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diff --git a/test_data/exp932/jupynb/HB1_ToolBox.ipynb b/test_data/exp932/jupynb/HB1_ToolBox.ipynb
deleted file mode 100644
index fbd043cc..00000000
--- a/test_data/exp932/jupynb/HB1_ToolBox.ipynb
+++ /dev/null
@@ -1,1616 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "id": "bd048c5e-01d0-40a1-9924-0a0bef6e140c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>.container { width:150% !important; }</style>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from IPython.display import display, HTML\n",
-    "display(HTML(\"<style>.container { width:150% !important; }</style>\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8c107f90-7487-467b-a39b-71d8e7787c60",
-   "metadata": {},
-   "source": [
-    "# PROGRAM LAYOUT FROM IXSFIT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "id": "2197a354-ffe6-47f9-80cc-bf703793f956",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "Cannot load backend 'TkAgg' which requires the 'tk' interactive framework, as 'qt' is currently running",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[173], line 20\u001b[0m\n\u001b[0;32m     18\u001b[0m os\u001b[38;5;241m.\u001b[39menviron[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMPLCONFIGDIR\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m tempfile\u001b[38;5;241m.\u001b[39mgettempdir()\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m---> 20\u001b[0m matplotlib\u001b[38;5;241m.\u001b[39muse(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTkAgg\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     21\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmpl_toolkits\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmplot3d\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Axes3D\n",
-      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\__init__.py:1249\u001b[0m, in \u001b[0;36muse\u001b[1;34m(backend, force)\u001b[0m\n\u001b[0;32m   1244\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m plt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1245\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1246\u001b[0m         \u001b[38;5;66;03m# we need this import check here to re-raise if the\u001b[39;00m\n\u001b[0;32m   1247\u001b[0m         \u001b[38;5;66;03m# user does not have the libraries to support their\u001b[39;00m\n\u001b[0;32m   1248\u001b[0m         \u001b[38;5;66;03m# chosen backend installed.\u001b[39;00m\n\u001b[1;32m-> 1249\u001b[0m         plt\u001b[38;5;241m.\u001b[39mswitch_backend(name)\n\u001b[0;32m   1250\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[0;32m   1251\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m force:\n",
-      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\pyplot.py:350\u001b[0m, in \u001b[0;36mswitch_backend\u001b[1;34m(newbackend)\u001b[0m\n\u001b[0;32m    347\u001b[0m     current_framework \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39m_get_running_interactive_framework()\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (current_framework \u001b[38;5;129;01mand\u001b[39;00m required_framework\n\u001b[0;32m    349\u001b[0m             \u001b[38;5;129;01mand\u001b[39;00m current_framework \u001b[38;5;241m!=\u001b[39m required_framework):\n\u001b[1;32m--> 350\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[0;32m    351\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot load backend \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m which requires the \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m interactive \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    352\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mframework, as \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m is currently running\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m    353\u001b[0m                 newbackend, required_framework, current_framework))\n\u001b[0;32m    355\u001b[0m \u001b[38;5;66;03m# Load the new_figure_manager() and show() functions from the backend.\u001b[39;00m\n\u001b[0;32m    356\u001b[0m \n\u001b[0;32m    357\u001b[0m \u001b[38;5;66;03m# Classically, backends can directly export these functions.  This should\u001b[39;00m\n\u001b[0;32m    358\u001b[0m \u001b[38;5;66;03m# keep working for backcompat.\u001b[39;00m\n\u001b[0;32m    359\u001b[0m new_figure_manager \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(module, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnew_figure_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
-      "\u001b[1;31mImportError\u001b[0m: Cannot load backend 'TkAgg' which requires the 'tk' interactive framework, as 'qt' is currently running"
-     ]
-    }
-   ],
-   "source": [
-    "#!/usr/bin/env python\n",
-    "import os\n",
-    "import sys\n",
-    "import glob\n",
-    "import socket\n",
-    "import smtplib\n",
-    "import datetime\n",
-    "import subprocess\n",
-    "import itertools\n",
-    "import threading\n",
-    "import time\n",
-    "import sys\n",
-    "import webbrowser\n",
-    "import numpy as np\n",
-    "import math\n",
-    "import os\n",
-    "import tempfile\n",
-    "os.environ[\"MPLCONFIGDIR\"] = tempfile.gettempdir()\n",
-    "import matplotlib\n",
-    "matplotlib.use('TkAgg')\n",
-    "import matplotlib.pyplot as plt\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "import numpy as np\n",
-    "import tkinter as tk\n",
-    "from tkinter import BOTH, END, LEFT\n",
-    "from tkinter import *\n",
-    "import subprocess as sub\n",
-    "from tkinter import filedialog\n",
-    "import webbrowser\n",
-    "import ufit\n",
-    "import numpy as np\n",
-    "from ufit.lab import *\n",
-    "#import tkMessageBox\n",
-    "#os.environ[ 'MPLCONFIGDIR' ] = 'D:\\.matplotlib'\n",
-    "\n",
-    "#Main master GUI properties\n",
-    "specle = tk.Tk()\n",
-    "specle.title('HB1 ToolBox 2024 (c) Avishek Maity')\n",
-    "#master.geometry('300x700+10+10')\n",
-    "\n",
-    "set_datatemplate('C:/Users/num/Documents/cycle 506/exp932/Datafiles/HB1_exp0932_scan%04d.dat')\n",
-    "ufit.set_dataformat('simple')\n",
-    "\n",
-    "\n",
-    "global xye_file\n",
-    "xye_file='X:/path/to/spec_file.dat'\n",
-    "\n",
-    "n=0\n",
-    "#tk.Label(spec, text=\"SUPPLEM\", font='TkFixedFont', width=12, anchor='w').grid(row=n+0, column=0);  \n",
-    "e0_1 = tk.Entry(specle, width=37); e0_1.insert(END,xye_file);  e0_1.grid(row=n+1, column=0, columnspan=2, pady=0)\n",
-    "#tk.Label(spec, text=\"SUMMARY\", font='TkFixedFont', width=12, anchor='w').grid(row=n+1, column=0);      \n",
-    "\n",
-    "def UploadSupplement():\n",
-    "    global spec_file, fname\n",
-    "    spec_file = filedialog.askopenfilename()\n",
-    "    for i in range(len(spec_file)):\n",
-    "        if spec_file[i]=='/':\n",
-    "            titl=spec_file[i+1:]\n",
-    "    n=0\n",
-    "    e0_1 = tk.Entry(specle, width=37); e0_1.insert(END,titl); e0_1.grid(row=n+1, column=0,columnspan=2,pady=0)\n",
-    "    #xye_file=str(e0_1.get())\n",
-    "    try:\n",
-    "        os.mkdir('./extracted')\n",
-    "    except:\n",
-    "        pass\n",
-    "    fout=open('./extracted/extractor.ini','w')\n",
-    "    \n",
-    "    fname=spec_file\n",
-    "    webbrowser.open(fname)\n",
-    "def MakeSummary():\n",
-    "    global fname\n",
-    "    #print(fname)\n",
-    "    fout0=open('./Summary.txt','w')\n",
-    "    f =open(fname, 'r')\n",
-    "    for line in f:\n",
-    "        if '#S' in line:\n",
-    "            linen=next(f)\n",
-    "            linen=linen.replace(\"\\n\",\"    \")\n",
-    "            fout0.write(linen + ':' + line)\n",
-    "    fout0.close()\n",
-    "    webbrowser.open('Summary.txt')\n",
-    "    \n",
-    "def scan2pol():\n",
-    "    scn_Pzz,scn_Pzx,scn_Pzy,scn_Pxx,scn_Pxy,scn_Pyy,scn_Pyx,scn_Pxz,scn_Pyz=eval()\n",
-    "    plabel=['Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz']\n",
-    "    P = np.full((3,3), np.nan)\n",
-    "    u=[]\n",
-    "    d=[]\n",
-    "    u1=[]\n",
-    "    d1=[]\n",
-    "    i=0\n",
-    "    for n in [scn_Pzz,scn_Pzx,scn_Pzy,scn_Pxx,scn_Pxy,scn_Pyy,scn_Pyx,scn_Pxz,scn_Pyz]:    \n",
-    "        try:\n",
-    "            data=read_data(n)\n",
-    "            plt.plot(data['col_ds_nut'],55555*data['col_detector']/data['col_monitor'],'^-', label='scan'+str(n)+':'+plabel[i])\n",
-    "            \n",
-    "            u.append(data['col_detector'][0]/data['col_monitor'][0])\n",
-    "            d.append(data['col_detector'][1]/data['col_monitor'][1])\n",
-    "    \n",
-    "            u1.append(data['col_detector'][0])\n",
-    "            d1.append(data['col_detector'][1])\n",
-    "            i=i+1\n",
-    "            #print(n)\n",
-    "        except:\n",
-    "            pass\n",
-    "            print(n)\n",
-    "    plt.xlabel('SF                                                  ds_nut_amps (A)                                              NSF')\n",
-    "    plt.xticks([-3.2,3.2])\n",
-    "    plt.ylabel('counts / 55555 mon [~ 1 min]')\n",
-    "    plt.legend(fontsize=9, ncol=3)\n",
-    "    plt.title(data['subtitle'][:-4]+', T ='+str('%6.2f'%data['sample'])+' K'+', time ='+str('%6.2f'%data['time'])+' sec')\n",
-    "    up=np.zeros([3,3])\n",
-    "    dn=np.zeros([3,3])\n",
-    "      \n",
-    "    up[2,2],up[2,0],up[2,1],up[0,0],up[0,1],up[1,1],up[1,0],up[0,2],up[1,2]=u[0],u[1],u[2],u[3],u[4],u[5],u[6],u[7],u[8]\n",
-    "    dn[2,2],dn[2,0],dn[2,1],dn[0,0],dn[0,1],dn[1,1],dn[1,0],dn[0,2],dn[1,2]=d[0],d[1],d[2],d[3],d[4],d[5],d[6],d[7],d[8]\n",
-    "    \n",
-    "    up1=np.zeros([3,3])\n",
-    "    dn1=np.zeros([3,3])\n",
-    "       \n",
-    "    up1[2,2],up1[2,0],up1[2,1],up1[0,0],up1[0,1],up1[1,1],up1[1,0],up1[0,2],up1[1,2]=u1[0],u1[1],u1[2],u1[3],u1[4],u1[5],u1[6],u1[7],u1[8]\n",
-    "    dn1[2,2],dn1[2,0],dn1[2,1],dn1[0,0],dn1[0,1],dn1[1,1],dn1[1,0],dn1[0,2],dn1[1,2]=d1[0],d1[1],d1[2],d1[3],d1[4],d1[5],d1[6],d1[7],d1[8]\n",
-    "    \n",
-    "    P=(up1-dn1)/(up1+dn1)\n",
-    "    \n",
-    "    txt_up='NSF=\\n'\\\n",
-    "     '['+str('%5d'%up1[0,0])+', '+str('%5d'%up1[0,1])+', '+str('%5d'%up1[0,2])+']'+'\\n'\\\n",
-    "    +'['+str('%5d'%up1[1,0])+', '+str('%5d'%up1[1,1])+', '+str('%5d'%up1[1,2])+']'+'\\n'\\\n",
-    "    +'['+str('%5d'%up1[2,0])+', '+str('%5d'%up1[2,1])+', '+str('%5d'%up1[2,2])+']'\n",
-    "    \n",
-    "    txt_dn='SF=\\n'\\\n",
-    "     '['+str('%5d'%dn1[0,0])+', '+str('%5d'%dn1[0,1])+', '+str('%5d'%dn1[0,2])+']'+'\\n'\\\n",
-    "    +'['+str('%5d'%dn1[1,0])+', '+str('%5d'%dn1[1,1])+', '+str('%5d'%dn1[1,2])+']'+'\\n'\\\n",
-    "    +'['+str('%5d'%dn1[2,0])+', '+str('%5d'%dn1[2,1])+', '+str('%5d'%dn1[2,2])+']'\n",
-    "\n",
-    "    txt_P='P=\\n'\\\n",
-    "     '['+str('%+5.4f'%P[0,0])+', '+str('%+5.4f'%P[0,1])+', '+str('%+5.4f'%P[0,2])+']'+'\\n'\\\n",
-    "    +'['+str('%+5.4f'%P[1,0])+', '+str('%+5.4f'%P[1,1])+', '+str('%+5.4f'%P[1,2])+']'+'\\n'\\\n",
-    "    +'['+str('%+5.4f'%P[2,0])+', '+str('%+5.4f'%P[2,1])+', '+str('%+5.4f'%P[2,2])+']'\n",
-    "    \n",
-    "    \n",
-    "    plt.text(-3.2, 0.65*55555*np.max(u), txt_up, fontsize=9, color='blue')\n",
-    "    plt.text(-1.2, 0.65*55555*np.max(u), txt_dn, fontsize=9, color='red')\n",
-    "    plt.text(0, 0.25*55555*np.max(u), txt_P, fontsize=9, color='green')\n",
-    "    \n",
-    "    figname=data['subtitle'][:-4]+'_T'+str(int(data['sample']))+'K'+'count'+str(int(data['time']))+'sec.jpg'\n",
-    "    \n",
-    "    print('saved as:',figname)\n",
-    "    plt.savefig(figname, dpi=600)\n",
-    "    plt.show()\n",
-    "\n",
-    "def ScanExtract():\n",
-    "    global fname, rawscanlist\n",
-    "    rawscanlist=[]\n",
-    "    f =open(fname, 'r')\n",
-    "    try:\n",
-    "        os.mkdir('./extracted')\n",
-    "    except:\n",
-    "        pass\n",
-    "    fout=open('./extracted/extractor.ini','w')\n",
-    "    for line in f:\n",
-    "        if '#S' in line:\n",
-    "            rawscanlist.append('./extracted/scan_'+str(int(line[2:6]))+'.dat')\n",
-    "            fout=open('./extracted/scan_'+str(int(line[2:6]))+'.dat','w')\n",
-    "            fout.write(line)\n",
-    "        if '#D' in line:\n",
-    "            fout.write(line)\n",
-    "        if '#L' in line:\n",
-    "            fout.write(line)\n",
-    "        if '#' not in line:\n",
-    "            fout.write(line)    \n",
-    "def PlotRawData():\n",
-    "    UploadScan()\n",
-    "    for i in range(len(scan)):\n",
-    "        if scan[i]=='/':\n",
-    "            titl=scan[i+1:]\n",
-    "    mon,p=eval(e0_2.get())\n",
-    "        \n",
-    "    \n",
-    "    d=np.loadtxt(scan)\n",
-    "    plt.rcParams['figure.figsize'] = [9, 4.5]\n",
-    "    plt.rcParams.update({'font.size': 12})\n",
-    "    plt.rcParams['axes.linewidth'] = 1\n",
-    "    plt.rcParams[\"legend.markerscale\"] = 1\n",
-    "    import matplotlib as mpl\n",
-    "    mpl.rcParams['patch.linewidth'] = 0\n",
-    "    from matplotlib.ticker import AutoMinorLocator\n",
-    "    fig, ax1 = plt.subplots(1, 1, sharex=False, sharey=False,squeeze=True)\n",
-    "    ax1.tick_params(axis=\"y\",direction=\"in\", width=1.5)\n",
-    "    ax1.tick_params(axis=\"x\",direction=\"in\", width=1.5)\n",
-    "    ax1.tick_params(bottom=True, top=True, left=True, right=True,\\\n",
-    "                labelbottom=True, labeltop=False, labelleft=True, labelright=False)\n",
-    "    ax1.xaxis.set_minor_locator(AutoMinorLocator())\n",
-    "    ax1.yaxis.set_minor_locator(AutoMinorLocator())\n",
-    "    ax1.tick_params(axis=\"x\", which=\"minor\", direction=\"in\", width=1,\n",
-    "                      top=True, labeltop=False, bottom=True, labelbottom=False)\n",
-    "    ax1.tick_params(axis=\"y\", which=\"minor\", direction=\"in\", width=1,\n",
-    "                      right=True, labelright=False, left=True, labelleft=False)\n",
-    "    plt.title(titl)\n",
-    "    if len(p)==3:\n",
-    "        x,y,e=d[:,int(p[0])], d[:,int(p[1])]*(mon/d[:,int(p[2])]), np.sqrt(d[:,int(p[1])])*(mon/d[:,int(p[2])])\n",
-    "        ax1.errorbar(x,y,xerr=None, yerr=e, marker='o', color='r',\\\n",
-    "                 ms=5, mfc='none', linewidth=1., elinewidth=1,\\\n",
-    "                 capsize=1, capthick=1)\n",
-    "    plt.legend()\n",
-    "    plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n",
-    "    plt.ylabel('Y')\n",
-    "    plt.xlabel('X')\n",
-    "    plt.show()\n",
-    "def SeparateAna():\n",
-    "    global rawscanlist, rawscanlistana\n",
-    "    rawscanlistana=[]\n",
-    "    for i in range(len(rawscanlist)):\n",
-    "        fin=open(rawscanlist[i], 'r')\n",
-    "        d=np.loadtxt(rawscanlist[i])\n",
-    "        stype, xcol,ycol,mon,analyzer= eval(e1_1.get())\n",
-    "        if analyzer==1 or analyzer==2 or analyzer==1  or analyzer==3  or analyzer==4  or analyzer==5  or analyzer==6  or analyzer==7  or analyzer==8 or analyzer==9:\n",
-    "            if stype=='hklscan':\n",
-    "                for line in fin:\n",
-    "                    if '#S' and stype in line:\n",
-    "                        fout=open(rawscanlist[i][:-4]+'_ana_'+str(analyzer)+'.dat','w')\n",
-    "                        rawscanlistana.append(rawscanlist[i][:-4]+'_ana_'+str(analyzer)+'.dat')\n",
-    "                        fout.write(line)    \n",
-    "                        for i in range(len(d)):\n",
-    "                            dataline=str('%.10e'%d[i,xcol])+'\\t'+ str('%.10e'%d[i, ycol]) +'\\t'+ str('%.10e'%d[i, mon])\n",
-    "                            fout.write(dataline+'\\n')\n",
-    "\n",
-    "def NormalizeScans():\n",
-    "    global rawscanlist, rawscanlistana, rawscanlistananorm\n",
-    "    rawscanlistananorm=[]\n",
-    "    for i in range(len(rawscanlistana)):\n",
-    "        fin=open(rawscanlistana[i], 'r')\n",
-    "        d=np.loadtxt(rawscanlistana[i])\n",
-    "        fout=open(rawscanlistana[i][:-4]+'_norm.dat','w')\n",
-    "        rawscanlistananorm.append(rawscanlistana[i][:-4]+'_norm.dat')\n",
-    "        for line in fin:\n",
-    "            if '#S' in line:\n",
-    "                fout.write(line)\n",
-    "            if '#L' in line:\n",
-    "                fout.write(line)\n",
-    "        fout.write('#NORMALIZED BY MONITOR:'+str(eval(e2_1.get()))+'\\n')\n",
-    "        for i in range(len(d)):\n",
-    "            norm=eval(e2_1.get())/d[i, 2]\n",
-    "            dataline=str('%.10e'%d[i,0])+'\\t'+ str('%.10e'%(d[i, 1]*norm)) +'\\t'+ str('%.10e'%(d[i, 2]*norm))\n",
-    "            fout.write(dataline+'\\n')\n",
-    "def CorrectE0line():\n",
-    "    from lmfit.models import PseudoVoigtModel\n",
-    "    from scipy.signal import find_peaks\n",
-    "    scan=filedialog.askopenfilename()\n",
-    "    for i in range(len(scan)):\n",
-    "        if scan[i]=='/':\n",
-    "            titl=scan[i+1:]\n",
-    "    d=np.loadtxt(scan)\n",
-    "    el,er, lfp, rfp, pwdth, choice=eval(e3_1.get())\n",
-    "    x,y,e=d[:,0], d[:,1], np.sqrt( d[:,1])\n",
-    "    p,prop=find_peaks(y, distance=None , width=pwdth)\n",
-    "    cen0,wid0, hight0=0,0,0\n",
-    "    peak0=[cen0,wid0, hight0]\n",
-    "    peak1=[cen0,wid0, hight0]\n",
-    "    peak2=[cen0,wid0, hight0]\n",
-    "    peak3=[cen0,wid0, hight0]\n",
-    "    for i in range(len(p)):\n",
-    "        if  el<x[p[i]]<er:\n",
-    "            cen0=x[p[i]]\n",
-    "            xmin=x[prop[\"left_ips\"][i].astype(int)]\n",
-    "            xmax=x[prop[\"right_ips\"][i].astype(int)]\n",
-    "            wid0=xmax-xmin\n",
-    "            hight0=prop[\"prominences\"][i]\n",
-    "            #height=prop[\"prominences\"][pos]\n",
-    "            peak0=[cen0,wid0, hight0]\n",
-    "            n=p[i]\n",
-    "        elif 2.5<x[p[i]]<7.:\n",
-    "            cen0=x[p[i]]\n",
-    "            xmin=x[prop[\"left_ips\"][i].astype(int)]\n",
-    "            xmax=x[prop[\"right_ips\"][i].astype(int)]\n",
-    "            wid0=xmax-xmin\n",
-    "            hight0=prop[\"prominences\"][i]\n",
-    "            #height=prop[\"prominences\"][pos]\n",
-    "            peak1=[cen0,wid0, hight0]\n",
-    "        elif 7<x[p[i]]<11.5:\n",
-    "            cen0=x[p[i]]\n",
-    "            xmin=x[prop[\"left_ips\"][i].astype(int)]\n",
-    "            xmax=x[prop[\"right_ips\"][i].astype(int)]\n",
-    "            wid0=xmax-xmin\n",
-    "            hight0=prop[\"prominences\"][i]\n",
-    "            #height=prop[\"prominences\"][pos]\n",
-    "            peak2=[cen0,wid0, hight0]\n",
-    "        elif 11.5<x[p[i]]<15.5:\n",
-    "            cen0=x[p[i]]\n",
-    "            xmin=x[prop[\"left_ips\"][i].astype(int)]\n",
-    "            xmax=x[prop[\"right_ips\"][i].astype(int)]\n",
-    "            wid0=xmax-xmin\n",
-    "            hight0=prop[\"prominences\"][i]\n",
-    "            #height=prop[\"prominences\"][pos]\n",
-    "            peak3=[cen0,wid0, hight0]\n",
-    "            \n",
-    "    pvmodel=PseudoVoigtModel(prefix='pv1_')\n",
-    "    pvmodel.nan_policy='omit'\n",
-    "    pvmodel.set_param_hint('pv1_amplitude', vary=True,  value=peak0[1])#, min=0, max=5.0e-4)#%\n",
-    "    pvmodel.set_param_hint('pv1_center',    vary=True,  value=peak0[0])#,  min=-2.5, max=2.5)#%\n",
-    "    pvmodel.set_param_hint('pv1_fwhm',      vary=True,  value=peak0[2])#,   min=0.1, max=2)#%\n",
-    "    pvmodel.set_param_hint('pv1_fraction',  vary=True,  value=0.5)#,      min=0, max=1)#%\n",
-    "    result = pvmodel.fit(y[n-lfp:n+rfp],x=x[n-lfp:n+rfp])\n",
-    "    peak0[0]=result.params['pv1_center'].value\n",
-    "    peak0[1]=result.params['pv1_fwhm'].value\n",
-    "    peak0[2]=result.params['pv1_height'].value\n",
-    "    if peak1[0] != 0 and  peak1[1]!=0 and peak1[2]!=0:\n",
-    "        peak1[0]=peak1[0]-peak0[0]\n",
-    "    if peak2[0] != 0 and  peak2[1]!=0 and peak2[2]!=0:\n",
-    "        peak2[0]=peak2[0]-peak0[0]\n",
-    "\n",
-    "        \n",
-    "    plt.rcParams['figure.figsize'] = [9, 4.5]\n",
-    "    plt.rcParams.update({'font.size': 12})\n",
-    "    plt.rcParams['axes.linewidth'] = 1\n",
-    "    plt.rcParams[\"legend.markerscale\"] = 1\n",
-    "    import matplotlib as mpl\n",
-    "    mpl.rcParams['patch.linewidth'] = 0\n",
-    "    from matplotlib.ticker import AutoMinorLocator\n",
-    "    \n",
-    "    fig, ax1 = plt.subplots(1, 1, sharex=False, sharey=False,squeeze=True)\n",
-    "    ax1.tick_params(axis=\"y\",direction=\"in\", width=1.5)\n",
-    "    ax1.tick_params(axis=\"x\",direction=\"in\", width=1.5)\n",
-    "    ax1.tick_params(bottom=True, top=True, left=True, right=True,\\\n",
-    "                labelbottom=True, labeltop=False, labelleft=True, labelright=False)\n",
-    "    ax1.xaxis.set_minor_locator(AutoMinorLocator())\n",
-    "    ax1.yaxis.set_minor_locator(AutoMinorLocator())\n",
-    "    ax1.tick_params(axis=\"x\", which=\"minor\", direction=\"in\", width=1,\n",
-    "                      top=True, labeltop=False, bottom=True, labelbottom=False)\n",
-    "    ax1.tick_params(axis=\"y\", which=\"minor\", direction=\"in\", width=1,\n",
-    "                      right=True, labelright=False, left=True, labelleft=False)    \n",
-    "      \n",
-    "    plt.title(titl)\n",
-    "    plt.plot(x,y,'--', label='raw data') \n",
-    "    plt.plot(x[p], y[p], \"k*\", ms=10, label='possible peaks')\n",
-    "    plt.hlines(y=prop[\"width_heights\"], xmin=x[prop[\"left_ips\"].astype(int)],\n",
-    "               xmax=x[prop[\"right_ips\"].astype(int)], color = \"b\")\n",
-    "    plt.vlines(x=x[p], ymin=y[p]-prop[\"prominences\"],ymax=y[p], color = \"b\")\n",
-    "    plt.errorbar(x[n-lfp:n+rfp],y[n-lfp:n+rfp],yerr=e[n-lfp:n+rfp], xerr=None, marker='o', mfc=None,label='data to fit')\n",
-    "    plt.plot(x[n-lfp:n+rfp], result.best_fit, 'k-', lw=1, label='pv fit')\n",
-    "    plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n",
-    "    plt.ylabel('Normalized counts')\n",
-    "    plt.xlabel('E (meV)')\n",
-    "    plt.show()   \n",
-    "    \n",
-    "    peaks=[peak0, peak1, peak2, peak3]\n",
-    "    d[:,0]=x-peak0[0]\n",
-    "    if choice==True:\n",
-    "        fout=open(scan[:-4]+'_e0.dat','w')\n",
-    "        fin=open(scan,'r')\n",
-    "        for line in fin:\n",
-    "            if '#' in line:\n",
-    "                fout.write(line)\n",
-    "        np.savetxt(fout,\\\n",
-    "               d, fmt='%.10e', delimiter='\\t', newline='\\n',\\\n",
-    "               header='', footer='', comments='# ', encoding=None)\n",
-    "        fout.close()\n",
-    "def UploadScan():\n",
-    "    global scan\n",
-    "    scan=filedialog.askopenfilename()  \n",
-    "def PlotXYEData():\n",
-    "    #global scan, stddev, titl\n",
-    "    UploadScan()\n",
-    "    for i in range(len(scan)):\n",
-    "        if scan[i]=='/':\n",
-    "            titl=scan[i+1:]\n",
-    "    d=np.loadtxt(scan)\n",
-    "    plt.rcParams['figure.figsize'] = [9, 4.5]\n",
-    "    plt.rcParams.update({'font.size': 12})\n",
-    "    plt.rcParams['axes.linewidth'] = 1\n",
-    "    plt.rcParams[\"legend.markerscale\"] = 1\n",
-    "    import matplotlib as mpl\n",
-    "    mpl.rcParams['patch.linewidth'] = 0\n",
-    "    from matplotlib.ticker import AutoMinorLocator\n",
-    "\n",
-    "    fig, ax1 = plt.subplots(1, 1, sharex=False, sharey=False,squeeze=True)\n",
-    "    ax1.tick_params(axis=\"y\",direction=\"in\", width=1.5)\n",
-    "    ax1.tick_params(axis=\"x\",direction=\"in\", width=1.5)\n",
-    "    ax1.tick_params(bottom=True, top=True, left=True, right=True,\\\n",
-    "                labelbottom=True, labeltop=False, labelleft=True, labelright=False)\n",
-    "    ax1.xaxis.set_minor_locator(AutoMinorLocator())\n",
-    "    ax1.yaxis.set_minor_locator(AutoMinorLocator())\n",
-    "    ax1.tick_params(axis=\"x\", which=\"minor\", direction=\"in\", width=1,\n",
-    "                      top=True, labeltop=False, bottom=True, labelbottom=False)\n",
-    "    ax1.tick_params(axis=\"y\", which=\"minor\", direction=\"in\", width=1,\n",
-    "                      right=True, labelright=False, left=True, labelleft=False)\n",
-    "    \n",
-    "    p=eval(e4_1.get())\n",
-    "    legen=['energy', 'sum0', 'std3', 'std5']\n",
-    "\n",
-    "    plt.title(titl) \n",
-    "    \n",
-    "    if len(p)==1:\n",
-    "        x,y,e=d[:,0], d[:,int(p[0])], np.sqrt(d[:,int(p[0])])\n",
-    "        ax1.errorbar(x,y,xerr=None, yerr=e, marker='o', color='r',\\\n",
-    "                 ms=5, mfc='none', linewidth=1., elinewidth=1,\\\n",
-    "                 capsize=1, capthick=1, label=legen[int(p[0])])\n",
-    "\n",
-    "    if len(p)==2:\n",
-    "        x,y,e=d[:,0], d[:,int(p[0])], np.sqrt(d[:,int(p[0])])\n",
-    "        ax1.errorbar(x,y,xerr=None, yerr=e, marker='o', color='r',\\\n",
-    "                 ms=5, mfc='none', linewidth=1., elinewidth=1,\\\n",
-    "                 capsize=1, capthick=1, label=legen[int(p[0])])\n",
-    "        x,y,e=d[:,0], d[:,int(p[1])], np.sqrt(d[:,int(p[1])])\n",
-    "        ax1.errorbar(x,y,xerr=None, yerr=e, marker='o', color='b',\\\n",
-    "                 ms=5, mfc='none', linewidth=1., elinewidth=1,\\\n",
-    "                 capsize=1, capthick=1, label=legen[int(p[1])])\n",
-    "        \n",
-    "    if len(p)==3:\n",
-    "        x,y,e=d[:,0], d[:,int(p[0])], np.sqrt(d[:,int(p[0])])\n",
-    "        ax1.errorbar(x,y,xerr=None, yerr=e, marker='o', color='r',\\\n",
-    "                 ms=5, mfc='none', linewidth=1., elinewidth=1,\\\n",
-    "                 capsize=1, capthick=1, label=legen[int(p[0])])\n",
-    "        x,y,e=d[:,0], d[:,int(p[1])], np.sqrt(d[:,int(p[1])])\n",
-    "        ax1.errorbar(x,y,xerr=None, yerr=e, marker='o', color='b',\\\n",
-    "                 ms=5, mfc='none', linewidth=1., elinewidth=1,\\\n",
-    "                 capsize=1, capthick=1, label=legen[int(p[1])])\n",
-    "        x,y,e=d[:,0], d[:,int(p[2])], np.sqrt(d[:,int(p[2])])\n",
-    "        ax1.errorbar(x,y,xerr=None, yerr=e, marker='o', color='g',\\\n",
-    "                 ms=5, mfc='none', linewidth=1., elinewidth=1,\\\n",
-    "                 capsize=1, capthick=1, label=legen[int(p[2])])\n",
-    "        \n",
-    "    plt.legend()\n",
-    "    plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n",
-    "    plt.ylabel('Normalized counts')\n",
-    "    plt.xlabel('E (meV)')\n",
-    "    plt.show()  \n",
-    "def RemoveSpikes():\n",
-    "\n",
-    "    scan=filedialog.askopenfilename()\n",
-    "    for i in range(len(scan)):\n",
-    "        if scan[i]=='/':\n",
-    "            titl=scan[i+1:]\n",
-    "    d=np.loadtxt(scan)\n",
-    "    threshold,threshold1,choice=eval(e5_1.get())\n",
-    "    d1=(d[:,1].max()-d[:,1].min())*10e-8\n",
-    "    d2=(d[:,2].max()-d[:,2].min())*10e-8\n",
-    "    d3=(d[:,3].max()-d[:,3].min())*10e-8\n",
-    "    \n",
-    "    for i in range(len(d)-2):\n",
-    "        try:\n",
-    "            #r1=(d[i+1,1]/d[i,1])/(d[i+2,1]/d[i+1,1])\n",
-    "            r1=((d[i+1,1]+d1)/(d[i,1]+d1))/((d[i+2,1]+d1)/(d[i+1,1]+d1))\n",
-    "            if r1>threshold and r1<threshold1:\n",
-    "                d[i+1,0],d[i+1,1]=None,None\n",
-    "        except:\n",
-    "            pass        \n",
-    "        try:            \n",
-    "            #r2=(d[i+1,2]/d[i,2])/(d[i+2,2]/d[i+1,2])\n",
-    "            r2=((d[i+1,2]+d2)/(d[i,2]+d2))/((d[i+2,2]+d2)/(d[i+1,2]+d2))\n",
-    "            if r2>threshold and r1<threshold1:\n",
-    "                d[i+1,0],d[i+1,2]=None,None    \n",
-    "        except:\n",
-    "            pass        \n",
-    "        try:            \n",
-    "            #r3=(d[i+1,3]/d[i,3])/(d[i+2,3]/d[i+1,3])\n",
-    "            r3=((d[i+1,3]+d3)/(d[i,3]+d3))/((d[i+2,3]+d3)/(d[i+1,3]+d3))\n",
-    "            if r3>threshold and r1<threshold1:\n",
-    "                d[i+1,0],d[i+1,3]=None,None                 \n",
-    "        except:\n",
-    "            pass            \n",
-    "    if choice==True:\n",
-    "        fout=open(scan[:-4]+'_rsp.dat','w')\n",
-    "        fin=open(scan,'r')\n",
-    "        for line in fin:\n",
-    "            if '#' in line:\n",
-    "                fout.write(line)\n",
-    "        np.savetxt(fout,\\\n",
-    "               d, fmt='%.18e', delimiter='\\t', newline='\\n',\\\n",
-    "               header='', footer='', comments='# ', encoding=None)\n",
-    "        fout.close()\n",
-    "        \n",
-    "    p=[1,2,3] \n",
-    "    legen=['energy', 'sum0', 'std3', 'std5']\n",
-    "    plt.rcParams['figure.figsize'] = [9, 4.5]\n",
-    "    plt.rcParams.update({'font.size': 12})\n",
-    "    plt.rcParams['axes.linewidth'] = 1\n",
-    "    plt.rcParams[\"legend.markerscale\"] = 1\n",
-    "    import matplotlib as mpl\n",
-    "    mpl.rcParams['patch.linewidth'] = 0\n",
-    "    from matplotlib.ticker import AutoMinorLocator\n",
-    "\n",
-    "    fig, ax1 = plt.subplots(1, 1, sharex=False, sharey=False,squeeze=True)\n",
-    "    ax1.tick_params(axis=\"y\",direction=\"in\", width=1.5)\n",
-    "    ax1.tick_params(axis=\"x\",direction=\"in\", width=1.5)\n",
-    "    ax1.tick_params(bottom=True, top=True, left=True, right=True,\\\n",
-    "                labelbottom=True, labeltop=False, labelleft=True, labelright=False)\n",
-    "    ax1.xaxis.set_minor_locator(AutoMinorLocator())\n",
-    "    ax1.yaxis.set_minor_locator(AutoMinorLocator())\n",
-    "    ax1.tick_params(axis=\"x\", which=\"minor\", direction=\"in\", width=1,\n",
-    "                      top=True, labeltop=False, bottom=True, labelbottom=False)\n",
-    "    ax1.tick_params(axis=\"y\", which=\"minor\", direction=\"in\", width=1,\n",
-    "                      right=True, labelright=False, left=True, labelleft=False)    \n",
-    "    plt.title(titl)\n",
-    "    if len(p)==3:\n",
-    "        x,y,e=d[:,0], d[:,int(p[0])], np.sqrt(d[:,int(p[0])])\n",
-    "        ax1.errorbar(x,y,xerr=None, yerr=e, marker='o', color='r',\\\n",
-    "                 ms=5, mfc='none', linewidth=1., elinewidth=1,\\\n",
-    "                 capsize=1, capthick=1, label=legen[int(p[0])])\n",
-    "        x,y,e=d[:,0], d[:,int(p[1])], np.sqrt(d[:,int(p[1])])\n",
-    "        ax1.errorbar(x,y,xerr=None, yerr=e, marker='o', color='b',\\\n",
-    "                 ms=5, mfc='none', linewidth=1., elinewidth=1,\\\n",
-    "                 capsize=1, capthick=1, label=legen[int(p[1])])\n",
-    "        x,y,e=d[:,0], d[:,int(p[2])], np.sqrt(d[:,int(p[2])])\n",
-    "        ax1.errorbar(x,y,xerr=None, yerr=e, marker='o', color='g',\\\n",
-    "                 ms=5, mfc='none', linewidth=1., elinewidth=1,\\\n",
-    "                 capsize=1, capthick=1, label=legen[int(p[2])])\n",
-    "        plt.legend()\n",
-    "        plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n",
-    "        plt.ylabel('Normalized counts')\n",
-    "        plt.xlabel('E (meV)')\n",
-    "        plt.show()    \n",
-    "        \n",
-    "n=1\n",
-    "tk.Button(specle,text='LOAD',command=UploadSupplement, bg='pink', font='Times 10').grid(row=n+0, column=2, rowspan=1, columnspan=1, pady=0, padx=0)\n",
-    "\n",
-    "tk.Button(specle,text='MAKE SUMMARY',command=MakeSummary,  bg='pink', font='Times 10').grid(row=n+1, column=0, rowspan=1, columnspan=1, pady=0, padx=0)\n",
-    "tk.Button(specle,text='EXTRACT SCANS',command=ScanExtract, bg='pink', font='Times 10').grid(row=n+1, column=1, rowspan=1, columnspan=1, pady=0, padx=0)\n",
-    "tk.Button(specle,text='PLOT RAWDATA  ',command=PlotRawData, bg='pink', font='Times 10').grid(row=n+4, column=0, rowspan=1, columnspan=1, pady=0, padx=0)\n",
-    "e0_2 = tk.Entry(specle, width=17); e0_2.insert(END,'1000000,[0,41,47]');  e0_2.grid(row=n+4, column=1, columnspan=1, pady=0)\n",
-    "n=5\n",
-    "tk.Button(specle,text=' SEPARATE ANA   ',command=SeparateAna, bg='pink', font='Times 10').grid(row=n+3, column=0, rowspan=1, columnspan=1, pady=0, padx=0)\n",
-    "e1_1 = tk.Entry(specle, width=17); e1_1.insert(END,'[\"hklscan\",0,41,47,6]');  e1_1.grid(row=n+3, column=1, columnspan=1, pady=0)\n",
-    "\n",
-    "n=9\n",
-    "tk.Button(specle,text='NORMALIZATION',command=NormalizeScans,  bg='pink', font='Times 10').grid(row=n+2, column=0, rowspan=1, columnspan=1, pady=0, padx=0)\n",
-    "e2_1 = tk.Entry(specle, width=17); e2_1.insert(END,'1000000');  e2_1.grid(row=n+2, column=1, columnspan=1, pady=0)\n",
-    "\n",
-    "tk.Button(specle,text='CORRECT E0-LINE ',command=CorrectE0line, bg='pink', font='Times 10').grid(row=n+3, column=0, rowspan=1, columnspan=1, pady=0, padx=0)\n",
-    "e3_1 = tk.Entry(specle, width=17); e3_1.insert(END,'-1,1,5,5,2,True');  e3_1.grid(row=n+3, column=1, columnspan=1, pady=0)\n",
-    "\n",
-    "\n",
-    "\n",
-    "tk.Button(specle,text='PLOT XYE DATA  ',command=PlotXYEData, bg='pink', font='Times 10').grid(row=n+4, column=0, rowspan=1, columnspan=1, pady=0, padx=0)\n",
-    "e4_1 = tk.Entry(specle, width=17); e4_1.insert(END,'[1,2,3]');  e4_1.grid(row=n+4, column=1, columnspan=1, pady=0)\n",
-    "\n",
-    "tk.Button(specle,text='  REMOVE SPIKES  ',command=RemoveSpikes, bg='pink', font='Times 10').grid(row=n+5, column=0, rowspan=1, columnspan=1, pady=0, padx=0)\n",
-    "e5_1 = tk.Entry(specle, width=17); e5_1.insert(END,'5, np.inf, True');  e5_1.grid(row=n+5, column=1, columnspan=1, pady=0)\n",
-    "\n",
-    "n=80\n",
-    "tk.Button(specle, text='QUIT',command=specle.destroy, font='Times 10').grid(row=n+0, column=2, rowspan=1, columnspan=1,  pady=0)\n",
-    "#tk.mainloop()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6a406892-50f7-45ff-83c6-420730adb33f",
-   "metadata": {},
-   "source": [
-    "# HB1_ToolBox"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "3f47db18-e9fc-4378-ab92-7a2220c2fe69",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#!/usr/bin/env python\n",
-    "import os\n",
-    "import sys\n",
-    "import glob\n",
-    "import socket\n",
-    "import smtplib\n",
-    "import datetime\n",
-    "import subprocess\n",
-    "import itertools\n",
-    "import threading\n",
-    "import time\n",
-    "import sys\n",
-    "import webbrowser\n",
-    "import numpy as np\n",
-    "import math\n",
-    "import os\n",
-    "import tempfile\n",
-    "os.environ[\"MPLCONFIGDIR\"] = tempfile.gettempdir()\n",
-    "import matplotlib\n",
-    "matplotlib.use('TkAgg')\n",
-    "import matplotlib.pyplot as plt\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "import numpy as np\n",
-    "import tkinter as tk\n",
-    "from tkinter import BOTH, END, LEFT\n",
-    "from tkinter import *\n",
-    "import subprocess as sub\n",
-    "from tkinter import filedialog\n",
-    "import webbrowser\n",
-    "import ufit\n",
-    "import numpy as np\n",
-    "from ufit.lab import *\n",
-    "#import tkMessageBox\n",
-    "#os.environ[ 'MPLCONFIGDIR' ] = 'D:\\.matplotlib'\n",
-    "\n",
-    "#Main master GUI properties\n",
-    "ptax = tk.Tk()\n",
-    "ptax.title('HB1 ToolBox 2024 (c) Avishek Maity')\n",
-    "#master.geometry('300x700+10+10')\n",
-    "\n",
-    "set_datatemplate('C:/Users/num/Documents/cycle 506/exp932/Datafiles/HB1_exp0932_scan%04d.dat')\n",
-    "ufit.set_dataformat('simple')\n",
-    "\n",
-    "\n",
-    "global hb1_file\n",
-    "hb1_file='X:/path/to/HB1_data_file.dat'\n",
-    "# Define Our Images\n",
-    "                 \n",
-    "n=0\n",
-    "m=0\n",
-    "e0_1 = tk.Entry(ptax, width=37); e0_1.insert(END,hb1_file);  e0_1.grid(row=n+1, column=m+0, columnspan=2, pady=0)\n",
-    "e0_2 = tk.Entry(ptax, width=37); e0_2.insert(END,hb1_file);  e0_2.grid(row=n+2, column=m+0, columnspan=2, pady=0)\n",
-    "def load():\n",
-    "    global ptax_file, fname, n, m\n",
-    "    ptax_file = filedialog.askopenfilename()\n",
-    "    for i in range(len(spec_file)):\n",
-    "        if ptax_file[i]=='/':\n",
-    "            titl=ptax_file[i+1:]\n",
-    "    e0_1 = tk.Entry(ptax, width=37); e0_1.insert(END,titl); e0_1.grid(row=n+1, column=m+0,columnspan=2,pady=0)\n",
-    "    \n",
-    "\n",
-    "def view():\n",
-    "    fname=ptax_file\n",
-    "    webbrowser.open(fname)\n",
-    "\n",
-    "tk.Button(ptax,text='LOAD',command=load,  bg='pink', font='Times 10').grid(row=n+1, column=m+2, rowspan=1, columnspan=1, pady=0, padx=0)\n",
-    "tk.Button(ptax,text='VIEW',command=view,  bg='pink', font='Times 10').grid(row=n+1, column=m+3, rowspan=1, columnspan=1, pady=0, padx=0)\n",
-    "n=80\n",
-    "tk.Button(ptax, text='QUIT',command=ptax.destroy, font='Times 10').grid(row=n+0, column=2, rowspan=1, columnspan=1,  pady=0)\n",
-    "#tk.mainloop()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f1f0ef4a-e631-4219-aa56-cda70834341d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "82212a02-6c5d-4a6a-beaa-b81f5958b9a6",
-   "metadata": {},
-   "source": [
-    "# NON GUI VERSION"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "00860f43-a719-412f-9ecb-bdd36ba2ab4f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>.container { width:150% !important; }</style>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'instrument': 'TAIPAN',\n",
-       " 'filenumber': 51,\n",
-       " 'date': '6/19/2024',\n",
-       " 'time': 30.0,\n",
-       " 'proposal': '32296',\n",
-       " 'experiment': '944',\n",
-       " 'experiment_number': '944',\n",
-       " 'command': 'scan ds_nut 3.2 -3.2 6.4',\n",
-       " 'builtin_command': 'scan ds_nut 3.2 -3.2 6.4',\n",
-       " 'users': 'Arnab Banerjee, Bishnu Prasad Belbase, Colten Koogler, Fankang Li, Gavin Hester, Guga Khundzakishvili, Kevin Goodman, Neel Jain, Chenyang Jiang, Robert Cooper, Jacob Tosado, Yong Chen',\n",
-       " 'local_contact': 'Masa Matsuda',\n",
-       " 'subtitle': 'Bi2Se3_Feanor T = 5K I = -100mA (000) no_chopper Pyy',\n",
-       " 'monochromator': 'Heusler',\n",
-       " 'analyzer': 'Heusler',\n",
-       " 'sense': '+-+',\n",
-       " 'collimation': '48-80-60-999',\n",
-       " 'samplename': 'Bismuth Selenide Bi2Se3 Feanor',\n",
-       " 'sampletype': 'crystal',\n",
-       " 'samplemosaic': '30.000000',\n",
-       " 'latticeconstants': '4.130000,4.130000,28.560000,90.000000,90.000000,120.000000',\n",
-       " 'ubmatrix': '0.141570,0.273890,0.007001,-0.030032,-0.055866,0.034307,0.239219,-0.005717,0.000164',\n",
-       " 'mode': '0',\n",
-       " 'plane_normal': '0.019101,-0.008668,0.999780',\n",
-       " 'ubconf': 'UB19Jun2024_20932PM.ini',\n",
-       " 'preset_type': 'normal',\n",
-       " 'preset_channel': 'time',\n",
-       " 'preset_value': '30.000000',\n",
-       " 'def_x': 'ds_nut',\n",
-       " 'def_y': 'detector',\n",
-       " 'headers': '',\n",
-       " 'title': 'Spherical Polarization Analysis for Measuring Spin Hall Materials, Bismuth Selenide Bi2Se3 Feanor',\n",
-       " 'environment': ['T = 5.000 K'],\n",
-       " 'Pt.': 1.5,\n",
-       " 'ds_nut': 0.0,\n",
-       " 'detector': 49293.0,\n",
-       " 'monitor': 27919.0,\n",
-       " 'mcu': 30.572499999999998,\n",
-       " 'm1': -49.1828,\n",
-       " 'm2': 41.9674,\n",
-       " 'marc': 0.507,\n",
-       " 'mtrans': 9.0,\n",
-       " 'mfocus': 279.9974,\n",
-       " 's1': 10.0,\n",
-       " 'us_guide': -3.5,\n",
-       " 'us_nut': -3.0,\n",
-       " 'up_prec': -3.5937,\n",
-       " 'ds_prec': 3.1778,\n",
-       " 'ds_guide': 3.0,\n",
-       " 'comp': 1.0,\n",
-       " 'up_prec_ramp': 0.1,\n",
-       " 'ds_prec_ramp': 0.1,\n",
-       " 'ds_guide_ramp': 0.5,\n",
-       " 'ds_nut_ramp': 0.5,\n",
-       " 'comp_ramp': 0.1,\n",
-       " 'theta_2': -92.25,\n",
-       " 'theta_1': 80.25,\n",
-       " 's2': -0.0009,\n",
-       " 'sgl': -1.0945,\n",
-       " 'sgu': -0.4968,\n",
-       " 'stl': 0.0,\n",
-       " 'stu': 0.0,\n",
-       " 'a1': 20.9835,\n",
-       " 'a2': 41.9547,\n",
-       " 'q': 0.0007,\n",
-       " 'h': 0.0,\n",
-       " 'k': 0.0,\n",
-       " 'l': 0.0033,\n",
-       " 'ei': 13.5003,\n",
-       " 'ef': 13.508,\n",
-       " 'e': -0.0078,\n",
-       " 'us_guide_amps': -3.424,\n",
-       " 'us_nut_amps': -2.9493,\n",
-       " 'up_prec_amps': -3.5898,\n",
-       " 'ds_prec_amps': 3.1772,\n",
-       " 'ds_guide_amps': 3.0037,\n",
-       " 'ds_nut_amps': 9.999999999998899e-05,\n",
-       " 'comp_amps': 1.0017,\n",
-       " 'vti': 5.32715,\n",
-       " 'sample': 7.775449999999999,\n",
-       " 'temp': 5.0,\n",
-       " 'temp_2': 5.0,\n",
-       " 'snp_status': 0.0,\n",
-       " 'datafilename': 'C:/Users/num/Documents/cycle506/exp944/Datafiles/HB1_exp0944_scan0051.dat',\n",
-       " 'col_Pt.': array([1., 2.]),\n",
-       " 'col_ds_nut': array([ 3.2, -3.2]),\n",
-       " 'col_time': array([30., 30.]),\n",
-       " 'col_detector': array([91381.,  7205.]),\n",
-       " 'col_monitor': array([27817., 28021.]),\n",
-       " 'col_mcu': array([30.461, 30.684]),\n",
-       " 'col_m1': array([-49.1828, -49.1828]),\n",
-       " 'col_m2': array([41.9674, 41.9674]),\n",
-       " 'col_marc': array([0.507, 0.507]),\n",
-       " 'col_mtrans': array([9., 9.]),\n",
-       " 'col_mfocus': array([279.9974, 279.9974]),\n",
-       " 'col_s1': array([10., 10.]),\n",
-       " 'col_us_guide': array([-3.5, -3.5]),\n",
-       " 'col_us_nut': array([-3., -3.]),\n",
-       " 'col_up_prec': array([-3.5937, -3.5937]),\n",
-       " 'col_ds_prec': array([3.1778, 3.1778]),\n",
-       " 'col_ds_guide': array([3., 3.]),\n",
-       " 'col_comp': array([1., 1.]),\n",
-       " 'col_up_prec_ramp': array([0.1, 0.1]),\n",
-       " 'col_ds_prec_ramp': array([0.1, 0.1]),\n",
-       " 'col_ds_guide_ramp': array([0.5, 0.5]),\n",
-       " 'col_ds_nut_ramp': array([0.5, 0.5]),\n",
-       " 'col_comp_ramp': array([0.1, 0.1]),\n",
-       " 'col_theta_2': array([-92.25, -92.25]),\n",
-       " 'col_theta_1': array([80.25, 80.25]),\n",
-       " 'col_s2': array([-0.0009, -0.0009]),\n",
-       " 'col_sgl': array([-1.0945, -1.0945]),\n",
-       " 'col_sgu': array([-0.4968, -0.4968]),\n",
-       " 'col_stl': array([0., 0.]),\n",
-       " 'col_stu': array([0., 0.]),\n",
-       " 'col_a1': array([20.9835, 20.9835]),\n",
-       " 'col_a2': array([41.9547, 41.9547]),\n",
-       " 'col_q': array([0.0007, 0.0007]),\n",
-       " 'col_h': array([0., 0.]),\n",
-       " 'col_k': array([0., 0.]),\n",
-       " 'col_l': array([0.0033, 0.0033]),\n",
-       " 'col_ei': array([13.5003, 13.5003]),\n",
-       " 'col_ef': array([13.508, 13.508]),\n",
-       " 'col_e': array([-0.0078, -0.0078]),\n",
-       " 'col_us_guide_amps': array([-3.424, -3.424]),\n",
-       " 'col_us_nut_amps': array([-2.9493, -2.9493]),\n",
-       " 'col_up_prec_amps': array([-3.5898, -3.5898]),\n",
-       " 'col_ds_prec_amps': array([3.1772, 3.1772]),\n",
-       " 'col_ds_guide_amps': array([3.0037, 3.0037]),\n",
-       " 'col_ds_nut_amps': array([ 3.1946, -3.1944]),\n",
-       " 'col_comp_amps': array([1.0017, 1.0017]),\n",
-       " 'col_vti': array([5.3219, 5.3324]),\n",
-       " 'col_sample': array([7.7646, 7.7863]),\n",
-       " 'col_temp': array([5., 5.]),\n",
-       " 'col_temp_2': array([5., 5.]),\n",
-       " 'col_snp_status': array([0., 0.]),\n",
-       " 'filedesc': 'TAIPAN:944:51'}"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import display, HTML\n",
-    "display(HTML(\"<style>.container { width:150% !important; }</style>\"))\n",
-    "\n",
-    "import ufit\n",
-    "import numpy as np\n",
-    "from ufit.lab import *\n",
-    "\n",
-    "DIR='C:/Users/num/Documents/cycle506/exp944/Datafiles/'\n",
-    "FIL_TEMPLATE='HB1_exp0944_scan%04d.dat'\n",
-    "DATA_TEMPLATE=DIR+FIL_TEMPLATE\n",
-    "\n",
-    "# UFIT PACKAGE\n",
-    "set_datatemplate('C:/Users/num/Documents/cycle506/exp944/Datafiles/HB1_exp0944_scan%04d.dat')\n",
-    "ufit.set_dataformat('taipan')\n",
-    "d=read_data(51)\n",
-    "d['meta']\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8f513849-bcf7-4637-9514-300078e9e158",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "7d44c486-f53f-45b6-90f7-8673799bcc31",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# MAKE SUMMARY\n",
-    "#f=open('summary.txt', 'w')\n",
-    "#plt.figure()\n",
-    "f=None\n",
-    "ufit.set_dataformat('taipan')\n",
-    "for n in range(0,1000):\n",
-    "    try:\n",
-    "            d=read_data(n)\n",
-    "        #if 'scan h 0 k 0 l' in '%50s'%d['command'] and d['hguide']>0.5 and d['vguide']>0.5 and d['tbguide']<0.5 and d['cguide']>17.5:\n",
-    "            if d['hguide']<0.5 and d['vguide']<0.5 and d['cguide']>17.5:\n",
-    "                print(TCYAN +'%3d'%n, '%50s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%10s'%'[NSF P-X] floff, guide -16 parq', file=f)\n",
-    "    \n",
-    "            elif d['hguide']<0.5 and d['vguide']<0.5 and d['cguide']<-17.5:\n",
-    "                print(TYELO +'%3d'%n, '%50s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%10s'%'[NSF PX] floff, guide 16 parq', file=f)        \n",
-    "    \n",
-    "            elif d['hguide']<0.5 and d['vguide']<0.5 and d['tbguide']>4.5:\n",
-    "                print(TBLACK + '%3d'%n, '%50s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%10s'%'[NSF PZ] floff, guide 18 perpq', file=f)\n",
-    "                \n",
-    "            elif d['hguide']>0.5 and d['vguide']>0.5 and d['tbguide']<0.5 and d['cguide']>17.5:\n",
-    "                print(TGREEN + '%3d'%n, '%50s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%22s'%'[SF P-X] flon, guide -16 parq', file=f)\n",
-    "                \n",
-    "            elif d['hguide']>0.5 and d['vguide']>0.5 and d['tbguide']<0.5 and d['cguide']<-17.5:\n",
-    "                print(TPURP + '%3d'%n, '%50s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%20s'%'[SF PX] flon, guide 16 parq', file=f)\n",
-    "    \n",
-    "            elif d['hguide']>0.5 and d['vguide']>0.5 and d['tbguide']<0.5 and 7<d['cguide']<9:\n",
-    "                print(TBLUE + '%3d'%n, '%50s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%22s'%'[SF PY] flon, guide 18 perpqh', file=f)\n",
-    "                    \n",
-    "            elif d['hguide']>0.5 and d['vguide']>0.5 and d['tbguide']>4.5:\n",
-    "                print(TRED + '%3d'%n, '%50s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|', '%21s'%'[SF PZ] flon, guide 18 perpq', file=f)\n",
-    "\n",
-    "\n",
-    "            #plt.errorbar(d['col_l'], 1000*d['col_detector']/d['col_monitor'],yerr=1000*np.sqrt(d['col_detector'])/d['col_monitor'], xerr=None,\\\n",
-    "                        #marker='o', ms=5)\n",
-    "\n",
-    "    except:\n",
-    "        pass\n",
-    "#plt.xlabel('(0,0,l) r.l.u')\n",
-    "#plt.ylabel('normalized counts')\n",
-    "#plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8b660ac7-7918-447a-a781-02c3f92412e8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# MAKE SUMMARY\n",
-    "x,y,y1,y2,y3,y4,y5,y6=[],[],[],[],[],[],[],[]\n",
-    "ufit.set_dataformat('taipan')\n",
-    "for n in range(1000):\n",
-    "    try:\n",
-    "        x.append(n)\n",
-    "        d=read_data(n)\n",
-    "        y.append(d['fguide'])\n",
-    "        y1.append(d['hguide'])\n",
-    "        y2.append(d['vguide'])\n",
-    "        \n",
-    "        y3.append(d['tbguide'])\n",
-    "        y4.append(d['aguide'])\n",
-    "        y5.append(d['bguide'])\n",
-    "        y6.append(d['cguide'])\n",
-    "    except:\n",
-    "        pass\n",
-    "\n",
-    "plt.figure()\n",
-    "plt.plot(x[:284],y ,'o', ms=1, label='fguide')\n",
-    "plt.plot(x[:284],y1,'o', ms=1, label='hguide')\n",
-    "plt.plot(x[:284],y2,'o', ms=1, label='vguide')\n",
-    "plt.plot(x[:284],y3,'o', ms=1, label='tbguide')\n",
-    "plt.plot(x[:284],y4,'o', ms=1, label='aguide')\n",
-    "plt.plot(x[:284],y5,'o', ms=1, label='bguide')\n",
-    "plt.plot(x[:284],y6,'o', ms=1, label='cguide')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "id": "4906e5ae-62c7-44c9-9dcf-d39473fdf5a3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f.close()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "83fa3398-1f49-4570-865d-61acd90c0825",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[30mIt doens't reset! \u001b[30m\n",
-      "\u001b[31mIt doens't reset! \u001b[31m\n",
-      "\u001b[32mIt doens't reset! \u001b[32m\n",
-      "\u001b[33mIt doens't reset! \u001b[33m\n",
-      "\u001b[34mIt doens't reset! \u001b[34m\n",
-      "\u001b[35mIt doens't reset! \u001b[35m\n",
-      "\u001b[36mIt doens't reset! \u001b[36m\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "TBLACK =  '\\033[30m'\n",
-    "print (TBLACK + \"It doens't reset!\" , TBLACK)\n",
-    "TRED =  '\\033[31m'\n",
-    "print (TRED + \"It doens't reset!\" , TRED)\n",
-    "TGREEN =  '\\033[32m' \n",
-    "print (TGREEN + \"It doens't reset!\" , TGREEN)\n",
-    "TYELO =  '\\033[33m'\n",
-    "print (TYELO + \"It doens't reset!\" , TYELO)\n",
-    "TBLUE =  '\\033[34m'\n",
-    "print (TBLUE + \"It doens't reset!\" , TBLUE)\n",
-    "TPURP =  '\\033[35m'\n",
-    "print (TPURP + \"It doens't reset!\" , TPURP)\n",
-    "TCYAN =  '\\033[36m'\n",
-    "print (TCYAN + \"It doens't reset!\" , TCYAN)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "0bfabf66-c2c0-45e7-a339-a08f94418953",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "UFitError",
-     "evalue": "Unknown data format: 'ptax', available formats are ill, nicos, old nicos, simple, simple comma-separated, trisp, llb, taipan, nist, desy, zebra, dynacool, cascade",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mUFitError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[60], line 15\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;66;03m#ufit.set_dataformat('taipan')\u001b[39;00m\n\u001b[0;32m     14\u001b[0m f\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m---> 15\u001b[0m ufit\u001b[38;5;241m.\u001b[39mset_dataformat(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mptax\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     17\u001b[0m \u001b[38;5;66;03m# text colors\u001b[39;00m\n\u001b[0;32m     18\u001b[0m TBLACK,TRED,TGREEN,TYELO,TBLUE,TPURP,TCYAN \u001b[38;5;241m=\u001b[39m\\\n\u001b[0;32m     19\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[30m\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[31m\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[32m\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[33m\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[34m\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[35m\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[36m\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
-      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\ufit\\data\\__init__.py:90\u001b[0m, in \u001b[0;36mset_dataformat\u001b[1;34m(fmt)\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Set the input data format.\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \n\u001b[0;32m     72\u001b[0m \u001b[38;5;124;03mNormally ufit autodetects file formats, but this can be overridden using\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[38;5;124;03m* ``'simple comma-separated'`` - simple comma-separated multi-column files\u001b[39;00m\n\u001b[0;32m     88\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m data_formats:\n\u001b[1;32m---> 90\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m UFitError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnknown data format: \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m, available formats are \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m     91\u001b[0m                     \u001b[38;5;241m%\u001b[39m (fmt, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(data_formats)))\n\u001b[0;32m     92\u001b[0m global_loader\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m=\u001b[39m fmt\n",
-      "\u001b[1;31mUFitError\u001b[0m: Unknown data format: 'ptax', available formats are ill, nicos, old nicos, simple, simple comma-separated, trisp, llb, taipan, nist, desy, zebra, dynacool, cascade"
-     ]
-    }
-   ],
-   "source": [
-    "#library\n",
-    "import ufit\n",
-    "import numpy as np\n",
-    "from ufit.lab import *\n",
-    "\n",
-    "# data directory\n",
-    "DIR='C:/Users/num/Documents/cycle506/exp944/Datafiles/'\n",
-    "FIL_TEMPLATE='HB1_exp0944_scan%04d.dat'\n",
-    "DATA_TEMPLATE=DIR+FIL_TEMPLATE\n",
-    "\n",
-    "# ufit data template\n",
-    "set_datatemplate('C:/Users/num/Documents/cycle506/exp945/Datafiles/HB1_exp0944_scan%04d.dat')\n",
-    "#ufit.set_dataformat('taipan')\n",
-    "f=None\n",
-    "ufit.set_dataformat('ptax')\n",
-    "\n",
-    "# text colors\n",
-    "TBLACK,TRED,TGREEN,TYELO,TBLUE,TPURP,TCYAN =\\\n",
-    "'\\033[30m','\\033[31m','\\033[32m','\\033[33m','\\033[34m','\\033[35m','\\033[36m'\n",
-    "\n",
-    "\n",
-    "# scan ranges\n",
-    "for n in range(14,50):\n",
-    "    try:\n",
-    "            d=read_data(n)\n",
-    "            if d['hguide']<0.5 and d['vguide']<0.5 and d['cguide']>17.5:\n",
-    "                print(TCYAN +'%3d'%n, '|','%80s'%d['subtitle'], '|', '%55s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%10s'%'[NSF P-X] floff, guide -16 parq')\n",
-    "    \n",
-    "            elif d['hguide']<0.5 and d['vguide']<0.5 and d['cguide']<-17.5:\n",
-    "                print(TYELO +'%3d'%n, '|','%80s'%d['subtitle'], '|', '%55s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%10s'%'[NSF PX] floff, guide 16 parq')        \n",
-    "    \n",
-    "            elif d['hguide']<0.5 and d['vguide']<0.5 and d['tbguide']>4.5:\n",
-    "                print(TBLACK + '%3d'%n, '|','%80s'%d['subtitle'], '|', '%55s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%10s'%'[NSF PZ] floff, guide 18 perpq')\n",
-    "                \n",
-    "            elif d['hguide']>0.5 and d['vguide']>0.5 and d['tbguide']<0.5 and d['cguide']>17.5:\n",
-    "                print(TGREEN + '%3d'%n, '|','%80s'%d['subtitle'], '|', '%55s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%22s'%'[SF P-X] flon, guide -16 parq')\n",
-    "                \n",
-    "            elif d['hguide']>0.5 and d['vguide']>0.5 and d['tbguide']<0.5 and d['cguide']<-17.5:\n",
-    "                print(TPURP + '%3d'%n, '|','%80s'%d['subtitle'], '|', '%55s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%20s'%'[SF PX] flon, guide 16 parq')\n",
-    "    \n",
-    "            elif d['hguide']>0.5 and d['vguide']>0.5 and d['tbguide']<0.5 and 7<d['cguide']<9:\n",
-    "                print(TBLUE + '%3d'%n, '|','%80s'%d['subtitle'], '|', '%55s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%22s'%'[SF PY] flon, guide 18 perpqh')\n",
-    "                    \n",
-    "            elif d['hguide']>0.5 and d['vguide']>0.5 and d['tbguide']>4.5:\n",
-    "                print(TRED + '%3d'%n, '|','%80s'%d['subtitle'], '|', '%55s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|', '%21s'%'[SF PZ] flon, guide 18 perpq')\n",
-    "\n",
-    "    except:\n",
