46 3.3384346565482974 60.94437171520663
47 3.340132898435447 57.4237944160557
48 3.341851008990326 54.10659052400482
49 3.343592804731414 50.98101175483791
50 3.3453598915062583 48.035988488201376
51 3.3471554403472323 45.26109056318691
52 3.348976635453718 42.646490338638024
53 3.35083365736286 40.182927887354175
54 3.3527214660750033 37.8616782009235
55 3.354650074334381 35.67452028903597
56 3.3566197648868212 33.61370806383846
57 3.3586354712237774 31.67194290621508
58 3.360696485206898 29.842347816832884
59 3.3628054067325253 28.11844306040575
60 3.3649644059493116 26.494123216917526
61 3.3671738324726395 24.963635558528658
62 3.3694363555287765 23.52155967558533
63 3.3717529546913534 22.16278827957425
64 3.374124180229041 20.882509115034292
65 3.376553126783588 19.676187916363897
66 3.379035945555129 18.539552349163618
67 3.3815780220725893 17.46857687924012
68 3.3841778585709865 16.459468515683387
69 3.3868360827736295 15.50865337752444
70 3.3895573885506165 14.61276403639781
71 3.3923431217156605 13.768627590381175
72 3.3951962839862815 12.97325442677427
73 3.398123437080362 12.223827634019026
74 3.4011200224703986 11.517693025261982
75 3.404200088221612 10.852349738226197
76 3.407359130606531 10.225441378100918
77 3.410606126579972 9.634747672080511
78 3.413953164793916 9.078176605996154
79 3.417379024568635 8.55375701519121
80 3.4209395512558136 8.059631603399964
81 3.424549484104815 7.594050364905223
82 3.4282951826504897 7.1553643866785706
83 3.4321632603552503 6.742020008552706
84 3.4361645434813837 6.352553320743543
85 3.4402567463724663 5.985584979234244
86 3.4445143286126867 5.63981532065931
87 3.4488291600676253 5.314019759387448
88 3.453334484549061 5.007044450501445
89 3.4579573001298756 4.717802203314957
90 3.4627403259223883 4.445268630953416
91 3.4675792273597024 4.188478522362348
92 3.4725945780708107 3.9465224238940992
93 3.4777747629196947 3.718543418366262
94 3.4830561521612236 3.5037340901844303
95 3.4885203673043086 3.3013336657808967
96 3.4941501951980154 3.1106253192418034
97 3.49997158910582 2.930933633580299
98 3.5059524368444466 2.7616222086644835
99 3.512087894604094 2.6020914073283317
100 3.5187802462345745 2.4517762316831613
101 3.5254725978650545 2.310144322108355
102 3.5321649494955345 2.1766940718345014
103 3.5388589290860684 2.0509528504415364
104 3.5464047737131486 1.9324753299801725
105 3.5539506183402287 1.8208419077883768
106 3.561496462967309 1.7156572204171077
107 3.5695575984047307 1.616548743402195
108 3.5778642716775946 1.5231654719232852
109 3.586170944950458 1.435176677677243
110 3.5949403361576024 1.3522707375633238
111 3.60386809082258 1.274154030031763
112 3.612795845487558 1.2005498951870635
113 3.6222907860346525 1.131197654963066
114 3.6318229676964804 1.065851689899617
115 3.6413551493583083 1.0042805692511467
116 3.6513938017369187 0.9462662313463103
117 3.661563831825913 0.891603211295852
118 3.6719695708231943 0.8400979133135114
119 3.6828148439144033 0.7915679250728149
120 3.6936601170056127 0.7458413716714607
121 3.704842420942301 0.7027563069152848
122 3.716473825640061 0.6621601397659602
123 3.7281976115391062 0.6239090939211316
124 3.7404364934079943 0.587867698613015
125 3.75293156527188 0.5539083088220679
126 3.765841993790791 0.5219106532065046
127 3.7791800703189855 0.4917614081465972
128 3.7926911342899934 0.4633537963951839
129 3.806815303963397 0.43658720891295943
130 3.821140127075321 0.41136684854922934
131 3.835992052830391 0.38760339430618046
132 3.851300285081342 0.3652126849976177
133 3.8669349563729747 0.3441154211818066
134 3.883138824916383 0.32423688431277947
135 3.8994631018469343 0.30550667211544585
136 3.916618509068262 0.287858449247305
137 3.933849073980586 0.27122971236369886
138 3.952009354046345 0.2555615687545555
139 3.9704370899326284 0.24079852776863633
140 3.989282289851493 0.22688830428659332
141 4.008912348919417 0.21378163354680915
142 4.029178028886164 0.20143209666820508
143 4.050146332500644 0.18979595625208343
144 4.071760717134821 0.17883200148076853
145 4.094212810474566 0.16850140216444437
146 4.117351802841806 0.1587675712192773