-    "        pass\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 218,
-   "id": "7b04ac3f-3a44-4be3-b63d-c2eef8d9c7e7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d=read_data(196)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 224,
-   "id": "2e5b0f66-a146-44ef-bf40-50e461d0d00b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-0.0001, -0.0002, -0.0002, -0.0001, -0.0001, -0.0001, -0.0001,\n",
-       "       -0.0001, -0.0001, -0.0001, -0.0001, -0.0002, -0.0001, -0.0001])"
-      ]
-     },
-     "execution_count": 224,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d['col_h']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0028e512-1104-4eaf-b1da-c101e21f0307",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a822456e-e464-40ff-b571-03c7c0f15979",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "58fe6e97-aa5f-4f3b-a4c2-c09b4bbe422e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b164a144-caa1-481a-ad63-aed0ee879713",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 334,
-   "id": "c1f5e054-a473-491a-bbfe-d748a1d2a39b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>.container { width:150% !important; }</style>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[30m  1                                scanrel s1 1 -1 0.1 | 4.511   0.001   0.004 | 5.116   0.000  -0.000  -0.000 | [NSF PZ] floff, guide 18 perpq\n",
-      "\u001b[30m  2                                scan sgu 0 -3.5 0.5 | 4.511   0.001   0.004 | 5.116   0.000  -0.000  -0.000 | [NSF PZ] floff, guide 18 perpq\n",
-      "\u001b[30m  3                                     th2th 2 -2 0.2 | 4.511   0.001   0.004 | 5.116   0.000   0.001  -0.000 | [NSF PZ] floff, guide 18 perpq\n",
-      "\u001b[30m  4                                scanrel s1 1 -1 0.1 | 4.511   0.001   0.004 | 5.116   0.000  -0.000  -0.000 | [NSF PZ] floff, guide 18 perpq\n",
-      "\u001b[30m  5              scan slit_pre_tp 0 24 2 preset time 1 | 4.511   0.001   0.004 | 5.116  -0.000  -0.000  -0.000 | [NSF PZ] floff, guide 18 perpq\n",
-      "\u001b[30m  6              scan slit_pre_bt 0 24 2 preset time 1 | 4.511   0.001   0.004 | 5.116   0.000   0.000  -0.000 | [NSF PZ] floff, guide 18 perpq\n",
-      "\u001b[30m  7             scan slit_pre_tp 26 30 2 preset time 1 | 4.511   0.001   0.004 | 5.116   0.000   0.000   0.000 | [NSF PZ] floff, guide 18 perpq\n",
-      "\u001b[30m  8             scan slit_pre_lf -2 16 2 preset time 1 | 4.511   0.001   0.004 | 5.116   0.000   0.000  -0.000 | [NSF PZ] floff, guide 18 perpq\n",
-      "\u001b[30m  9             scan slit_pre_rt -2 16 2 preset time 1 | 4.511   0.001   0.004 | 5.116   0.000  -0.001  -0.000 | [NSF PZ] floff, guide 18 perpq\n",
-      "\u001b[31m 10                  scan vguide 1 4 0.1 preset time 1 | 4.511   1.799   2.054 | 5.116   0.000   0.000  -0.000 | [SF PZ] flon, guide 18 perpq\n",
-      "\u001b[33m 13                  scanrel s1 2 -2 0.1 preset time 1 | 4.511  -0.001   0.004 | 0.007  -0.158   4.330 -17.767 | [NSF PX] floff, guide 16 parq\n",
-      "\u001b[33m 14                                     th2th 2 -2 0.2 | 4.511  -0.001   0.004 | 0.007  -0.159   4.336 -17.767 | [NSF PX] floff, guide 16 parq\n",
-      "\u001b[35m 30                            scanrel s1 1.1 -1.1 0.1 | 4.511   3.199   2.400 | 0.007  -1.227   4.988 -15.694 | [SF PX] flon, guide 16 parq\n",
-      "\u001b[35m 31                            scanrel s1 1.1 -1.1 0.1 | 4.511   1.799   2.400 | 0.007  -1.230   4.969 -15.694 | [SF PX] flon, guide 16 parq\n"
-     ]
-    }
-   ],
-   "source": [
-    "from IPython.display import display, HTML\n",
-    "display(HTML(\"<style>.container { width:150% !important; }</style>\"))\n",
-    "\n",
-    "import ufit\n",
-    "import numpy as np\n",
-    "from ufit.lab import *\n",
-    "exp_no=936\n",
-    "\n",
-    "DIR='C:/Users/num/Documents/cycle506/exp'+'%3s'%(exp_no)+'/Datafiles/'\n",
-    "FIL_TEMPLATE='HB1_exp0'+'%3s'%(exp_no)+'_scan%04d.dat'\n",
-    "DATA_TEMPLATE=DIR+FIL_TEMPLATE\n",
-    "f=None\n",
-    "# UFIT PACKAGE\n",
-    "\n",
-    "set_datatemplate('C:/Users/num/Documents/cycle506/exp'+'%3s'%(exp_no)+'/Datafiles/HB1_exp0'+'%3s'%(exp_no)+'_scan%04d.dat')\n",
-    "\n",
-    "\n",
-    "for n in range(0,1000):\n",
-    "    try:\n",
-    "            d=read_data(n)\n",
-    "        #if 'scan h 0 k 0 l' in '%50s'%d['command'] and d['hguide']>0.5 and d['vguide']>0.5 and d['tbguide']<0.5 and d['cguide']>17.5:\n",
-    "            if d['hguide']<0.5 and d['vguide']<0.5 and d['cguide']>17.5:\n",
-    "                print(TCYAN +'%3d'%n, '%50s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%10s'%'[NSF P-X] floff, guide -16 parq', file=f)\n",
-    "    \n",
-    "            elif d['hguide']<0.5 and d['vguide']<0.5 and d['cguide']<-17.5:\n",
-    "                print(TYELO +'%3d'%n, '%50s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%10s'%'[NSF PX] floff, guide 16 parq', file=f)        \n",
-    "    \n",
-    "            elif d['hguide']<0.5 and d['vguide']<0.5 and d['tbguide']>4.5:\n",
-    "                print(TBLACK + '%3d'%n, '%50s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%10s'%'[NSF PZ] floff, guide 18 perpq', file=f)\n",
-    "                \n",
-    "            elif d['hguide']>0.5 and d['vguide']>0.5 and d['tbguide']<0.5 and d['cguide']>13.5:\n",
-    "                print(TGREEN + '%3d'%n, '%50s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%22s'%'[SF P-X] flon, guide -16 parq', file=f)\n",
-    "                \n",
-    "            elif d['hguide']>0.5 and d['vguide']>0.5 and d['tbguide']<0.5 and d['cguide']<-13.5:\n",
-    "                print(TPURP + '%3d'%n, '%50s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%20s'%'[SF PX] flon, guide 16 parq', file=f)\n",
-    "    \n",
-    "            elif d['hguide']>0.5 and d['vguide']>0.5 and d['tbguide']<0.5 and 7<d['cguide']<9:\n",
-    "                print(TBLUE + '%3d'%n, '%50s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|','%22s'%'[SF PY] flon, guide 18 perpqh', file=f)\n",
-    "                    \n",
-    "            elif d['hguide']>0.5 and d['vguide']>0.5 and d['tbguide']>4.5:\n",
-    "                print(TRED + '%3d'%n, '%50s'%d['command'],\\\n",
-    "                      '|','%5.3f'%d['fguide'], '%7.3f'%d['hguide'], '%7.3f'%d['vguide'],\\\n",
-    "                      '|', '%5.3f'%d['tbguide'], '%7.3f'%d['aguide'], '%7.3f'%d['bguide'], '%7.3f'%d['cguide'],\\\n",
-    "                      '|', '%21s'%'[SF PZ] flon, guide 18 perpq', file=f)\n",
-    "\n",
-    "\n",
-    "            #plt.errorbar(d['col_l'], 1000*d['col_detector']/d['col_monitor'],yerr=1000*np.sqrt(d['col_detector'])/d['col_monitor'], xerr=None,\\\n",
-    "                        #marker='o', ms=5)\n",
-    "\n",
-    "    except:\n",
-    "        pass\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 319,
-   "id": "d4f1548a-b18e-4729-8625-6a3f1f49154d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'instrument': 'TAIPAN',\n",
-       " 'filenumber': 193,\n",
-       " 'date': '4/23/2024',\n",
-       " 'time': 5.0,\n",
-       " 'proposal': '32165',\n",
-       " 'experiment': '939',\n",
-       " 'experiment_number': '939',\n",
-       " 'command': 'scanrel caen1 0 0.12 0.003',\n",
-       " 'builtin_command': 'scan caen1 @(caen1)+0 @(caen1)+0.12 0.003000',\n",
-       " 'users': 'Fankang Li, Mason Klemm, Yaofeng Xie',\n",
-       " 'local_contact': 'Masa Matsuda',\n",
-       " 'subtitle': 'CAEN power supply stability test at 10A trial: 9',\n",
-       " 'monochromator': 'Heusler',\n",
-       " 'analyzer': 'Heusler',\n",
-       " 'sense': '+-+',\n",
-       " 'collimation': '48-80-60-999',\n",
-       " 'samplename': 'FeGe',\n",
-       " 'sampletype': 'crystal',\n",
-       " 'samplemosaic': '30.000000',\n",
-       " 'latticeconstants': '5.000000,5.000000,5.000000,90.000000,90.000000,90.000000',\n",
-       " 'ubmatrix': '0.141421,0.141421,-0.000000,-0.000000,-0.000000,0.200000,0.141421,-0.141421,0.000000',\n",
-       " 'mode': '0',\n",
-       " 'plane_normal': '0.000000,0.000000,1.000000',\n",
-       " 'ubconf': 'UB22Apr2024_103013AM.ini',\n",
-       " 'preset_type': 'normal',\n",
-       " 'preset_channel': 'time',\n",
-       " 'preset_value': '5.000000',\n",
-       " 'def_x': 'caen1',\n",
-       " 'def_y': 'detector',\n",
-       " 'headers': '',\n",
-       " 'title': 'In-plane Symmetry Breaking of Annealed FeGe, FeGe',\n",
-       " 'environment': ['T = 1.000 K'],\n",
-       " 'Pt.': 14.5,\n",
-       " 'caen1': 10.0405,\n",
-       " 'detector': 4987.035714285715,\n",
-       " 'monitor': 3692.5,\n",
-       " 'mcu': 4.043392857142857,\n",
-       " 'm1': -49.18280000000001,\n",
-       " 'm2': 40.99949999999999,\n",
-       " 'marc': 0.5069999999999999,\n",
-       " 'mtrans': 9.0,\n",
-       " 'mfocus': 279.9974000000001,\n",
-       " 's1': -9.999500000000001,\n",
-       " 's2': 0.0017,\n",
-       " 'sgl': -0.0005000000000000001,\n",
-       " 'sgu': -0.0030000000000000005,\n",
-       " 'stl': 0.0,\n",
-       " 'stu': 0.0,\n",
-       " 'a1': 20.983899999999995,\n",
-       " 'a2': 41.957699999999996,\n",
-       " 'q': 0.056999999999999995,\n",
-       " 'h': 0.0055000000000000005,\n",
-       " 'k': 0.0055000000000000005,\n",
-       " 'l': -0.0447,\n",
-       " 'ei': 14.116199999999997,\n",
-       " 'ef': 13.506199999999996,\n",
-       " 'e': 0.6098999999999999,\n",
-       " 'psa_dcct': -10.124274999999997,\n",
-       " 'psb_dcct': -0.0003,\n",
-       " 'fluxgate_1': 9.027860714285714,\n",
-       " 'fluxgate_2': 68.28192142857142,\n",
-       " 'fluxgate_3y': -310.69625,\n",
-       " 'fluxgate_3z': -1.6078928571428566,\n",
-       " '34972a_status': 0.0,\n",
-       " 'caen1_amps': 10.042085714285713,\n",
-       " 'coldtip': 0.0,\n",
-       " 'pt100': 295.09817857142855,\n",
-       " 'hotstage_1': 295.19278571428566,\n",
-       " 'temp': 1.0,\n",
-       " 'shield_pt-100': 295.2953928571429,\n",
-       " 'hotstage_2': 295.52914285714286,\n",
-       " 'temp_2': 1.0,\n",
-       " 'datafilename': 'C:/Users/num/Documents/cycle506/exp939/Datafiles/HB1_exp0939_scan0193.dat',\n",
-       " 'col_Pt.': array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,\n",
-       "        14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26.,\n",
-       "        27., 28.]),\n",
-       " 'col_caen1': array([10.   , 10.003, 10.006, 10.009, 10.012, 10.015, 10.018, 10.021,\n",
-       "        10.024, 10.027, 10.03 , 10.033, 10.036, 10.039, 10.042, 10.045,\n",
-       "        10.048, 10.051, 10.054, 10.057, 10.06 , 10.063, 10.066, 10.069,\n",
-       "        10.072, 10.075, 10.078, 10.081]),\n",
-       " 'col_time': array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
-       "        5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.]),\n",
-       " 'col_detector': array([4450., 4766., 5101., 5190., 5408., 5558., 5884., 6049., 6116.,\n",
-       "        6171., 6165., 6232., 6010., 6012., 5870., 5548., 5359., 5167.,\n",
-       "        4870., 4528., 4345., 4300., 4136., 4072., 4115., 4005., 4210.,\n",
-       "           0.]),\n",
-       " 'col_monitor': array([3781., 3796., 3864., 3853., 3973., 3845., 3817., 3790., 3734.,\n",
-       "        3832., 3811., 3763., 3830., 3690., 3845., 3789., 3905., 3884.,\n",
-       "        3774., 3948., 3897., 3778., 3855., 3798., 3840., 3829., 3869.,\n",
-       "           0.]),\n",
-       " 'col_mcu': array([4.14 , 4.157, 4.231, 4.219, 4.351, 4.21 , 4.18 , 4.15 , 4.089,\n",
-       "        4.196, 4.173, 4.121, 4.194, 4.041, 4.21 , 4.149, 4.276, 4.253,\n",
-       "        4.133, 4.323, 4.267, 4.137, 4.221, 4.159, 4.205, 4.193, 4.237,\n",
-       "        0.   ]),\n",
-       " 'col_m1': array([-49.1828, -49.1828, -49.1828, -49.1828, -49.1828, -49.1828,\n",
-       "        -49.1828, -49.1828, -49.1828, -49.1828, -49.1828, -49.1828,\n",
-       "        -49.1828, -49.1828, -49.1828, -49.1828, -49.1828, -49.1828,\n",
-       "        -49.1828, -49.1828, -49.1828, -49.1828, -49.1828, -49.1828,\n",
-       "        -49.1828, -49.1828, -49.1828, -49.1828]),\n",
-       " 'col_m2': array([40.9995, 40.9995, 40.9995, 40.9995, 40.9995, 40.9995, 40.9995,\n",
-       "        40.9995, 40.9995, 40.9995, 40.9995, 40.9995, 40.9995, 40.9995,\n",
-       "        40.9995, 40.9995, 40.9995, 40.9995, 40.9995, 40.9995, 40.9995,\n",
-       "        40.9995, 40.9995, 40.9995, 40.9995, 40.9995, 40.9995, 40.9995]),\n",
-       " 'col_marc': array([0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507,\n",
-       "        0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507,\n",
-       "        0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507,\n",
-       "        0.507]),\n",
-       " 'col_mtrans': array([9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9.,\n",
-       "        9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9.]),\n",
-       " 'col_mfocus': array([279.9974, 279.9974, 279.9974, 279.9974, 279.9974, 279.9974,\n",
-       "        279.9974, 279.9974, 279.9974, 279.9974, 279.9974, 279.9974,\n",
-       "        279.9974, 279.9974, 279.9974, 279.9974, 279.9974, 279.9974,\n",
-       "        279.9974, 279.9974, 279.9974, 279.9974, 279.9974, 279.9974,\n",
-       "        279.9974, 279.9974, 279.9974, 279.9974]),\n",
-       " 'col_s1': array([-9.9995, -9.9995, -9.9995, -9.9995, -9.9995, -9.9995, -9.9995,\n",
-       "        -9.9995, -9.9995, -9.9995, -9.9995, -9.9995, -9.9995, -9.9995,\n",
-       "        -9.9995, -9.9995, -9.9995, -9.9995, -9.9995, -9.9995, -9.9995,\n",
-       "        -9.9995, -9.9995, -9.9995, -9.9995, -9.9995, -9.9995, -9.9995]),\n",
-       " 'col_s2': array([0.0017, 0.0017, 0.0017, 0.0017, 0.0017, 0.0017, 0.0017, 0.0017,\n",
-       "        0.0017, 0.0017, 0.0017, 0.0017, 0.0017, 0.0017, 0.0017, 0.0017,\n",
-       "        0.0017, 0.0017, 0.0017, 0.0017, 0.0017, 0.0017, 0.0017, 0.0017,\n",
-       "        0.0017, 0.0017, 0.0017, 0.0017]),\n",
-       " 'col_sgl': array([-0.0005, -0.0005, -0.0005, -0.0005, -0.0005, -0.0005, -0.0005,\n",
-       "        -0.0005, -0.0005, -0.0005, -0.0005, -0.0005, -0.0005, -0.0005,\n",
-       "        -0.0005, -0.0005, -0.0005, -0.0005, -0.0005, -0.0005, -0.0005,\n",
-       "        -0.0005, -0.0005, -0.0005, -0.0005, -0.0005, -0.0005, -0.0005]),\n",
-       " 'col_sgu': array([-0.003, -0.003, -0.003, -0.003, -0.003, -0.003, -0.003, -0.003,\n",
-       "        -0.003, -0.003, -0.003, -0.003, -0.003, -0.003, -0.003, -0.003,\n",
-       "        -0.003, -0.003, -0.003, -0.003, -0.003, -0.003, -0.003, -0.003,\n",
-       "        -0.003, -0.003, -0.003, -0.003]),\n",
-       " 'col_stl': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
-       " 'col_stu': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
-       " 'col_a1': array([20.9839, 20.9839, 20.9839, 20.9839, 20.9839, 20.9839, 20.9839,\n",
-       "        20.9839, 20.9839, 20.9839, 20.9839, 20.9839, 20.9839, 20.9839,\n",
-       "        20.9839, 20.9839, 20.9839, 20.9839, 20.9839, 20.9839, 20.9839,\n",
-       "        20.9839, 20.9839, 20.9839, 20.9839, 20.9839, 20.9839, 20.9839]),\n",
-       " 'col_a2': array([41.9577, 41.9577, 41.9577, 41.9577, 41.9577, 41.9577, 41.9577,\n",
-       "        41.9577, 41.9577, 41.9577, 41.9577, 41.9577, 41.9577, 41.9577,\n",
-       "        41.9577, 41.9577, 41.9577, 41.9577, 41.9577, 41.9577, 41.9577,\n",
-       "        41.9577, 41.9577, 41.9577, 41.9577, 41.9577, 41.9577, 41.9577]),\n",
-       " 'col_q': array([0.057, 0.057, 0.057, 0.057, 0.057, 0.057, 0.057, 0.057, 0.057,\n",
-       "        0.057, 0.057, 0.057, 0.057, 0.057, 0.057, 0.057, 0.057, 0.057,\n",
-       "        0.057, 0.057, 0.057, 0.057, 0.057, 0.057, 0.057, 0.057, 0.057,\n",
-       "        0.057]),\n",
-       " 'col_h': array([0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055,\n",
-       "        0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055,\n",
-       "        0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055,\n",
-       "        0.0055, 0.0055, 0.0055, 0.0055]),\n",
-       " 'col_k': array([0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055,\n",
-       "        0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055,\n",
-       "        0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055, 0.0055,\n",
-       "        0.0055, 0.0055, 0.0055, 0.0055]),\n",
-       " 'col_l': array([-0.0447, -0.0447, -0.0447, -0.0447, -0.0447, -0.0447, -0.0447,\n",
-       "        -0.0447, -0.0447, -0.0447, -0.0447, -0.0447, -0.0447, -0.0447,\n",
-       "        -0.0447, -0.0447, -0.0447, -0.0447, -0.0447, -0.0447, -0.0447,\n",
-       "        -0.0447, -0.0447, -0.0447, -0.0447, -0.0447, -0.0447, -0.0447]),\n",
-       " 'col_ei': array([14.1162, 14.1162, 14.1162, 14.1162, 14.1162, 14.1162, 14.1162,\n",
-       "        14.1162, 14.1162, 14.1162, 14.1162, 14.1162, 14.1162, 14.1162,\n",
-       "        14.1162, 14.1162, 14.1162, 14.1162, 14.1162, 14.1162, 14.1162,\n",
-       "        14.1162, 14.1162, 14.1162, 14.1162, 14.1162, 14.1162, 14.1162]),\n",
-       " 'col_ef': array([13.5062, 13.5062, 13.5062, 13.5062, 13.5062, 13.5062, 13.5062,\n",
-       "        13.5062, 13.5062, 13.5062, 13.5062, 13.5062, 13.5062, 13.5062,\n",
-       "        13.5062, 13.5062, 13.5062, 13.5062, 13.5062, 13.5062, 13.5062,\n",
-       "        13.5062, 13.5062, 13.5062, 13.5062, 13.5062, 13.5062, 13.5062]),\n",
-       " 'col_e': array([0.6099, 0.6099, 0.6099, 0.6099, 0.6099, 0.6099, 0.6099, 0.6099,\n",
-       "        0.6099, 0.6099, 0.6099, 0.6099, 0.6099, 0.6099, 0.6099, 0.6099,\n",
-       "        0.6099, 0.6099, 0.6099, 0.6099, 0.6099, 0.6099, 0.6099, 0.6099,\n",
-       "        0.6099, 0.6099, 0.6099, 0.6099]),\n",
-       " 'col_psa_dcct': array([-10.1242, -10.1243, -10.1243, -10.1242, -10.1243, -10.1243,\n",
-       "        -10.1243, -10.1242, -10.1243, -10.1243, -10.1241, -10.1242,\n",
-       "        -10.1242, -10.1243, -10.1242, -10.1243, -10.1243, -10.1242,\n",
-       "        -10.1244, -10.1244, -10.1244, -10.1243, -10.1241, -10.1243,\n",
-       "        -10.1242, -10.1243, -10.1244, -10.1244]),\n",
-       " 'col_psb_dcct': array([-0.0003, -0.0003, -0.0003, -0.0003, -0.0003, -0.0003, -0.0003,\n",
-       "        -0.0003, -0.0003, -0.0003, -0.0003, -0.0003, -0.0003, -0.0003,\n",
-       "        -0.0003, -0.0003, -0.0003, -0.0003, -0.0003, -0.0003, -0.0003,\n",
-       "        -0.0003, -0.0003, -0.0003, -0.0003, -0.0003, -0.0003, -0.0003]),\n",
-       " 'col_fluxgate_1': array([9.1552, 9.1531, 9.1413, 9.1454, 9.1463, 8.9963, 8.9984, 9.0021,\n",
-       "        8.9933, 9.0028, 9.0037, 9.0054, 8.9989, 9.0019, 9.0005, 9.0008,\n",
-       "        8.9972, 8.9981, 8.9964, 8.9945, 8.9964, 8.9988, 9.0033, 9.0083,\n",
-       "        9.0118, 9.0116, 9.0115, 9.0068]),\n",
-       " 'col_fluxgate_2': array([68.7583, 68.685 , 68.689 , 68.8002, 68.7347, 68.1161, 68.1434,\n",
-       "        68.0851, 68.1285, 68.124 , 68.1366, 68.1386, 68.1446, 68.1828,\n",
-       "        68.1605, 68.2354, 68.2193, 68.2496, 68.2644, 68.2249, 68.255 ,\n",
-       "        68.2629, 68.2315, 68.2307, 68.1783, 68.177 , 68.1725, 68.1649]),\n",
-       " 'col_fluxgate_3y': array([-310.687, -310.695, -310.695, -310.687, -310.687, -310.704,\n",
-       "        -310.706, -310.687, -310.698, -310.692, -310.696, -310.706,\n",
-       "        -310.68 , -310.722, -310.687, -310.689, -310.692, -310.702,\n",
-       "        -310.704, -310.695, -310.702, -310.693, -310.684, -310.696,\n",
-       "        -310.698, -310.731, -310.692, -310.688]),\n",
-       " 'col_fluxgate_3z': array([-9.9230e-01, -1.6720e+00,  6.2100e-02, -1.6773e+00, -1.7095e+00,\n",
-       "        -1.9825e+00, -4.5806e+00, -2.0732e+00,  3.3000e-03,  1.4250e-01,\n",
-       "         1.3180e-01,  1.9400e-02,  1.9500e-02, -8.9580e-01,  1.9300e-02,\n",
-       "        -5.8543e+00, -5.9882e+00, -3.3659e+00, -3.7674e+00, -5.5922e+00,\n",
-       "        -2.9538e+00, -3.2750e+00, -1.7149e+00, -1.6881e+00, -6.8720e-01,\n",
-       "         1.9408e+00,  2.8451e+00,  2.6540e-01]),\n",
-       " 'col_34972a_status': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
-       " 'col_caen1_amps': array([10.12  ,  9.9999, 10.003 , 10.0086, 10.0089, 10.0118, 10.0149,\n",
-       "        10.018 , 10.0211, 10.024 , 10.0272, 10.0331, 10.033 , 10.036 ,\n",
-       "        10.0389, 10.0418, 10.045 , 10.0481, 10.0507, 10.0542, 10.0573,\n",
-       "        10.0601, 10.0627, 10.0661, 10.069 , 10.0721, 10.075 , 10.0779]),\n",
-       " 'col_coldtip': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
-       " 'col_pt100': array([295.098, 295.098, 295.098, 295.098, 295.098, 295.098, 295.097,\n",
-       "        295.098, 295.098, 295.098, 295.098, 295.098, 295.099, 295.098,\n",
-       "        295.098, 295.099, 295.098, 295.099, 295.099, 295.098, 295.098,\n",
-       "        295.098, 295.098, 295.098, 295.099, 295.098, 295.098, 295.099]),\n",
-       " 'col_hotstage_1': array([295.191, 295.193, 295.193, 295.191, 295.19 , 295.192, 295.191,\n",
-       "        295.193, 295.192, 295.192, 295.19 , 295.189, 295.186, 295.189,\n",
-       "        295.193, 295.194, 295.195, 295.193, 295.192, 295.191, 295.193,\n",
-       "        295.195, 295.198, 295.197, 295.198, 295.197, 295.195, 295.195]),\n",
-       " 'col_temp': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
-       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),\n",
-       " 'col_shield_pt-100': array([295.296, 295.296, 295.295, 295.296, 295.295, 295.295, 295.295,\n",
-       "        295.295, 295.295, 295.295, 295.295, 295.295, 295.295, 295.295,\n",
-       "        295.295, 295.296, 295.295, 295.296, 295.295, 295.296, 295.296,\n",
-       "        295.295, 295.295, 295.295, 295.296, 295.296, 295.296, 295.296]),\n",
-       " 'col_hotstage_2': array([295.532, 295.533, 295.53 , 295.53 , 295.53 , 295.531, 295.531,\n",
-       "        295.529, 295.53 , 295.531, 295.528, 295.528, 295.527, 295.524,\n",
-       "        295.525, 295.528, 295.53 , 295.53 , 295.527, 295.526, 295.522,\n",
-       "        295.524, 295.533, 295.533, 295.528, 295.528, 295.532, 295.536]),\n",
-       " 'col_temp_2': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
-       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),\n",
-       " 'filedesc': 'TAIPAN:939:193'}"
-      ]
-     },
-     "execution_count": 319,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d['meta']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 277,
-   "id": "e12e2518-af27-4958-b793-1186e56d1463",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "861.8095238095239"
-      ]
-     },
-     "execution_count": 277,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.average(d['col_monitor'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2fecf638-79aa-4aaf-be01-c026bf8a88f6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/test_data/exp932/jupynb/HB1_alignment-checkpoint.ipynb b/test_data/exp932/jupynb/HB1_alignment-checkpoint.ipynb
deleted file mode 100644
index 363fcab7..00000000
--- a/test_data/exp932/jupynb/HB1_alignment-checkpoint.ipynb
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/test_data/exp932/jupynb/HB1_alignment.ipynb b/test_data/exp932/jupynb/HB1_alignment.ipynb
deleted file mode 100644
index d4fb3e8f..00000000
--- a/test_data/exp932/jupynb/HB1_alignment.ipynb
+++ /dev/null
@@ -1,1014 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "2b6d9128-2b1e-450d-bb22-62bd741d9195",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "from IPython.display import Image\n",
-    "import ufit\n",
-    "import numpy as np\n",
-    "from ufit.lab import *\n",
-    "from lmfit.models import LinearModel, GaussianModel, LorentzianModel, PseudoVoigtModel, Model\n",
-    "set_datatemplate('C:/Users/num/Documents/cycle506/exp943/Datafiles/HB1_exp0943_scan%04d.dat')\n",
-    "ufit.set_dataformat('simple')\n",
-    "\n",
-    "def gauss_fit(x,y,\\\n",
-    "        center1,center1_vary,center1_min, center1_max,\\\n",
-    "        fwhm1,fwhm1_vary, fwhm1_min, fwhm1_max,\\\n",
-    "        amplitude1,amplitude1_vary, amplitude1_min, amplitude1_max,\\\n",
-    "        results):\n",
-    "    erry =np.sqrt(y)\n",
-    "\n",
-    "\n",
-    "    pgvt1 = GaussianModel(prefix='g1_')\n",
-    "    pars = pgvt1.guess(y, x=x)\n",
-    "    pars['g1_center'].set(value=center1,\n",
-    "                           vary=center1_vary,\n",
-    "                           min=center1_min,\n",
-    "                           max=center1_max)\n",
-    "    pars['g1_sigma'].set(value=fwhm1 / 2.,\n",
-    "                          vary=fwhm1_vary,\n",
-    "                          min=fwhm1_min,\n",
-    "                          max=fwhm1_max)\n",
-    "    pars['g1_amplitude'].set(value=amplitude1,\n",
-    "                              vary=amplitude1_vary,\n",
-    "                              min=amplitude1_min,\n",
-    "                              max=amplitude1_max)\n",
-    "\n",
-    "    mod1 =pgvt1\n",
-    "    init = mod1.eval(pars, x=x)\n",
-    "    out = mod1.fit(y, pars, x=x)\n",
-    "    print_fit_report = results\n",
-    "    if print_fit_report == 1:\n",
-    "        print((out.fit_report()))\n",
-    "    if print_fit_report == 0:\n",
-    "        #print('#--------peak1---------#')\n",
-    "        print(('g1_center:', out.params['g1_center'].value))\n",
-    "        print(('g1_fwhm:', out.params['g1_fwhm'].value))\n",
-    "        print(('g1_amplitude:', out.params['g1_amplitude'].value))\n",
-    "        print('chisqr, redchisqr', out.chisqr, out.redchi)\n",
-    "    if print_fit_report == -1:\n",
-    "        pass\n",
-    "\n",
-    "    #--------------------------------------\n",
-    "    #plt.figure()\n",
-    "    plt.errorbar(x, y, erry, xerr=None, fmt='', marker='o', ms= 3, mfc='none', mec=None,\\\n",
-    "                 ecolor=None, elinewidth=0.5, capsize=0,capthick=0,lw=0,\\\n",
-    "                 label='cen:'+str('%10.3f'%out.params['g1_center'].value)+'\\nfwhm :'+str('%10.3f'%out.params['g1_fwhm'].value))\n",
-    "    plt.plot(x, out.best_fit, '--r', linewidth=0.5)\n",
-    "\n",
-    "\n",
-    "\n",
-    "def lgauss_fit(x,y,\\\n",
-    "        slope,slope_vary,slope_min,slope_max,\\\n",
-    "        intercept,intercept_vary, intercept_min, intercept_max,\\\n",
-    "        center1,center1_vary,center1_min, center1_max,\\\n",
-    "        fwhm1,fwhm1_vary, fwhm1_min, fwhm1_max,\\\n",
-    "        amplitude1,amplitude1_vary, amplitude1_min, amplitude1_max,\\\n",
-    "        results):\n",
-    "    erry =np.sqrt(y)\n",
-    "\n",
-    "    lin_mod = LinearModel(prefix='l1_')\n",
-    "    pars = lin_mod.guess(y, x=x)\n",
-    "    pars['l1_slope'].set(value=slope,\n",
-    "                          vary=slope_vary,\n",
-    "                          min=slope_min,\n",
-    "                          max=slope_max)\n",
-    "    pars['l1_intercept'].set(value=intercept,\n",
-    "                              vary=intercept_vary,\n",
-    "                              min=intercept_min,\n",
-    "                              max=intercept_min)    \n",
-    "\n",
-    "    pgvt1 = GaussianModel(prefix='g1_')\n",
-    "    pars.update(pgvt1.make_params())\n",
-    "    pars['g1_center'].set(value=center1,\n",
-    "                           vary=center1_vary,\n",
-    "                           min=center1_min,\n",
-    "                           max=center1_max)\n",
-    "    pars['g1_sigma'].set(value=fwhm1 / 2.,\n",
-    "                          vary=fwhm1_vary,\n",
-    "                          min=fwhm1_min,\n",
-    "                          max=fwhm1_max)\n",
-    "    pars['g1_amplitude'].set(value=amplitude1,\n",
-    "                              vary=amplitude1_vary,\n",
-    "                              min=amplitude1_min,\n",
-    "                              max=amplitude1_max)\n",
-    "\n",
-    "    mod1 =lin_mod+pgvt1\n",
-    "    init = mod1.eval(pars, x=x)\n",
-    "    out = mod1.fit(y, pars, x=x)\n",
-    "    print_fit_report = results\n",
-    "    if print_fit_report == 1:\n",
-    "        print((out.fit_report()))\n",
-    "    if print_fit_report == 0:\n",
-    "        #print('#--------peak1---------#')\n",
-    "        print('g1_center:', out.params['g1_center'].value, '+-', out.params['g1_center'].stderr)\n",
-    "        print('g1_fwhm:', out.params['g1_fwhm'].value, '+-', out.params['g1_fwhm'].stderr)\n",
-    "        print('g1_amplitude:', out.params['g1_amplitude'].value, '+-', out.params['g1_amplitude'].stderr)\n",
-    "        print('chisqr, redchisqr', out.chisqr, out.redchi)\n",
-    "    if print_fit_report == -1:\n",
-    "        pass\n",
-    "\n",
-    "    #--------------------------------------\n",
-    "    #plt.figure()\n",
-    "    plt.errorbar(x, y, erry, xerr=None, fmt='', marker='o', ms= 3, mfc='none', mec=None,\\\n",
-    "                 ecolor=None, elinewidth=0.5, capsize=0,capthick=0,lw=0,\\\n",
-    "                 label='cen:'+str('%10.3f'%out.params['g1_center'].value)+'\\nfwhm :'+str('%10.3f'%out.params['g1_fwhm'].value))\n",
-    "    plt.plot(x, out.best_fit, '--r', linewidth=0.5)\n",
-    "\n",
-    "\n",
-    "\n",
-    "def lgauss2_fit(x,y,\\\n",
-    "        slope,slope_vary,slope_min,slope_max,\\\n",
-    "        intercept,intercept_vary, intercept_min, intercept_max,\\\n",
-    "        amplitude1,amplitude1_vary, amplitude1_min, amplitude1_max,\\\n",
-    "        center1,center1_vary,center1_min, center1_max,\\\n",
-    "        fwhm1,fwhm1_vary, fwhm1_min, fwhm1_max,\\\n",
-    "        amplitude2,amplitude2_vary, amplitude2_min, amplitude2_max,\\\n",
-    "        center2,center2_vary,center2_min, center2_max,\\\n",
-    "        fwhm2,fwhm2_vary, fwhm2_min, fwhm2_max,\\\n",
-    "        results):\n",
-    "    erry =np.sqrt(y)\n",
-    "\n",
-    "    lin_mod = LinearModel(prefix='l1_')\n",
-    "    pars = lin_mod.guess(y, x=x)\n",
-    "    pars['l1_slope'].set(value=slope,\n",
-    "                          vary=slope_vary,\n",
-    "                          min=slope_min,\n",
-    "                          max=slope_max)\n",
-    "    pars['l1_intercept'].set(value=intercept,\n",
-    "                              vary=intercept_vary,\n",
-    "                              min=intercept_min,\n",
-    "                              max=intercept_min)    \n",
-    "\n",
-    "    pgvt1 = GaussianModel(prefix='g1_')\n",
-    "    pars.update(pgvt1.make_params())\n",
-    "    pars['g1_center'].set(value=center1,\n",
-    "                           vary=center1_vary,\n",
-    "                           min=center1_min,\n",
-    "                           max=center1_max)\n",
-    "    pars['g1_sigma'].set(value=fwhm1 / 2.,\n",
-    "                          vary=fwhm1_vary,\n",
-    "                          min=fwhm1_min,\n",
-    "                          max=fwhm1_max)\n",
-    "    pars['g1_amplitude'].set(value=amplitude1,\n",
-    "                              vary=amplitude1_vary,\n",
-    "                              min=amplitude1_min,\n",
-    "                              max=amplitude1_max)\n",
-    "\n",
-    "    pgvt2 = GaussianModel(prefix='g2_')\n",
-    "    pars.update(pgvt2.make_params())\n",
-    "    pars['g2_center'].set(value=center2,\n",
-    "                           vary=center2_vary,\n",
-    "                           min=center2_min,\n",
-    "                           max=center2_max)\n",
-    "    pars['g2_sigma'].set(value=fwhm2 / 2.,\n",
-    "                          vary=fwhm2_vary,\n",
-    "                          min=fwhm2_min,\n",
-    "                          max=fwhm2_max)\n",
-    "    pars['g2_amplitude'].set(value=amplitude2,\n",
-    "                              vary=amplitude2_vary,\n",
-    "                              min=amplitude2_min,\n",
-    "                              max=amplitude2_max)\n",
-    "    \n",
-    "    mod1 =lin_mod+pgvt1+pgvt2\n",
-    "    init = mod1.eval(pars, x=x)\n",
-    "    out = mod1.fit(y, pars, x=x)\n",
-    "    print_fit_report = results\n",
-    "    if print_fit_report == 1:\n",
-    "        print((out.fit_report()))\n",
-    "    if print_fit_report == 0:\n",
-    "        #print('#--------peak1---------#')\n",
-    "        print('g1_center:', out.params['g1_center'].value, '+-', out.params['g1_center'].stderr)\n",
-    "        print('g1_fwhm:', out.params['g1_fwhm'].value, '+-', out.params['g1_fwhm'].stderr)\n",
-    "        print('g1_amplitude:', out.params['g1_amplitude'].value, '+-', out.params['g1_amplitude'].stderr)\n",
-    "        print('chisqr, redchisqr', out.chisqr, out.redchi)\n",
-    "    if print_fit_report == -1:\n",
-    "        pass\n",
-    "\n",
-    "    #--------------------------------------\n",
-    "    #plt.figure()\n",
-    "    plt.errorbar(x, y, erry, xerr=None, fmt='', marker='o', ms= 3, mfc='none', mec=None,\\\n",
-    "                 ecolor=None, elinewidth=0.5, capsize=0,capthick=0,lw=0,\\\n",
-    "                 label='cen:'+str('%10.3f'%out.params['g1_center'].value)+' fwhm :'+str('%10.3f'%out.params['g1_fwhm'].value)+'\\n'+'cen:'+str('%10.3f'%out.params['g2_center'].value)+' fwhm :'+str('%10.3f'%out.params['g2_fwhm'].value))\n",
-    "    plt.plot(x, out.best_fit, '--r', linewidth=0.5)\n",
-    "\n",
-    "def tcal(lamda, h,k,l):\n",
-    "    a,b,c=3.524041, 3.524041, 3.524041\n",
-    "    g=np.sqrt(((h*h)/(a*a))+((k*k)/(b*b))+((l*l)/(c*c)))\n",
-    "    d=1/g\n",
-    "    t=(180/np.pi)*np.arcsin(lamda/(2*d))\n",
-    "    return t\n",
-    "def e2l(e):\n",
-    "    return 9.045/np.sqrt(e)\n",
-    "\n",
-    "def l2e(l):\n",
-    "    return (9.045*9.045)/(l*l)\n",
-    "\n",
-    "def plot_params(x, y, font, lw, ms):\n",
-    "    plt.rcParams['figure.figsize'] = [x, y]\n",
-    "    plt.rcParams.update({'font.size': font})\n",
-    "    plt.rcParams['axes.linewidth'] = lw\n",
-    "    plt.rcParams[\"legend.markerscale\"] = ms\n",
-    "    import matplotlib as mpl\n",
-    "    mpl.rcParams['patch.linewidth'] = 0.0\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "99dca93d-48e3-47c2-ab22-4916ad871839",
-   "metadata": {},
-   "source": [
-    "# HB-1 Instrument Calibration\n",
-    "\n",
-    "- CYCLE 506, EXP 932, Proposal 9880 \n",
-    "\n",
-    "- Avishek Maity (Trainee), Masaki Matsuda\n",
-    "\n",
-    "- Date: 09-APR-2024\n",
-    "\n",
-    "\n",
-    "- Initial Spectrometer configuration:\n",
-    "\n",
-    "      The monochromator is Heusler with a d-spacing of 3.437 Angstroms.\n",
-    "\n",
-    "      Collimation: 48'-80'-60'-999'\n",
-    "\n",
-    "      The spectrometer is configured to have a fixed final energy of 13.5000 meV\n",
-    "\n",
-    "      Scattering sense = +-+\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "761416c0-2035-4826-badf-9fa2ba562107",
-   "metadata": {},
-   "source": [
-    "## Step 1\n",
-    "\n",
-    "#Alignment of m1 with monitor\n",
-    "\n",
-    "- Drive the final energy to 13.5 meV\n",
-    "      #drive ef 13.5\n",
-    "\n",
-    "- Drive energy transfer to zero\n",
-    "      #drive e 0\n",
-    "\n",
-    "- Remove the shielding from the top of the analyzer drum\n",
-    "\n",
-    "- Remove the Heusler Analyzer\n",
-    "  \n",
-    "- Drive analyzer two theta (a2) to ZERO\n",
-    "      #drive a2 0\n",
-    "  \n",
-    "- Set slit for a narrow oppening\n",
-    "      #drive slit_pre_tp 25 slit_pre_bt 25 slit_pre_rt 3 slit_pre_lf 3\n",
-    "  \n",
-    "- Monitor was placed after the slit and befor the sample\n",
-    "      #note: The guide between sample and slit needs to be moved out for this purpose.\n",
-    "\n",
-    "- Change Counter to the monitor with counting time 5 sec\n",
-    "      #defcount monitor\n",
-    "      #preset time 5\n",
-    "\n",
-    "- Scan m1 between +-0.6 degree relative to the current position\n",
-    "      #scanrel m1 -0.6 0.6 0.1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 314,
-   "id": "a63891ea-c592-480d-b5c9-e48916edea83",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\num\\AppData\\Local\\anaconda3\\Lib\\site-packages\\ufit\\data\\loader.py:108: RuntimeWarning: invalid value encountered in sqrt\n",
-      "  datarr[:, 2] = sqrt(datarr[:, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "d=read_data(1)\n",
-    "x=d['col_m1']\n",
-    "y=d['col_monitor']\n",
-    "plot_params(4,3,10,1,1)\n",
-    "plt.figure()\n",
-    "gauss_fit(x,y,\\\n",
-    "            -49.2, True, None, None,\\\n",
-    "             0.5,  True, None, None,\\\n",
-    "             1000, True, None, None,\\\n",
-    "             -1)\n",
-    "plt.ylabel('monitor counts')\n",
-    "plt.xlabel('m1 (deg.)')\n",
-    "plt.legend()\n",
-    "plt.savefig('m1-alignment.jpg')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c184e361-ba68-4306-b794-774d5468120f",
-   "metadata": {},
-   "source": [
-    "- Move m1 to the peak center of the gaussian fit, in this case -49.2\n",
-    "      #drive m1 -49.2\n",
-    "  \n",
-    "- Put back the monitor before sample, as was placed before.\n",
-    "  \n",
-    "- Put back the guide to the optical rail inbetween slit and sample, as was placed before.\n",
-    "  \n",
-    "- Use needle magnet to check guide field direction (up) if necessary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d68e228-0a00-4f0a-a184-102bde63eb99",
-   "metadata": {},
-   "source": [
-    "## Step 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "59bd7659-13b7-4a5a-a7d9-b1b7cbf414f3",
-   "metadata": {},
-   "source": [
-    "#Alignment of m2 and s2\n",
-    "\n",
-    "- Set slits to a broader opening, #drive slit_pre_tp 25 slit_pre_bt 25 slit_pre_rt 15 slit_pre_lf 15\n",
-    "   \n",
-    "- Reset your counter from  'monitor' to 'detector', #defcount detector\n",
-    "\n",
-    "- Move sample 2theta (s2=30), this will allow some space for the next steps. \n",
-    "\n",
-    "- Remove the PG-filter (for using $\\lambda/2$ for Ni-powder calibration ) following the steps:\n",
-    "  \n",
-    "      - Move out the guide field after the sample.\n",
-    "  \n",
-    "      - Remove the shielding around the filter (take care of the guide inside).\n",
-    "\n",
-    "      - Make a note that the filter was already out by 5-7 mm not to hit the Solid State collimator in the analyzer shielding.\n",
-    "  \n",
-    "      - Then move out the PG filter (use the dedicated tool to pull it out).\n",
-    "  \n",
-    "      - Place the 'Ni-powder' sample for the calibration measurements.\n",
-    "\n",
-    "\n",
-    "\n",
-    "- Ni-powder calibration can be started from the GUI (or from command line)\n",
-    "\n",
-    "      - powcalib ni 1.000000 energy 13.500000 maxpeaks 6 (at 13.5 meV, count for 1 sec)\n",
-    "\n",
-    "      - Using (0.5,0.5,0.5), (1,0,0), (1,1,0), (1.5,0.5,0.5), (1,1,1), (2,0,0)\n",
-    "\n",
-    "      - After the scans are done accept the new zero angle for m2 and s2 from the GUI.\n",
-    "          #zero m2 xx\n",
-    "          #zero s2 xx\n",
-    "\n",
-    "      - Drive the spectrometer to the elastic configuration e=0\n",
-    "          #drive e 0\n",
-    "\n",
-    "      - Drive the sample two theta to -30 for comfortable access to PG-filter shileding area.\n",
-    "          #drive s2 -30"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 312,
-   "id": "73c9d0b9-d435-4b1b-bc10-d14519f82940",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\num\\AppData\\Local\\anaconda3\\Lib\\site-packages\\ufit\\data\\loader.py:108: RuntimeWarning: invalid value encountered in sqrt\n",
-      "  datarr[:, 2] = sqrt(datarr[:, 1])\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "g1_center: -35.15217205564274 +- 0.004924963439253939\n",
-      "g1_fwhm: 1.163369748418447 +- 0.015554589478892478\n",
-      "g1_amplitude: 9885.921096965823 +- 157.68456498779528\n",
-      "chisqr, redchisqr 1137072.4117574596 30731.686804255667\n",
-      "g1_center: -40.83165755849498 +- 0.0058390927369921875\n",
-      "g1_fwhm: 1.1632071141795224 +- 0.01613209092917668\n",
-      "g1_amplitude: 5542.866750040352 +- 88.99325741825706\n",
-      "chisqr, redchisqr 380218.73552323325 10561.631542312034\n",
-      "g1_center: -59.168083083143614 +- 0.004856142666209839\n",
-      "g1_fwhm: 1.261910913958208 +- 0.012886971168631208\n",
-      "g1_amplitude: 5638.921366916988 +- 62.400176785495475\n",
-      "chisqr, redchisqr 175613.2647010808 4878.146241696689\n",
-      "g1_center: -88.56850552351406 +- 0.006218641634431643\n",
-      "g1_fwhm: 2.0954885285772082 +- 0.01654687534149\n",
-      "g1_amplitude: 9540.47669160506 +- 82.21023149360275\n",
-      "chisqr, redchisqr 109949.73283497548 3054.1592454159854\n",
-      "g1_center: -70.74860921381517 +- 0.005577045159714189\n",
-      "g1_fwhm: 1.4276666445876862 +- 0.016115734871368623\n",
-      "g1_amplitude: 8324.582260687606 +- 113.0222302931228\n",
-      "chisqr, redchisqr 322891.40184358956 8969.205606766376\n",
-      "g1_center: -74.4196305968323 +- 0.0061026189747815955\n",
-      "g1_fwhm: 1.9619971939820608 +- 0.020902444099093653\n",
-      "g1_amplitude: 18442.09485085329 +- 324.86728449289035\n",
-      "chisqr, redchisqr 345832.3067937221 9606.452966492281\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 750x750 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_params(7.5,7.5,10,1,1)\n",
-    "\n",
-    "plt.figure()\n",
-    "plt.subplot(3,2,1)\n",
-    "plt.title('Ni Powder (0.5,0.5,0.5), dval=4.06921, 2theta=35.9052', fontsize=9)\n",
-    "d=read_data(4)\n",
-    "x=d['col_s2']\n",
-    "y=d['col_detector']\n",
-    "lgauss_fit(x,y,\\\n",
-    "          0, False, None, None,\\\n",
-    "          1, True, None, None,\\\n",
-    "            -36.2, True, None, None,\\\n",
-    "             0.5,  True, None, None,\\\n",
-    "             1000, True, None, None,\\\n",
-    "             0)\n",
-    "plt.ylabel('detector counts / sec')\n",
-    "plt.xlabel('s2 (deg.)')\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.subplot(3,2,2)\n",
-    "plt.title('Ni Powder (1.0,0.0,0.0), dval=3.52404, 2theta=41.69895', fontsize=9)\n",
-    "d=read_data(5)\n",
-    "x=d['col_s2']\n",
-    "y=d['col_detector']\n",
-    "lgauss_fit(x,y,\\\n",
-    "          0, True, None, None,\\\n",
-    "          1, True, None, None,\\\n",
-    "            -41.2, True, None, None,\\\n",
-    "             0.5,  True, None, None,\\\n",
-    "             1000, True, None, None,\\\n",
-    "             0)\n",
-    "plt.ylabel('detector counts / sec')\n",
-    "plt.xlabel('s2 (deg.)')\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.subplot(3,2,3)\n",
-    "plt.title('Ni Powder (1.0,1.0,0.0), dval=2.49187, 2theta=60.44225', fontsize=9)\n",
-    "d=read_data(6)\n",
-    "x=d['col_s2']\n",
-    "y=d['col_detector']\n",
-    "lgauss_fit(x,y,\\\n",
-    "          0, True, None, None,\\\n",
-    "          1, True, None, None,\\\n",
-    "            -59.2, True, None, None,\\\n",
-    "             0.5,  True, None, None,\\\n",
-    "             1000, True, None, None,\\\n",
-    "             0)\n",
-    "plt.ylabel('detector counts / sec')\n",
-    "plt.xlabel('s2 (deg.)')\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.subplot(3,2,4)\n",
-    "plt.title('Ni Powder (2.0,0.0,0.0), dval=1.76202, 2theta=90.76772', fontsize=9)\n",
-    "d=read_data(8)\n",
-    "x=d['col_s2']\n",
-    "y=d['col_detector']\n",
-    "lgauss_fit(x,y,\\\n",
-    "          0, True, None, None,\\\n",
-    "          1, True, None, None,\\\n",
-    "            -88.2, True, None, None,\\\n",
-    "             0.5,  True, None, None,\\\n",
-    "             1000, True, None, None,\\\n",
-    "             0)\n",
-    "plt.ylabel('detector counts / sec')\n",
-    "plt.xlabel('s2 (deg.)')\n",
-    "plt.legend()\n",
-    "\n",
-    "\n",
-    "plt.subplot(3,1,3)\n",
-    "plt.title('Ni Powder (1.5,0.5,0.5), dval=2.12508, 2theta=72.34479/ (1.0,1.0,1.0), dval=2.03461, 2theta=76.11636', fontsize=9)\n",
-    "d=read_data(7)\n",
-    "x=d['col_s2'][:-40]\n",
-    "y=d['col_detector'][:-40]\n",
-    "lgauss_fit(x,y,\\\n",
-    "           0, True, None, None,\\\n",
-    "           1, True, None, None,\\\n",
-    "           -71.2, True, None, None,\\\n",
-    "           1,  True, None, None,\\\n",
-    "           1000, True, None, None,\\\n",
-    "           0)\n",
-    "\n",
-    "x=d['col_s2'][40:]\n",
-    "y=d['col_detector'][40:]\n",
-    "lgauss_fit(x,y,\\\n",
-    "           0, True, None, None,\\\n",
-    "           1, True, None, None,\\\n",
-    "           -74.2, True, None, None,\\\n",
-    "           1,  True, None, None,\\\n",
-    "           1000, True, None, None,\\\n",
-    "           0)\n",
-    "\n",
-    "\n",
-    "plt.ylabel('detector counts / sec')\n",
-    "plt.xlabel('s2 (deg.)')\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('Ni-calibration.jpg')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cc464528-1b41-4338-b6cc-6946e6ea29b3",
-   "metadata": {},
-   "source": [
-    "- Performing calibration using Ni(d-spacing=3.524041) with the following data\n",
-    "- h    k    l      d-spacing     position\n",
-    "  \n",
-    "- 0.50 0.50 0.50        4.06921     -35.149420+/-0.005235\n",
-    "- 1.00 0.00 0.00        3.52404     -40.829179+/-0.005926\n",
-    "- 1.00 1.00 0.00        2.49187     -59.182585+/-0.007981\n",
-    "- 1.50 0.50 0.50        2.12508     -70.754354+/-0.008555\n",
-    "- 1.00 1.00 1.00        2.03461     -74.464934+/-0.007335\n",
-    "- 2.00 0.00 0.00        1.76202     -88.573833+/-0.007014\n",
-    "\n",
-    "\n",
-    "#h, k, l, dval, 2theta_cal, 2theta_exp, err\n",
-    "\n",
-    "calib=np.array([\n",
-    "\n",
-    "[0.50, 0.50, 0.50, 4.06921, -35.21188556, -35.1521720556427, 0.004924963439253939],\n",
-    "\n",
-    "[1.00, 0.00, 0.00, 3.52404, -40.88422223, -40.8316575584949, 0.005839092736992187],\n",
-    "\n",
-    "[1.00, 1.00, 0.00, 2.49187, -59.19835818, -59.1680830831436, 0.004856142666209839],\n",
-    "\n",
-    "[1.50, 0.50, 0.50, 2.12508, -70.78627965, -70.7486092138151, 0.005577045159714189], \n",
-    "\n",
-    "[1.00, 1.00, 1.00, 2.03461, -74.44869058, -74.4196305968323, 0.006102618974781595],\n",
-    "\n",
-    "[2.00, 0.00, 0.00, 1.76202, -88.61703456, -88.5685055235140, 0.006218641634431643]], dtype=float)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 315,
-   "id": "00e52889-1950-4c13-b119-2250cb95779f",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 750x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 750x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "calib=np.array([\n",
-    "[0.50, 0.50, 0.50, 4.06921, -35.21188556, -35.1521720556427, 0.004924963439253939],\n",
-    "[1.00, 0.00, 0.00, 3.52404, -40.88422223, -40.8316575584949, 0.005839092736992187],\n",
-    "[1.00, 1.00, 0.00, 2.49187, -59.19835818, -59.1680830831436, 0.004856142666209839],\n",
-    "[1.50, 0.50, 0.50, 2.12508, -70.78627965, -70.7486092138151, 0.005577045159714189], \n",
-    "[1.00, 1.00, 1.00, 2.03461, -74.44869058, -74.4196305968323, 0.006102618974781595],\n",
-    "[2.00, 0.00, 0.00, 1.76202, -88.61703456, -88.5685055235140, 0.006218641634431643]], dtype=float)\n",
-    "lamda1=2.461619 #(for 13.5 meV)\n",
-    "plot_params(7.5,3.5,10,1,1)\n",
-    "x,y,yerr=np.sin((np.pi/180)*calib[:,4]/2), lamda1*np.sin((np.pi/180)*calib[:,5]/2), lamda1*np.sin((np.pi/180)*calib[:,6]/2)\n",
-    "plt.figure()\n",
-    "plt.subplot(2,1,1)\n",
-    "#plt.errorbar(x,y, yerr)\n",
-    "lin_fit(x, y, slope=1, slope_vary=True, slope_min=None, slope_max=None, intercept=0, intercept_vary=True, intercept_min=None, intercept_max=None, results=-1)\n",
-    "plt.legend()\n",
-    "plt.xlabel(r'$sin(\\theta_1)$')\n",
-    "plt.ylabel(r'$\\lambda_1 \\times sin(\\theta_2)$')\n",
-    "\n",
-    "plt.show()\n",
-    "\n",
-    "x,y,yerr=calib[:,5], -2*tcal(2.46309225863336, calib[:,0],calib[:,1],calib[:,2]), 2*tcal(0.0005766054673102825, calib[:,0],calib[:,1],calib[:,2])\n",
-    "plt.subplot(2,1,2)\n",
-    "#plt.errorbar(x,y, yerr)\n",
-    "lin_fit(x, y, slope=1, slope_vary=True, slope_min=None, slope_max=None, intercept=0, intercept_vary=True, intercept_min=None, intercept_max=None, results=-1)\n",
-    "plt.legend()\n",
-    "plt.xlabel(r'$2\\theta$(Experiment)')\n",
-    "plt.ylabel(r'$2\\theta$(Calculated)')\n",
-    "plt.savefig('lamda2.jpg')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e3d2f00e-0b8b-4ba7-96cf-7915fabd51b6",
-   "metadata": {},
-   "source": [
-    "## Step 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b8a1276-4045-44f3-9e7a-efce2704de3e",
-   "metadata": {},
-   "source": [
-    "#Alignment of a1 and a2\n",
-    "\n",
-    "- Put back the PG-filter\n",
-    "  \n",
-    "      - DO NOT insert the filter completely inside the analyzer shielding\n",
-    "      - leave 5-7 mm out side the shielding to avoid the collision with the solid sate collimator\n",
-    "  \n",
-    "  \n",
-    "- Remove the shielding around the filter (take care of the guide inside the shielding).\n",
-    "\n",
-    "- Put back the guide field after the sample.\n",
-    "\n",
-    "- Put the Remove Heusler analyzer back in the anlyzer drum\n",
-    "\n",
-    "- Put back the shielding on the top of the analyzer drum\n",
-    "\n",
-    "- For analyzer calibration put the 'plastic standard' sample\n",
-    "\n",
-    "- Check with speedometer for some intensity by driving relative +- 1.5 degrees is necessary\n",
-    "  \n",
-    "- make a a1 scan around the relative postion and find the peak\n",
-    "  \n",
-    "- Change the counting time 5 sec #preset time 5\n",
-    "\n",
-    "- Scan m1 between +-2.5 degree relative to the current position #scanrel a1 -2.5 2.5 0.25\n",
-    "\n",
-    "- Move a1 to the peak center of the gaussian fit, in this case 20.579 #drive a1 20.579\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 316,
-   "id": "749b8d1b-94e3-4875-b0c9-738bb9263783",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "d=read_data(9)\n",
-    "x=d['col_a1']\n",
-    "y=d['col_detector']\n",
-    "plot_params(4,3,10,1,1)\n",
-    "plt.figure()\n",
-    "gauss_fit(x,y,\\\n",
-    "            20.2, True, None, None,\\\n",
-    "             0.5,  True, None, None,\\\n",
-    "             1000, True, None, None,\\\n",
-    "             -1)\n",
-    "plt.ylabel('detector counts')\n",
-    "plt.xlabel('a1 (deg.)')\n",
-    "plt.legend()\n",
-    "plt.savefig('a1_alignment.jpg')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f95debe1-d73f-400a-b388-bb77c11e25dd",
-   "metadata": {},
-   "source": [
-    "- Make a theta2theta scans to correct for the a2 and a1 anlges\n",
-    "\n",
-    "- Fit the two scans and move the a1 nad a2 to the peak postion.\n",
-    "\n",
-    "- Set in the GUI the \"Current anlyzer settings corresponds to Ef=13.5 meV\" "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 317,
-   "id": "df50e5df-b0e4-4793-a126-646ed1d5eff7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 700x300 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "d=read_data(10)\n",
-    "x=d['col_a1']\n",
-    "y=d['col_detector']\n",
-    "plot_params(7,3,10,1,1)\n",
-    "plt.figure()\n",
-    "plt.subplot(1,2,1)\n",
-    "gauss_fit(x,y,\\\n",
-    "            20.2, True, None, None,\\\n",
-    "             0.5,  True, None, None,\\\n",
-    "             1000, True, None, None,\\\n",
-    "             -1)\n",
-    "plt.ylabel('detector counts')\n",
-    "plt.xlabel('a1 (deg.)')\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.subplot(1,2,2)\n",
-    "x=d['col_a2']\n",
-    "y=d['col_detector']\n",
-    "\n",
-    "gauss_fit(x,y,\\\n",
-    "            42.2, True, None, None,\\\n",
-    "             0.5,  True, None, None,\\\n",
-    "             1000, True, None, None,\\\n",
-    "             -1)\n",
-    "plt.ylabel('detector counts')\n",
-    "plt.xlabel('a2 (deg.)')\n",
-    "plt.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('a1_a2_alignment.jpg')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07ca975a-a45b-45eb-831e-9385530ebd0c",
-   "metadata": {},
-   "source": [
-    "## Step 4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ffc28918-4a52-49d9-9ef2-6ad757af2b31",
-   "metadata": {},
-   "source": [
-    "#Flipper calibration: correction of the flipper guide fileds 'hguide' and 'vguide'\n",
-    "\n",
-    "- Connect all the flipper coil and the guide field colils to the power supply\n",
-    "\n",
-    "- Initialize the flipper and fields with 'init' command\n",
-    "  \n",
-    "- Put the Si crystal for flipper calibration\n",
-    "\n",
-    "- set just the a lattice parameters a=3.135 and leave the others as the default (5)\n",
-    "\n",
-    "-  Scattering plane remained as [100] [001]\n",
-    "\n",
-    "-  calculate the s2 and s1 from the UB calculateor, in this case s2=-46.23300, s1=-23.00000\n",
-    "\n",
-    "-  Drive sample angles to the respective peak positions #drive s2 -46.233 s1 -23\n",
-    "\n",
-    "-  Turn on the flipper fixed vertical guide field and the helmohltz vertical guide field at the sample position\n",
-    "\n",
-    "      - drive fguide 4.5\n",
-    "        \n",
-    "      - guide 18 perpq\n",
-    "        \n",
-    "-  check for the peak by relatively driving the s1 and following the speedometer and go to the maximum\n",
-    "\n",
-    "-  then check the optimum sgu by driving the sgu in the limit of +-3\n",
-    "\n",
-    "-  drive hguide to 1.8 and make a scan of the flipper fixed compensastion field vguide between 1 and 4 with count time 1 sec\n",
-    "\n",
-    "      - drive hguide 1.8 (i.e. )\n",
-    "\n",
-    "      - preset time 1\n",
-    "\n",
-    "      - scan vguide 1 4 0.1\n",
-    " \n",
-    "- drive the vguide to the peak position, in this case vguide 2.34 gauss\n",
-    "\n",
-    "- With the field on fguide 4.5109   hguide 1.7993   vguide 2.343  tbguide 5.1158   count for 10 secs\n",
-    "\n",
-    "      - count preset time 10 (gives FLIPPER ON counts 27905.000)\n",
-    "\n",
-    "- then switch off the flipper #floff drive hguide off vguide off\"\n",
-    "\n",
-    "      - count preset time 10 (gives FLIPPER OFF counts 280553.000)\n",
-    "\n",
-    "- Flipping ratio FR = (counts FLIPPER OFF/ counts FLIPPER ON) = 10\n",
-    "\n",
-    "- Beam polarization P = (FR-1)/(FR+1) = 9/11= 82%\n",
-    "\n",
-    "- One may finally do a scan for the fguide which is generally stays at 4.5 gauss\n",
-    "\n",
-    "      - flon: drive hguide on vguide on\n",
-    "      - scan fguide 2 6 0.25\n",
-    "      - drive fguide to the maximum, in this case 4.43 gauss.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 347,
-   "id": "08978e8d-bf16-42c6-ba59-ba1fceb30865",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('g1_center:', 2.342522938055374)\n",
-      "('g1_fwhm:', 1.732357570036023)\n",
-      "('g1_amplitude:', -21294.280969892512)\n",