147 4.141384298519994 0.14959603508977368
148 4.166294098000499 0.14095431165645733
149 4.192191598331225 0.13281179519646158
150 4.218900929609588 0.12513964798961003
151 4.2466617965835445 0.11791069818609551
152 4.275420393370698 0.11109934357404312
153 4.305192945548419 0.10468146090613875
154 4.335919966990351 0.09863432046419131
155 4.367610474108864 0.09293650555904945
156 4.400134020618158 0.08756783668077193
157 4.433423737247819 0.0825093000304194
158 4.467461743345362 0.07774298018035442
159 4.5021879162596985 0.07325199662455868
160 4.537693539359212 0.06902044399425149
161 4.573978640564434 0.06503333572707659
162 4.610959232242574 0.061276550990354485
163 4.648736781378808 0.05773678467042246
164 4.687296976846007 0.05440150025094364
165 4.726621287927182 0.051258885413297546
166 4.766677746723316 0.048297810201805846
167 4.807511106714135 0.04550778760562982
168 4.848984569947229 0.04287893641773602
169 4.8911735566815855 0.0404019462393903
170 4.933979920401898 0.03806804450623939
171 4.977378580873001 0.035868965419198894
172 5.021316799363022 0.03379692067011253
173 5.065844466423054 0.03184457185850415
174 5.111094020370926 0.03000500450173285
175 5.1571775577879615 0.02827170354650511
176 5.204239044456156 0.026638530295014923
177 5.252440936701529 0.02509970066399301
178 5.299929284777306 0.023649764699666894
179 5.339548589812631 0.022283587276081554
180 5.370682993246003 0.020996329908421364
181 5.395740066034322 0.019783433616923
182 5.416744833019113 0.018640602780689765
183 5.434752558253089 0.017563789924223625
184 5.450760589894486 0.016549181382794542
185 5.4650425666014 0.01559318379587939
186 5.47799138453746 0.014692411380835135
187 5.489940702127 0.01384367394173461
188 5.500991943105089 0.013043965570896533
189 5.5113367407500755 0.012290454003095001
190 5.521027798560731 0.01158047058474501
191 5.5301695908696535 0.01091150082253864
192 5.538833780235197 0.010281175478058782

209
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@@ -0,0 +1,209 @@
Comment: 30.07.2021, FP and CMN, 16mK--2.3K extrapolated elsewhere
Sensor Model: RX-1000-BF0.007
Serial Number: UO6898
Data Format: 4 (Log Ohms/Kelvin)
SetPoint Limit: 0.0167 (Kelvin)
Temperature coefficient: 1 (Negative)
Number of Breakpoints: 198
No. Units Temperature (K)
1 3.2191180247435516 2.29950691832369
2 3.226985325932204 2.0097161300123414
3 3.2347126420220187 1.7698268083328295
4 3.242304867636779 1.5656215409996737
5 3.249766645091846 1.4044548585918006
6 3.2571023814429747 1.2723444343778547
7 3.2643162641190093 1.1587693166893545
8 3.2714122752773003 1.0588882280084686
9 3.2783942050050667 0.9708797410003432
10 3.285265663476279 0.8932217714634569
11 3.2920300921616823 0.8245049116194323
12 3.298690774179099 0.7634734536223046
13 3.30525084386195 0.7090773607234804
14 3.3117132956157804 0.6604825504936115
15 3.3180809921254553 0.6170128935600759
16 3.324356671969315 0.5780798776688211
17 3.330542956690987 0.5430880946966065
18 3.336642357374558 0.511404571696989
19 3.3426572807643797 0.4824992878480597
20 3.348590034966843 0.455949417663389
21 3.3544428347679145 0.43151272119405476
22 3.3602178065971016 0.40903218201071395
23 3.365916993165687 0.38836120026332716
24 3.3715423578045485 0.3693540051327973
25 3.3770957885246133 0.3518298511568298
26 3.3825791018209665 0.3356158854973798
27 3.387994046239784 0.32056293864739577
28 3.393342305725622 0.3065429216956495
29 3.3986255027650953 0.2934543854283516
30 3.4038452013416287 0.281213301029395
31 3.4090029097147374 0.2697448362931856
32 3.414100083036208 0.25898214040025697
33 3.4191381258145217 0.2488673792077368
34 3.4241183942379636 0.2393536006629945
35 3.4290421983660386 0.23039862595713706
36 3.4339108041980437 0.22196368208516906
37 3.4387254356269743 0.21401309193758467
38 3.443487276286304 0.20651205474559298
39 3.4481974712966155 0.1994240124885713
40 3.4528571289185255 0.19271530665406983
41 3.4574673221178696 0.18635561227935576
42 3.4620290900486865 0.1803175605823477
43 3.4665434394591204 0.17457671803644545
44 3.4710113460249996 0.1691138222957068
45 3.4754337556155193 0.16391268874241216
46 3.4798115854951233 0.15895824350530083
47 3.484145725465414 0.1542364204295611
48 3.488437038950643 0.14973408558152645
49 3.4926863640300936 0.14543880207480547
50 3.496894514420445 0.1413376698953472
51 3.5010622804110003 0.13741816795031556
52 3.505190429754458 0.13366876357919796
53 3.5092797085157517 0.13007882581667837
54 3.5133308418812925 0.12663854361666163
55 3.5173445349308197 0.12333885720059015
56 3.5213214733739076 0.12017183201443447
57 3.5252623242530547 0.11713086848202323
58 3.5291677366151633 0.11420983531041214
59 3.5330383421530884 0.11140295102345926
60 3.5368747558188587 0.108704761356224
61 3.540677576410038 0.10611011826904865
62 3.5444473871306483 0.10361412196711942
63 3.548184756127948 0.10121159021533338
64 3.551890237006312 0.09889726210958212
65 3.555564369319379 0.09666623415284005
66 3.559207679041547 0.09451394222732153
67 3.562820679019862 0.09243613441058249
68 3.566403869407262 0.09042884626687994
69 3.5699577380780934 0.0884883783617504
70 3.5734827610267654 0.08661127577601353
71 3.5769794027503554 0.08479445279481848
72 3.580448116615933 0.08303565995657396
73 3.5838893452133327 0.08133292315387707
74 3.5873035206940593 0.07968433454402822
75 3.5906910650969732 0.07808805102163871
76 3.5940523906613766 0.0765422926528761
77 3.59738790012807 0.07504534106387294
78 3.600697987028943 0.07359553779645431
79 3.6039830359656038 0.07219128264269653
80 3.6072434228775583 0.07083102781608791
81 3.6104795153003915 0.06951307701457524
82 3.6136916726144026 0.06823552408812261
83 3.6168802462841128 0.06699654769295077
84 3.620045580089044 0.06579442872664083
85 3.6231880103461505 0.06462754361361508
86 3.6263078661242556 0.06349435809407272
87 3.629405469450848 0.06239342147408471
88 3.63248113551155 0.06132336129847827
89 3.635535172842572 0.06028287841165721
90 3.6385678835164526 0.05927074237468046
91 3.641579563321351 0.05828580478162973
92 3.6445705019341714 0.057327148422392074
93 3.6475409830877643 0.056393990383649395
94 3.650491284732447 0.05548557396340301
95 3.6534216791920784 0.05460116664084014
96 3.656332433314898 0.05374005935093514
97 3.6592238086193505 0.05290156577245305
98 3.6620960614350815 0.05208502163012042
99 3.6649494430393066 0.05128978401157684
100 3.6677841997887266 0.05051523069959573
101 3.670600573247166 0.04976075951994638
102 3.6733988003091 0.049025787705167016
103 3.676179113319227 0.0483097305297635
104 3.678941740188239 0.047611875735435
105 3.6816869045049305 0.04693148053575122
106 3.684414825644789 0.0462678393702357
107 3.687125718875195 0.04562028201716805
108 3.68981979545736 0.04498817154617889
109 3.69249726274512 0.04437090240669901
110 3.6951583242807042 0.04376789864211611
111 3.6978031798875888 0.043178612220332535
112 3.7004320257605356 0.04260252147217671
113 3.703045054552927 0.04203912962981675
114 3.705642455461487 0.04148796345795638
115 3.7082244143084826 0.04094857197117103
116 3.7107911136215 0.04042052547245066
117 3.7133427327108732 0.039903428081875025
118 3.7158794477448516 0.03939692255237446
119 3.7184014318225884 0.038900668352304005
120 3.7209088550450193 0.03841433903090943
121 3.7234018845837062 0.03793762153385209
122 3.725880684747718 0.03747021555688869
123 3.7283454170486134 0.03701183293530849
124 3.730796240263589 0.03656219706689588
125 3.7332333104968543 0.03612104236634222
126 3.7356567812392996 0.03568811374917144
127 3.738066803426503 0.03526316614337954
128 3.740463525495138 0.03484596402710735
129 3.7428470934378337 0.03443628099077986
130 3.7452176508565396 0.03403389932225026
131 3.7475753390144373 0.033638610605193645
132 3.749920296886455 0.03325022874966554
133 3.7522526612084244 0.0328685849331552
134 3.754572566524923 0.032493515882363544
135 3.7568801452358467 0.032124863381159455
136 3.7591755276417516 0.031762474081957336
137 3.7614588419880053 0.031406199325266435
138 3.7637302145077793 0.03105589496701136
139 3.7659897694639275 0.030711421213242496
140 3.768237629189779 0.030372642461878298
141 3.77047391412888 0.03003942715113998