-      "chisqr, redchisqr 171378022.474991 6855120.89899964\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\num\\AppData\\Local\\Temp\\ipykernel_13976\\3648376987.py:16: RuntimeWarning: invalid value encountered in sqrt\n",
-      "  erry =np.sqrt(y)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('g1_center:', 4.432143880628567)\n",
-      "('g1_fwhm:', 2.7253472301742176)\n",
-      "('g1_amplitude:', -21961.30523171658)\n",
-      "chisqr, redchisqr 1881196.4194543501 171017.85631403184\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\num\\AppData\\Local\\Temp\\ipykernel_13976\\3648376987.py:16: RuntimeWarning: invalid value encountered in sqrt\n",
-      "  erry =np.sqrt(y)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "d=read_data(11)\n",
-    "x=d['col_vguide']\n",
-    "y=d['col_detector']\n",
-    "plot_params(4,3,10,1,1)\n",
-    "plt.figure()\n",
-    "gauss_fit(x,y-y.max(),\\\n",
-    "            2.25, True, None, None,\\\n",
-    "             1.5,  True, None, None,\\\n",
-    "             -5000, True, None, None,\\\n",
-    "             0)\n",
-    "plt.ylabel('counts - maxcount')\n",
-    "plt.xlabel('vguide (gauss)')\n",
-    "plt.legend()\n",
-    "plt.savefig('vguide_alignment.jpg')\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "d=read_data(12)\n",
-    "x=d['col_fguide'][:-1]\n",
-    "y=d['col_detector'][:-1]\n",
-    "plot_params(4,3,10,1,1)\n",
-    "plt.figure()\n",
-    "gauss_fit(x,y-y.max(),\\\n",
-    "            4.5, True, None, None,\\\n",
-    "             .1,  True, None, None,\\\n",
-    "             1, True, None, None,\\\n",
-    "             0)\n",
-    "plt.ylabel('counts - maxcount')\n",
-    "plt.xlabel('fguide (gauss)')\n",
-    "plt.legend()\n",
-    "plt.savefig('fguide_alignment.jpg')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b4fe2ea8-f392-442d-a11d-d23c948c40bb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tkinter import messagebox, ttk\n",
-    "import tkinter as tk\n",
-    "\n",
-    "def display_selection():\n",
-    "    # Get the selected value.\n",
-    "    selection = combo.get()\n",
-    "    messagebox.showinfo(\n",
-    "        message=f\"The selected value is: {selection}\",\n",
-    "        title=\"Selection\"\n",
-    "    )\n",
-    "\n",
-    "main_window = tk.Tk()\n",
-    "main_window.config(width=300, height=200)\n",
-    "main_window.title(\"Combobox\")\n",
-    "combo = ttk.Combobox(\n",
-    "    state=\"readonly\",\n",
-    "    values=[\"Python\", \"C\", \"C++\", \"Java\"]\n",
-    ")\n",
-    "combo.place(x=50, y=50)\n",
-    "button = ttk.Button(text=\"Display selection\", command=display_selection)\n",
-    "button.place(x=50, y=100)\n",
-    "main_window.mainloop()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652b6960-88f8-468d-921b-ba195d7d6f21",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/test_data/exp932/jupynb/Ni-calibration.jpg b/test_data/exp932/jupynb/Ni-calibration.jpg
deleted file mode 100644
index 7242d6f6..00000000
Binary files a/test_data/exp932/jupynb/Ni-calibration.jpg and /dev/null differ
diff --git a/test_data/exp932/jupynb/SNP943.ipynb b/test_data/exp932/jupynb/SNP943.ipynb
deleted file mode 100644
index 56ec9a98..00000000
--- a/test_data/exp932/jupynb/SNP943.ipynb
+++ /dev/null
@@ -1,3861 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "c861903f-7cfe-4366-b950-43e488092648",
-   "metadata": {},
-   "source": [
-    "# Alignment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 580,
-   "id": "6d3cce43-aa4d-40f0-aa8b-46d3dd18e693",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[30mIt doens't reset! \u001b[30m\n",
-      "\u001b[31mIt doens't reset! \u001b[31m\n",
-      "\u001b[32mIt doens't reset! \u001b[32m\n",
-      "\u001b[33mIt doens't reset! \u001b[33m\n",
-      "\u001b[34mIt doens't reset! \u001b[34m\n",
-      "\u001b[35mIt doens't reset! \u001b[35m\n",
-      "\u001b[36mIt doens't reset! \u001b[36m\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "from IPython.display import Image\n",
-    "import ufit\n",
-    "import numpy as np\n",
-    "from ufit.lab import *\n",
-    "from lmfit.models import LinearModel, GaussianModel, LorentzianModel, PseudoVoigtModel, Model\n",
-    "set_datatemplate('C:/Users/num/Documents/cycle506/exp943/Datafiles/HB1_exp0943_scan%04d.dat')\n",
-    "ufit.set_dataformat('simple')\n",
-    "\n",
-    "def gauss_fit(x,y,\\\n",
-    "        center1,center1_vary,center1_min, center1_max,\\\n",
-    "        fwhm1,fwhm1_vary, fwhm1_min, fwhm1_max,\\\n",
-    "        amplitude1,amplitude1_vary, amplitude1_min, amplitude1_max,\\\n",
-    "        results):\n",
-    "    erry =np.sqrt(y)\n",
-    "\n",
-    "\n",
-    "    pgvt1 = GaussianModel(prefix='g1_')\n",
-    "    pars = pgvt1.guess(y, x=x)\n",
-    "    pars['g1_center'].set(value=center1,\n",
-    "                           vary=center1_vary,\n",
-    "                           min=center1_min,\n",
-    "                           max=center1_max)\n",
-    "    pars['g1_sigma'].set(value=fwhm1 / 2.,\n",
-    "                          vary=fwhm1_vary,\n",
-    "                          min=fwhm1_min,\n",
-    "                          max=fwhm1_max)\n",
-    "    pars['g1_amplitude'].set(value=amplitude1,\n",
-    "                              vary=amplitude1_vary,\n",
-    "                              min=amplitude1_min,\n",
-    "                              max=amplitude1_max)\n",
-    "\n",
-    "    mod1 =pgvt1\n",
-    "    init = mod1.eval(pars, x=x)\n",
-    "    out = mod1.fit(y, pars, x=x)\n",
-    "    print_fit_report = results\n",
-    "    if print_fit_report == 1:\n",
-    "        print((out.fit_report()))\n",
-    "    if print_fit_report == 0:\n",
-    "        #print('#--------peak1---------#')\n",
-    "        print(('g1_center:', out.params['g1_center'].value))\n",
-    "        print(('g1_fwhm:', out.params['g1_fwhm'].value))\n",
-    "        print(('g1_amplitude:', out.params['g1_amplitude'].value))\n",
-    "        print('chisqr, redchisqr', out.chisqr, out.redchi)\n",
-    "    if print_fit_report == -1:\n",
-    "        pass\n",
-    "\n",
-    "    #--------------------------------------\n",
-    "    #plt.figure()\n",
-    "    plt.errorbar(x, y, erry, xerr=None, fmt='', marker='o', ms= 3, mfc='none', mec=None,\\\n",
-    "                 ecolor=None, elinewidth=0.5, capsize=0,capthick=0,lw=0,\\\n",
-    "                 label='cen:'+str('%10.3f'%out.params['g1_center'].value)+'\\nfwhm :'+str('%10.3f'%out.params['g1_fwhm'].value))\n",
-    "    plt.plot(x, out.best_fit, '--r', linewidth=0.5)\n",
-    "\n",
-    "\n",
-    "\n",
-    "def lgauss_fit(x,y,\\\n",
-    "        slope,slope_vary,slope_min,slope_max,\\\n",
-    "        intercept,intercept_vary, intercept_min, intercept_max,\\\n",
-    "        center1,center1_vary,center1_min, center1_max,\\\n",
-    "        fwhm1,fwhm1_vary, fwhm1_min, fwhm1_max,\\\n",
-    "        amplitude1,amplitude1_vary, amplitude1_min, amplitude1_max,\\\n",
-    "        results):\n",
-    "    erry =np.sqrt(y)\n",
-    "\n",
-    "    lin_mod = LinearModel(prefix='l1_')\n",
-    "    pars = lin_mod.guess(y, x=x)\n",
-    "    pars['l1_slope'].set(value=slope,\n",
-    "                          vary=slope_vary,\n",
-    "                          min=slope_min,\n",
-    "                          max=slope_max)\n",
-    "    pars['l1_intercept'].set(value=intercept,\n",
-    "                              vary=intercept_vary,\n",
-    "                              min=intercept_min,\n",
-    "                              max=intercept_min)    \n",
-    "\n",
-    "    pgvt1 = GaussianModel(prefix='g1_')\n",
-    "    pars.update(pgvt1.make_params())\n",
-    "    pars['g1_center'].set(value=center1,\n",
-    "                           vary=center1_vary,\n",
-    "                           min=center1_min,\n",
-    "                           max=center1_max)\n",
-    "    pars['g1_sigma'].set(value=fwhm1 / 2.,\n",
-    "                          vary=fwhm1_vary,\n",
-    "                          min=fwhm1_min,\n",
-    "                          max=fwhm1_max)\n",
-    "    pars['g1_amplitude'].set(value=amplitude1,\n",
-    "                              vary=amplitude1_vary,\n",
-    "                              min=amplitude1_min,\n",
-    "                              max=amplitude1_max)\n",
-    "\n",
-    "    mod1 =lin_mod+pgvt1\n",
-    "    init = mod1.eval(pars, x=x)\n",
-    "    out = mod1.fit(y, pars, x=x)\n",
-    "    print_fit_report = results\n",
-    "    if print_fit_report == 1:\n",
-    "        print((out.fit_report()))\n",
-    "    if print_fit_report == 0:\n",
-    "        #print('#--------peak1---------#')\n",
-    "        print('g1_center:', out.params['g1_center'].value, '+-', out.params['g1_center'].stderr)\n",
-    "        print('g1_fwhm:', out.params['g1_fwhm'].value, '+-', out.params['g1_fwhm'].stderr)\n",
-    "        print('g1_amplitude:', out.params['g1_amplitude'].value, '+-', out.params['g1_amplitude'].stderr)\n",
-    "        print('chisqr, redchisqr', out.chisqr, out.redchi)\n",
-    "    if print_fit_report == -1:\n",
-    "        pass\n",
-    "\n",
-    "    #--------------------------------------\n",
-    "    #plt.figure()\n",
-    "    plt.errorbar(x, y, erry, xerr=None, fmt='', marker='o', ms= 3, mfc='none', mec=None,\\\n",
-    "                 ecolor=None, elinewidth=0.5, capsize=0,capthick=0,lw=0,\\\n",
-    "                 label='cen:'+str('%10.3f'%out.params['g1_center'].value)+'\\nfwhm :'+str('%10.3f'%out.params['g1_fwhm'].value))\n",
-    "    plt.plot(x, out.best_fit, '--r', linewidth=0.5)\n",
-    "\n",
-    "\n",
-    "\n",
-    "def lgauss2_fit(x,y,\\\n",
-    "        slope,slope_vary,slope_min,slope_max,\\\n",
-    "        intercept,intercept_vary, intercept_min, intercept_max,\\\n",
-    "        amplitude1,amplitude1_vary, amplitude1_min, amplitude1_max,\\\n",
-    "        center1,center1_vary,center1_min, center1_max,\\\n",
-    "        fwhm1,fwhm1_vary, fwhm1_min, fwhm1_max,\\\n",
-    "        amplitude2,amplitude2_vary, amplitude2_min, amplitude2_max,\\\n",
-    "        center2,center2_vary,center2_min, center2_max,\\\n",
-    "        fwhm2,fwhm2_vary, fwhm2_min, fwhm2_max,\\\n",
-    "        results):\n",
-    "    erry =np.sqrt(y)\n",
-    "\n",
-    "    lin_mod = LinearModel(prefix='l1_')\n",
-    "    pars = lin_mod.guess(y, x=x)\n",
-    "    pars['l1_slope'].set(value=slope,\n",
-    "                          vary=slope_vary,\n",
-    "                          min=slope_min,\n",
-    "                          max=slope_max)\n",
-    "    pars['l1_intercept'].set(value=intercept,\n",
-    "                              vary=intercept_vary,\n",
-    "                              min=intercept_min,\n",
-    "                              max=intercept_min)    \n",
-    "\n",
-    "    pgvt1 = GaussianModel(prefix='g1_')\n",
-    "    pars.update(pgvt1.make_params())\n",
-    "    pars['g1_center'].set(value=center1,\n",
-    "                           vary=center1_vary,\n",
-    "                           min=center1_min,\n",
-    "                           max=center1_max)\n",
-    "    pars['g1_sigma'].set(value=fwhm1 / 2.,\n",
-    "                          vary=fwhm1_vary,\n",
-    "                          min=fwhm1_min,\n",
-    "                          max=fwhm1_max)\n",
-    "    pars['g1_amplitude'].set(value=amplitude1,\n",
-    "                              vary=amplitude1_vary,\n",
-    "                              min=amplitude1_min,\n",
-    "                              max=amplitude1_max)\n",
-    "\n",
-    "    pgvt2 = GaussianModel(prefix='g2_')\n",
-    "    pars.update(pgvt2.make_params())\n",
-    "    pars['g2_center'].set(value=center2,\n",
-    "                           vary=center2_vary,\n",
-    "                           min=center2_min,\n",
-    "                           max=center2_max)\n",
-    "    pars['g2_sigma'].set(value=fwhm2 / 2.,\n",
-    "                          vary=fwhm2_vary,\n",
-    "                          min=fwhm2_min,\n",
-    "                          max=fwhm2_max)\n",
-    "    pars['g2_amplitude'].set(value=amplitude2,\n",
-    "                              vary=amplitude2_vary,\n",
-    "                              min=amplitude2_min,\n",
-    "                              max=amplitude2_max)\n",
-    "    \n",
-    "    mod1 =lin_mod+pgvt1+pgvt2\n",
-    "    init = mod1.eval(pars, x=x)\n",
-    "    out = mod1.fit(y, pars, x=x)\n",
-    "    print_fit_report = results\n",
-    "    if print_fit_report == 1:\n",
-    "        print((out.fit_report()))\n",
-    "    if print_fit_report == 0:\n",
-    "        #print('#--------peak1---------#')\n",
-    "        print('g1_center:', out.params['g1_center'].value, '+-', out.params['g1_center'].stderr)\n",
-    "        print('g1_fwhm:', out.params['g1_fwhm'].value, '+-', out.params['g1_fwhm'].stderr)\n",
-    "        print('g1_amplitude:', out.params['g1_amplitude'].value, '+-', out.params['g1_amplitude'].stderr)\n",
-    "        print('chisqr, redchisqr', out.chisqr, out.redchi)\n",
-    "    if print_fit_report == -1:\n",
-    "        pass\n",
-    "\n",
-    "    #--------------------------------------\n",
-    "    #plt.figure()\n",
-    "    plt.errorbar(x, y, erry, xerr=None, fmt='', marker='o', ms= 3, mfc='none', mec=None,\\\n",
-    "                 ecolor=None, elinewidth=0.5, capsize=0,capthick=0,lw=0,\\\n",
-    "                 label='cen:'+str('%10.3f'%out.params['g1_center'].value)+' fwhm :'+str('%10.3f'%out.params['g1_fwhm'].value)+'\\n'+'cen:'+str('%10.3f'%out.params['g2_center'].value)+' fwhm :'+str('%10.3f'%out.params['g2_fwhm'].value))\n",
-    "    plt.plot(x, out.best_fit, '--r', linewidth=0.5)\n",
-    "\n",
-    "def tcal(lamda, h,k,l):\n",
-    "    a,b,c=3.524041, 3.524041, 3.524041\n",
-    "    g=np.sqrt(((h*h)/(a*a))+((k*k)/(b*b))+((l*l)/(c*c)))\n",
-    "    d=1/g\n",
-    "    t=(180/np.pi)*np.arcsin(lamda/(2*d))\n",
-    "    return t\n",
-    "def e2l(e):\n",
-    "    return 9.045/np.sqrt(e)\n",
-    "\n",
-    "def l2e(l):\n",
-    "    return (9.045*9.045)/(l*l)\n",
-    "\n",
-    "def plot_params(x, y, font, lw, ms):\n",
-    "    plt.rcParams['figure.figsize'] = [x, y]\n",
-    "    plt.rcParams.update({'font.size': font})\n",
-    "    plt.rcParams['axes.linewidth'] = lw\n",
-    "    plt.rcParams[\"legend.markerscale\"] = ms\n",
-    "    import matplotlib as mpl\n",
-    "    mpl.rcParams['patch.linewidth'] = 0.0\n",
-    "\n",
-    "\n",
-    "TBLACK =  '\\033[30m'\n",
-    "print (TBLACK + \"It doens't reset!\" , TBLACK)\n",
-    "TRED =  '\\033[31m'\n",
-    "print (TRED + \"It doens't reset!\" , TRED)\n",
-    "TGREEN =  '\\033[32m' # Green Text\n",
-    "print (TGREEN + \"It doens't reset!\" , TGREEN)\n",
-    "TYELO =  '\\033[33m'\n",
-    "print (TYELO + \"It doens't reset!\" , TYELO)\n",
-    "TBLUE =  '\\033[34m'\n",
-    "print (TBLUE + \"It doens't reset!\" , TBLUE)\n",
-    "TPURP =  '\\033[35m'\n",
-    "print (TPURP + \"It doens't reset!\" , TPURP)\n",
-    "TCYAN =  '\\033[36m'\n",
-    "print (TCYAN + \"It doens't reset!\" , TCYAN)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8fd63ade-3f5f-4519-aa97-12e17807b526",
-   "metadata": {},
-   "source": [
-    "# Direct beam P matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 581,
-   "id": "8d5ce83b-8e32-41d0-b8f6-2e3446d16dec",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.85269972, -0.00563103, -0.01101298],\n",
-       "       [ 0.02027053,  0.85476527, -0.08905623],\n",
-       "       [-0.03210473,  0.01320895,  0.83179268]])"
-      ]
-     },
-     "execution_count": 581,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "up=np.array([\n",
-    "[23470, 12626,  13605],\n",
-    "[13162, 23626,  11129],\n",
-    "[12421, 12925,  23185],])\n",
-    "dn=np.array([\n",
-    "[1866 , 12769, 13908],\n",
-    "[12639, 1850 , 13305],\n",
-    "[13245, 12588, 2129],])\n",
-    "P=(up-dn)/(up+dn)\n",
-    "P\n",
-    "#errup=np.sqrt(up)\n",
-    "#errdn=np.sqrt(dn)\n",
-    "#errP=np.sqrt(2*dn*((errup/(up+dn))**2)+2*up*((errdn/(up+dn))**2))\n",
-    "#errP=np.sqrt((errup/(up+dn))**2+(errdn/(up+dn))**2)\n",
-    "#errP"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d5cecb5-f93f-4694-b5fa-d7233bd19548",
-   "metadata": {},
-   "source": [
-    "# Silicon(111) P mat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 582,
-   "id": "562a8124-9022-419b-bbf1-cc0a1c5d1e67",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.85515946, -0.01048124, -0.00821066],\n",
-       "       [ 0.01626918,  0.85336295, -0.08237344],\n",
-       "       [-0.03137585,  0.01579267,  0.84029436]])"
-      ]
-     },
-     "execution_count": 582,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "up=np.array([\n",
-    "[71086, 37669,  40828],\n",
-    "[39416, 70501,  33698],\n",
-    "[37401, 39332,  70521],])\n",
-    "dn=np.array([\n",
-    "[5550 , 38467, 41504],\n",
-    "[38154, 5578 , 39748],\n",
-    "[39824, 38109, 6120],])\n",
-    "P=(up-dn)/(up+dn)\n",
-    "\n",
-    "P"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9586b822-5221-4a8e-b7ee-d124fda7e541",
-   "metadata": {},
-   "source": [
-    "# Selective History LOG"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 625,
-   "id": "ee412a37-c6fd-4e13-ae5a-c65215e82150",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2:16:21 PM  6/11/2024    Executing \"ubcalc file \"C:\\SPICE\\User\\exp943\\UBConf\\tmp\\UB11Jun2024_21616PM.ini\"\"\n",
-      "2:19:48 PM  6/11/2024    Executing \"drive comp 1.5\"\n",
-      "2:20:55 PM  6/11/2024    Executing \"drive comp 0.5\"\n",
-      "2:22:39 PM  6/11/2024    Executing \"drive comp 0\"\n",
-      "2:24:49 PM  6/11/2024    Executing \"drive comp -0.5\"\n",
-      "2:25:21 PM  6/11/2024    Executing \"drive comp 0.5\"\n",
-      "2:26:37 PM  6/11/2024    Executing \"drive comp 0\"\n",
-      "2:27:06 PM  6/11/2024    Executing \"drive comp 0.7\"\n",
-      "2:27:37 PM  6/11/2024    Executing \"drive comp 1\"\n",
-      "2:28:35 PM  6/11/2024    Executing \"drive comp 0\"\n",
-      "3:35:43 PM  6/11/2024    Executing \"drive s2 -10\"\n",
-      "3:36:06 PM  6/11/2024    Executing \"drive s2 0\"\n",
-      "3:36:39 PM  6/11/2024    Executing \"drive s2 -2\"\n",
-      "3:39:44 PM  6/11/2024    Executing \"drive s2 -1\"\n",
-      "3:39:51 PM  6/11/2024    Executing \"drive s2 0\"\n",
-      "3:40:22 PM  6/11/2024    Executing \"drive ds_nut -2.5\"\n",
-      "3:40:54 PM  6/11/2024    Executing \"drive ds_nut 2.5\"\n",
-      "4:06:54 PM  6/11/2024    Executing \"drive s2 -30\"\n",
-      "4:50:31 PM  6/11/2024    Executing \"drive s2 0\"\n",
-      "4:51:47 PM  6/11/2024    Executing \"lowerlimit m1 -99.000000 m2 23.000000 marc -9.850000 mtrans -11.900000 mfocus -0.100000 up_prec -60.000000 ds_prec -60.000000 ds_guide -60.000000 ds_nut -60.000000 comp -60.000000 up_prec_ramp 0.100000 ds_prec_ramp 0.100000 ds_guide_ramp 0.100000 ds_nut_ramp 0.100000 comp_ramp 0.100000 theta_2 -360.000000 theta_1 -360.000000 s1 -100.000000 s2 -70.000000 sgl -5.000000 sgu -5.000000 stl -14.900000 stu -14.900000 a1 -87.813820 a2 -75.000000\"\n",
-      "4:51:48 PM  6/11/2024    Executing \"upperlimit m1 250.000000 m2 46.000000 marc 9.850000 mtrans 11.850000 mfocus 300.000000 up_prec 60.000000 ds_prec 60.000000 ds_guide 60.000000 ds_nut 60.000000 comp 60.000000 up_prec_ramp 99.900000 ds_prec_ramp 99.900000 ds_guide_ramp 99.900000 ds_nut_ramp 99.900000 comp_ramp 99.900000 theta_2 356.000000 theta_1 356.000000 s1 100.000000 s2 1.000000 sgl 5.000000 sgu 5.000000 stl 14.900000 stu 14.700000 a1 171.462286 a2 89.000000\"\n",
-      "4:52:13 PM  6/11/2024    Executing \"hide m1 0 m2 0 marc 1 mtrans 1 mfocus 1 up_prec 0 ds_prec 0 ds_guide 0 ds_nut 0 comp 0 up_prec_ramp 0 ds_prec_ramp 0 ds_guide_ramp 0 ds_nut_ramp 0 comp_ramp 0 theta_2 0 theta_1 0 s1 0 s2 0 sgl 1 sgu 1 stl 1 stu 1 a1 0 a2 0 q 0 h 0 k 0 l 0 ei 0 ef 0 e 0 up_prec_amps 0 ds_prec_amps 0 ds_guide_amps 0 ds_nut_amps 0 snp_status 1\"\n",
-      "4:54:44 PM  6/11/2024    Executing \"zero m1 -0.000016 m2 -0.767483 marc 0.000000 mtrans 0.000000 mfocus 0.000000 up_prec 0.000000 ds_prec 0.000000 ds_guide 0.000000 ds_nut 0.000000 comp 0.000000 up_prec_ramp 0.000000 ds_prec_ramp 0.000000 ds_guide_ramp 0.000000 ds_nut_ramp 0.000000 comp_ramp 0.000000 theta_2 4.000000 theta_1 4.000000 s1 0.000625 s2 0.371334 sgl 0.000000 sgu 0.000000 stl 0.000000 stu 0.000000 a1 -55.932682 a2 -4.281899 up_prec_amps 0.000000 ds_prec_amps 0.000000 ds_guide_amps 0.000000 ds_nut_amps 0.000000 snp_status 0.000000\"\n",
-      "4:54:47 PM  6/11/2024    Executing \"hold m1 0 m2 0 marc 0 mtrans 0 mfocus 0 up_prec 0 ds_prec 0 ds_guide 0 ds_nut 0 comp 0 up_prec_ramp 0 ds_prec_ramp 0 ds_guide_ramp 0 ds_nut_ramp 0 comp_ramp 0 theta_2 0 theta_1 0 s1 1 s2 0 sgl 0 sgu 0 stl 0 stu 0 a1 0 a2 0\"\n",
-      "4:55:48 PM  6/11/2024    Executing \"drive theta2 @(theta2)+4\"\n",
-      "4:56:26 PM  6/11/2024    Executing \"drive theta_2 @(theta_2)+4\"\n",
-      "4:57:11 PM  6/11/2024    Executing \"drive theta_2 -3\"\n",
-      "4:59:11 PM  6/11/2024    Executing \"drive theta_1 -3\"\n",
-      "4:59:33 PM  6/11/2024    Executing \"initialize\"\n",
-      "5:00:46 PM  6/11/2024    Executing \"drive theta_1 -3\"\n",
-      "5:05:28 PM  6/11/2024    Executing \"drive s2 -20\"\n",
-      "5:07:56 PM  6/11/2024    Executing \"initialize\"\n",
-      "5:17:15 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+1\"\n",
-      "5:17:20 PM  6/11/2024    Executing \"drive theta_2 @(theta_2)+1\"\n",
-      "5:32:41 PM  6/11/2024    Executing \"initialize\"\n",
-      "5:34:57 PM  6/11/2024    Executing \"initialize\"\n",
-      "5:36:11 PM  6/11/2024    Executing \"drive s2 0\"\n",
-      "5:36:26 PM  6/11/2024    Executing \"zero m1 -0.000016 m2 -0.767483 marc 0.000000 mtrans 0.000000 mfocus 0.000000 up_prec 0.000000 ds_prec 0.000000 ds_guide 0.000000 ds_nut 0.000000 comp 0.000000 up_prec_ramp 0.000000 ds_prec_ramp 0.000000 ds_guide_ramp 0.000000 ds_nut_ramp 0.000000 comp_ramp 0.000000 theta_2 4.000000 theta_1 4.000000 s1 0.000000 s2 0.371334 sgl 0.000000 sgu 0.000000 stl 0.000000 stu 0.000000 a1 -55.932682 a2 -4.281899 up_prec_amps 0.000000 ds_prec_amps 0.000000 ds_guide_amps 0.000000 ds_nut_amps 0.000000 vti 0.000000 sample 0.000000 temp 0.000000 temp_2 0.000000 snp_status 0.000000\"\n",
-      "5:36:29 PM  6/11/2024    Executing \"hold m1 0 m2 0 marc 0 mtrans 0 mfocus 0 up_prec 0 ds_prec 0 ds_guide 0 ds_nut 0 comp 0 up_prec_ramp 0 ds_prec_ramp 0 ds_guide_ramp 0 ds_nut_ramp 0 comp_ramp 0 theta_2 0 theta_1 0 s1 1 s2 0 sgl 0 sgu 0 stl 0 stu 0 a1 0 a2 0 temp 0 temp_2 0\"\n",
-      "5:36:50 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+1\"\n",
-      "5:40:19 PM  6/11/2024    Executing \"initialize\"\n",
-      "5:42:11 PM  6/11/2024    Executing \"initialize\"\n",
-      "5:43:21 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-1\"\n",
-      "5:43:27 PM  6/11/2024    Executing \"drive theta_2 @(theta_2)+1\"\n",
-      "5:45:06 PM  6/11/2024    Executing \"drive ds_nut -2.5\"\n",
-      "5:46:01 PM  6/11/2024    Executing \"drive ds_nut -3\"\n",
-      "5:46:51 PM  6/11/2024    Executing \"scan ds_nut -3 -4 0.25\"\n",
-      "5:51:12 PM  6/11/2024    Executing \"scan ds_nut -4 -3 0.5\"\n",
-      "5:52:16 PM  6/11/2024    Executing \"tolerance m1 0.005000 m2 0.010000 marc 0.005000 mtrans 0.005000 mfocus 0.050000 up_prec 0.005000 ds_prec 0.005000 ds_guide 0.005000 ds_nut 0.010000 comp 0.010000 up_prec_ramp 0.000000 ds_prec_ramp 0.000000 ds_guide_ramp 0.000000 ds_nut_ramp 0.000000 comp_ramp 0.000000 theta_2 0.050000 theta_1 0.050000 s1 0.020000 s2 0.020000 sgl 0.050000 sgu 0.050000 stl 0.050000 stu 0.050000 a1 0.050000 a2 0.050000 temp 0.500000 temp_2 0.500000\"\n",
-      "5:52:26 PM  6/11/2024    Executing \"scan ds_nut -3 -4 0.25\"\n",
-      "5:54:37 PM  6/11/2024    Executing \"tolerance m1 0.005000 m2 0.010000 marc 0.005000 mtrans 0.005000 mfocus 0.050000 up_prec 0.005000 ds_prec 0.005000 ds_guide 0.005000 ds_nut 0.020000 comp 0.010000 up_prec_ramp 0.000000 ds_prec_ramp 0.000000 ds_guide_ramp 0.000000 ds_nut_ramp 0.000000 comp_ramp 0.000000 theta_2 0.050000 theta_1 0.050000 s1 0.020000 s2 0.020000 sgl 0.050000 sgu 0.050000 stl 0.050000 stu 0.050000 a1 0.050000 a2 0.050000 temp 0.500000 temp_2 0.500000\"\n",
-      "5:54:42 PM  6/11/2024    Executing \"scan ds_nut -3 -4 0.25\"\n",
-      "5:55:23 PM  6/11/2024    Executing \"tolerance m1 0.005000 m2 0.010000 marc 0.005000 mtrans 0.005000 mfocus 0.050000 up_prec 0.005000 ds_prec 0.005000 ds_guide 0.005000 ds_nut 0.020000 comp 0.010000 up_prec_ramp 0.000000 ds_prec_ramp 0.000000 ds_guide_ramp 0.000000 ds_nut_ramp 0.000000 comp_ramp 0.000000 theta_2 0.050000 theta_1 0.050000 s1 0.020000 s2 0.020000 sgl 0.050000 sgu 0.050000 stl 0.050000 stu 0.050000 a1 0.050000 a2 0.050000 temp 0.500000 temp_2 0.500000\"\n",
-      "5:56:18 PM  6/11/2024    Executing \"scan ds_nut -4 -2 0.25\"\n",
-      "5:57:11 PM  6/11/2024    Executing \"scan ds_nut 2 4 0.25\"\n",
-      "5:58:36 PM  6/11/2024    Executing \"drive comp 1\"\n",
-      "5:59:16 PM  6/11/2024    Executing \"scan ds_nut 4 2 0.25\"\n",
-      "6:03:21 PM  6/11/2024    Executing \"drive ds_nut -2\"\n",
-      "6:03:58 PM  6/11/2024    Executing \"scan ds_nut -2 -4 0.25\"\n",
-      "6:05:19 PM  6/11/2024    Executing \"drive theta_2 @(theta_2)+2\"\n",
-      "6:09:21 PM  6/11/2024    Executing \"drive s2 -20\"\n",
-      "6:10:56 PM  6/11/2024    Executing \"drive s2 0\"\n",
-      "6:13:04 PM  6/11/2024    Executing \"drive ds_nut 4\"\n",
-      "6:14:06 PM  6/11/2024    Executing \"count\"\n",
-      "6:14:06 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 1.00\n",
-      "         time     detector      monitor          mcu\n",
-      "        1.000     2460.000      794.000        0.869\n",
-      "6:15:16 PM  6/11/2024    Executing \"count preset time 10\"\n",
-      "6:15:16 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    25568.000     8155.000        8.930\n",
-      "6:15:35 PM  6/11/2024    Executing \"drive ds_nut -4\"\n",
-      "6:15:54 PM  6/11/2024    Executing \"count preset time 10\"\n",
-      "6:15:54 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000     5125.000     8042.000        8.806\n",
-      "6:21:05 PM  6/11/2024    Executing \"drive ds_nut 4\"\n",
-      "6:21:33 PM  6/11/2024    Executing \"count\"\n",
-      "6:21:33 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 1.00\n",
-      "         time     detector      monitor          mcu\n",
-      "        1.000     1874.000      868.000        0.951\n",
-      "6:21:43 PM  6/11/2024    Executing \"count preset time 10\"\n",
-      "6:21:43 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    17710.000     8524.000        9.334\n",
-      "6:22:04 PM  6/11/2024    Executing \"drive ds_nut -4\"\n",
-      "6:22:21 PM  6/11/2024    Executing \"count preset time 10\"\n",
-      "6:22:21 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000     3663.000     8378.000        9.174\n",
-      "6:24:02 PM  6/11/2024    Executing \"count preset time 10\"\n",
-      "6:24:02 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000     3605.000     9133.000       10.001\n",
-      "6:24:25 PM  6/11/2024    Executing \"drive ds_nut 4\"\n",
-      "6:25:02 PM  6/11/2024    Executing \"count preset time 10\"\n",
-      "6:25:02 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    17156.000     8258.000        9.043\n",
-      "6:25:53 PM  6/11/2024    Executing \"scan ds_guide 3 4 0.2\"\n",
-      "6:27:33 PM  6/11/2024    Executing \"tolerance m1 0.005000 m2 0.010000 marc 0.005000 mtrans 0.005000 mfocus 0.050000 up_prec 0.005000 ds_prec 0.005000 ds_guide 0.020000 ds_nut 0.020000 comp 0.010000 up_prec_ramp 0.000000 ds_prec_ramp 0.000000 ds_guide_ramp 0.000000 ds_nut_ramp 0.000000 comp_ramp 0.000000 theta_2 0.050000 theta_1 0.050000 s1 0.020000 s2 0.020000 sgl 0.050000 sgu 0.050000 stl 0.050000 stu 0.050000 a1 0.050000 a2 0.050000 temp 0.500000 temp_2 0.500000\"\n",
-      "6:27:54 PM  6/11/2024    Executing \"scan ds_guide 3 2 0.2\"\n",
-      "6:29:21 PM  6/11/2024    Executing \"scan ds_guide 2 1 0.2\"\n",
-      "6:35:29 PM  6/11/2024    Executing \"drive ds_guide 0\"\n",
-      "6:36:01 PM  6/11/2024    Executing \"drive ds_nut 0\"\n",
-      "6:36:37 PM  6/11/2024    Executing \"drive comp 0\"\n",
-      "6:37:15 PM  6/11/2024    Executing \"drive ds_guide 3\"\n",
-      "6:37:33 PM  6/11/2024    Executing \"drive ds_nut 2\"\n",
-      "6:37:49 PM  6/11/2024    Executing \"drive ds_nut -2\"\n",
-      "6:38:12 PM  6/11/2024    Executing \"drive ds_nut -3\"\n",
-      "6:38:29 PM  6/11/2024    Executing \"drive ds_nut -4\"\n",
-      "6:38:47 PM  6/11/2024    Executing \"drive ds_nut -4.5\"\n",
-      "6:39:02 PM  6/11/2024    Executing \"drive ds_nut -5\"\n",
-      "6:39:17 PM  6/11/2024    Executing \"drive ds_nut -5.5\"\n",
-      "6:39:35 PM  6/11/2024    Executing \"drive ds_nut -6\"\n",
-      "6:39:44 PM  6/11/2024    Executing \"drive ds_nut -5\"\n",
-      "6:40:01 PM  6/11/2024    Executing \"drive ds_nut -4.5\"\n",
-      "6:40:15 PM  6/11/2024    Executing \"drive ds_nut -5.25\"\n",
-      "6:40:32 PM  6/11/2024    Executing \"drive ds_nut 5\"\n",
-      "6:41:00 PM  6/11/2024    Executing \"drive ds_nut 5.5\"\n",
-      "6:41:15 PM  6/11/2024    Executing \"drive ds_nut -4.5\"\n",
-      "6:41:36 PM  6/11/2024    Executing \"drive ds_nut -4\"\n",
-      "6:41:47 PM  6/11/2024    Executing \"drive ds_nut 3\"\n",
-      "6:42:19 PM  6/11/2024    Executing \"drive ds_nut -2\"\n",
-      "6:42:42 PM  6/11/2024    Executing \"drive ds_nut 1\"\n",
-      "6:42:59 PM  6/11/2024    Executing \"drive ds_nut 0\"\n",
-      "6:43:08 PM  6/11/2024    Executing \"drive ds_nut -0.5\"\n",
-      "6:43:35 PM  6/11/2024    Executing \"drive ds_nut 0\"\n",
-      "6:45:10 PM  6/11/2024    Executing \"drive ds_nut 2\"\n",
-      "6:45:32 PM  6/11/2024    Executing \"drive ds_nut -2\"\n",
-      "6:50:03 PM  6/11/2024    Executing \"drive ds_nut 2\"\n",
-      "6:50:26 PM  6/11/2024    Executing \"drive ds_nut 3\"\n",
-      "6:50:42 PM  6/11/2024    Executing \"drive ds_nut 4\"\n",
-      "6:50:54 PM  6/11/2024    Executing \"drive ds_nut 4.5\"\n",
-      "6:51:06 PM  6/11/2024    Executing \"drive ds_nut 5\"\n",
-      "6:51:22 PM  6/11/2024    Executing \"drive ds_nut 4\"\n",
-      "6:51:33 PM  6/11/2024    Executing \"count preset 10\"\n",
-      "6:51:33 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    17282.000     8283.000        9.070\n",
-      "6:51:52 PM  6/11/2024    Executing \"drive ds_nut -3\"\n",
-      "6:52:16 PM  6/11/2024    Executing \"drive ds_nut -1\"\n",
-      "6:52:29 PM  6/11/2024    Executing \"drive ds_nut 0\"\n",
-      "6:52:37 PM  6/11/2024    Executing \"drive ds_nut -1\"\n",
-      "6:52:48 PM  6/11/2024    Executing \"drive ds_nut -2\"\n",
-      "6:52:57 PM  6/11/2024    Executing \"drive ds_nut -1\"\n",
-      "6:53:10 PM  6/11/2024    Executing \"count preset 10\"\n",
-      "6:53:10 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000     3107.000     8235.000        9.018\n",
-      "7:29:31 PM  6/11/2024    Executing \"drive ds_nut 2\"\n",
-      "7:29:59 PM  6/11/2024    Executing \"preset time 10\"\n",
-      "7:30:02 PM  6/11/2024    Executing \"count\"\n",
-      "7:30:02 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000     4124.000     8785.000        9.620\n",
-      "7:30:24 PM  6/11/2024    Executing \"drive ds_nut -2\"\n",
-      "7:30:37 PM  6/11/2024    Executing \"count\"\n",
-      "7:30:37 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000      326.000     8533.000        9.344\n",
-      "7:32:33 PM  6/11/2024    Executing \"count\"\n",
-      "7:32:33 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    29946.000     8394.000        9.192\n",
-      "7:32:53 PM  6/11/2024    Executing \"drive ds_nut 2\"\n",
-      "7:33:15 PM  6/11/2024    Executing \"count\"\n",
-      "7:33:15 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000   374351.000     8326.000        9.117\n",
-      "7:36:21 PM  6/11/2024    Executing \"count\"\n",
-      "7:36:21 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000     7743.000     9173.000       10.045\n",
-      "7:36:42 PM  6/11/2024    Executing \"drive ds_nut -2\"\n",
-      "7:36:53 PM  6/11/2024    Executing \"count\"\n",
-      "7:36:53 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000      662.000     8645.000        9.467\n",
-      "7:42:11 PM  6/11/2024    Executing \"drive ds_nut 2\"\n",
-      "7:43:00 PM  6/11/2024    Executing \"count\"\n",
-      "7:43:00 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000     6034.000     8441.000        9.243\n",
-      "7:43:30 PM  6/11/2024    Executing \"drive ds_nut -2\"\n",
-      "7:43:45 PM  6/11/2024    Executing \"count\"\n",
-      "7:43:45 PM  6/11/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000      519.000     8457.000        9.261\n",
-      "7:46:38 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+4\"\n",
-      "7:47:42 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+16\"\n",
-      "7:48:24 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+25\"\n",
-      "7:49:08 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+45\"\n",
-      "7:49:47 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+20\"\n",
-      "7:50:15 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+25\"\n",
-      "7:50:55 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+25\"\n",
-      "7:51:28 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+20\"\n",
-      "7:52:25 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+10\"\n",
-      "7:52:51 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+15\"\n",
-      "7:54:40 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+10\"\n",
-      "7:55:18 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+10\"\n",
-      "7:55:49 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+10\"\n",
-      "7:56:15 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+10\"\n",
-      "7:56:47 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+10\"\n",
-      "7:57:33 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+10\"\n",
-      "7:58:15 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+15\"\n",
-      "7:59:28 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+15\"\n",
-      "8:00:27 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+15\"\n",
-      "8:01:07 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+10\"\n",
-      "8:02:26 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+10\"\n",
-      "8:03:04 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+10\"\n",
-      "8:03:55 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+10\"\n",
-      "8:04:29 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+10\"\n",
-      "8:13:59 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-10\"\n",
-      "8:14:24 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-10\"\n",
-      "8:14:53 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-10\"\n",
-      "8:15:23 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-10\"\n",
-      "8:15:51 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-10\"\n",
-      "8:16:26 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-10\"\n",
-      "8:17:05 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "8:17:38 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-10\"\n",
-      "8:18:15 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-15\"\n",
-      "8:18:43 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-15\"\n",
-      "8:19:17 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-10\"\n",
-      "8:19:42 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-10\"\n",
-      "8:20:13 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-10\"\n",
-      "8:20:39 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-10\"\n",
-      "8:21:03 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-20\"\n",
-      "8:21:37 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "8:22:06 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "8:22:32 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "8:23:03 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "8:23:46 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "8:24:42 PM  6/11/2024    Executing \"drive theta_1 @(theta_1)+-20\"\n",
-      "11:52:48 PM  6/11/2024    Executing \"initialize\"\n",
-      "11:59:23 PM  6/11/2024    Executing \"initialize\"\n",
-      "12:03:02 AM  6/12/2024    Executing \"initialize\"\n",
-      "12:19:21 AM  6/12/2024    Executing \"initialize\"\n",
-      "12:22:20 AM  6/12/2024    Executing \"initialize\"\n",
-      "12:24:24 AM  6/12/2024    Executing \"drive ds_nut 2\"\n",
-      "12:25:01 AM  6/12/2024    Executing \"drive comp 0.1\"\n",
-      "12:25:13 AM  6/12/2024    Executing \"drive comp 0\"\n",
-      "12:25:24 AM  6/12/2024    Executing \"drive up_prec 1\"\n",
-      "12:25:37 AM  6/12/2024    Executing \"drive up_prec 0\"\n",
-      "12:25:45 AM  6/12/2024    Executing \"drive ds_prec 1\"\n",
-      "12:28:13 AM  6/12/2024    Executing \"drive us_guide 1\"\n",
-      "12:28:31 AM  6/12/2024    Executing \"drive us_guide 0\"\n",
-      "12:28:42 AM  6/12/2024    Executing \"drive us_nut 1\"\n",
-      "12:29:34 AM  6/12/2024    Executing \"drive us_guide -3.5\"\n",
-      "12:29:39 AM  6/12/2024    Executing \"drive us_nut -3\"\n",
-      "12:30:14 AM  6/12/2024    Executing \"drive ds_[rec 0\"\n",
-      "12:30:20 AM  6/12/2024    Executing \"drive ds_prec 0\"\n",
-      "12:36:01 AM  6/12/2024    Executing \"count\"\n",
-      "12:36:01 AM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000   574162.000    11883.000       13.012\n",
-      "12:36:22 AM  6/12/2024    Executing \"drive ds_nut -2\"\n",
-      "12:36:25 AM  6/12/2024    Executing \"count\"\n",
-      "12:36:25 AM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000   151646.000    10297.000       11.276\n",
-      "12:36:48 AM  6/12/2024    Executing \"count\"\n",
-      "12:36:48 AM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    57587.000     9582.000       10.493\n",
-      "12:37:42 AM  6/12/2024    Executing \"drive ds_nut -3\"\n",
-      "12:37:55 AM  6/12/2024    Executing \"count\"\n",
-      "12:37:55 AM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    54131.000     8511.000        9.320\n",
-      "12:38:25 AM  6/12/2024    Executing \"drive ds_nut -4\"\n",
-      "12:38:41 AM  6/12/2024    Executing \"count\"\n",
-      "12:38:41 AM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    52946.000     8350.000        9.144\n",
-      "12:39:18 AM  6/12/2024    Executing \"drive ds_nut 3\"\n",
-      "12:39:43 AM  6/12/2024    Executing \"count\"\n",
-      "12:39:43 AM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000   577313.000     8430.000        9.231\n",
-      "12:42:49 AM  6/12/2024    Executing \"drive us_nut -4\"\n",
-      "12:43:00 AM  6/12/2024    Executing \"count\"\n",
-      "12:43:00 AM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000   575359.000     8315.000        9.105\n",
-      "12:43:41 AM  6/12/2024    Executing \"drive us_nut -3\"\n",
-      "12:44:16 AM  6/12/2024    Executing \"drive ds_nut 4\"\n",
-      "12:44:47 AM  6/12/2024    Executing \"drive ds_nut 4.5\"\n",
-      "12:45:02 AM  6/12/2024    Executing \"count\"\n",
-      "12:45:02 AM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000   576794.000     8199.000        8.978\n",
-      "12:45:18 AM  6/12/2024    Executing \"drive ds_nut 3\"\n",
-      "12:45:49 AM  6/12/2024    Executing \"drive ds_guide 3.5\"\n",
-      "12:46:06 AM  6/12/2024    Executing \"drive ds_guide 4\"\n",
-      "12:46:18 AM  6/12/2024    Executing \"drive ds_guide 2\"\n",
-      "12:46:32 AM  6/12/2024    Executing \"drive ds_guide 1\"\n",
-      "12:46:43 AM  6/12/2024    Executing \"drive ds_guide 3\"\n",
-      "12:47:01 AM  6/12/2024    Executing \"drive ds_guide -3\"\n",
-      "12:47:19 AM  6/12/2024    Executing \"drive ds_guide -4\"\n",
-      "12:47:33 AM  6/12/2024    Executing \"count\"\n",
-      "12:47:33 AM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000   571787.000     8483.000        9.289\n",
-      "12:48:10 AM  6/12/2024    Executing \"drive ds_guide 3\"\n",
-      "12:48:31 AM  6/12/2024    Executing \"drive ds_nut -3\"\n",
-      "12:48:47 AM  6/12/2024    Executing \"count\"\n",
-      "12:48:47 AM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    53558.000     8284.000        9.071\n",
-      "12:49:17 AM  6/12/2024    Executing \"drive ds_nut 3\"\n",
-      "12:50:56 AM  6/12/2024    Executing \"drive ds_nut 2.5\"\n",
-      "12:51:00 AM  6/12/2024    Executing \"wait 20\"\n",
-      "12:52:05 AM  6/12/2024    Executing \"drive comp 1\"\n",
-      "12:52:31 AM  6/12/2024    Executing \"drive comp 0\"\n",
-      "12:53:25 AM  6/12/2024    Executing \"drive ds_nut -3\"\n",
-      "12:53:51 AM  6/12/2024    Executing \"drive ds_nut 3\"\n",
-      "12:58:35 AM  6/12/2024    Executing \"preset time 60\"\n",
-      "1:02:39 AM  6/12/2024    Executing \"drive us_guide -3\"\n",
-      "1:02:52 AM  6/12/2024    Executing \"drive us_guide -4\"\n",
-      "1:03:05 AM  6/12/2024    Executing \"drive us_guide -3.5\"\n",
-      "1:03:19 AM  6/12/2024    Executing \"drive us_guide -4.5\"\n",
-      "1:03:30 AM  6/12/2024    Executing \"drive us_guide -3.75\"\n",
-      "1:03:44 AM  6/12/2024    Executing \"drive us_guide -3.5\"\n",
-      "1:05:25 AM  6/12/2024    Executing \"preset time 30\"\n",
-      "1:06:51 AM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan ++, no comp\"\"\n",
-      "1:06:51 AM  6/12/2024    Executing \"scan theta_1 -4 356 5\"\n",
-      "1:48:46 AM  6/12/2024    Executing \"drive ds_nut -3\"\n",
-      "1:48:47 AM  6/12/2024    Executing \"wait 30\"\n",
-      "1:49:17 AM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan +-, no comp\"\"\n",
-      "1:49:17 AM  6/12/2024    Executing \"scan theta_1 356 -4 5\"\n",
-      "2:31:11 AM  6/12/2024    Executing \"drive comp 1\"\n",
-      "2:31:11 AM  6/12/2024    Executing \"wait 15\"\n",
-      "2:31:27 AM  6/12/2024    Executing \"drive ds_nut 3\"\n",
-      "2:31:27 AM  6/12/2024    Executing \"wait 30\"\n",
-      "2:31:57 AM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan ++, comp 1A\"\"\n",
-      "2:31:57 AM  6/12/2024    Executing \"scan theta_1 -4 356 5\"\n",
-      "3:13:50 AM  6/12/2024    Executing \"drive ds_nut -3\"\n",
-      "3:13:51 AM  6/12/2024    Executing \"wait 30\"\n",
-      "3:14:21 AM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan +-, comp 1A\"\"\n",
-      "3:14:21 AM  6/12/2024    Executing \"scan theta_1 356 -4 5\"\n",
-      "3:56:16 AM  6/12/2024    Executing \"drive comp 1.2\"\n",
-      "3:56:17 AM  6/12/2024    Executing \"wait 15\"\n",
-      "3:56:32 AM  6/12/2024    Executing \"drive ds_nut 3\"\n",
-      "3:56:33 AM  6/12/2024    Executing \"wait 30\"\n",
-      "3:57:03 AM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan ++, comp 1.2A\"\"\n",
-      "3:57:03 AM  6/12/2024    Executing \"scan theta_1 -4 356 5\"\n",
-      "4:39:00 AM  6/12/2024    Executing \"drive ds_nut -3\"\n",
-      "4:39:00 AM  6/12/2024    Executing \"wait 30\"\n",
-      "4:39:31 AM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan +-, comp 1.2A\"\"\n",
-      "4:39:31 AM  6/12/2024    Executing \"scan theta_1 356 -4 5\"\n",
-      "5:21:26 AM  6/12/2024    Executing \"drive comp 1.4\"\n",
-      "5:21:26 AM  6/12/2024    Executing \"wait 15\"\n",
-      "5:21:42 AM  6/12/2024    Executing \"drive ds_nut 3\"\n",
-      "5:21:42 AM  6/12/2024    Executing \"wait 30\"\n",
-      "5:22:12 AM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan ++, comp 1.4A\"\"\n",
-      "5:22:12 AM  6/12/2024    Executing \"scan theta_1 -4 356 5\"\n",
-      "6:04:11 AM  6/12/2024    Executing \"drive ds_nut -3\"\n",
-      "6:04:12 AM  6/12/2024    Executing \"wait 30\"\n",
-      "6:04:42 AM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan +-, comp 1.4A\"\"\n",
-      "6:04:42 AM  6/12/2024    Executing \"scan theta_1 356 -4 5\"\n",
-      "6:46:41 AM  6/12/2024    Executing \"drive comp 0\"\n",
-      "9:22:25 AM  6/12/2024    Executing \"drive s2 -10\"\n",
-      "9:23:18 AM  6/12/2024    Executing \"drive s2 -10\"\n",
-      "9:23:36 AM  6/12/2024    Executing \"drive s2 -20\"\n",
-      "9:30:22 AM  6/12/2024    Executing \"drive up_prec 2\"\n",
-      "9:30:44 AM  6/12/2024    Executing \"drive up_prec 0\"\n",
-      "9:57:52 AM  6/12/2024    Executing \"drive up_prec 1\"\n",
-      "9:58:15 AM  6/12/2024    Executing \"drive up_prec 0\"\n",
-      "10:00:42 AM  6/12/2024    Executing \"scan up_prec 0 1 0.1 preset countfile wait15sec\"\n",
-      "10:04:02 AM  6/12/2024    Executing \"drive up_prec 0\"\n",
-      "10:04:08 AM  6/12/2024    Executing \"drive s2 0\"\n",
-      "10:06:11 AM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan ++, no comp\"\"\n",
-      "10:06:23 AM  6/12/2024    Executing \"scan theta_1 -4 356 5\"\n",
-      "10:07:24 AM  6/12/2024    Executing \"drive ds_nut 3\"\n",
-      "10:07:32 AM  6/12/2024    Executing \"preset time 10\"\n",
-      "10:07:45 AM  6/12/2024    Executing \"scan theta_1 -4 356 5\"\n",
-      "10:11:30 AM  6/12/2024    Executing \"drive theta_1 1\"\n",
-      "10:11:49 AM  6/12/2024    Executing \"drive theta_1 -4\"\n",
-      "10:12:01 AM  6/12/2024    Executing \"scan theta_1 -4 356 5\"\n",
-      "10:33:58 AM  6/12/2024    Executing \"drive theta_1 331\"\n",
-      "10:34:55 AM  6/12/2024    Executing \"drive s2 -20\"\n",
-      "10:36:33 AM  6/12/2024    Executing \"drive theta_1 300\"\n",
-      "10:37:18 AM  6/12/2024    Executing \"drive theta_1 270\"\n",
-      "10:38:01 AM  6/12/2024    Executing \"drive theta_1 240\"\n",
-      "10:38:41 AM  6/12/2024    Executing \"drive theta_1 220\"\n",
-      "10:38:56 AM  6/12/2024    Executing \"drive theta_1 200\"\n",
-      "10:39:17 AM  6/12/2024    Executing \"drive theta_1 100\"\n",
-      "11:12:07 AM  6/12/2024    Executing \"initialize\"\n",
-      "11:16:47 AM  6/12/2024    Executing \"initialize\"\n",
-      "11:18:28 AM  6/12/2024    Executing \"drive up_prec 1\"\n",
-      "11:18:51 AM  6/12/2024    Executing \"drive up_prec 0\"\n",
-      "11:21:09 AM  6/12/2024    Executing \"scan ds_nut 3 4 0.25\"\n",
-      "11:23:26 AM  6/12/2024    Executing \"scan ds_nut 4 3 0.25\"\n",
-      "11:29:58 AM  6/12/2024    Executing \"drive theta_1 150\"\n",
-      "11:31:39 AM  6/12/2024    Executing \"drive theta_1 180\"\n",
-      "11:34:19 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+30\"\n",
-      "11:34:57 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+30\"\n",
-      "11:36:44 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+30\"\n",
-      "11:37:55 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+30\"\n",
-      "11:40:21 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "11:44:16 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "11:50:49 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+30\"\n",
-      "11:51:20 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+30\"\n",
-      "11:52:21 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+30\"\n",
-      "11:52:36 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+30\"\n",
-      "11:53:10 AM  6/12/2024    Executing \"drive theta_1 356\"\n",
-      "11:54:01 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "11:54:13 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "11:55:14 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "11:55:36 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "11:55:52 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "11:56:07 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "11:56:24 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "11:56:41 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "11:57:02 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "11:57:20 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "11:57:43 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "11:58:01 AM  6/12/2024    Executing \"drive theta_1 @(theta_1)+-30\"\n",
-      "11:59:34 AM  6/12/2024    Executing \"initialize\"\n",
-      "12:00:59 PM  6/12/2024    Executing \"scan ds_nut 4 3 0.25\"\n",
-      "12:03:09 PM  6/12/2024    Executing \"scan ds_nut 4 0 1\"\n",
-      "12:04:30 PM  6/12/2024    Executing \"drive ds_nut_ramp 0.1\"\n",
-      "12:05:05 PM  6/12/2024    Executing \"scan ds_nut 0 4 1\"\n",
-      "12:12:08 PM  6/12/2024    Executing \"scan ds_nut 4 0 1\"\n",
-      "12:21:51 PM  6/12/2024    Executing \"initialize\"\n",
-      "12:23:31 PM  6/12/2024    Executing \"initialize\"\n",
-      "12:27:41 PM  6/12/2024    Executing \"initialize\"\n",
-      "12:29:07 PM  6/12/2024    Executing \"drive s1 1\"\n",
-      "12:29:13 PM  6/12/2024    Executing \"drive s1 0\"\n",
-      "12:29:41 PM  6/12/2024    Executing \"scan ds_nut 0 4 1\"\n",
-      "12:31:43 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "12:31:49 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "12:31:53 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "12:31:56 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "12:32:00 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "12:32:03 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "12:32:10 PM  6/12/2024    Executing \"drive s1 @(s1)+10\"\n",
-      "12:39:50 PM  6/12/2024    Executing \"drive ds_nut_ramp 0.5\"\n",
-      "12:40:08 PM  6/12/2024    Executing \"drive ds_nut -2\"\n",
-      "12:40:33 PM  6/12/2024    Executing \"drive ds_nut 2\"\n",
-      "12:44:44 PM  6/12/2024    Executing \"drive s1 @(s1)+-10\"\n",
-      "12:44:49 PM  6/12/2024    Executing \"drive s1 @(s1)+-10\"\n",
-      "12:44:54 PM  6/12/2024    Executing \"drive s1 @(s1)+-10\"\n",
-      "12:45:01 PM  6/12/2024    Executing \"drive s1 @(s1)+-7\"\n",
-      "12:59:48 PM  6/12/2024    Executing \"method temp set_setpoint d 170.000000\"\n",
-      "12:59:52 PM  6/12/2024    Executing \"method temp_2 set_setpoint d 170.000000\"\n",
-      "1:05:40 PM  6/12/2024    Executing \"drive s2 -46.224\"\n",
-      "1:06:37 PM  6/12/2024    Executing \"drive s1 @(s1)+-10\"\n",
-      "1:06:46 PM  6/12/2024    Executing \"drive s1 @(s1)+10\"\n",
-      "1:08:13 PM  6/12/2024    Executing \"drive s1 @(s1)+10\"\n",
-      "1:08:25 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "1:08:29 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "1:08:34 PM  6/12/2024    Executing \"drive s1 @(s1)+1\"\n",
-      "1:08:37 PM  6/12/2024    Executing \"drive s1 @(s1)+1\"\n",
-      "1:08:39 PM  6/12/2024    Executing \"drive s1 @(s1)+1\"\n",
-      "1:08:42 PM  6/12/2024    Executing \"drive s1 @(s1)+1\"\n",
-      "1:08:44 PM  6/12/2024    Executing \"drive s1 @(s1)+1\"\n",
-      "1:08:47 PM  6/12/2024    Executing \"drive s1 @(s1)+1\"\n",
-      "1:08:54 PM  6/12/2024    Executing \"drive s1 @(s1)+-1\"\n",
-      "1:08:59 PM  6/12/2024    Executing \"drive s1 @(s1)+-0.5\"\n",
-      "1:09:06 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "1:09:10 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "1:09:11 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "1:09:13 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "1:09:15 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "1:09:17 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "1:09:18 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "1:09:20 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "1:09:25 PM  6/12/2024    Executing \"drive s1 @(s1)+-1\"\n",
-      "1:09:32 PM  6/12/2024    Executing \"drive s1 @(s1)+0.5\"\n",
-      "1:09:37 PM  6/12/2024    Executing \"drive s1 @(s1)+0.1\"\n",
-      "1:09:39 PM  6/12/2024    Executing \"drive s1 @(s1)+0.1\"\n",
-      "1:09:41 PM  6/12/2024    Executing \"drive s1 @(s1)+0.1\"\n",
-      "1:09:43 PM  6/12/2024    Executing \"drive s1 @(s1)+0.1\"\n",
-      "1:09:44 PM  6/12/2024    Executing \"drive s1 @(s1)+0.1\"\n",
-      "1:09:46 PM  6/12/2024    Executing \"drive s1 @(s1)+0.1\"\n",
-      "1:09:49 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:09:52 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:09:54 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:09:56 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:09:58 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:10:01 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:10:03 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:10:06 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:16:28 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:16:37 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:18:57 PM  6/12/2024    Executing \"admin\" ... \n",
-      "1:19:02 PM  6/12/2024    Executing \"method s1 set_motor_position d -41+@(s1.zero)\"\n",
-      "1:20:03 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "1:20:59 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "1:21:11 PM  6/12/2024    Executing \"drive s1 @(s1)+-1\"\n",
-      "1:21:52 PM  6/12/2024    Executing \"drive s1 @(s1)+-1\"\n",
-      "1:21:58 PM  6/12/2024    Executing \"drive s1 @(s1)+-1\"\n",
-      "1:22:02 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "1:22:10 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "1:22:56 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "1:23:16 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "1:24:27 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "1:24:46 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "1:25:00 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:25:06 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:25:11 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:25:15 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:25:19 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:26:03 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:27:46 PM  6/12/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "1:28:56 PM  6/12/2024    Executing \"scan s1 80 40 0.5 preset time 1\"\n",
-      "1:32:26 PM  6/12/2024    Executing \"drive s1 60\"\n",
-      "1:33:31 PM  6/12/2024    Executing \"drive s1 60\"\n",
-      "1:35:02 PM  6/12/2024    Executing \"drive s1 60\"\n",
-      "1:36:12 PM  6/12/2024    Executing \"drive s1 65\"\n",
-      "1:37:07 PM  6/12/2024    Executing \"drive s1 70\"\n",
-      "1:37:36 PM  6/12/2024    Executing \"drive s1 75\"\n",
-      "1:38:50 PM  6/12/2024    Executing \"drive s1 @(s1)+-23\"\n",
-      "1:39:51 PM  6/12/2024    Executing \"drive s1 @(s1)+-10\"\n",
-      "1:40:18 PM  6/12/2024    Executing \"drive s1 @(s1)+-10\"\n",
-      "1:42:23 PM  6/12/2024    Executing \"drive s1 @(s1)+20\"\n",
-      "1:43:06 PM  6/12/2024    Executing \"drive s1 @(s1)+-3\"\n",