142 3.772698742873721 0.029711647614356705
143 3.7749122322034694 0.029389179940838474
144 3.7771144971207566 0.02907190384252694
145 3.7793056508875273 0.028759702526153712
146 3.781485805059993 0.028452462570646497
147 3.783655069522714 0.028150073809834017
148 3.785813552521831 0.027852437555167312
149 3.787961360697478 0.0275594871552878
150 3.7900985991153946 0.027271162792434148
151 3.7922253712977674 0.02698740449987387
152 3.7943417792533194 0.026708152233679342
153 3.796447923506665 0.026433345939659215
154 3.7985439031269648 0.026162925615698777
155 3.8006298157558858 0.025896831369755083
156 3.802705757634895 0.025635003473739194
157 3.8047718236319064 0.025377382413507073
158 3.8068281072672923 0.025123908935172197
159 3.808874700739287 0.024874524087939928
160 3.8109116949487896 0.024629169263657366
161 3.8129391795235934 0.024387786233260177
162 3.814957242842046 0.02415031718029197
163 3.8169659720561686 0.02391670473166033
164 3.8189654531142376 0.02368689198578812
165 3.820955770782853 0.02346081726654466
166 3.822937008668501 0.02323826342281111
167 3.824909249238627 0.023018846986179224
168 3.826872573842234 0.02280219668365782
169 3.828827062730015 0.022587962678799832
170 3.8307727950740382 0.022375815581227494
171 3.832709848986988 0.02216544553857752
172 3.834638301540983 0.021956561402791078
173 3.8365582287859765 0.021748889963373312
174 3.8384697057677526 0.021542175240878217
175 3.8403728065455263 0.02133617783444549
176 3.8422676042091624 0.02113067431773675
177 3.8441541708960187 0.0209254566780917
178 3.8460325778074242 0.02072033179415623
179 3.847902895224804 0.020515120947629317
180 3.849765192525459 0.020309659365139145
181 3.851619538198005 0.020103795786591445
182 3.853465999857491 0.019897392056640425
183 3.855304644260187 0.019690324402365966
184 3.8571355373180696 0.019482636461923223
185 3.858958744112999 0.019274649134078236
186 3.8607743289105994 0.01906669435902634
187 3.8625823551738527 0.018859085066472027
188 3.864382885576409 0.01865211569832508
189 3.8661759820156214 0.018446062763581402
190 3.8679617056253157 0.01824118541948394
191 3.869740116788295 0.01803772607353675
192 3.8715112751485927 0.01783591100140398
193 3.8732752396234744 0.01763595097617088
194 3.8750320684151984 0.017438041904868005
195 3.8767818190225367 0.017242365468563767
196 3.87852454825207 0.017049089762712917
197 3.8802603122292516 0.016858369934812715
198 3.8819891664092534 0.016670348816755363

514
exp_temp/Rocking_curves_with_hexapod.ipynb Normal file
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@@ -0,0 +1,514 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "911f65de-cee5-4218-b508-5ab1063fdc3a",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-05-12 at 14:39:29 | INFO | Loading started.\n",
"2025-05-12 at 14:39:29 | WARNING | Cannot write log file to snapshots.\n",
"2025-05-12 at 14:39:29 | WARNING | [Errno 13] Permission denied: '/sf/cristallina/applications/beamline/snapshots/slic_logs/cristallina.log'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/gfa/.mounts/sf_cristallina/applications/slic/cristallina\n",
"\u001b]0;⊚slic\u0007could not set up DBusNotify: org.freedesktop.DBus.Error.NotSupported: Using X11 for dbus-daemon autolaunch was disabled at compile time, set your DBUS_SESSION_BUS_ADDRESS instead\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-05-12 at 14:39:35 | WARNING | No transmission value reported from SARFE10-OATT053\n",
"2025-05-12 at 14:39:35 | WARNING | No transmission value reported from SAROP31-OATA150\n",
"2025-05-12 at 14:39:36 | INFO | Using undulator (Aramis) offset to PSSS energy of 0 eV.\n",
"2025-05-12 at 14:39:36 | INFO | Photon energy offsets: PSSS -8 eV , DCCM -12 eV, undulator -16 eV.\n",
"2025-05-12 at 14:39:36 | WARNING | Error: Could not connect to dilution fridge. name 'Dilution' is not defined\n",
"2025-05-12 at 14:39:36 | INFO | Connected to stand server\n",
"2025-05-12 at 14:39:36 | WARNING | Cannot write log file to pgroup p22478.\n",
"2025-05-12 at 14:39:36 | INFO | Using CristallinaQ setup for detectors.\n",
"2025-05-12 at 14:39:36 | INFO | Running at cristallina with pgroup p22478. Experiment type: Q.\n",
"2025-05-12 at 14:39:36 | INFO | Loading finished.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"SAT functions\n"
]
}
],
"source": [
"import sys\n",
"sys.path.append('/sf/cristallina/applications/slic/cristallina/crq_exp/')\n",
"from tqdm.notebook import tqdm\n",
"%cd /sf/cristallina/applications/slic/cristallina\n",
"%run /sf/cristallina/applications/slic/cristallina/cristallina.py"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "0e264fea-8070-4173-bd1b-2e86ea783386",
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"import smaract.smarpod as smarpod\n",
"from smaract.smarpod import Pose as Pose"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "564f383e-a516-4160-b446-0bae514e62dd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"api version = 1.9.20\n",
"lib version = 1.11.4\n",
"device opened successfully\n",
"referencing...\n",
"movement status = 1 (1)\n",
"movement status = 0 (0)\n",
"pose = 9.53363734703935e-10,-4.3032376366873044e-10,-0.0016776778132903925,-9.549834512512486e-07,-3.8592949236139845e-06,7.816448044732403\n"
]
}
],
"source": [
"def check_lib_compatibility():\n",
" \"\"\"\n",
" checks that the major version numbers of the Python API and the\n",
" loaded shared library are the same to avoid errors due to \n",
" incompatibilites.\n",
" raises a RuntimeError if the major version numbers are different.\n",
" \"\"\"\n",
" vapi = smarpod.api_version\n",
" vlib = smarpod.GetDLLVersion()\n",
" if vapi[0] != vlib[0]:\n",
" raise RuntimeError(\"incompatible python api and library version\") \n",
"\n",
"def print_info():\n",
" vapi = smarpod.api_version\n",
" print(\"api version = %s.%s.%s\" % (vapi[0], vapi[1], vapi[2]))\n",
" vlib = smarpod.GetDLLVersion()\n",
" print(\"lib version = %s.%s.%s\" % (vlib[0], vlib[1], vlib[2]))\n",
"\n",
"def print_controllers():\n",
" syslist = smarpod.FindSystems().splitlines()\n",
" for sys in syslist:\n",
" print(\"controllers found:\\n%s\\n\" % sys) \n",
"\n",
"def pose_to_str(pose):\n",
" return \"%s,%s,%s,%s,%s,%s\" % (\n",
" pose.positionX, pose.positionY, pose.positionZ,\n",
" pose.rotationX, pose.rotationY, pose.rotationZ)\n",
"\n",
"def pose_print(pose):\n",
" p = pose_to_str(pose)\n",
" print(f'X={pose.positionX:.3e}\\nY={pose.positionY:.3e}\\nZ={pose.positionZ:.3e}\\nRX={pose.rotationX:.3e}\\nRY={pose.rotationY:.3e}\\nRZ={pose.rotationZ:.3e}')\n",
" \n",
"def check_pose_list(h,list):\n",
" \"\"\"\n",
" returns True if all poses in list are reachable\n",
" \"\"\"\n",
" for pose in list:\n",
" if not smarpod.IsPoseReachable(h,pose):\n",
" return False\n",
" return True\n",
"\n",
"def move_sequence(h,seq):\n",
" \"\"\"\n",
" moves to each pose in the list seq\n",
" \"\"\"\n",
" for pose in seq:\n",
" print(\"moving to %s\" % pose_to_str(pose))\n",
" smarpod.Move(h,pose,0,True)\n",
"\n",
"\n",
"h = None # SmarPod device handle\n",
"try:\n",
" check_lib_compatibility()\n",
" print_info()\n",
"\n",
" # change model to the model code of your SmarPod model code before\n",
" # starting the program\n",
" model = 10068\n",
"\n",
"\n",
" # uncomment the following line to show available controllers\n",
" #print_controllers()\n",
" \n",
" # change the locator of your positioner controller: if it is connected\n",
" # over USB, \"usb:ix:0\" should work. If if it connected over ethernet,\n",
" # use the network locator format and replace the ip address by the one\n",
" # of your controller. (for MCS 1 controllers append the port number\n",
" # :5000, for MCS 2 controllers, when specifying the locator with an IPv4 \n",
" # address, no port should be appended. alternatively, for MCS 2 the\n",
" # serial number can be used in the locator)\n",
" locator = 'network:MCS2-00002749'\n",
" #locator = \"usb:id:123456789\" # MCS 1\n",
" #locator = \"network:198.168.1.200:5000\" # MCS 1\n",
" #locator = \"network:ip:198.168.1.200\" # MCS 2\n",
" #locator = \"network:sn:MCS2-000001234\" # MCS 2\n",
"\n",
" # set to True to perform a calibration of the positioner sensors\n",