-      "1:43:18 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "1:43:25 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "1:43:31 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "1:44:05 PM  6/12/2024    Executing \"preset time 1\"\n",
-      "1:44:14 PM  6/12/2024    Executing \"scan s1 @(s1)+1 @(s1)+-1 0.100000\"\n",
-      "1:44:47 PM  6/12/2024    Executing \"drive s1 @(s1)--1\"\n",
-      "1:45:20 PM  6/12/2024    Executing \"drive s1 50\"\n",
-      "1:45:59 PM  6/12/2024    Executing \"scan s2 @(s2)+2 @(s2)+-2 0.200000 s1 @(s1)+(2/2) @(s1)+(-2/2) 0.100000\"\n",
-      "1:46:49 PM  6/12/2024    Executing \"drive s1 @(s1)-((@(s2)-@(com))/2) s2 @(com)\"\n",
-      "1:47:13 PM  6/12/2024    Executing \"drive s2 -46.2416 s1 49.9950\"\n",
-      "1:50:21 PM  6/12/2024    Executing \"preset time 5\"\n",
-      "1:50:26 PM  6/12/2024    Executing \"scan s1 @(s1)+1 @(s1)+-1 0.100000\"\n",
-      "1:51:10 PM  6/12/2024    Executing \"drive s2 -46.2416 s1 49.9950\"\n",
-      "1:51:17 PM  6/12/2024    Executing \"scan s1 @(s1)+-1 @(s1)+1 0.100000\"\n",
-      "1:53:23 PM  6/12/2024    Executing \"drive s1 @(s1)-1\"\n",
-      "1:53:30 PM  6/12/2024    Executing \"preset countfile wait10sec\"\n",
-      "1:53:33 PM  6/12/2024    Executing \"scan s1 @(s1)+-1 @(s1)+1 0.100000\"\n",
-      "1:59:09 PM  6/12/2024    Executing \"drive s1 @(s1)-1\"\n",
-      "1:59:12 PM  6/12/2024    Executing \"drive s1 50.1\"\n",
-      "2:09:44 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "2:09:55 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "2:10:11 PM  6/12/2024    Executing \"drive s1 @(s1)+-20\"\n",
-      "2:10:45 PM  6/12/2024    Executing \"drive s1 @(s1)+3\"\n",
-      "2:11:15 PM  6/12/2024    Executing \"drive s1 @(s1)+-0.2\"\n",
-      "2:11:26 PM  6/12/2024    Executing \"drive s1 @(s1)+-0.1\"\n",
-      "2:14:01 PM  6/12/2024    Executing \"drive s1 @(s1)+-0.2\"\n",
-      "2:14:06 PM  6/12/2024    Executing \"drive s1 @(s1)+-0.2\"\n",
-      "2:14:14 PM  6/12/2024    Executing \"drive s1 @(s1)+0.1\"\n",
-      "2:17:04 PM  6/12/2024    Executing \"drive s1 @(s1)+1\"\n",
-      "2:17:09 PM  6/12/2024    Executing \"drive s1 @(s1)+-2\"\n",
-      "2:17:19 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "2:17:27 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "2:17:34 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "2:20:28 PM  6/12/2024    Executing \"scan s1 @(s1)+-1 @(s1)+1 0.100000\"\n",
-      "2:24:34 PM  6/12/2024    Executing \"drive s1 @(s1)-1\"\n",
-      "2:24:36 PM  6/12/2024    Executing \"drive s1 45.2\"\n",
-      "2:24:51 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "2:25:01 PM  6/12/2024    Executing \"drive s1 @(s1)+-0.4\"\n",
-      "2:25:11 PM  6/12/2024    Executing \"drive s1 @(s1)+0.2\"\n",
-      "2:26:09 PM  6/12/2024    Executing \"drive s1 44.229\"\n",
-      "2:26:34 PM  6/12/2024    Executing \"drive s1 45.19800\"\n",
-      "2:30:35 PM  6/12/2024    Executing \"drive s1 @(s1)+0.1\"\n",
-      "2:30:43 PM  6/12/2024    Executing \"drive s1 @(s1)+-0.1\"\n",
-      "2:33:48 PM  6/12/2024    Executing \"initialize\"\n",
-      "2:35:33 PM  6/12/2024    Executing \"initialize\"\n",
-      "2:49:27 PM  6/12/2024    Executing \"initialize\"\n",
-      "2:52:22 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "2:52:52 PM  6/12/2024    Executing \"drive s1 @(s1)+-5\"\n",
-      "2:53:14 PM  6/12/2024    Executing \"scan s1 @(s1)+-1 @(s1)+1 0.100000\"\n",
-      "2:54:14 PM  6/12/2024    Executing \"scan s1 @(s1)+-1 @(s1)+1 0.100000\"\n",
-      "2:56:06 PM  6/12/2024    Executing \"count preset time 1\"\n",
-      "2:56:06 PM  6/12/2024   Executing count command with preset channel \"time\" and preset value 1.00\n",
-      "         time     detector      monitor          mcu\n",
-      "        1.000        0.000      882.000        0.966\n",
-      "2:56:26 PM  6/12/2024    Executing \"drive s1 49.2\"\n",
-      "2:56:40 PM  6/12/2024    Executing \"scan s1 @(s1)+-1 @(s1)+1 0.100000\"\n",
-      "2:58:22 PM  6/12/2024    Executing \"preset countfile wait0sec\"\n",
-      "2:58:27 PM  6/12/2024    Executing \"drive s1 49.2\"\n",
-      "2:58:35 PM  6/12/2024    Executing \"scan s1 @(s1)+-1 @(s1)+1 0.100000\"\n",
-      "3:01:48 PM  6/12/2024    Executing \"drive s1 @(s1)-1\"\n",
-      "3:02:19 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "3:02:42 PM  6/12/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "3:03:25 PM  6/12/2024    Executing \"drive s1 @(s1)+-10\"\n",
-      "3:03:53 PM  6/12/2024    Executing \"drive s1 @(s1)+-2\"\n",
-      "3:04:03 PM  6/12/2024    Executing \"drive s1 @(s1)+-2\"\n",
-      "3:04:18 PM  6/12/2024    Executing \"drive s1 @(s1)++0.5\"\n",
-      "3:04:32 PM  6/12/2024    Executing \"drive s1 @(s1)++0.25\"\n",
-      "3:04:49 PM  6/12/2024    Executing \"drive s1 @(s1)+-0.25\"\n",
-      "3:05:03 PM  6/12/2024    Executing \"scan s1 @(s1)+-1 @(s1)+1 0.100000\"\n",
-      "3:08:17 PM  6/12/2024    Executing \"drive s1 @(s1)-1\"\n",
-      "3:09:36 PM  6/12/2024    Executing \"drive s1 45.77\"\n",
-      "3:09:49 PM  6/12/2024    Executing \"scan s1 @(s1)+1 @(s1)+-1 0.100000\"\n",
-      "3:12:03 PM  6/12/2024    Executing \"drive s1 @(s1)--1\"\n",
-      "3:13:14 PM  6/12/2024    Executing \"scan s2 @(s2)+2 @(s2)+-2 0.200000 s1 @(s1)+(2/2) @(s1)+(-2/2) 0.100000\"\n",
-      "3:15:31 PM  6/12/2024    Executing \"drive s1 @(s1)-((@(s2)-@(com))/2) s2 @(com)\"\n",
-      "3:18:11 PM  6/12/2024    Executing \"drive s2 -46.191\"\n",
-      "3:18:34 PM  6/12/2024    Executing \"preset time 10\"\n",
-      "3:18:42 PM  6/12/2024    Executing \"count\"\n",
-      "3:18:42 PM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    23976.000     8729.000        9.559\n",
-      "3:19:08 PM  6/12/2024    Executing \"drive ds_nut -2\"\n",
-      "3:19:52 PM  6/12/2024    Executing \"count\"\n",
-      "3:19:52 PM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000     2078.000     8803.000        9.640\n",
-      "3:20:15 PM  6/12/2024    Executing \"drive ds_nut 2\"\n",
-      "3:20:25 PM  6/12/2024    Executing \"count\"\n",
-      "3:20:25 PM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    23653.000     8920.000        9.768\n",
-      "3:22:09 PM  6/12/2024    Executing \"drive s2 0\"\n",
-      "3:25:58 PM  6/12/2024    Executing \"drive s1 @(s1)+1\"\n",
-      "3:26:37 PM  6/12/2024    Executing \"scan ds_nut 2 4 0.2\"\n",
-      "3:29:20 PM  6/12/2024    Executing \"scan ds_nut -2 -4 0.2\"\n",
-      "3:34:03 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "3:34:50 PM  6/12/2024    Executing \"scan theta_1 -4 356 5\"\n",
-      "3:54:14 PM  6/12/2024    Executing \"drive ds_nut -2.2\"\n",
-      "3:54:53 PM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan +-, no comp\"\"\n",
-      "3:55:07 PM  6/12/2024    Executing \"scan theta_1 356 -4 5\"\n",
-      "4:16:25 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "4:16:45 PM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan ++, no comp with mu metal cover on\"\"\n",
-      "4:16:58 PM  6/12/2024    Executing \"scan theta_1 -4 356 5\"\n",
-      "4:18:12 PM  6/12/2024    Executing \"scan theta_1 -4 356 5\"\n",
-      "4:36:30 PM  6/12/2024    Executing \"drive ds_nut -2.2\"\n",
-      "4:37:02 PM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan +-, no comp with mu metal cover on\"\"\n",
-      "4:37:21 PM  6/12/2024    Executing \"scan theta_1 356 -4 5\"\n",
-      "4:55:46 PM  6/12/2024    Executing \"drive theta_1 -8 theta_2 -4\"\n",
-      "4:56:06 PM  6/12/2024    Executing \"drive ds_nutator 3.2\"\n",
-      "4:56:16 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "4:56:47 PM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan ++, no comp with mu metal cover on\"\"\n",
-      "4:57:14 PM  6/12/2024    Executing \"scan theta_1 -8 352 5\"\n",
-      "5:15:00 PM  6/12/2024    Executing \"drive ds_nut -2.2\"\n",
-      "5:15:12 PM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan +-, no comp with mu metal cover on\"\"\n",
-      "5:15:12 PM  6/12/2024    Executing \"scan theta_1 352 -8 5\"\n",
-      "5:33:47 PM  6/12/2024    Executing \"drive ds_nut 3.2 comp 1\"\n",
-      "5:34:00 PM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan ++, comp 1A with mu metal cover on\"\"\n",
-      "5:34:00 PM  6/12/2024    Executing \"scan theta_1 -8 352 5\"\n",
-      "5:51:43 PM  6/12/2024    Executing \"drive ds_nut -2.2\"\n",
-      "5:51:55 PM  6/12/2024    Executing \"scantitle \"Upstream nutator 360 degree scan +-, comp 1A with mu metal cover on\"\"\n",
-      "5:51:55 PM  6/12/2024    Executing \"scan theta_1 352 -8 5\"\n",
-      "6:19:04 PM  6/12/2024    Executing \"drive theta_1 @(theta_1)+90\"\n",
-      "6:20:40 PM  6/12/2024    Executing \"drive theta_2 @(theta_2)+-90\"\n",
-      "6:22:03 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "6:23:02 PM  6/12/2024    Executing \"drive theta_2 @(theta_2)+4\"\n",
-      "6:23:26 PM  6/12/2024    Executing \"drive ds_nut -2.2\"\n",
-      "6:23:45 PM  6/12/2024    Executing \"drive ds_nut -3.2\"\n",
-      "6:24:06 PM  6/12/2024    Executing \"drive ds_nut -4\"\n",
-      "6:24:22 PM  6/12/2024    Executing \"drive ds_nut -3.5\"\n",
-      "6:24:36 PM  6/12/2024    Executing \"drive ds_nut -2.5\"\n",
-      "6:24:47 PM  6/12/2024    Executing \"drive ds_nut -3\"\n",
-      "6:25:37 PM  6/12/2024    Executing \"drive theta_2 @(theta_2)+-4\"\n",
-      "6:26:04 PM  6/12/2024    Executing \"scantitle \"quick upstream precession scan\"\"\n",
-      "6:26:16 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "6:27:15 PM  6/12/2024    Executing \"scan up_prec @(up_prec)+-6 @(up_prec)+6 0.500000\"\n",
-      "6:41:48 PM  6/12/2024    Executing \"scan up_prec -6 6 0.5\"\n",
-      "6:48:28 PM  6/12/2024    Executing \"drive ds_nut -3.2\"\n",
-      "6:48:42 PM  6/12/2024    Executing \"scan up_prec 6 -6 0.5\"\n",
-      "6:56:18 PM  6/12/2024    Executing \"scantitle \"finer upstream precession scan\"\"\n",
-      "6:56:55 PM  6/12/2024    Executing \"drive up_prec -10\"\n",
-      "6:58:10 PM  6/12/2024    Executing \"scan up_prec -10 10 0.25\"\n",
-      "7:20:58 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "7:21:26 PM  6/12/2024    Executing \"scan up_prec 10 -10 0.25\"\n",
-      "7:40:17 PM  6/12/2024    Executing \"drive up_prec 0\"\n",
-      "7:43:05 PM  6/12/2024    Executing \"drive ds_prec -10\"\n",
-      "7:44:46 PM  6/12/2024    Executing \"scantitle \"finer downstream precession scan\"\"\n",
-      "7:45:04 PM  6/12/2024    Executing \"scan ds_prec -10 10 0.25\"\n",
-      "8:03:40 PM  6/12/2024    Executing \"drive ds_nut -3.2\"\n",
-      "8:03:55 PM  6/12/2024    Executing \"scan ds_prec 10 -10 0.25\"\n",
-      "8:22:20 PM  6/12/2024    Executing \"drive ds_prec 0\"\n",
-      "8:24:01 PM  6/12/2024    Executing \"drive theta_1 -8\"\n",
-      "8:24:52 PM  6/12/2024    Executing \"count\"\n",
-      "8:24:52 PM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000   333243.000     9038.000        9.897\n",
-      "8:25:19 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "8:25:32 PM  6/12/2024    Executing \"count\"\n",
-      "8:25:32 PM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000   322231.000     9029.000        9.887\n",
-      "8:26:55 PM  6/12/2024    Executing \"scan up_prec -7 7 0.5\"\n",
-      "8:37:16 PM  6/12/2024    Executing \"drive ds_nut -3.2\"\n",
-      "8:37:36 PM  6/12/2024    Executing \"scan up_prec 7 -7 0.5\"\n",
-      "8:45:34 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "8:47:25 PM  6/12/2024    Executing \"drive theta_1 -7 theta_2 -95\"\n",
-      "8:48:32 PM  6/12/2024    Executing \"scan up_prec -7 7 0.5\"\n",
-      "8:57:03 PM  6/12/2024    Executing \"drive ds_nut -3.2\"\n",
-      "8:57:27 PM  6/12/2024    Executing \"scan up_prec 7 -7 0.5\"\n",
-      "9:05:48 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "9:06:13 PM  6/12/2024    Executing \"drive theta_1 -9 theta_2 -93\"\n",
-      "9:06:28 PM  6/12/2024    Executing \"scan up_prec -7 7 0.5\"\n",
-      "9:14:35 PM  6/12/2024    Executing \"drive ds_nut -3.2\"\n",
-      "9:14:48 PM  6/12/2024    Executing \"scan up_prec 7 -7 0.5\"\n",
-      "9:23:39 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "9:24:28 PM  6/12/2024    Executing \"drive theta_1 -10 theta_2 -92\"\n",
-      "9:24:45 PM  6/12/2024    Executing \"scan up_prec -7 7 0.5\"\n",
-      "9:33:20 PM  6/12/2024    Executing \"drive ds_nut -3.2\"\n",
-      "9:33:35 PM  6/12/2024    Executing \"scan up_prec 7 -7 0.5\"\n",
-      "9:42:07 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "9:45:44 PM  6/12/2024    Executing \"drive theta_1 -9.5 theta_2 -92.5\"\n",
-      "9:46:05 PM  6/12/2024    Executing \"scan up_prec -7 7 0.5\"\n",
-      "9:53:45 PM  6/12/2024    Executing \"drive ds_nut -3.2\"\n",
-      "9:54:00 PM  6/12/2024    Executing \"scan up_prec 7 -7 0.5\"\n",
-      "10:01:40 PM  6/12/2024    Executing \"drive up_prec 0\"\n",
-      "10:04:43 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "10:05:05 PM  6/12/2024    Executing \"drive theta_1 -9.25 theta_2 -92.75\"\n",
-      "10:06:00 PM  6/12/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25\"\n",
-      "10:06:39 PM  6/12/2024    Executing \"scan up_prec -7 7 0.5\"\n",
-      "10:15:29 PM  6/12/2024    Executing \"drive ds_nut -3.2\"\n",
-      "10:15:42 PM  6/12/2024    Executing \"scan up_prec 7 -7 0.5\"\n",
-      "10:29:15 PM  6/12/2024    Executing \"drive theta_2 -2.25\"\n",
-      "10:30:13 PM  6/12/2024    Executing \"drive up_prec 0\"\n",
-      "10:37:19 PM  6/12/2024    Executing \"drive s2 -46.191\"\n",
-      "10:38:32 PM  6/12/2024    Executing \"drive s1 @(s1)+-1\"\n",
-      "10:38:56 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "10:41:28 PM  6/12/2024    Executing \"drive ds_nut -2.2\"\n",
-      "10:46:17 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "11:15:56 PM  6/12/2024    Executing \"lattice 5.431000 5.431000 5.431000 90.000000 90.000000 90.000000\"\n",
-      "11:22:33 PM  6/12/2024    Executing \"drive ds_nut -2.2\"\n",
-      "11:23:45 PM  6/12/2024    Executing \"drive ds_nutator 0\"\n",
-      "11:23:52 PM  6/12/2024    Executing \"drive ds_nut 0\"\n",
-      "11:27:32 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "11:31:42 PM  6/12/2024    Executing \"drive s2 0\"\n",
-      "11:32:10 PM  6/12/2024    Executing \"drive s1 @(s1)+1\"\n",
-      "11:33:37 PM  6/12/2024    Executing \"drive ds_nut -2.2\"\n",
-      "11:39:18 PM  6/12/2024    Executing \"drive ds_nut -3.2\"\n",
-      "11:39:45 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "11:41:21 PM  6/12/2024    Executing \"count\"\n",
-      "11:41:21 PM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000   586669.000     9055.000        9.916\n",
-      "11:42:01 PM  6/12/2024    Executing \"drive ds_nut -3.2\"\n",
-      "11:42:16 PM  6/12/2024    Executing \"count\"\n",
-      "11:42:16 PM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    47518.000     8971.000        9.824\n",
-      "11:43:47 PM  6/12/2024    Executing \"drive s2 -46.191\"\n",
-      "11:44:21 PM  6/12/2024    Executing \"drive s1 @(s1)+-1\"\n",
-      "11:45:03 PM  6/12/2024    Executing \"count\"\n",
-      "11:45:03 PM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000     2012.000     9905.000       10.846\n",
-      "11:45:17 PM  6/12/2024    Executing \"drive ds_nut 3.2\"\n",
-      "11:45:47 PM  6/12/2024    Executing \"count\"\n",
-      "11:45:47 PM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    22368.000     9099.000        9.964\n",
-      "11:55:13 PM  6/12/2024    Executing \"count\"\n",
-      "11:55:13 PM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    23185.000    10696.000       11.713\n",
-      "11:55:52 PM  6/12/2024    Executing \"drive ds_nut -3.2\"\n",
-      "11:56:10 PM  6/12/2024    Executing \"count\"\n",
-      "11:56:10 PM  6/12/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000     2129.000     9376.000       10.267\n",
-      "11:58:45 PM  6/12/2024    Executing \"drive theta_1 80.25 theta_2 -92.25\"\n",
-      "11:59:55 PM  6/12/2024    Executing \"scantitle \"silicon 111\"\"\n",
-      "12:00:10 AM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "12:03:05 AM  6/13/2024    Executing \"drive up_prec 1.84\"\n",
-      "12:03:54 AM  6/13/2024    Executing \"drive ds_nut -3.2\"\n",
-      "12:04:31 AM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "12:07:55 AM  6/13/2024    Executing \"scan up_prec -3 3 0.5\"\n",
-      "12:13:04 AM  6/13/2024    Executing \"drive up_prec 0\"\n",
-      "12:13:51 AM  6/13/2024    Executing \"scan ds_prec -3 3 0.5\"\n",
-      "12:21:40 AM  6/13/2024    Executing \"drive up_prec 8.105\"\n",
-      "12:23:35 AM  6/13/2024    Executing \"drive ds_prec 7.167\"\n",
-      "12:24:25 AM  6/13/2024    Executing \"count\"\n",
-      "12:24:25 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    23470.000     9027.000        9.885\n",
-      "12:24:52 AM  6/13/2024    Executing \"drive ds_nut -3.2\"\n",
-      "12:25:11 AM  6/13/2024    Executing \"count\"\n",
-      "12:25:11 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000     1866.000     9070.000        9.932\n",
-      "12:29:31 AM  6/13/2024    Executing \"drive ds_prec -6.262\"\n",
-      "12:32:00 AM  6/13/2024    Executing \"count\"\n",
-      "12:32:00 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000     2553.000     9085.000        9.949\n",
-      "12:33:00 AM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "12:33:14 AM  6/13/2024    Executing \"count\"\n",
-      "12:33:14 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    22996.000     9041.000        9.900\n",
-      "12:35:32 AM  6/13/2024    Executing \"drive ds_prec -5.667\"\n",
-      "12:35:52 AM  6/13/2024    Executing \"drive ds_prec -3.2\"\n",
-      "12:40:04 AM  6/13/2024    Executing \"drive ds_prec -2.361\"\n",
-      "12:40:47 AM  6/13/2024    Executing \"count\"\n",
-      "12:40:47 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    12626.000     9009.000        9.865\n",
-      "12:41:11 AM  6/13/2024    Executing \"drive ds_nut -3.2\"\n",
-      "12:41:32 AM  6/13/2024    Executing \"count\"\n",
-      "12:41:32 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    12769.000     8944.000        9.794\n",
-      "12:43:17 AM  6/13/2024    Executing \"drive up_prec 4.514\"\n",
-      "12:44:15 AM  6/13/2024    Executing \"count\"\n",
-      "12:44:15 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000     1850.000     9169.000       10.041\n",
-      "12:44:41 AM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "12:44:58 AM  6/13/2024    Executing \"count\"\n",
-      "12:44:58 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    23626.000     8940.000        9.790\n",
-      "12:48:15 AM  6/13/2024    Executing \"drive ds_prec -5.537\"\n",
-      "12:48:58 AM  6/13/2024    Executing \"count\"\n",
-      "12:48:58 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    13162.000     9052.000        9.912\n",
-      "12:49:30 AM  6/13/2024    Executing \"drive ds_nut -3.2\"\n",
-      "12:49:49 AM  6/13/2024    Executing \"count\"\n",
-      "12:49:49 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    12639.000     9084.000        9.947\n",
-      "12:51:31 AM  6/13/2024    Executing \"drive theta_2 @(theta_2)+90\"\n",
-      "12:52:13 AM  6/13/2024    Executing \"drive ds_prec 0\"\n",
-      "12:53:14 AM  6/13/2024    Executing \"count\"\n",
-      "12:53:14 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    13305.000     9149.000       10.019\n",
-      "12:53:50 AM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "12:54:09 AM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "12:54:17 AM  6/13/2024    Executing \"count\"\n",
-      "12:54:17 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    11129.000     9059.000        9.920\n",
-      "12:55:58 AM  6/13/2024    Executing \"drive up_prec 8.105\"\n",
-      "12:56:42 AM  6/13/2024    Executing \"count\"\n",
-      "12:56:42 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    13608.000     9152.000       10.022\n",
-      "12:57:09 AM  6/13/2024    Executing \"drive ds_nut -3.2\"\n",
-      "12:57:24 AM  6/13/2024    Executing \"count\"\n",
-      "12:57:24 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    13910.000     8932.000        9.781\n",
-      "12:57:53 AM  6/13/2024    Executing \"count\"\n",
-      "12:57:53 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    13908.000     9106.000        9.972\n",
-      "12:59:05 AM  6/13/2024    Executing \"drive theta_1 @(theta_1)+-90\"\n",
-      "12:59:56 AM  6/13/2024    Executing \"drive theta_2 @(theta_2)+-90\"\n",
-      "1:02:21 AM  6/13/2024    Executing \"drive up_prec 0 ds_prec -2.361\"\n",
-      "1:03:48 AM  6/13/2024    Executing \"count\"\n",
-      "1:03:48 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    12588.000     8916.000        9.763\n",
-      "1:04:13 AM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "1:04:29 AM  6/13/2024    Executing \"count\"\n",
-      "1:04:29 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    12925.000     9231.000       10.108\n",
-      "1:06:46 AM  6/13/2024    Executing \"drive ds_prec -5.537\"\n",
-      "1:07:22 AM  6/13/2024    Executing \"count\"\n",
-      "1:07:22 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    12421.000     9033.000        9.892\n",
-      "1:07:53 AM  6/13/2024    Executing \"drive ds_nut -3.2\"\n",
-      "1:08:07 AM  6/13/2024    Executing \"count\"\n",
-      "1:08:07 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000    13245.000     9118.000        9.985\n",
-      "1:09:21 AM  6/13/2024    Executing \"drive theta_2 @(theta_2)+90\"\n",
-      "1:09:41 AM  6/13/2024    Executing \"drive ds_prec 0\"\n",
-      "1:15:21 AM  6/13/2024    Executing \"scantitle \"Si (111) upstream nutator 360 degree overnight scan\"\"\n",
-      "1:15:43 AM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "1:16:00 AM  6/13/2024    Executing \"scantitle \"Si (111) upstream nutator 360 degree overnight scan ++\"\"\n",
-      "1:19:45 AM  6/13/2024    Executing \"preset time 180\"\n",
-      "1:20:08 AM  6/13/2024    Executing \"scan theta_1 -9.75 350.25 5\"\n",
-      "5:04:32 AM  6/13/2024    Executing \"scantitle \"Si (111) upstream nutator 360 degree overnight scan +-\"\"\n",
-      "5:04:32 AM  6/13/2024    Executing \"drive ds_nut -3.2\"\n",
-      "5:04:47 AM  6/13/2024    Executing \"scan theta_1 350.25 -9.75 5\"\n",
-      "9:12:41 AM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25\"\n",
-      "9:14:09 AM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "9:15:32 AM  6/13/2024    Executing \"preset time 60\"\n",
-      "9:15:53 AM  6/13/2024    Executing \"scantitle \"Si 111 upstream precession scan ++\"\"\n",
-      "9:16:17 AM  6/13/2024    Executing \"scan up_prec -8 8 0.5\"\n",
-      "9:54:12 AM  6/13/2024    Executing \"drive ds_nut -3.2\"\n",
-      "9:54:32 AM  6/13/2024    Executing \"scantitle \"Si 111 upstream precession scan +-\"\"\n",
-      "9:54:41 AM  6/13/2024    Executing \"scan up_prec 8 -8 0.5\"\n",
-      "10:32:03 AM  6/13/2024    Executing \"drive up_prec 0 ds_nut 3.2 ds_prec -8\"\n",
-      "10:33:25 AM  6/13/2024    Executing \"scantitle \"Si 111 downstream precession scan +-\"\"\n",
-      "10:33:50 AM  6/13/2024    Executing \"scantitle \"Si 111 downstream precession scan ++\"\"\n",
-      "10:34:04 AM  6/13/2024    Executing \"scan ds_prec -8 8 0.5\"\n",
-      "11:10:57 AM  6/13/2024    Executing \"drive ds_nut -3.2\"\n",
-      "11:11:11 AM  6/13/2024    Executing \"scantitle \"Si 111 downstream precession scan +-\"\"\n",
-      "11:11:17 AM  6/13/2024    Executing \"scan ds_prec 8 -8 0.5\"\n",
-      "11:48:02 AM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "11:49:02 AM  6/13/2024    Executing \"drive up_prec 8.105 ds_prec -5.537\"\n",
-      "11:50:25 AM  6/13/2024    Executing \"preset time 30\"\n",
-      "11:50:33 AM  6/13/2024    Executing \"count\"\n",
-      "11:50:33 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000    71086.000    27720.000       30.355\n",
-      "11:51:47 AM  6/13/2024    Executing \"drive ds_nut -3.2\"\n",
-      "11:52:02 AM  6/13/2024    Executing \"count\"\n",
-      "11:52:02 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000     5550.000    27677.000       30.308\n",
-      "11:54:02 AM  6/13/2024    Executing \"drive ds_prec -2.361\"\n",
-      "11:54:35 AM  6/13/2024    Executing \"count\"\n",
-      "11:54:35 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000    38467.000    27612.000       30.237\n",
-      "11:55:27 AM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "11:56:09 AM  6/13/2024    Executing \"count\"\n",
-      "11:56:09 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000    37669.000    27407.000       30.012\n",
-      "11:57:32 AM  6/13/2024    Executing \"drive up_prec 4.514\"\n",
-      "11:58:09 AM  6/13/2024    Executing \"count\"\n",
-      "11:58:09 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000    70501.000    27455.000       30.065\n",
-      "11:58:56 AM  6/13/2024    Executing \"drive ds_nut -3.2\"\n",
-      "11:59:10 AM  6/13/2024    Executing \"count\"\n",
-      "11:59:10 AM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000     5578.000    27407.000       30.012\n",
-      "12:01:34 PM  6/13/2024    Executing \"drive ds_prec -5.537\"\n",
-      "12:02:08 PM  6/13/2024    Executing \"count\"\n",
-      "12:02:08 PM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000    38154.000    27905.000       30.557\n",
-      "12:03:06 PM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "12:03:21 PM  6/13/2024    Executing \"count\"\n",
-      "12:03:21 PM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000    39416.000    27749.000       30.387\n",
-      "12:05:32 PM  6/13/2024    Executing \"drive theta_2 -2.25\"\n",
-      "12:06:08 PM  6/13/2024    Executing \"drive ds_prec 0\"\n",
-      "12:07:13 PM  6/13/2024    Executing \"count\"\n",
-      "12:07:13 PM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000    33698.000    27872.000       30.521\n",
-      "12:08:01 PM  6/13/2024    Executing \"drive ds_nut -3.2\"\n",
-      "12:08:22 PM  6/13/2024    Executing \"count\"\n",
-      "12:08:22 PM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000    39748.000    27615.000       30.240\n",
-      "12:10:16 PM  6/13/2024    Executing \"drive up_prec 8.105\"\n",
-      "12:11:40 PM  6/13/2024    Executing \"count\"\n",
-      "12:11:40 PM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000    41504.000    27729.000       30.365\n",
-      "12:12:57 PM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "12:13:15 PM  6/13/2024    Executing \"count\"\n",
-      "12:13:15 PM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000    40828.000    27549.000       30.168\n",
-      "12:14:50 PM  6/13/2024    Executing \"drive theta_1 -9.75\"\n",
-      "12:15:46 PM  6/13/2024    Executing \"drive up_prec 0\"\n",
-      "12:17:09 PM  6/13/2024    Executing \"count\"\n",
-      "12:17:09 PM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000    70521.000    27577.000       30.198\n",
-      "12:17:53 PM  6/13/2024    Executing \"drive ds_nut -3.2\"\n",
-      "12:18:07 PM  6/13/2024    Executing \"count\"\n",
-      "12:18:07 PM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000     6120.000    27847.000       30.494\n",
-      "12:19:10 PM  6/13/2024    Executing \"drive theta_2 @(theta_2)+-90\"\n",
-      "12:20:06 PM  6/13/2024    Executing \"drive ds_prec -2.362\"\n",
-      "12:20:35 PM  6/13/2024    Executing \"drive ds_prec -2.361\"\n",
-      "12:20:42 PM  6/13/2024    Executing \"count\"\n",
-      "12:20:42 PM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000    38109.000    27553.000       30.172\n",
-      "12:21:54 PM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "12:22:12 PM  6/13/2024    Executing \"count\"\n",
-      "12:22:12 PM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000    39332.000    27628.000       30.254\n",
-      "12:23:24 PM  6/13/2024    Executing \"drive ds_prec -5.537\"\n",
-      "12:23:58 PM  6/13/2024    Executing \"count\"\n",
-      "12:23:58 PM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000    37401.000    27477.000       30.089\n",
-      "12:24:42 PM  6/13/2024    Executing \"drive ds_nut -3.2\"\n",
-      "12:24:56 PM  6/13/2024    Executing \"count\"\n",
-      "12:24:56 PM  6/13/2024   Executing count command with preset channel \"time\" and preset value 30.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       30.000    39824.000    27685.000       30.316\n",
-      "1:08:12 PM  6/13/2024    Executing \"drive s1 0\"\n",
-      "1:31:09 PM  6/13/2024    Executing \"drive temp 1\"\n",
-      "1:38:40 PM  6/13/2024    Executing \"method temp set_setpoint d 1.000000\"\n",
-      "1:38:59 PM  6/13/2024    Executing \"method temp_2 set_setpoint d 1.000000\"\n",
-      "1:42:22 PM  6/13/2024    Executing \"lattice 5.200000 5.200000 14.000000 90.000000 90.000000 120.000000\"\n",
-      "1:46:14 PM  6/13/2024    Executing \"ubcalc file \"C:\\SPICE\\User\\exp943\\UBConf\\tmp\\UB13Jun2024_14612PM.ini\"\"\n",
-      "1:47:10 PM  6/13/2024    Executing \"drive s2 -50\"\n",
-      "1:47:31 PM  6/13/2024    Executing \"drive s2 -55\"\n",
-      "1:47:45 PM  6/13/2024    Executing \"drive s2 -60\"\n",
-      "1:48:06 PM  6/13/2024    Executing \"drive s1 -63.672\"\n",
-      "1:50:15 PM  6/13/2024    Executing \"drive s2 -63.672\"\n",
-      "1:50:19 PM  6/13/2024    Executing \"drive theta_2 -2.25\"\n",
-      "1:50:50 PM  6/13/2024    Executing \"drive ds_prec 0 ds_nut 3.2\"\n",
-      "1:51:59 PM  6/13/2024    Executing \"drive s1 -31\"\n",
-      "1:53:29 PM  6/13/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "1:53:48 PM  6/13/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "1:54:12 PM  6/13/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "1:54:32 PM  6/13/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "1:56:29 PM  6/13/2024    Executing \"drive s1 @(s1)+-5\"\n",
-      "1:56:45 PM  6/13/2024    Executing \"drive s1 @(s1)+-5\"\n",
-      "1:57:03 PM  6/13/2024    Executing \"drive s1 @(s1)+-5\"\n",
-      "1:57:29 PM  6/13/2024    Executing \"drive s1 -23\"\n",
-      "1:57:48 PM  6/13/2024    Executing \"drive s1 -22\"\n",
-      "1:57:59 PM  6/13/2024    Executing \"drive s1 -21\"\n",
-      "1:58:16 PM  6/13/2024    Executing \"drive s1 -22.5\"\n",
-      "1:58:46 PM  6/13/2024    Executing \"preset time 1\"\n",
-      "1:58:56 PM  6/13/2024    Executing \"scan s1 @(s1)+2 @(s1)+-2 0.200000\"\n",
-      "1:59:56 PM  6/13/2024    Executing \"drive s1 @(s1)--2\"\n",
-      "2:00:29 PM  6/13/2024    Executing \"drive s1 -30\"\n",
-      "2:00:52 PM  6/13/2024    Executing \"drive s1 -40\"\n",
-      "2:02:55 PM  6/13/2024    Executing \"drive s1 -20\"\n",
-      "2:05:39 PM  6/13/2024    Executing \"drive s1 -15\"\n",
-      "2:05:58 PM  6/13/2024    Executing \"drive s1 -10\"\n",
-      "2:06:18 PM  6/13/2024    Executing \"drive s1 -5\"\n",
-      "2:06:35 PM  6/13/2024    Executing \"drive s1 -10\"\n",
-      "2:06:47 PM  6/13/2024    Executing \"drive s1 -7\"\n",
-      "2:07:21 PM  6/13/2024    Executing \"scan s1 @(s1)+2 @(s1)+-2 0.200000\"\n",
-      "2:08:20 PM  6/13/2024    Executing \"drive s1 @(s1)--2\"\n",
-      "2:09:03 PM  6/13/2024    Executing \"ubcalc file \"C:\\SPICE\\User\\exp943\\UBConf\\tmp\\UB13Jun2024_20902PM.ini\"\"\n",
-      "2:11:00 PM  6/13/2024    Executing \"drive s2 -30.585\"\n",
-      "2:11:49 PM  6/13/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "2:12:19 PM  6/13/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "2:12:48 PM  6/13/2024    Executing \"drive s1 -15\"\n",
-      "2:13:44 PM  6/13/2024    Executing \"drive s1 -30\"\n",
-      "2:14:35 PM  6/13/2024    Executing \"ubcalc file \"C:\\SPICE\\User\\exp943\\UBConf\\tmp\\UB13Jun2024_21432PM.ini\"\"\n",
-      "2:14:55 PM  6/13/2024    Executing \"drive h 0.000000 k -1.000000 l 4.000000 e 0.000000\"\n",
-      "2:16:30 PM  6/13/2024    Executing \"drive s1 -90\"\n",
-      "2:17:53 PM  6/13/2024    Executing \"drive h 0.000000 k 1.000000 l 4.000000 e 0.000000\"\n",
-      "2:21:36 PM  6/13/2024    Executing \"drive s1 30\"\n",
-      "2:22:38 PM  6/13/2024    Executing \"drive s1 0\"\n",
-      "2:26:26 PM  6/13/2024    Executing \"drive s2 -63.672\"\n",
-      "2:26:48 PM  6/13/2024    Executing \"drive s1 -40\"\n",
-      "2:28:18 PM  6/13/2024    Executing \"drive s1 -60\"\n",
-      "2:34:01 PM  6/13/2024    Executing \"drive h 0.000000 k -1.000000 l 1.000000 e 0.000000\"\n",
-      "2:44:15 PM  6/13/2024    Executing \"drive h 0.000000 k 0.000000 l 6.000000 e 0.000000\"\n",
-      "2:46:47 PM  6/13/2024    Executing \"drive s2 @(s2)+-0.5\"\n",
-      "2:47:17 PM  6/13/2024    Executing \"drive s1 @(s1)+-20\"\n",
-      "2:48:08 PM  6/13/2024    Executing \"drive s1 @(s1)+40\"\n",
-      "2:49:47 PM  6/13/2024    Executing \"drive s2 @(s2)+-0.5\"\n",
-      "2:50:25 PM  6/13/2024    Executing \"drive s1 @(s1)+-40\"\n",
-      "2:52:31 PM  6/13/2024    Executing \"drive s2 @(s2)+1.5\"\n",
-      "2:52:59 PM  6/13/2024    Executing \"drive s1 @(s1)+40\"\n",
-      "2:54:27 PM  6/13/2024    Executing \"drive s1 -20\"\n",
-      "2:54:54 PM  6/13/2024    Executing \"drive s1 -20.5\"\n",
-      "2:55:06 PM  6/13/2024    Executing \"drive s1 -21\"\n",
-      "2:55:19 PM  6/13/2024    Executing \"drive s1 -21.5\"\n",
-      "2:55:40 PM  6/13/2024    Executing \"preset time 10\"\n",
-      "2:55:51 PM  6/13/2024    Executing \"scan s1 @(s1)+2 @(s1)+-2 0.200000\"\n",
-      "2:59:36 PM  6/13/2024    Executing \"drive s1 -21.3876\"\n",
-      "2:59:49 PM  6/13/2024    Executing \"scan s2 @(s2)+2 @(s2)+-2 0.200000 s1 @(s1)+(2/2) @(s1)+(-2/2) 0.100000\"\n",
-      "3:03:39 PM  6/13/2024    Executing \"drive s2 -63.3609 s1 -21.4776\"\n",
-      "3:04:16 PM  6/13/2024    Executing \"lattice a 5.200000 b 5.200000 c 14.061541\"\n",
-      "3:04:26 PM  6/13/2024    Executing \"lattice 5.200000 5.200000 14.061541 90.000000 90.000000 120.000000\"\n",
-      "3:04:42 PM  6/13/2024    Executing \"ubcalc file \"C:\\SPICE\\User\\exp943\\UBConf\\tmp\\UB13Jun2024_30440PM.ini\"\"\n",
-      "3:05:37 PM  6/13/2024    Executing \"ubcalc file \"C:\\SPICE\\User\\exp943\\UBConf\\tmp\\UB13Jun2024_30535PM.ini\"\"\n",
-      "3:05:49 PM  6/13/2024    Executing \"ubcalc file \"C:\\SPICE\\User\\exp943\\UBConf\\tmp\\UB13Jun2024_30547PM.ini\"\"\n",
-      "3:05:56 PM  6/13/2024    Executing \"drive h 0.000000 k -1.000000 l 4.000000 e 0.000000\"\n",
-      "3:07:46 PM  6/13/2024    Executing \"scan s2 @(s2)+2 @(s2)+-2 0.200000 s1 @(s1)+(2/2) @(s1)+(-2/2) 0.100000\"\n",
-      "3:11:37 PM  6/13/2024    Executing \"drive s2 -52.601 s1 -54.0866\"\n",
-      "3:12:20 PM  6/13/2024    Executing \"lattice a 5.233898 b 5.233898 c 14.061541\"\n",
-      "3:13:06 PM  6/13/2024    Executing \"ubcalc file \"C:\\SPICE\\User\\exp943\\UBConf\\tmp\\UB13Jun2024_31300PM.ini\"\"\n",
-      "3:13:59 PM  6/13/2024    Executing \"drive h 0.000000 k -1.000000 l 1.000000 e 0.000000\"\n",
-      "3:14:51 PM  6/13/2024    Executing \"zero m1 -0.000016 m2 -0.767483 marc 0.000000 mtrans 0.000000 mfocus 0.000000 s1 0.000000 us_guide 0.000000 us_nut 0.000000 up_prec 0.000000 ds_prec 0.000000 ds_guide 0.000000 ds_nut 0.000000 comp 0.000000 up_prec_ramp 0.000000 ds_prec_ramp 0.000000 ds_guide_ramp 0.000000 ds_nut_ramp 0.000000 comp_ramp 0.000000 theta_2 4.000000 theta_1 4.000000 s2 0.371334 sgl 0.000000 sgu 0.000000 stl 0.000000 stu 0.000000 a1 -55.932682 a2 -4.281899 us_guide_amps 0.000000 us_nut_amps 0.000000 up_prec_amps 0.000000 ds_prec_amps 0.000000 ds_guide_amps 0.000000 ds_nut_amps 0.000000 comp_amps 0.000000 vti 0.000000 sample 0.000000 temp 0.000000 temp_2 0.000000 snp_status 0.000000\"\n",
-      "3:14:53 PM  6/13/2024    Executing \"hold m1 0 m2 0 marc 0 mtrans 0 mfocus 0 s1 0 us_guide 0 us_nut 0 up_prec 0 ds_prec 0 ds_guide 0 ds_nut 0 comp 0 up_prec_ramp 0 ds_prec_ramp 0 ds_guide_ramp 0 ds_nut_ramp 0 comp_ramp 0 theta_2 0 theta_1 0 s2 0 sgl 1 sgu 1 stl 1 stu 1 a1 0 a2 0 temp 0 temp_2 0\"\n",
-      "3:15:44 PM  6/13/2024    Executing \"scan s2 @(s2)+2 @(s2)+-2 0.200000 s1 @(s1)+(2/2) @(s1)+(-2/2) 0.100000\"\n",
-      "3:19:46 PM  6/13/2024    Executing \"drive s1 @(s1)-((@(s2)-@(com))/2) s2 @(com)\"\n",
-      "3:19:58 PM  6/13/2024    Executing \"drive h 0.000000 k 0.000000 l 6.000000 e 0.000000\"\n",
-      "3:22:44 PM  6/13/2024    Executing \"count preset time 10\"\n",
-      "3:22:44 PM  6/13/2024   Executing count command with preset channel \"time\" and preset value 10.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       10.000       92.000     9578.000       10.488\n",
-      "3:28:07 PM  6/13/2024    Executing \"drive s1 @(s1)+-5\"\n",
-      "3:28:24 PM  6/13/2024    Executing \"drive s1 @(s1)+-5\"\n",
-      "3:28:38 PM  6/13/2024    Executing \"drive s1 @(s1)+20\"\n",
-      "3:29:34 PM  6/13/2024    Executing \"drive s1 @(s1)+10\"\n",
-      "3:30:03 PM  6/13/2024    Executing \"drive h 0.000000 k 0.000000 l 6.000000 e 0.000000\"\n",
-      "3:30:53 PM  6/13/2024    Executing \"drive s1 @(s1)+-5\"\n",
-      "3:31:12 PM  6/13/2024    Executing \"drive s1 @(s1)+1\"\n",
-      "3:31:19 PM  6/13/2024    Executing \"drive s1 @(s1)+1\"\n",
-      "3:31:51 PM  6/13/2024    Executing \"drive s1 @(s1)+2\"\n",
-      "3:32:00 PM  6/13/2024    Executing \"drive s1 @(s1)+-1\"\n",
-      "3:32:26 PM  6/13/2024    Executing \"scan s1 @(s1)+2 @(s1)+-2 0.200000\"\n",
-      "3:36:32 PM  6/13/2024    Executing \"drive s1 @(s1)--2\"\n",
-      "3:36:51 PM  6/13/2024    Executing \"drive s1 @(s1)+-10\"\n",
-      "3:37:13 PM  6/13/2024    Executing \"drive s1 @(s1)+-10\"\n",
-      "3:37:48 PM  6/13/2024    Executing \"drive s1 @(s1)+0.5\"\n",
-      "3:43:08 PM  6/13/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "3:43:36 PM  6/13/2024    Executing \"drive s1 @(s1)+10\"\n",
-      "3:44:05 PM  6/13/2024    Executing \"drive s1 @(s1)+10\"\n",
-      "3:44:29 PM  6/13/2024    Executing \"drive s1 -27\"\n",
-      "3:48:40 PM  6/13/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "3:49:09 PM  6/13/2024    Executing \"drive s1 @(s1)+5\"\n",
-      "3:49:31 PM  6/13/2024    Executing \"drive s1 @(s1)+-20\"\n",
-      "3:50:20 PM  6/13/2024    Executing \"drive s1 -28.5\"\n",
-      "3:50:55 PM  6/13/2024    Executing \"drive s1 -27\"\n",
-      "3:51:06 PM  6/13/2024    Executing \"drive s1 -27.2\"\n",
-      "3:51:23 PM  6/13/2024    Executing \"preset time 1\"\n",
-      "3:51:31 PM  6/13/2024    Executing \"scan s1 @(s1)+1 @(s1)+-1 0.100000\"\n",
-      "3:52:20 PM  6/13/2024    Executing \"drive s1 @(s1)--1\"\n",
-      "3:53:18 PM  6/13/2024    Executing \"drive s1 -27.3\"\n",
-      "3:53:47 PM  6/13/2024    Executing \"scan s2 @(s2)+2 @(s2)+-2 0.200000 s1 @(s1)+(2/2) @(s1)+(-2/2) 0.100000\"\n",
-      "3:54:41 PM  6/13/2024    Executing \"drive s1 @(s1)-((@(s2)-@(com))/2) s2 @(com)\"\n",
-      "3:55:19 PM  6/13/2024    Executing \"drive s2 -63.5061 s1 -27.3774\"\n",
-      "3:55:48 PM  6/13/2024    Executing \"ubcalc file \"C:\\SPICE\\User\\exp943\\UBConf\\tmp\\UB13Jun2024_35547PM.ini\"\"\n",
-      "3:56:11 PM  6/13/2024    Executing \"drive h 0.000000 k -1.000000 l 4.000000 e 0.000000\"\n",
-      "3:57:31 PM  6/13/2024    Executing \"scan s2 @(s2)+2 @(s2)+-2 0.200000 s1 @(s1)+(2/2) @(s1)+(-2/2) 0.100000\"\n",
-      "3:58:26 PM  6/13/2024    Executing \"drive s1 @(s1)-((@(s2)-@(com))/2) s2 @(com)\"\n",
-      "3:59:31 PM  6/13/2024    Executing \"drive s2 -52.7344 s1 -59.8077\"\n",
-      "3:59:58 PM  6/13/2024    Executing \"lattice a 5.201352 b 5.201352 c 14.061541\"\n",
-      "4:00:23 PM  6/13/2024    Executing \"lattice a 5.201352 b 5.201352 c 14.032562\"\n",
-      "4:00:52 PM  6/13/2024    Executing \"lattice a 5.190272 b 5.190272 c 14.032562\"\n",
-      "4:01:19 PM  6/13/2024    Executing \"ubcalc file \"C:\\SPICE\\User\\exp943\\UBConf\\tmp\\UB13Jun2024_40115PM.ini\"\"\n",
-      "4:01:34 PM  6/13/2024    Executing \"drive h 0.000000 k -1.000000 l 1.000000 e 0.000000\"\n",
-      "4:03:24 PM  6/13/2024    Executing \"preset time 10\"\n",
-      "4:03:35 PM  6/13/2024    Executing \"scan s2 @(s2)+2 @(s2)+-2 0.200000 s1 @(s1)+(2/2) @(s1)+(-2/2) 0.100000\"\n",
-      "4:07:37 PM  6/13/2024    Executing \"drive s1 @(s1)-((@(s2)-@(com))/2) s2 @(com)\"\n",
-      "4:07:45 PM  6/13/2024    Executing \"drive h 0.000000 k 0.000000 l 6.000000 e 0.000000\"\n",
-      "4:13:11 PM  6/13/2024    Executing \"method temp set_setpoint d 10.000000\"\n",
-      "4:13:22 PM  6/13/2024    Executing \"method temp_2 set_setpoint d 10.000000\"\n",
-      "4:21:43 PM  6/13/2024    Executing \"preset time 1\"\n",
-      "4:21:50 PM  6/13/2024    Executing \"scan s1 @(s1)+1 @(s1)+-1 0.100000\"\n",
-      "4:22:39 PM  6/13/2024    Executing \"drive s1 @(s1)--1\"\n",
-      "4:22:49 PM  6/13/2024    Executing \"drive s1 -27.1\"\n",
-      "4:28:04 PM  6/13/2024    Executing \"scan s1 @(s1)+1 @(s1)+-1 0.100000\"\n",
-      "4:28:54 PM  6/13/2024    Executing \"drive s1 @(s1)--1\"\n",
-      "4:29:00 PM  6/13/2024    Executing \"drive s1 -26.4\"\n",
-      "4:29:31 PM  6/13/2024    Executing \"scan s1 @(s1)+1 @(s1)+-1 0.100000\"\n",
-      "4:30:20 PM  6/13/2024    Executing \"drive s1 @(s1)--1\"\n",
-      "4:30:33 PM  6/13/2024    Executing \"drive s1 -26.3\"\n",
-      "4:30:53 PM  6/13/2024    Executing \"ubcalc file \"C:\\SPICE\\User\\exp943\\UBConf\\tmp\\UB13Jun2024_43051PM.ini\"\"\n",
-      "4:31:51 PM  6/13/2024    Executing \"drive h 0.000000 k 1.000000 l 2.000000 e 0.000000\"\n",
-      "4:34:15 PM  6/13/2024    Executing \"method temp set_setpoint d 1.000000\"\n",
-      "4:34:16 PM  6/13/2024    Executing \"method temp_2 set_setpoint d 1.000000\"\n",
-      "4:35:23 PM  6/13/2024    Executing \"scan s2 @(s2)+2 @(s2)+-2 0.200000 s1 @(s1)+(2/2) @(s1)+(-2/2) 0.100000\"\n",
-      "4:36:18 PM  6/13/2024    Executing \"drive s1 @(s1)-((@(s2)-@(com))/2) s2 @(com)\"\n",
-      "4:36:45 PM  6/13/2024    Executing \"drive h 0.000000 k -1.000000 l 2.000000 e 0.000000\"\n",
-      "4:41:23 PM  6/13/2024    Executing \"scan s2 @(s2)+2 @(s2)+-2 0.200000 s1 @(s1)+(2/2) @(s1)+(-2/2) 0.100000\"\n",
-      "4:42:18 PM  6/13/2024    Executing \"drive s1 @(s1)-((@(s2)-@(com))/2) s2 @(com)\"\n",
-      "4:43:09 PM  6/13/2024    Executing \"drive s2 -37.9538 s1 -70.8970\"\n",
-      "4:44:06 PM  6/13/2024    Executing \"drive h 0.000000 k -1.000000 l 2.000000 e 0.000000\"\n",
-      "4:44:38 PM  6/13/2024    Executing \"preset time 60\"\n",
-      "4:46:04 PM  6/13/2024    Executing \"scantitle \"Ni2InSbO6_R_2K_0-12_th2th_1min\"\"\n",
-      "4:46:47 PM  6/13/2024    Executing \"scan s2 @(s2)+1.5 @(s2)+-1.5 0.150000 s1 @(s1)+(1.5/2) @(s1)+(-1.5/2) 0.075000\"\n",
-      "4:47:41 PM  6/13/2024    Executing \"drive h 0.000000 k -1.000000 l 2.000000 e 0.000000\"\n",
-      "4:50:02 PM  6/13/2024    Executing \"scan h 0 k -0.95 -1.05 0.005 l 2 e 0 preset time 60\"\n",
-      "5:12:53 PM  6/13/2024    Executing \"drive ds_nut -3.2\"\n",
-      "5:13:23 PM  6/13/2024    Executing \"scantitle \"Ni2InSbO6_R_2K_0-12_K_sweep_SpinFlip_zz_1min\"\"\n",
-      "5:13:32 PM  6/13/2024    Executing \"scan h 0 k -0.95 -1.05 0.005 l 2 e 0 preset time 60\"\n",
-      "5:42:19 PM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "5:43:19 PM  6/13/2024    Executing \"drive s2 -37.8682\"\n",
-      "5:43:47 PM  6/13/2024    Executing \"drive s1 -70.773\"\n",
-      "5:44:30 PM  6/13/2024    Executing \"drive s1 -70.697\"\n",
-      "5:45:16 PM  6/13/2024    Executing \"drive s2 -37.8129\"\n",
-      "5:49:29 PM  6/13/2024    Executing \"drive theta1 -9.75 theta2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "5:49:29 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzz\"\"\n",
-      "5:49:30 PM  6/13/2024    Executing \"ds_nut 3.2 -3.2 6.4\" ... \n",
-      "5:49:30 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -5.6881\"\n",
-      "5:50:29 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzx\"\"\n",
-      "5:50:29 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "5:50:40 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -2.5102\"\n",
-      "5:51:14 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzy\"\"\n",
-      "5:51:15 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "5:51:26 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.9423 ds_prec -5.6881\"\n",
-      "5:54:07 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "5:55:28 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzz\"\"\n",
-      "5:55:28 PM  6/13/2024    Executing \"ds_nut 3.2 -3.2 6.4\" ... \n",
-      "5:55:28 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -5.6881\"\n",
-      "5:56:52 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "5:57:51 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzz\"\"\n",
-      "5:57:51 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "6:00:06 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -5.6881\"\n",
-      "6:01:05 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzx\"\"\n",
-      "6:01:05 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "6:03:34 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -2.5102\"\n",
-      "6:04:08 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzy\"\"\n",
-      "6:04:08 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "6:06:37 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.9423 ds_prec -5.6881\"\n",
-      "6:07:58 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pxx\"\"\n",
-      "6:07:58 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "6:10:27 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.9423 ds_prec -2.5102\"\n",
-      "6:11:00 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pxy\"\"\n",
-      "6:11:00 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "6:13:29 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.3486 ds_prec -2.5102\"\n",
-      "6:14:07 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pyy\"\"\n",
-      "6:14:07 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "6:16:36 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.3486 ds_prec -5.6881\"\n",
-      "6:17:10 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pyx\"\"\n",
-      "6:17:10 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "6:19:39 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 7.9423 ds_prec 0\"\n",
-      "6:20:38 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pxz\"\"\n",
-      "6:20:38 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "6:23:07 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 4.3486 ds_prec 0\"\n",
-      "6:23:45 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pyz\"\"\n",
-      "6:23:45 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "6:35:05 PM  6/13/2024    Executing \"preset time 300\"\n",
-      "6:35:35 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "6:36:20 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzz\"\"\n",
-      "6:36:20 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "6:37:00 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "6:37:00 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzz\"\"\n",
-      "6:37:01 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "6:47:16 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -5.6881\"\n",
-      "6:48:15 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzx\"\"\n",
-      "6:48:15 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "6:58:44 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -2.5102\"\n",
-      "6:59:18 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzy\"\"\n",
-      "6:59:18 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:09:47 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.9423 ds_prec -5.6881\"\n",
-      "7:11:08 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pxx\"\"\n",
-      "7:11:08 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:21:37 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.9423 ds_prec -2.5102\"\n",
-      "7:22:10 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pxy\"\"\n",
-      "7:22:10 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:32:39 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.3486 ds_prec -2.5102\"\n",
-      "7:33:17 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pyy\"\"\n",
-      "7:33:17 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:43:46 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.3486 ds_prec -5.6881\"\n",
-      "7:44:19 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pyx\"\"\n",
-      "7:44:20 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:54:48 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 7.9423 ds_prec 0\"\n",
-      "7:55:47 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pxz\"\"\n",
-      "7:55:47 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "8:06:16 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 4.3486 ds_prec 0\"\n",
-      "8:06:54 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pyz\"\"\n",
-      "8:06:55 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "8:19:12 PM  6/13/2024    Executing \"drive theta_1 -9.75 up_prec 0 ds_nut 3.2\"\n",
-      "8:20:06 PM  6/13/2024    Executing \"drive h 0.000000 k -1.000000 l 1.000000 e 0.000000\"\n",
-      "8:20:38 PM  6/13/2024    Executing \"preset time 60\"\n",
-      "8:24:05 PM  6/13/2024    Executing \"scan h 0 k -1.06 -0.94 0.005 l 1 e 0 preset time 60\"\n",
-      "8:24:57 PM  6/13/2024    Executing \"scantitle \"Ni2InSbO6_R_0-11_Pzz_NSF_1min_2K\"\"\n",
-      "8:25:07 PM  6/13/2024    Executing \"scan h 0 k -1.06 -0.94 0.005 l 1 e 0 preset time 60\"\n",
-      "8:28:14 PM  6/13/2024    Executing \"scantitle \"Ni2InSbO6_R_0-11_Pzz_NSF_1min_2K\"\"\n",
-      "8:28:18 PM  6/13/2024    Executing \"scan h 0 k -1.06 -0.94 0.005 l 1 e 0 preset time 60\"\n",
-      "8:57:16 PM  6/13/2024    Executing \"drive s2 -37.8097 s1 -70.6970\"\n",
-      "9:02:01 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "9:02:02 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzz\"\"\n",
-      "9:02:02 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "9:02:32 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -5.6527\"\n",
-      "9:03:30 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzx\"\"\n",
-      "9:03:30 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "9:04:50 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -2.4749\"\n",
-      "9:05:30 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "9:05:57 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzz\"\"\n",
-      "9:05:57 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "9:08:26 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -5.6527\"\n",
-      "9:09:24 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzx\"\"\n",
-      "9:09:25 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "9:11:53 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -2.4749\"\n",
-      "9:12:27 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pzy\"\"\n",
-      "9:12:27 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "9:14:56 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.9822 ds_prec -5.6527\"\n",
-      "9:16:18 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pxx\"\"\n",
-      "9:16:18 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "9:18:47 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.9822 ds_prec -2.4749\"\n",
-      "9:19:20 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pxy\"\"\n",
-      "9:19:20 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "9:21:50 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.388 ds_prec -2.4749\"\n",
-      "9:22:27 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pyy\"\"\n",
-      "9:22:27 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "9:24:56 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.388 ds_prec -5.6527\"\n",
-      "9:25:29 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pyx\"\"\n",
-      "9:25:30 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "9:27:58 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 7.9822 ds_prec 0\"\n",
-      "9:28:56 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pxz\"\"\n",
-      "9:28:56 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "9:31:25 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 4.388 ds_prec 0\"\n",
-      "9:32:03 PM  6/13/2024    Executing \"scantitle \"(0-12)_Ni2InSbO6-R_Pyz\"\"\n",
-      "9:32:03 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "9:38:18 PM  6/13/2024    Executing \"drive s2 @(s2)+2\"\n",
-      "9:40:01 PM  6/13/2024    Executing \"drive s2 -34.1132 s1 -84.1788\"\n",
-      "9:41:06 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "9:41:52 PM  6/13/2024    Executing \"drive ds_nut 3.2\"\n",
-      "9:42:51 PM  6/13/2024    Executing \"count\"\n",
-      "9:42:51 PM  6/13/2024   Executing count command with preset channel \"time\" and preset value 60.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       60.000       20.000    55558.000       60.839\n",
-      "9:46:05 PM  6/13/2024    Executing \"preset time 3600\"\n",
-      "9:51:47 PM  6/13/2024    Executing \"preset time 60\"\n",
-      "9:51:51 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "9:51:52 PM  6/13/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pzz\"\"\n",
-      "9:51:52 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "9:54:07 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -5.7534\"\n",
-      "9:55:06 PM  6/13/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pzx\"\"\n",
-      "9:55:06 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "9:57:35 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -2.5756\"\n",
-      "9:58:08 PM  6/13/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pzy\"\"\n",
-      "9:58:08 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "10:00:36 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.8684 ds_prec -5.7534\"\n",
-      "10:01:57 PM  6/13/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pxx\"\"\n",
-      "10:01:57 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "10:04:26 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.8684 ds_prec -2.5756\"\n",
-      "10:05:00 PM  6/13/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pxy\"\"\n",
-      "10:05:00 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "10:07:30 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.2747 ds_prec -2.5756\"\n",
-      "10:08:07 PM  6/13/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pyy\"\"\n",
-      "10:08:07 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "10:10:36 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.2747 ds_prec -5.7534\"\n",