" do_calibrate = False\n",
" # the sensor power mode to set\n",
" sensor_mode = smarpod.SensorPowerMode.ENABLED\n",
"\n",
" # enable z-safe ref method to prefer z-negative direction\n",
" # (this will fail with a FEATURE_UNAVAILABLE error if the SmarPod model \n",
" # doesn't support the selected method)\n",
" ref_zsafe = False\n",
" # perform a referencing even if SmarPod is already the referenced state\n",
" force_referencing = False\n",
"\n",
" \n",
" # open the connection to the SmarPod device.\n",
" # on success, a handle is returned, else an execption is thrown\n",
" # if Open raises an exception with ErrorCode.SYSTEM_CONFIGURATION\n",
" # the model code and the controller configuration don't match.\n",
" h = smarpod.Open(model, locator)\n",
" print(\"device opened successfully\")\n",
"\n",
" # set the frequency used for referencing and calibration\n",
" smarpod.Set_ui(h, smarpod.Property.FREF_AND_CAL_FREQUENCY, 8000)\n",
"\n",
" # if do_calibrate is True, perform calibration\n",
" if do_calibrate:\n",
" print(\"calibrating...\")\n",
" smarpod.Calibrate(h)\n",
"\n",
" # if not referenced, perform referencing\n",
" do_referencing = force_referencing or not smarpod.IsReferenced(h)\n",
" if do_referencing:\n",
" # set referencing method and parameters\n",
" if (ref_zsafe):\n",
" smarpod.Set_ui(h, smarpod.Property.FREF_METHOD, smarpod.FrefMethod.ZSAFE)\n",
" smarpod.Set_ui(h, smarpod.Property.FREF_ZDIRECTION, smarpod.Direction.NEGATIVE)\n",
" else:\n",
" smarpod.Set_ui(h, smarpod.Property.FREF_METHOD, smarpod.DEFAULT)\n",
" print(\"referencing...\")\n",
" smarpod.FindReferenceMarks(h)\n",
"\n",
" smarpod.SetSensorMode(h, sensor_mode)\n",
"\n",
" mstat = smarpod.GetMoveStatus(h)\n",
" print(\"movement status = %s (%s)\" % (smarpod.MoveStatus(mstat), mstat))\n",
"\n",
" smarpod.Stop(h)\n",
"\n",
" mstat = smarpod.GetMoveStatus(h)\n",
" print(\"movement status = %s (%s)\" % (smarpod.MoveStatus(mstat), mstat))\n",
"\n",
"\n",
" # move sequence of poses\n",
"\n",
" # set speed and maximum closed-loop piezo frequency\n",
" smarpod.SetSpeed(h, 1, 3e-3)\n",
" smarpod.SetMaxFrequency(h, 18500)\n",
"\n",
" # pivot point should move with the top plate: set to RELATIVE mode\n",
" smarpod.Set_ui(h, smarpod.Property.PIVOT_MODE, smarpod.PivotMode.RELATIVE)\n",
" # set pivot point\n",
" smarpod.SetPivot(h,[0,0,20e-3])\n",
"\n",
" pHome = Pose(0, 0, 0, 0, 0, 0)\n",
" pSequence = [\n",
" pHome,\n",
" Pose(0, 0, 0.002, 0, 0, 0),\n",
" Pose(0, 0, -0.002, 0, 0, 0),\n",
" Pose(-0.002, 0, 0, 0, 0, 0),\n",
" Pose(-0.002, -0.002, 0, 0, 0, 0),\n",
" Pose(0.002, 0.002, 0, 0, 0, 0),\n",
" Pose(0, 0, 0, 0, 0, -5),\n",
" Pose(0, 0, 0, -0.02, 0, 5),\n",
" pHome\n",
" ]\n",
"\n",
" if not check_pose_list(h,pSequence):\n",
" raise Exception(\"not all poses in sequence are reachable\")\n",
"\n",
" # if all poses are reachable, move the sequence of poses\n",
" #move_sequence(h,pSequence)\n",
"\n",
" # query current pose (should be close to pHome)\n",
" pose = smarpod.GetPose(h)\n",
" print(\"pose = %s\" % pose_to_str(pose))\n",
"\n",
"except smarpod.Error as err:\n",
" print(\"SMARPOD ERROR: %s -> %s (%s) \" % (err.func, smarpod.ErrorCode(err.code), err.code))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "228d8308-11d5-4af7-bac7-250e02d81e82",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X=-4.226e-10\n",
"Y=2.440e-10\n",
"Z=1.155e-09\n",
"RX=9.547e-07\n",
"RY=1.654e-06\n",
"RZ=-5.730e-06\n"
]
}
],
"source": [
"smarpod.Move(h,Pose(0, 0, 0, 0, 0, 0),100,True)\n",
"pose = smarpod.GetPose(h)\n",
"pose_print(pose)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7ed3d2c9-f918-4725-9dda-191e8bb17884",
"metadata": {},
"outputs": [],
"source": [
"def pose_to_str(pose):\n",
" return \"%s,%s,%s,%s,%s,%s\" % (\n",
" pose.positionX, pose.positionY, pose.positionZ,\n",
" pose.rotationX, pose.rotationY, pose.rotationZ)\n",
"\n",
"def pose_to_array(pose):\n",
" return np.asarray([pose.positionX, pose.positionY, pose.positionZ,\n",
" pose.rotationX, pose.rotationY, pose.rotationZ])\n",
"\n",
"def array_to_pose(arr):\n",
" arr = np.asarray(arr)\n",