-      "10:11:10 PM  6/13/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pyx\"\"\n",
-      "10:11:10 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "10:13:40 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 7.8684 ds_prec 0\"\n",
-      "10:14:39 PM  6/13/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pxz\"\"\n",
-      "10:14:40 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "10:17:08 PM  6/13/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 4.2747 ds_prec 0\"\n",
-      "10:17:46 PM  6/13/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pyz\"\"\n",
-      "10:17:46 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "10:20:36 PM  6/13/2024    Executing \"preset time 3600\"\n",
-      "10:20:51 PM  6/13/2024    Executing \"drive theta_1 -9.75 theta_2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "10:21:36 PM  6/13/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pzz\"\"\n",
-      "10:21:36 PM  6/13/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "12:22:05 AM  6/14/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -5.7534\"\n",
-      "12:23:04 AM  6/14/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pzx\"\"\n",
-      "12:23:05 AM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "2:23:33 AM  6/14/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -2.5756\"\n",
-      "2:24:07 AM  6/14/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pzy\"\"\n",
-      "2:24:07 AM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "4:24:36 AM  6/14/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.8684 ds_prec -5.7534\"\n",
-      "4:25:57 AM  6/14/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pxx\"\"\n",
-      "4:25:57 AM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "6:26:25 AM  6/14/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.8684 ds_prec -2.5756\"\n",
-      "6:26:58 AM  6/14/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pxy\"\"\n",
-      "6:26:58 AM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "8:27:26 AM  6/14/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.2747 ds_prec -2.5756\"\n",
-      "8:28:05 AM  6/14/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pyy\"\"\n",
-      "8:28:05 AM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "10:28:33 AM  6/14/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.2747 ds_prec -5.7534\"\n",
-      "10:29:07 AM  6/14/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pyx\"\"\n",
-      "10:29:07 AM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "12:29:36 PM  6/14/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 7.8684 ds_prec 0\"\n",
-      "12:30:35 PM  6/14/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pxz\"\"\n",
-      "12:30:36 PM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "2:31:05 PM  6/14/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 4.2747 ds_prec 0\"\n",
-      "2:31:42 PM  6/14/2024    Executing \"scantitle \"(0-11)_-q_Ni2InSbO6-R_Pyz\"\"\n",
-      "2:31:43 PM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "4:35:46 PM  6/14/2024    Executing \"drive h 0.000000 k 1.000000 l -1.000000 e 0.000000\"\n",
-      "4:40:54 PM  6/14/2024    Executing \"preset time 60\"\n",
-      "4:44:23 PM  6/14/2024    Executing \"scantitle \"k-scan_01-1_1min_Ni2InSbO6-R\"\"\n",
-      "4:44:27 PM  6/14/2024    Executing \"scan h 0 k 0.94 1.06 0.005 l -1 e 0 preset time 60\"\n",
-      "5:05:21 PM  6/14/2024    Executing \"drive h 0.000000 k 0.000000 l 6.000000 e 0.000000\"\n",
-      "5:09:27 PM  6/14/2024    Executing \"drive theta_1 -9.75 theta_2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "5:10:11 PM  6/14/2024    Executing \"drive ds_nut 3.2\"\n",
-      "5:10:45 PM  6/14/2024    Executing \"drive s1 @(s1)+-5\"\n",
-      "5:11:11 PM  6/14/2024    Executing \"drive s1 @(s1)+-5\"\n",
-      "5:11:32 PM  6/14/2024    Executing \"drive s1 @(s1)+20\"\n",
-      "5:12:56 PM  6/14/2024    Executing \"drive s1 @(s1)+10\"\n",
-      "5:13:30 PM  6/14/2024    Executing \"drive s1 -6.5\"\n",
-      "5:14:05 PM  6/14/2024    Executing \"drive s1 @(s1)+-1\"\n",
-      "5:14:14 PM  6/14/2024    Executing \"drive s1 @(s1)+-2\"\n",
-      "5:14:23 PM  6/14/2024    Executing \"drive s1 @(s1)+4\"\n",
-      "5:14:38 PM  6/14/2024    Executing \"drive s1 -5.5\"\n",
-      "5:14:57 PM  6/14/2024    Executing \"scan s1 @(s1)+0.5 @(s1)+-0.5 0.100000\"\n",
-      "5:15:14 PM  6/14/2024    Executing \"preset time 5\"\n",
-      "5:15:17 PM  6/14/2024    Executing \"scan s1 @(s1)+0.5 @(s1)+-0.5 0.100000\"\n",
-      "5:16:30 PM  6/14/2024    Executing \"drive s1 @(s1)--0.5\"\n",
-      "5:16:55 PM  6/14/2024    Executing \"drive s1 -5.5\"\n",
-      "5:17:41 PM  6/14/2024    Executing \"drive s1 -5.7\"\n",
-      "5:18:00 PM  6/14/2024    Executing \"preset time 2\"\n",
-      "5:18:13 PM  6/14/2024    Executing \"scan s1 @(s1)+0.5 @(s1)+-0.5 0.100000\"\n",
-      "5:18:53 PM  6/14/2024    Executing \"drive s1 @(s1)--0.5\"\n",
-      "5:19:03 PM  6/14/2024    Executing \"drive s1 -5.7955\"\n",
-      "5:19:38 PM  6/14/2024    Executing \"ubcalc file \"C:\\SPICE\\User\\exp943\\UBConf\\tmp\\UB14Jun2024_51934PM.ini\"\"\n",
-      "5:19:41 PM  6/14/2024    Executing \"lattice 5.190272 5.190272 14.032562 90.000000 90.000000 120.000000\"\n",
-      "5:19:44 PM  6/14/2024    Executing \"ubcalc file \"C:\\SPICE\\User\\exp943\\UBConf\\tmp\\UB14Jun2024_51942PM.ini\"\"\n",
-      "5:22:18 PM  6/14/2024    Executing \"drive s1 @(s1)+1\"\n",
-      "5:22:44 PM  6/14/2024    Executing \"scan s1 @(s1)+0.5 @(s1)+-0.5 0.100000\"\n",
-      "5:23:23 PM  6/14/2024    Executing \"drive s1 @(s1)--0.5\"\n",
-      "5:23:36 PM  6/14/2024    Executing \"drive s1 -4.9725\"\n",
-      "5:23:47 PM  6/14/2024    Executing \"ubcalc file \"C:\\SPICE\\User\\exp943\\UBConf\\tmp\\UB14Jun2024_52346PM.ini\"\"\n",
-      "5:24:44 PM  6/14/2024    Executing \"drive h 0.000000 k 1.000000 l 1.000000 e 0.000000\"\n",
-      "5:28:51 PM  6/14/2024    Executing \"drive h 0.000000 k 1.000000 l -1.000000 e 0.000000\"\n",
-      "5:31:48 PM  6/14/2024    Executing \"scantitle \"Ni2InSbO6-R_01-1_k-scan\"\"\n",
-      "5:32:22 PM  6/14/2024    Executing \"scan h 0 k 0.94 1.06 0.005 l -1 e 0 preset time 60\"\n",
-      "5:58:52 PM  6/14/2024    Executing \"drive h 0.000000 k 1.000000 l 1.000000 e 0.000000\"\n",
-      "6:00:52 PM  6/14/2024    Executing \"scantitle \"Ni2InSbO6-R_011_k-scan_1min\"\"\n",
-      "6:01:03 PM  6/14/2024    Executing \"scan h 0 k 0.94 1.06 0.005 l 1 e 0 preset time 60\"\n",
-      "6:27:12 PM  6/14/2024    Executing \"drive h 0 k 0.9847 l 1.0044 e 0\"\n",
-      "6:29:03 PM  6/14/2024    Executing \"count\"\n",
-      "6:29:03 PM  6/14/2024   Executing count command with preset channel \"time\" and preset value 2.00\n",
-      "         time     detector      monitor          mcu\n",
-      "        2.000        1.000     1848.000        2.024\n",
-      "6:29:22 PM  6/14/2024    Executing \"count preset time 60\"\n",
-      "6:29:22 PM  6/14/2024   Executing count command with preset channel \"time\" and preset value 60.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       60.000       27.000    56409.000       61.771\n",
-      "6:31:33 PM  6/14/2024    Executing \"drive h 0 k 0.985 l 1.0 e 0\"\n",
-      "6:31:43 PM  6/14/2024    Executing \"count preset time 60\"\n",
-      "6:31:43 PM  6/14/2024   Executing count command with preset channel \"time\" and preset value 60.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       60.000       30.000    56357.000       61.714\n",
-      "6:36:11 PM  6/14/2024    Executing \"count preset time 60\"\n",
-      "6:36:11 PM  6/14/2024   Executing count command with preset channel \"time\" and preset value 60.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       60.000       38.000    58382.000       63.931\n",
-      "7:28:18 PM  6/14/2024    Executing \"drive theta_1 -9.75 theta_2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "7:28:19 PM  6/14/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pzz\"\"\n",
-      "7:28:19 PM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:28:36 PM  6/14/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -5.7739\"\n",
-      "7:29:41 PM  6/14/2024    Executing \"preset time 60\"\n",
-      "7:29:55 PM  6/14/2024    Executing \"drive theta_1 -9.75 theta_2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "7:30:54 PM  6/14/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pzz\"\"\n",
-      "7:30:55 PM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:33:24 PM  6/14/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -5.7739\"\n",
-      "7:34:24 PM  6/14/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pzx\"\"\n",
-      "7:34:24 PM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:36:52 PM  6/14/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -2.5961\"\n",
-      "7:37:26 PM  6/14/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pzy\"\"\n",
-      "7:37:27 PM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:39:56 PM  6/14/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.8452 ds_prec -5.7739\"\n",
-      "7:41:16 PM  6/14/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pxx\"\"\n",
-      "7:41:17 PM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:43:46 PM  6/14/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.8452 ds_prec -2.5961\"\n",
-      "7:44:19 PM  6/14/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pxy\"\"\n",
-      "7:44:20 PM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:46:49 PM  6/14/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.2515 ds_prec -2.5961\"\n",
-      "7:47:26 PM  6/14/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pyy\"\"\n",
-      "7:47:27 PM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:49:56 PM  6/14/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.2515 ds_prec -5.7739\"\n",
-      "7:50:29 PM  6/14/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pyx\"\"\n",
-      "7:50:30 PM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:52:59 PM  6/14/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 7.8452 ds_prec 0\"\n",
-      "7:53:59 PM  6/14/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pxz\"\"\n",
-      "7:53:59 PM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:56:28 PM  6/14/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 4.2515 ds_prec 0\"\n",
-      "7:57:05 PM  6/14/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pyz\"\"\n",
-      "7:57:06 PM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "8:00:10 PM  6/14/2024    Executing \"preset time 3600\"\n",
-      "8:00:10 PM  6/14/2024    Executing \"drive theta_1 -9.75 theta_2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "8:00:54 PM  6/14/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pzz\"\"\n",
-      "8:00:55 PM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "10:01:24 PM  6/14/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -5.7739\"\n",
-      "10:02:24 PM  6/14/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pzx\"\"\n",
-      "10:02:24 PM  6/14/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "12:02:53 AM  6/15/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -2.5961\"\n",
-      "12:03:26 AM  6/15/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pzy\"\"\n",
-      "12:03:27 AM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "2:03:56 AM  6/15/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.8452 ds_prec -5.7739\"\n",
-      "2:05:16 AM  6/15/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pxx\"\"\n",
-      "2:05:16 AM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "4:05:45 AM  6/15/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.8452 ds_prec -2.5961\"\n",
-      "4:06:19 AM  6/15/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pxy\"\"\n",
-      "4:06:19 AM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "6:06:48 AM  6/15/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.2515 ds_prec -2.5961\"\n",
-      "6:07:26 AM  6/15/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pyy\"\"\n",
-      "6:07:26 AM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "8:07:55 AM  6/15/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.2515 ds_prec -5.7739\"\n",
-      "8:08:29 AM  6/15/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pyx\"\"\n",
-      "8:08:29 AM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "10:08:58 AM  6/15/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 7.8452 ds_prec 0\"\n",
-      "10:09:58 AM  6/15/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pxz\"\"\n",
-      "10:09:58 AM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "12:10:27 PM  6/15/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 4.2515 ds_prec 0\"\n",
-      "12:11:05 PM  6/15/2024    Executing \"scantitle \"(011)_-q_Ni2InSbO6-R_Pyz\"\"\n",
-      "12:11:05 PM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "2:22:30 PM  6/15/2024    Executing \"drive h 0.000000 k 1.015000 l 1.000000 e 0.000000\"\n",
-      "2:25:36 PM  6/15/2024    Executing \"preset time 60\"\n",
-      "2:25:54 PM  6/15/2024    Executing \"count\"\n",
-      "2:25:54 PM  6/15/2024   Executing count command with preset channel \"time\" and preset value 60.00\n",
-      "         time     detector      monitor          mcu\n",
-      "       60.000       42.000    57252.000       62.694\n",
-      "2:35:03 PM  6/15/2024    Executing \"preset time 60\"\n",
-      "2:35:03 PM  6/15/2024    Executing \"drive theta_1 -9.75 theta_2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "2:35:48 PM  6/15/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pzz\"\"\n",
-      "2:35:48 PM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "2:38:17 PM  6/15/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -5.7573\"\n",
-      "2:39:17 PM  6/15/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pzx\"\"\n",
-      "2:39:17 PM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "2:41:46 PM  6/15/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -2.5795\"\n",
-      "2:42:20 PM  6/15/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pzy\"\"\n",
-      "2:42:20 PM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "2:44:49 PM  6/15/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.864 ds_prec -5.7573\"\n",
-      "2:46:10 PM  6/15/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pxx\"\"\n",
-      "2:46:10 PM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "2:48:39 PM  6/15/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.864 ds_prec -2.5795\"\n",
-      "2:49:13 PM  6/15/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pxy\"\"\n",
-      "2:49:14 PM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "2:51:42 PM  6/15/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.2703 ds_prec -2.5795\"\n",
-      "2:52:20 PM  6/15/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pyy\"\"\n",
-      "2:52:20 PM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "2:54:49 PM  6/15/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.2703 ds_prec -5.7573\"\n",
-      "2:55:23 PM  6/15/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pyx\"\"\n",
-      "2:55:23 PM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "2:57:52 PM  6/15/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 7.864 ds_prec 0\"\n",
-      "2:58:52 PM  6/15/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pxz\"\"\n",
-      "2:58:52 PM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "3:01:21 PM  6/15/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 4.2703 ds_prec 0\"\n",
-      "3:01:59 PM  6/15/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pyz\"\"\n",
-      "3:01:59 PM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "3:05:38 PM  6/15/2024    Executing \"preset time 3600\"\n",
-      "3:05:38 PM  6/15/2024    Executing \"drive theta_1 -9.75 theta_2 -2.25 up_prec 0 ds_prec 0\"\n",
-      "3:06:23 PM  6/15/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pzz\"\"\n",
-      "3:06:24 PM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "5:06:52 PM  6/15/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -5.7573\"\n",
-      "5:07:52 PM  6/15/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pzx\"\"\n",
-      "5:07:52 PM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:08:21 PM  6/15/2024    Executing \"drive theta_1 -9.75 theta_2 -92.25 up_prec 0 ds_prec -2.5795\"\n",
-      "7:08:55 PM  6/15/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pzy\"\"\n",
-      "7:08:55 PM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "9:09:24 PM  6/15/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.864 ds_prec -5.7573\"\n",
-      "9:10:45 PM  6/15/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pxx\"\"\n",
-      "9:10:45 PM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "11:11:14 PM  6/15/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 7.864 ds_prec -2.5795\"\n",
-      "11:11:48 PM  6/15/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pxy\"\"\n",
-      "11:11:49 PM  6/15/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "1:12:19 AM  6/16/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.2703 ds_prec -2.5795\"\n",
-      "1:12:56 AM  6/16/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pyy\"\"\n",
-      "1:12:56 AM  6/16/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "3:13:25 AM  6/16/2024    Executing \"drive theta_1 80.25 theta_2 -92.25 up_prec 4.2703 ds_prec -5.7573\"\n",
-      "3:13:59 AM  6/16/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pyx\"\"\n",
-      "3:13:59 AM  6/16/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "5:14:29 AM  6/16/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 7.864 ds_prec 0\"\n",
-      "5:15:29 AM  6/16/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pxz\"\"\n",
-      "5:15:29 AM  6/16/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "7:15:59 AM  6/16/2024    Executing \"drive theta_1 80.25 theta_2 -2.25 up_prec 4.2703 ds_prec 0\"\n",
-      "7:16:37 AM  6/16/2024    Executing \"scantitle \"(011)_+q_Ni2InSbO6-R_Pyz\"\"\n",
-      "7:16:37 AM  6/16/2024    Executing \"scan ds_nut 3.2 -3.2 6.4\"\n",
-      "10:20:00 AM  6/16/2024    Executing \"method temp set_setpoint d 100.000000\"\n",
-      "10:20:25 AM  6/16/2024    Executing \"method temp_2 set_setpoint d 100.000000\"\n",
-      "10:35:50 AM  6/16/2024    Executing \"drive h 0.000000 k -1.000000 l 1.000000 e 0.000000\"\n",
-      "10:41:33 AM  6/16/2024    Executing \"preset time 2\"\n",
-      "10:42:05 AM  6/16/2024    Executing \"drive s1 @(s1)+1\"\n",
-      "10:42:13 AM  6/16/2024    Executing \"drive s1 @(s1)+1\"\n",
-      "10:42:25 AM  6/16/2024    Executing \"drive s1 @(s1)+-4\"\n",
-      "10:42:39 AM  6/16/2024    Executing \"drive h 0.000000 k -1.000000 l 1.000000 e 0.000000\"\n",
-      "10:42:53 AM  6/16/2024    Executing \"preset time 60\"\n",
-      "10:43:20 AM  6/16/2024    Executing \"scan s1 @(s1)+0.5 @(s1)+-0.5 0.100000\"\n",
-      "10:53:29 AM  6/16/2024    Executing \"drive s1 @(s1)--0.5\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "f=open('C:/Users/num/Documents/cycle506/exp943/LogFile.txt','r')\n",
-    "\n",
-    "for line in f:\n",
-    "    if 'Executing' in line:\n",
-    "        print(line,end='')\n",
-    "        #pass\n",
-    "        \n",
-    "    if 'time     detector      monitor          mcu' in line:\n",
-    "        print(line, end='')\n",
-    "        print(next(f), end='')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bbd58189-c451-45b2-8f7d-ad48ea72abd5",
-   "metadata": {},
-   "source": [
-    "# Scan Summary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 657,
-   "id": "d64c7c09-3782-41c5-8f56-f6e26d04d7b6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>.container { width:150% !important; }</style>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[30m 45        USN 360 SCN ++, no comp            scan ds_nut 2 4 0.2 |    10 | -2.949  -3.424  +0.003 | +0.000 | +2.995  +3.004  -0.001 |  -4.000   0.000 | 165.3\n",
-      "\u001b[30m 46        USN 360 SCN ++, no comp          scan ds_nut -2 -4 0.2 |    10 | -2.949  -3.424  +0.003 | +0.000 | -2.995  +3.004  -0.001 |  -4.000   0.000 | 163.5\n",
-      "\u001b[30m 47        USN 360 SCN ++, no comp          scan theta_1 -4 356 5 |    10 | -2.949  -3.424  +0.003 | +0.000 | +3.195  +3.004  -0.001 | 176.000   0.000 | 166.3\n",
-      "\u001b[30m 48        USN 360 SCN +-, no comp          scan theta_1 356 -4 5 |    10 | -2.949  -3.424  +0.003 | +0.000 | -2.197  +3.004  -0.001 | 176.000   0.000 | 169.7\n",
-      "\u001b[30m 49  USN 360 SCN ++, no comp mu on          scan theta_1 -4 356 5 |    10 | -2.949  -3.424  +0.003 | +0.000 | +3.195  +3.004  -0.001 |   3.500   0.000 | 170.4\n",
-      "\u001b[30m 50  USN 360 SCN ++, no comp mu on          scan theta_1 -4 356 5 |    10 | -2.949  -3.424  +0.003 | +0.000 | +3.195  +3.004  -0.001 | 176.000   0.000 | 170.6\n",
-      "\u001b[30m 51  USN 360 SCN +-, no comp mu on          scan theta_1 356 -4 5 |    10 | -2.949  -3.424  +0.003 | +0.000 | -2.197  +3.004  -0.001 | 176.000   0.000 | 170.7\n",
-      "\u001b[30m 52  USN 360 SCN ++, no comp mu on          scan theta_1 -8 352 5 |    10 | -2.950  -3.424  +0.003 | +0.000 | +3.195  +3.004  -0.001 | 172.000  -4.000 | 170.8\n",
-      "\u001b[30m 53  USN 360 SCN +-, no comp mu on          scan theta_1 352 -8 5 |    10 | -2.949  -3.424  +0.003 | +0.000 | -2.197  +3.004  -0.001 | 172.000  -4.000 | 170.8\n",
-      "\u001b[30m 54  USN 360 SCN ++, comp 1A mu on          scan theta_1 -8 352 5 |    10 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.001 | 172.000  -4.000 | 170.7\n",
-      "\u001b[30m 55  USN 360 SCN +-, comp 1A mu on          scan theta_1 352 -8 5 |    10 | -2.949  -3.424  +0.003 | +1.000 | -2.197  +3.004  -0.001 | 172.000  -4.000 | 170.8\n",
-      "\u001b[30m 56                   qck USP scan       scanrel up_prec -6 6 0.5 |    10 | -2.949  -3.424  -5.996 | +1.000 | +3.195  +3.004  -0.001 |  82.000 -94.000 | 170.9\n",
-      "\u001b[30m 57                   qck USP scan          scan up_prec -6 6 0.5 |    10 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  82.000 -94.000 | 170.9\n",
-      "\u001b[30m 58                   qck USP scan          scan up_prec 6 -6 0.5 |    10 | -2.949  -3.424  +0.004 | +1.000 | -3.195  +3.004  -0.000 |  82.000 -94.000 | 170.9\n",
-      "\u001b[30m 59                   fin USP scan       scan up_prec -10 10 0.25 |    10 | -2.949  -3.424  +0.003 | +1.000 | -3.195  +3.004  -0.000 |  82.000 -94.000 | 170.9\n",
-      "\u001b[30m 60                   fin USP scan       scan up_prec 10 -10 0.25 |    10 | -2.950  -3.424  +0.005 | +1.000 | +3.195  +3.004  -0.000 |  82.000 -94.000 | 170.9\n",
-      "\u001b[30m 61                   fin DSP scan       scan ds_prec -10 10 0.25 |    10 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.002 |  82.000 -94.000 | 170.9\n",
-      "\u001b[30m 62                   fin DSP scan       scan ds_prec 10 -10 0.25 |    10 | -2.950  -3.424  +0.003 | +1.000 | -3.195  +3.004  +0.006 |  82.000 -94.000 | 170.9\n",
-      "\u001b[30m 63                   fin DSP scan          scan up_prec -7 7 0.5 |    10 | -2.950  -3.424  +0.004 | +1.000 | +3.195  +3.004  -0.000 |  -8.000 -94.000 | 170.9\n",
-      "\u001b[30m 64                   fin DSP scan          scan up_prec 7 -7 0.5 |    10 | -2.950  -3.424  +0.004 | +1.000 | -3.195  +3.004  -0.000 |  -8.000 -94.000 | 170.9\n",
-      "\u001b[30m 65                   fin DSP scan          scan up_prec -7 7 0.5 |    10 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -7.000 -95.000 | 170.9\n",
-      "\u001b[30m 66                   fin DSP scan          scan up_prec 7 -7 0.5 |    10 | -2.950  -3.424  +0.004 | +1.000 | -3.195  +3.004  -0.000 |  -7.000 -95.000 | 170.9\n",
-      "\u001b[30m 67                   fin DSP scan          scan up_prec -7 7 0.5 |    10 | -2.950  -3.424  +0.001 | +1.000 | +3.195  +3.004  -0.000 |  -9.000 -93.000 | 171.0\n",
-      "\u001b[30m 68                   fin DSP scan          scan up_prec 7 -7 0.5 |    10 | -2.949  -3.424  +0.004 | +1.000 | -3.195  +3.004  -0.000 |  -9.000 -93.000 | 171.0\n",
-      "\u001b[30m 69                   fin DSP scan          scan up_prec -7 7 0.5 |    10 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 | -10.000 -92.000 | 171.0\n",
-      "\u001b[30m 70                   fin DSP scan          scan up_prec 7 -7 0.5 |    10 | -2.950  -3.424  +0.004 | +1.000 | -3.195  +3.004  -0.000 | -10.000 -92.000 | 171.0\n",
-      "\u001b[30m 71                   fin DSP scan          scan up_prec -7 7 0.5 |    10 | -2.950  -3.424  +0.002 | +1.000 | +3.195  +3.004  -0.000 |  -9.500 -92.500 | 170.9\n",
-      "\u001b[30m 72                   fin DSP scan          scan up_prec 7 -7 0.5 |    10 | -2.950  -3.424  +0.005 | +1.000 | -3.195  +3.004  -0.000 |  -9.500 -92.500 | 170.9\n",
-      "\u001b[30m 73                   fin DSP scan          scan up_prec -7 7 0.5 |    10 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750 -92.250 | 170.9\n",
-      "\u001b[30m 74                   fin DSP scan          scan up_prec 7 -7 0.5 |    10 | -2.949  -3.424  +0.005 | +1.000 | -3.195  +3.004  -0.000 |  -9.750 -92.250 | 170.9\n",
-      "\u001b[30m 75                    silicon 111          scan up_prec -3 3 0.5 |    10 | -2.949  -3.424  +0.004 | +1.000 | +3.195  +3.004  -0.000 |  80.250 -92.250 | 170.5\n",
-      "\u001b[30m 76                    silicon 111          scan ds_prec -3 3 0.5 |    10 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  +0.002 |  80.250 -92.250 | 170.4\n",
-      "\u001b[30m 77        Si (111) USN 360 SCN ++    scan theta_1 -9.75 350.25 5 |   180 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 | 170.250  -2.250 | 170.9\n",
-      "\u001b[30m 78        Si (111) USN 360 SCN +-    scan theta_1 350.25 -9.75 5 |   180 | -2.949  -3.424  +0.003 | +1.000 | -3.195  +3.004  -0.000 | 170.250  -2.250 | 170.9\n",
-      "\u001b[30m 79 Si 111 upstream precession scan ++          scan up_prec -8 8 0.5 |    60 | -2.949  -3.424  +0.004 | +1.000 | +3.195  +3.004  -0.000 |  80.250 -92.250 | 170.6\n",
-      "\u001b[30m 80 Si 111 upstream precession scan +-          scan up_prec 8 -8 0.5 |    60 | -2.949  -3.424  +0.004 | +1.000 | -3.195  +3.004  -0.000 |  80.250 -92.250 | 170.6\n",
-      "\u001b[30m 81 Si 111 downstream precession scan ++          scan ds_prec -8 8 0.5 |    60 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  +0.000 |  80.250 -92.250 | 170.8\n",
-      "\u001b[30m 82 Si 111 downstream precession scan +-          scan ds_prec 8 -8 0.5 |    60 | -2.950  -3.424  +0.003 | +1.000 | -3.195  +3.004  +0.001 |  80.250 -92.250 | 170.9\n",
-      "\u001b[30m 83 Si 111 downstream precession scan +-            scanrel s1 2 -2 0.2 |     1 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 170.3\n",
-      "\u001b[30m 84 Si 111 downstream precession scan +-            scanrel s1 2 -2 0.2 |     1 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 151.7\n",
-      "\u001b[30m 85 Si 111 downstream precession scan +-            scanrel s1 2 -2 0.2 |    10 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  9.2\n",
-      "\u001b[30m 86 Si 111 downstream precession scan +-                 th2th 2 -2 0.2 |    10 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  3.4\n",
-      "\u001b[30m 87 Si 111 downstream precession scan +-                 th2th 2 -2 0.2 |    10 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  2.0\n",
-      "\u001b[30m 88 Si 111 downstream precession scan +-                 th2th 2 -2 0.2 |    10 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  2.0\n",
-      "\u001b[30m 89 Si 111 downstream precession scan +-            scanrel s1 2 -2 0.2 |    10 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  3.2\n",
-      "\u001b[30m 90 Si 111 downstream precession scan +-            scanrel s1 1 -1 0.1 |     1 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  7.1\n",
-      "\u001b[30m 91 Si 111 downstream precession scan +-                 th2th 2 -2 0.2 |     1 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  7.1\n",
-      "\u001b[30m 92 Si 111 downstream precession scan +-                 th2th 2 -2 0.2 |     1 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  7.0\n",
-      "\u001b[30m 93 Si 111 downstream precession scan +-                 th2th 2 -2 0.2 |    10 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  6.6\n",
-      "\u001b[30m 94 Si 111 downstream precession scan +-            scanrel s1 1 -1 0.1 |     1 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 10.5\n",
-      "\u001b[30m 95 Si 111 downstream precession scan +-            scanrel s1 1 -1 0.1 |     1 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 10.5\n",
-      "\u001b[30m 96 Si 111 downstream precession scan +-            scanrel s1 1 -1 0.1 |     1 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 10.5\n",
-      "\u001b[30m 97 Si 111 downstream precession scan +-                 th2th 2 -2 0.2 |     1 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  6.3\n",
-      "\u001b[30m 98 Si 111 downstream precession scan +-                 th2th 2 -2 0.2 |     1 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  3.7\n",
-      "\u001b[30m 99 Ni2InSbO6_R_2K_0-12_th2th_1min            th2th 1.5 -1.5 0.15 |    60 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  2.3\n",
-      "\u001b[30m100 Ni2InSbO6_R_2K_0-12_th2th_1min scan h 0 k -0.95 -1.05 0.005  l 2 e 0 preset time 60 |    60 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  3.3\n",
-      "\u001b[30m101 Ni2InSbO6_R_2K_0-12_K_sweep_SpinFlip_zz_1min scan h 0 k -0.95 -1.05 0.005  l 2 e 0 preset time 60 |    60 | -2.949  -3.424  +0.003 | +1.000 | -3.195  +3.004  -0.000 |  -9.750  -2.250 |  5.7\n",
-      "\u001b[30m102         (0-12)_Ni2InSbO6-R_Pzx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -5.685 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m103         (0-12)_Ni2InSbO6-R_Pzy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -2.508 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m104         (0-12)_Ni2InSbO6-R_Pzz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m105         (0-12)_Ni2InSbO6-R_Pzx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +0.003 | +1.000 | +0.000  +3.004  -5.685 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m106         (0-12)_Ni2InSbO6-R_Pzy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -2.508 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m107         (0-12)_Ni2InSbO6-R_Pxx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +7.947 | +1.000 | +0.000  +3.004  -5.685 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m108         (0-12)_Ni2InSbO6-R_Pxy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +7.947 | +1.000 | +0.000  +3.004  -2.508 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m109         (0-12)_Ni2InSbO6-R_Pyy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +4.353 | +1.000 | +0.000  +3.004  -2.508 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m110         (0-12)_Ni2InSbO6-R_Pyx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +4.353 | +1.000 | -0.000  +3.004  -5.685 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m111         (0-12)_Ni2InSbO6-R_Pxz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +7.947 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m112         (0-12)_Ni2InSbO6-R_Pyz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +4.353 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m113         (0-12)_Ni2InSbO6-R_Pzz       scan ds_nut 3.2 -3.2 6.4 |   300 | -2.950  -3.424  +0.003 | +1.000 | +3.183  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m114         (0-12)_Ni2InSbO6-R_Pzz       scan ds_nut 3.2 -3.2 6.4 |   300 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m115         (0-12)_Ni2InSbO6-R_Pzx       scan ds_nut 3.2 -3.2 6.4 |   300 | -2.950  -3.424  +0.003 | +1.000 | +0.000  +3.004  -5.685 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m116         (0-12)_Ni2InSbO6-R_Pzy       scan ds_nut 3.2 -3.2 6.4 |   300 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -2.508 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m117         (0-12)_Ni2InSbO6-R_Pxx       scan ds_nut 3.2 -3.2 6.4 |   300 | -2.950  -3.424  +7.947 | +1.000 | +0.000  +3.004  -5.685 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m118         (0-12)_Ni2InSbO6-R_Pxy       scan ds_nut 3.2 -3.2 6.4 |   300 | -2.949  -3.424  +7.947 | +1.000 | +0.000  +3.004  -2.508 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m119         (0-12)_Ni2InSbO6-R_Pyy       scan ds_nut 3.2 -3.2 6.4 |   300 | -2.950  -3.424  +4.353 | +1.000 | +0.000  +3.004  -2.508 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m120         (0-12)_Ni2InSbO6-R_Pyx       scan ds_nut 3.2 -3.2 6.4 |   300 | -2.949  -3.424  +4.353 | +1.000 | -0.000  +3.004  -5.685 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m121         (0-12)_Ni2InSbO6-R_Pxz       scan ds_nut 3.2 -3.2 6.4 |   300 | -2.949  -3.424  +7.947 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m122         (0-12)_Ni2InSbO6-R_Pyz       scan ds_nut 3.2 -3.2 6.4 |   300 | -2.949  -3.424  +4.353 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m123         (0-12)_Ni2InSbO6-R_Pyz scan h 0 k -1.06 -0.94 0.005 l 1 e 0 preset time 60 |     0 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m124 Ni2InSbO6_R_0-11_Pzz_NSF_1min_2K scan h 0 k -1.06 -0.94 0.005 l 1 e 0 preset time 60 |    60 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m125 Ni2InSbO6_R_0-11_Pzz_NSF_1min_2K scan h 0 k -1.06 -0.94 0.005 l 1 e 0 preset time 60 |    60 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m126         (0-12)_Ni2InSbO6-R_Pzz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m127         (0-12)_Ni2InSbO6-R_Pzx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -5.650 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m128         (0-12)_Ni2InSbO6-R_Pzz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m129         (0-12)_Ni2InSbO6-R_Pzx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -5.650 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m130         (0-12)_Ni2InSbO6-R_Pzy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +0.003 | +1.000 | +0.000  +3.004  -2.473 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m131         (0-12)_Ni2InSbO6-R_Pxx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +7.987 | +1.000 | +0.000  +3.004  -5.650 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m132         (0-12)_Ni2InSbO6-R_Pxy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +7.987 | +1.000 | +0.000  +3.004  -2.473 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m133         (0-12)_Ni2InSbO6-R_Pyy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +4.393 | +1.000 | +0.000  +3.004  -2.473 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m134         (0-12)_Ni2InSbO6-R_Pyx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +4.393 | +1.000 | -0.000  +3.004  -5.650 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m135         (0-12)_Ni2InSbO6-R_Pxz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +7.987 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m136         (0-12)_Ni2InSbO6-R_Pyz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +4.393 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m137      (0-11)_-q_Ni2InSbO6-R_Pzz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m138      (0-11)_-q_Ni2InSbO6-R_Pzx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | -0.003  +3.004  -5.750 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m139      (0-11)_-q_Ni2InSbO6-R_Pzy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | -0.005  +3.004  -2.573 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m140      (0-11)_-q_Ni2InSbO6-R_Pxx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +7.873 | +1.000 | +0.000  +3.004  -5.750 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m141      (0-11)_-q_Ni2InSbO6-R_Pxy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +7.873 | +1.000 | +0.000  +3.004  -2.573 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m142      (0-11)_-q_Ni2InSbO6-R_Pyy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +4.279 | +1.000 | +0.000  +3.004  -2.573 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m143      (0-11)_-q_Ni2InSbO6-R_Pyx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +4.279 | +1.000 | +0.000  +3.004  -5.750 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m144      (0-11)_-q_Ni2InSbO6-R_Pxz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +7.873 | +1.000 | +0.003  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m145      (0-11)_-q_Ni2InSbO6-R_Pyz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +4.279 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m146      (0-11)_-q_Ni2InSbO6-R_Pzz       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.950  -3.424  +0.003 | +1.000 | +0.000  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m147      (0-11)_-q_Ni2InSbO6-R_Pzx       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +0.003 | +1.000 | +0.004  +3.004  -5.750 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m148      (0-11)_-q_Ni2InSbO6-R_Pzy       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -2.573 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m149      (0-11)_-q_Ni2InSbO6-R_Pxx       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.950  -3.424  +7.873 | +1.000 | -0.000  +3.004  -5.750 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m150      (0-11)_-q_Ni2InSbO6-R_Pxy       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.950  -3.424  +7.873 | +1.000 | -0.004  +3.004  -2.573 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m151      (0-11)_-q_Ni2InSbO6-R_Pyy       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +4.279 | +1.000 | +0.004  +3.004  -2.573 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m152      (0-11)_-q_Ni2InSbO6-R_Pyx       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +4.279 | +1.000 | +0.000  +3.004  -5.750 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m153      (0-11)_-q_Ni2InSbO6-R_Pxz       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.950  -3.424  +7.873 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m154      (0-11)_-q_Ni2InSbO6-R_Pyz       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.950  -3.424  +4.279 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m155   k-scan_01-1_1min_Ni2InSbO6-R scan h 0 k 0.94 1.06 0.005 l -1 e 0 preset time 60 |    60 | -2.949  -3.424  +4.279 | +1.000 | -3.194  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m156   k-scan_01-1_1min_Ni2InSbO6-R        scanrel s1 0.5 -0.5 0.1 |    60 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m157   k-scan_01-1_1min_Ni2InSbO6-R        scanrel s1 0.5 -0.5 0.1 |     5 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m158   k-scan_01-1_1min_Ni2InSbO6-R        scanrel s1 0.5 -0.5 0.1 |     2 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m159   k-scan_01-1_1min_Ni2InSbO6-R        scanrel s1 0.5 -0.5 0.1 |     2 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m160        Ni2InSbO6-R_01-1_k-scan scan h 0 k 0.94 1.06 0.005 l -1 e 0 preset time 60 |    60 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m161    Ni2InSbO6-R_011_k-scan_1min scan h 0 k 0.94 1.06 0.005 l 1 e 0 preset time 60 |    60 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m162       (011)_-q_Ni2InSbO6-R_Pzz       scan ds_nut 3.2 -3.2 6.4 |     1 | -2.950  -3.424  +0.003 | +1.000 | +0.000  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m163       (011)_-q_Ni2InSbO6-R_Pzz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m164       (011)_-q_Ni2InSbO6-R_Pzx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -5.771 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m165       (011)_-q_Ni2InSbO6-R_Pzy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -2.594 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m166       (011)_-q_Ni2InSbO6-R_Pxx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +7.850 | +1.000 | +0.000  +3.004  -5.771 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m167       (011)_-q_Ni2InSbO6-R_Pxy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +7.850 | +1.000 | +0.000  +3.004  -2.594 |  80.250 -92.250 |  2.0\n",
-      "\u001b[30m168       (011)_-q_Ni2InSbO6-R_Pyy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +4.255 | +1.000 | +0.000  +3.004  -2.594 |  80.250 -92.250 |  2.0\n",
-      "\u001b[30m169       (011)_-q_Ni2InSbO6-R_Pyx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +4.255 | +1.000 | +0.000  +3.004  -5.771 |  80.250 -92.250 |  2.0\n",
-      "\u001b[30m170       (011)_-q_Ni2InSbO6-R_Pxz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +7.850 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.0\n",
-      "\u001b[30m171       (011)_-q_Ni2InSbO6-R_Pyz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +4.255 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.0\n",
-      "\u001b[30m172       (011)_-q_Ni2InSbO6-R_Pzz       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m173       (011)_-q_Ni2InSbO6-R_Pzx       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +0.003 | +1.000 | +0.004  +3.004  -5.771 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m174       (011)_-q_Ni2InSbO6-R_Pzy       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -2.594 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m175       (011)_-q_Ni2InSbO6-R_Pxx       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +7.850 | +1.000 | +0.000  +3.004  -5.771 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m176       (011)_-q_Ni2InSbO6-R_Pxy       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +7.850 | +1.000 | +0.000  +3.004  -2.594 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m177       (011)_-q_Ni2InSbO6-R_Pyy       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +4.255 | +1.000 | +0.000  +3.004  -2.594 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m178       (011)_-q_Ni2InSbO6-R_Pyx       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.950  -3.424  +4.255 | +1.000 | +0.000  +3.004  -5.771 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m179       (011)_-q_Ni2InSbO6-R_Pxz       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.950  -3.424  +7.850 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m180       (011)_-q_Ni2InSbO6-R_Pyz       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.950  -3.424  +4.255 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m181       (011)_+q_Ni2InSbO6-R_Pzz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m182       (011)_+q_Ni2InSbO6-R_Pzx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +0.003 | +1.000 | +0.000  +3.004  -5.754 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m183       (011)_+q_Ni2InSbO6-R_Pzy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -2.577 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m184       (011)_+q_Ni2InSbO6-R_Pxx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +7.869 | +1.000 | +0.000  +3.004  -5.754 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m185       (011)_+q_Ni2InSbO6-R_Pxy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +7.869 | +1.000 | -0.004  +3.004  -2.577 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m186       (011)_+q_Ni2InSbO6-R_Pyy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +4.275 | +1.000 | +0.000  +3.004  -2.577 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m187       (011)_+q_Ni2InSbO6-R_Pyx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +4.275 | +1.000 | +0.003  +3.004  -5.754 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m188       (011)_+q_Ni2InSbO6-R_Pxz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +7.869 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m189       (011)_+q_Ni2InSbO6-R_Pyz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +4.275 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m190       (011)_+q_Ni2InSbO6-R_Pzz       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -0.000 |  -9.750  -2.250 |  2.1\n",
-      "\u001b[30m191       (011)_+q_Ni2InSbO6-R_Pzx       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -5.754 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m192       (011)_+q_Ni2InSbO6-R_Pzy       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -2.577 |  -9.750 -92.250 |  2.1\n",
-      "\u001b[30m193       (011)_+q_Ni2InSbO6-R_Pxx       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +7.869 | +1.000 | +0.000  +3.004  -5.754 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m194       (011)_+q_Ni2InSbO6-R_Pxy       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.950  -3.424  +7.869 | +1.000 | +0.000  +3.004  -2.577 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m195       (011)_+q_Ni2InSbO6-R_Pyy       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +4.275 | +1.000 | +0.000  +3.004  -2.577 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m196       (011)_+q_Ni2InSbO6-R_Pyx       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +4.275 | +1.000 | +0.000  +3.004  -5.754 |  80.250 -92.250 |  2.1\n",
-      "\u001b[30m197       (011)_+q_Ni2InSbO6-R_Pxz       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +7.869 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m198       (011)_+q_Ni2InSbO6-R_Pyz       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +4.275 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  2.1\n",
-      "\u001b[30m199       (011)_+q_Ni2InSbO6-R_Pyz        scanrel s1 0.5 -0.5 0.1 |    60 | -2.528  -2.935  +3.664 | +0.857 | -2.738  +2.575  -0.000 |  68.214  -2.500 | 81.3\n",
-      "\u001b[30m200       (011)_+q_Ni2InSbO6-R_Pyz        scanrel s1 0.5 -0.5 0.1 |    60 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 100.3\n",
-      "\u001b[30m201       (011)_+q_Ni2InSbO6-R_Pyz        scanrel s1 0.5 -0.5 0.1 |    60 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 100.4\n",
-      "\u001b[30m202       (011)_+q_Ni2InSbO6-R_Pyz        scanrel s1 0.5 -0.5 0.1 |    60 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 100.4\n",
-      "\u001b[30m203       (011)_+q_Ni2InSbO6-R_Pyz scan h 0 k -1.05 -0.95 0.01 l 1 e 0 preset time 60 |    60 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 100.5\n",
-      "\u001b[30m204       (011)_+q_Ni2InSbO6-R_Pyz scanrel s1 0.5 -0.5 0.1 preset time 30 |    60 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 100.4\n",
-      "\u001b[30m205       (011)_+q_Ni2InSbO6-R_Pyz scanrel s1 0.5 -0.5 0.1 preset time 30 |    60 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 100.4\n",
-      "\u001b[30m206       (011)_+q_Ni2InSbO6-R_Pyz        scanrel s1 0.5 -0.5 0.1 |    30 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 100.4\n",
-      "\u001b[30m207       (011)_+q_Ni2InSbO6-R_Pyz scan h 0 k -1.05 -0.95 0.01 l 1 e 0 preset time 60 |    60 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 100.5\n",
-      "\u001b[30m208 (0-11)_k-scan_100K_5min_Ni2InSbO6-R scan h 0 k -1.06 -0.94 0.005 l 1 e 0 preset time 120 |   120 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 100.5\n",
-      "\u001b[30m209  (0-11)-q_100K_Ni2InSbO6-R_Pzz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | +0.003  +3.004  -0.000 |  -9.750  -2.250 | 100.4\n",
-      "\u001b[30m210  (0-11)-q_100K_Ni2InSbO6-R_Pzx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | +0.003  +3.004  -5.749 |  -9.750 -92.250 | 100.4\n",
-      "\u001b[30m211  (0-11)-q_100K_Ni2InSbO6-R_Pzy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | -0.003  +3.004  -2.573 |  -9.750 -92.250 | 100.4\n",
-      "\u001b[30m212  (0-11)-q_100K_Ni2InSbO6-R_Pxx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +7.874 | +1.000 | +0.000  +3.004  -5.749 |  80.250 -92.250 | 100.3\n",
-      "\u001b[30m213  (0-11)-q_100K_Ni2InSbO6-R_Pxy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +7.874 | +1.000 | +0.000  +3.004  -2.573 |  80.250 -92.250 | 100.3\n",
-      "\u001b[30m214  (0-11)-q_100K_Ni2InSbO6-R_Pyy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +4.279 | +1.000 | +0.000  +3.004  -2.573 |  80.250 -92.250 | 100.3\n",
-      "\u001b[30m215  (0-11)-q_100K_Ni2InSbO6-R_Pyx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +4.279 | +1.000 | +0.000  +3.004  -5.749 |  80.250 -92.250 | 100.2\n",
-      "\u001b[30m216  (0-11)-q_100K_Ni2InSbO6-R_Pxz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +7.874 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 | 100.2\n",
-      "\u001b[30m217                            Pyz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +4.279 | +1.000 | -0.000  +3.004  -0.000 |  80.250  -2.250 | 100.2\n",
-      "\u001b[30m218  (0-11)-q_100K_Ni2InSbO6-R_Pzz       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -0.000 |  -9.750  -2.250 | 100.2\n",
-      "\u001b[30m219  (0-11)-q_100K_Ni2InSbO6-R_Pzx       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -5.749 |  -9.750 -92.250 | 100.2\n",
-      "\u001b[30m220  (0-11)-q_100K_Ni2InSbO6-R_Pzy       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +0.003 | +1.000 | -0.004  +3.004  -2.573 |  -9.750 -92.250 | 100.2\n",
-      "\u001b[30m221  (0-11)-q_100K_Ni2InSbO6-R_Pxx       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +7.874 | +1.000 | +0.000  +3.004  -5.749 |  80.250 -92.250 | 100.2\n",
-      "\u001b[30m222  (0-11)-q_100K_Ni2InSbO6-R_Pxy       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +7.874 | +1.000 | -0.003  +3.004  -2.573 |  80.250 -92.250 | 100.2\n",
-      "\u001b[30m223  (0-11)-q_100K_Ni2InSbO6-R_Pyy       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +4.279 | +1.000 | +0.000  +3.004  -2.573 |  80.250 -92.250 | 100.2\n",
-      "\u001b[30m224  (0-11)-q_100K_Ni2InSbO6-R_Pyx       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.950  -3.424  +4.279 | +1.000 | +0.000  +3.004  -5.749 |  80.250 -92.250 | 100.2\n",
-      "\u001b[30m225  (0-11)-q_100K_Ni2InSbO6-R_Pxz       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +7.874 | +1.000 | -0.004  +3.004  -0.000 |  80.250  -2.250 | 100.2\n",
-      "\u001b[30m226  (0-11)-q_100K_Ni2InSbO6-R_Pyz       scan ds_nut 3.2 -3.2 6.4 |  3600 | -2.949  -3.424  +4.279 | +1.000 | -0.004  +3.004  -0.000 |  80.250  -2.250 | 100.2\n",
-      "\u001b[30m227  (011)_k-scan_100K_Ni2InSbO6-R scan h 0 k 0.94 1.06 0.005 l 1 e 0 preset time 60 |    60 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 100.5\n",
-      "\u001b[30m228  (011)_k-scan_100K_Ni2InSbO6-R            scanrel s1 1 -1 0.1 |     5 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 123.9\n",
-      "\u001b[30m229  (011)_k-scan_100K_Ni2InSbO6-R                 th2th 2 -2 0.2 |     5 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 122.7\n",
-      "\u001b[30m230  (011)_k-scan_100K_Ni2InSbO6-R                 th2th 2 -2 0.2 |     5 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 117.9\n",
-      "\u001b[30m231  (011)_k-scan_100K_Ni2InSbO6-R             th2th 0.5 -0.5 0.1 |    20 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 114.4\n",
-      "\u001b[30m232  (011)_k-scan_100K_Ni2InSbO6-R            scanrel s1 1 -1 0.1 |     5 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 108.5\n",
-      "\u001b[30m233  (011)_k-scan_100K_Ni2InSbO6-R             th2th 0.5 -0.5 0.1 |     5 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 104.7\n",
-      "\u001b[30m234  (011)_k-scan_100K_Ni2InSbO6-R        scanrel s1 0.5 -0.5 0.1 |     5 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 93.9\n",
-      "\u001b[30m235     NiCo2TeO6_1-domain_86K_00L scan h 0 k 0 l 1.1 1.9 0.05 e 0 preset time 10 |    10 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 80.7\n",
-      "\u001b[30m236     NiCo2TeO6_1-domain_74K_003 scan h 0 k 0 l 2.9 3.1 0.01 e 0 preset time 10 |    10 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 | 66.3\n",
-      "\u001b[30m237      NiCo2TeO6_1-domain_4K_00L scan h 0 k 0 l 1.1 1.9 0.05 e 0 preset time 10 |    10 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  2.8\n",
-      "\u001b[30m238    NiCo2TeO6_1-domain_2K_001p2                 th2th 1 -1 0.1 |     5 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m239    NiCo2TeO6_1-domain_2K_001p2             th2th 0.5 -0.5 0.1 |    10 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m240    NiCo2TeO6_1-domain_2K_001p2        scanrel s1 0.5 -0.5 0.1 |    10 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m241    NiCo2TeO6_1-domain_2K_001p2             th2th 0.5 -0.5 0.1 |    10 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m242                         001p75 scan h 0 k 0 l 1.6 1.9 0.01 e 0 preset time 10 |    10 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m243                         001p25 scan h 0 k 0 l 1.1 1.5 0.01 e 0 preset time 10 |     9 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m244                         001p25        scanrel s1 0.5 -0.5 0.1 |    10 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m245                         001p25        scanrel s1 0.3 -0.3 0.1 |    10 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m246                           1p25        scanrel s1 0.5 -0.5 0.1 |    10 | -2.949  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m247     NiCo2TeO6_2K_L-scan_001p25 scan h 0 k 0 l 1.1 1.4 0.01 e 0 preset time 10 |    10 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m248 NiCo2TeO6_2K_L-scan_001p25_with app scan h 0 k 0 l 1.15 1.35 0.01 e 0 preset time 10 |    10 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m249 NiCo2TeO6_2K_L-scan_001p25_with app        scanrel s1 0.5 -0.5 0.1 |    10 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m250 NiCo2TeO6_2K_L-scan_001p25_with app scan h 0 k 0 l 1.15 1.35 0.01 e 0 preset time 10 |     6 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m251 NiCo2TeO6_2K_L-scan_001p25_with app scan h 0 k 0 l 1.15 1.35 0.01 e 0 preset time 10 |    10 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m252 NiCo2TeO6_2K_L-scan_001p25_with app        scanrel s1 0.5 -0.5 0.1 |    10 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m253 NiCo2TeO6_2K_L-scan_001p25_with app        scanrel s1 0.5 -0.5 0.1 |     8 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m254 NiCo2TeO6_2K_L-scan_001p25_with app        scanrel s1 0.5 -0.5 0.1 |    10 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m255 NiCo2TeO6_2K_L-scan_001p25_with app scan h 0 k 0 l 1.15 1.35 0.01 e 0 preset time 30 |    30 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m256 NiCo2TeO6_2K_L-scan_001p75_with app scan h 0 k 0 l 1.65 1.85 0.01 e 0 preset time 30 |    30 | -2.950  -3.424  +0.003 | +1.000 | +3.195  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m257 NiCo2TeO6-1domain-001p76_2K_Pzz       scan ds_nut 3.2 -3.2 6.4 |    30 | -2.949  -3.424  +0.003 | +1.000 | +0.004  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m258 NiCo2TeO6-1domain-001p76_2K_Pzz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +0.003 | +1.000 | +0.000  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m259 NiCo2TeO6-1domain-001p76_2K_Pzx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +0.003 | +1.000 | +0.000  +3.004  -6.035 |  -9.750 -92.250 |  1.9\n",
-      "\u001b[30m260 NiCo2TeO6-1domain-001p76_2K_Pzy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -2.858 |  -9.750 -92.250 |  1.9\n",
-      "\u001b[30m261 NiCo2TeO6-1domain-001p76_2K_Pxx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +7.550 | +1.000 | +0.000  +3.004  -6.035 |  80.250 -92.250 |  1.9\n",
-      "\u001b[30m262 NiCo2TeO6-1domain-001p76_2K_Pxy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +7.550 | +1.000 | +0.000  +3.004  -2.858 |  80.250 -92.250 |  1.9\n",
-      "\u001b[30m263 NiCo2TeO6-1domain-001p76_2K_Pyy       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +3.956 | +1.000 | +0.000  +3.004  -2.858 |  80.250 -92.250 |  1.9\n",
-      "\u001b[30m264 NiCo2TeO6-1domain-001p76_2K_Pyx       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +3.956 | +1.000 | +0.000  +3.004  -6.035 |  80.250 -92.250 |  1.9\n",
-      "\u001b[30m265 NiCo2TeO6-1domain-001p76_2K_Pxz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +7.550 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  1.9\n",
-      "\u001b[30m266 NiCo2TeO6-1domain-001p76_2K_Pyz       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.950  -3.424  +3.956 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  1.9\n",
-      "\u001b[30m267 NiCo2TeO6-1domain-001p76_2K_Pzz       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.949  -3.424  +0.003 | +1.000 | +0.000  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m268 NiCo2TeO6-1domain-001p76_2K_Pzx       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.950  -3.424  +0.003 | +1.000 | +0.000  +3.004  -6.035 |  -9.750 -92.250 |  1.9\n",
-      "\u001b[30m269 NiCo2TeO6-1domain-001p76_2K_Pzy       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.950  -3.424  +0.003 | +1.000 | +0.000  +3.004  -2.858 |  -9.750 -92.250 |  1.9\n",
-      "\u001b[30m270 NiCo2TeO6-1domain-001p76_2K_Pxx       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.950  -3.424  +7.550 | +1.000 | +0.000  +3.004  -6.035 |  80.250 -92.250 |  1.9\n",
-      "\u001b[30m271 NiCo2TeO6-1domain-001p76_2K_Pxy       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.950  -3.424  +7.550 | +1.000 | +0.000  +3.004  -2.858 |  80.250 -92.250 |  1.9\n",
-      "\u001b[30m272 NiCo2TeO6-1domain-001p76_2K_Pyy       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.949  -3.424  +3.956 | +1.000 | +0.000  +3.004  -2.858 |  80.250 -92.250 |  1.9\n",
-      "\u001b[30m273 NiCo2TeO6-1domain-001p76_2K_Pyx       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.949  -3.424  +3.956 | +1.000 | +0.000  +3.004  -6.035 |  80.250 -92.250 |  1.9\n",
-      "\u001b[30m274 NiCo2TeO6-1domain-001p76_2K_Pxz       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.950  -3.424  +7.550 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  1.9\n",
-      "\u001b[30m275 NiCo2TeO6-1domain-001p76_2K_Pyz       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.949  -3.424  +3.956 | +1.000 | +0.003  +3.004  -0.000 |  80.250  -2.250 |  1.9\n",
-      "\u001b[30m276 NiCo2TeO6-1domain-001p26_2K_Pzz       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.949  -3.424  +0.003 | +1.000 | +0.003  +3.004  -0.000 |  -9.750  -2.250 |  1.9\n",
-      "\u001b[30m277 NiCo2TeO6-1domain-001p26_2K_Pzx       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.950  -3.424  +0.003 | +1.000 | +0.000  +3.004  -6.126 |  -9.750 -92.250 |  1.9\n",
-      "\u001b[30m278 NiCo2TeO6-1domain-001p26_2K_Pzy       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.950  -3.424  +0.003 | +1.000 | +0.000  +3.004  -2.949 |  -9.750 -92.250 |  1.9\n",
-      "\u001b[30m279 NiCo2TeO6-1domain-001p26_2K_Pxx       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.950  -3.424  +7.448 | +1.000 | +0.000  +3.004  -6.126 |  80.250 -92.250 |  1.9\n",
-      "\u001b[30m280 NiCo2TeO6-1domain-001p26_2K_Pxy       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.950  -3.424  +7.448 | +1.000 | +0.003  +3.004  -2.949 |  80.250 -92.250 |  1.9\n",
-      "\u001b[30m281 NiCo2TeO6-1domain-001p26_2K_Pyy       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.949  -3.424  +3.854 | +1.000 | +0.000  +3.004  -2.949 |  80.250 -92.250 |  1.9\n",
-      "\u001b[30m282 NiCo2TeO6-1domain-001p26_2K_Pyx       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.949  -3.424  +3.854 | +1.000 | -0.000  +3.004  -6.126 |  80.250 -92.250 |  1.9\n",
-      "\u001b[30m283 NiCo2TeO6-1domain-001p26_2K_Pxz       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.949  -3.424  +7.448 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  1.9\n",
-      "\u001b[30m284 NiCo2TeO6-1domain-001p26_2K_Pyz       scan ds_nut 3.2 -3.2 6.4 |  1800 | -2.949  -3.424  +3.854 | +1.000 | +0.000  +3.004  -0.000 |  80.250  -2.250 |  1.9\n",
-      "\u001b[30m285 NiCo2TeO6-1domain-001p26_2K_Pxx_5min       scan ds_nut 3.2 -3.2 6.4 |   300 | -2.949  -3.424  +7.448 | +1.000 | +0.000  +3.004  -6.126 |  80.250 -92.250 |  1.9\n",
-      "\u001b[30m286 NiCo2TeO6-1domain-001p76_2K_Pxx_5min       scan ds_nut 3.2 -3.2 6.4 |   300 | -2.949  -3.424  +7.550 | +1.000 | +0.000  +3.004  -6.035 |  80.250 -92.250 |  1.8\n",
-      "\u001b[30m287 NiCo2TeO6-1domain-001p76_2K_P-xx_5min_2       scan ds_nut 3.2 -3.2 6.4 |    60 | -2.949  -3.424  +0.403 | +1.000 | +0.000  +3.004  -6.035 |  80.250 -92.250 |  1.8\n",
-      "\u001b[30m288 NiCo2TeO6-1domain-001p76_2K_P-xx_5min_3       scan ds_nut 3.2 -3.2 6.4 |   300 | -2.949  -3.424  +0.403 | +1.000 | +0.004  +3.004  -6.035 |  80.250 -92.250 |  1.8\n",
-      "\u001b[30m289 NiCo2TeO6-1domain-001p76_100K_P-xx_10min       scan ds_nut 3.2 -3.2 6.4 |   600 | -2.949  -3.424  +0.403 | +1.000 | +0.000  +3.004  -6.035 |  80.250 -92.250 | 100.5\n"
-     ]
-    }
-   ],
-   "source": [
-    "from IPython.display import display, HTML\n",
-    "display(HTML(\"<style>.container { width:150% !important; }</style>\"))\n",
-    "\n",
-    "import ufit\n",
-    "import numpy as np\n",
-    "from ufit.lab import *\n",
-    "exp_no=943\n",
-    "\n",
-    "DIR='C:/Users/num/Documents/cycle506/exp'+'%3s'%(exp_no)+'/Datafiles/'\n",
-    "FIL_TEMPLATE='HB1_exp0'+'%3s'%(exp_no)+'_scan%04d.dat'\n",
-    "DATA_TEMPLATE=DIR+FIL_TEMPLATE\n",
-    "f=None\n",
-    "# UFIT PACKAGE\n",
-    "\n",
-    "set_datatemplate('C:/Users/num/Documents/cycle506/exp'+'%3s'%(exp_no)+'/Datafiles/HB1_exp0'+'%3s'%(exp_no)+'_scan%04d.dat')\n",
-    "ufit.set_dataformat('taipan')\n",
-    "\n",
-    "for n in range(45,1000):\n",
-    "    try:\n",
-    "        d=read_data(n)\n",
-    "        title=d['subtitle']\n",
-    "        if 'Upstream nutator 360 degree scan' in d['subtitle']:\n",
-    "            title=d['subtitle'][:].replace('Upstream nutator 360 degree scan','USN 360 SCN')\n",
-    "        if 'with mu metal cover on' in d['subtitle']:    \n",
-    "            title=title.replace('with mu metal cover on','mu on')\n",
-    "        if 'quick upstream precession scan' in d['subtitle']:    \n",
-    "            title=title.replace('quick upstream precession scan','qck USP scan')\n",
-    "        if 'finer upstream precession scan' in d['subtitle']:    \n",
-    "            title=title.replace('finer upstream precession scan','fin USP scan')           \n",
-    "        if 'finer downstream precession scan' in d['subtitle']:    \n",
-    "            title=title.replace('finer downstream precession scan','fin DSP scan') \n",
-    "        if 'Si (111) upstream nutator 360 degree overnight scan' in d['subtitle']:    \n",
-    "            title=title.replace('Si (111) upstream nutator 360 degree overnight scan','Si (111) USN 360 SCN') \n",
-    "\n",
-    "        \n",
-    "        print(TBLACK + '%3d'%n , '%30s'%title, '%30s'%d['command'], '|','%5d'%d['time'],\\\n",
-    "             '|','%+5.3f'%d['us_nut_amps'], '%+7.3f'%d['us_guide_amps'], '%+7.3f'%d['up_prec_amps'],\\\n",
-    "             '|','%+5.3f'%d['comp'],\\\n",
-    "             '|','%+5.3f'%d['ds_nut_amps'], '%+7.3f'%d['ds_guide_amps'], '%+7.3f'%d['ds_prec_amps'],\\\n",
-    "             '|','%7.3f'%d['theta_1'],'%7.3f'%d['theta_2'],'|','%4.1f'%d['sample']) \n",
-    "    except:\n",
-    "        pass\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 585,
-   "id": "a5ece33e-8786-4efa-8938-412234ad9a7a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filter_string=\"'%3d'%n , '%50s'%d['subtitle'], '%30s'%d['command'], '|','%5d'%d['time']\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b9e54804-13a3-42c5-a75a-da071330cfe7",
-   "metadata": {},
-   "source": [
-    "# '(0-12)_Ni2InSbO6-R [scan 128:136] T=2.1 K'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 615,
-   "id": "d9784d9a-cc22-4306-a673-5e99ab710e2a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "#plt.title('(0-12)_Ni2InSbO6-R [scan 128:136] T=2.1 K')\n",
-    "plabel=['Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz']\n",
-    "\n",
-    "u=[]\n",
-    "d=[]\n",
-    "u1=[]#def polamat([scn_Pzz,scn_Pzx,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy])\n",
-    "\n",
-    "plt.figure()\n",
-    "#plt.title('(0-12)_Ni2InSbO6-R [scan 128:136] T=2.1 K')\n",
-    "plabel=['Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz']\n",
-    "P = np.full((3,3), np.nan)\n",
-    "u=[]\n",
-    "d=[]\n",
-    "u1=[]\n",
-    "d1=[]\n",
-    "i=0\n",
-    "for n in range(114,123):    \n",
-    "    try:\n",
-    "        data=read_data(n)\n",
-    "        plt.plot(data['col_ds_nut'],55555*data['col_detector']/data['col_monitor'],'o-', label='scan'+str(n)+':'+plabel[i])\n",
-    "        \n",
-    "        u.append(data['col_detector'][0]/data['col_monitor'][0])\n",
-    "        d.append(data['col_detector'][1]/data['col_monitor'][1])\n",
-    "\n",
-    "        u1.append(data['col_detector'][0])\n",
-    "        d1.append(data['col_detector'][1])\n",
-    "        i=i+1\n",
-    "        #print(n)\n",
-    "    except:\n",
-    "        pass\n",
-    "        print(n)\n",
-    "plt.xlabel('SF                                                  ds_nut_amps (A)                                              NSF')\n",
-    "plt.xticks([-3.2,3.2])\n",
-    "plt.ylabel('counts / 55555 mon [~ 1 min]')\n",
-    "plt.legend(fontsize=9, ncol=3)\n",
-    "plt.title(data['subtitle'][:-4]+', T ='+str('%6.2f'%data['sample'])+' K'+', time ='+str('%6.2f'%data['time'])+' sec')\n",
-    "up=np.zeros([3,3])\n",
-    "dn=np.zeros([3,3])\n",
-    "  \n",
-    "up[2,2],up[2,0],up[2,1],up[0,0],up[0,1],up[1,1],up[1,0],up[0,2],up[1,2]=u[0],u[1],u[2],u[3],u[4],u[5],u[6],u[7],u[8]\n",
-    "dn[2,2],dn[2,0],dn[2,1],dn[0,0],dn[0,1],dn[1,1],dn[1,0],dn[0,2],dn[1,2]=d[0],d[1],d[2],d[3],d[4],d[5],d[6],d[7],d[8]\n",
-    "\n",
-    "up1=np.zeros([3,3])\n",
-    "dn1=np.zeros([3,3])\n",
-    "   \n",
-    "up1[2,2],up1[2,0],up1[2,1],up1[0,0],up1[0,1],up1[1,1],up1[1,0],up1[0,2],up1[1,2]=u1[0],u1[1],u1[2],u1[3],u1[4],u1[5],u1[6],u1[7],u1[8]\n",
-    "dn1[2,2],dn1[2,0],dn1[2,1],dn1[0,0],dn1[0,1],dn1[1,1],dn1[1,0],dn1[0,2],dn1[1,2]=d1[0],d1[1],d1[2],d1[3],d1[4],d1[5],d1[6],d1[7],d1[8]\n",
-    "\n",
-    "P=(up1-dn1)/(up1+dn1)\n",
-    "\n",
-    "txt_up='NSF=\\n'\\\n",
-    " '['+str('%5d'%up1[0,0])+', '+str('%5d'%up1[0,1])+', '+str('%5d'%up1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[1,0])+', '+str('%5d'%up1[1,1])+', '+str('%5d'%up1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[2,0])+', '+str('%5d'%up1[2,1])+', '+str('%5d'%up1[2,2])+']'\n",
-    "\n",
-    "txt_dn='SF=\\n'\\\n",
-    " '['+str('%5d'%dn1[0,0])+', '+str('%5d'%dn1[0,1])+', '+str('%5d'%dn1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[1,0])+', '+str('%5d'%dn1[1,1])+', '+str('%5d'%dn1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[2,0])+', '+str('%5d'%dn1[2,1])+', '+str('%5d'%dn1[2,2])+']'\n",
-    "\n",
-    "txt_P='P=\\n'\\\n",
-    " '['+str('%+5.4f'%P[0,0])+', '+str('%+5.4f'%P[0,1])+', '+str('%+5.4f'%P[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[1,0])+', '+str('%+5.4f'%P[1,1])+', '+str('%+5.4f'%P[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[2,0])+', '+str('%+5.4f'%P[2,1])+', '+str('%+5.4f'%P[2,2])+']'\n",
-    "\n",
-    "\n",
-    "plt.text(-3.2, 0.75*55555*np.max(u), txt_up, fontsize=9, color='blue')\n",
-    "plt.text(-1.2, 0.75*55555*np.max(u), txt_dn, fontsize=9, color='red')\n",
-    "plt.text(0, 20, txt_P, fontsize=9, color='green')\n",
-    "\n",
-    "plt.savefig('(0-12)_Ni2InSbO6-R_T2K_count300sec.jpg', dpi=600)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f291d57-15dc-42c8-8b1b-ec49107d452a",
-   "metadata": {},
-   "source": [
-    "# (0-11)_-q_Ni2InSbO6-R [scan 137-145]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 587,
-   "id": "c17ee17b-8a7a-4c10-a8e4-aab11a586729",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\num\\AppData\\Local\\Temp\\ipykernel_14356\\3606654044.py:12: RuntimeWarning: divide by zero encountered in divide\n",
-      "  plt.plot(data['col_ds_nut'],55555*data['col_detector']/data['col_monitor'],'o-', label='scan'+str(n)+':'+plabel[i])\n",
-      "C:\\Users\\num\\AppData\\Local\\Temp\\ipykernel_14356\\3606654044.py:14: RuntimeWarning: divide by zero encountered in scalar divide\n",
-      "  u.append(data['col_detector'][0]/data['col_monitor'][0])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "plt.figure()\n",
-    "plabel=['Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz']\n",
-    "P = np.full((3,3), np.nan)\n",
-    "u=[]\n",
-    "d=[]\n",
-    "u1=[]\n",
-    "d1=[]\n",
-    "i=0\n",
-    "for n in [137,138,139,140,141,142,143,144,145]:    \n",
-    "    try:\n",
-    "        data=read_data(n)\n",
-    "        plt.plot(data['col_ds_nut'],55555*data['col_detector']/data['col_monitor'],'o-', label='scan'+str(n)+':'+plabel[i])\n",
-    "        \n",
-    "        u.append(data['col_detector'][0]/data['col_monitor'][0])\n",
-    "        d.append(data['col_detector'][1]/data['col_monitor'][1])\n",
-    "\n",
-    "        u1.append(data['col_detector'][0])\n",
-    "        d1.append(data['col_detector'][1])\n",
-    "        i=i+1\n",
-    "        #print(n)\n",
-    "    except:\n",
-    "        pass\n",
-    "        print(n)\n",
-    "plt.xlabel('SF                                                  ds_nut_amps (A)                                              NSF')\n",
-    "plt.xticks([-3.2,3.2])\n",
-    "plt.ylabel('counts / 55555 mon [~ 1 min]')\n",
-    "plt.legend(fontsize=9, ncol=3)\n",
-    "plt.title(data['subtitle'][:-4]+', T ='+str('%6.2f'%data['sample'])+' K'+'scan 137 problematic')\n",
-    "up=np.zeros([3,3])\n",
-    "dn=np.zeros([3,3])\n",
-    "  \n",
-    "up[2,2],up[2,0],up[2,1],up[0,0],up[0,1],up[1,1],up[1,0],up[0,2],up[1,2]=u[0],u[1],u[2],u[3],u[4],u[5],u[6],u[7],u[8]\n",
-    "dn[2,2],dn[2,0],dn[2,1],dn[0,0],dn[0,1],dn[1,1],dn[1,0],dn[0,2],dn[1,2]=d[0],d[1],d[2],d[3],d[4],d[5],d[6],d[7],d[8]\n",
-    "\n",
-    "up1=np.zeros([3,3])\n",
-    "dn1=np.zeros([3,3])\n",
-    "   \n",
-    "up1[2,2],up1[2,0],up1[2,1],up1[0,0],up1[0,1],up1[1,1],up1[1,0],up1[0,2],up1[1,2]=u1[0],u1[1],u1[2],u1[3],u1[4],u1[5],u1[6],u1[7],u1[8]\n",
-    "dn1[2,2],dn1[2,0],dn1[2,1],dn1[0,0],dn1[0,1],dn1[1,1],dn1[1,0],dn1[0,2],dn1[1,2]=d1[0],d1[1],d1[2],d1[3],d1[4],d1[5],d1[6],d1[7],d1[8]\n",
-    "\n",
-    "P=(up1-dn1)/(up1+dn1)\n",
-    "\n",
-    "txt_up='NSF=\\n'\\\n",
-    " '['+str('%5d'%up1[0,0])+', '+str('%5d'%up1[0,1])+', '+str('%5d'%up1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[1,0])+', '+str('%5d'%up1[1,1])+', '+str('%5d'%up1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[2,0])+', '+str('%5d'%up1[2,1])+', '+str('%5d'%up1[2,2])+']'\n",
-    "\n",
-    "txt_dn='SF=\\n'\\\n",
-    " '['+str('%5d'%dn1[0,0])+', '+str('%5d'%dn1[0,1])+', '+str('%5d'%dn1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[1,0])+', '+str('%5d'%dn1[1,1])+', '+str('%5d'%dn1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[2,0])+', '+str('%5d'%dn1[2,1])+', '+str('%5d'%dn1[2,2])+']'\n",
-    "\n",
-    "txt_P='P=\\n'\\\n",
-    " '['+str('%+5.4f'%P[0,0])+', '+str('%+5.4f'%P[0,1])+', '+str('%+5.4f'%P[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[1,0])+', '+str('%+5.4f'%P[1,1])+', '+str('%+5.4f'%P[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[2,0])+', '+str('%+5.4f'%P[2,1])+', '+str('%+5.4f'%P[2,2])+']'\n",
-    "\n",
-    "\n",
-    "plt.text(-3.2, 40, txt_up, fontsize=9, color='blue')\n",
-    "plt.text(-1.2, 40, txt_dn, fontsize=9, color='red')\n",
-    "plt.text(0, 20, txt_P, fontsize=9, color='green')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f8892236-7bd5-4d63-9d86-b04b26bfd6bf",
-   "metadata": {},
-   "source": [
-    "# (0-11)_-q_Ni2InSbO6-R [scan 146-154]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 588,
-   "id": "00551244-fda2-449e-b8f5-fb885e7f5aff",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "52.94494949209521\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#def polamat([scn_Pzz,scn_Pzx,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy])\n",
-    "\n",
-    "plt.figure()\n",
-    "#plt.title('(0-12)_Ni2InSbO6-R [scan 128:136] T=2.1 K')\n",
-    "plabel=['Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz']\n",
-    "P = np.full((3,3), np.nan)\n",
-    "u=[]\n",
-    "d=[]\n",
-    "u1=[]\n",
-    "d1=[]\n",
-    "i=0\n",
-    "for n in [146,147,148,149,150,151,152,153,154]:    \n",
-    "    try:\n",
-    "        data=read_data(n)\n",
-    "        plt.plot(data['col_ds_nut'],55555*data['col_detector']/data['col_monitor'],'o-', label='scan'+str(n)+':'+plabel[i])\n",
-    "        \n",
-    "        u.append(data['col_detector'][0]/data['col_monitor'][0])\n",
-    "        d.append(data['col_detector'][1]/data['col_monitor'][1])\n",
-    "\n",
-    "        u1.append(data['col_detector'][0])\n",
-    "        d1.append(data['col_detector'][1])\n",
-    "        i=i+1\n",
-    "        #print(n)\n",
-    "    except:\n",
-    "        pass\n",
-    "        print(n)\n",
-    "plt.xlabel('SF                                                  ds_nut_amps (A)                                              NSF')\n",
-    "plt.xticks([-3.2,3.2])\n",
-    "plt.ylabel('counts / 55555 mon [~ 1 min]')\n",
-    "plt.legend(fontsize=9, ncol=3)\n",
-    "plt.title(data['subtitle'][:-4]+', T ='+str('%6.2f'%data['sample'])+' K'+', time ='+str('%6.2f'%data['time'])+' sec')\n",
-    "up=np.zeros([3,3])\n",
-    "dn=np.zeros([3,3])\n",
-    "  \n",
-    "up[2,2],up[2,0],up[2,1],up[0,0],up[0,1],up[1,1],up[1,0],up[0,2],up[1,2]=u[0],u[1],u[2],u[3],u[4],u[5],u[6],u[7],u[8]\n",
-    "dn[2,2],dn[2,0],dn[2,1],dn[0,0],dn[0,1],dn[1,1],dn[1,0],dn[0,2],dn[1,2]=d[0],d[1],d[2],d[3],d[4],d[5],d[6],d[7],d[8]\n",
-    "\n",
-    "up1=np.zeros([3,3])\n",
-    "dn1=np.zeros([3,3])\n",
-    "   \n",
-    "up1[2,2],up1[2,0],up1[2,1],up1[0,0],up1[0,1],up1[1,1],up1[1,0],up1[0,2],up1[1,2]=u1[0],u1[1],u1[2],u1[3],u1[4],u1[5],u1[6],u1[7],u1[8]\n",
-    "dn1[2,2],dn1[2,0],dn1[2,1],dn1[0,0],dn1[0,1],dn1[1,1],dn1[1,0],dn1[0,2],dn1[1,2]=d1[0],d1[1],d1[2],d1[3],d1[4],d1[5],d1[6],d1[7],d1[8]\n",
-    "\n",
-    "P=(up1-dn1)/(up1+dn1)\n",
-    "\n",
-    "txt_up='NSF=\\n'\\\n",
-    " '['+str('%5d'%up1[0,0])+', '+str('%5d'%up1[0,1])+', '+str('%5d'%up1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[1,0])+', '+str('%5d'%up1[1,1])+', '+str('%5d'%up1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[2,0])+', '+str('%5d'%up1[2,1])+', '+str('%5d'%up1[2,2])+']'\n",
-    "\n",
-    "txt_dn='SF=\\n'\\\n",
-    " '['+str('%5d'%dn1[0,0])+', '+str('%5d'%dn1[0,1])+', '+str('%5d'%dn1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[1,0])+', '+str('%5d'%dn1[1,1])+', '+str('%5d'%dn1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[2,0])+', '+str('%5d'%dn1[2,1])+', '+str('%5d'%dn1[2,2])+']'\n",
-    "\n",
-    "txt_P='P=\\n'\\\n",
-    " '['+str('%+5.4f'%P[0,0])+', '+str('%+5.4f'%P[0,1])+', '+str('%+5.4f'%P[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[1,0])+', '+str('%+5.4f'%P[1,1])+', '+str('%+5.4f'%P[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[2,0])+', '+str('%+5.4f'%P[2,1])+', '+str('%+5.4f'%P[2,2])+']'\n",
-    "\n",
-    "print(55555*np.max(u))\n",
-    "plt.text(-3.2, 0.75*55555*np.max(u), txt_up, fontsize=9, color='blue')\n",
-    "plt.text(-1.2, 0.75*55555*np.max(u), txt_dn, fontsize=9, color='red')\n",
-    "plt.text(0, 20, txt_P, fontsize=9, color='green')\n",
-    "\n",
-    "#plt.savefig('(0-11)_-q_Ni2InSbO6-R_T2K_count3600sec.jpg', dpi=600)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 647,
-   "id": "eee5e450-3a1c-4953-a7ed-6e2cce6d2190",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "saved as: _T100Kcount60sec.jpg\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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lUklRUVHS888/3+e4v/76a2nJkiWSt7e35O7uLi1durRbFuIPPvhAmjdvnqRWq6WAgADpkksukdLT053a7N27V0pJSZG0Wq0UHBwsrV+/XqqoqOjz/AJBT8gkSRReEEwcli5dyocffuiI6BCMLjExMRw7dmy0hyEQCMYZwudEMGH46quvaGtrE8JkjPD6668THx8/2sMQCATjELFyIpgwFBUV4eXlhb+//2gPRYA9vX1ERMRpQ34FAoGgJ4Q4EQgEAoFAMKYQZh2BQCAQCARjCiFOBAKBQCAQjCmEOBEIBAKBQDCmGHdJ2Gw2G6WlpY5aFgKBQCAQCMY+kiTR1NREWFhYn8VLx504KS0tJSIiYrSHIRAIBAKBYAAUFxej0+lO22bciRMvLy/AfnE9VSoVCAQCgUAw9mhsbCQiIsLxHD8doyZOcnJyuPPOO9m/fz/e3t7ccsstPPjgg30u9XSYcry9vYU4EQgEAoFgnOGKS8aoOMQ2NzdzwQUXMHXqVEpKSti1axcffPABTz755GgMRyAQCAQCwRhiVMTJ999/T2VlJa+88goeHh5MmzaNhx9+mM2bNyNywgkEAoFAMLkZFbOO1WpFpVKhVCod2+RyORUVFdTX1+Pn5+fYbjQaMRqNjveNjY0jOlbB0GK1WjGbzaM9DIFAIBAME0qlEoVCMag+RkWcLFmyBK1Wy4MPPshjjz1GVVUVzz33HABtbW1O4mTjxo088cQTozFMwRDT3NyMXq8Xq2MCgUAwgZHJZOh0Ojw9PQfex2jV1jl8+DD33HMP6enpREdHs27dOn7zm9/Q0tLiVCisp5WTiIgIGhoahEPsOMJqtXLs2DHc3d0JCgoSOWoEAoFgAiJJElVVVbS2thITE+O0gtLY2IiPj49Lz+9RWTkxmUxYLBa+++47x0Nq8+bNzJ49u1sFU7VajVqtHo1hCoYQs9mMJEkEBQWh1WpHezgCgUAgGCaCgoIoLCzEbDYP2LwzKg6xkiSxYsUK/va3vyFJEocOHeLpp5/mrrvuGo3hCEYQsWIiEAgEE5uhuM+PijhRq9V8/vnn/PWvf8Xb25vLL7+c+++/n5tuumk0hiMQCAQCgWAMMWpJ2JYuXcqhQ4dG6/QCgUAgEAjGKKIqsWDc8P2xas5/cQffH6se7aEMioaGBtatW0dQUBAhISHccccdmEwmpza1tbVERkZSWFjo2GY2m7n55psJCgoiODiYe+65B5vN1uM5ZDIZHh4eeHp64uXlhb+/P2vXrqW+vn4Yr2xkadmzh4KfrqJlz57RHsqgGOh8cOW4DibDfDAcq6P8xYMYjtWN9lAGxUDnQ1NTEwqFAk9PT8frxRdf7PEc42E+CHHSheLsWt7dsI/i7NrRHorgFCRJ4o9f55Bf2cwfv84Z1+HI9913HwaDgRMnTpCRkUFaWhrPP/+8Y39aWhrLli2jqKjI6bhXXnmFoqIiCgsLyczM5Ouvv+btt9/u9TyZmZk0NzfT1NREYWEhZWVl/OpXvxq26xpJJEmi8sVNmAoKqHxx06ScD30ddyoTfT40fF2IpbKNhq8LJ+V8SE9PJyEhgebmZsfrnnvu6fU8Y30+CHHSjiRJ7PusgLryVvZ9VjCuJ/d4QJIkWk0Wl1/fZFWQrm8AIF3fwDdZFf063pXv02q1cvPNNxMYGIhOp2P9+vWOm8SKFSvw8vIiOjqaTz/9FIDKykrWrFnD1KlT0Wq1pKSkUFJSAsD69eu55557mDdvHt7e3qxatYra2lrHtT/yyCN4enoSFBTE1Vdfzd69ewHIyspi1apV3Hvvvd3Gl5+fj9VqxWq1AvbEhR2RT//4xz+Ij4/v9dq8vb259NJLycjI4OTJk06/rrRaLTKZjMzMTOLj4532yWQyXnnlFVe/1gEhSRK21tZ+vZq3bcNw9CgAhqNHad62zeVjXf3bHuvz4XTH9Wc+lJWV4efn57iOr776isDAQEpLS1m8eDF33HEHYK8I7+/vzzfffOPS5zdQJEnCZrL269WWVYNZ3wyAWd9MW1aNy8dOlPlw5MgR5syZ0+PY+zMfUlNTmTJliuM+A7B8+XI++OCDEZ0P464q8XBRnFVLZVETAJVFTRRn1TI1PmCURzVxaTNbmf3Y1wM+/uZ3+uevlPX7C3BXnX66f/rpp2RlZXHy5EnMZjPnnnsuH330EX/6058499xz+eKLL0hLS+OCCy5gyZIl3H///QQEBFBQUEBbWxs/+9nPePnll9m4cSMA7777Lrt27SIwMJDzzjuPzZs38/DDD/P66687nffLL7/kjDPOACAiIoKCggI8PT1Zv369U7sbb7yRFStW4Ofnh81mY82aNVxxxRUArF27lrVr1/Z6bUVFRfzjH/8gJSWFqVOn0tzc7Ni3Zs0a3N3diY+PJzMz07H9pZde4tVXX+Xaa6/t+wMeBFJbG7lnzhtUH/rbf+Ny29gfDiE7JWVBT4z1+XC64/ozH0JDQ3nhhRe48847WbhwITfddBOvvPIKYWFhvPnmm8yfP5/169fz+OOPc8UVV/CTn/ykz89uMEhmG6WPDc5UV/tOtsttw36/GJmq73DXsT4fjhw5Ql5eHrGxsTQ3N3PllVeyceNGVCpVv+bDsmXLUCgU7Nq1i5SUFCoqKjh06BCrVq0iKSlpxOaDECfYleruT/Kdtn37VhaX/m4ePkF938QEEwMfHx9yc3PZunUrq1atIi0tjRMnTnDDDTewZ88eVCoVS5YsYceOHXh5efHss8/i6emJ1WqluLgYf39/ysvLHf1deumlxMTEAHDRRRdRUFDQ7Zz3338/2dnZbN26FeC0pcSNRiNr167lscceo6GhgVWrVvHKK6/w61//usf2SUlJyOVyJEnC29ubCy64wHFj7OCZZ54hPz+fPaf4bezcuZMnnniC3bt3DyrL43hmrM+H0x3XE6ebD9dffz3vv/8+ycnJLFmyxCF6Z82axUMPPcTFF1+MRqPhvffec+3Dm4CM9fng6elJSkoKDz74IA0NDVx66aU888wzbNiwocf2vc0HuVzOmjVr+OSTT0hJSeGTTz7hoosuwsPDY0TngxAn2FdNaktbnLa1NZnZ+ug+IpMCSUrRoZvlJ3J0DCFapYKs31/QZztJkrjitX1klTVi67L6KpfB7FBvPrhloUvfi1bZ9y+jFStWsHHjRl577TVuu+02Fi9ezMaNGwkICHBKBDhvnv1XfmZmJrfffjsnTpwgMTERg8HgdPMICgpy/N/Nzc3JedVisXDrrbeSmprKtm3bCAjoe5XuuuuuY8uWLfj5+eHn58fDDz/Mc88916s4SU9PJzIystf+vvzySzZt2sSBAwecEuMVFxdz+eWX88YbbxAbG9vnuAaLTKsl9gfXVsIkSaJo3S8x5uRAV2dguRx1XBzT3nm7z/kgczEJ4FifD/09rq/5cN1113H11Vdz9dVXO21fv349jz32GL/5zW9GRKjKlHLCfr/YpbaSJFH9Wjrmshboap2RgTLUg8BbkvqeD0rXvBvG+nx44YUXHP/38fHhgQce4Omnn+5VnJxuPlxxxRWsWbOGv/zlL3z44Yfceeedjn0jNR8mvc+JJEns/9dxepu/henV/OsvP/LeE/vJ2K7HZLCM7AAnKDKZDHeVW5+vg0X1HC11FiYANgmOljZysKjepX5cETDHjx9nyZIlpKWlUVJSQlhYGI888gg1NTVOJRRefPFFcnJyWLt2LTfeeCOVlZVs27aN5ORkl67daDRyySWXkJGRwd69e5kxY4ZLx+n1eievfaVS6VQ8sz8cO3aMdevW8c477xAVFeXYbjAY+PnPf84NN9zA6tWrB9R3f5HJZMjd3V16tf1wGGNWlrMwAbDZMGZl0fbD4T77cPVHxlifDwM9ridaW1t5+OGHWbt2Lb/97W9pbW117Lv77rtZvXo1b731FhkZGQM+h6vIZDLkKoVLL3NRE+bSU4QJgATm0hbMRU199jFR5sPjjz/O8ePHnfrRaDQuHXsqixYtws3Nja+//pojR46wcuVKx76Rmg+TXpx0+Jr05hMVmRSIUq2grryVne/nseWB3ez6II/6itaeDxAMGZIk8cL/cnsVjjIZvPC/3CFzXt62bRtXXXUVlZWV+Pv7o9FoiI6OZv78+WzYsAGz2czu3bt56qmn8PX1pb6+Hg8PDwD27NnD1q1bXaq4/Jvf/Ia6ujpSU1MJDg52eXwXXnghjz76KA0NDZSXl/OHP/yByy67rN/X2dTUxOrVq7n33nu58MILnfZ1hCo/+eST/e53uJEkiao//5nTTYiqP/950syHgR7XEw899BAzZ85k69atzJgxg0ceeQSAzz//nJ07d/Lmm29y3333ccMNNzg5So4mkiTR8L9C6E1byKDhf0MXuTPW58Phw4d56KGHaG1tpaioiGeffZZ169YN6FplMhmXX345v/vd7xwmHBjZ+TCpxUnHqsnpJndrg5FrNy7mnCti8J3ijslgJT1Vzz8e38e/XzpC0dEapFN/1guGBJPVRml9W6/CUZKgrN6Aydpzro/+cv3113POOecQHx9PQEAAtbW1PPPMM7z//vscOXKE4OBgbrjhBt59911CQkLYvHkzDz74ID4+Ptx5551cf/31ZGef3hGvoaGBN998k8OHDxMcHOyIiun6y6Q3Xn31VcLCwoiOjubMM8/k/PPP5+677wb69sbvyj//+U+ysrLYtGkTXl5ejjFs2bKFd955h/379+Pn5+fYfuutt7rU73Ajmc2Yy8o43YQwl5cjufAAcIWxPB/6Oq4/82H//v288cYbjqisV155hddee40dO3Zw22238eKLL+Lr68t9991HU1MTmzZtcqnfYccqYa03dl816UDCvt86NPfnsTwfwO4gbTab0el0JCcns3r1asffbn/mQwdXXnklGRkZXHnllY6xjeR8GLWqxAOlP1UN+8JqtrHlod20NfV+M3P3VvHLpxejUMqRbBIns2vJSNVTlFnj+KPwCdaSuExH3OJQ1FrhxtMTHSF3UVFR/VpqLK1vo7al58RSAAGeKkJ9RCHByYK5rAxLbe95iNwCAlCGhIzgiASjiaXeiK2l9/u33FOJm48oHDsQKioqiI+Pp6ysrN/m497u9/15fk9qcQLQVGvA0Nz75NZ6KfH06/4wra9s5ej2ErL3lGIy2Je1lGoFsQtDSEzR4R/qMeixTSQGKk4EAoFAMLJkZWXxyiuvIJfLeemll/p9/FCIk0n/M9/LX4OXf/8flr7B7px9eQwLLokib3856dtLqCtr4eiOEo7uKEEX50fSch3TEgORy0WUj0AgEAjGB5dddhkKhYJvv/121MYw6cXJYFFp3EhYpiN+aTj63DoyUvUUplejz6lDn1OHd6CGhKU6Zi0JReMxsMgKgUAgEAhGiqysrNEeghAnQ4VMJiMizp+IOH8aq9s4uqOErN2lNFYb2PNpPgf+fZyZC0NIStERED45k1oJBAKBQOAKQpwMA96BWhZfGk3yxXaTT8Z2PTUlLWTtKiVrVynhM31JXK4jKikQuWJSB0wJBAKBQNANIU6GEaVKQfw54cw+O4zSY/VkpOo5fqSakrx6SvLq8fRTk7DMvl/rqRrt4QoEAoFAMCYQ4mQEkMlkhM/0I3ymH021Bo7uLCHr+1Ka64zs++w4aV8UErNgCkkpOoKmulZLQyAQCASCiYoQJyOMl7+GRatnkPzTSI6lVZKxXU/VySZy9pSRs6eM0Bk+JC7XMf2MIBTC5CMQCASCSYh4+o0SbkoFsxaHsubB+fzivnnEzA9GLpdRVtDA/97I5J2H9pD2nxO0NvaegGzSUZAKLy+w/zuOaWhoYN26dQQFBRESEsIdd9zhVDMHoLa2lsjISAoLCx3bmpqauO666wgICGDatGls3ry513PIZDI8PDzw9PTEy8sLf39/1q5dS319/TBd1chTnF3Luxv2UZzde1K28cBA50NDQwNXXnkl/v7+BAUFcffdd2Ox9Fz7azLMh4KCAl5++eUeq/uOJwY6HwCef/55wsLC8PPzY+3atbS1tfV4jvEwH4Q4GWVkMhmhM3xYcWMCv3xmMfN/GonWW0VLg4kD/z7Blod28+3fs6gobBztoY4ukgTbnoDqXPu/4yt3oBP33XefI0lRRkYGaWlpPP/88479aWlpLFu2jKKiIqfjfvvb31JYWMixY8fYvn07zz//PJ999lmv58nMzKS5uZmmpiYKCwspKyvjV7/61XBd1ogiSRL7PiugrryVfZ8VDFn9lNFgMPNBoVBQUlJCbm4uqampbNmypdfzTPT5sG3bNqqrq9m2bduknA8ffvghmzdvZvfu3RQXF1NVVcUf/vCHXs8z1ueDECdjCA9fNWddPJ1rn17M+dfNZkqUNzaLRO7+cj5+9iAf/+EgufvLsVqGppbMqCJJYGpx/ZX7JZQeth9betj+vj/Hu3Czslqt3HzzzQQGBqLT6Vi/fr3jJrFixQq8vLyIjo7m008/BaCyspI1a9YwdepUtFotKSkplJSUAPay4vfccw/z5s3D29ubVatWUduedl2SJB555BE8PT0JCgri6quvZu/evYA9v8CqVau49957u43vs88+46mnnsLf35+oqChuvfVWx8Oor9oZ3t7eXHrppWRkZHDy5ElHzQ5PT0+0Wi0ymYzMzEwWL17MHXfcYf+YS0vx9/fnm2++cfFLHRiSJGE2Wvv1OnGkmsqiJgAqi5o4caTa5WNdfXCN9fmwefNm/va3v6FWqykvL8dgMODv7w/0bz6UlZXh5+fnuI6vvvqKwMBASkpK8PDwcKo8+8QTTwz7A0ySJEwmU79eubm5lJaWAvZ5m5ub6/KxE2U+vP766zz++ONERUXh6enJO++8w/r164GhmQ/l5eXEx8c73TtkMpmjJtNQI3xOxiAKpZzYs0KIPSuEihONpG8vJv9gJRUnGqk4kcXuT/KJPyeMhHPC8fAdp3UjzK3wTNjAj3//6v61f6gUVKcvKfDpp5+SlZXFyZMnMZvNnHvuuXz00Uf86U9/4txzz+WLL74gLS2NCy64gCVLlnD//fcTEBBAQUEBbW1t/OxnP+Pll19m48aNALz77rvs2rWLwMBAzjvvPDZv3szDDz/M66+/7nTeL7/8kjPOOAOAiIgICgoK8PT0dNxYOrBarbi7uzveKxQKxxL22rVrWbt2ba/XVlRUxD/+8Q9SUlKYOnUqzc3Njn1r1qzB3d2d+Ph43nzzTebPn8/69et5/PHHueKKK/jJT37S9+c7CCwmG/93545B9fHVq66Xbr/5z8tQqhV9thvr86Gj3slll13GJ598wooVK7j44ouB/s2H0NBQXnjhBe68804WLlzITTfdxCuvvEJ4eDirVq3ik08+ITExEbD/Oh+uh1EHZrOZZ555ZlB9vP/++y63feihh1Cp+o6WHOvz4ccff2T16tXMnTuX8vJyLrvsMl544QVgaOZDSEgImZmZjmNeeuklXn31Va699lqXPuf+IsTJGGdKlDc/iYpnyaUxZO4qIXNnCS0NJg7+p5AfvipixplBJC6PIGS6N7LeSskLXMLHx4fc3Fy2bt3KqlWrSEtL48SJE9xwww3s2bMHlUrFkiVL2LFjB15eXjz77LN4enpitVopLi7G39+f8vJyR3+XXnopMTExAFx00UU92sLvv/9+srOz2bp1KwBeXr1Ha61atYrHH3+ct99+m7q6Ot58883TlmBPSkpCLpcjSRLe3t5ccMEFjhtjB8888wz5+fns2bMHgFmzZvHQQw85yqS/9957rn+AE4yxPh862Lp1Ky+//DIXX3wxjz76aLfvuIPTzYfrr7+e999/n+TkZJYsWcIVV1wB2CvTPvbYY2zYsIGsrCzq6upYunRp/z7ICcJYnw91dXW89dZbfPHFF6jValavXs3GjRvZsGFDj+0HMh862LlzJ0888QS7d+/G03N4kooKcTJOcPdWkfzTKM68cBrHD1eRkaqnrKCBYwcrOXawkqCpXiSm6IhJDsZN2fevwlFH6W5fzegLSYK3LoLyoyBZO7fLFBCSAOu/BFdEmdK9zyYrVqxg48aNvPbaa9x2220sXryYjRs3EhAQgFrduUI1b948wG6zvf322zlx4gSJiYkYDAanm0dQUJDj/25ubthsneY4i8XCrbfeSmpqKtu2bSMgIKDP8f3pT3/itttuIyYmhhkzZnDttdfy0Ucf9do+PT2dyMjIXvd/+eWXbNq0iQMHDqDVdlZ2Xr9+PY899hi/+c1vhu3G0xU3lZyb/7zMpbaSJPHZC4ep1jc5WepkMgjUebH6t2f0KdLdVK5Zs8f6fOhAo9EQEhLCAw88wIYNG3oVJ33Nh+uuu46rr76aq6/uXJVcuXIl69evJy8vjw8//JA1a9Yglw+vN4BSqeShhx5yqa0kSbz11luUl5c7mWdkMhkhISGsX7++z/ngasXdsT4fVCoVd9xxBzqdDrD7JD3zzDO9ipOBzAeA4uJiLr/8ct544w1iY2P7HNdAET4n4wyFQk7M/Cn84r55XP5QMnGLQ1G4yak62cR3b2ez5cE97P2sgKZaw2gP9fTIZHYzS1+v4n1QdsRZmID9fdkR+35X+nFBwBw/fpwlS5aQlpZGSUkJYWFhPPLII9TU1GA0Gh3tXnzxRXJycli7di033ngjlZWVbNu2jeTkZJcu3Wg0cskll5CRkcHevXuZMWOGS8dVVFTw6quvUlVVxb59+6ivr2fOnDkuHXsqx44dY926dbzzzjtERUU57bv77rtZvXo1b731lpO/wXAhk8lQqhUuvcoLGqgqburmQiRJUFXcRHlBQ599uLrCONbnw6pVq/jPf/7j1I+vr69Lx55Ka2srDz/8MGvXruW3v/0tra2tgF34/OxnP+Pzzz/ns88+48orrxxQ//1BJpOhUqlcehUXF1NWVtbNb0SSJMrKyiguLu6zj4kyH2bOnElDQ4PjvdVqdRI8/aG3+WAwGPj5z3/ODTfcwOrVqwfUt6sIcTKOCZrqxXm/nMW1zy5m4erpePqpMTSb+eG/RbzzyF7++38ZlB6rG7+e65IE3z1F79NUbt8/RNe3bds2rrrqKiorK/H390ej0RAdHc38+fPZsGEDZrOZ3bt389RTT+Hr60t9fT0eHnY/lj179rB169bTmlk6+M1vfkNdXR2pqakEBwe7PL6nn36ahx9+GKvVSlpaGq+//jo33nhjv6+zqamJ1atXc++993LhhRc67fv888/ZuXMnb775Jvfddx833HADVqu1l55GFkmS2P+v49Dbs0QG+/91fMjm+1ifD2eeeSZPP/00dXV1FBcX8+yzz7Ju3boBXetDDz3EzJkz2bp1KzNmzOCRRx5x7Lviiit4++23qa+vZ+HChQPqfziQJInvvvvutG2+++67STMf1q1bx4svvkhxcTHV1dU8//zzXHrppQO61t7mw80330xQUBBPPvnkgPrtD0KcTAC0nirmXRjJuqcWceEtCYTP9EWySRT8UMU/XzjMB0+lkfV9KWbT2HjIuIzVBA0lQG/q3waNJfZ2Q8D111/POeecQ3x8PAEBAdTW1vLMM8/w/vvvc+TIEYKDg7nhhht49913CQkJYfPmzTz44IP4+Phw5513cv3115OdnX3aczQ0NPDmm29y+PBhgoODHV7vK1eu7HN8zz33HMeOHcPPz4+rr76al156yfGw6Msbvyv//Oc/ycrKYtOmTXh5eTnGsGXLFm677TZefPFFfH19ue+++2hqamLTpk0u9Tvc2CySfUWwt2eNBM11RmyWoXkYjfX58Mgjj3DmmWcSGxvLkiVLuPrqq7npppuA/s2H/fv388YbbzgcXV955RVee+019u/fD9jNGaWlpVx++eVjyq/NarU6rRT0RGNj45CJ67E+H+6++26uv/56zj77bGJiYpg/fz733XcfMDTzYceOHbzzzjvs378fPz8/x9huvfVWl/rtLzJpnP2sbmxsxMfHh4aGBry9vUd7OGOWmpJm0rfrydtXjsVsf7ir3d2YvSSMhGXheAdq++hhaOkIuYuKikKj0bh+YIMeWqp73+8RBD7hgx+gYFzQVGvA0Nz7r0+tlxJPv37ML4FLzJ49m61bt3LmmWeO9lCcaGhooKWlpdf9Hh4e+Pj4jOCIBND7/b4/z2/hEDtBCQj3ZPnaOBatnkH27jIyduhpqjFw+JuT/PjtSSKTAklcrkMX6zemfg11w0dnfwkE2Ms/ePkL8TFSFBUV8d1336FQKMacMAF7BI0QHxMTIU4mOBoPJWesmMqc8yMoyqgmPVWPPqeOE0eqOXGkGr9QD5JSwpl5VggqjZgOAoGgk2effZaPP/6Yjz/+eLSHIphkCLPOJKS2rIWM7Xpy9pVjMdrtsSqtG7MWhZKQEo5vcN9ht/1lwGYdgUAgEIwrhFlHMCD8Qz1YdlUsC1fPIGdPGRnb9TRUtXHku2KOpBYzLT6ApOU6Imb5I5OPYZOPQCAQCCYkQpxMYtRaN+acF0HSch0ns2pJT9VzMrOGoqP2l+8UdxJTwolbGIpKK6aKQCAQCEYG8cQRIJPLmJYQwLSEAOorWsnYoSdnTxn1Fa3s+uAY+z47TtyiUBJTwvELOX19GoFAIBAIBosQJwInfKe4c87lMznrkunk7isnY7ueuvJWMrbrydiuJ2K2P0kpOqYlBAiTj0AgEAiGBSFOBD2i0riRmKIjYVk4+uw60rfrKcyopjirluKsWrwDNSSm6Ji1OBS1u2u1KQQCgUAgcAWRIVZwWmQyGRGz/fnpbUlc8/tFzD0/ArW7G43VBnZ/nM9bD+xm+z9yqCltHvax7C3dy88++xl7S/cO+7mGk4aGBtatW0dQUBAhISHccccdmEymPvfZbDbuuusuAgICCA4O5g9/+EOv55DJZHh4eODp6YmXlxf+/v6sXbuW+vr6kbjEEaEo/Uf+fs+vKEr/cbSHMihO9513UFtbS2RkJIWFhT32sW7dOtavX9/rOSbDfKit3c3efRdQW7t7tIcyKAY6H+Lj4x1ZWz09PVGpVMycObPHc4yH+SDEicBlfIK0LLkshms3LiFlbSz+YR5YTDYyd5Xy/u8P8NmmHzh+uAqbbeij0yVJ4s8//JnjDcf58w9/Hr/1goD77rvPEWqXkZFBWloazz//fJ/7XnrpJfbt28exY8fYs2cPr776Ktu2bev1PJmZmTQ3N9PU1ERhYSFlZWX86le/GpFrHG4kSWLX+1uoLSlm1/tbJux8AEhLS2PZsmUUFRX1ePznn3/Ou+++2+d5Jvp8yC94jtbWfPILnpuU86Hj+21ubqakpISIiAhefPHFXs8z1ueDECeCfqNUK4g/J5wrH13A6rvPYPoZQchkUJJbz1evZfDOI3v44eui06YZlySJVnOry6/Uk6lk1mQCkFmTSerJ1H4d78rNymq1cvPNNxMYGIhOp2P9+vWOm8SKFSvw8vIiOjqaTz/9FIDKykrWrFnD1KlT0Wq1pKSkUFJSAsD69eu55557mDdvHt7e3qxatYra2lrHtT/yyCN4enoSFBTE1Vdfzd69e/vc9+6773Lvvffi7+9PdHQ0t99+O6+//jrQd+0Mb29vLr30UjIyMjh58qTTLyytVotMJiMzMxMPDw+nSsRPPPHEsN+wJEnCbDD061VwcD8VBccAqCg4RsHB/S4f6+qDayzMh6ysLFatWsW9997b4xirq6v53e9+x3XXXee0vT/zITU1lSlTpjjVoFm+fDlbtmwZtflgtbb261Vd/S1NTfZxNjVlUF39rcvHTqT50MH999/Pueeey6pVq4ChmQ8ffPBBt9UZmUzmqMEz1IyKz8kPP/zAXXfdRXp6Olqtlssvv5w//vGPqNXq0RiOYIDIZDLCY/0Ij/WjqdbA0R0lZH1fSnOtkb3/LODAFyeYuWAKSct1eAY6+6W0Wdo4692zBnzuO7ff2a/2+6/ej7vy9MnlPv30U7Kysjh58iRms5lzzz2Xjz76iD/96U+ce+65fPHFF6SlpXHBBRewZMkS7r//fgICAigoKKCtrY2f/exnvPzyy2zcuBGwi4ldu3YRGBjIeeedx+bNm3n44YcdgqKDL7/8kjPOOAPgtPtycnKIi4tz7IuNjWXLli0ArF27lrVr1/Z6bUVFRfzjH/8gJSWFqVOn0tzcaYZbs2YN7u7uxMfHs2rVKj755BMSExMB+PDDD4ft5tOBxWjkL9deNqg+Pn/+KZfb3rHlY5QuJAIcC/MhIiKCgoICPD09ezTb3Hbbbfzud7+juLjYaYm/P/Nh2bJlKBQKdu3aRUpKChUVFRw6dIgvvviCL7/8csTng83WxvYdiYPqIz3D9WJ0KcsyUCj6Tjw5HuYDQHZ2Nu+99x4FBQWObUMxH1atWsUVV1zhOOall17i1Vdf5dprr+3zsxsIIy5ObDYbq1at4oEHHmD79u2UlpZy/vnnExgYyKOPPjrSwxEMEV7+Ghb9fAbJP40kL62CjO16qoubyd5dRvbuMiKSfIhcoh7Ty60+Pj7k5uaydetWVq1aRVpaGidOnOCGG25gz549qFQqlixZwo4dO/Dy8uLZZ5/F09MTq9VKcXEx/v7+lJeXO/q79NJLiYmJAeCiiy5yull0cP/995Odnc3WrVv73NfS0oK7e+dN1N3dndbW1l6vJykpCblcjiRJeHt7c8EFFzhujB0888wz5Ofns2fPHgCuvPJKHnvsMTZs2EBWVhZ1dXUsXbq0H5/ixGEszAcvL69ex/fBBx/Q2NjIDTfcwIYNG/q8nt7mg1wuZ82aNXzyySekpKTwySefcNFFF+Hh4SHmQxfG+nzoYNOmTdxwww0EBgaett1A5kMHO3fu5IknnmD37t14enq69Pn1lxEXJ3V1dZSVlWGz2RwPKrlc7nTTFYxf3FQKZi8JY9biUMoKGshI1VNwuIqqoiZCkuTUV7Ri85Oj8VCz/+r9ffYnSRLXfX0duXW52CSbY7tcJifWL5a/X/B3lwoXat36rsK8YsUKNm7cyGuvvcZtt93G4sWL2bhxIwEBAU6revPmzQPsNtvbb7+dEydOkJiYiMFgcLp5BAUFdX4ubm7YbJ3jt1gs3HrrraSmprJt2zYCAgL63Ofu7k5bW5ujXWtr62lvDOnp6URGRva6/8svv2TTpk0cOHAArdb++axcuZL169eTl5fHhx9+yJo1a5DLh9f666ZWc8cW12q3SJLEB088QFXRCaQun6dMLidoWhRXPP5sn/PBzcUV2rEyH3qioqKChx56iO3bt7t0LXD6+XDFFVewZs0a/vKXv/Dhhx9y5532lcnRmA9yuZaUZRl9N8Q+H344fBVNTdmArcseOV5eszjzjPf6nA9yuWsV2sfyfOjAZDLx4Ycfsnt3307BA5kPAMXFxVx++eW88cYbxMbGujSugTDiPicBAQHcfffd/Pa3v0WtVhMREcHMmTO5++67e2xvNBppbGx0egnGPjKZjLBoXy64KYFfPr2YhGXhyORgs0q01BupKW3B0iBDaVPjrnTv9fVj1Y9k12Y7CRMAm2QjuzabH6t+PO3xHS9XBMzx48dZsmQJaWlplJSUEBYWxiOPPEJNTQ1Go9HR7sUXXyQnJ4e1a9dy4403UllZybZt20hOTnbpszEajVxyySVkZGSwd+9eZsyY4dK+uLg48vLyHO9zc3MHfHM4duwY69at45133iEqKsqxXaPR8LOf/YzPP/+czz77jCuvvHJA/fcHmUyGUqNx6VWam0XliQInYQIg2WxUniigNDerzz5crcI9FuZDb3zzzTeUl5eTmJiIr68vzz77LO+++y5JSUkunfNUFi1ahJubG19//TVHjhxh5cqVwOjNB4XC3aVXQ8MhmpoycRYmADaamjJpaDjUZx8TYT50sHv3bqZMmXJa/xJX6G0+GAwGfv7zn3PDDTewevXqQZ2jL0ZcnNhsNrRaLS+//DItLS0cPXqUrKwsHn/88R7bb9y40VEW28fHh4iIiBEesWCwePqpmXNuBB6+ajx9VbipFCCBocVMXXkLtWUtGFrM3Uw+kiTx0uGXkNHzzUOGjJcOvzRkpqJt27Zx1VVXUVlZib+/PxqNhujoaObPn8+GDRswm83s3r2bp556Cl9fX+rr6x1LnXv27GHr1q2Yzb07AXfwm9/8hrq6OlJTUwkODnZ535VXXsmzzz5LZWUlBQUFvPzyy6e1I/dGU1MTq1ev5t577+XCCy/stv+KK67g7bffpr6+noULF/a7/+FCkiS+/3Ar9PYwkcn4/sOtE2o+9MY111xDS0sL9fX11NfX88ADD3D11VeTnp4+oGuVyWRcfvnl/O53v+Piiy92KtY2ludDwfEXoZf7A8goOP7ipJgPHRw4cGBIvqPe5sPNN99MUFAQTz755KDP0RcjLk7++c9/8sknn/CrX/0KtVpNfHw8jz/+OH/96197bP/ggw/S0NDgeBUXF4/wiAVDhUwmQ+2hwj/UA78QdzQedidZi8lKY3UbNSXNtNQbsVrsv4LMNjPlLeVI9HxzkZAobynHbOv7D94Vrr/+es455xzi4+MJCAigtraWZ555hvfff58jR44QHBzMDTfcwLvvvktISAibN2/mwQcfxMfHhzvvvJPrr7+e7Ozs056joaGBN998k8OHDxMcHOzwel+5cuVp9wHccccdnHPOOSQlJbF48WJuvfVWLrnkEqBvb/yu/POf/yQrK4tNmzbh5eXlOM8//vEPwL58XVpayuWXX+7yr8qRwGqx0FRdBb09bCSJpupqrBbLkJxvtOfDYOjPfOjgyiuvJCMjo9vqyFidD5Jkwmgog17uDyBhNJYhSaZe9veP8TAfioqKCA0N7bZ9KObDyZMneeedd9i/fz9+fn6Osd16q+vOx/1BJo2wh+KmTZv4y1/+wokTJxzbOmxaZWVlfR7fn5LLgrFDbyW0rRYbhmYzbc0mbNbOqah2V+LuraTaXEWdsa7Xfv01/oR4hAzr2Ccjs2fPZuvWrZx55pmjPRQnGquraGts6HW/u48vXgGndwQU9ExFRQXx8fGUlZWhVDpH143V+WAwlGIy1/a6X6UMQKPp/rAW9M3p5kNf9Ha/78/ze8QdYi+44AIefPBBnnnmGe6//36Kiop46qmnuOaaa0Z6KIIxgMJNjoevGncfFcZWC21NJsxGK8ZWM8ZWM2qVN1FeAWjclaKWzwhQVFTEd999h0KhGHMPIgDvwCC8A4P6bijoF1lZWbzyyitcddVVTg+isT4fNJowNJqw0R7GhKO3+TCSjLg4mT17Nl988QWPPPIIf/zjH/Hx8eGaa67p1edEMDmQyWRoPJRoPJSYjVbamkwYWi1YTFaaaqy01BnReCnReqpQuIncgcPFs88+y8cff8zHH7sWPSOYGFx22WUoFAq+/fZbp+1iPkxOepsPI8mIm3UGy3CadVr27KH86WcIefghPBYvHtK+Jzu9LfOdDpvVRluzmbYmMzZrpze+2l2J1kuJUq0YUzZwgUAgEAyNWUf8BG1HkiQqX9yEqaCAyhc3jelkYZMFuUKOh4+agHAPvAO1KNUKAIytZuorWqkrb6Wt2YQ0DLV8BAKBQDB6CHHSTsv3uzEcPQqA4ehRWr4f35UtxyoDEX0dJh+/EA/8Qj3QeCqRyWTtJh8D1SXNNNcZHFE+AoFAIBg9huLH/ajU1hlrSJJE1Z//3LlBJqPimWfQfvQhimFKzTvZUCrtgqKqqoqgoKBBmWNUHjLcNAoMrRaMLWZsJglTrZGGWlBpFKg9lShVwuQjEAgEI40kSVRVVdmTKw7CmVaIE5xXTQCQJEwnTpCXvAB1dDSaOUloE5PQzklCHR2NzE18bP1FoVCg0+nQ6/VOBcoGiySB1WzFZLBiNXdJb6+QodQohF+KQCAQjDAymQydTodCoRh4H5PdIVaSJArXXI4hKwtsfZsFZFotmvjZdrGSlIg2KQm3sDDxAHQRq9XqUpbEgVBX2cKxA5Wc+LEKS7tQUWkVTD8jiJnJIXj5u+aIKxAIBIKBo1QqexQm/Xl+T3px0rzre4pvuqnX/YG3/xrJbMGQkU5bega2LqXmO1AEBqJNTESblIgmKQltYiIKkSBu1DC2msneU0bGjhIaq9oL5ckgMiGAxOU6Imb5CzEpEAgEI4wQJy7iWDXJzOw5JbZMhiY+nsiPPkQmkyHZbJgKC2k7km4XK0fSMeTmQg/pslVRUZ1iJSkJTWwsMpVqUOMV9A/JJlGUWUNGqp6TWZ1ZJH2nuJO0XEfswhBUGmGiEwgEgpFAiBMXsZlM5C8/F2tNTa9tFIGBRH+3DXkvwsJmNGLMzqYt3b6y0paejvnkyW7tZEol6tmzHL4r2sRElNOmiV/wI0RdeQsZO0rI2VuG2WAFQKlRMGtRKIkpOnynuI/yCAUCgWBiI8RJPzCXlWGp7b02g1tAAMqQ/tVusdTVYcjIaBcrRzCkZ2Ctr+/WTu7j024OSkLT4b/i79/fSxD0A1ObhZx95WRs11Nf0erYPjXen8QUHdPiA0SafIFAIBgGhDgZY0iShFmvdzYHZWUhmbpXy1TqdM7moNmzkbuYUVXgOpJNojinlvRUPUVHaxyFTX2CtCSm6IhbHIpaK0w+AoFAMFQIcTIOkEwmDHnHHGKlLSMDU0FB94YKBerYmWiTkhwRQqrp05ENIkRL4ExDVSsZ20vI3lOGqc3uP+SmVhB3VgiJKTr8wzxGeYQCgUAw/hHiZJxibWrCcPSow3elLf0I1qrqbu3kHh5oEhK6mIPmoJwSPAojnliYDBbyDlSQnqqnrqzFsV0X50diio7IpEDkwuQjEAgEA0KIkwmCJElYysudfFfaMjORWlu7tXWbMqVTrCQmoUlIQOEpfvEPBEmSKMmtIz1VT2F6tSOQyytAQ8KycGYvCUPjMTplxAUCgWC8IsTJBEayWjHmF3SKlYwMjHl53RPIyWSoo2e0511pz24bEyOy2/aTxuo2ju4sIWt3KcaWdpOPUs7Ms0JIWq4jIFyUNxAIBAJXEOJkkmFrbcWQleXwXWlLP4KltKxbO5lGgyY+3h4hNCcJTWISynCR3dYVzCYrx9LsJp8afWcivrAYX5KW64iaE4hcIepoCgQCQW8IcSLAUlXVLlTSMaSn05ZxFFtTU7d2ioAAtImJDt8VbWICCh+fURjx+ECSJMryG0hP1XP8xyokm/3Px9NPbTf5nB2G1lMk2xMIBIJTEeJE0A17dtuiTnNQent22x7q3KgiIzvFSlIi6ri4XpPQTWaa6wx2k8/3pbQ12T9HhZucmORgkpZHEDTVa5RHKBAIBGMHIU4ELtGZ3bY9OigjHXNRL9ltZ83qYg5KRBUZKcxB7VjMVvIPVpKeqqfqZOfqVMh0H5KW65h+ZhAKYfIRCASTHCFOBAPGUlfXHs6c3m4SysBaV9etndzHB21CgkOsaJOScAsIGIURjx0kSaLiRCPpqXoKDlViazf5uPuoSFgaTvw54bh7ixUogUAwORHiRDBkOLLbdviupGfYs9sajd3aKsPDHY622qREe3ZbrXYURj36tDQYydxZwtFdpbQ12jMByxUyoucFk7hcR0iU8OsRCASTCyFOBMOKZDZjyMuz1w86YjcHmQqOd6/srFCgntme3ba9dtBky25rtdgo+MFu8qk40ejYHhzpTdJyHdFnBqNQCpOPQCCY+AhxIhhxrE1NGDIzHWLFcCQdS1VVt3Zyd3d7dtsOc9CcOSinTBmFEY88lUV2k8+xgxXYLPY/O62XkvhzwklYGo6Hr3qURygQCATDhxAnglFHkiQsFRWdxQ7TM2g7erTn7LbBwV3MQUloEuJReE7c5GatjSayvi/h6I4SWhraTT5yGdPPDCIpRUfIDB/hbCwQCCYcQpwIxiSS1YqxoMDhu9KWno7x2DGwWp0bdmS3Tew0B6ljYpApJ1bKeKvVxvHDVWRs11OW3+DYHhjhSdJyHTHJU3BTTh4TmEAgmNgIcSIYNziy26Z3Jowzl5Z2ayfTaNDMnt0ZzpyUhDI8fMKsMFQVN5GRqicvrQKr2V6KQOOhZPbZYSQsC8fLXzPKIxQIBILBIcSJYFxjqa62i5V235W2jIyes9v6+3fPbuvrO/IDHkIMzWaydpeSsUNPc609Ikomg6i5dpNP2EzfCSPIBALB5EKIE8GEoiO7rcN3JT0dQ05Oz9ltp02zFztsjxBSz5o1LrPb2qw2CtNrSN9eTEluvWN7QLgHiSk6Zp4VglIlTD4CgWD8IMSJYMJjM5mcstsa0tMxFRV1b6hUoomLc4gVTWISqshpyOTjJ3y3pqSZjO16cveXYzHZTT5qdzdmLQkjcVk43oGTM5eMQCAYXwhxIpiUWOvracs46lQ/qMfstt7eaBMS0MxJQpuYhHbO+Mhua2gxk72njKM79DRWG+wbZRCZGEjSch26OD9h8hEIBGMWIU4EAtqz25aU2KOD2n1XDJmZPWe3DQtzEitjObutzSZRdLSGjNRiirM7xZdfiDuJKTpiF4ag0riN4ggFAoGgO0KcCAS9IJnNGI8da68dlEFb+pHes9vGxHSag5KSUM+YMeay29aVt5CRqidnXzlmoz0kW6VRELc4lMQUHb7B7qM8QoFAILAjxIlA0A+szc0YjmbafVcy7KsslsrKbu0c2W3bxYo2KQllSMgojLg7pjYL2XvLyNiup6GyzbF9anwASct1TJ3tj0wuTD4CgWD0EOJEIBgk5ooK2o4ccdQPMhw9iq2X7LaapMROc1BCwqhmt5VsEieza8lI1VN0tMax3SdYS2KKjlmLQlFphclHIBCMPEKcCARDjCO7raPYYQbGvLwes9uqZkzvFCuJiWhmzhyV7Lb1Fa1k7NCTs6cMk8E+TqVaQdzCEBKX6/AL8RjxMQkEgsmLECcCwQhga2vrkt3WHiFkLinp1k6mVtuz23Y1B+l0IxZZYzJYyN1XTsZ2PXXlnas/EbP8SFwewbSEAOTC5CMQCIaZIRcnJ0+e7NcApk6d2q/2/UGIE8FYxlJdbY8K6rLCYmts7NZO4efXntm2o9hhAm5+fsM6NkmS0OfUkZ6qpzCjGtr/8r0DNSQs0zFrcSgaj4lVv0ggEIwdhlycyOVyl37lSZKEXC7HYrG4Ptp+IsSJYDwhSRKmwkJnc1B2NlIP2W2V06bazUFds9uq1cMyrsbqNjJ2lJC9uxRjq/3v1U0lZ+ZZISSl6AgIn7hVoQUCwegw5OLE09OTzMzMPk8sSRIJCQk0Nze7Ptp+IsSJYLxjM5kw5uS0i5V0DOkZmAoLuzdUKtHExtpXVtrrBw11dluzyUrefrvJp6akxbE9fKYvScsjiEwKQK4YP9l0BQLB2KU/z2+X3PZvuukmpk2b5tLJb7rpJpfaCQSTFblK5TDndGCtr6ftaKZzdtvaWgxHj2I4ehTebT/WywttYkKX+kFJuAUGDngsSpWC+HPCmX12GKXH6slI1XP8SDUlefWU5NXj6a8mcZmO2UvC0HgKk49AIBgZ+u0QazKZqKysxGazOW3vj5/JP/7xD2655ZZu/cpkMow9ZO/silg5EUwG7NltSzGkH+msH9RLdlu3sND2qsyJndlt3QeefK2p1sDRnSVk7SrF0GI3PymUcmYmTyFxuY6gCK8B9y0QCCYvwxat89FHH3HLLbfQ0NDg2CZJEjKZDOupIZX9oKSkhOTkZP74xz9yzTXXnLatECeCyYpkNmPMz+9iDkrHmF/Qe3bbxESHOUgd3f/sthazlWNplaSnFlNd3GmqDY32ITFFx/QzglAIk49AIHCRYRMns2fP5tJLL+Xaa69FeUreBlfNPqciSRLnnXceM2bM4PXXX++zvRAnAkEn1uYWDEePOsRKW3oGloqKbu1k7u5o4+MdYkWblIhbSIjLju7lBQ2kb9dz/IcqbDb7LcPDV03C0jBmnx2Ou7dqyK9NIBBMLIZNnHh5eVFXV4eb29BlmHznnXe49957yc/Px8ur+3Kx0Wh0MvU0NjYSEREhxIlA0Avmigq7GahdrBgyMnrObhsU1MV3JdGe3baHv8GutNQbObqzhMxdJbQ12U0+cjcZMfPsJp8pkeJvUiAQ9MywiZNly5bx0ksvkdTFkW8w2Gw24uLiuP3227njjjt6bLNhwwaeeOKJbtuFOBEIXEOyWjEdP+7wXWnLSMeY20t22+nTO31XEpPQxPac3dZqtpH/QyXpqXoqCzvzuEyJ8iZpuY4ZZwajcBMmH4FA0MmwiZOHHnqI119/nTVr1hBySsGzxx57rN8D3bZtG6tXr6a8vBwPj55TaYuVE4Fg6LG1tWHIznZaYTHr9d3aydRqNLNmOcSKNikRZUSEkzmo4kQj6duLyT9Yic1qv524e6uIPyeM+KXhePgMT64WgUAwvhg2cbJ8+fKeO5HJ+O677/o3SuC2226jubmZt99+2+VjhM+JQDA8WGpq7Nlt28VKW0YGti7O7x0ofH2dfFc0iYm4+fnR2mgic1cJR3eW0NpgAkAulzFjXjBJy3VMifIesZT9AoFg7DFuauskJSVxxx13cOONN7p8jBAnAsHIIEkS5qIiuykoPcNuDsrqJbvt1KmdmW3jEykxBXH0+wrKCjrFTdBUL5KW64ieH4ybsn+RQwKBYPwz5OLkvffe46qrrup1hUMmk7Fu3bp+D9TT05OPPvqIlStXunyMECcCwehhM5kw5ubSdiQdQ0Y6bUfSe85u6+aGJjYWw6xFFKriKapQOVxcNJ5K4s8OI2FZOJ5+mhEdv0AgGD2GXJwkJCRw9OhRoqKieu5EJuP48eMDG20/GU5xUlBQwFdffcXKlSuZMWPGkPYtEExUrA0NtGUcdYiVjuy2XTEpPSmLXE5J2DkYZHb/Mpkcps8NImm5jtBoX2HyEQgmOOPGrDMQhkucSJLE66+/TmlpKWFhYdx0003iZikQDABJkrCUlnaagzqy2xoM2GRyqgOS0OuWUe8703GMr9ZI/DxvZv00EbWfWBEVCCYiwypOvv/+ewoLC53S1w/UrDMQhkuc5Ofns3XrVsf7a665hujo6CHrXyCYzEgWC8Zjx9rFir1+UE1ZK/qwpZRPWYBNYU/i5mZuIcKYw8ypJgLOmIk2KQn1jBnIhjC3kkAgGB2GTZz86le/4o033iAsLAx5l8qo492s03XVpIOQkBBuueUWsXoiEAwT1uYWDJmZNBw+Sl56MwWGcAwqP/tOyUZgdTq6kh34m/S4z57tlDDOLTRU/G0KBMNEUfqPfPfWa5y7/hamJc0dsn6HTZz4+fnx7bffMm/evEEPcqAMhzg5ddWkg7CwMObPn8/MmTPx9PQcknMJBIKesdkkCnYVkLGtiLLKTuHh0VKKrmQHIeUHUNjsIcqKoEC0iV2y2yYm9pndViAQ9I0kSfzj4XuoKDjGlBkxrH36xSH7ITBs4iQyMpLc3FzU6tFLqjTU4qRj1aSstJTePggZMsJ8pjAzYgZxM2cSEBaM3FuFXCXCIQWC4aC2rIWM7Xpy9pZhMdlNyEqZhfCWo4Rk/Qv35u71gzqy22rmJKHtyG6rEjV/BIL+kJ+2j8+ff8rx/tIHnyBy7tAsSAybOHnjjTfYsWMH9913H76+vk77pk6dOqDB9pehFie9rZp04GXT0iRvc9oWYPNkmjWISGUoQd7+uPmoUXirUXirUPioUHip7f96q5F7KpHJxfKzQDAQjG0WcvaUkbFdT0NV+9+hDHQRSma4l+BblIYhI73n7LYqFZpZsxxiRTsnqVt2W4FAAK2NDRw/dIBjaXs5figN2n+qy+RygqNmDNnqybCJk5dffpm7777byRlWkiRkMhnWU+t0DBNDKU568jU5lSlegVwUtZT88hMU1BdTZqp2WmHxsmmJtAUxzRpEsOSDnFO+QDkoPFXIfdrFi7fKWci0/1+uEQ5/AkFvSDaJoswaMrbrOZnZGabsO8WdxJRwomM1WPMyMbRntjWkp2PtKbutj49zscOkJNz8/EbyUgSCMUFDZTn5afvJT9tLSU4WkmTrte1QrZ4MmziZMmUKTzzxBCtWrEChcDZpTJs2bWCj7SdDKU4sFgsvPv8irYbuFVs78NC4c/e99zgqMbe0tJCbm0tOVjYFJ447iTJ3Nw2R2jAiZcGEtnoja7HSq63oFGQqRfuqiwpFFyEj9+5chVF4KZEpRDE1weSmvqKVjO16sveWYTbY//6UGgVxC0NJTAnHL8TDnt325Mku4cxHes9uGxHhJFY0s2Yh14jkcIKJhSRJVBWdID9tL/lp+6gqOuG0P2jadFob62mtr6OrLBjK1ZNhEycBAQHU1NQManCDZahXTo7/ZS9NFXU9iwgZeE3xY/odi3r8UoxGI/n5+eTk5JCXl+dUoFClUjEzZiYzp0UTFRCBW5uEtcmEtcGEtdGItbH93wYTktHFVScZyD2UTuKlcxWmc5tM6yaWrgUTHpPBQu6+cjK266kr7/yBETHbn6QUHdMSApxMqpLJhCE316nYoenEie4dt2e31SQlOsxBqqgoZHLxw0AwvrBZrZTkZpGfto/8tH00VnX6aslkcnSz4olOXkh08iJqS4r5ZOPjvfY1FKsnwyZO7rvvPiIiIrjjjjsGNcDBMKTixGKj7NkD2Jq7/5rqQO6pJPSBBcj6KP9usVgoLCwkJyeHnJwcmpubHfsUCgXTp08nLi6O2NjYbpE/NqO1i2AxYW0wYms0OW9rNIHNxa/KTd7DKswppiQvFTKluNkKxj+SJKHPriN9u57CjGrHDw3vIC2Jy8KZtTgUtbuyx2OtjY12M1BGRmd22x5+gMk9PdEkJjjEiiYxEWVw8HBelkAwIMwmI0XpP5J/YC8FPxzA0NTo2OemUhM55wyikxcRdcZ83L19gC4ROsfzoSdJIJMxZXr0oFdPhk2cLF26lO+//x4vLy/8/f2dBjle85xY6o3YWk4vTtz6WfLdZrNRUlJCdnY2OTk51HZJ5S2TyYiIiGDWrFnExcXh56K9W7JJ2FrMnSsu7SKmQ7jY2rfZWi0uj1Pu7mYXKj4q5F2FjI+6XdiokLsLh17B+KGhqo2MHXpy9pRhbP9bcFMriD0rhKQUHf5hHqc93pHdtkOsZKRjyMxCamvr1tYtNBRtYqJDrGjj45F7nL5/gWA4aGtu4vihA+Sn7aMw/QcsXVbxNZ5ezJi3gBnJC4lMOgOlurvJ0mI28/qvr6O1ob7Xc7j7+HHTK3/DTdmz0HeFYRMnW7Zs6XXftdde6/oIB8F4K/wnSRJVVVUOoVJWVua0f8qUKQ6hMmXKlEGbYySzzdls1HiqKcn+fywufu0KmV2oeHdfhZF32SbCqgVjCbPRSu5+u8mntrTFsT081o+k5ToikwKRuyi6JYsFY35+u/9KOob0DIz5+WA7xYFQLkcdHW03B7U73aqjo0V2W8Gw0FhdSX7afgoO7qU46yhSl/noFRhEdPJCYpIXER4Xj1zR9/25sbqKtsbuTuQduPv44hUQOLgxi9o6Y5f6+nqH6aeoqMjJ8cjPz4+4uDji4uKIiIhwysI7lEiShK3V4rTi0nUVpkPInM7cdSoyjcKxCtObOUnuqRKrMIIRRZIkSvLqyUjVc+JIlWPF2stfQ8KycGafHYbGo/+/BK3NLRiyMh2+K23p6VjKy7u1k2m1aOJno02agzYpEW1iIm5hYcInTNBvJEmipriI/LR9HEvbS+WJAqf9gVMjiU5eRHTyQoIjp4/JOSbEyTihpaWFvLw8srOzKSgocIr88fDwIDY2llmzZhEVFeWIFhpJJIsNa3P3lRfbKUJGMvUeguZER1h1h2jx6dmpV6ZWjMk/LMH4prGmjcydJWR+X4qxxW7yUSjlzFwwhaTlOgJ1g8swa66oxHC0izko4yi2Lr5nHSgCA53NQYmJKMb5vUwwPNhsVkrzcshP20dB2j7qK7qsvMtkhMfOtju0zl+Ib0jo6A3URYQ4GYf0FfkTExPDrFmziImJGdUMvaciSRKS0drjyktXUWNrMvUjrFreKVi8u+aI6ZroTiXCqgUDwmKykpdWQcZ2PdXFneIhNNqHpOURTJ8biHwI5pZks2E6caJTrBxJx5CXB5bufmGqqCi0SUkOk5AmNlZkt52kWEwmTh49Qn7aXgoOHXDyA1EolUxLnEt08iJmzFuAu4/vqI1zIAhxMs4ZTOTPWEWySdh6WIWxNhidQqwlQz/Dqruuwpzi1Cv3UiF3F2HVgp6RJImyggbSv9Nz/McqpPZoOE8/NfFLw4k/Owyt19AKBJvBgCErG0NGpznIXFzcrZ1MqUQ9e1anOSgpCeXUqWIuT1AMLc2cOHyQ/LR9nPjxEGZDpwO22sOD6WcuIDp5IZFzzkSl0Y7iSAeHECcTiI7In5ycHLKzs3uM/ImLi2PWrFkuR/6MZWwma+/h1B2rM00msPYjrNqRE6YXc5K3WoRVT3Ka6wwc3VlC1veltDXZfa0UbnJi5geTuFxH8LThu9dY6uqcfFf6zG7bxSTk5u8/bOMSDC9NtdUUpO0n/+A+ijPTsXUx63v6B7Sbaxahm52AYoI4VQtxMkEZ6cifsYpkk7C1mu2rLU1dREuDEVuXVZj+h1W3Z+T17iU6yUOEVU90LGYr+YcqyUjVU1nU5NgeMt2bxOU6ZpwRjKKPnEeDpTO7badYMWRnI5lM3doqdbou5qA5aGaL7LZjFUmSqC3R2zO0HtxHeX6e0/4A3VSH/8iUGTET8v495OLk7bff7vOkv/zlL10f4SCYzOLkVMZC5M9YRjLb7OKlPRNvb+YkLC469HYNq+6yCnOqoBFh1eMfSZKoONFIeqqegh8qsbWv1Ln7qIg/J5z4c8Lw6Gf+o0GNx2TCkJvn8F1py8jA1FNuKTc3NDNnOsSKNikR1fTpIrvtKCHZbJTl5zlSxteVlXTulMkIjYklJnkRM+YvxD8sfPQGOkIMuThZvHgx+/fv77XysEwmG7dJ2CYKYz3yZ6wiSRJSm6W76ejUjL0tZtcdetUKp6KOPZmS5J4qZIqJ98toItLSYCRzVymZO0tobbSvXsgVMmacGUzSuTpConxGZVzWxkYMR492qR+UjrW6uls7uYeHIyrIbg5KQjlFZLcdLixmM8WZ6XaH1oP7aamvc+xTuLkxNWEO0cmLmD5vAZ5+k8ssN+TipLW1lWXLlrF+/Xp+/etfD9lAB4IQJ31jNBopKCggOzt7XEX+jGUkqw1rk9mxCmPrZRVGMvXDodfr1FBqFQqvLkLGRy3CqscQVouNgsN2k0/58c6U4MHTvEhariN63hQUo+i7JEkSlrIyJ3NQW2Zmz9ltQ0KcxIo2QWS3HQzG1lZO/Nju0Hr4IKa2zlpPKq2WqDOSiU5eSNTc+ajd3UdxpKPLsPicZGZmcv7551NUVIRqFEPchDjpHxMx8mcsYzNYOldeGkzdVmFsDUaszSZw0ZIkU8q7V6nuWiPJuz2sepj9IATOVBY1kpGqJ+9gBbb2bMtaL2W7ySccT7+xIfoliwVjQQFtR4446gf1mt12xgwnc5A6JkZktz0NLfV1FBzcz7G0vZzMOILN2unj5uHrx4z5ZxGTvAhdfNKgUr5PJIbNITY1NZW5c+eOalSIECcD53SRPwBTp06dUJE/YxV7WHXnKoy1qQch02BCMvTDoddTeUpmXrt4kXcRMSKseuhpbTSR9X0pR3eW0FJvX6GUy2VMPyOIxOU6Qmf4jLnP3NbSgiEry8kcZDnFuR5AptGgiY9vT8VvD2ee7Nlta0tLHA6tZcdynYrk+YWGE71gEdHzFxIaPVP4+fSAiNYR9ElH5E+HUJmskT9jGZvJ6hxO3UONJGtjf8KqZU7J7XozJ8mUwqG3v1itNk78WE16ajFl+Z1hwIERniSm6JiZPAW3Mewoba6stK+spGfQln6k9+y2AQEOsaJJTEKbmIDCZ3R8bkYCSZKoKDhG/sF95Kfto0Z/0ml/SPRMoucvJDp5EQG6iFEa5fhBiBNBvxGRP+MTR1h1V8HSYDolR4wRW0v/wqrlPazCdA2vFmHVvVOtbyI9VU/egQqsZrv5ROOhZPbZoSQs0+HlP/ZDfR3ZbdMz7AnjjqRjyM3tObttZGSn78qcJNSxscjHcXZbq8WCPusox9L2UnBwH821NY59coWCiPgkoucvZEbyWXj5D64Q3mRDiBPBoOiI/MnJyaGgoABLlxtSR+RPXFwc06dPF5E/4wTJYuteWqCpM8Ta1i5uJLOrdZLaw6q7ZOZ1VKn26vSJkavH7mrBcGNoNpO1u5SjO0poqjUAIJNB1JwgkpbrCJvpO65WJG1GI4asLIfvSltGBuaTJ7u1kymVqGfNcjIHKadNG9PXajK0UfjjIfLT9nH8cBrGls5K1kq1hqgz5tsdWs+Yj8ZD+OYNFCFOBEOGq5E/0dHRaETyp3FNt7DqnvLDNBrt1ar7E1bt3fsqjNxbhWKCh1XbbBKF6dWkp+opye0MK/UP8yBpuY6ZC0JQjlMRZ6mr6yJW0jGkZ2Ctr+/WTu7jY48OSkq0Z7lNShr17LatDfUUHDpAftpeijJ+xGrurMKu9fYhev5ZRCcvYmrCHNzG8UrQWEKIE8GwYLFYKCoqcmSoFZE/kxPJasPabO5SYqBnISMZ+xFW7anqXh/plMgkmWb8h1XXlDaTsb2E3H1lWNqreavd3Zi12G7y8Qkav3VToD27bXFxp+9KegaGrKzTZLdNdJiDNLNmIdcO7/XXV5S3J0TbS0lutpNDq8+UEKKTFxGTvIjQmbHI5eNTMI5lhlWcvPDCC5xzzjksWLBgUIMcKEKcjA1E5I+gL2xGS7coJNupie6a+hlW3RFOfWp9pA5zkvf4CKs2tprJ3lNGxnY9jdV2kw8yiEwMJClFh26W37gXYh1IJhOGvGMOsdKWkYGpoKB7Q4UCdexMtIlJDpOQavp0ZIqBiwRJkqgsPO7I0Fp9stBpf3DUDGKSFxGdvJCAiLFtepoIDKs4+eGHH7j55ps5ePDgoAY5UIQ4GXu4EvnTIVRE5I+gK5JNwtZi7iJYelmFaeuHQ6+jWnUXB95TzUljJKxaskkUZdaQnqqnOKtT4PuFuJOYoiN2YQgqzcTz67I2Ndmz27b7rrSlH8Fa1Ut224QEJ3OQcsqU0/Zts1rRZ2eSf9AuSJqqqxz7ZHI5EbMTmDF/EdHJZ+EdKDLljiTDKk7Ky8tJSEiguoc0ySOBECdjn9NF/vj6+jpClEXkj8BVbCarU1HH3oSMy2HVCplzaYFezEkjGVZdV95CxvYScvaWYW43iak0CuIWhZKYosN3ysTNLCpJEpby8k7flSOnyW47ZUqnWElMQpOQgE2poDD9MAVp+yg4dABDc2fRRjeVmsg5ZxKdvJDp8xag9fQayUsTdGFYxclLL73EN998w7/+9a9BDXKgCHEyvhCRP4KRQpIkbK0WxyqMczh1pznJ1mLuu7N2ZFq3boUeuzn1DnFYtanNQs6+MjK2l1Bf0ZkGfWq8P4kpOqbFB0yKMG57dtvjneag9HSMx445stuaFHIqvT2o8PGg2tsDa5ePROPpxYx2h9ZpiXNQqoWz/lhgWMXJWWedxcaNGzn33HMHNciBIsTJ+MVkMpGfn09OTg55eXkYDAbHPhH5IxgpJEtHtWpTj+Yk+wqNsR9h1bT7u3QxIfWwCiNX9098SzaJ4uxa0rfrKTpa44iQ8gnSkpiiI25xKGrt5BL0dScLyf3y3xT8eIiKumqnoDGtycyUhhamNLTgbwX32bM7E8YlzUEZPrmz244FhlWcBAcHU1paOmq/coU4mRhYrVYKCwt7jfyJiopi1qxZIvJHMCpIkoRksJ4+O2+DCVuzqf9h1T2YkxyCppew6vrKVo7uKCF7Txmmdv8bN7WCuIUhJKbo8A+dmEX7JEmi+mQh+Wn2DK2Vhc6OtEHTopg+O4lwdy/cS8owZmTQlnEUW1NTt74UAQFoExM76wdN8Oy2Y5FhFScXX3wxt99+OxdccMGgBjlQhDiZeNhsNkpLSx1Cpaamxml/R+RPXFwc/qOcG0Eg6IpklbA1d69O3TU7r7Whv2HVyu5lBtrDqW1qBSfy6zmyq5S68k6Tjy7Oj6TlOqYlBiIf5yYfm81KaW62I8KmobLCsU8mkxMeN5voZLtDq09wSLfjJZsNU2FhZ2Xm9Ax7dltzd3Oeato0NHPsvivaOUmo4+LGdXbbsc6wO8QePHiQVatWDWqQA0WIk4mNiPwRTERsRms3/5dudZOaTGBz7XYsU8qRNG40m6zUN5lps0kYJJB5uBE+N5jpS0Jxn+IxLsKqAcwmIyczfiQ/bR8FB/fT1tTo2OemVDFtzhlEz7c7tLp793+1w2Y0YszOdhQ6bMtIx1x0muy2iYntKfkTUU2bJor4DRFDLk4uvfRSPvnkE5dO3p+2A0GIk8lFfX09ubm5ZGdn9xj50yFUROSPYLzjCKt2WoXpFDS2jlwxra6HVaNxQ+nX6f8i75LUrmNlRu6hHBWRb2hu5vjhNPLT9lL44w+YjZ0+aBoPT6afmUx08iIi55yJchh80Cx1dV3Cme0RQj1mt/X27mIOas9uGxAw5OOZDAy5OPHw8OC///0vriyyrFy5kpYudQmGGiFOJi+tra3k5uaKyB/BpEYyW3sIpTZhrjfQUtqCpc6A0iahcFVwKGSnhFJ3j0ySe6uQD0FV5aaaanuF3wN70WcfxWbtNHd5BgQSPX8hMQsWER4Xj2KE/4YlScKs19uLHGa0m4OyspC6lOzoQBke3um7kpSIZvbsYc9uOxEYcnHSn1+kMpkMq9VF++oAEOJEAK5F/sTFxRETEyMifwSTCkmSKD1WR9a2Yioya1EDGjn4uCsJCXXHx1MJ7Ss0tuZ+hFVr3HoIpVah8Or0iZF7OodVS5JEjf6kw6G14vgxpz4DI6YRnbyQ6ORFBEfNGHNmWslsxpCX5/BdaUtPx3T8uFPae8Ce3XbmTCdzkHrGjEFlt52IiNo6gkmFiPwRCHqmqdZA5s4SMr8vxdAuRBRucmKSg0laHkFgmAfW5i5OvO2+L7YGZ/+Y/oRVyz1V2FQ2mk311NbqqW+soM3aRKulmTZbM76R4UQtmE/0/LPwCw0fxqsfHhzZbTv8V3rLbuvubs9u2y5WtElJKEO6O/BOJsa8OKmtreWuu+7iP//5DzabjWXLlrF582ZCQ0P7PFaIE8Hp6CvyJyIiwpGhVkT+CCYLFrOV/IOVpKfqqTrZGWYbOsOHxOU6pp8RhELR8wq5JElIRmsvJQY6/m/E2mxG5mpYtUrRQ2bernWT1Ci8lMh6GdNYQpIkLBUVneagjuy2ra3d2roFBzubgxISUEyiH0xjXpwsX74cPz8//va3v6FQKFi/fj1Go5Evvviiz2OFOBG4StfIn5ycHEpLS532i8gfwWRDkiQqTjSS/l0xBT9UYWuPDvLwURG/NJz4c8Jx93YtlNbY2sLxwwfJT9vHicMHsRgMqBUeuCs88XIPRBc5m+DgKLzdA5BarA4hIxn6EVbtoezuC+PtLGhk2rFRJ6krktWKMb+gU6xkZGDMy3Nkt3Ugk6GaMd0hVrRJSahjYpAplaMz8GFmTIuTQ4cOcfbZZ1NRUeEYXG1tLWVlZcTHx/d5vBAngoHS0NDgCFEWkT+CyU5LvZGju0rI3FVKW6MJALmbjOh5wSSlRDAlqvv9tbm2hoJD+8lP28fJo+nYrF2c0v38iZ6/kOjkhUTEJ6Jw6/kBazNZu5cYaDcndV2dcTWsGjf5aesj2VdhVMiUo/s3bWttxZCV5TAHGdLTMZ/ygwlAplajac9uq0lKRDtnDsrw8DEnwAbCmBYnmzdv5o033uDqq69m8+bNtLS0cOGFF/LCCy/0uMxuNBoxdvGWbmxsJCIiQogTwaAQkT8CgR2rxUb+oUoytuupONGZXyQ40puk5Tr8Q0yc+PEA+Qf2Upaf63Ssf5jO7tC6YBEh02OGLB+IZJOwtZq7Z+Y9RdT0J6xa7u7miEKSdxUyPup2YaNC7j60dZL6wlJVRVvG0c76QRkZPWe39ffvnt3W13fExjlUDKs4MZlMVFZWYjtleWrq1KkuHf/000+zYcMGbrjhBp577jlaW1tZt24dKpWqR7POhg0beOKJJ7ptF+JEMFSIyB+BwE5FYSPp350kd386FsMxbKYCJFutU5vQ6FiiFyxixvyzCAiPGKWR2pHMNrt46alidUNnll4s/ahW7aXqZjrqzBHTXj9pCMKqe7wemw1TYZGTOciQk9N7dtukJEeEkHrWrDGf3XbYxMlHH33ELbfcQkNDg2ObJEn9Ch9+/vnnefDBB2lqanLc6NPS0jjrrLNobGzsFk0hVk4EI0lH5E+Hn0pTl18xHZE/Han0ReSPYKJgtZgpzsxoz9C6j+a6roJEjtwtAoU6hulnLmDeynhCpnuPGzODJElIbZZeCz12mJNsLWbX6yRpFN3qI3Ur9OipGpJVGJvRiDEnxyFW2tKP9JjdFqUSTVxcl3DmJFSRA8tuW1u7m9y83xM78zH8/ZcM+ho6GDZxMnv2bC699FKuvfZalKc47EybNs2lPr788ksuvvhi6urqHIPbt28fixcvpqGhAS8vr9MeL3xOBCOFiPwRTGRMba2c+PEH8tP2cuLwQYytnckzlRotUWfMZ/q8BcjkkeTsraUsv/NHadBULxJTdMQkB+OmnBi5PCSrDWuTuUskUkeJAedVGMnkYli1DLv5qIekdl1XZmRqRb+FniO7bXq63RyUno61rq5bO7m3N9qEhM76QUmJuAUGnv5zkCT2b/8JLdIJPGRRnJXyzZAJ0WETJ15eXtTV1Q3KBm82m5k9ezZz5szhrbfeoq2tjSuvvBIfHx8+/fTTPo8X4kQwGvQV+RMcHOwQKiEhIePmV6VgctFSX9fp0JrxI9YuvlbuPr6dDq0Jc3A75Qdo1ckm0rfrOXagAqvF/oDWeCqZfXYYCUvD8fKfHCZPm8HSczh1R5mBBiPWZhO4qmFUcudyAj059XqpThtWLUkS5pIS2o4ccYiVXrPbhoXZzUFJdrGiiY93ym5bXb2DI+nXO97PSfobgYHLXP+ATsOwiZNly5bx0ksvkZSUNKgBlpaWcs8997Bjxw4MBgOXXHIJf/7zn/F1wcFHiBPBWEBE/gjGC3XlpY4MraV52U7ZTX1DQtsr/C4iNGYmcnnfqyBtzSayvi/l6I4SmuvsDz+ZXMb0OYEkLtcRFuM76cW5ZJOwNTuvwjiZk9qLPUoGFx16O8Kqu67CnOLUK/dSIXfvDKt2ZLfNyHDUDzIV9JLdNiYGzZwE2uZJnHT/BJvcHr2FDTzkUZy1fGhWT4ZNnDz00EO8/vrrrFmzhpBTMt099thjAxttPxHiRDDWaG1tJS8vj+zs7G6RP+7u7sTGxjJr1iyioqK6mUMFgqFGkiQqTxSQn7aX/LR9VBcXOe2fMj2mPWX8QgJ0Uwf80LFZbZxIryYjVU9JXr1je0C4J4kp4cw8KwTlMDmOThRsJqtTUccenXqbTGDtR1i1w4m3uzkJNwvmk3kYMtsjhI6kY2yrpPVsGy1Lrdh8e+52qFZPhk2cLF++vOdOZDK+++67/o1ygAhxIhjLiMgfwWhgtVgoycnk2IG9FBzcT1NNlWOfTC4nYnaiPcJm3ll4BwYN+flrSppJ364nb185lvZU92p3N2YtCSNxWTjegaIo3kBxhFWf4shrczInGbG19C+s2hRcQm3o19R77kKStUcDWWUgl6CrXh3C1ZMxnedksAhxIhgvnC7yRy6XM336dBH5IxgwZoOBwiN2h9bjP6RhaOmsKeWmVhM1dx7RyYuYfkYymhGaX4YWM9l7yji6Q09jdbswl0FUkt3ko4v1m/Qmn+FCsti6mY2sTc5mJUtjK83+B6mb+i1tfnmOYzUNUWhr46iL+qrX/odi9WRYxcnnn3/Oa6+9RmFhIaGhodxwww1cffXVgxpwfxDiRDAeEZE/gqGgtbGB44cOkH9wH0VHDmMxmxz7tF7ezJh/FtHJC5maOBelSj1q47TZJIqO1pCRWkxxdmcUiV+oB0ntJh+VRiQ3HClMplpKSz9AX7IVo7EcABkK/N3OJdi8GnVjFDnKX2PQFtJjgaQhWj0ZNnHy7rvvctttt3HLLbcQFRVFfn4+r7/+Oi+88AI33njjgAfcH4Q4EUwEqqqqHEJFRP4ITkdDZTn5afvJT9tLSU4WktQZBuITPKXdoXUhYbGzXHJoHWlqy1rI2K4nZ185FqM9H5ZK68asRaEkpITjG+w+yiOcuDQ1ZVGsf5uKis+x2exCVqn0Jzz8anThV6NWTwHAajWw66s5WN17Nw0pWtw456IjKBQDN0cPmziZM2cOf/rTn5x8T1JTU/n1r39NVlbWgAfcH4Q4EUw0OiJ/cnJyKCws7DHyJy4ujqlTp4rIn0mAJElUFZ1wOLRWFZ1w2h8cOcPh0Bo4NXLciFdjm4WcPWVkbNfTUNVm3yiDafEBJC7XMXWW/4imjp+o2GwWqqu/pVi/hfr6A47tXl4JROiuZcqUnyKXO6+q2Uwm8n55LW6JF/XaryXjv8x8+++DykI7bOLE19eXuro6pz8Gm82Gr68vjY2Npzly6BDiRDCREZE/kxOb1UpJbpYj5LexqsKxTyaTo5sV3y5IFuEdFDyKIx08kk3iZFYt6al6TmZ2mjd9grUkpuiYtSgUlVaYfPqL2VxHSemH6PXvYDSWASCTuREcdCG6iF/i431mr0JWkiQq/pSGpaJ7XpQO3KaomXJX8oiZdfo1A3Q6HTt37mTZsk6nmJ07d7pcV0cgEJwed3d35s6dy9y5czGZTBQUFJCdnU1eXh6tra0cPnyYw4cPi8ifCYDZZKQo/UfyD+yl4IcDGJo6f+C5qdREzjmD6ORFRJ0xH3dvn1Ec6dAik8uYlhDAtIQA6itaydihJ2dPGQ2VbXz/4TH2f36cuIUhJC7X4RfiMdrDHfM0NeegL95CecXn2Gx2caFU+hMediXhurVo1CF99ABYJWwtp88aZ2ux2UOa3UZmdatfKydvvPEG9913H7fccgvTp0+noKCA1157jU2bNnHdddcN5zgdiJUTwWRERP5MDNqam+wOrWn7KEz/AUuXDJ4aTy9mzFvAjOSFRCadgVI9eQSnyWAhd185Gdv11JW3OrZHzPYnKUXH1IQA5MLk48Bms1Bds43i4i3U1+93bPfyjEcX8UumBF+MQtE/h2hLfXt9oV6Qeypx8xmck/WwRuu89dZb/P3vf6eiooLIyEhuvPFGLrvsskENuD8IcSKY7HRE/nRkqO0p8qcjQ62I/Bl9GqsryU/bT8HBvRRnHUXqUtHdKzCI6OSFxCQvIjwuHrli7Dm0jiSSJKHPqSM9VU9hRrWjEJ93oMZu8lkcitp98pozzeZ6Sks/RF+yFYOhBACZTEFQ0AVE6K7Fx2fekPggfX+smg3/zmTDxfGcHXP6Wjz9QeQ5EQgmESLyZ2whSRI1xUXkp+3jWNpeKk8UOO0PnBrpiLAJjpwuvpNeaKxuI2NHCdm7SzG22n2v3FRyYs8KITFFR0D45FkhbG7OpVj/NuXln2Gz2fPHKJV+hIVdiS78ajSasCE7lyRJ/OyV3aTrG0jS+fD5r5eM/cJ/x48f55lnnqGwsBCbzdk+JTLECgSjj4j8GR1sNiuleTnkp+2jIG0f9RVlnTtlMsJjZ9sdWucvxDckdPQGOg4xG63kHSgnPVVPbWln5eTwWF+SUiKITApAfpqieOMVSbJSXf0dxfot1NXtdWz39JxFhG49U6asGlRYb2/syKvi2r91RvlsuX4By2YOTVbhYRMnZ511FiqVinPPPbfbje3xxx8f2Gj7iRAnAoFrdET+5OTkkJ+fLyJ/hhiLycTJo0fIT9tLwaEDtDbUO/YplEqmJc4lOnkRM+YtwN3Hd9TGOVGQJInSvHrSt+s58WOVo36dp7+axGU6Zi8JQ+M5/uex2dxAadlH6PXvYDDo27fKCQpaQYTuWnx9BxcxY7NJNBks1LaaqG0xUddioq7V/qppNvHhwWLqWu2+J3IZJIQP3erJsIkTLy8vKisr0WpHr06CECcCQf85