" shape = len(arr.shape)\n",
" assert shape <3, KeyError('Your array needs to be 2D max')\n",
" if shape == 1: \n",
" return Pose(*arr)\n",
" else:\n",
" poses = []\n",
" for i in range(arr.shape[0]):\n",
" poses.append(Pose(*arr[i,:]))\n",
" return poses\n",
"\n",
"def make_positions(base_array, index,new_values):\n",
" result = np.tile(base_array, (len(new_values),1)).astype('float64')\n",
" result[:,index] = new_values\n",
" return result\n",
"\n",
"def convert_results_to_pandas(readbacks,times,timestrings):\n",
" d = pd.DataFrame({'Time':timestrings,\n",
" 'X (m)':readbacks[:,0],\n",
" 'Y (m)':readbacks[:,1],\n",
" 'Z (m)':readbacks[:,2],\n",
" 'RX (deg)':readbacks[:,3],\n",
" 'RY (deg)':readbacks[:,4],\n",
" 'RZ (deg)':readbacks[:,5],\n",
" 'Epoch time':times\n",
" })\n",
" return d"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "164d4bf4-5eac-4681-904f-e3e8f4d44b7b",
"metadata": {},
"outputs": [],
"source": [
"def move_around_current_pose(pose,axis_index,movement_array,hold_time=1000,folder=None,filename=None,press_to_continue=True):\n",
" assert (type(axis_index) == int) & (axis_index>=0) & (axis_index<6), KeyError('Axis index must be an int between 0 and 5 (e.g. RX=3)')\n",
" pose_beg = pose_to_array(pose)\n",
" positions = make_positions(pose_beg, axis_index, movement_array)\n",
" poses = array_to_pose(positions) \n",
" readbacks,times,timestrings = [], [],[]\n",
" for i,pos in enumerate(poses):\n",
" print(f'Step {i}: Go to position {pose_to_str(pos)}')\n",
" if press_to_continue:\n",
" input('Press any key to continue')\n",
" smarpod.Move(h,pos,hold_time,True)\n",
" readbacks.append(pose_to_array( smarpod.GetPose(h) ))\n",
" times.append(time.time())\n",
" timestrings.append(time.strftime('%Y-%m-%d %H:%M:%S'))\n",
" results = convert_results_to_pandas(np.asarray(readbacks),times,timestrings)\n",
" if folder:\n",
" if filename == None:\n",
" filename = f'{time.strftime(\"%Y%m%d_%H%M%S\")}_axis={axis_index}.csv'\n",
" else:\n",
" filename=filename+'.csv'\n",
" filepath=os.path.join(folder,filename)\n",
" results.to_csv(filepath)\n",
" return results\n",
" \n",
"#res = move_around_current_pose(smarpod.GetPose(h),5,[-5,0,5])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b2e7239e-ae18-4f04-b3d0-15d6251bda66",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">╭─ daq.acquire = &lt;bound method SFAcquisition.acquire of SF DAQ (status: idle, last run: None) BrokerAPI @ http://─╮</span>\n",
"<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff; font-style: italic\">def </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">SFAcquisition.acquire</span><span style=\"font-weight: bold\">(</span>filename, <span style=\"color: #808000; text-decoration-color: #808000\">data_base_dir</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #808000; text-decoration-color: #808000\">detectors</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #808000; text-decoration-color: #808000\">channels</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #808000; text-decoration-color: #808000\">pvs</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n",
"<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000\">scan_info</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #808000; text-decoration-color: #808000\">n_pulses</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span>, <span style=\"color: #808000; text-decoration-color: #808000\">n_repeat</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #808000; text-decoration-color: #808000\">is_scan_step</span>=<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, <span style=\"color: #808000; text-decoration-color: #808000\">wait</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, **kwargs<span style=\"font-weight: bold\">)</span>: <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n",
"<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n",
"<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27</span><span style=\"font-style: italic\"> attribute(s) not shown.</span> Run <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">inspect</span><span style=\"font-weight: bold\">(</span>inspect<span style=\"font-weight: bold\">)</span> for options. <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n",