NfLn1Jo/0dHRzJo1S0T+9IGhpZkThw+Sn7aPEz8ewmxoc+xTe3gw/cwFRCcvJHLOmag0op7McNFUa+DojhKyvi/F0O7EqVDKmZk8hcTlOoIivEZ5hP2nueUYev3blJX9E5vNPq/c3HwJD7sCne6aHk03kiTRZLRQ19IuNFpN1LaYqW/t+t5EXYuZ2lYT9a0m6lrNWG398+YYqtWTYRMnZ599Nn//+9+JiYkZ9CAHihAnAsHgcDXyJzY2Fi+v8XeTH2qaaqspSNtP/sF9FGemY7NaHfs8/QPazTWL0M1OQOEm8nOMJBaTlWMHK0hP1VNd3FlbKDTah6TlEUTNDUQxhk0+kmSlumY7+uIt1NbtdmxXqKKxua+hXkqhrk1hFx+tJicRUtdqpq7FhKWfQqMDT7Ubvu5K/D1U+Lmr8HNX8n1+NTXNJrr2OJSrJ8MmTn744QdWr17NpZdeip+fn9O+xx57bGCj7SdCnAgEQ4eI/OmZGn2xPUPrwX2U5+c57QvQTXX4j0yZESMcWscAkiRRXtBA+nY9x3+owtb+wPbwVZOwNJzZZ4fh7j3wzKYDGU+b2eq0auEwn7SYaGytxYdviNR+hbeyEgCbJONwZRLbTi4lty4a59LAp8ddpbALDA8lfu4qh+Dw91Dh52EXHv7u9v/7e6jwdVeidnOODDvV1+RUhmL1ZNjEySWXXML3339PQkICii4hbzKZTDjECgQTgKqqKodQmUyRP5LNRll+HvkH7Rla60r1nTtlMkJjYolJXsSM+QvxDwsfvYEK+qS5zkjmrhIyd5XQ1tTuO+EmI2b+FJKW6wie1v/nhqFdaHQ1ldS3mp1NJ+0mlQ4RYrR0T2oW6lHOuRE7WRx2AI2bvdZNi9mdnfpFpBafQ43BH7WbnAAPFb5dxIW/u7KLsFC1C43OVQ+NcnAh6B0ROhklDfSkCGQySByC1ZNhEyeenp4UFBQwZcqUAQ9usAhxIhCMDBM98sdqMVN8NJ1jaXspOLiflvrO6rkKNzemJswhOnkR0+ctwNNv8qwaTRSsZhv5P1SSnqqnsrAz+25QpBeRC0Nwn+FFg9HSLjTahUU3Xw27+aTNbD3NmXpHpZAT4OHGGVNyOGvKNnTuGY59ZlkkVu1lePquIsDLu12EqNCqRj7XjdFiZcmz31HdbOq1TZCnmu8fWN5txaU/DJs4iY2N5cCBA/j4jF4qZSFOBIKRx5XIn7i4OKZPnz6mI3+Mra2c+LHdofXwQUxtndlIVVotUWckE528kKi581G7i2q5Yx2TxWYXFqc6fnb10Wg1Y6syEFJpYWqLhKLdXNIskziisnBEbaHFBW2tVMjafTOcVy0cPhunmFS81UYaaj5DX/I2bW0n23uRERh4HhG6a/HzWzSmVh9L69uobeldnAR4qgj1GZyT97CJk9dff513332XO+64A39/f6cPdunSpQMfcT8Q4kQgGF3GW+RPS30dBQf3k5+2l5NHj2DtIqw8fP2YMf8sYpIXoYtPwm0MC6uJjtlqo77V7LRqYY8wMTu9d2xvMdNktPTdcRc8bJBkcmOu0Q1Pyf78ssmgMUCJaboHnmFa/DzUXQRHpwnFU+3mkphoaTmOvuRtyso+xWq152Vxc/MiLPRydLp1aLUR/f9wJgjDJk56W7qVyWRYrQNb9uovQpwIBGMHq9VKUVGRI0PtqZE/UVFRzJo1a8Qjf+rKShwZWsuO5dLVkO4XGk70gkVEz19IaPRMZOPQJDXWsdqk9rBVu7mktksuDXvESXcTSpOhf0KjA7kM/NxVTpEnnb4anQ6hXd97uMk58WM16al6yo83OPoKmupF0nId0fODceuHH4ck2aip3Ym+eAs1tTsd2z08YtDpfkloyGoUinG0EleQCl/dDyv/ADOWD1m3In39QBmmL0QgmAyMZuSPJElUFBxzOLTW6E867Q+Jnkn0/IVEJy8iQDd5f7kOBJtNoqHN3Gsoq7NTqF2INBrMPTpW9oVMBr5aZbug6BAaylOERvu2dhHirVEOqihg1ckm0lOLOZZWibXdiVXrpWT22WEkLA3H06/31T+LpYmysk8p1r9NW1thx1W0m25+iZ/f4jFlunEJSYLXl0PpYQg7A25KtX8xQ4AQJwNhGL8QgWAy0lfkT4dQGWjkj9ViQZ91lPyDe8lP20dzbacYkisURMQnET1/ITOSz8LLf+iKl41nesoO2pGcqyPSpKvppK7FREObmQGm0sBHq3SErjpCWd07hIVbe+SJ0iE4fLRKFB1TwfFokgb53rU2bc0WsvZVc3R3Dc0N9igfmRymJ/iQdLY/oVHujuDeVsNJiis+oaz6P1htdr8lhcKDsICfEhH8C7Tq0CEY41B/Di6+P7kf/nt/5/mv+QSiz2coEOJkIOT+F967AgDZEw3cf8X3PPtQR30MicISd6IuWoX04weARFaBN799YS570gORJBlnxNby9K+PcPYZVYBEyk0/YV9GIG4K53Cyb175hkVJle3d9mfi9rC/X30M1Xv6135YxzRE/Q9FHydr4e97ILfcvi8qENYugFkh8Oi/IK+SzrtuO4+uhNjgYRjjEF3ToD5H5/cNFjdyWv3IafWj0OiN1CWHg6/CQJy2ljhtLVNVDdh/BPd8DpNZorBWQX6VguPVbhgtnf0oFRJR/maigyxE+ZvRKKXu83U0Ppf+/s24+FCVAEmSHC+b4//t2+l8T/v7juM6PjVZe3+d/55mu8z+Xta1Xft3JUPW/q/zGJ3ejxNskpwTxgWkt6yi1Bzv2B7gdpzYqe9DVB61AZ2+Se6tFiJKDIRUGHDrHj08vpEpIDRpyH6sC3HSXyQJXlsK5emAXZy4yc3suX4FyeE/AFBYP5WoP2cgPe6D1SZnxl9+5J5Fr/Cr+W8ik0m89eNa7vrvRvLvOIMQz0pS3vqC9XPfZf3cd4dmjIKxi02CvzTDIhXMV9nv3D+a4b8GuMMTPm6DuUqYO3JJoMYyrajJYzo5RJPPNCx0udHTSizHiSOf6ZxEiZVWi5KCZn/ymwIoavHDKnX6iGgVJqK9aoj2rGGqRz1u8nF1OxOMcarN00g3rqAyxA3v6F2ovcvtOyTwqYGosib8G6ztIqz94S3rlHj9e3+6Nn3tH6L3FiO0VtONIVo96c/zW+RaBijY5hAmHaxLep/r/vMmPzx1Hyo3C1S153aZcS41jT4UNUzjylVVKH1SALhxZhm51m1UBlxCyNSToPWHKQkw88L2HjsnwTOf/YxnPl/dbRiv3fgma8/Z2639ad+70qbf7+ln++Ec42CPH8ox0vP+umZo2AC3PQR+Xvb9KwDPf0FiMmz7FOIXwIqF3Y/f8jW8/XX3sdx/NVx41mnG6OqYR+q962Nyl8mYC8xFhslio6CikZySenJLG2g1uXOYBA6TgEIG7uZWLJXlKJoakNnsTvc+ft5Ez55BzKwZhEaEIHckhHRx/g7z5yIhYbTYaDRYaGyz0mgwt//fHl3SYLDQ2Nb+Mpjb35ux2JzXGDrXHZzXH07drlEq8NYo8dLazSLejn9V+GiV+GhVeGuVeLur8NXa26kV8iH+7odo/gzr/OzfMa1tRdSWbEVe+hFBVntqfJtFS/3xJdTlL8fSEkxlUiBJl+sIj/Ubf74lp9Lh2tBWx161G88G+PFATR2LjBb47imYcV7Pf+/DhEsrJ7m5ucTGxjreHzhwgI8//hitVsu1117L9OnTh3WQXRnylZOOL6QsHST7zU/2RAN5v5nPmn++z6pfzuCpp2UUFkJUVOfq6jnnQFUVXHstLF8O8+ZB1yjElBRYv97+EkwCTjchxGToE0mSKDuez8Ed2zlWkE8zCiSlqmsDAr08SUhKYt6ixSMa+dNTdtCO5Fw9ZQetbTVh6iE7qCt0ZAf1cwpl7cwO2lOejcFmBxV0IkkStXW70RdvobomlQ5J6O4ehU53LVOCV6PPMpCxXY8+p85xnH+YB4kpOmLPCkGpHqffR/63sPVSJOCqsClkqtXEG428V1phl21DsHoy5GYdb29vGhvtGfb+85//cOmll3L++edjNBrZu3cvX331Feecc86gBu0qQy5O2r+QrsieaODEnYnUtfmy5O3tfL9bgb+/szhpbYWXXoJPPoEffgBPT7j9dnjySbu4TEmBAwdA1eX+evPN8Mc/Dn7IgjHI6SbE8uViMvSAzWpFn53pcGhtqq7q3CmXEzgrEbeQCGraDNTW1Tsd2xH5ExcXR0BAgMvnNJitPaYdr+slO2htqwmDeWBCQ6WQO9U26Rpx0iE4Tg17HY3soAKwWFooL/+MYv3btLbmO7YHBCwjQrcef/+zkcmcQ85rS1vI2K4nZ385FqP9h61K68asxaEkpoTjEzSOQocdASFH2K1VcWtIsGPXq+WVLGkzQdicQfueDLk48fLycuQvWLBgAXfddRdXX301AH/729/4v//7P/bt2zfgAfeHIRUnXb4Q6LwBdYiTSF89j6b9mX/p1/HxxzJmzuzu72cfE3z5pf1Z9NRTcOutp/+x/Oyz9tep/PWv0P6xCsYzp06I998Xk6Eds9FAYfphCtL2UXDoAIbmzrwobio1kXPOJDp5IdPnLUDr2bk6crrIHx//AALCp6MJmopB6eVI5NWRHbRruGuraWD5mDqygzoiT07JDtrTdneVYvwv9U9w2tpOotdvpbTsQywW+1xUKDwIDb2UCN0vcXeP6rMPY6uZnL3lpG/X01jVZt8og8iEABKX64iI80c2iFDnocZoNdJobKTR1P4yNtLYVkPj/x6iwdLCh16e1CgUIJMhlyRmmUz21RPPYLjrKLipB3zuYV05CQoKoqysDLf20uBWqxVfX1+n5EvDyZCKE4sRNiVAS6XT5k5xchKTJpwztx5l/nw5W7bYxcn778PTT0NGhnN3t91mF5WvvCJW8icVfU2IzMxJPRnamho5/kMa+Wl7KTxyGIvJ6Nin8fJmxrwFRM07C+/p8TRZZd2ygTryabS/b2luwqOtkim2GkLkjXS97zdLKk5a/Siy+lIpeTlFBXWgkMscoaudRdScc2c4VjX6mR1UMPaRJIm6uj0U69+munobHaYbrTaSCN0vCQ39BW5u/TcbSjaJoswaMlL1nMyqdWz3neJOYoqOuEUhqDSDd/OUJIk2S5uzuOjh/02mph73m2y9p6jvjVfPuI8l084Dn8EVvRxyh1ir1Yper0en0xEfH8/JkycdfiYnT54c1Vo7g8JNDTenQssp3slPAFd9ABEmVB5B/P08OYsWde4+/3z49a9hwwa4+277Cv7hw/Cf/9h/8AomGX1NiMzM0R7hiGKx2igpLiHnwF6KD6fRcCIXpM6VSYu7Lw3BsZR4R1OkDKamwkLTp7XArn6cJRAIRCOzEKtpJkJRh7+lFk+ZidluFcx2q0CuVOMdMo3wyBlERk0nyNsdPw8V3hohNCYjVmsrZeWfode/TUvLMcf2AP+l6CKuJcB/aTfTTX+QyWVEJgYSmRhIXXkLGTtKyNlbRn1FK7s+yGPf5wXELQolKUWHd7CGFnOLk3joTUw47WvfZpEGlk23A7lMjpfKC2+VN94qb7xUXngpvThYcZB6Y7097LxL25dOfsnixHU9SP3hwyVxMnXqVCIjIwkICECr1fLAAw/w4Ycf8t1333H77bdzxRVXDPc4hw8fnf11KlNmQ5j9v8nJcO+98Ic/2N8HBsLOnfDggzB9OhgM9n+fegp++tORG7pgjNDXhHjuudEe4YCx2iQae8gOWttibk/c1f6+2YilpgyfyhzCGgoINjkL/ipVAMfdozjuHkW1KsC+otQE0FmXp2t2UH/3jpLxp/pqOBdY65odtKPmT05ODrm5uRgMBuqL86gvziNvn5KYmBji4uKYOXPmmKj5IxgZ2tr06EveobT0QywWuwXAbrr5BbrwX+LhMbCADqvNSrO52SEgGkwNDvHQZGqiMbqRptAWpBxvPPOmQrM3Gal6MlL1FPtmkxGyk5O+2SAbWPi7m8wNb7V3p8BQd4oNx0vt/P8OQeKh9EB+ihDbXbKbb05+0+08NslGZk0me0r3sCR8yYDGOhBcznPS1NTEoUOHSEtLQyaTce+99/LCCy9QXFzMc889N2KVSEVtHYFgYPSWHbQn00nHv/Vtvachl0k2Qo3lTG8pZHrrCXwsnWXpbciodA+lJiCWttA43AOCnSJOOkwoXZ1CfbRKFENkmx+rNX8EI4MkSdTV70NfvIWq6m10+BRqtVPR6X5JWOhluLl5YbaZ+1y5aDI39bia0Wxu7seAZOgaZpJQvpRpdbORYRcGjZpqTuh+oGZaAe4emm5iomNVoyexoXXTDtkKoCRJXPWfq8iqyXJaNelAhozZAbN576fvDeqcIgmbQDDBkSSJJqOl22qGczryzhLyde1tBpqG3Evjhr+HigC1DF2bnsDaPNzLcpEZWxxt5G5KguOSmJ68kPizFuLt5zdEVzs4JEmitLTUIVSqq51XdXQ6HbNmzep35I9gbNDVwbPBUEVD9f+w1P0PubnM0aZWNoVcSUeBSUuDqVNstFnaBn1+rZvWJTHho/bBS+WFW5OW8gMmCg/UYWqzO2i7qRXEnRVCYooO/zCPQY+pv5isJlZ8vIIaQ02vbQI0Afzvsv+hUgw8meSQi5M33niDG2+80fH+hRde4IMPPkCr1XLrrbdy1VVXDXiw/UWIE8FEQ5Ikmo0WR4hr3wXW7CYVywCVhqfazZ4nw2E66b3Amp+HEo3NhD79kN2h9ccfMBs7TTEaD0+mn5lMdPIiIueciXIcmEs6In9ycnIoKSlx2tdR8ycuLo7Q0FDhmzIC9OXg2dvKRcdKh9FqxE9h42xPCws9LHi0R2MbbXCgxY3vm92osJzel8RT6dmzGUTp1eNqRlcRolQMzGpgNlrJ3V9OxnY9taWdIl8X50diio7IpMBBFTTsL+Ut5dQaanvd76/xJ8QjZFDnGNZonT/+8Y/85S9/4a677sJgMPCXv/yFJ598kltuuWVQg3YVIU4EYxlJkmg1WR25M7oLjfbtp+TZMFsHJjTcVYreQ1x78NHwdVeidus7l0ZTTbWjwq8+KwObtTME1zMgkOj5C4lZsIjwuHgUbuM30XRDQwO5ublkZ2dTWFhI19uhj4+Pozjh1KlTkcsH7iw50ZEkye5/0SEaeokgcUSSGJuc3ltsA3HwlJihtrHU00Ki1uqI2mq0qciXplLtNhN3TaCzmGgXFz4qH4fY8FR6opCPXn4ZSZIoya0jPVVPYXq1w4zqFaAhYVk4s5eEofEYGbeJ4WZY85zExcXxzjvvkJycDMDevXv55S9/ybFjx07XxZAhxIlgJGlrFxrdk3Od4hA6xNlB7UKie3bQU0XIUGUHlSSJ2pJi8tP2cezAXiqOO/89B0ZMIzp5IdHJiwiOmjEhVxRaW1vJy8sjJyeH/Px8LJbOB6a7uzuxsbHExcUxffr0EfOxG0l6cvB0NYKkydSETRrYvO+gRwdPZffVDG83DRpDOta6b7AYCh3H+/stQRdxLYEBKchk4zOZXWNNG0d3lJC1uxRji33+uSnlzFwwhcTlEQTqPEd5hINjWFdOQkJCKCkpQaHo/PK7ipfhZrjFSUeaeg8P2LoVVq8eeF/PPw8vvghtbXDRRfDGG6DVDtVInWlqAl9f5/5//3u4557hOV9X1q0DhQLeegs++wyuuQZaWuDECYiMHP7zu0pv2UFrW0zdt/cjO6iuoYLvX72BFqWGu1f9lv/NXOSUHbRbPo1T05F3ZAeV2ewhyf/8pz105Zpr7JNomH6xSw0NtF63HtU332CUydgXE8mR6Gn2nTIZYTNn2QXJ/LPwCx1cfgMA4uOhqKjzvclknyB//OOYmzQ9Rf50oFSO3cgfs83c68rFqeGoTqYTUxNN5sHfw1VyVZ9mkN729+XgaTCUoi/5ByUl72Ox1AMgl2sJDf05uvB1eHrOHPT4xwoWk5W8tArSU/XU6Dsdb8NifElM0TF9biByxfhbyRvyPCcmk4mtW7cyb948zjnnHPbs2eNIV5+amkp4+BDcuMYYzf1wxO6JDz+EzZth924ICoJf/MIeirxhw5AMrxvp6ZCQAEeODE//vfH55/Duu3aBAnYx19w8/PWhTBabfeWiB8dPu9gYnuygfqeYSfzcVegaAuBVKDhexqPuKjYNNDvon16yP7wLC+3p8FNSIClpSJO3Wcxmio8eIT9tH+Gb/oxnTS3/Pm8xKrOFNTv34zE7Ho/165kx7yw8fIfYobVrvpeGBjjzTLt6X7VqZCZNP1CpVMyaNYtZs2Y5In86/FQaGxvJysoiKytrWCJ/ujp4dgiKBmNDNzHhZCoZBgfPvsRET/vUioFnD+0JSZKor09Dr3+bqur/IbXXP9NodOh06wgLXYNSOU7zbJ0GN5WC2UvCmLU4lLL8BtJT9Rz/sYrSY/WUHqvH009N/NJw4s8OQ+s1MauduyROrrvuOv785z+TkZGB2Wymrq6Ob7/9lv/7v//jnnvuYdOmTcM9znHH66/D44/bV2EA3nnHvoIyXBw5AnPmDF//PVFdDb/7HVx3HVgGkRPIbLVR19pl9cLJKdTsECGd2800Gwd2wq7ZQTuLqHXPDurfpcBan9lBC+37knS+AxqTg/x8sFrtL7CvmAzBUpuxtYUThw9yLG0fhT8exNQ+EZecOMmX5y4h8tyfEJ28CM/UHSxKOwjnXdhHj0PA/ffDuefahckYR6FQMH36dKZPn87KlSu7Rf4UFBRQUFDAF198gU6nIy4ujqiYKBSeCpdWLk5d6TBajX0Pqg9OdfDsLYKka+6LjtdAHTyHEqvVSEXFvynWb6G5Ocux3c9vERG6awkMPHfcmm76g0wmIyzGl7AYX5rrDBzdWULW96U01xnZ//lxDv6nkJjkYJKWRxA0dWKFxLskTjZv3gzYV1COHDlCQ0MDAFOmTGHr1q2sHoDt44MPPmDt2rVOS6I///nPeeedd/rd11jkxx/tqwhz50J5OVx2GbzwwvCd78gRyMuD2Fj7j9Arr4SNG51rzQ01t91mFyfFxfYf+2DPDlrfZgbU/FhcT06zoY9KriaaDAMTGnIZTmYRhy9G16Rdp4iQMZ0d9MYbYcUK8PMDmw3WrIEBJjhsrquloN2h9eTRdGzWzs/Yw8+f6PkLUX/9PT9/4lkUyQvsO/YegIKCobiS05OdDe+9NzLnGiCSJHVm8OzJ58K/kcb5jbTVtWEpt6CsVqJp0aDX69Hr9fAtNCgbKPUopdS9lHpVPf1JrylD1uvKRU9Co8PB00vlhafKEzf5+HRSNhjKKCn5ByWlH2A22yNH5HINISGridD9Ek/P2FEe4ejh6adh4c9mMP+iSPIPVfL/7d13XFNnFwfwXwJhhr0hAVRUwF3BVa1aq3W2WutoHWiHo+Pt0rbaobZ11Wrr2/lWq+Kuo2rdtu4tThw4WJKwd1jZz/vHhUBkQ0IY5/v58Gnz3HuTcyGSw7PO7ZNSpD3Ow/2LKbh/MQWerR3QeaAIrZ9yg1kTHPJ5Up33OSkoKICFhUWdJ4bNmTMHmZmZWL9+fa2ua6g5J/Xd/cXcHOjWjZs+YGnJJSqDBxtvWOejjwCBgNukNDcXGDuW25y0Pq+n0TLkFpUpolZm8ufpI5a4fMQegz54gPM7PJCdagGPUZHILVIBAB4vHwGfWSdg7lCz7qIndwetuJJrafuTu4OalKHeNJcvc3V6vvyS+yGOHAnMns3NQ6mBrCQpoiMuITriIpIfPdA75uwt4uaP9OgNz9ZtwePzgbAwIDsb2LiR++/IkYBKxWW5xjRjBrfF/6pV+u08nkHnnOhN8FRVU4PkiRUldZngaa22hlehF7wLvOEmdwMfpR8QKgsVlC5KmHmYwdbdFg5WDlUmGxXt4NlcMcaQm3sNEmk40tOPlg7dWHpzQzfe4yEQOJo2yEaIMYbUOBkiT0oRcy0N2uKtBWwcLNDxGR8E9/WGrYNhh9nqy+BzTsLCwhAeHg4AyMnJwZQpU3D48GHw+XxMnz4dP/74Iyxq+Sd6REQExo8fX6trmhILC+A//wFExTvjf/QRsGSJ8ZKTsr0yDg7Ap59ytehKXk+rZZDJVciuaOikUImcsnM2qtkdVFNggZTNfeDxyiX8GyVHTq4D1AoerIoTkxIiJxt4iyxLV548sTto2XZD7g7aZE2fDoSHcz0nTk7AZ59xW99XkpwwrRYpsY+4hOTKRWQlSfWOewW0R0CP3mgT0hMuPuLyT/DDD1z3V9u2QJs2XLKyc6cRbqwMpZKbkHX+fI1Or2iCZ0XJREW7fOar8ivc7bI2Kprg+eQwyJNDJw6WDrDQWCAhNkG38gdKQJAsAJIBSxtL+LbzRVBQEFr7N8+VPzWh0SiQlnYAEmk48vJK5yM5OvaEWBwGV5dB4DfRHqCGwOPx4NnaAZ6tHVDwcgDunknE3bNJKMxV4sr+OFw9FI+A7u7oNFAEz1ZNb15OjX7ye/bs0SUnn3zyCQoKCnDp0iXI5XJ8/PHH+OSTT2o170Sr1eL69euwtbXFt99+C41Gg+HDh2P58uVwemJXSYVCAYWidAy2ZNVQY9euHffHbwmNhuupNwTGGGRytd5S1l9XWaPbszkwcyxEdoESEf8KkZDrhudWXTfY7qAlCUXyNXekFlkja+MA8Hg8qJQ8MAZYabxx7ooajtYCCJYD22f0agwLL5oOqZT78C4hEHBfZWjUKkju3kZ0xCXEXL2E/OzSTZP4Zubw7dgZAaG90KZ7Twidq9ntNDUV+O03LpsFgPnzjTJxSaFR6BIK7Ynj8Haxx3GrWMiibuolG98AmHN6DmIjtbpJn4Xqwnq/vrW5deVzLiqZ8FlyvpV53VfiOHd1RteuXcut/CksLMTNmzdx8+bNRr3yx1jkihQkSrcgMWl7maEbS3h6vAiROAx2wkATR9j02DpYoseo1ug+zB8x19MQeVKK1DgZHl5JxcMrqXD3s0PngSIEdPeAmaBp9MjVep8TX19fXLhwAaLiLoGEhAT06NEDKSkpNX7R1NRUjBs3DtOmTcPEiRORkZGBsLAw2NjY4ODBg3rnLly4EIsWLSr3HMYY1jn3KAOfhEfjwuJe9e6hX7kS+Pln4PRpbk7jiBHACy9wfwyXVbI7aHZB8VyM6gqsVbI7aNruEPDMNXAZfgvaQkuk7Q6BXbfHsOuWoHde2d1BdbuAVlRgrXg/DScbCwiqGb9cuJAb2diwobTNwD30jZuhhnXGj+dmGe/Zw82eHjUKGD8eynfeRtzN64iOuIi4G1ehKCzdTVJgZY1W3UIQENoLrbuFwNKmFltfT5nCJSarVwPXr3Pr3ffvB3r10jvtyR08K+u5qGzCZ9kJnq8dTEfrJAU+f7N8sc3b0+7g+RXtkORWvhe27ATP6nounkw+GsMEzxIVrfwpUbLyp2SH2uZU84cxBpnsBhIkG4qHbrg5UJaWXhCJpsDHezwEgsZR7qC5SHvMDfk8upoKrZr73WRtJ0CHfj7o0M8HQqeGH/Ix6j4n/v7+uH//vi7D12g0cHZ21k2SrauIiAj07NkTubm5ev8oK+o5EYvFBk9OGGN48efzuH5XicTfnoVWy2o9cbJkd9CsAiWy8lX47ypz7P/TEgX5PHQdKEO/yYnIUylx/YQdbh/0RvC7Fw22O6iV2gY3trdG4j0hzM2BQWPy8cZ7RXAWCnD+iA3W/mSFyNusRruD1hYlJ/GGSU6ysrixwKNHoTU3R8aAZ3ChU3vE37uNttHx6BkVjfCh/WHj4IiAkF4ICO0FcccuMK/BsEBFEzzl0ni0/3ApnG7cR6GzECfeHIhL/f0hU8rQ6WgkRv15G1NWPFWPHTxLlUzwnBcugVpoi4Mz+pZLJt7o/CZOn90AQeu2eslFU57gWZWSmj/3799HVFRUs6z5o9UqkJp6qHjo5rau3dGxR/Gqm+do6MbICmVK3DuXhDtnElGQw32W8vk8tH7KDZ0HiODZxqHBFgkYPDkRCASYOnUqQkJCcPDgQbz88suYVrz3wnfffYc9e/bgfA3HkAEgMjISW7duxdKlS3XflHPnzmHAgAEoKCiApWXlGZ2xJsSefpiOsHVXoM61RuJvz+LUg3T08HfWK5pWdnfQkra67g6avvcpuI2+rnv85O6gXI9G+d1By+6zUZvdQceNM/50grIoOam97JSk4gmtl5D0MErv+Rw9vTDq4g3krPsVlmJ35KvzK9xIq7Itw+sywXPlTwn46B1f3WMznlmlwyLVLVUVCoTVT/BsUW+a8jIyMnRLlJ+s+ePm5qZLVJpCzR+FIg2JiVshTdwKlYorJsfnW8DD40WIRVNhZxds4ghbHo1Gi7ibGYg8KUFydGlngqtYiM4DRWgb4gFzC+4zRRKVhbN/PkS/Ce0gDnI2WAwGT0527NiBiIgIRERE4Pr163jqqadw6tQpzJs3Dz/99BMOHTqk25StJqRSKQIDA/Hll1/iww8/RFJSEiZMmIAOHTpg7dq1Bru5mmKMYcR/z+Jecp4uOfH/5GCdp9JZmPMrXcrqbCNA8kMh8tIs8epkVro7qIXx1uyfO8et2gwLM9pLlNOiPmdqkZzoTfBUyJAc+xApt+4g50401Gk5eucWuZojw4cHiWcR3OKlcE3Oxb6+jvUKtewEz8qGQews7CCOfAznxGyoJr8KB0sHg5dor1CLetNUTSaT6YZ+4uPjoS0zYa2k5k9gYCB8fX31dus2tdzcm5BIw5GWdqjM0I0nRD6T4e09ARYWhvugI3WXLsnD7VNSPLySCk3xLthWtgIE9/VGh2e8cfT3O0h7nAd3Pzu8/GmIwf7dGzw5KYsxhvT0dLi7u+P27dtwdnau0w6xp0+fxrx583Dnzh1YWVlh4sSJ+Pbbb6udEGaM5KSk1wSALjnhCdRwHXkTDkFper0WVRVYK2mv0+6gzURj3r7eUJQapV7vhCLmAXr2mQiVtSWOL56GyL6ty68uKT6/SFkIjywr+KZawzfVBkJ5aZe2lseQ4ixHgkchEjyKUGhd8Y62tZ3gWbayan0meBpNS3jT1ENRUREePnyIqKiocjV/rK2t0b59e27lj4lq/mi1CqSmHYZUEg5ZXqSu3cEhBGJxGNxcB4PPbzzzfkgpeb4K984n4fZpKfKzKt78b9S7XeDbwTDDikZNTgAgLy8PZ8+ehbW1NZ555pkGzdwNnZyUzDW5k5irt5qFzwOCvOyx/52nqRppM1MywbO6YZDK6pPINfLqX6QMczUP3hlW8E21gTjNGpaq0n8vGjMgX2wJdWtHmAd4wN7eWa96amOf4EkallKpRGxsLKKiovDw4UMUldl2uuzKn7Zt28LaWIW8iikU6UhM2obExC1QKrn5MjyeBTw9RkEkngp7u45GfX1iOFqNFvGRmbh1MgFJD0uHfHg8wM3XcL0nBt/npHXr1oiNjQUAREVFYfDgwcjLy4NKpULr1q1x+PBhiMUV7KPQBJx5lIFIafnJvFoG3E2S4Wx0Jvq3czNBZKQqT07wfDKZKFuLpKLKqoaa4FnZnAuh2hLmsblQP0hG/iMJWNm/du3s0SakJwJCe8G3U1cILBrXRkmk8bKwsNAN6dSk5o8xVv7IZJGQSMKRmnYQjHF7G1laeMBHNAk+3hNgYeFqsNciDYNvxkfrbm4wt+Aj6WFpgTbGgLTHeZDcyzJY70lN1Xop8ciRI9G2bVusWrUKarUa77zzDtLS0rBnzx6jBwsYtuekpNfkdmKubrqA3rDOqJvoNVCOfW8/XaessSGrEldW8NWYm31WdH9Hj9a8h17LtBX2XtSkTHueMg8aVrdCfiUqm+BZWc9F2WREb4Jn8ZwTZmODmA/+g2sChsSoe2BlJqA6uHtwO7SG9oZ3+yDw+RX0Nprih1iibGlpY8nNBd55BzhyhHut8eO5N9GhQzSsU081WfkTGBiIoKCgOq380WqVSEs7Aol0I2SyG7p2B4enIBaFwc3teRq6aeIYY9i17CrSE/L0ps8ZsvfE4D0nZQO6cuUK/vzzT/B4PAgEAqxcuVK350lTo9RokZRTVOE8Rt8PjwIAknMsodRoa70Et6GrEldW8NVYSu7v9Fk1LB1kmDzeGh9+KcPk96Ox87YMw1sPw7aobbDMSK60PokhdvAU8AVVVk6taNKng6UD7CzsYGNuU69/bIwxpD+Og+Tg3+gOYNXIgcCj0uWS7v5tihOSXnD19a/+tRr6h1jiydLSxjJ3LiCXcwlIURG38c9333GbvzWyqsRNDY/Hg4+PD3x8fDBo0KByK39Kav78+++/tVr5o1BmIDFxGxITt0KpTCt+LQt4eIyAWDQV9vadG+oWiZFJ7mUh7XFeuXZT9Z7UeoG5h4cHFAoFbG25zZ60Wm2T3X7Z0twMf7/TF1kFpbtyJkp4GPobcODdvgAAF6FFnfYGaeiqxGXVtuDrkxM8K0smyvZynPpmPhwH78dL53YDANSjXXBCaYlz/yYVP+swbLi7ARZuSZW/cLFyEzyr2bWzbFtDT/DUajRIfHBPt+RXlp4K+4JCdAfA4/EhCurA7dAa0gsO7h51f6GGqtprqNLSNcEY8PnnXF0doRB49VXg2DHjvmYL5erqin79+qFfv37lVv6kp6cjPT0dZ86cqXTlj0wWCYk0HKmph8AY9/vRwsINIp9J8PZ5BZY0dNOsMMZw+e9YrjhlRX8v8oDLf8dCHOzcYIs9apSc5Ofno02bNggJCYFQKMTy5cuxfPlyJCQkYO7cuejfv7+x4zQab0dreDuWjrUIi8vDdPSpXy2ChqxKzBiDXCOHTCHD9dtF2LxVjM1nLmJfdGaNyrTXdoInAOTGt4JlFwGSvtgJda4rXHqeRMfpG+Bo2x72lva4A2CI/xD4+mmrrKzqYOHQ6Cd4qpQKPI68ieiIi4i9dgVFeaW7epoLLODXqT1w8CRm/b4JNvYGqGHRkFV7KyotbSxr1ug/PnSIq45JjMre3h49evRAjx49yq38yc3NxeXLl3H58mXY2FgiOFgOB8drUCqjylzfDWLRVLi7DwWfb8Qy58RktGqGvCx5xYkJADAgP1sBrZrBTNCIkpOUlBTdPif5+fm6DYJ+/PFHPHz4EH///bdRg2woF5Mu4stj6wCsqfbc6mRnc8P3Bw6UViVeurTyYZ2SCZ6VJRO5ilz9Y0/Mw1Bpuawqcf0CWPU5gy9urahVvCUTPCsr067Xq2Fhj2cKneB2dw7+OSGHs50tXn5pHPo+GKe7v/UAPgr5qMlOHyjKz0Pc9Qg8unIR8ZHXoS6zS7GV0A5tuvdAm9Be8O/UDYKUFGDZKsMkJgDw/ffA668Drkb+6/TPPwGZjHstY403VuaTT7gkbPPmhn3dFs7a2hpdunRBly5ddCt/7t+PgEx2AK5ud2FpWQSlEtBq+VAqO8PT4xUEB48w+sofYlpmAj7GzQuFPF9V6TnWdoIGrctTo+TE3d0dI0aMwIgRI/TaFyxYgBUravch2FgxxrD6+mokyLJ1j2vbfVV2gqfAwgfDp8binjoWskIZuo5zwh+/dwT/+f9VuLrEEBM8eRpLyCKex9NLPoGPa6dK98KoaMJnjXbwLMPCApjzgSU6BHArTYxddbkhyDLSiwvqXYTk3h2wMhtf2bm6cfNHQnpDFNQBfGMtn69l1d46S03l5nqcOmXc13mSWg3MmgWcPAkcPw400W3ZmwO54iG02o1wcNwPO3tu6IYxO6SmBCI+3g8qlTWAOzh48F6zrflDStk5W8HOufHsg1Sj5OTBgwdo37697vGVK1ewa9cuWFtbY9q0aWhVMrGiCbuQdAF3M+8C8AYA7Ineg7aObatcjvrkHI18ZZkJnq47sebKX/jLfhsAIPfREGQUemHnw6r3kK9sgmfZjbQq23jr8lkbzBbxcPq9X4z5rQJg3KrLDYUxhkzJY27+yNVLSI2N1jvu6uuvW2Hj7t+6YcZaz58HPDy4lTvG9M8/3Hhjp07cY7mc+wFevw5ERlZ9bV0pFMCYMUBmJnDxIuDubpzXIZXSalVITz8GiTQcubnXdO32dp0hFk+Du/sw8HiCcit/YmJiEBMTg4MHD9Z75Q8hNVGj5CQ0NFRX+O/gwYMYO3YsnnvuOSgUCqxcuRKHDx+u1fb1jQ1jDN9f+16vbcGFBXV+Pmtza4j6n0LSP6+hz+B0OAotcfjk23hmWBxGd3mrfM9FmXkYVmZWdf4QjIgoV1DWaKZM4RaSvPgitzz6u++AsWMb5rXrQ6vVIPnhA0RfvYToKxeRk5pcepDHg0/7IASE9kZASC84eno1fIBXrjTMD3HyZO6rREXVGw3t3Xe58c6TJwEbG+O9DilHqcxEUtKfkCZugULBVZDn8czh7j4cYlEYHBy66p3/5MqfkkSlopU/JYlKU6j5Q5qOGiUnZbdCWbRoEdatW4dXX30VALBu3TrMnTsXly5dMk6EDeBC0gU8yH5Qrt3Z0hluNm5VzsOo6JiFmQW0r3DDHGu++B4yGbcw4fvvxbCweAZbtgDzluivHDWEx48Brwo+T7ds4WIx5Ot98AG3+qhvX+jub+5cwz2/IamVSiTcuYXoiIuIuXYFhbk5umNmAgH8OnVFQGhvtOneAzYOjiaLE0DD/hCrYujXy80F/vgDEAj0e0z69QMOHzbMa5By8vLuFa+6+RtaLTd0IxC4QOTzKnx8XoGlZfUrylxdXdG3b1/07du30pU/Z8+ebdQ1f0jTU6NN2Ozt7XU9J25ubkhOToa5OZfXaDQaODo66jZpMzZjbF//ysFXEJUVBS3TQpnujYdzj6JzeBcEOQdh24htRvlroKGrBLe0qsTygnzE3biK6IhLiLt5DSp56TpuSxtbtH4qFAGhveDftTssrOo52c9AVYmrRW8aUgNarRoZGf9CIg1HTs4VXbudXUeIRdPg4TEcfH79dyUuWflz//59REdHQ6UqnUxZUvMnMDAQbdq0abLbTRDDMvgmbBqNBlKpFCKRCB06dEBCQgJat24NAEhISICDg4FWKZhA6VwTfVqmxd3Mu7iQdAFP+zxt0Nc8d87421eY8vVMJS8rAzFXryA64iIkdyOh1ZROMBY6u5ROaA3uCDPzWm/xY1r0piHVUKmykZi0A1LpJigU3HAlj2cOd7ehEIvDYG/fzaB/aFW08qdszZ+bN2/i5s2bEAgECAgIQFBQUIPU/CHNQ416ToKCgvDo0SO4uLjA2toaPXr0wI4dO3DixAm88847GDZsGFYaaxOPJxh6+/pXDr6Ce5n3dBNZS3pO+JaFEM2Yh95DUo3We9IcNXSB2cxECaKvXET01UtIidbf5t1F5FuckPSCR+sA8IxVwLGk58TWllsaO3q0cV6nuaKqxPWSl38fUkk4UlL3QavllrwLBM7w8XkFPj6vwsrSs0Hj0Wg0SEhI0O1QW9LrDgB8Ph/+/v4ICgpC+/btDVZZnjQNRqlKnJeXh2vXriEiIgI8Hg9z5szBypUrIZFIsGLFigbrtjNkcqLUKDFk1xBkyjMrPcfFygXHXj4GCzPafKgxYFotkqMfchNaIy4hO0mqd9yrXSACQrgVNs7ePiaKkhDj4oZujhcP3VzWtdsJO0AsDoO7+0iYmZm+oGTZmj/3799Henq63vGSlT+BgYFwNfa+PsTkjJKcNBaGnnOSUpCCLHlWpcedrZzhaduwf3kQfRq1CpI7kVxCcvUyCrJLf158M3P4duqCtqG90bp7DwidnE0YKSHGpVLlcKtupJshV3ClIXg8M7i5PV+86qZ7o+7lfXLlT1m08qf5q83nd8Nt99ZIedp6ItglWPdlkxeMDq7B6OEXjIdng6tMTHJzgYkTAWdnrrjfBx+Uliep6lhZU6YA06bV/z6qi2XKFK7d0xP4z3+4vb4AbjuNklInQiG3uVq7dsaLpawn710/FgYLgRY+7lmYNeQHtA8NxuA334ZEag4La2u07/MMRrz3Md5auxVj5y1C5+eGmi4xiY/nJnMKhdwQRVXq+qap6Te1Nqp6Y6hUwIwZ3DF3d+DDD0s3ssnL42rxuLgAfn5cBcj6qur+tFrg/fe513N35ypoVnfd3r3cz4PHM/62/A0gP/8Bou5/hnPnn0Z0zLeQK5IgEDjB3282+vQ+jU4df4SjY/2rxhpbycqfN998Ex9++CGGDx+O1q1bg8/n61b9/P777/jhhx9w+PBhxMXFQaOp3+aUpGlqYrMCG05+fvXnfPQRV/k9MZFbVvvss0B4OLcbeFXHShiyGGxVr1dVMVhjFMKt671fOZ+NmKuXER1xEQ+uP8LKwysxtP3vCHSLwOrXIjH7900Y+tYH6D04EOaNcfa/Md80Nfmm1lZVb4yff+aWNcfHA4WFwIABQOfOXDb50Udc+6NH3Jvmuee45c/1mWtT1f39+CNw6RL3ellZwODBQEgIMGhQ1dc18UrHjGm4oRtJOLJzSrdqEAqDilfdjISZWePZ0bO2Kqr5U7Lyp2zNH1r50zJRclIPv/7K/VEnEHC/3+Vy7g+46o4Bhi8GW9Xr1bQYrKEK4dbm3gvzChDx9xFER1xC0qP7uuW4f19/G8H+0Qib5YuA0AnwCmiH2b8D4uBOMG/Kv5vq+qap7ptaF1W9MaKjuW1/S/5q5fO53fYArldizx7u9Z2due3ow8Prl5xUdX9bt3KJVMnrvfMOV0Rw0CDjfF9MTKXKRVLyDm7oRs7NqeLxzODmOgQicRgcHRp/D0ltlV35o1KpEBMTg/v37+PBgwe08qeFqlFy8sMPP2DMmDHw8/MzdjxNSkkC//LLwO7dwJAhwKhR1R8DDF8MtqrXq0kxWEMWwq0qFsYYXpuajzEDbuN+ZDYSUyxxBut113oGtIOZ52DcPfw8YmJ4cHNrujsPV6iub5rq3lB1UdUb4403uNdwcuI+/MeNAyZM4I5pNPo7vJqZ1f+NU9X93b8PBAaWntu+PZcMVXddE5Of/xBS6UYkp+yFVsvty2Nu7ggfn4kQ+UyClZW3iSNsGAKBQDdJtqKVP1FRUYiKiqKVP81cjeacpKenY9iwYejevTsWL16MqKio6i9qQTZvBpKTuR7nL76o/ljZYrANGQtQWgz2o4/0241RCFcXSybDf2am4vi6XzHj+V/x8MZDuOUsQaEsFzweD36du2HQa7Mx45cNmLR4FU7cHoY33uDDza15/XWop7Zvmpocq48n3xgKBTBpEtfNFRcH3LvHDfUAXNfaggVATg537I8/uB4LQ6jo/goK9JMhGxtuqKm665oAxjRIT/8X129MweUrw5CYtA1abRGEwkAEBS5F36fPI6DN3BaTmDzJzMwMrVq1wvDhw/HBBx9gxowZ6NevH9zc3KDVahEbG4uDBw9i1apVWLt2Lc6dO4eMjAxTh00MoFardaKiovDXX3/hr7/+QmFhIcaMGYOXXnoJISEhxoxRj6FX6zypPpt97t7NlSi5fbvyY//+C/TpwxWDFYuNV9LkyVjKFoM9dgxo06b0XKWSm2d4/rzh6s0p5UWIv3Ud0RGX8NdfwMHrYzGz/+f46fgKvPv8Qjz1tD+O3HkFMqUPNm0uHaepKpZGu4Gosd80tT1WG5W9MYKDud6J0FDu8bZtwIoVXGHA7Gyu6+/ff7nzX3qJ20k2IqJ+sZRV9v7s7blCgSVviAMHuOGomzervg5otG8alUqG5OSdkEg3QS6XFLfy4eY2GGJRGBwdezS7oRtDo5U/TY/Bd4gtERQUhM8++wyfffYZHj9+jN27d+P9999HYmIi4uLi6hV0UzRyJDB7NjBiBPdYoQAcHas+ZqxisFXFUl0xWEMVwi2U5SLm2mVMf8sf3Tx3INCdm8RXVNgftlZyFLlMQaHGHd8f/w04ztPd+63I0ntvqKK8JlOXN011x+qqqjeGVFq6cgfghk9KhlBSU4HffgNKdoaePx/o0qV+sVR1f4GBwMOHpW+KBw+4oZ3qrmuECgqiIZFuRErKHmg0XO+PubkDfLwnwMdnMqytaW+emnqy5s+DBw8QFRVFNX+aiTpPiPXz88OHH36IDz/8EKmpqYaMqcl46ilg8WKuJyQ/H1i2jJurV9UxYxWDrSqW6orB1qcQbk5qCqIjLnITWh9EgTEtnPiT8M/tsej4Uhq8gvtg7c3RmLPQArNmBWL5utJrK7r3hirKazJ1edNUd6yuqnpjDB3KDY/s2cOtglm+HBg/nju2eDGXmKxezWXVa9YA+/fXL5aq7m/iRO7x009zy5h/+ol77equayQY0yIz8xQkknBkZZ/TtdvatoNYFAZPzxdhZkYTO+vD3t4eoaGhCA0NpZU/zYRBVut4eFRf2bI5+vxzboi7fXvAyorr6X7zzeqPVaWuxWAre72aFIOtTSFcxhjS4mMRHXEJMREXkZ4Qr3eNe6s2+GIMH+GHfbB472pYHeHhrbeAmTNrdh+VxdJs1PVNU9Wxurxpqntj/PYbt+9JQAB3TlgYt4cIwA3vhIVxk2U9PLilviUZpaHfwAAXR1ISt5SZMW7PlRdeqP46E1Or85CUvAtS6UYUFSUUt/Lg5vocROIwODn2ouEGI6jtyp/AwEC0a9eOVv40Mi1+h9gnNVSB2ao0dDHYqowbB/y5XYPE+3cRHXEJ0VcvQZaepjvO4/MhDu6INiG9ERDaE/au7lU8W/000ukD9KZ5UmOKxQRvmoKCGEilm5CcsrvM0I09vL3HQ+QzGdbW4gaLhZQqWflTMk+Fav40PKPNOSHG11iKwaoUcuzaGA1f60T8OmMD5Pl5umPmFpbw7/IUAkJ7ofVTobC2o3/IJtVY3jRA44qlAXFDN6chkYYjK+usrt3Wti1Eoqnw8hwNM7MKxlRJgylZ+dOqVSsMHToUycnJuiXK6enpiI2N1a3+8fHxQVBQENX8MaFa95xoNBrdhKLDhw/D1dUVoSUz+htAQ/WctMQCs0V5MsRej0B0xEXE37oBtVKhO2ZlZ482T/VAQGgv+HXuCoFlw+1M2eiL1rbkN01j1UBvGrU6D8nJuyGRbkJRUXxxKw+uroMgFk2Fk1MfGrppAkpW/ty/fx9SqX4x0ZKVP4GBgfD29qafZz0YrfDf/v378cYbbyA1NRXffPMNFi9eDB6Ph9WrV+PNBhrnNXZy0tLI0tN0FX6lUXfASuqnALB3c0dAaG8EhPaCT/tg8GmWOyEAgMLCOEikG5GcvBsaTQEAwNzcDt5e4yESTYa1ta+JIyR19eTKH23Z34n29rolyrTyp/aMlpz07NkTb7zxBl5//XV4eXlhw4YNcHd3x4QJExAdHV3vwGuCkpP6YYwhQ/IY0Ve4FTZp8fo7e7r5tUJAaC8EhPaGm18r+iuBkGKMaZGVdRYSaTgyM0/r2m1s2hSvuhkNc3NbE0ZIDK2oqAiPHj1CVFQUoqOjoVKpdMdo5U/tGS05cXV1RUZGBm7cuIFnnnkG2dnZMDc3h52dHfLy8qp/AgOg5KT2tFoNkh5E6Sa05qam6I7xeHz4BAYjILQX2oT0gqNH5VWYCWmJ1Op8JKf8Bal0IwoLS/Zz4sHVZSBE4jA4Oz1NSXwLUNHKnxK08qdmjDYh1sbGBmlpadi/fz/69u0Lc3NzREZGwsXFpV4BE8NTK5V4fPsmt+T32mUUyXJ1x8wEAvh17oa2ob3RunsP2Ng7mDBSQhqnwsJ4SKWbkJS8CxoNV3HazEwIb+9xEPlMho2Nv2kDJA2qopo/ZVf+PFnzp+Rc+iO6bmqVnLz22mvo1q0bsrOzsXv3bly7dg1Dhw7FnDlzjBUfqQV5fj7ibkTgUcRFxN+8DpWitN6Jpa1t8YTW3vDr0g0WVpTZE/Ikxhiyss4VD92cAsB1LNvYtC5edTMG5uZCk8ZITK+mK38OHTpEK3/qqNardU6dOgUrKyv06tULEokEEREReOmll4wVXzk0rKMvLzOjdELrvdvQlpS4ByB0cUVASC8EhPaCKKgjzMxp5TghFVGrC5CSsgcS6UYUFpbOw3JxGQCxKAzOzn3B49WoTipp4apa+ePq6qpLVFriyh+jzTl58cUXsW/fvnLt/fv3x+nTpyu4wvBaenLCGENWooSbPxJxESkxj/SOu4h8ERDaG2179IZ7qzYt7s1PSG0UFj6GNHEzkpN3Qq3m5s2ZmQnh5TUWYtEU2Ni0MnGEpCmjlT/6DJqcxMfHY+PGjQCAJUuWYP78+XrHc3NzsW7dOmRnZ9cz7JppickJ02qRHP0Aj65cRMzVS8hOTio9yOPBu10Qt8ImpCecvKhwGCFVYYwhO/sCJNJwZGScQMnQjbW1P8SiqfDyegnm5namDZI0O9Wt/GnXrh2CgoKa9cofg06I9fX1xZ07d5Ceng61Wo2TJ0/qHbeyssIvv/xSv4hJOWqVCpI7t3QrbApzc3THzMzN4dupK7fCpntP2Do6mS5QQpoIjaYQySl7IZVuREFBaY+ji/MzEInD4OL8DA3dEKOxtrZG586d0blz5wpX/ty6dQu3bt2ilT/FajWs8+abb2LNmjXGjKdazbnnRFFYgLgbVxEdcQlxN69CWWapmoW1DVo/FYqA0N5o1fUpWFjTVtiE1ERRkaR41c1OqNVcPRUzM1t4eb0Ekc9U2Nq2NnGEpCWrSc2f5rLyx2hzTgBAqVQiLS1Nb+wM4HpYGkJzS07ys7MQc/UyoiMuIuFOJLQate6YrZOzbkKruEMnmJk3z64+QgyNG7q5WDx0cxylQze+EImmwtvrZRq6IY0OY6zcyp+ymvrKH6MlJ7t27cKbb76pl9kxxsDj8aAps0rEmJpDcpKVJNVNaE1+9EDvmLO3SLdDq2ebtuDxqZuZkJrSaIqQkrIXEmm43tCNs3M/iEVhcHHpT0M3pMnIzMzUJSrNYeWP0ZKT4OBgjB07FmFhYeUm7Pj5+dU6UI1Gg0GDBsHf3x8bNmyo0TVNMTlhWi1SYh8VJySXkJUo0TvuFdAebUK5HhIXHyqnTkhtFRVJIU3cjKSkHVCruQ0Hzcxs4On5EsSiKbC1DTBxhITUT8nKn/v37yMuLq7ClT+BgYHw8/NrtCt/jJac2NnZ6basN4Qvv/wSixcvxpQpU5pdcqJRqyC5d4fbofXqJeRnZeqO8c3M4duxs25Cq9CZdtglpLYYY8jJuQyJNBzp6f8C4H5ZW1v5QiSaAi+vlyEQNN7fEYTUVVNd+WO07eufeuop3Lt3D507d65XgABw4sQJ7N69G2PHjq33czUWyqJCxN28juiIi4i7cRWKwgLdMYGVNVp1C0FAaC+06todVra0yyQhdaHRFCEl9W9IJeHILygdFnV2ehoicRhcXQaAx2ucfzkSYghPrvyJjY1FVFRUhSt/2rRpg6CgoCa38qdWycnTTz+NQYMGYdy4cfD01C8Q9+WXX9b4edLS0vD6669j7969+P7776s8V6FQQKFQ6B6Xne/SGBTkZCPm2hXEXL2Ex7dvQlMmg7VxcESbkJ4ICO0F345dYd6IMlhCmhq5PAlS6WYkJv0JtToHAMDnW8PLawxEoqkQ2rY1bYCEmIBAIED79u3Rvn17vZU/9+/fR25uru7/m9rKn1oN6wwcOLDiJ+HxcOLEiRo9h1arxdChQzFq1Ci8++67mDZtGgBUOqyzcOFCLFq0qFy7KYd1clKSER1xEY8iLiHpYRRQ5lvo6OmFgNDeCAjtDa+27cDn019whNQVN3QTUTx0cwwlQzdWViKIRFPg7TUOAgEVriTkSSUrf0qWKFe08qdkh9onV/7ExMTg8OHDGDZsGNq0aWOwmIy6lLi+Fi9ejMuXL+Pvv/8GgGqTk4p6TsRisVGSk8eRN3Fiw//w7LSZ8OvcVdfOGENaXAyiIy4iOuISMiSP9a7zaN22eIVNL7iIfJvErGlCGjONRo7U1P2QSMORnx+la3dy6g2xKAyurs/S0A0htVDTlT9eXl5Yu3YtkpKS4O3tjTfffNNgn2lGS05KtrGvyNSpU2v0HIGBgUhKSgK/eIlsYWEhAMDGxgY5OTnVXm+sCbGMMWz57EOkxjyCR5u2mLjoWyQ9uKdbYZOXWZp18vh8iIM7cRNaQ3rB3tXNYHEQ0pLJ5UmQJm5FUtJ2qFRcSQw+3wqenqMhFk2FUNjexBES0vTl5eXphnueXPljY2Oj+1wGgMmTJyMgwDCr3YyWnLRqpV8EKysrCwUFBejbty9OnTpVp2Cr6zl5krGSk/ib17B76QLdY4GlFVQKue6xuaUlWnXpjoAevdGqWwishbSBEyGGwBhDbu614qGbo2CM2zPJytKbG7rxHg+BwNG0QRLSTJWs/Ll//z4ePnwItbp0I1AejwcvLy+D9Z4YbbVOXFyc3mPGGJYvX46srKzaR9mIMMZwatMfem0qhRxWQjvdcI1vp64QWFiaKEJCmh+NRoHUtP2QSjYiL/+urt3RsSfE4jC4ugwCn2+YbQsIIRUru/Ln/v372L59u+4YYwxJSUmIiYkxWO9JTdV7zolGo4GPjw9SUlIMFVOVjNFz8mSvSYkxny5A626hBnkNQghHrkhBonQLEpO2Q6Xi/rDh8y3h6fEiROIw2AkDTRwhIS0PYwxr1qxBcnIyyqYFhuw9MVrPSUUePHjQpCeAMsZwbsdm8Ph8sDLjbjw+Hxd2bkWrriFN+v4IaQwYY8iVXYdEUjJ0w3UdW1p6QSSaAh/v8RAIqLo2IaYSExODpKSkcu2m6j2pVXIycOBAvQ9qpVKJyMhITJ482eCBNZTHt64jNeZRuXam1SI15hEe37oO/67dTRAZIU2fVqtAaupBSKThyMu7o2t3dOxRvOrmORq6IcTEGGPVbgdy4sQJtGnTpsH+WK/Vb4UBAwboPTYzM8MHH3yA0aNHGzCkhlPSawIeT2+vEh0eD+d2bIZfl6eo94SQWlAoUiFN3IrExG1QqbjSDXy+BTw8XoRYNBV2dsEmjpAQUkKj0SA3N7fKc2QyGTQajcHK11SnVq+yYEHpvIy0tDQ4Ozs3WKDGoFGrkZeRXnFiAgCMIS8jAxq1mnZ3JaQajDHIZDchkYYjLe1wmaEbT4h8JsPbewIsLJxNHCUh5Enm5uaYMWMGCgoKKj3H1ta2QT/va/VKKpUKH3/8MdasWYOioiJYWlpi8uTJ+PHHH2Fp2fRWspgLBJi05HsUySrPGG0cHCkxIaQKWq0CqWmHIZWEQ5YXqWt3cAiBWBwGN9fB4PPp3xAhjZmDgwMcHBrPbsu1Sk6+/vprnDx5Ejt37kSrVq0QHR2Nzz77DF988QW+/fZbY8VoVPaubrSJGiF1oFCkITFxGxKTtkKpzAAA8HgW8PQYBZF4KuztOpo4QkJIU1WrpcRt2rTBP//8g9atW+vaYmJi8MwzzyAxMdEoAT7JWJuwEUJqJld2C1JJOFLTDoExrtClpYUHfEST4OM9ARYWrtU8AyGkJTLaUuKsrCz4+vrqtfn6+uptdUsIaX60WiXS0o5AIg2HTHZT1+7g8BTEojC4uT1PQzeEEIOpVXLSuXNn/Pbbb3jnnXd0bb/99hs6depk8MAIIaanUGZwQzeJW6BUcvWleDwLeHiMgFg0Ffb2nU0cISGkOapVcvLNN99gyJAh2Lx5M1q3bo2YmBjcu3cPR48eNVZ8hBATkMkiIZGGIzX1EBhTAgAsLNwh8nkV3j6vwJKGbgghRlSr5KRfv364desWtmzZgtTUVIwZMwY7duyAn5+fseIjhDQQrVaFtPQjkErCkSu7oWu3t+8GsWgq3N2Hgs+3MGGEhJCWotaLlk+fPo2ZM2fC29sbmzdvxj///IM33njDGLERQhqAUpmBxMTtSEzcCoUyFQDA4wng4T4CIvFUONh3MXGEhJCWptabsG3YsEG3U6y9vT2+/PJLZGdnY+7cucaIjxBiJLK8O5BKwpGSeqDM0I0rfHwmwcf7FVha0hJ7Qohp1GopsUgkwpkzZ8otJX722Wfx+PFjowT4JFpKTEjdabUqpKcfg0Qajtzca7p2e/suEIvC4O4+jIZuCCFGYbSlxDKZrMKlxPn5+bWPkhDSYJTKTCQl/Qlp4hYoFCkAAB7PHO7uwyEWhcHBoatpAySEkDJqlZx0794dy5Ytw+eff65r++6779C1a1dDx0UIMYC8vLuQSDciNfVvaLXc0I1A4AKRz6vw8XkFlpYeJo6QEELKq1VysnLlSgwZMgS///47xGIxJBIJVCoVLSUmpBHRatVIz/gHUkk4cnIjdO12dh0hFk2Dh8dw8PlNrxYWIaTlqFVy8tRTT+HRo0fYv38/kpOTIRaLMWLEiEZVLIiQlkqpzEJS0g5IEzdDoUgGUDx04zYUYnEY7O27gcfjmThKQgipXq2XEjs5OWHq1KnGiIUQUgd5eVGQSjciJXUftFoFAEAgcIaPzyvw8XkVVpaeJo6QEEJqp9bJCSHE9LRaNTIyjkMiDUdOzmVdu52wA8TiMLi7j4SZGQ3dEEKaJkpOCGlCVKocbtWNdDPkiiQAAI9nBje354tX3XSnoRtCSJNHyQkhTUB+/gNIpOFISdkHrVYOABAInODjPRE+PpNgZeVl4ggJIcRwKDkhpJFiTMMN3UjCkZ1zSdcuFAZDLAqDh8dImJlZmTBCQggxDkpOCGlkVKpcJCXv4IZu5FIApUM3ItFUODqE0NANIaRZo+SEkEYiP/8hpNKNSE7ZC622CABgbu4IH5+JEPlMgpWVt4kjJISQhkHJCSEmxA3dnIREGo7s7Au6dqEwsHjo5gUauiGEtDiUnBBiAiqVDMnJOyGRboJcLilu5cPNbTDEojA4OvagoRtCSItFyQkhDaigIBoS6UYkJ/9VZujGAT7eE+DjMxnW1j4mjpAQQkyPkhNCjIwxLTIzT0EiCUdW9jldu61tO4jF0+Dp8QLMzKxNGCEhhDQulJwQYiRqdR6SkndBKt2IoqKE4lY+3FwHQSQOg5NjLxq6IYSQClByQoiBFRTEQCrdhOSU3dBoCgEA5ub28PYeD5HPZFhbi00cISGENG6UnBBiANzQzWlIpOHIyjqra7e1bQuRaCq8PEfDzMzGhBESQkjTQckJIfWgVuchOXk3JNKNKCp6XNzKg6vrIIhFU+Hk1IeGbgghpJYoOSGkDgoL44pX3eyGRlMAADA3t4O313iIRJNhbe1r4ggJIaTpouSEkBpiTIusrLOQSMORmXla125j0wZiURg8PUfD3NzWhBESQkjzQMkJIdVQq/ORnPIXpNKNKCyMK27lwdVlIETiMDg7PU1DN4QQYkCUnBBSicLCeEilm5CUvAsaTT4AwMxMCG/vcRD5TIaNjb9pAySEkGaKkhNCymCMISvrXPHQzSkADABgY9O6zNCN0KQxEkJIc0fJCSEA1OoCpKTsgUS6EYWFMbp2F5eBEIvC4Oz8NHg8vgkjJISQlsNkycmJEycwb948REVFwcbGBuPGjcO3334La2vaxps0nMLCx5AmbkZS0g69oRsvr7EQi6bAxqaViSMkhJCWxyTJSXp6OkaMGIFff/0VU6dORWpqKoYMGYJly5Zh0aJFpgiJtCCMMWRnX4BEGo6MjBMoGbqxtvaHWDQVXl4vwdzczrRBEkJIC2aS5MTNzQ1paWmws7MDYwyZmZmQy+Vwc3MzRTikhdBoCpGcshcSSTgKC6N17S7Oz0AkDoOL8zM0dEMIIY2AyYZ17Oy4v0zFYjESExPRr18/TJ8+vdx5CoUCCoVC91gmkzVYjKR5KCqSFK+62Qm1mnv/mJnZwstrLEQ+U2Br29rEERJCCCmLxxhjpgygqKgI2dnZmDRpEqysrHD48GG94wsXLqxwqCc3Nxf29vYNFSZpYrihm4vFQzfHUTp041c8dDOWhm4IIaQByWQyODg41Ojz2+TJSYkrV66gZ8+eyMrKgpOTk669op4TsVhMyQmpkEZTiJSUfZBIw1FQ8EjX7uzcD2JRGFxc+tPQDSGEmEBtkhOTDOtcuHABr732GiIjI2FhYQGAS0IsLCxga6u//belpSUsLS1NESZpQoqKpLpVN2p1LgDAzMwGnp4vQSyaAlvbABNHSAghpKZMkpx07twZhYWF+PTTT7Fs2TIkJydjzpw5eP3113XJCiHVYYwhJ+cyJNJwpKf/C0ALALC28oVIPBVenmMhEFDvGiGENDUmSU6EQiGOHDmC999/Hx4eHnBwcMDkyZPxxRdfmCIc0sRoNEVISf0bUkk48gse6NqdnfpCLC4ZujEzYYSEEELqw2SrdYKDg3Hs2DFTvTxpguTyJEilm5GY9CfU6hwAAJ9vDS+vlyASTYHQtq1pAySEEGIQtH09adS4oZuI4qGbYygZurGyEhWvunkZAoGDaYMkhBBiUJSckEZJo5EjNXU/JNJw5OdH6dqdnHpDLAqDq+uzNHRDCCHNFCUnpFGRy5MgTdyKpKTtUKmyAQB8vhW8PMdwQzfC9iaOkBBCiLFRckJMjjGG3NxrxUM3R8GYBgBgZeUDkWgKvL3GQSBwNG2QhBBCGgwlJ8RkNBoFUtP2QyrZiLz8u7p2J8deEIvD4Oo6iIZuCCGkBaLkhDQ4uSIFidItSEzaDpUqCwDA51vC03M0xKIwGrohhJAWjpIT0iBKh242Ij39iG7oxtLSCyLRFPh4j4dA4FTNsxBCCGkJKDkhRqXVKpCaehASaTjy8u7o2h0dexavuhkEPp/ehoQQQkrRpwIxCoUiFdLErUhM3AaVKhNA8dCNx4sQiabCzi7IxBESQghprCg5IQbDGINMdhMSaTjS0g6DMTUAwNLSEyKfKfD2Hg8LC2cTR0maAt4iHmwENuCBBx6PBwFfgGFth+Hn4T/D0crR1OERQoyMkhNSb1qtAqlphyGVhEOWF6lrd3QIhUgcBjfXwTR0Q2rt7lt34e/oDwCQKWQYvX00Zh+cjW1jt5k2MEKI0dEnBqkzhSINiYlbkZi0DUplBgCAz7eAh8cLEIumws6ug4kjJM2FvaU9xgaNxa9XfwUAdPilAx7nPC53Xv78/IYOjRBiBJSckFrLld2CVBKO1LRDYEwFoGToZhK8vSfAwsLFxBGS5uZxzmNsub0FA/wHAOB6VQghzRclJ6RGtFol0tKOQCINh0x2U9fu4NAdYlEY3NyGgM8XmC5A0ux0/rUz+Dw+GBjsLe3xfJvnsXTQUlOHRQhpAJSckCoplBlITNyGxMQtUCrTAQA8ngU8PUZCJJoKe/tOJo6QNFeRsyN1c06e1PnXzkjITSjXnvNpjnGDIoQ0CEpOSIVkskhIpOFITT2oG7qxsHCHyGcSfHwmwsLC1cQRkpYscnZk9ScRQposSk6ITunQzUbIZDd07Q723SASh8Hd7Xnw+RYmjJAQQkhLQMkJgVKZgcTE7ZAmboFSmQYA4PEE8PAYAbEoDPb2nU0cISGEkJaEkpMWTCa7Dal0I1JSD4AxJQDAwsINPj6T4OM9EZaWbiaOkLRUbAEzdQiEEBOi5KSF0WpVSE8/Bok0HLm513Tt9vZdIRaFwd19KA3dEEIIMSlKTloIpTITiUnbkZi4FQpFCoDioRv34RCJpsLBoatpAySEEEKKUXLSzOXl3YVEEo7UtP3QarmhG4HApXjVzSuwtHQ3cYSEEEKIPkpOmiGtVo30jH8gkWxAbu5VXbudXSeIxdPg4T4MfL6lCSMkhBBCKsc3dQDEcJTKLMTH/4YLF/vjzp13kJt7FTyeOTw8RiGk+y6EhuyBl+doSkxIoxafEw/eIh6ES4TYe39vhecsO7cMHt95wGm5Ez48+iG0TFvlc/4V9RcGhg+s8FhqfircVrjhVPwpXdu5hHMIXRMK+6X2CP45GPvu79Mdu5J4BT3W9ID9Unv0W98Pd9Lu1PoeASC9IB3DtwyH3VI7tP2xLY5EH6n03K9Pfw3x92I4LXfCqG2jIJVJdcd+vvKz7tiL219EWkGa7ljI7yGwXWIL4RIhhEuEGPPnGADArAOzdG3CJUJYL7YGbxEPibLEWt/HoUeH0P6n9rBdYotR20YhvSC9wvOkMile2PYCnJc7Q7RKhK9Pf607VjYW4RIhzL8yx5BNQ+p83X8v/xe2S2zBW8Sr9f2QRoI1Mbm5uQwAy83NNXUojYZMdo/du/cpO3EyiP17vDX793hrdvpMCIuOWcXk8hRTh0dIrcRlxzEsrPxX096ovazN6jbscc5jlpKXwrr/rztbe21thedqtBq2+tJqZvWNFeu/vn+F54zePprxF/HZybiTjDHG8hR5zGW5C9t+ezvTaDXs35h/md0SOxabFctyinKY27du7IeLPzClWsk23tzIfFb6sHxFfq3vc/T20eztg28zhVrBDj86zJyWObHsouxy522J3MLa/9iexWfHM7lKzt468BYbvHEwY4yxG8k3mNd3XuxxzmMmV8nZpN2TWNieMMYYY2qNmll9Y8UyCzOrjeWlP19ic4/NrfU9JOclM/ul9uxk3ElWpCpib+x7g03+a3KF5w7eOJj959B/mEKtYHHZcazN6jZsS+SWcufFZ8czr++82NXEq/W6rrr3EWl4tfn8pp6TJkqrVSMt7QiuXX8FVyJGIil5B7RaBezsOiA4aAWe7nMObVp/AEtLD1OHSohBbb2zFbNCZsHXwRceQg/M6zsPa66vqfDcz45/hj/v/okPen1Q4fFNtzaBz+NDbC/WtUllUgxvOxwTOk4An8fHoNaD0NalLa4nX8d5yXk4WDngvV7vQWAmwJQuU+Bg5YB/Y/+t1T3kK/Ox/8F+LOi/ABZmFhgaMBRP+z6N7Xe2lzs3qygL8/rOg5+jHyzNLTErZBYuSi8CAKKzoqFlWqi1agCAGd8M1ubWAIAHmQ/gZuMGZ2vnKmPZfmc7otKjsPjZxbW6BwDYE7UH/Xz7YYD/AFiZW2HZc8uw4+4OyBQyvfO0TAtrgTXm95sPCzML+Dv648X2L+Ki5GK555x1cBbeDn0b3b271/k60vTRnJMmRqXKRmLSDkilm6BQJAMAeDwzuLkNhVgcBgf7p8DjUVcmab7uZ9zHlM5TdI/bu7bHvfR7FZ77n57/wdLnlmLDzQ24ILmgdywpLwlfnfkK5187jx5reujaA10DsXHMRt3j+Jx43Eu/h47uHfEw8yFsBDZ6z2PGM0NMdkyt7uFR5iM4WjnCzbZ0L6H2LhXfxzs93tF7fPDRQXRy52paPd/mefjY+6DNf9vAjGeGAOcAXHidu89bKbdgxjdDr7W9EJsdi76+ffHT8J/gbeetey61Vo1P/v0E/xv5PwjMal+4837GfQS6Buoeu9i4wM7CDtFZ0XjK6yldO5/Hx76JpUNjKo0Kx2KP4d0e7+o937GYY7iXfk93bl2vI00f9Zw0Efn5DxAVNQ/nzj+NmJhvoVAkQyBwhr/fW+jT+zQ6dfwvHB26U2JCmr0CZYFegmAjsEGhqrDCc73svCp9njf+fgNfD/wa7raVr1hLL0jHyK0j8Xq319HetT36iPtAkitB+M1wqDQqbLu9DVEZUZCr5bW7B1VBuSSnqvso8feDv/HNmW+wYvAKAECRugjdvbrj3lv3kP1JNrp5dcPMAzMBcL0Vod6h2DFuB2Lfi4WztTNe3f2q3vPtuLsDLtYuGBowtFbx1+c+NFoNpu2bBkszS4R1CdM79t2F7/BR749gYVZ+r6W6XkeaJuo5acQY0yAj4zgkknBk51zStQuFwRCLw+DhPgpmZjS5lTRfCbkJCP45WPf48KTDsBHYoEhVpGsrVBVCaCGs1fOuvb4WVuZWmNhxYqXnRGdFY+jmoejv1x//HfZfAFzPwN6Je/H+kffx0bGPMLHjRAxpMwQOlg5Vvt6Ss0uw5OwSAICfox82jdmEInWR3jnV3ceaa2sw55852DFuB572fRoAsPDUQrR1bosgtyAAwKohq+C9yhu58lxM6jwJkzpP0l2//LnlcF3hilx5LhysuHg3RW7C691erzL2sjr80gGPcx4DAOb3m1/uZ1HdfRQoCzB+13ik5qfiyOQjsDQv/f2VnJeMM4/P4M+X/zTYdaTpouSkEVKpcpGUzA3dyOXc7Hlu6OZ5iEVhcKAeEtJC+Dr4In9+vl5boGsgHmY+xAiMAAA8yHiA9q7ta/W8O+/txEXJRTgucwQAyBQyjNw6Ep8/8zk+7fspridfx/Obn8fboW9j4YCFuuvkajnsLe1xfeZ1AFzvhN8PfpjXd16Vrze/33zM7zdf91imkCFHnoOsoizdnJAHmQ8wPGB4hdd/dfor/BzxM/6Z8g96+JQOQUllUvjY+egeC8wE4IEHc745Nt3aBA+hB4a04Va9KDQK8Hl83Qe7XC3HibgTWPfCuhp+14C7b93Ve/xLxC84GnNU9zijMAM58hwEOAeUuza7KBuDNw2Gl50XTk87DVsLW73jR6KPoL9/fzhZOxnkOtK00bBOI5Kf/wBR9z/DufN9EB29DHJ5IgQCJ/j5zUaf3qfQqeOPcHQMocSEtGgTO07ETxE/ITY7FmkFaVh6bikmdZpU/YVlHJ18FLJ5MuR8moOcT3Pg6+CLA68ewKd9P0VmYSZGbB2B+X3n6yUmADfnod/6frgouQilRoklZ5fA0coRfcR9avX69pb2GBYwDJ8d/wxytRxHo4/i7OOzGBM0pty5W29vxX8v/xfnpp/TS0wAYGjAUPwc8TMeZDyAXC3HvH/nYVjbYbC1sEV6YTreO/IepDIp8pX5mPvPXLwc/DKszK0AcHNSPIWeVQ59VefF9i/idPxp/BPzD+RqOeYfn48X2r9QYc/JhF0T4Ovgi70T9pZLMABuiXYvn14Gu440bdRzYmLc0M0JSKThyM4unYEuFAZCLJoGD49RMDOzMmGEhDQuLwW9hOisaPTf0B+FqkKEdQnTTRo9+/gshm0ZVq63pTa23dmGlPwUfHHyC3xx8gtd+/9G/g+TOk/Clpe2YOreqUjNT0VvcW/sf2U/+Dzu77xhW4ahn28/vV6Syqx9YS1m7J8Bz+884SH0wPaXt8NT6AmA24cEAH4b+RtWXVyFXEUuuv2vm971+fPzMTtkNtIK0vDcpudQqCrEoFaDsOHFDQCA93q+h+S8ZIT8HoJCVSGGtx2O/438n+76x7mP4SUsn5iUDKXde/sefB18q7wHH3sf/Pnyn7okqJ9fP4SPDtcdFy4R4vCkw3CydsI/sf/AytwKDstKh8Amd56M30b+pouni2cXvee/k3anTteRpo/HGGtS5T9lMhkcHByQm5sLe3t7U4dTZyqVrMzQTcmGSny4uQ2BWBQGR8dQ6iEhLVJ8TjxarW7VJCsTRyRG4GT8SXz89MemDqVe3j74NhYMWFDlZOHGrim/j5qr2nx+U89JA8sveASpdBOSk/+CVstNJDM3d4SP9wT4+EyCtbVPNc9ACGmsDkcfxvSu000dRr3kKfJgaW7ZpBMT0vRRz0kDYEyDjMxTkErCkZV9XtcutG0PkTgMnh4vwMzM2oQREtJ4lPzFayuwxeaXNmN04GhTh0SamP9e/i/mHZ+HQlUh9Zw0IrX5/KbkxIhUKhmSk3dBKt2EInlCcSsfbm7PFQ/d9KShG0IIIS1CbT6/abWOERQUxOD+gwU4f+FpPIpejCJ5AszN7eHr+yb69D6Jzp1+hZNTL0pMCKmAIQv/Pcp8hH7r+0G4RIiuv3XF1SSuSvfZx2fLFY3jLeLp9iLJKMzAmD/HwH6pPXy/90X4zdJJnoYq/FeWlmnx/pH34fKtC9xXuGP5ueWVnrvv/j50+KUD7JfaI+T3EJxP4Hpjt0RuqfCett7eWq/r6qpIVYRJf02C4zJHiL8X630Pn/Tyjpdhvdha9/rdfy/dgr6qwoJ1Oeb5nScEXwuw8NTCet0fMTKjVvkxgsZa+E+r1bD09BPs+o0wXfG9f4+3ZhcvPc+k0q1MrS4wdYiENAmGLPzX7bdubNnZZUypVrJ119cxv+/9mFqjLnfevvv7WNv/tmU5RTmMMcaGbR7GZu2fxRRqBbuedJ0JlwhZdGa0QQr/he0JY+tvrNdr++HiD6znmp4sszCTPcp8xPx/8Gf/xvxb7trYrFhmv9SenYo7xTRaDdt8azNzXu7MZHJZuXNXX1rN+vzRhynVyjpfV1P91/fXFU4s8f7h99mL215kBcoCFpEYwVyWu7AHGQ8qvD7gvwHsetL1cu1VFRas6zHGuJ/BgpMLanx/xDBq8/lNyUk9qVQy9jhhHTt/YWCZpKQNu3lrBsvMPM+0Wq2pQySkSakuORm/czxbcX6F7vGuu7tYzzU9y513L+0es19qr5eMdPylIzv86LDeebnyXOaxwoOdiT/DGGNMkithtottWaGyUHfO3bS7TCaXsYMPD7KA/wboXR/8czDbG7W3xvdXUXLSY00PtvPuTt3j785/xybsnFDu2lNxp9hHRz/Sa3Ne7qyrxFsiLjuOOS5zZI8yH9XrupqqKDnxWOHBIhIjdI/fOfgO++SfT8pdm6/IZxZfWzC5Sl7u2C9XfmEjtozQPc4oyGAWX1uwXHlunY8xRsmJqdTm85tW69RRQUEspIkbkZz8FzSaAgCAubkdvL3GQySaDGvrqvcHIITUTU0L/93PuI+2zm1hxjcrPbe4uF7ZWjLLzi3DAP8B6OfXDwC3OVl71/ZYem4p1l5fC3tLeywdtBTBbsHQaDV1KvyXkJuAzr92BsBt777j7g68f+R99PXtiwOvHihXQK+9a3uE3yo/DNLfvz/6+/fXPb4kvYRCVSHaurTVO2/+8fmY2X2mbqfWul5XlXMJ5zBy60gAXJXlkVtHwpxvjlc7vYrFzy5GakFquXs6FnOs3PNEpkbCVmCL4VuHIzI1El08uuCn4T8h0DWwysKCdT1WtiAhabxMkpzcunULc+bMwbVr12BhYYEhQ4Zg1apVcHV1NUU4NcaYFplZZyCVhCMz64yu3cYmAGJxGDw9XoS5efkdDAkhhlPTwn81KUpXoCzAb1d/w6lpp3Rt2fJs3Eq5hWEBwxD3XhwuSi/ihW0voIN7B73Cf692ehW77u2qUeE/Xwdf5HyaAwCYtncaBvgPwLSu02p9T2VFZ0Vj7I6x+Hrg17C3LJ1cmJCbgP0P9yPmPxUnTHW97kl9ffvq7mnAhgFYOGAhBvgPAMBtq19yH9XdU4GqAL1EvfDdkO/Q2qk1lp5dihe2vYC7b91FgaoA3lbeeueXPE9dj5GmocEnxBYVFWHYsGHo06cPUlJScPfuXWRmZmL69Ma7N4BanQeJJBwXLw3GrVuvFycmPLi6DkK3rhvRq+cRiHxepcSEEANLyE3Qm6h59vHZGhf+sxHYVFtc78DDA2jj3AadPTrr2izMLGDGN8PCAQthaW6JAf4DMMB/AI7FHNMV/lt9eTW8VnrhvOR8jQr/Vae2xQyvJF5Bnz/64M2n3sScPnP0jm27vQ1DA4ZWuE9JXa+rrZKkpCb39Fzr53Bo0iEEuwXDytwKCwcsRGpBKqIyoqosLFjXY6RpaPCek4SEBHTp0gVffvklzMzM4OLigpkzZ2LKlCkVnq9QKKBQKHSPZTKZ0WLLyjqPBw+/Qvt2X8LZ+WkUFsZBIt2E5OTd0Gi47bDNzITw9h4Pkc9k2Nj4GS0WQkj9Cv8FugYiJisGWqbVbS//IPOBXhXeQ9GH8FLgS3rXtXNpB7VWjUJVoa5nQcM00DJtnQv/Vafknjq4d6jyngDgaPRRjN81HiuHrMQbT71R7vih6EOY1X2Wwa6rC2drZ7jZuOFh5kN08+K23X+Q8QDtXcrf04GHB5CvzNdViNYwDdRaNazMrRDoGlhpYcG6HiNNRAPMganWlClT2MCBAys8tmDBAgag3JehJ8RqtVp2+cqL7N/jrdn5C4PY9RvT9VbdXLj4HJNINjGVquaz8gkhtVfdhNjd93az1qtbs5isGJaan8q6/687W31pdYXndv61M/vm9DdMoVaw9TfWM6/vvFiRqkh3PPCnQHYi9oTeNVqtlnX6pRObfWA2U6qV7HjscWa72JbFZMUwmVzGhEuE7ELCBaZQK9jXp79mHX/pyDRaTb3ueeWFlazHmh4sNT+VRWdGM/8f/Nm++/vKnfcw4yETLhGyXXd3Vfg8Gq2G2S62ZbFZsQa5rj7ePfQuG7V1FJPJZexq4lXmvNyZ3Ui+Ue683fd2M/cV7uxe2j0mV8nZnKNzdBOcpblS5rDUgR2LPsaKVEXszb/fZGO2j6nXMcZoQqypNJnVOlqtln322WfM0dGRRUZGVniOXC5nubm5ui+JRGKU5CQ19aheMlLydePm6ywj4wzT1vOXDyGkZqpLThhjbPm55Uy0SsSclzuzD458oEsOzsSfYbaLbXXnRWdGswEbBjC7JXas629d2RXpFb3nsf7GmkWlR5V7fmmulI3aOoo5LXNirVe3Zrvv7dYd23d/Hwv4bwCzW2LHhmwawuKy43THhm4eyhafWVzre1ZpVOyjox8xjxUezH2FO1t2dpnu2OZbm1nwz8GMMcY+OvoR4y3kMdvFtnpfJSuNUvNTGRZCLwGrz3WMcauRNt/aXOt7ylfks2l7pzGX5S5MvErMwm+G644tPrOYDd08VPf423PfMtEqEbNdbMuGbh7KEnISdMeOPDrCgn4KYnZL7NjwLcNZekF6vY9RcmIatUlOTLZDrEwmw/Tp03Ht2jXs378fnTp1qvF1ht4hljGGy1eGoaDgka5NIHBB96e2w9a2tUFegxBSM025YFtzKfxX1s67O8HAML7DeFOHYjDT9k6Dv6M/Fg5YaOpQWpRGv0NsTEwMQkNDIZPJcPXq1RonJsaSlXVWLzEBAJUqs0y1YEIIqd7h6MN4peMrpg7DoC5JL+ktvSakITR4cpKdnY1nn30Wffr0wdGjR02+fJgxhpjYVSj/reAjJnYVTNSxREiLV9X29Y3Vl/2/hNhBbOowDGrl8yv1lhw3dZ7feWLL7S2mDoNUo8GHdVatWoWPPvoINjY25WrL5OfnV3JVKUMP62RmnsHNW5UvY+7aZT1cXJ6p9+sQQgghLRlVJa4hxhgiro5BXt4dcIuAnsSDnV1HhIbsoSJ9hBBCSD00+jknjQVjSijkyag4MQEABoUiGYwpGzIsQlq0mlQlLquqyrRlVVUltyYVdOOy4+C4zFGv7U7aHZh9Zaa3Udzue7trdb8V2XBzA3y/94XdUjtM3TO13IZiJdIL0jF8y3DYLbVD2x/b4kj0Eb17em3fa3Ba7gTP7zwrrHR8JfEK2v5YuoX9k5veCZcIYfaVGWbsn1Hve6pvJWmg8mrRVVWZrurYuJ3jYPWNFQZsGFDv+yMGZrQ1Q0Zi6MJ/RUWJLFd2u9KvoqIkg7wOIaRmKltKfDLuJOu/vr9eW3XVZ8uqqkpudRV0j0YfZeJV4nJxbb61mb2w7YU63+v6G+tZ2J4wvbYbyTeY67euLDIlksnkMjZ8y3D2+fHPK7x+9PbR7O2DbzOFWsEOPzrMnJY5seyibMYYY7MPzGajto5ieYo8Fpcdx7y+89IrzrclcgtzWubE/L73qzS+iMQI5r3Smz3OeVzje1pwckG5ZbqGqiRdWbXoJz1ZZbqqY+tvrC/3viLGUZvP7xbdcwIAVlbesLfrWOmXlZWXqUMkhFRiT9Qe9PPthwH+A2BlboVlzy3Djrs7IFOU30l6251t+PyZz2EjsEGIdwhe6fgK1t1YV+2xo9FH8eb+N/Fp30/LPeet1Fvo4tHFoPe07fY2TOgwAZ08OsHO0g5fDfgKa2+sLXdevjIf+x/sx4L+C2BhZoGhAUPxtO/T2H5nO5QaJTbe2oifhv8EoYUQ/o7+ODP9DDq5cysj/3f1f1h8dnGF91RCo9Vg+r7p+Pa5b+HrUL9CplvvbMWskFnwdfCFh9AD8/rOw5rra8qdF5UehZjsGMzpMwcCMwGmd5sOO0s7/BP7D6QyKc48PoNVz6+ChZkFunl1w+U3Lpfbbl+mkGHG/hn444U/4GDlUONjpHGhqsSEkEZt5NaROJdwDmqtGnK1XDe0Ejk7ssbVZ7OLsiutklvVMQAI8Q7Bo3cfISkvqVxst1JvQalRwu8HP/DAw8zuMzGvX/Vb2Xf+tTMSchOg1Cih1qp1w1c5n+bgfuZ9DG49WC+WlPwUZBVlwdnaWdf+KPMRHK0c4WbrVnpucdXlR5mPYC2wxv4H+7HiwgrweDx83OdjzA6dDQAYHTgab3Z/E2cen8EvEb9UGOMfN/6A0EKISZ0nVXs/AHQ/l5IiiD9c+gG+Dr66n1N9K0lrtJpKq0WX9WSV6ZoeI40LJSeEkEbtwKsHAACn4k9h4amFehWEa1p9tkBVoDv25HlVHQO4hKcyLtYu6O7VHbNCZuFx7mOM3DoSXnZeehWHKxI5OxIAN7fkVPwpbBi9oTTWCioUA1zhurLJSWVVl3PkOciWZyO7KBtXk6/i7lt3EZ0Vjec2PYcgtyAM8B8AD6FHlfExxrDy4kqsGLyiyvPKKqlQvPDUQu6/ZTY4M0Ql6aqqRbdzaad7nSerTJeNobJjpPFp8cM6hJCmq6bVZ6uqklubCrpP2jp2Kz7q8xFsLWwR7BaMt0Pfxt8P/q7z/ZTE+mQsACq8p8qqLluYWUDDNPhqwFewtbBFF88ueKXjKzjw8ECNYriSeAW58lyMaDuiXveiF2s9K0lXVS26REVVpmtyjDQ+lJwQQpqsQNdAPMx6qHtcWfXZslVyS5RUya3qWFWKVEWYe2wucuW5ujaFRgErc6v639MTsXjYesDRylHvvADnAOTIc5BVlFV6biYXd4BzAHjgIVdRGptGq6l0hcyTDj06hBfbv6g3vFIfFd1TdZWkdecW31PZatElSqpF6+KuoMp0TY6RxoeSE0JIkzDAf0C5LvkX27+I0/Gn8U/MP5Cr5Zh/fD5eaP9ChX+VT+w4EQtOLUCeIg/Xkq5h652teKXTK9Ueq4y1wBpHY47iq9NfQaVR4U7aHfwc8bPe3IrqTOs6TW9IBwAmdJiArXe24nrydeQp8vDlqS/xaqdXy11rb2mPYQHD8NnxzyBXy3E0+ijOPj6LMUFj4GztjBHtRuCzE5+hUFWIyNRIbLuzDS8F1ezD+UrSFfQS9arxfZS1cMDCcjVrJnaciJ8ifkJsdizSCtKw9NxSTOpUfi5LsFswxA5iLD27FEqNEhtubkBmYSYGthqILh5d0MGtAz7991OoNCqciDuB0/GnMbLdyNK4EyuPu6pjpPGh5IQQ0mT52Pvgz5f/xHtH3oP7Cnck5iXi91G/644Llwhx9vFZAMDSQUvhYuOCVqtbYcyfY/D989+jq2fXao9VZff43YhMi4TrClcM2zIM8/vOx7C2wwAAS84uwbAtw2p9T929u2PVkFUYt3McRN+L4GztjMXPLgZQug9JQm4CAGDtC2uRnJ8Mz+888Z8j/8H2l7fDU+gJANg0ZhPsLe3h/4M/hm0ZhsXPLkZf3741iuFxzmN42ZVfqTjrwCzMOjCr1vf0UtBLmNl9Jvpv6I+gn4PwjN8zeKfHOwBK9ygp8df4v/Bv3L9w/dYVqy+vxr6J+2BlbgUej4fDkw5DKpPC4zsPvLn/TWwcsxGtnUqLs1YWd3XHSOPToneIJYQ0Pk25KnFZaq0aU/ZMwbax20wdisGkF6RjwakF+GVExSt8mqINNzdwE5NpoqzR0Q6xhBBiYnvv78W0LtNMHYZB7by3E28+9aapwyAtQJNbSlzS0SOTld9kiRDS9OXl5QHghmR+H/o7RgaMrOaKxmmIaAiA5vW7anL7yQCazz1NPTAVR2KPINQrtNncU2NW8j2uyYBNkxvWkUqlEIubV0lyQgghpKWQSCQQiURVntPkkhOtVoukpCTY2dkZvFKwTCaDWCyGRCKh+SyEEEJaJGN9FjLGkJeXB29vb/D5Vc8qaXLDOnw+v9qMq77s7e0pOSGEENKiGeOz0MGhZjWNaEIsIYQQQhoVSk4IIYQQ0qhQclKGpaUlFixYAEtLS1OHQgghhJhEY/gsbHITYgkhhBDSvFHPCSGEEEIaFUpOCCGEENKoUHJCCCGEkEaFkhNCCCGENCotOjm5efMmnn32WTg4OMDV1RVTpkxBZmZmpefv3r0bXbt2hb29Pfz9/bFo0SJotdoGjJgQQgipvxMnTqBnz56wt7eHp6cn3n33XRQVFVV47m+//Yb27dvDzs4O7dq1wy+/GL8qdYtNTpRKJYYPH46BAwciMzMT0dHRSE5Oxocffljh+deuXcOUKVPwzTffICcnB4cPH8aGDRvw/fffN3DkhBBCSN2lp6djxIgRmD17NnJycnDjxg2cOnUKy5YtK3fu3r17MW/ePISHh0MmkyE8PByfffYZdu/ebdQYm9z29YZiYWGBR48ewdraGnw+H9nZ2SgoKICbm1uF58fHx2PWrFkYOZKrkBoUFIQxY8bgzJkz+OijjxoydEIIIaTO3NzckJaWBjs7OzDGkJmZCblcXuHnX1JSEj799FP06tULANC7d28MHDgQZ86cwdixY40WY7Pe56SoqAiJiYkVHvPy8oKtrS0A4Omnn8aFCxcQHByMEydOwMPDo0bP3bVrV0yaNAlffvmlQeMmhBBCGoJIJEJiYiL69euHw4cP6z4XK5OWlobg4GCsWrUKU6dONVpczTo5OXXqFAYOHFjhsT179mD06NEAuERDLpdj9uzZuHv3Lm7evAkzM7NKnzcvLw8vv/wyUlNTcfbsWdjZ2RkjfEIIIcSoioqKkJ2djUmTJsHKygqHDx+u9NyUlBSMGDECrq6uOHjwIMzNjTf40qyTk9pKS0uDh4cHrl+/jm7dulV4zoMHDzB27Fh4eHhg165dcHJyauAoCSGEEMO6cuUKevbsiaysrAo/1y5duoRx48ahX79+WL9+vdG3tm+xE2Lj4+PRqlUrJCcn69oUCgUAwNnZucJrDh06hB49emDo0KE4evQoJSaEEEKanAsXLiAwMBBKpVLXplAoYGFhUeGwzrp16zBo0CC8//772Lp1a4PU3GmxPSeMMYSEhKBt27ZYu3Yt5HI5pk+fDo1Gg0OHDpU7/9KlS+jfvz9+/fVXvPbaayaImBBCCKm//Px8BAcH4+WXX8ayZcuQnJyM8ePHo3v37uWWCe/evRuvvvoq/v77bzz//PMNFmOL7Tnh8XjYt28fVCoV/Pz80KVLF/j6+mLbtm26czp06IAlS5YAAJYsWQKVSoX//Oc/EAqFuq9hw4aZ6hYIIYSQWhMKhThy5Aju3LkDDw8P9O/fH4MHD9ZtjSEUCrFlyxYAwKJFi6BWqzF27Fi9z75Zs2YZNcYW23NCCCGEkMapxfacEEIIIaRxouSEEEIIIY0KJSeEEEIIaVQoOSGEEEJIo0LJCSGEEEIaFUpOCCGEENKoUHJCCCGEkEaFkhNCSIOIjo42dQiEkCaiUSQn2dnZeOuttyAWi2FrawsvLy+EhYVBKpXqzpk2bRoEAoHeDnUlXxqNxoTRE9J4xcfHg8fjIT4+3qRxzJ07F998841JYzCUd999V28naQA4ePAgeDwe3nvvvXLnL1myBKtWrWqo8AhpMAMGDIBYLEZmZqZe+5O/d7RaLb799lsEBwdDKBTCyckJQ4cOxcWLF3XXbNiwAXw+X/e53iiSkwkTJiAjIwMREREoKCjAzZs3oVAoMHjwYKjVat15kyZNQn5+frkvMzMzE0ZPCKlOenq6qUMwiOPHj+PGjRt45ZVX9Np/+uknzJ49G+vWrUN2drbesTlz5mDNmjWIiopqyFAJaRBSqRRTp05FVZvNL1u2DBs2bMCuXbuQl5eHxMREPPvss3j22Wf1elR9fX11n+uNIjk5d+4cxowZA09PTwCAh4cHfvjhB/Ts2bPcP3RCSOXy8vIQFhYGBwcH+Pj4YNOmTXrHf/31V7Rp0waOjo7o3Lkz1q5dW6Pn3bBhA/r27Yv33nsPbm5ucHd3xxtvvAGVSgWA69mcNm2a3jU8Hg+nTp3C119/jS1btmDLli3o0qVLjV5v3bp16N69O1xcXGBnZ4eRI0fqEpyFCxdi/PjxCAsLg729PUQiEXbu3Imvv/4aHh4ecHZ21uul4fF4WLFiBXx9fXXPlZSUpPt+TZw4Ea6urvD09MTzzz9fZRIxb948vPvuu3pt0dHROHHiBBYsWIDOnTvjf//7n95xCwsLTJs2DQsWLKjRvRPSlEydOhXnz5/HihUrKj3n3Llz6NevH4KDg8Hj8WBjY4OPP/4YM2bMQGpqasUXsUbgtddeY/b29mz27Nnszz//ZPHx8eXOCQsLY2FhYQ0fHCFNyJQpU1jv3r1Zamoqy8jIYIMHD2YAWFxcHIuJiWGWlpbs/v37jDHGjhw5wqysrFhSUlK1z7t+/XoGgH3zzTdMqVSyK1euMFtbW7Zt2zbGWMX/PgGwkydPVnq8MpcvX2Y2Njbs8uXLjDHGJBIJa9euHfv8888ZY4wtWLCA8Xg8tnPnTqbVatknn3zCzMzM2Jw5c5hSqWR79+5lAHS/RwCwDh06sNjYWJaTk8NeeOEF1rdvX8YYY19++SUbNmwYKywsZHK5nIWFhbEXXnihwrhK7rmwsFCv/b333mMTJ05kjDG2bds25u3tzRQKhd45jx8/Znw+n6Wmptboe0BIU9C/f3+2YMECtmvXLmZubs7Onz/PGGMsLi5O93uHMcbCw8OZmZkZGzt2LFuzZg2LjIxkGo1G77nWr1/P/Pz8dI8bRc/JmjVr8PPPPyMhIQEzZsyAv78/AgICdFURS2zduhWOjo56X0ePHjVR1IQ0LgqFAjt27MCiRYvg7u4OFxcXvb9mzM3NwRjDb7/9hvPnz2PQoEEoKCiAl5dXjZ7f2toa8+fPh0AgQGhoKLp06YKHDx8a/D46deqEu3fvokePHsjOzkZSUhLc3NyQmJioO6ek3DuPx8PgwYOh0Wh0sY0aNQoA8PjxY935X3/9NVq1agUHBwesWLEC586dQ3x8PKytrXHr1i1s3LgRSUlJWLduHfbt21dhXCdOnEC3bt1gbW2taysoKMCGDRvw/vvvAwBefvll8Pn8cnNSfH194eXlhZMnTxrq20RIozF27FjMnDkTEydORFZWVrnjU6dOxcmTJ2FjY4NFixahc+fOcHNzw/z58/WmbiQkJOg+2xtFcsLn8zF58mQcOHAA2dnZuHv3LsaNG4cpU6bg+PHjuvNeffVV5OTk6H09//zzJoyckMYjMzMTCoUCvr6+urY2bdro/t/X1xenTp1CfHw8Ro4cCWdnZ3zwwQeQy+U1en53d3fweDzdY4FAAK1Wa7gbKGZmZobVq1fD3d0d3bt3x5IlSyCTyfRey8XFRe98AHBycgLA/T4BoHd+27Ztdf9f8v1JTk7GJ598grlz5+KPP/5A27ZtERQUhL/++qvCuBISEuDj46PXtnHjRuTm5mLEiBG6oaG0tDSsXLmy3PUikQgSiaRW3wtCmoqVK1fCzc0NYWFhFc4/6devHzZu3AiJRAKpVIqlS5fil19+waJFi3Tn+Pr66j7bTZ6cHD16FEKhUJdt8Xg8BAcHY+nSpejWrRtu3Lhh4ggJaRpcXV1hZWWF2NhYXVvZFW9paWnQaDTYs2cPMjMzcejQIWzfvh1//PFHvV/bzMwMSqVS9zgjI6POz/X999/j2LFjuH37NmJjY7F37174+fnpnVM2SaqJsr0ucXFxALhfhJGRkRg1ahSuXLmCzMxMTJs2DRMmTEBubm655+Dz+eWSsZ9//hlff/01bt68qfu6fPky7t27h2PHjumdq1arafI+abYsLS3x559/4syZM3rJeX5+PmxtbXHgwAFdm4+PD2bMmIHXX3+90s94kycnzzzzDDw8PDB9+nTcvn0bKpUKeXl52LJlCx49eoQRI0aYOkRCmgQLCwtMnToVX3zxBaRSKXJzc/Hxxx/rjickJGDw4ME4ceIE+Hw+vL29AXBJTX0FBQXh7NmzSExMRFFRERYtWqSXQFhZWVX4gV+R3NxcCAQCWFhYQK1WY/PmzThy5Ihe8lNbixYtQkpKCnJycjBnzhwMHToUPj4+WLt2LaZOnYq0tDTY29vDwcEBQqEQlpaW5Z7Dz89PL8k5ceIEHj58iBkzZkAkEum+unbtiuHDh+O7777Tuz4pKUmvV4uQ5iYgIAC///47fv75Z12bUCjEiy++iLlz5+LUqVMoKCiAUqnE+fPnsWvXLowdO7bC5zJ5cmJtbY1z587B09MTo0aNgoODA8RiMTZv3ox//vkHQUFBpg6RkCZj9erV6NGjBzp16oS2bduiZ8+eumMhISH4+eefMWvWLAiFQjzzzDN46623MH78+Hq/7syZM9GnTx906dIFAQEB8PX11fsgnjBhAs6fP1+jD+c5c+ZALBbDz88P3t7e2Lx5M95++23cvn27zvF1794dffv2hb+/P5ycnHTz2ZYuXYqAgAB06NABdnZ2WL9+Pfbt2wcrK6tyzzFkyBBcu3ZNNwz2008/Yfjw4XB3dy937syZM/HPP/8gMjISABAbG4uMjAwMGjSozvdASFMwYcIEzJw5U69t/fr1mDRpEt59913dirr3338fixcvxvTp0yt8Hh6raHCIEEKaCR6Ph5MnT2LAgAH1fq6QkBDMnTsXEyZMqNV1S5cuxa1bt7B9+/Z6x0BIS2DynhNCCGkqli5dih9++KFW1ygUCvzxxx/46quvjBMUIc0QJSeEtHAREREVloUo+erQoYPBXistLa3K1xIKhQZ7LWMYPHgwunXrVm6bg6qsWLECM2fORLt27YwYGSHNCw3rEEIIIaRRoZ4TQgghhDQqlJwQQgghpFGh5IQQQgghjQolJ4QQQghpVCg5IYQQQkijQskJIYQQQhoVSk4IIYQQ0qhQckIIIYSQRuX/+CCGm2wzzxQAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scan2pol(218,219,220,212,213,214,215,216,217)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "06142770-9162-4f05-a65a-d2106a957908",
-   "metadata": {},
-   "source": [
-    "# (011)_-q_Ni2InSbO6-R  [scan 163-171]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 592,
-   "id": "aa5434a0-08a2-48b9-a0dd-df3aa0404e76",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#def polamat([scn_Pzz,scn_Pzx,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy])\n",
-    "\n",
-    "plt.figure()\n",
-    "#plt.title('(0-12)_Ni2InSbO6-R [scan 128:136] T=2.1 K')\n",
-    "plabel=['Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz']\n",
-    "P = np.full((3,3), np.nan)\n",
-    "u=[]\n",
-    "d=[]\n",
-    "u1=[]\n",
-    "d1=[]\n",
-    "i=0\n",
-    "for n in range(163,172):    \n",
-    "    try:\n",
-    "        data=read_data(n)\n",
-    "        plt.plot(data['col_ds_nut'],55555*data['col_detector']/data['col_monitor'],'^-', label='scan'+str(n)+':'+plabel[i])\n",
-    "        \n",
-    "        u.append(data['col_detector'][0]/data['col_monitor'][0])\n",
-    "        d.append(data['col_detector'][1]/data['col_monitor'][1])\n",
-    "\n",
-    "        u1.append(data['col_detector'][0])\n",
-    "        d1.append(data['col_detector'][1])\n",
-    "        i=i+1\n",
-    "        #print(n)\n",
-    "    except:\n",
-    "        pass\n",
-    "        print(n)\n",
-    "plt.xlabel('SF                                                  ds_nut_amps (A)                                              NSF')\n",
-    "plt.xticks([-3.2,3.2])\n",
-    "plt.ylabel('counts / 55555 mon [~ 1 min]')\n",
-    "plt.legend(fontsize=9, ncol=3)\n",
-    "plt.title(data['subtitle'][:-4]+', T ='+str('%6.2f'%data['sample'])+' K'+', time ='+str('%6.2f'%data['time'])+' sec')\n",
-    "up=np.zeros([3,3])\n",
-    "dn=np.zeros([3,3])\n",
-    "  \n",
-    "up[2,2],up[2,0],up[2,1],up[0,0],up[0,1],up[1,1],up[1,0],up[0,2],up[1,2]=u[0],u[1],u[2],u[3],u[4],u[5],u[6],u[7],u[8]\n",
-    "dn[2,2],dn[2,0],dn[2,1],dn[0,0],dn[0,1],dn[1,1],dn[1,0],dn[0,2],dn[1,2]=d[0],d[1],d[2],d[3],d[4],d[5],d[6],d[7],d[8]\n",
-    "\n",
-    "up1=np.zeros([3,3])\n",
-    "dn1=np.zeros([3,3])\n",
-    "   \n",
-    "up1[2,2],up1[2,0],up1[2,1],up1[0,0],up1[0,1],up1[1,1],up1[1,0],up1[0,2],up1[1,2]=u1[0],u1[1],u1[2],u1[3],u1[4],u1[5],u1[6],u1[7],u1[8]\n",
-    "dn1[2,2],dn1[2,0],dn1[2,1],dn1[0,0],dn1[0,1],dn1[1,1],dn1[1,0],dn1[0,2],dn1[1,2]=d1[0],d1[1],d1[2],d1[3],d1[4],d1[5],d1[6],d1[7],d1[8]\n",
-    "\n",
-    "P=(up1-dn1)/(up1+dn1)\n",
-    "\n",
-    "txt_up='NSF=\\n'\\\n",
-    " '['+str('%5d'%up1[0,0])+', '+str('%5d'%up1[0,1])+', '+str('%5d'%up1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[1,0])+', '+str('%5d'%up1[1,1])+', '+str('%5d'%up1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[2,0])+', '+str('%5d'%up1[2,1])+', '+str('%5d'%up1[2,2])+']'\n",
-    "\n",
-    "txt_dn='SF=\\n'\\\n",
-    " '['+str('%5d'%dn1[0,0])+', '+str('%5d'%dn1[0,1])+', '+str('%5d'%dn1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[1,0])+', '+str('%5d'%dn1[1,1])+', '+str('%5d'%dn1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[2,0])+', '+str('%5d'%dn1[2,1])+', '+str('%5d'%dn1[2,2])+']'\n",
-    "\n",
-    "txt_P='P=\\n'\\\n",
-    " '['+str('%+5.4f'%P[0,0])+', '+str('%+5.4f'%P[0,1])+', '+str('%+5.4f'%P[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[1,0])+', '+str('%+5.4f'%P[1,1])+', '+str('%+5.4f'%P[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[2,0])+', '+str('%+5.4f'%P[2,1])+', '+str('%+5.4f'%P[2,2])+']'\n",
-    "\n",
-    "\n",
-    "plt.text(-3.2, 0.65*55555*np.max(u), txt_up, fontsize=9, color='blue')\n",
-    "plt.text(-1.2, 0.65*55555*np.max(u), txt_dn, fontsize=9, color='red')\n",
-    "plt.text(0, 0.25*55555*np.max(u), txt_P, fontsize=9, color='green')\n",
-    "\n",
-    "plt.savefig('(011)_-q_Ni2InSbO6-R_T2K_count60sec.jpg', dpi=600)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00a9668b-4e8e-4d6b-8932-b2975967c25e",
-   "metadata": {},
-   "source": [
-    "# (011) scans Only magnetic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 622,
-   "id": "12c47ee4-89a0-45a6-84f2-64a6870ab076",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "plt.figure()\n",
-    "data=read_data(100)\n",
-    "plt.plot(data['col_k'], data['col_detector'],'-o', label='NSF(0,-1,2)')\n",
-    "data=read_data(101)\n",
-    "plt.plot(data['col_k'], data['col_detector'],'-o', label='SF(0,-1,2)')\n",
-    "plt.xlabel('0K1 (r.l.u)')\n",
-    "plt.ylabel('counts/min')\n",
-    "plt.legend()\n",
-    "plt.show()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 620,
-   "id": "f1d9c4fc-bee8-4096-b74a-b939a5a8bcfe",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'instrument': 'TAIPAN',\n",
-       " 'filenumber': 100,\n",
-       " 'date': '6/13/2024',\n",
-       " 'time': 60.0,\n",
-       " 'proposal': '32196',\n",
-       " 'experiment': '943',\n",
-       " 'experiment_number': '943',\n",
-       " 'command': 'scan h 0 k -0.95 -1.05 0.005  l 2 e 0 preset time 60',\n",
-       " 'builtin_command': 'scan h 0 k -0.95 -1.05 0.005 l 2 e 0 preset time 60',\n",
-       " 'users': 'James Beare, Junjie Yang, Masaaki Matsuda, Chenyang Jiang, Yunpeng Gao',\n",
-       " 'local_contact': 'Masa Matsuda',\n",
-       " 'subtitle': 'Ni2InSbO6_R_2K_0-12_th2th_1min',\n",
-       " 'monochromator': 'Heusler',\n",
-       " 'analyzer': 'Heusler',\n",
-       " 'sense': '+-+',\n",
-       " 'collimation': '48-80-60-999',\n",
-       " 'samplename': 'Si',\n",
-       " 'sampletype': 'crystal',\n",
-       " 'samplemosaic': '30.000000',\n",
-       " 'latticeconstants': '5.190272,5.190272,14.032562,90.000000,90.000000,120.000000',\n",
-       " 'ubmatrix': '-0.006863,-0.021086,0.070929,-0.112404,-0.221467,-0.006744,0.191867,-0.001517,-0.001414',\n",
-       " 'mode': '0',\n",
-       " 'plane_normal': '0.011994,-0.005443,0.627796',\n",
-       " 'ubconf': 'UB13Jun2024_43053PM.ini',\n",
-       " 'preset_type': 'normal',\n",
-       " 'preset_channel': 'time',\n",
-       " 'preset_value': '60.000000',\n",
-       " 'def_x': 'k',\n",
-       " 'def_y': 'detector',\n",
-       " 'headers': '',\n",
-       " 'title': 'Investigate the helical-cycloidal magnetic structure in the chiral-polar magnet Ni2InSbO6, Si',\n",
-       " 'environment': ['T = 1.000 K'],\n",
-       " 'Pt.': 11.0,\n",
-       " 'h': 0.0,\n",
-       " 'k': -1.0004904761904763,\n",
-       " 'l': 1.9986857142857144,\n",
-       " 'e': -0.008000000000000002,\n",
-       " 'detector': 105.14285714285714,\n",
-       " 'monitor': 55323.142857142855,\n",
-       " 'mcu': 60.58166666666667,\n",
-       " 'm1': -49.18280000000001,\n",
-       " 'm2': 41.9678,\n",
-       " 'marc': 0.5069999999999999,\n",
-       " 'mtrans': 9.0,\n",
-       " 'mfocus': 279.9974000000001,\n",
-       " 's1': -70.88285714285713,\n",
-       " 'us_guide': -3.5,\n",
-       " 'us_nut': -3.0,\n",
-       " 'up_prec': 0.0,\n",
-       " 'ds_prec': 0.0,\n",
-       " 'ds_guide': 3.0,\n",
-       " 'ds_nut': 3.2000000000000006,\n",
-       " 'comp': 1.0,\n",
-       " 'up_prec_ramp': 0.10000000000000002,\n",
-       " 'ds_prec_ramp': 0.10000000000000002,\n",
-       " 'ds_guide_ramp': 0.5,\n",
-       " 'ds_nut_ramp': 0.5,\n",
-       " 'comp_ramp': 0.10000000000000002,\n",
-       " 'theta_2': -2.25,\n",
-       " 'theta_1': -9.75,\n",
-       " 's2': -37.95963333333334,\n",
-       " 'sgl': -1.0945,\n",
-       " 'sgu': -0.4968000000000001,\n",
-       " 'stl': 0.0,\n",
-       " 'stu': 0.0,\n",
-       " 'a1': 20.9835,\n",
-       " 'a2': 41.9547,\n",
-       " 'q': 1.6605,\n",
-       " 'ei': 13.5,\n",
-       " 'ef': 13.507999999999997,\n",
-       " 'us_guide_amps': -3.4241952380952383,\n",
-       " 'us_nut_amps': -2.9495000000000005,\n",
-       " 'up_prec_amps': 0.002609523809523809,\n",
-       " 'ds_prec_amps': -0.00031428571428571427,\n",
-       " 'ds_guide_amps': 3.003990476190476,\n",
-       " 'ds_nut_amps': 3.194666666666667,\n",
-       " 'comp_amps': 1.0018761904761904,\n",
-       " 'vti': 2.7223142857142864,\n",
-       " 'sample': 3.2636428571428575,\n",
-       " 'temp': 1.0,\n",
-       " 'temp_2': 1.0,\n",
-       " 'snp_status': 0.0,\n",
-       " 'datafilename': 'C:/Users/num/Documents/cycle506/exp943/Datafiles/HB1_exp0943_scan0100.dat',\n",
-       " 'col_Pt.': array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,\n",
-       "        14., 15., 16., 17., 18., 19., 20., 21.]),\n",
-       " 'col_h': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "        0., 0., 0., 0.]),\n",
-       " 'col_k': array([-0.9504, -0.9555, -0.9605, -0.9655, -0.9705, -0.9755, -0.9805,\n",
-       "        -0.9855, -0.9905, -0.9954, -1.0005, -1.0055, -1.0105, -1.0155,\n",
-       "        -1.0205, -1.0255, -1.0305, -1.0355, -1.0405, -1.0455, -1.0505]),\n",
-       " 'col_l': array([1.9986, 1.9988, 1.9987, 1.9986, 1.9989, 1.9989, 1.9986, 1.9989,\n",
-       "        1.9987, 1.9984, 1.9986, 1.9988, 1.9987, 1.9987, 1.9988, 1.9987,\n",
-       "        1.9984, 1.9987, 1.9988, 1.9985, 1.9986]),\n",
-       " 'col_e': array([-0.008, -0.008, -0.008, -0.008, -0.008, -0.008, -0.008, -0.008,\n",
-       "        -0.008, -0.008, -0.008, -0.008, -0.008, -0.008, -0.008, -0.008,\n",
-       "        -0.008, -0.008, -0.008, -0.008, -0.008]),\n",
-       " 'col_time': array([60., 60., 60., 60., 60., 60., 60., 60., 60., 60., 60., 60., 60.,\n",
-       "        60., 60., 60., 60., 60., 60., 60., 60.]),\n",
-       " 'col_detector': array([   3.,    5.,    7.,    7.,    6.,    8.,    6.,   59.,  163.,\n",
-       "        1064.,  581.,  218.,    9.,    5.,    9.,   10.,   13.,    8.,\n",
-       "           5.,   13.,    9.]),\n",
-       " 'col_monitor': array([55080., 54980., 55113., 55460., 55569., 55135., 55788., 54697.,\n",
-       "        55422., 55225., 55424., 55192., 55212., 55091., 55349., 55570.,\n",
-       "        55206., 55339., 55713., 55538., 55683.]),\n",
-       " 'col_mcu': array([60.315, 60.206, 60.352, 60.731, 60.851, 60.376, 61.091, 59.896,\n",
-       "        60.69 , 60.474, 60.692, 60.438, 60.46 , 60.327, 60.61 , 60.852,\n",
-       "        60.453, 60.599, 61.009, 60.817, 60.976]),\n",
-       " 'col_m1': array([-49.1828, -49.1828, -49.1828, -49.1828, -49.1828, -49.1828,\n",
-       "        -49.1828, -49.1828, -49.1828, -49.1828, -49.1828, -49.1828,\n",
-       "        -49.1828, -49.1828, -49.1828, -49.1828, -49.1828, -49.1828,\n",