"<span style=\"color: #000080; text-decoration-color: #000080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[34m╭─\u001b[0m\u001b[34m daq.acquire = <bound method SFAcquisition.acquire of SF DAQ (status: idle, last run: None) BrokerAPI @ http://\u001b[0m\u001b[34m─╮\u001b[0m\n",
"\u001b[34m│\u001b[0m \u001b[3;96mdef \u001b[0m\u001b[1;31mSFAcquisition.acquire\u001b[0m\u001b[1m(\u001b[0mfilename, \u001b[33mdata_base_dir\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mdetectors\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mchannels\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mpvs\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[34m│\u001b[0m\n",
"\u001b[34m│\u001b[0m \u001b[33mscan_info\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mn_pulses\u001b[0m=\u001b[1;36m100\u001b[0m, \u001b[33mn_repeat\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mis_scan_step\u001b[0m=\u001b[3;91mFalse\u001b[0m, \u001b[33mwait\u001b[0m=\u001b[3;92mTrue\u001b[0m, **kwargs\u001b[1m)\u001b[0m: \u001b[34m│\u001b[0m\n",
"\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
"\u001b[34m│\u001b[0m \u001b[1;36m27\u001b[0m\u001b[3m attribute(s) not shown.\u001b[0m Run \u001b[1;35minspect\u001b[0m\u001b[1m(\u001b[0minspect\u001b[1m)\u001b[0m for options. \u001b[34m│\u001b[0m\n",
"\u001b[34m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"daq.acquire?"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "c5a5a484-2130-45a9-8a75-bf558238d71b",
"metadata": {},
"outputs": [],
"source": [
"pose = smarpod.GetPose(h)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "14e64a15-f7d8-4fa3-b234-55e16d8e6da8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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"data": {
"text/plain": [
"\n",
"\u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m-2.74212563e-09\u001b[0m, \u001b[1;36m-3.44389924e-09\u001b[0m, \u001b[1;36m4.59956670e-08\u001b[0m,\n",
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" \u001b[1m[\u001b[0m\u001b[1;36m-2.74212563e-09\u001b[0m, \u001b[1;36m-3.44389924e-09\u001b[0m, \u001b[1;36m4.59956670e-08\u001b[0m,\n",
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" \u001b[1m[\u001b[0m\u001b[1;36m-2.74212563e-09\u001b[0m, \u001b[1;36m-3.44389924e-09\u001b[0m, \u001b[1;36m4.59956670e-08\u001b[0m,\n",
" \u001b[1;36m-2.65212072e-05\u001b[0m, \u001b[1;36m1.69357078e-06\u001b[0m, \u001b[1;36m1.00000000e+00\u001b[0m\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"make_positions(pose_to_array(pose), 5, [-1,0,1])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "bf5fec3c-1680-4505-afcc-1af088636c29",
"metadata": {},
"outputs": [],
"source": [
"def hexapod_rocking_curve(positions,scan_name,settle_time=1,n_pulses=100,folder=os.getcwd(),filename=None):\n",
" readbacks,times,timestrings = [], [],[]\n",
" poses = array_to_pose(positions)\n",
" \n",
" for i,pos in enumerate(poses):\n",
" if i==0:\n",
" is_scan_step=False\n",
" else:\n",
" is_scan_step=True\n",
"\n",
" smarpod.Move(h,pos,1000,True)\n",
" time.sleep(settle_time)\n",
"\n",
" # Record hexapod positions\n",
" readbacks.append(pose_to_array( smarpod.GetPose(h) ))\n",
" times.append(time.time())\n",
" timestrings.append(time.strftime('%Y-%m-%d %H:%M:%S'))\n",
"\n",
" # Record the data\n",
" daq.acquire(scan_name,n_pulses=n_pulses,is_scan_step=is_scan_step)\n",
"\n",
" # Save encoder readbacks readbacks\n",
" results = convert_results_to_pandas(np.asarray(readbacks),times,timestrings)\n",
" if filename == None:\n",
" filename = f'{time.strftime(\"%Y%m%d_%H%M%S\")}_axis={axis_index}.csv'\n",
" else:\n",
" filename=filename+'.csv'\n",
" filepath=os.path.join(folder,filename)\n",
" results.to_csv(filepath)\n",
" return results\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0aaf3507-518f-4529-8483-418ba1ea5688",
"metadata": {},
"outputs": [],
"source": []
}
],
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