-       "        -49.1828, -49.1828, -49.1828]),\n",
-       " 'col_m2': array([41.9678, 41.9678, 41.9678, 41.9678, 41.9678, 41.9678, 41.9678,\n",
-       "        41.9678, 41.9678, 41.9678, 41.9678, 41.9678, 41.9678, 41.9678,\n",
-       "        41.9678, 41.9678, 41.9678, 41.9678, 41.9678, 41.9678, 41.9678]),\n",
-       " 'col_marc': array([0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507,\n",
-       "        0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507,\n",
-       "        0.507, 0.507, 0.507]),\n",
-       " 'col_mtrans': array([9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9.,\n",
-       "        9., 9., 9., 9.]),\n",
-       " 'col_mfocus': array([279.9974, 279.9974, 279.9974, 279.9974, 279.9974, 279.9974,\n",
-       "        279.9974, 279.9974, 279.9974, 279.9974, 279.9974, 279.9974,\n",
-       "        279.9974, 279.9974, 279.9974, 279.9974, 279.9974, 279.9974,\n",
-       "        279.9974, 279.9974, 279.9974]),\n",
-       " 'col_s1': array([-68.854, -69.064, -69.272, -69.478, -69.682, -69.887, -70.091,\n",
-       "        -70.294, -70.496, -70.698, -70.898, -71.098, -71.295, -71.493,\n",
-       "        -71.692, -71.887, -72.085, -72.28 , -72.472, -72.666, -72.858]),\n",
-       " 'col_us_guide': array([-3.5, -3.5, -3.5, -3.5, -3.5, -3.5, -3.5, -3.5, -3.5, -3.5, -3.5,\n",
-       "        -3.5, -3.5, -3.5, -3.5, -3.5, -3.5, -3.5, -3.5, -3.5, -3.5]),\n",
-       " 'col_us_nut': array([-3., -3., -3., -3., -3., -3., -3., -3., -3., -3., -3., -3., -3.,\n",
-       "        -3., -3., -3., -3., -3., -3., -3., -3.]),\n",
-       " 'col_up_prec': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "        0., 0., 0., 0.]),\n",
-       " 'col_ds_prec': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "        0., 0., 0., 0.]),\n",
-       " 'col_ds_guide': array([3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n",
-       "        3., 3., 3., 3.]),\n",
-       " 'col_ds_nut': array([3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2,\n",
-       "        3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2]),\n",
-       " 'col_comp': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
-       "        1., 1., 1., 1.]),\n",
-       " 'col_up_prec_ramp': array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,\n",
-       "        0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]),\n",
-       " 'col_ds_prec_ramp': array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,\n",
-       "        0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]),\n",
-       " 'col_ds_guide_ramp': array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
-       "        0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]),\n",
-       " 'col_ds_nut_ramp': array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
-       "        0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]),\n",
-       " 'col_comp_ramp': array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,\n",
-       "        0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]),\n",
-       " 'col_theta_2': array([-2.25, -2.25, -2.25, -2.25, -2.25, -2.25, -2.25, -2.25, -2.25,\n",
-       "        -2.25, -2.25, -2.25, -2.25, -2.25, -2.25, -2.25, -2.25, -2.25,\n",
-       "        -2.25, -2.25, -2.25]),\n",
-       " 'col_theta_1': array([-9.75, -9.75, -9.75, -9.75, -9.75, -9.75, -9.75, -9.75, -9.75,\n",
-       "        -9.75, -9.75, -9.75, -9.75, -9.75, -9.75, -9.75, -9.75, -9.75,\n",
-       "        -9.75, -9.75, -9.75]),\n",
-       " 'col_s2': array([-36.568 , -36.7093, -36.8455, -36.9818, -37.1237, -37.2615,\n",
-       "        -37.3965, -37.5392, -37.6763, -37.8129, -37.9542, -38.0963,\n",
-       "        -38.2341, -38.3753, -38.5178, -38.6564, -38.7961, -38.9402,\n",
-       "        -39.0822, -39.2211, -39.3639]),\n",
-       " 'col_sgl': array([-1.0945, -1.0945, -1.0945, -1.0945, -1.0945, -1.0945, -1.0945,\n",
-       "        -1.0945, -1.0945, -1.0945, -1.0945, -1.0945, -1.0945, -1.0945,\n",
-       "        -1.0945, -1.0945, -1.0945, -1.0945, -1.0945, -1.0945, -1.0945]),\n",
-       " 'col_sgu': array([-0.4968, -0.4968, -0.4968, -0.4968, -0.4968, -0.4968, -0.4968,\n",
-       "        -0.4968, -0.4968, -0.4968, -0.4968, -0.4968, -0.4968, -0.4968,\n",
-       "        -0.4968, -0.4968, -0.4968, -0.4968, -0.4968, -0.4968, -0.4968]),\n",
-       " 'col_stl': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "        0., 0., 0., 0.]),\n",
-       " 'col_stu': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "        0., 0., 0., 0.]),\n",
-       " 'col_a1': array([20.9835, 20.9835, 20.9835, 20.9835, 20.9835, 20.9835, 20.9835,\n",
-       "        20.9835, 20.9835, 20.9835, 20.9835, 20.9835, 20.9835, 20.9835,\n",
-       "        20.9835, 20.9835, 20.9835, 20.9835, 20.9835, 20.9835, 20.9835]),\n",
-       " 'col_a2': array([41.9547, 41.9547, 41.9547, 41.9547, 41.9547, 41.9547, 41.9547,\n",
-       "        41.9547, 41.9547, 41.9547, 41.9547, 41.9547, 41.9547, 41.9547,\n",
-       "        41.9547, 41.9547, 41.9547, 41.9547, 41.9547, 41.9547, 41.9547]),\n",
-       " 'col_q': array([1.6018, 1.6078, 1.6135, 1.6193, 1.6253, 1.6311, 1.6368, 1.6428,\n",
-       "        1.6486, 1.6544, 1.6603, 1.6663, 1.6721, 1.678 , 1.684 , 1.6899,\n",
-       "        1.6957, 1.7018, 1.7078, 1.7136, 1.7196]),\n",
-       " 'col_ei': array([13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5,\n",
-       "        13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5]),\n",
-       " 'col_ef': array([13.508, 13.508, 13.508, 13.508, 13.508, 13.508, 13.508, 13.508,\n",
-       "        13.508, 13.508, 13.508, 13.508, 13.508, 13.508, 13.508, 13.508,\n",
-       "        13.508, 13.508, 13.508, 13.508, 13.508]),\n",
-       " 'col_us_guide_amps': array([-3.424 , -3.4243, -3.424 , -3.4243, -3.4243, -3.4243, -3.4243,\n",
-       "        -3.4243, -3.424 , -3.4243, -3.4243, -3.4243, -3.424 , -3.4236,\n",
-       "        -3.4243, -3.424 , -3.4243, -3.4243, -3.4243, -3.4243, -3.4243]),\n",
-       " 'col_us_nut_amps': array([-2.9496, -2.9496, -2.9496, -2.9496, -2.9493, -2.9496, -2.9493,\n",
-       "        -2.9496, -2.9493, -2.9496, -2.9496, -2.9493, -2.9496, -2.9493,\n",
-       "        -2.9496, -2.9496, -2.9493, -2.9493, -2.9496, -2.9496, -2.9496]),\n",
-       " 'col_up_prec_amps': array([0.0026, 0.0026, 0.0027, 0.0026, 0.0026, 0.0026, 0.0027, 0.0026,\n",
-       "        0.0026, 0.0026, 0.0026, 0.0026, 0.0026, 0.0026, 0.0026, 0.0026,\n",
-       "        0.0026, 0.0026, 0.0026, 0.0026, 0.0026]),\n",
-       " 'col_ds_prec_amps': array([-0.0003, -0.0003, -0.0003, -0.0003, -0.0003, -0.0003, -0.0004,\n",
-       "        -0.0003, -0.0003, -0.0004, -0.0003, -0.0003, -0.0003, -0.0003,\n",
-       "        -0.0003, -0.0003, -0.0003, -0.0003, -0.0003, -0.0004, -0.0003]),\n",
-       " 'col_ds_guide_amps': array([3.004 , 3.004 , 3.004 , 3.004 , 3.004 , 3.0039, 3.004 , 3.004 ,\n",
-       "        3.004 , 3.004 , 3.0039, 3.004 , 3.004 , 3.004 , 3.004 , 3.004 ,\n",
-       "        3.004 , 3.004 , 3.004 , 3.004 , 3.004 ]),\n",
-       " 'col_ds_nut_amps': array([3.1947, 3.1947, 3.1947, 3.1947, 3.1946, 3.1947, 3.1947, 3.1947,\n",
-       "        3.1947, 3.1947, 3.1947, 3.1947, 3.1947, 3.1946, 3.1946, 3.1946,\n",
-       "        3.1946, 3.1946, 3.1947, 3.1946, 3.1947]),\n",
-       " 'col_comp_amps': array([1.0019, 1.0019, 1.0019, 1.0019, 1.0019, 1.0019, 1.0019, 1.0019,\n",
-       "        1.0019, 1.0019, 1.0019, 1.0019, 1.0019, 1.0018, 1.0019, 1.0018,\n",
-       "        1.0018, 1.0019, 1.0019, 1.0018, 1.0018]),\n",
-       " 'col_vti': array([1.5245, 1.5223, 1.5186, 1.5203, 1.528 , 1.5336, 1.5944, 1.6917,\n",
-       "        1.8015, 1.9696, 2.1428, 2.331 , 2.525 , 2.9925, 3.7101, 3.9983,\n",
-       "        4.2128, 4.4971, 4.7502, 4.8582, 4.9461]),\n",
-       " 'col_sample': array([2.1683, 2.1624, 2.1562, 2.1545, 2.159 , 2.1678, 2.205 , 2.2725,\n",
-       "        2.373 , 2.5086, 2.6736, 2.839 , 3.0105, 3.3942, 4.1143, 4.4408,\n",
-       "        4.6826, 4.968 , 5.2342, 5.3792, 5.4728]),\n",
-       " 'col_temp': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
-       "        1., 1., 1., 1.]),\n",
-       " 'col_temp_2': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
-       "        1., 1., 1., 1.]),\n",
-       " 'col_snp_status': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "        0., 0., 0., 0.]),\n",
-       " 'filedesc': 'TAIPAN:943:100'}"
-      ]
-     },
-     "execution_count": 620,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data['meta']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "338e7913-3e90-4908-9bb2-a69c2f73a59f",
-   "metadata": {},
-   "source": [
-    "# (011)_-q_Ni2InSbO6-R  [scan 172-180]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 593,
-   "id": "f54bc7f3-c463-4207-946a-63e6f3e7b370",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#def polamat([scn_Pzz,scn_Pzx,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy])\n",
-    "\n",
-    "plt.figure()\n",
-    "#plt.title('(0-12)_Ni2InSbO6-R [scan 128:136] T=2.1 K')\n",
-    "plabel=['Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz']\n",
-    "P = np.full((3,3), np.nan)\n",
-    "u=[]\n",
-    "d=[]\n",
-    "u1=[]\n",
-    "d1=[]\n",
-    "i=0\n",
-    "for n in [172,173,174,175,176,177,178,179,180]:    \n",
-    "    try:\n",
-    "        data=read_data(n)\n",
-    "        plt.plot(data['col_ds_nut'],55555*data['col_detector']/data['col_monitor'],'^-', label='scan'+str(n)+':'+plabel[i])\n",
-    "        \n",
-    "        u.append(data['col_detector'][0]/data['col_monitor'][0])\n",
-    "        d.append(data['col_detector'][1]/data['col_monitor'][1])\n",
-    "\n",
-    "        u1.append(data['col_detector'][0])\n",
-    "        d1.append(data['col_detector'][1])\n",
-    "        i=i+1\n",
-    "        #print(n)\n",
-    "    except:\n",
-    "        pass\n",
-    "        print(n)\n",
-    "plt.xlabel('SF                                                  ds_nut_amps (A)                                              NSF')\n",
-    "plt.xticks([-3.2,3.2])\n",
-    "plt.ylabel('counts / 55555 mon [~ 1 min]')\n",
-    "plt.legend(fontsize=9, ncol=3)\n",
-    "plt.title(data['subtitle'][:-4]+', T ='+str('%6.2f'%data['sample'])+' K'+', time ='+str('%6.2f'%data['time'])+' sec')\n",
-    "up=np.zeros([3,3])\n",
-    "dn=np.zeros([3,3])\n",
-    "  \n",
-    "up[2,2],up[2,0],up[2,1],up[0,0],up[0,1],up[1,1],up[1,0],up[0,2],up[1,2]=u[0],u[1],u[2],u[3],u[4],u[5],u[6],u[7],u[8]\n",
-    "dn[2,2],dn[2,0],dn[2,1],dn[0,0],dn[0,1],dn[1,1],dn[1,0],dn[0,2],dn[1,2]=d[0],d[1],d[2],d[3],d[4],d[5],d[6],d[7],d[8]\n",
-    "\n",
-    "up1=np.zeros([3,3])\n",
-    "dn1=np.zeros([3,3])\n",
-    "   \n",
-    "up1[2,2],up1[2,0],up1[2,1],up1[0,0],up1[0,1],up1[1,1],up1[1,0],up1[0,2],up1[1,2]=u1[0],u1[1],u1[2],u1[3],u1[4],u1[5],u1[6],u1[7],u1[8]\n",
-    "dn1[2,2],dn1[2,0],dn1[2,1],dn1[0,0],dn1[0,1],dn1[1,1],dn1[1,0],dn1[0,2],dn1[1,2]=d1[0],d1[1],d1[2],d1[3],d1[4],d1[5],d1[6],d1[7],d1[8]\n",
-    "\n",
-    "P=(up1-dn1)/(up1+dn1)\n",
-    "\n",
-    "txt_up='NSF=\\n'\\\n",
-    " '['+str('%5d'%up1[0,0])+', '+str('%5d'%up1[0,1])+', '+str('%5d'%up1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[1,0])+', '+str('%5d'%up1[1,1])+', '+str('%5d'%up1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[2,0])+', '+str('%5d'%up1[2,1])+', '+str('%5d'%up1[2,2])+']'\n",
-    "\n",
-    "txt_dn='SF=\\n'\\\n",
-    " '['+str('%5d'%dn1[0,0])+', '+str('%5d'%dn1[0,1])+', '+str('%5d'%dn1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[1,0])+', '+str('%5d'%dn1[1,1])+', '+str('%5d'%dn1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[2,0])+', '+str('%5d'%dn1[2,1])+', '+str('%5d'%dn1[2,2])+']'\n",
-    "\n",
-    "txt_P='P=\\n'\\\n",
-    " '['+str('%+5.4f'%P[0,0])+', '+str('%+5.4f'%P[0,1])+', '+str('%+5.4f'%P[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[1,0])+', '+str('%+5.4f'%P[1,1])+', '+str('%+5.4f'%P[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[2,0])+', '+str('%+5.4f'%P[2,1])+', '+str('%+5.4f'%P[2,2])+']'\n",
-    "\n",
-    "\n",
-    "plt.text(-3.2, 0.65*55555*np.max(u), txt_up, fontsize=9, color='blue')\n",
-    "plt.text(-1.2, 0.65*55555*np.max(u), txt_dn, fontsize=9, color='red')\n",
-    "plt.text(0, 0.25*55555*np.max(u), txt_P, fontsize=9, color='green')\n",
-    "\n",
-    "plt.savefig('(011)_-q_Ni2InSbO6-R_T2K_count3600sec.jpg', dpi=600)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f8e1d8d-bc2e-4b6e-989c-341b691c7539",
-   "metadata": {},
-   "source": [
-    "# (011)_+q_Ni2InSbO6-R  [scan 181-189]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 594,
-   "id": "0b8a3d34-ca4f-47c2-a06a-525d0a4abe24",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#def polamat([scn_Pzz,scn_Pzx,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy])\n",
-    "\n",
-    "plt.figure()\n",
-    "#plt.title('(0-12)_Ni2InSbO6-R [scan 128:136] T=2.1 K')\n",
-    "plabel=['Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz']\n",
-    "P = np.full((3,3), np.nan)\n",
-    "u=[]\n",
-    "d=[]\n",
-    "u1=[]\n",
-    "d1=[]\n",
-    "i=0\n",
-    "for n in [181,182,183,184,185,186,187,188,189]:    \n",
-    "    try:\n",
-    "        data=read_data(n)\n",
-    "        plt.plot(data['col_ds_nut'],55555*data['col_detector']/data['col_monitor'],'^-', label='scan'+str(n)+':'+plabel[i])\n",
-    "        \n",
-    "        u.append(data['col_detector'][0]/data['col_monitor'][0])\n",
-    "        d.append(data['col_detector'][1]/data['col_monitor'][1])\n",
-    "\n",
-    "        u1.append(data['col_detector'][0])\n",
-    "        d1.append(data['col_detector'][1])\n",
-    "        i=i+1\n",
-    "        #print(n)\n",
-    "    except:\n",
-    "        pass\n",
-    "        print(n)\n",
-    "plt.xlabel('SF                                                  ds_nut_amps (A)                                              NSF')\n",
-    "plt.xticks([-3.2,3.2])\n",
-    "plt.ylabel('counts / 55555 mon [~ 1 min]')\n",
-    "plt.legend(fontsize=9, ncol=3)\n",
-    "plt.title(data['subtitle'][:-4]+', T ='+str('%6.2f'%data['sample'])+' K'+', time ='+str('%6.2f'%data['time'])+' sec')\n",
-    "up=np.zeros([3,3])\n",
-    "dn=np.zeros([3,3])\n",
-    "  \n",
-    "up[2,2],up[2,0],up[2,1],up[0,0],up[0,1],up[1,1],up[1,0],up[0,2],up[1,2]=u[0],u[1],u[2],u[3],u[4],u[5],u[6],u[7],u[8]\n",
-    "dn[2,2],dn[2,0],dn[2,1],dn[0,0],dn[0,1],dn[1,1],dn[1,0],dn[0,2],dn[1,2]=d[0],d[1],d[2],d[3],d[4],d[5],d[6],d[7],d[8]\n",
-    "\n",
-    "up1=np.zeros([3,3])\n",
-    "dn1=np.zeros([3,3])\n",
-    "   \n",
-    "up1[2,2],up1[2,0],up1[2,1],up1[0,0],up1[0,1],up1[1,1],up1[1,0],up1[0,2],up1[1,2]=u1[0],u1[1],u1[2],u1[3],u1[4],u1[5],u1[6],u1[7],u1[8]\n",
-    "dn1[2,2],dn1[2,0],dn1[2,1],dn1[0,0],dn1[0,1],dn1[1,1],dn1[1,0],dn1[0,2],dn1[1,2]=d1[0],d1[1],d1[2],d1[3],d1[4],d1[5],d1[6],d1[7],d1[8]\n",
-    "\n",
-    "P=(up1-dn1)/(up1+dn1)\n",
-    "\n",
-    "txt_up='NSF=\\n'\\\n",
-    " '['+str('%5d'%up1[0,0])+', '+str('%5d'%up1[0,1])+', '+str('%5d'%up1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[1,0])+', '+str('%5d'%up1[1,1])+', '+str('%5d'%up1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[2,0])+', '+str('%5d'%up1[2,1])+', '+str('%5d'%up1[2,2])+']'\n",
-    "\n",
-    "txt_dn='SF=\\n'\\\n",
-    " '['+str('%5d'%dn1[0,0])+', '+str('%5d'%dn1[0,1])+', '+str('%5d'%dn1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[1,0])+', '+str('%5d'%dn1[1,1])+', '+str('%5d'%dn1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[2,0])+', '+str('%5d'%dn1[2,1])+', '+str('%5d'%dn1[2,2])+']'\n",
-    "\n",
-    "txt_P='P=\\n'\\\n",
-    " '['+str('%+5.4f'%P[0,0])+', '+str('%+5.4f'%P[0,1])+', '+str('%+5.4f'%P[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[1,0])+', '+str('%+5.4f'%P[1,1])+', '+str('%+5.4f'%P[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[2,0])+', '+str('%+5.4f'%P[2,1])+', '+str('%+5.4f'%P[2,2])+']'\n",
-    "\n",
-    "\n",
-    "plt.text(-3.2, 0.65*55555*np.max(u), txt_up, fontsize=9, color='blue')\n",
-    "plt.text(-1.2, 0.65*55555*np.max(u), txt_dn, fontsize=9, color='red')\n",
-    "plt.text(0, 0.25*55555*np.max(u), txt_P, fontsize=9, color='green')\n",
-    "\n",
-    "plt.savefig('(011)_+q_Ni2InSbO6-R_T2K_count60sec.jpg', dpi=600)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a101dc0-df69-47ee-8ccb-9efc9aac1863",
-   "metadata": {},
-   "source": [
-    "# (011)_+q_Ni2InSbO6-R  [scan 190-198]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 627,
-   "id": "fccccfe0-e071-44ad-9823-b22af5e14cac",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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hoWzfvp3XXnuNmjVrsmPHDp555hmmTZtmVtbHx4f27dubfnt5edGhQwf2798PKPfa0tIyT5ssLCxMZQFSU1OJjY1l7969DBw4kF69evH777/j5OTEZ599BoDBYCAtLc20ZO3mqlatmun+/fHHH3Tp0gVvb29++OEHJk6c+MBmBwkhzGy8f8lrJk9+9U6fPp1ly5bx7rvv0q9fvxK1+8svv2T69OkMHTqUyZMn51v+22+/pUuXLuzatYuAgAAmTpxYovbkRMeOHU3/9/LyAjDzQ3d3d0CZtQZKV0v37t2xs7MzXX8nJye6dOnCvn37cj3O/X52/3I/ffv25ciRIyxatIgtW7bQp08fQPFpgKpVq7J9+3Z69uzJiBEj2Lp1Kzdv3mTDhg1AwZ6ZeZUBcn0GFLTunMiv7u7du3P9+nVat27NvHnzOH/+PMOGDWPKlCkAHDx4ECEEffr0Mbt+ffv2RafTcezYMa5fv05YWBj9+/c3q3vq1Kn8999/WFtb53jsiowMXsoBWq0We3t7s3UuLi7ExcVlK5uQkICzs3Op2GFvb49Wqy1weSsrK9q0aWNarKys8Pb2Nv1u1KgRANHR0Xh6embbv3r16sW2+cknn2T8+PF8+eWXnDx5ssD7HTp0iI4dO6JWqzl48GC24DEn7h/zoVarc8yrYWdnR//+/fn666+5fPky/v7+PPHEEyxevNhsTFHGiyMrVapUISYmBlACvPDw8Dz7yjPGHdSqVQvANK25Ro0apjKOjo507NiR//77D4B69ephaWlpWsaMGWMqm/WePvvss/z555+4ubnRq1cvrl27lt8lKjHWrVtnZuP9y0cffVToOnU6HUOHDmXRokW88847zJ8/v8TsNRqNTJs2jalTpzJs2DDWr19foECvV69e7Nixgz59+vD666+zYcMG1q1bV2J25YSTk1O2dXmNZ4qKimLz5s3Z7sHu3bsJCQnJdb/7/ez+5X6aN2/OE088wVtvvcXSpUv566+/OHHiBI6OjgA899xzZgFA+/btcXFxMfl1QZ6ZeZUBcn22FrRuwGysSkHqHjJkCBs3bsTBwYE5c+bQqlUr6taty6ZNmwDl+gM0bdrU7Pq1a9cOgJCQEFOZKlWq5HiMyohFWRsgAQ8PD9NXTgaNGjVi7969GI1Gsz/YgIAAmjRpUip2xMTE4OHhUeL1enh4EBAQkG19xh9ccVm4cCF//vkno0ePLtCX9MaNGxk1ahQNGzbkzz//NHvRFxWDwUC9evUYOXIkc+fONdtWv359vvzySx577DH8/Pxo1qwZgClIycq9e/dMD6C+ffvyzTffsHPnToYOHZrjcbdu3WoqC9CgQYMcB+jp9XpsbW0B+O2338zK5HXP7ezs+OGHH3j88ccZM2YMx48ffyCtL3369OHcuXO5bvf29i5UfVqtll69enHq1CkWLVrEW2+9VVwTTaSmpvLiiy+yY8cOpk6dyuLFiwt8jQYNGoSVlRUAn332Gfv37+e1116jQ4cOpoHkZY2LiwtPPfVUtpZDyGz5y4n7/Swnbt26xcGDBxk+fDg2Njam9W3btgUgODiY3r17o1ar8/XrRo0amQ14zyAgIMD0om/UqBFxcXFERESYfVAFBARQu3ZtU133U5DnccbHWkBAgMn+jN/W1tbUrVs31+swdOhQhg4dilar5a+//uKzzz5jxIgRPPHEE6ag6ODBg6ZALis+Pj5EREQAmP7NIDo6mn/++YcOHTrg4OCQ6/ErIrLlpRxQq1YtgoODzdb17NmT+Ph49u7da1oXERHBkSNHzEa8lyTBwcGmL/iS5KmnniIwMJCzZ8+a1kVHRxeqpSQvHB0d+e6777h+/bqpqyo39uzZw8svv0zHjh05ceJEiQQuoMwoql69Ot9//32OQVlGq0VG4ALKQy1rUBccHMzJkyfp3r07AE8//TSdO3dm+vTpBAUFZavz77//5vPPP2fIkCE0aNAAUL7k//vvP65cuWIqFxUVxYkTJ0yzMpo3b27WYpZfsrw2bdowfvx4Tp48WeqtAhm4u7ub2Xj/UpjgJS0tzRQMbd68uUQDF4CRI0eyc+dOlixZwpIlS4oc3Nna2vLTTz+RmprKkCFDCj1LRKPRFOm4+dG1a1f8/Pxo2bKl6fq3bt2aJUuWsGPHjlz3u9/P7l9ACV7GjRvH9u3bzfbNSFL36KOP4uDgQJcuXdi+fbvZNTlw4ACJiYkmv+7ZsydXrlwxSxzn5+fHlStXTM/Mp59+GoBffvnFVCYlJYXdu3fn+VwtyPO4Y8eO2Nvbm9Ut0mdKde3aNdeumyFDhphmKDk7OzNo0CA++OADDAYDISEhdO3aFYDIyEiz6xcVFcWsWbOIioqicePGeHh4sHPnTrO6f/rpJ5577jl0Ol2u51ZhKZuhNpKsfPjhh8LDwyPbYMhu3boJV1dXsXr1arF9+3bRokULUb16dREdHZ1jPcUZsGs0GoW7u7uYO3du0U8kF3Q6nXjssceEt7e3WLt2rdi1a5do166dUKlUxRqwez/jxo0zDfjLIOs5Jycni2rVqgkXFxfx119/5TqDJbcBu/fnsLn/ev7999/CwcFB+Pj4iEWLFomDBw+Kffv2iQ8++EA4ODiICRMmmMqOHDlS2NraiiZNmogdO3aIX375RTzyyCOiWrVqZsnJbt++LZo3by7c3d3Fxx9/LA4cOCD++usv8c477wg7OzvRuXNnERMTYyofHR0tfHx8TDNKfv31V9G2bVvh4uKS7wydvPK8REZGCldXV1G1alVTHpuMGUEFTYJWVgN2ly5dakpMmNesl4CAgDyT5uU0YHfnzp0CEH379s2x7twGSuaVSyhj4Ocbb7xhWufr6yv+/fffPM/zhRdeENWqVRMHDhwQ0dHRuQ7YzUpOf0/373fhwgVhZ2cnnnnmGbFz507x559/iv79+2dLvFgU0tLSRPfu3YWrq6tYvny5KcmfpaWlGD9+vKncyZMnhZWVlejRo4fYs2eP+OGHH0SVKlXE448/bprRp9PpRKNGjYSPj4/YuHGj2Lhxo/Dx8REtWrQwG0Q7cuRIYW1tLRYtWiR+++030b17d+Hk5CT8/f1NZXLyhYI8j2fPni1UKpWYNWuW2LNnjxg4cKCwtLQUx48fz/UaZMwSmjZtmjhw4ID45ZdfRLNmzUSDBg1MyfVGjBghnJ2dxeeffy4OHjwoVqxYIVxdXUWrVq1M5798+XIBiIkTJ4q//vrLNHng9ddfL8YdKr/I4KUccOHCBQGIM2fOmK2Pjo4Wo0aNEi4uLsLJyUk899xz4urVq7nWU5zg5fTp0wIQvr6+RTuJfIiMjBQjR44UTk5Ows3NTcyYMUN06dKlRIMXrVYratasmWvwcuDAgTxnR2UEJ0UNXoQQwt/fX4wZM0bUqVNH2NjYCHt7e9GuXTuxevVqs+B05MiR4vHHHxdLly4VXl5ewtbWVvTu3VsEBARkO6+EhATxxRdfiMcee0w4OjoKFxcX0bFjR7Fy5cpsMxuEUDLbDhs2TLi6ugp7e3vxzDPPmCXuyo38Eud9/fXXAhBTpkwRQmTei4yXXH6UVfDSpUuXPO97BhmzrXIjp+DlpZdeyrPunPxUiLyDF4PBIDp37iwAsWvXLtOx80tqePDgQeHj4yOsrKzETz/9VGLBixBKMr5nn31WODo6CgcHB9G+fXuTbcVFq9WKadOmidq1awsrKyvRqFEjsXjx4mzTzE+cOCG6desmbG1thbu7uxg7dqxZ4C6EEuz3799fODg4CFdXVzFkyBAREhJiVkan04mpU6eKKlWqCDs7O9GlS5dsz96cfKEgz2Oj0Sg+/vhjUbNmTWFjYyNatWplNrsrN7766ivRpEkTYWtrK9zc3MTgwYNFYGCgabterxcfffSRqFu3rrC0tBQ1atQQEydOzDYte+3ataJp06bCyspK1KlTR3z00Udm2YUrEyohpJJXeaBPnz5UqVKFNWvWlMnxR48eTUxMTLZmx9IkI0FdeddekeTOyy+/zKRJk8xmq0hKntTUVFq1alVqSSQlkoqGHPNSTvj000/55ZdfuH379gM/9u3bt9m+fTsff/wxoMycyGuKY25THYuKyGdabHGnx0pKh/Pnz3PmzBmzbL6S0mHu3Lk89dRTZW2GRFJukMFLOaF58+bMnDmTGTNmPPBjz5gxg3fffdeUh2XMmDF5TnHMWAIDA0vk+EeOHCnQ8YoyPVZSetSsWZP9+/c/UNmAh5UXX3yRhQsXlrUZEkm5QXYbSbIRGBhIZGRkvuVatGhhmuZZHOLj4wuUQ8Tb27vQU2QlEolEUvmQwYtEIpFIJJIKhew2kkgkEolEUqGQwYtEIpFIJJIKRaWTBzAajYSEhODo6PjAROQkEolEIpEUDyEE8fHxeHt75yuUW+mCl5CQEGrWrFnWZkgkEolEIikCwcHB+Uq3VLrgJUO4Kjg4OEf1VIlEIpFIJOWPuLg4atasmaMA5f1UuuAlo6vIyclJBi8SiUQikVQwCjLkQw7YlUgkEolEUqGQwYtEIpFIJJIKRaXrNsqPDB0dg8FQ1qZIJBKJpJSwtLREo9GUtRmSUuKhCl5SU1MJDQ0lKSmprE2RSCQSSSmiUqmoUaMGDg4OZW2KpBR4aIIXo9HIrVu30Gg0eHt7Y2VlJfPASCQSSSVECEFERAR37tyhQYMGsgWmEvLQBC+pqakYjUZq1qwpVXAlEomkkuPp6UlgYCB6vV4GL5WQh27Abn5Z+yQSiURS8ZEt65Ub+SaXSCQSiURSoZDBi0QikUgkkgqFDF4KyXH/SJ5afITj/pFlbUqJEB0dTe3atQkMDDSti4+PZ/To0bi7u1OrVi1WrFhh2mY0Gpk6dSru7u5UqVKFzz77LNe6VSoV9vb2ODg44OjoiJubG8OHDyc2NrYUz+jBknjyJDf+15vEkyfL2pQSobD+oNVqefHFF3Fzc8PT05M333yTtLS0HOt+GPxB5x/DvcV/o/OPKWtTSoTC+gPAwoUL8fb2xtXVleHDh5OcnJxj3Q+DP0hKDxm8FAIhBJ/vvUpAeAKf772KEKKsTSoW586do2vXrgQFBZmtnzZtGoGBgfj7+3P48GEWLlzIzp07AVi2bBmnT5/G39+fkydPsnLlSg4cOJDrMXx9fUlISCA+Pp7AwEBCQ0OZOHFiaZ7WA0MIQfjiJaTeuEH44iUPpT9MmzYNjUbD3bt3uXbtGocOHWLdunW5HqOy+4N2byBp4clo9wY+lP6wZcsWVqxYwYkTJwgODiYiIiLPD5zK7A+VmfIQpD+0wYsQgqTUtEIt+/zCuHhHC8DFO1r2+YUVeN+CPsgMBgPjx4/Hw8ODGjVqMGrUKHQ6Hbdu3aJnz544OjpSv359tm/fDkB4eDiDBg3Cx8cHW1tbunXrxt27dwEYNWoUb731Fq1bt8bJyYnevXsTHR0NgJ+fH71792b69OnZbNi5cyfz5s3Dzc2NOnXqMGHCBNMLaePGjUyfPh03Nzfq16/P66+/zurVqwH46aefaNq0aa7n5uTkxIABA7h06RK3b9/GwcHBtNja2qJSqfD19aVp06Zm21QqFcuXLy/gnS0aQgiMSUmFWhIOHEB3+TIAusuXSThwoMD7VhZ/WLFiBd9//z3W1tbcu3cPnU6Hm5sbUDh/CA0NxdXV1XQef/zxBx4eHoSEhNCxY0cmT54MKKrxbm5u7Nu3r0DXr6gIITCmGgq1JPtFob+TAID+TgLJflEF3rey+MPq1auZPXs2derUwcHBgR9//JFRo0YBhfOHQ4cOUbVqVbNkot27d2fz5s1l4g+STMpLkP7QTJW+n2S9gSYf7i1WHeN//KfAZf0+egY7q/wv9/bt2/Hz8+P27dvo9Xp69OjB1q1bWbp0KT169GD37t2cO3eOZ555hk6dOjFjxgzc3d25ceMGycnJPP/883z99dfMnz8fUIKNY8eO4eHhwZNPPsmKFSt4//33qVmzJjdu3MDBwcH0cMnAYDCYTSfXaDTcuHEDgKtXr9K4cWPTtkaNGpkeXMOHD2f48OG5nltQUBA//fQT3bp1w8fHh4SEBNO2QYMGYWdnR9OmTfH19TWtX7ZsGStXrmTkyJH5XrviIJKTudaqdbHquPP6GwUu2+jff1AVYMp+efcHS0tLAAYOHMi2bdvo2bMnffr0AQrnD9WqVWPRokVMmTKF9u3b88orr7B8+XK8vb1Zs2YNbdq0YdSoUcyePZshQ4bw9NNPF/haFwWhNxLyYfG6AqN/vFLgst4fdURllf903vLuD//99x/9+vWjZcuW3Lt3j4EDB7Jo0SKgcP7QtWtXNBoNx44do1u3boSFhfHPP//Qu3dvWrRo8cD9QZJJ8qVIsyA9xT8Wm4auD9yOh7blpbzi7OzMtWvX2LBhA4mJiZw7d46OHTty6dIl5s2bh5WVFZ06deLIkSM4OjqyYMECFi5ciMFgIDg4GDc3N+7du2eqb8CAATRo0ABXV1d69eplesg4Ojrmmnmyd+/ezJ49m9jYWG7dusWaNWvQ6XQAJCYmmj247Ozs8sxY3KJFC1xcXHB2dqZz5840adLE9ODM4NNPPyUgIICVK1earT969Chz585l+/btD22WzPLuDxls2LCB0NBQoqOj+eCDD3I9n7z8YcyYMTzyyCO0bduWjh07MmTIEAAeeeQRZs6cSZ8+ffDz8+OLL74o1jWtyJR3f4iJiWHt2rXs3r2bS5cucf78+Wx/71nJzR/UajWDBg1i27ZtAGzbto1evXphb28v/aEM0QVqid58LXOFCrR/lU3ry0Pb8mJrqcHvo2cKVFYIwZBVp/ELjcOY5R6pVdCkmhObX22fb04BW8uCJUnq2bMn8+fPZ9WqVUyaNImOHTsyf/583N3dsba2NpVr3VppJfD19eX111/n1q1bNG/eHJ1Oh6Ojo6mcp6en6f8WFhYYjcZ8bVi6dCmTJk2iQYMG1KtXj5EjR7J161ZACVayDsBLSkrKM7C4ePEitWvXznX7nj17WLJkCWfPnsXW1ta0Pjg4mMGDB/Pdd9/RqFGjfG0uLipbWxr9W7CWNCEEQS+9TMrVq5D1eqrVWDduTK0f1+frD6os55oX5d0fMrCxscHLy4t3332XOXPm5PrCys8fRo8ezbBhwxg2bJjZ+lGjRvHhhx/yxhtvPJBAVmWpxvujjgUqK4QgctVF9KGJkPUZrgLLavZ4vNoif3+wLNh3ZHn3BysrKyZPnkyNGjUAZXzMp59+ypw5c3KsKy9/GDJkCIMGDeKrr75iy5YtTJkyxbTtQfvDw44wCuKP3CHur0BzHxdl1/ry0La8qFQq7KwsCrT8HRTL5RDzwAXAKOBySBx/B8XmW0dBEybdvHmTTp06ce7cOe7evYu3tzezZs0iKiqKlJQUU7nFixdz9epVhg8fzrhx4wgPD+fAgQO0bdu22NcmLCyMlStXEhERwenTp4mNjeXRRx8FoHHjxly/ft1U9tq1a0UOLvz9/XnppZf48ccfqVOnjmm9Tqejf//+jB07ln79+hXrXAqKSqVCbWdXoCX53/Ok+PmZBy4ARiMpfn4k/3s+3zoqiz/07t2b33//3VQ2JSUFFxeXIh0nKSmJ999/n+HDhzNt2jSzFr0333yTfv36sXbtWi5dulSs8ykIKpUKtZWmQIs+KB59yH2BCygP9pBE9EHx+dZRWfyhYcOGaLVaU1mDwVCggCgnOnTogIWFBXv37uXChQs899xzpm0P2h8eZgzxqUR+f5m4vYHZfRzKrPXloQ1eCooQgkV/XSO3Z4tKBYv+ulZiN+7AgQMMHTqU8PBw3NzcsLGxoX79+rRp04Y5c+ag1+s5ceIE8+bNw8XFhdjYWOzt7QE4efIkGzZsQK/XF8uGTz75hPfffx+DwcC5c+dYvXo148aNA+DFF19kwYIFhIeHc+PGDb7++us8+7FzIz4+nn79+jF9+nSeffZZs23jx4/H09OTjz/+uFjnURoIIYj48kvycoiIL798aPyhVatWfPLJJ8TExBAcHMyCBQt46aWXinScmTNn0rBhQzZs2EC9evWYNWsWALt27eLo0aOsWbOGt99+m7Fjx5YbVXghBNq/AiG32KOEH+zl3R9eeuklFi9eTHBwMJGRkSxcuJABAwYU6TgqlYrBgwfzzjvv0KdPH2xsbIDy7Q+VDd31GMK+/JeUgFjQ5OLkWVpfHiQyeMmHVIORkNhkcnv2CAGhsTpSDUX7urifMWPG0KVLF5o2bYq7uzvR0dF8+umnbNq0iQsXLlClShXGjh3Lxo0b8fLyYsWKFbz33ns4OzszZcoUxowZw5UrBR8omBNffPEF/v7+uLq6MmzYMJYtW0b79u0BmDx5Ml26dKFFixZ07NiRCRMm0LdvXyD/2QRZ2bFjB35+fixZssTUv+7g4MC6dev48ccfOXPmDK6urqb1EyZMKNY5lRRCr0cfGkpeDqG/dw9RzBdEBuXdH2bNmkWrVq1o1KgRnTp1YtiwYbzyyitA4fzhzJkzfPfdd6ZZZcuXL2fVqlUcOXKESZMmsXjxYlxcXHj77beJj49nyZIlxTqnEsMgMMSm5PxFCiBQthtKJngp7/7w5ptvMmbMGDp37kyDBg1o06YNb7/9NlA4f8jgxRdf5NKlS7z44ouAkleoXPtDJUEYjGj/vEXk95cxJujRVLHFwsP2gQXpBUElKnoygvuIi4vD2dkZrVaLk5OTaX3GdMI6deqYIviCEhKbTHRiaq7b3R2sqOZcsDEMkoqPPjSUtPQppTlh4e6OpZfXA7RIUpakxaZgTMw9WFU7WGLhbJ3rdknuhIWF0bRpU0JDQ00z2wpKcZ75DzNpMTqif75K6u14AOwf98L52drcW/QPxoS8/bzau+1QWRS9TSS393dOPLQDdguDt4st3i4yOJEoWFarhmW1amVthqScYOFiDS4yOClp/Pz8WL58OUOHDi104CIpGsmXI4n+xR+hS0Nlo8H1hQbYtfAEIajS5RrGQ8vBmEMG7V5foH6keIFLYZHBi0QikUjKHQMHDkSj0bB///6yNqXSI/RGYn+/SeLpUACsajriNrQxFm42kBgFv76BxbX0gfn3xycqDVz8GB4/9EBtLtMxLwaDgW7dupklQZo4cSLW1tZmGVa//fbbsjNSIpFIJA8cPz8/Ll26RNWqVcvalEqNPjyJ8OX/mQIXh6418JzQQglcbh6BlZ3g2u9KkJITwgAh5+FG7jIxpUGZtrzMnTuXY8eOmc3zP3fuHN9++22pZ1SVSCQSieRhRQhB0j/hxO4KQOiNqO0tcRvcEJtGbpCWCvs+gRNfAgLc6oPGAiKuAzlNTlHDwXlQ78ncZ2KWMGUWvBw8eJBt27aZTaNLSUnh0qVLtGnTpqzMkkgkEomkUmNMSSN25w2SzocDYF3PGbchjdE4WUHUDdg2VmlNAWg9Cp6cDcsfJ+fABWV93F0wpILFgxn/VSbBS3h4OGPHjmXnzp1mU9wuXLiAXq/nww8/5Pjx4zg7OzN27Fjefvtt1Oqce7hSUlLMkjPFxcWVuv0SiUQikVREUu8mEP3zVdIik0EFTk/XwrFbTaXB5L+N8Pt00CeCjQv0/QqaPK/sOP4QJEbmXrG95wMLXKAMghej0ciIESN46623TFkZM9BqtXTr1o3JkyezadMmzp8/T//+/VGr1aZcAfczf/585s6d+yBMl0gkEomkQiKEIOFkCNo9t8Ag0Dhb4Ta0Mda1nSE5Fna/Cb6KGjm1OsMLq8C5RmYFzjXMf5cxD3zA7vz587GxseGNN7Ir8D799NMcPHiQrl27YmlpSbt27Zg6dSqbN2/Otb733nsPrVZrWoKDg0vTfIlEIpFIKhTGJD1RP15B+9tNMAhsHnGj6pRWSuASdApWdlYCF5UGenwAI38tV4FKTjzw4OXHH3/k8OHDuLi44OLiwsaNG9m4cSMuLi7s3LmTVatWmZVPSUkxE+y7H2tra5ycnMyWUuXGIfi6nfJvJSA6OpratWsTGBhoWhcfH8/o0aNxd3enVq1arFixokD73Y9KpcLe3h4HBwccHR1xc3Nj+PDhxMbGlvyJlBHBV6LZOOc0wVdyT1pXkSiqP4CSGj7rzMH7eRj8IUMyI0OduaJTWH9o2rSp2UxRKysrGjZsmGPdD4M/lAdSArWEfXkenV8UaFS49KmL+8tNUFur4NB8WNsLtMHgWhvG/gVPTAd1wYSEy5IHHrxcvXqVuLg4YmNjiY2NNSnIxsbGIoTgzTff5MCBAwghOHXqFF9++SWvvvrqgzYzZ4SAA3Mh8prybwVPTnzu3Dm6du1KUFCQ2fpp06YRGBiIv78/hw8fZuHChezcuTPf/XLC19eXhIQE4uPjCQwMJDQ0lIkTJ5b0qZQJQghO77xBzL0kTu+8USay8CVJUf0BFL2ZjRs35nuMyu4PBw4cIDIy0vQMq8gUxR8y7m9CQgJ3796lZs2aLF68ONdjVGZ/KGuEURB34DYRqy5i0KZg4WFLlUktcehUHVXsbSVoObIAhBEeHQqvHoMaFWeyTLnSNurfvz9Llixh0qRJODg4MGLECObOncuIESNK/mBCQGpi4ZZrezJHYIecV34XdN8CPsgMBgPjx4/Hw8ODGjVqMGrUKFOa6549e+Lo6Ej9+vXZvl3pmwwPD2fQoEH4+Phga2tLt27duHv3LqDIxr/11lu0bt0aJycnevfuTXR6Wns/Pz969+7N9OnTs9mwc+dO5s2bh5ubG3Xq1GHChAmsW7cu3/3y0y5xcnJiwIABXLp0idu3b5t9odna2qJSqfD19aVjx45MnjxZucwhIbi5ubFv374CXb+iIoRAn2Io1HLrQiThQUoK7fCgeG5diCzwvgV9sZV3fwCIjIzknXfeYfTo0Wb7FcYfQkNDcXV1NZ3HH3/8gYeHB3fv3sXe3t5MOXju3Lml/oITQpCamlqo5dq1a4SEhACK3167dq3A+1Ymf8hgxowZ9OjRg969ewMl4w/37t3L1rqjUqlMmlgSBUNcKpFrLhG3LwgE2D1WhSpvtMSqugNc+kXpJgo+A9ZO8MJ30H8l2JRyr0UJU+YZdteuXWv2+9VXX30wLS36JPjUu3h1bBpW8LIzQ8DKPt9i27dvx8/Pj9u3b6PX6+nRowdbt25l6dKl9OjRg927d3Pu3DmeeeYZOnXqxIwZM3B3d+fGjRskJyfz/PPP8/XXXzN//nwANm7cyLFjx/Dw8ODJJ59kxYoVvP/++9SsWZMbN27g4OCQranfYDBgZ2dn+q3RaEzN4HntN3z48DwVpoOCgvjpp5/o1q0bPj4+JCQkmLYNGjQIOzs7mjZtypo1a2jTpg2jRo1i9uzZDBkyhKeffjrfa1cc0lKNfDvlSLHq+GPlpfwLpTP+y65YWuffNFve/QFg0qRJvPPOOwQHB5t1LxTGH6pVq8aiRYuYMmUK7du355VXXmH58uVUr16d3r17s23bNpo3bw7Ali1bSv1lpdfr+fTTT4tVx6ZNmwpcdubMmVhZWeVbriL4A8CVK1f4+eefzdaXhD94eXnh6+tr2mfZsmWsXLlS5gXLgu5aNNFbrmNM1KOyVOPSrz52raqgSk2AnTPgv5+UgjXawoDvlO6iCkiZBy8Sc5ydnbl27RobNmygd+/enDt3jlu3bjF27FhOnjyJlZUVnTp14siRIzg6OrJgwQIcHBwwGAwEBwfj5ubGvXv3TPUNGDCABg0aANCrVy/Tw8TR0TFXG3r37s3s2bNZv349MTExrFmzBn26SnJe++VEixYtUKvVCCFwcnLimWeeMT04M/j0008JCAjg5MmTADzyyCPMnDmTPn36YGNjw88//1yoY1Ymyrs/bN68mbi4OMaOHcucOXPyPZ+8/GHMmDFs2rSJtm3b0qlTJ4YMGQIoysIffvghc+bMwc/Pj5iYGJ544okiXc+KTnn3hwyWLFnC2LFj8fDwyPN8iuIPGRw9epS5c+dy4sQJHBwcCn4RKynCYET7VxAJR+4AYOllj9uwxlhWsYM7/yi5W2JugUoNXaZD1xlK4rkKSsW1vLhY2imtIQVBCKV/8N5lJRVyBioNeDWDUXvyzypoaZf39nR69uzJ/PnzWbVqFZMmTaJjx47Mnz8fd3d3rK0z59C3bt0aUPqMX3/9dW7dukXz5s3R6XRmDx5PT0/T/y0sLDAac0sylMnSpUuZNGkSDRo0oF69eowcOZKtW7cWyP77uXjxolkG5fvZs2cPS5Ys4ezZs2YDs0eNGsWHH37IG2+88UAeTBZWasZ/2bVAZYUQ7Fx0nsg78Wa9gSoVeNRwpN+0x1Dl4w8WVgXrsS3P/hAWFsbMmTM5fPhwgc4F8veH0aNHm8bBZfDcc88xatQorl+/zpYtWxg0aFCueZ9KCktLS2bOnFmgskII1q5dy71798y6f1QqFV5eXowaNSpffyio8GB59ocMUlNT2bJlCydOnMi3rqL4A0BwcDCDBw/mu+++o1GjRvkep7KTFp2uBB2crgTdvhou/6uLSiPg2GI49IkiqOhUA174Fmp3KmOLi0+5GvPyQFGplG6cgizBpyH0gnngAsrv0AvK9vzqKGDK5Js3b9KpUyfOnTvH3bt38fb2ZtasWURFRZkl41u8eDFXr15l+PDhjBs3jvDwcA4cOEDbtm2LfWnCwsJYuXIlERERnD59mtjY2Gw5eUoCf39/XnrpJX788Ufq1Kljtu3NN9+kX79+rF271my8Q2mhUqmwtNYUaLl3Q0tEcHy2YUxCQERwPPduaPOtI7+XWQbl2R/27dvHvXv3aN68OS4uLixYsICNGzfSokWLIh0nKSmJ999/n+HDhzNt2jSSkpIAsLGx4fnnn2fXrl3s3LmTF198sdjnlB8qlQorK6sCLcHBwYSGhmYbtyKEIDQ0lODg4HzrqAz+kMGJEyeoWrVqnuNbCkJu/qDT6ejfvz9jx46lX79+xTpGZSDpUgRhX/1LanA8KhsL3Ec8gmu/+qiS78H655XJJcY0JdncxOOVInCBhzl4KShCKJoNuV6qdE2HEppZcODAAYYOHUp4eDhubm7Y2NhQv3592rRpw5w5c9Dr9Zw4cYJ58+bh4uJCbGws9vbKWJqTJ0+yYcOGbE24heWTTz7h/fffx2AwcO7cOVavXs24ceNK4vRMxMfH069fP6ZPn86zzz5rtm3Xrl0cPXqUNWvW8PbbbzN27FgMBkMuNT1YhBCc+fUm5PauUcGZX2+W2EyT8uwPI0aMIDEx0TRz8N1332XYsGFcvHixSMeZOXMmDRs2ZMOGDdSrV49Zs2aZtg0ZMoT169cTGxtL+/bti3U+JYkQgoMHD+ZZ5uDBgw+FP2Rw9uzZErlHufnD+PHj8fT05OOPPy72MSoyQm8gZoc/0T9dRegMWPk4UnXyY9g284Aru2FFRwg8prT69/0aBq0DW9eyNrvEkMFLfhhSQXuXAmk6lABjxoyhS5cuNG3aFHd3d6Kjo/n000/ZtGkTFy5coEqVKowdO5aNGzfi5eXFihUreO+993B2dmbKlCmMGTOGK1euFMuGL774An9/f1xdXRk2bBjLli0r0MMov9kEWdmxYwd+fn4sWbIER0dH08yBdevWMWnSJBYvXoyLiwtvv/028fHxZjISZYkxTRAfrYPc3kUCEmJSMKaVzMvqYfGHM2fO8N1335kG4i5fvpxVq1Zx5swZQOkuCQkJYfDgwQVupXgQGAwGtFptnmXi4uJKLPiuCP4QFBREtWrVsu1XEv5w5MgRfvzxR86cOYOrq6vpuTFhwoRinVNFw6QEfUYZv+TYrQaer7bAwsEIv02FzcMhOQaqtVSmQLd66YEJJj4oVKKiJyO4j7i4OJydndFqtWYJ6zKmE9apUwcbG5vCVaq9k7+mg3P1IlosqWjER+vQJeT+9WrraImDayF9TJIvTZo0YcOGDbRq1aqsTTFDq9WSmJiY63Z7e3ucnZ0foEUSKOYzv5wihCDp7zBif72hKEE7WOI2uBE2DV3h3iX4ZayShwyg42QlW65F/rPYygu5vb9z4uEdsFsYypmmg6RscXSzwdGtcjwMKwJBQUEcPHgQjUZT7gIXUGYAyeBEUtoYdWnE7Awg+b8IAKzru+A2pBEaB0s4vQL2faj0ADh4KXlb6nUvY4tLFxm8SCSScs2CBQv45Zdf+OWXX8raFImkTEi9E0/Uz1cxROlADU49a+P4RA1USRHw00QI2K8UbPgcPP812Oc9Rb0yIIMXiURSrlmxYkWuekoSSWVGCEHCiRC0f6QrQbtYK0rQtZzAfx/snAiJEWBhAz3nQdtxlW5sS27I4EUikUgkknKGIVFPzNbr6K4qkg02Td1xG9AAtaUB/ngXzqQH9FWawIA1ULVJGVr74JHBi0QikUgk5YiUm1qiN13FEJcKFipc/lcX+/bVUEVcg23jICw991W7V+Hpj8Dy4RuDJ4MXiUQikUjKAcIoiD94m7gDt0GAhYctbsMaY1XNHv75Af6cCWnJYOcO/VZAw2fK2uQyQwYvEolEIpGUMYa4FKI3XSPlppI3yK5VFVyer4/aoIXNr8LV3UrBej2UwMXRqwytLXtk8CKRSCQSSRmSfDWamK3XMCamobJSlKDtW1WFm0dgx6sQHwpqS3hqDrSfBKWs7VURkFegkJwKOcXzO5/nVMipsjalRIiOjqZ27doEBgaa1sXHxzN69Gjc3d2pVauW2UwPrVbLSy+9hKenJ15eXkyePJnU1JyzC6tUKuzt7XFwcMDR0RE3NzeGDx9ObGxsKZ/VgyPo4n/88NZEgi7+V9amlAiF9Yf4+Hg0Go0p06mDgwOLFy/Ose6HwR+io09w6vQzREfnL0pYESisP+j1elP6/ipVqvDWW2/lKvb4MPhDfog0I7G/3yRqrS/GxDQsq9lT5Y3HsG/hCvtmK9pE8aHg3gBeOQAdX5eBSzryKhQCIQRf/vslN7U3+fLfL0tMr6SsOHfuHF27diUoKMhs/bRp0wgMDMTf35/Dhw+zcOFCdu7cCcDbb79tylx56dIlzp07x8KFC3M9hq+vLwkJCcTHxxMYGEhoaCgTJ04szdN6YAghOLZpHdF3gzm2ad1D6Q8XL16kWbNmJCQkmJa33nor12NUdn8IuPEFSUkBBNz44qH0h+XLlxMUFERgYCC+vr7s3buX9evX53qMyuwP+ZEWlUz4ygskHLsLgENHb6pMaomlOhS+7wknlgICWo2EV49AtZIXx63IPLTBixCCJH1SoZZDtw/hG+ULgG+UL4duHyrwvgV9kBkMBsaPH4+Hhwc1atRg1KhRpmChZ8+eODo6Ur9+fbZv3w5AeHg4gwYNwsfHB1tbW7p168bdu8ofw6hRo3jrrbdo3bo1Tk5O9O7dm+hoZdqdn58fvXv3Zvr06dls2LlzJ/PmzcPNzY06deowYcIE1q1bZ7pus2bNwsHBAU9PT4YNG8apU0orVH7aJU5OTgwYMIBLly5x+/Zts691W1tbVCoVvr6+2NvbmylJz507t9QfaEII9DpdoZYbf58h7IY/AGE3/Lnx95kC71tZ/OHChQu5Ko4Xxh8OHTpE1apVzTSAunfvzrp168rMHwyGpEItkZH7iY9X7IyPv0Rk5P4C71tZ/CEgIACDwWC6j2q1GltbW6Bk/GHz5s00bdrU7NmhUqlMGkgVhaQLEYR9dR79nQRUtha4v9QElz51UflugpVdIOQ82LjA4PXQ9yuwsi9rk8sdD+2Yl+S0ZB7f+Hix6phyeEqBy54ZdgY7S7t8y23fvh0/Pz9u376NXq+nR48ebN26laVLl9KjRw92797NuXPneOaZZ+jUqRMzZszA3d2dGzdukJyczPPPP8/XX3/N/PnzAdi4cSPHjh3Dw8ODJ598khUrVvD+++9Ts2ZNbty4gYODA6NGjTKzwWAwYGeXaatGo+HGjRsArF692qzsnj17eOyxxwAYPnw4w4cPz/XcgoKC+Omnn+jWrRs+Pj4kJCSYtg0aNAg7OzuaNm1K79692bZtG82bNwdgy5Ytpf5wSktJ4auRA4tVx66F8wpcdvK6X7AsgN5KefeHCxcucP36dRo1akRCQgIvvvgi8+fPx8rKqlD+0LVrVzQaDceOHaNbt26EhYXxzz//sHv3bvbs2fPA/cFoTObwkebFquPipYKLBXbregmNpuI/H8aNG0fPnj1xdXXFaDQyaNAghgwZAhTu+ZCbP/Tu3dtUH8CyZctYuXIlI0eOLPC1LkuMqQa0u2+SeFYRVLSq5YTb0EZYWOtg21i4vE0pWKszvLBKytLkwUPb8lJecXZ25tq1a2zYsIHExETOnTtHx44duXTpEvPmzcPKyopOnTpx5MgRHB0dWbBgAQsXLsRgMBAcHIybmxv37t0z1TdgwAAaNGiAq6srvXr1Mj1kMpScc6J3797Mnj2b2NhYbt26xZo1a9DpdNnKzZgxgytXrjBt2rRcz6dFixa4uLjg7OxM586dadKkienBmcGnn35KQEAAK1euBODFF19k2zblj9jPz4+YmBieeOKJwl3ISkJ59wcHBwe6devG2bNnOXXqFEePHuXTTz/N9Xxy8we1Ws2gQYNM933btm306tULe3t76Q9ZKO/+kJKSwvDhw4mMjOTWrVv4+fnlGWgWxR8yOHr0KHPnzmX79u252lqe0IclKkrQZ++BChy718RzfAss4v5TWlsubwOVRhFTHPmrDFzy4aFtebG1sOXMsDMFKiuEYPTe0VyLuYZRZA4+U6vUNHJtxA/P/IAqn5TMtha2BTpWz549mT9/PqtWrWLSpEl07NiR+fPn4+7ujrW1talc69atAaXP+PXXX+fWrVs0b94cnU6Ho6OjqZynp6fp/xYWFrkOnsvK0qVLmTRpEg0aNKBevXqMHDmSrVu3mranpaUxYcIEDh06xIEDB3B3d8+1rosXL1K7du1ct+/Zs4clS5Zw9uxZU/Pyc889x6hRo7h+/Tpbtmxh0KBBqEt5kJqFtTWT1xVMO0cIwea57xIRdAuR5Xqq1Go8a9VhyOwF+fqDRZZ7mRfl3R8WLVpkKufs7My7777LJ598wpw5c3KsKy9/GDJkCIMGDeKrr75iy5YtTJmitGyWhT+o1bZ063op/4Io/vDv+aHEx18Bsl5PNY6Oj9DqsZ/z9Qe1unI8H0aPHs26detwdXXF1dWV999/ny+++ILXXnstx7qK4g8AwcHBDB48mO+++45GjRrla3NZIoQg8dw9tL/dVJSgHS1xG9IImzqOcOwzOPIZCCO41lYy5dZoU9YmVwge2pYXlUqFnaVdgZb/Iv7jSvQVs8AFwCiMXIm+wn8R/+VbR34Prwxu3rxJp06dOHfuHHfv3sXb25tZs2YRFRVFSkqKqdzixYu5evUqw4cPZ9y4cYSHh3PgwAHatm1b7GsTFhbGypUriYiI4PTp08TGxprGNaSkpNC3b18uXbrEqVOnqFevXpGP4+/vz0svvcSPP/5InTp1TOttbGx4/vnn2bVrFzt37uTFF18s9jnlh0qlwtLGpkBLyDU/wm/dMAtcAITRSPitG4Rc88u3jsriD7Nnz+bmzZumsikpKdgUoDssJzp06ICFhQV79+7lwoULPPfcc0DZ+YNGY1egRav9h/h4X8wDFwAj8fG+aLX/5FtHZfGHO3fumM0+tLS0xNLSskjHyc0fdDod/fv3Z+zYsfTr16/Y51OaGHVpRP98ldjtAQi9EesGLlSd3Aobj3hY+z84PF8JXFq8CK8ek4FLIXhog5eCIoRg2fllqMj54aJCxbLzy0psZsGBAwcYOnQo4eHhuLm5YWNjQ/369WnTpg1z5sxBr9dz4sQJ5s2bh4uLC7Gxsaam1JMnT7Jhwwb0en2xbPjkk094//33MRgMnDt3jtWrVzNu3DgA3njjDWJiYjh06BBVqlQp8jHi4+Pp168f06dP59lnn822fciQIaxfv57Y2Fjat29f5OOUNEIIjm/ZkLv4mUrF8S0bHhp/OH/+PDNnziQpKYmgoCAWLFjASy+9VKTjqFQqBg8ezDvvvEOfPn3MgqDy7A83bi6GXJ4PoOLGzcUPjT88++yzfPDBB2i1Wu7du8dnn33GwIFFG0uWmz9kTMX++OOPi3UepU1qcDxhX50n+WIkqFU4P1cbj9HN0AT+Cis6Q/BpsHKEF1Yr41tsnMra5AqFDF7yQW/Ucy/xHoKcHz4Cwb3Ee+iNxXsgZDBmzBi6dOlC06ZNcXd3Jzo6mk8//ZRNmzZx4cIFqlSpwtixY9m4cSNeXl6sWLGC9957D2dnZ6ZMmcKYMWO4cuVKsWz44osv8Pf3x9XVlWHDhrFs2TLat2+PVqtlzZo1nD9/nipVqphG+2d8EeU3myArO3bswM/PjyVLlpj61x0cHPjpp58ApXk8JCSEwYMHF/ir9EFgSEsjPjICcnsZCUF8ZCSGtLQSOV559gdQBnDr9Xpq1KhB27Zt6devHxMmKANVC+MPGbz44otcunQpW+tKefUHIVJJ0YVCLs8HEKSkhCJEzrmQCkt594eVK1fi7e1N/fr1adWqFU899RRvvvkmUDL+cPv2bX788UfOnDmDq6ur6bmR4XPlAWEUxB+9Q/iKCxiidWhcrfGc0ALH9i6ofn1NGZibooUabWHCMWgxuKxNrpCoREVPRnAfcXFxODs7o9VqcXLKjGQzphPWqVOn0M3a9xLvEa2LznW7m40bXvYPd6rm0qBJkyZs2LCBVq1albUpZsRFRpAcp811u52zC47uHg/QospDWFgYTZs2JTQ0NFt3Q3n1B50uhFR97s8HK0t3bGyqPUCLKg95+UN+FOeZX1QMCamKEvS1GABsm7njOqAh6ugLiqBi9E1QqaHLdOj6DmiK1qVWWcnt/Z0TD+2A3cLgZe8lg5MHSFBQEAcPHkSj0ZS7FxWAk4cnTh6e+ReUFIqMmSlDhw41e1GVd3+wsfHGxsa7rM2odOTmD+UV3Y1Yojdfw5ihBN27HvZtq6A69RUcnAfGNHCqAS98C7U7lbW5FR4ZvEjKHQsWLOCXX37hl18KNvtHUjkYOHAgGo2G/fv3m62X/vBwkps/lDeEQRB38DbxB9OVoD1tcRv2CFb2WtjQD24dVQo2eR76fAm2rmVqb2VBdhtJJBKJpNLxIJ75adoUojddJfVWHAB2ravi8nw91Df+gF9fh+QYsLSD5z6Hx0bkPtBfAshuI4lEIpFISpXkK1HEbL2OMSkNlZUG1/71sWvqAH9Nh7+/VwpVe1TJ3eLRoGyNrYQ8dMFLQZIwSSQSiaRiU1qdCiLNiPaPWyScCAHAsroDbkMbY5kWAN/2hshrSsGOk5VsuRZWpWLHw85DE7xYWVmhVqsJCQnB09MTKyurcjXlUiKRSCQlgxCCiIgIJflkCQ72TYtMJurnq+jvKrpsDp28cX62Nqp/voV9H4IhFRy8oP8KqNejxI4ryc5DE7yo1Wrq1KlDaGgoISEhZW2ORCKRSEoRlUpFjRo10Gg0JVJf0n/hxOwIQKQYUNtZ4DqwIbY+Btg8BAL2KYUaPgfPfw32MlVCafPQBC+gtL74+PiQlpZmJrUukUgkksqFpaVliQQuxlQDsb/eIOnvMACsajvhNrQxFuHHYMUESIwACxvoOQ/ajpODch8QD1XwApiaEStC3gCJRCKRlB36e4lEbbxCWniyogTdwwenJ6qiOjQHTn+jFKrSRBmUW7VJmdr6sPHQBS8SiUQikeSFEILEs/eI/e0mpBlRO1rh9mIjbJzC4PunICxdcbzdq/D0XLAsmCq4pOQoU20jg8FAt27dGDVqlGndmTNnePzxx3FwcKBOnTqsWbOm7AyUSCQSyUOFMTmN6I1Xid0RAGlGbBq5UnVyS2xitsOqrkrgYucOQzdDr89l4FJGlGnwMnfuXI4dO2b6HRMTQ69evXj55ZeJjY1lzZo1vPnmm5w9e7YMrZRIJBLJw0DK7TjCvvqX5EvpStC96uA+uBqa38fA7jchLRnqdoeJJ6HRs2Vt7kNNmXUbHTx4kG3btjFgwADTum3btuHu7s5rr70GQI8ePRg+fDjLly+nXbt2ZWWqRCKRSCoxwihIOHYX7d5AMAo0bja4D22Mlf5fWPkqxIeC2hKemgPtJ4G6TL/7JZRRy0t4eLhJtt3Ozs603tfXl+bNm5uVbdKkCRcuXMi1rpSUFOLi4swWiUQikUgKgiEhlci1vmj/uAVGgW1zD6q+1gyra4tg/fNK4OLeAF45AB1fl4FLOeGBt7wYjUZGjBjBW2+9xaOPPmq2LT4+Hnt7e7N1dnZ2JCQk5Frf/PnzmTt3bqnYKpFIJJLKiy4gRlGCjteDhRqXvnWxr5uI6qdeEPKvUqjVSHh2PljZ512Z5IHywEPI+fPnY2NjwxtvvJFtm729PUlJSWbrkpKScHR0zLW+9957D61Wa1qCg4NL3GaJRCKRVB6EQaDdG0jkmssY4/VYVLGj6muP4mB1CNW3XZXAxcYFBq+Hvl/JwKUc8sBbXn788UdCQkJwcXEBMAUrO3fu5IsvvuCvv/4yK+/n50ezZs1yrc/a2hpra+tSs1cikUgklYe02HQl6EBliIF9Wy+cn3ZH/ddkuLxNKVSrM7ywCpxrlKGlkrx44MHL1atXzX5nTJNeu3YtUVFRvPPOOyxdupTXXnuN48eP89NPP7Fr164HbaZEIpFIKhnJvlFE/3IdkZyGylqD6wv1sXO9CWteAO1tUGmg+3vQ+S1Ql4ysgKR0KFdJ6tzd3dm3bx9Tpkzhww8/xNPTk6+++oru3buXtWkSiUQiqaCINCPaPbdIOJmuBF3DAffBDbDw+xp2fgbCCK614YXvoGbbsjVWUiBUorR0w8uIuLg4nJ2d0Wq1ODk5lbU5EolEIilD9JHJRG+8gj4kEQCHztVx7qBBtXM8BJ9WCrV4EXp9ATbynVGWFOb9Xa5aXiQSiUQiKSkSz4cTuyMAkZquBD24EbZph+DbNyFFC1aO0HsxtBhc1qZKCokMXiQSiURSqTCmGIjdFUDSv+EAWNVxxv2F6mhOzIL/flIK1WgLL6wGtzplaKmkqMjgRSKRSCSVhtSQBKJ/vkpahKIE7fSkD46NIlD9/BRE3wSVGrpMh67vgMayrM2VFBEZvEgkEomkwiOEIPF0KLG/34Q0gdrJCvfBDbG+tw5++BiMaeBUXWltqd2prM2VFBMZvEgkEomkQmNM0hOzzZ9k3ygAbBq74fqsE5q9L8Oto0qhJs9Dny/B1rUMLZWUFDJ4kUgkEkmFJSUojuifr2KITQGNCudn6+Dgfh7VutchOQYs7eC5z+Cxl0ClKmtzJSWEDF4kEolEUuEQRkH80TvE/RUIRtC42+A+sDZWvp/Cge+VQtUehQFrwKNBmdoqKXlk8CKRSCSSCoUhPpXoLddI8Y8FwPZRT1w7pqL+7X8QeU0p1HEy9PgALKzKzlBJqSGDF4lEIpFUGHT+6UrQCXpUlmpc+tTFzrAT1foPwZAKDlWh/0qo16OsTZWUIjJ4kUgkEkm5RxiMxO27TfyRYBBgUdUO935VsTzxBgTsUwo1fBaeXw72HmVrrKTUkcGLRCKRSMo1aTE6ojddIzUoXQn6cS9cGgeh+mUIJEaAxhqe+QTajpODch8SZPAikUgkknJL8uVIon/xR+jSlaD718Hu3lew+RulQJUmyqDcqk3K1lDJA0UGLxKJRCIpdwi9kdg9N0k8FQqAZU1H3J+xwmLfIAi7pBRqNx6e/ggsbcvQUklZIIMXiUQikZQr9BFJRG+8ij40XQn6ieo4exxGtWkmpCWDnTs8/w00eraMLZWUFTJ4kUgkEkm5IfGfMGJ3BSBSjajtLXDt643tlZlwdrdSoG53ZTaRo1fZGiopU2TwIpFIJJIyx5hiIHZnAEnnFSVo67rOuHWMRbP3WYgPAbUlPDUb2r8GanUZWyspa2TwIpFIJJIyJfVuuhJ0ZIYSdA0cxXpUvywBBLjXVwblercsa1Ml5QQZvEgkEomkTBBCkHgyhNg9t8Ag0Dhb4dbLEeszYyDkX6VQq5fh2QVgZV+2xkrKFQUKXm7fvl2oSn18fIpkjEQikUgeDoxJeqJ/8Ufnl64E/Ygbro0vofl9GqQmgI0L9P1KUYOWSO6jQMFL7dq1URUg8Y8QArVaTVpaWrENk0gkEknlJCVQS/TP1zBo05Wgn/bCIeJTVH9sUwrU6gwvrALnGmVrqKTcUqDgxc7ODl9f33zLCSFo1qxZsY2SSCQSSeVDGAXxh4OJ2x8ERrBwt8GtRxpWR/uD9jaoNND9Pej8Fqg1ZW2upBxToODllVdeoVatWgWq8JVXXimWQRKJRCKpfBjiU4nefI2UgFgA7B71wMVzN+rdn4IwgkstZVBuzbZla6ikQqASQojC7JCamkp4eDhGo9FsfXkZ5xIXF4ezszNarRYnJ6eyNkcikUgeenTXY4jekkUJ+mlX7PzfQhV8SinQYgj0Wgg28pn9MFOY93ehZhtt3bqVV199Fa1Wa1onhEClUmEwGIpmrUQikUgqJcJgRPtXEAlH7gBg6WWHW9s7WB4dCilasHKE/y2CR4eUsaWSikahgpfZs2fz2muvMXLkSCwtLUvLJolEIpFUcNKidURvukrq7XgA7Nt64KJagWrfOqVA9TYw4Dtwq1OGVkoqKoXqNnJ0dCQmJgYLi/KbHkZ2G0kkEknZknQpkpht1xE6AyobDa7dNdhdmADRNwEVPDEdus4AjfwIlmRSat1GrVq1ws/PjxYtWhTLQIlEIpFUPoTeQOzumySeuQeAVU1H3BqcxOLIB2BMA6fq8MJqqN2pjC2VVHQKFbx06tSJJ598kkGDBuHlZS6K9eGHH5aoYRKJRCKpOOjD05Wg7ylK0I4dXHCKmYXq5GGlQJPnoc+XYOtadkZKKg2F6jbq3r17zpWoVBw8eLDEjCoOsttIIpFIHhxCCJL+CSN21w2E3ojawRK3DrHY/D0RkqPB0g6e+wweewkKkOxU8vBSat1Ghw4dKpZhEolEIqk8GFPSiNkRQPJ/EQBY13XEzWMzmmPfKAW8WsDA78GjQRlaKamMFCh4+fnnnxk6dCjr16/PcbtKpeKll14qUcMkEolEUn5JvZtA9MYrpEXpQA1O7W1wvD0B1cUrSoGOb0CPD8DCumwNlVRKCtRt1KxZMy5fvkydOjlPaVOpVNy8ebPEjSsKsttIIpFISg8hBAknQtD+kaEEbY1biytY//s2GFLAoSr0Xwn1epS1qZIKRmHe3+qCVHj58mUAbt26leNSXgKXB4VKBe++a74uMNC8O9fPD557DpydwckJunaF48czt3frBjY24OBgvpw69SDOQPLAycshpDNIKgiGRD1R6/3Q7r4JBoFNIweqVvsS63OTlcCl4bMw8aQMXCSlTqETthw/fpzAwEAzeYCHsdto0SIYMADa5iDDYTBAr17w1lvw669KULN2LTz7LAQEQMZErZUrYdSoB2m1pEzIzyFAOoOk3JNyS0v0z1cxxKWCRoVLu1Tsrw1GlRQOGmt45hNoO04OypU8EAoVvEycOJHvvvsOb29v1OrMRpuiBC8HDx7kvffe48qVK9jZ2TFo0CA+//xzbG1tmThxIt9//71ZFt/Fixczfvz4Qh2jNHnpJRg9Gv79F6yszLdFRUFQELz4ImScwrhxcO0ahIdnBi+58emnynI/q1bB8OElY7/kAZKfQ+SFdAZJGSOMgvhD6UrQAiw8bHCrtR+r8wuUAlWaKIKKVZuUraGSh4pCTZV2dXVl//79tG7dulgHjYiIwMfHhxUrVvDyyy8TFhZGz549eeGFF5g7dy5t2rThjTfeYOTIkYWu+0GMeVGp4Pp1GDQIeveGefOUbqM6dSDjanbpAhERMHIkdO8OrVtnvrdA6SkYNUp+bD805OUQ0hkk5RRDXArRm66RclPRs7NrYoNL/AzUEf8oBdqNh6c/AkvbMrRSUlko8TEvGTg7O9OsWbNiGQfg6elJeHg4o0aNQqVSERUVhU6nw9PTk5SUFC5dukSbNm2KfZzSxNISfvgBFi9WWl/uZ+9epWVmxw7o3Bk8PWHWrMzgBmDSJHBxyVzeeecBGS958OTnENIZJOWM5GvRhH35Lyk3tais1Li2DcPtdh8lcLFzh6GbodcXMnCRlAmFCl5mzZrFuHHjuHjxIrdv3zZbCoujoyMANWvWpHnz5lSrVo3Ro0dz4cIF9Ho9H374IVWrVqVhw4Z89tlnZmNsspKSkkJcXJzZ8qB47DGYNk15J+n15tvs7GDGDDh7FqKjlSENK1cqrf0ZfPMNxMZmLp9/rqxfsMD8PZaxbNxY+uckKSXycwjpDJJygkgzErvnJlE/+GJMTMOyqg1V6v6E/aWxkJYMdbsrg3IbPVvWpkoeYgoVvOh0OjZt2sRjjz1GnTp1qFOnDrVr1851CnVB8Pf35+7du2g0GgYOHIhWq6Vbt25MnjyZO3fusGHDBr766isWLVqU4/7z58/H2dnZtNSsWbPItuTHcf9Inlp8xGzdBx8o4zE/+SRz3aZN0Lx55m8nJ2W4w+DBcOlS/sd5913z91jGMmxY8c9BUgYUxyGkM0geIGlRyYSvukjC0bsA2DcVVDGMwTLwJ1BbQs95MGI7OOYzcE8iKWUKFbx8/PHHLFu2DH9/f27evMnNmzeLPVXa1tYWb29vPvvsM/7880/atGnDwYMH6dq1K5aWlrRr146pU6eyefPmHPd/77330Gq1piU4OLjItuSFEILP914lIDzB9BuUwbo//AAbNmSWfeopCAmBOXNAq1WCm7//ht9/VyadSB4ypENIKgBJFyMI++o8+uB4VDYa3FtcxPVGX1QJt8G9PozbrySeUxfqtSGRlAqF8sK0tDQmTJhA3bp1qVWrltlSGE6ePEnjxo1JTU01rUtJScHKyop9+/axKmvfSvo2W9uc+1Wtra1xcnIyW0qDPy7d4+Idren3mVvRpv+3bQvTp2eW9fCAo0eVsTB16yof2qNHKwN7//e/UjFPUp6RDiEpxxhTDcRs9yd641VEigErb0uqen6O7fWZgIBWL8OrR8G7ZVmbKpGYKNRso7fffpuaNWsyefLkYh00ISGBJk2aMHDgQBYsWEBoaCiDBw+mdevWPP300wwfPpzffvuNHj16cPr0afr27cuSJUsYMWJEvnWXxmwjIQQ9Fh7mVlSSaZ2dlYZBrWvQvIYLzas7U8/THguN/CKRSCQVB31YIlEbr5IWlgQqcHwkDqfgCaj0cWDjDH2+gqb9ytpMyUNCYd7fhQpennjiCY4fP46joyNubm6osiQjKmzXkZ+fH1OnTuXcuXM4OzszYsQIPvjgA6ytrVm1ahWLFy/mzp07eHl5MW3aNCZNmlSgeksjeDlyPYKR35/Ns4y1hZpHqjnRvLozzao70ay6Mw2qOGJlIQMaiURSvhBCkHQujNjf0pWg7S1wq/47NreXKQVqdYIXvgXnGmVrqOShotSCl3Xr1uW6rSg5WUqDkg5ehBA8v/wEl+9qMWa5UioVuNtbUcfDniuh8SSkpGXb10qjppGXI83SA5rm1Z1pWNURG0tNse2SSCSSomDUpStBX0hXgq6pwk33Dpp4X1BpoPt70PktUMvnlOTBUpj3d6Ey7JaXAOVBctQ/0mysSwZCQGRCKosGt6RLfQ8CoxK5HBLH5bta0xKnS+PSXS2X7mbub6FW0bCqo6l1pll1Zx7xcsLWSj4oJBJJ6ZJ6J56ojVcxRKcrQdcPwvH2FFSkgUstJVNuzRw0TySScobs08gDIQSL/rpmJtWRprUl6LP/cXvxMyT5VzVtr+vpQN9HvZnZ6xE2vtKeC7N7cvTt7nwzvBUTu9VDfeRx4va2JM0o8AuNY9OJUCaN19C2kT2Onik0GejPW1v+4/vjtzh8OZqXXjbi7g61asGKFSVzPgsXgrc3uLoqmeWTk7OXeekl80SvWq0yq9fNTcmr9uabkJa9kalQxMeDRmOuQbh4cea20aPJ8dz1ehg/XrGjShVFKshohJ07lTpUKiXTcbkjQ7XTwUExNi+aNjW/MFZW0LChsk2rVW6Qp6eiMTF5MmQMejcaYepU5cJVqQKffVYytufmNHkd70HbkpdDZSWrc5d7pylZhBDEH7tD+IoLGKJ1aJw0eFZfi9Pt15TApcUQmHBcBi6SCkOhhRkfJlINRkJik8mpY83nrb0AhMZak2owYm1h3nKiUqnwcbfDx90O/c1qBJ2Fl14SfPKuG5fvapn9tg36NDX13zxJRLQR/82PE2m8gl1DP6L+bE5ajKDt1CvUdXblw3kN0aqSmTTKFicby+zGFIAtW5RA4MQJ5d33wgvKO2XOnMwyu3Ypuc+yylRNm6a8F+7eVd4VPXrAunUwdmyRzADg4kVo1gwuXMi+bdo05V3i76+8q596CqpVg379YPlyRSIoMBCSkpSs+i1aKO+jhIQKoAeXkJB/GV/fzP9rtdCqVeaL+O23QaeDW7eUm9G3r/JCnzkTli2D06eVCxcdDU8/DW3awJNPFt3evJwmr+M9aFvycqgM7nfufv0qiNMUH0Oinpit19FdVWZI2tZMwVX7OuqIULByhP8tgkeHlLGVEknhkMFLHlhbaPj19c5EJ2ZO6b4brOLZlbD7jc4AuDtYZQtcshIZqWR6Hz0a0tJUVHexpbqLLS/9rWSK79SpG2FxOj5w0HP6dGPaPQ/rvvbCvd/f3E7UcjtRS1pDNR8tdmVl4DFqu9vRtLqzMjDYWxlL42JnlevxM1i9GmbPVvSXAH780bzlxdzOzPUrVigf0paWyjtTp1NaYYrDhQvw6KM5b9u5U7kubm7KMmGCEiz166cIMBsMygJKuolcZtBXDmbMUKLF3r2V30IokgIZrQvDhsFffynbNm5UgpuMC/f668pNL07AkJfT5HW8B21LXg4FuTv3Q0DKzViiNl3DGJcKFipcapzDPnSOErNVbwMDvgO3oicZlUjKChm85IO3iy3eLplvSId0GYBm1Z0LtP+kScpzMzjYvHXaYFAyxgNUdbKhsbcNp+Pgu5Ft2PEWbJzQBrVnLJfvatl424YLVx0ACIxKIjAqid8vhprqquFqSzNvZ5rXcKaptzKWxsPB2syO//5TAoCWLeHePRg4ELImLc7NzgztwIEDYds26NkT+vQp0KnnyoULirBlo0bKx++LL8L8+UoPSdbrAkqrz40byv/HjVOO7+qqBFSDBsGQyvrBeOUK/Pxz5smD8gLPyp49ikYFwNWr0Lhx5rZGjZSorzjk5TR5He9B25KXQ0Huzl2JEUZB3IHbxB+8rShBu6hws1yA1b1jSmvTE9Oh6wzQFK0lVyIpa+SYl1Jk82aIi8u5i6V3b+VDMjZWadFYs0Zp1cjYtvRzK1pWrcL/6jRAe6Em1R0dOP/B02wY+zgznm3M/1pUo5a78pa/E5PMn773+GLvNUb9cI428/bTYf4Bxq37m6X7r3PgShgxMYK1a2H3biUj/fnzyvM9Pzsz2LABQkOVXoAPPijedXFwULp8zp6FU6eU/G2ffpr/dUlJUYY6REYq2/z8lK6kSsmSJcoN8fDIefuMGUqAM22a8jsx0Tzqs7NT+taKQ0wMuTpNXsd70Lbk5VAFce5KhkGbQsTqS8QfUAIXuxqRVEkZjFX8MXCqDqN2Q49ZMnCRVGgK1PKyfv36fMu8/PLLxTamMhEWpgxFOHw45+1LlyofhA0aQL16MHIkbN2a9zZXeys6N/Cgc4PMF5o2WY9fxiynEGVm063IREK1OkK1OvZfCQPAqHqWqFpX+GBfEs2rO9NjUBV2rHNmwgQVM2eqcrUzAxsbZYzou+8qwwwy3htFIWuLj7OzUucnnyj15nVdRo9WPuBdXZXl/ffhiy/gtdeKbku5JDVVGeNx4kT2bWlpSl/aoUNw4IAyKBaUACFrP2BSkvJSLw5WVsqg4BrpuT6mTVOCgjlz8j7eg7YlN4eaODHvP8JKSPLVaGK2XMOYlIbKSoWLx27sI9NHvT/SF/p8CXbF7PeVSMoBBQpeVq5cyZkzZ/Dx8clxu0qlksHLfezbp7RuZ+jx6XRKV8e//yrjC8PCFFFh5/Tep5kzM7vt89p2P862lnSo506Heu6mdQkpaWYBzeW7WkLdEojVwqFrERy6FkHi1QTiQuvT/vXb3LnThIaPgEatIi1VlW6niosXlZaQiRMzs9inpCiixsVh9mwlKKlbN7NOG5v8z/3OnczJNaB0aVlWxo/HEyegalVl5lFWUlKgf3+IilJaGKpUydzWuLHSdZKxz7VrSjdKcWjYUBk0nIHBoDhxfsd70Lbk5lD5/RFWIkSaEe2fgSQcVwQVLd0MuKW9i2X0FbC0g+c+g8deeigGKEseEkQBSExMFG3atBFff/11QYqXKVqtVgBCq9WWSv23bglRsKtmzuzZQowcmfl7xAghXntNiLQ0Ic6eFcLDQ4hTp/LfVlTmLzCI6jXTxOIdt8Vray4JZx+tcHviqqg1Y7fZ4tzpmnB59I4Y+u0p8envfmLw+DjxWJs0ERlpFLdvC9G8uRCrVhXPlj59hBgyRIjERCECA5U6v/km/3MfNEiI7t2FiI0VIjRUiDZthPj888x6Qbk/5Y7COs2CBUK8/HL29a+8IkT79sqFu59Fi4Ro106IsDAhAgKEqF1biF27imyyEEKIhQuFqFNHiNu3hYiIUOqfNy//4z1oW/JyqKzc/0coRDl2moKjj0wS95b9K4JnHBXBM46KmKXfC+OHbkLMdhJiRWchIq6XtYkSSYEozPu7wE/Uy5cvCy8vL5GSklIs40qbihK8hIYK0bOnEI6OQtSvL8TPPxds24YNQjRpUvjjGwxCfPyxED4+Qri4CDFpkhDahDTx3+0YseF0oHh32wXR+6tjwq3zdWHf7LYpmPGZ/rtwbHVLaOx0wtZVJ556+Z7Y9k+w8A+LE+vXG4tky717QrzwghCurkJ4egrxwQdCGI35n3tUlBDDhysBTbVqQrz7rhB6feb2cvseKqzTTJwoxIwZ5utiY4VQq4WwthbC3j5zefZZZbteL8S0aUJUrSpElSpKAJRBSTpNxt9/Xsd70Lbk5VBZqYTBS+J/YeLOhydE8Iyj4u7sYyLpi1FK0DLbSYi97wuh15W1iRJJgSnM+7tQ8gCHDh2iZcuWuLq6llZDULEpDW2jrAQGKrM1C37VSp5BgzLHgZQ0qWlGrofF45s+fuby3TiuhMaRkmbMVtbWUkP8720YNyeMpt5ONK/hTH1PhzITqFSplIG8tWuXyeFzp7I7TWEpT7aUW6fJG2Oqgdhfb5D0tzKmzco9EbekN7EQIeBQFfqvhHo9ythKiaRwlJo8QPfu3YtlmKT4HD+emfajNLCyUJtkC4akJ9tMMxgJiEjg8t1M+QPfkDhibjmRVu0Oa0/eNe1vbaGmcTUnmld3Ss9Do+g5SYHKMqS0naYwlCdbKij6e+lK0OHKDC5Hz7M4xc1DpTJCw2fh+eVgn8ssNYmkklColpeKwINqebG3V6YP9+tX4oeoEBiMgluRSkBzKUtAk5NApaVGRSMvR5pXd6apt5Jgr5FXyQlU7twJI0YoM3TL5Ue0dJryR7l3muwIIUg8e4/Y325CmhG1rcDN8gtsUo+Cxhqe+QTajpODciUVllJTla4IlHbwIskdo1EQFJ2UKU4ZonQ7aZP12cpaqFU0qOpIM+9Mgcom1aRApUSSE8bkNGK2+5N8KRIAa7cI3BKnolFpwfMRGLgGqjbNpxaJpHwjgxcZvJQbhBDciUnmcrq6dobydlbJhQzUKqhfxYFm3s4mCYQm3k44WMtE0JKHl9TgeKI2XsEQkwJqcHbeg0PSClQqAe3Gw9MfgWVl1smQPCwU5v0tByIUkvwEgnftUtJbODkpWnRZ84wdOaIICTo5Qdu2cOZM5rbp05XUFBmyNVkTq545o2SBt7eHLl3MM8YXluhopYU8a5b03NSaM8hNzDcvAeQMVCoVNd3seK55Nd55tjHrx7Tjn1lPcfLdHnz7Umsm96hPj8ZV8HS0JvZMHU5+X5ft5+/y8W4/Bq86RfUnbqOxNGJlY8DGzoiLm9HUklOU69munWJnVuXsUqcwqtJ5KSQXROI7pxtcFBYvzn6R8nLSvPbbtk3J/eLgoNyoU6eKbldO55eXFDkUXhm7nChOC6Mg/mi6EnRMCho7PZ7W7+GY/A0qezcYuhl6fSEDF8nDSWGnMi1cuFCcOXOmsLs9MMpyqvTNm0I4OQlx+LAys3PDBiHc3ISIi1Pylri7C/HXX8oszuXLhahVK3Pfp54SYvv27HUmJyvTgn/6SZkZOneuEJ07F832s2eFaNYs++zQJUuU6ckJCUKEhyszWX/4Qdm2ebMQdesq5xYfL8TTTyszTu8nNlYp99tvhbdLpxNi1iwh1Gqj6NkvSSzdd12MW3dOtP90v7CpFS48+5/Llo+my4KDwsZBL17/4o44ci1cfLFYX6DrKUTOM2ZLlcJMlT5+XIgWLXLeNnasEMOGCZGUpMwbf/RRIb77LnN7bje4MGTejOwXKa+Lmtt+t24pU7pPn1Z+f/+9EDVqFM223M7vlVeE6NZNuSY3byqOuGOHsi0vB166VIjHH1f28/dX8tHs359ZbxlOo06LTxHhay6ZcrdEzv9BGD6spkyBXve8EHGhZWKXRFKaFOb9Xej2+O7duzN+/Hj+/vvvko+kyjnR0Sf49/wqIGe5hNu34ZVXoGtX5ffw4UpG8+vXlQ/CqCillUMI5eM6qyJybsK4hw4pH9rDhim/339fkb25X/suP/z8lEken3+e/cM4L7Xm/NSoM7hfALkwjBqlfDy/8ooKnc6WKU81MG3zXCxYOklFjMbF1PV0JyaZW3f16BIs2HLmLr9GRJBwwYfkpLq8+uMVmnk78/e/9ahRTw9Y53rcckleCsl5SXzndYMLQ+bNyBSVKohtue1Xu7aS5dbBQWk5iozMlDQoDHmdX15S5EVVxi5DdAGxRG++hjE+FTQCF9sN2CdvRqWxhKfmQfvXlD9SieQhptB/Ad7e3gQ+JMqsWRFCEHDjC5KTA02/76drV6WFOoPTpxVZlwYNlBb2ceOUNPtWVkoLfIbQbmioEti8+abSG9C+vbIvZA9SNBpF88fPr3D216ypdDeNHJl927hxSrZ0V1el9fyRRzLVmv/7T3kntWypaBt9/DFUq2a+f4YAclH1jhYvVvT2vLzM1ytCkCo2L/NgzuD6nP+6NQu79uC/D5/m5zda0el/cYT/0o7bC58j5tAjOD97nr2+YXy2I4jYGBVdB8RgaZ9KlXoJTPnqNvv9wrin1eV478oNWRWSq1dXNHwyNBEsLcHaGgYPVvrsatXKlPjO6wYXhrxuRm5Omtd+oAQuly4pAcKsWYogVWHJ6/zykiLPy4FzUr8u7B9WCSIMAu1fgUSuuYQxPhUL+0Sqat7AIW0zKo/6MG4/dHxDBi4SCUUIXrZu3UrHjh1Lw5ZyTXT0MeLjL5n9zouAABgwQHlWOjkpz1cnJ9i7V5mdOW+ekqsrKUn5GO3WTdGTu3tXCSZ691beFfcL9ELRRHodHXPXxstLrTkvMd8M8hNAzo/7g6EMcrsuhmQrOtT14PFGTuzdC7okNfM/USEOdODtJ5vQzac6rvVice5wA+9J+zE0vMnX73oxetUF2s8/wOpjtzgeEMGiv67x5+V73IlJKj8BTV4KyRnkJPGd1w0uDIW9GVFRee+XQePGSovMypXKH0ZEROHsyuv88pIiL6oy9gMmLTaFiNUXiT8YrChBO/5DlbSXsVQHKppE44+Ad8sysU0iKY8UOnjZsGEDU6dOLQVTyi9CCPwDFgCZ+ROu+89Dp7uHENkzz549Cx07Ki3o06cr67ZtUz70evZUPp6nTFHGPu7fr+jGHTgAHToorTLjxinvghMnsgv0QsmI9GZl9GilW8rVVWnlf/995fkP5mK+np5KQ8Du3Zn7Zgggjx1bcvZkkNd1uf96zpiuwdlBQ83kOmx45xGi/V0JWP04v7zWnoWzHHH1MOCR4I1aBUmpadzT6lh2MIAJG/6h82eHaPXxPl5ac4bP/rzK7xdDCYpKLJuAZtEiWLBAUab08VGChV9/NS+TVeI7680oTfK6GQUhQ0Vz9Gilxejo0ZKzbelSZTR7gwYwdKjSOpOh7JmXA5eG+nURSPaLIvyrf0kNjENlYcTN9ivc9LNR29rAoLXw/Ndg/eDtkkjKM4Ue83Lr1i2eeOKJ0rCl3BIdfYzExGtm65KSbnDiZCdUKktsrKthY+ONjU11zp7twOTJvfn44yBeecUCo7EaarV1NkVkyHyenzihdNtMnJi5LTVVeUc1bpzZvQRKC05AQPFFerOSl1pzXmK+kLsAckmQ13UJCCjI9bSgbW032tZ2Y4EtfDakGV26PcLUGan4XTfQsU1NLt3Vcj0snpgkPcf8IznmH2mqz9HGgmbezjSv4UzT9Hw0ddztUatLMQlYXpLbpSHxXVDyuhl5sX+/Motn3z7z/UrS7rykyIuqjP0AEGlGtH/cIuFECACWdpG4p72HhQiFWp2g/ypwqfnA7JFIKhKFDl4ef/xxDhw4wDPPPFMa9pQ7hBDcuLkYpZEqeyuLEHqSdbdJ1t3mzp3aTJr0IW+/M4VmzfdyKn1IgJWVJ1W9OnLs2AKWf/MbvXsnsmtnGyIjG9KuXRK3bjnx9tvKx2379vDNN0qr9xNPKIN7w8Jg/XplluyCBcqYl0ceKblzfPZZpfdhxw7lQ/Szz5RhFQAvvaQMZ3j+eWUQ78KFSqt/BmfPKjaXBtbW5HpdqlZVWoh27FBsW7dO6cHo1El5H+W2n42VhmrOtqR4wmcDWwCQkmbg2r14U7Zg3xAtV0PjidelcepmFKduRplssrfS0DRd9qBZdSeaV3emrqcDmpIKaM6fV16i33+vdK0sWJAZMLRqBZ98ojTrJSQo215/vWSOmx953Yy8aNkS/vkHNm2CgQOVICMtTTmHkuKTT5TA5csv4d9/lUG3v/2mbMvLgTP+oDp1UsbFfP21UscDQB+ZTPTPV9HfTQDAwe4AzoZlqDQCus+Czm+BWiZslEhyo9DBy+rVqx+qmUb3j3W5nyaPLMTGtgY63V02/Vwbnc6OLz5fzBefi/RuB8H8+WNo3mIXM97V8cXnU3nnnWrUruXPnLn9+e+CHxqNA29Ne4lhw14mMtKFJk2iWbvuKikpTtjYVGf3bk8mTlTz2mvKu2DLlszjN22qfGgOH170c1y5UmlZr19fabkYOVIZlwnKv8nJ0LkzxMUp3Utvv525b1BQzsMdfvpJGarh61t0u9q0Ud6Ro0YpQzxatlRa/G1slA/rDRuU8Z+jRinXYc+ezPw6ue2XE9YWGlrUcKFFDRfTOr0hXaAyQ/4gRMuV0DgSUw2cDYzmbGC0qaytpYZHqqXLH6Qn16tfxQHLoghUrl4NkyYp3RwWFsrMmQkTlG2zZinjXBo1Uk5m0iSlbzI/Svtm5IWHh9Lt9cYbynm0aqXcqIzpbCXhwF98oTitq6sS1S5blhlR5+XAkydDSIiSLEgIJcFR375Ft6OAJJ0PJ2ZHACLVgNpSj6tqAbbGM+BaCwasgZptS90GiaSiU6AMuwMGDGDbtm0FqrAwZUuDksywK4Tg3N/9iY+/DCiX6d696owYfpT9B+oBKhwdm9G2zQ5UOeiJCCHQ62PQpYSg091Fp8v+r14fnW2/+7m/ayrrv3v21MfCwo0XX7Qs1rmWBuVJPDgrc+YoucfWri3cfmkGIzciEk1Ttn1DFD2npFRDtrJWFmoe8XKkWXVnHlfF0bd/Z1L0aVhblNHXdHm9GVu3KoFDRlNfeaKEFaeNqQZid90g6Z90JWjbINyMH2KhioLmg+F/i8BGZgWXPLyUuKr0n3/+ybFjxwo0gPHPP/8smJUVACFSSdGFkhG45FCClJRQhEhFpcqeT0SlUmFl5YaVlRtOjs1yrMFgSM4SzGQENum/U0JISbln1jV1P7t2vcdLLy/j2HHbLIFNenBjnRHkVMfS8sE+FCujeLCFRk0jL0caeTkyoHUNIEOgMhHfEC2X7igtNL5344hPSePCHS0X7mg5og2jL9Bs9l4aVjVvoWlcggKVuVKeb8bp08o4n0pOamgi0RuvkBaRDCqBo9V2nIzrUFnbw/++hUeHlLWJEkmFokAtL+pC5BVQqVQYDNm/RB8UJa1tpNOFkJqldSQoyJIWzRthb2/g29V3eKG/FTY2+UwTLQZGYxqpqeEk6+6Skh7UZPw/Of230ZhD1rj70GgcsrTYZAY5tjbVsbbxxtqqCipV5c8f0a6dkvpj2LDCt7wUFKNRcDs6icshSgtN2IWrLH3vBRItbXiz9zT+atjBVFajVtGgioMyhsbbieY1nHmkmhN2VlLPqcwoQcVpIQSJZ0KJ3X0T0gRqyyTc+BgbzSWo3gYGrAa3uiVmukRSkZHCjA+RMGPOXVPmLTmF75rKHuRYW3uj0VSwbLXlCJEuUOmbHtBcvqsIVEblIlBZz1MJaJp6O5kEKh1tyl/XoCR3jMlpxGy7TvJlZcC3jbUvrnyCRhUPXaZBt3dBI++pRJKBDF4eouClIJh3TWX5Nz3gUbqm8m8ts7LyNB93Y501yKmOhYVjjmN/JDkjhOBenC5zllP6WJrw+JQcy9f1sE/vbnJSlLe9nXG2ky+/8kjK7TiiN17FEJsCKiPOFmtxUO9A5ewNL3wLtTuXtYkSSbmjUO/vkhRVKg88KGFGe/tM7bes7NypCBs6OgrRurWis5fB4cNCNG+ubGvTJlOrTgjl/y1bCmFnpwgvBgRkr9vXVwhr6+JpxUVFKYKQWeuIjdULtdoo7O3ThJ2dXtjZpYoZM34X58+PEnXqBAobm0RhY5MgbGwShIVFiqhe/abYf6Cu+PW35kKtTjNts7FJFJMnrxLn/xsrrlz9QNwKXClC7/0qYmP/Ecm6UGE0GvK0bdGi7FqA06Yp52xvryzu7pnb8rpmud2Htm2FsLQsI2HG3JwmK3FxwqhWC72dnUixsRPJ1rbiy14TsglTbmvaXWxt9qTo8tlBMXHD32LV7vMi9IUXhcHNTQgfHyG++aZ4Nt9/MzZsyLwJGQsoiqFC5H0zfvlFiEaNlH3atBHi5MnC2xMbK8SIEUJ4eAhRtaoQb7yhKJVmJWfnzn0/g0GIKVMU9VRPTyEWLFDW79iReX6F/GMzGoxCe+i2CH5PEVQMmfWrSPmgtSKouGmEEIlRhT93ieQhoTDvbxm8FJLSUJUuiHK0Xi9Eu3bFE7rNTZQ3LyFjIYQwGo0iNTVa3A72FbVqJYrvv/9LXLs+T6xd94WoX/+mOHK0jdh/oG6+y4GDjcSJE93E3/8MFb6+08WNG0vE3bubRUjICTFjRrRQq40FFjLO65rldR+EqJiq0pHxOnH4Wrj4+qC/WPHmQpGmUoutzZ40BTM/PfqMOOnTXLSY/LMY8PYGEe5ZXfw2d7k4eCVMhMUlF9zWvFSls/Lll0J07ChEamreN6OkVKVfeUWIgQMVZejwcCHatxfik08yt+elOJ3bfiWsKp0WlyLCv7uYqQQ963Nh+NBLiHleQvy9VvnDl0gkuVKqqtKS3CmqqnRBlKPnz1dSVZw9WzTb8hLlzUssGJRB2JaWrnwyz5Wnn4bRo58Gnmb/PiVb/BNdzhWwaypz1lRslvo/mbeUpKTz9Op1j7AwJ879/X36bKlqnD//JjVq/Et8vBs2Nt5YWDihUqnyvGZhYbnfh9ati3b9Hhi53Ax3B2u6NvSkq5sKJnwLY0bTV5dKtXGPc/mulj4rzjJzxFy0to78Daxu2pPWG35kdFItAKo6WStdTemznJpVd8LLySZ7N19eqtIZBAYqM4TOnVMSA+3fn7cDl4SqtBBKnhsHB2UZNgz++kvZlpdz57VfCapK6/xjiN5yDWO8HpU6DRf1N9ip/0JVrYWSu8WzYeHPWSIppwRd/I+Da1fRY9Sr1GrRskxsKHTwkpqaSnh4OMasOeIBHx+fEjOqotK1a+YLE8xVpZ2cMlWlMwKXAweUcnkpRzdurLzPNm9W3hWLFxfNtgxRXgeHnIOXDCHjhAQl8ej8+YosTAYZytEZYr3Z97PlxRfrMX9+vRzfTRmzprJPC7/Lm29txMHhEj/88Aph96yJi7tAXNwFoqI8iYmZwZSpSfj5Nqaa903eeH0RrVpHceDASKpXb05g0HHToOK6dVvg66thwAB1rveh3JPfzZg0Cd55B4KDsQoMpFN9DzrV9wALFcvGdebTR5rhGxIHCSfxuXGM+lUcuBGRQFhcCmFx4Ry4Gm46lIeDFU29M4OZpt7O1Fi0CJW3d2YynJyYORNefVXJagj5O3CGqnTLlkrivaLoMa1ebf57zx547DHl/3k5d1775aQqnVWLowAIgyBufxDxhxVBRQuLe7irP8JSfRs6vA5PfggWcqC7pPIghODYpnVE3w3m2KZ1+DR/tEzGOhYqeNm6dSuvvvoq2ixaIUKIMp8eXR7JS1W6a1clq+2gQUpQkJdydGqqomW3alVmS01RcHTMfVuGkPF77ykfxwMGKAlZ58zJLJOTcnRB9stArbYwBRnQxmxby5aKHx08oMOQpqN582/Q6UJIStLTpo0/Y8fsp3adD9nzezfee+8rflj7NFHRIRiFNTdufG6qR6//mb//2YpXtXOmaeD37jVh+PAhvPdeGBYWYDB4A+X4ZZLXRd28WckSO3Zs9oucrqzsuH497VVxcGgn2KjY/1ZXElPSuBIal55cLw7fEC3+4QlEJqRy5HoER65nKjy72FnSzDuYCf6R1EpIwhCZSC13u8yH0+3bSur9rFFsQaTPM1SlN2xQzunGDUUosSjMmKH84WzYoPzOy7nz2q+YqtJpsTqif75GalAcAPYWf+GsWYXawRn6b4f6hW/BkUjKO0EX/iXshj8AYTf8CbrwL7VbPvgm7UIFL7Nnz+a1115j5MiRWFoWb5bDwYMHee+997hy5Qp2dnYMGjSIzz//HFtbW86cOcPkyZPx9fXF09OTWbNmMbY0ZItLibNnlXfJpEnZVaUXLVJ+T5miZFvfvz9v5eiPPlLeZZ06lZ69GTaBIhHz7ruKXEzG+zFDOfp+AeH89isMKpUKjcYWS0tbqngqulk+NaFPb4DPAOjWNZk//1STlLgen5qp3Au1w8urn6kFJyXFFlubeHS6YHS6YE5fbcGs99+lT9/VdOr8lUlrKuj2u0RG1uPS5R3p08OzTguvbuqaKhNyu6gTJyotHocP57zf0qWKwzVooLR6jBxpyqhrb21Bm9putKntZiqu0xuUgCYkjsvpyfWuh8UTm6TneEAkbYKiuaeNZvrCwzjaWJimbPf7awO1uj+FnYdnpiR9QaTPM54Xo0crzYdHj5qLZBWEtDRFYuDQIaXZsqDdT7ntVwxV6WTfSKJ/8Uckp6FSp+CqWYqd5hg0eAaeXw4ORQzMJJIyxmg0kBgTQ3xUBPFRUen/RhIfFUFcZAQRgTdNZVVqNce3bKDWo60e+DOzUMFLcHAws2fPxsKieENlIiIi+N///seKFSt4+eWXCQsLo2fPnixYsICpU6fSq1cvPvroI1599VWOHj1Kv379aN68Oe3atSvWcR8Ee/cqmc4XLVK6iTLIS1U6L+Xod99VpGS+/z5ze4sWSstNxhCD4pKXkDHkrhyd337F5X4hY43GlrQ0qFKlKW5uSi6xpk2Ul73BAGFhgr59P6J27XH88Ucq773bmrff3kOfPv+RrKtvSuhnMCSh18cQHr4nx+MqCf2qmU0Dt7Guho2t8n9rK09UqlLKipvbRd23Txk70ry5sl6nU9SR//1XuUh5KSvngI2lhsd8XHnMx9W0LiXNwPV7CVwO0VLlljPoY7CyUBOvS+P0zWhO34zmqR2/MbPlcxyYs5cm6UrbT2k8aON3FY3BiIVGbe7AJaUqnZIC/fsrA8dOnYIqVYq/XxFUpYXeSOyemySeCgXAUnMTd80nWFjGwjMLoe04RVZAIimHCKORpDgt8ZFZApKoSBKiItN/R5IQE4W4b1hIXvWVVetLoaKQVq1a4efnR4sWLYp1UE9PT8LDw3F0dEQIQVRUFDqdDk9PT7Zt24a7uzuvvfYaAD169GD48OEsX7683Acv/v6KcO7atdk/Kp98MncVZEvL3JWjr141r0elUt5VJSS3AuQtZAy5K0fnt19xyUvIOGe1bRUtW1bB378K48dn3If+QH9A6ZpKS4tl/z49qSl6GjSYlS2pn14fjcGQQGKiP4mJ/jnapVJZYm3tZZah2DZL602xEvrldlFHjFCWDO4XaMpLWbmAWFtoaF7DmeY1nOGRqmCbjO/cZ/APUwIa3+AYWiy5wUyfR0hMNXAuMIZzgTFs1DtwPOgOC194h5tP9mbi6a20rFaTe87VaeDmjmVJqEq/8QbExCitJ/d3URV1v0KqSusjkojeeBV9aCIADpptOFv8iKpKAxi4A6o2zXVfiaS0EUKQHB9nCkJMLSbpgUpCdCTxUVEYDWn51qVSq3Fwc8fR3RNHdw8c3T1wcHPnvz93ow2/ZyYVVFatL4UKXjp16sSTTz7JoEGD8PLyMtv24YcfFurAjun91DVr1uTu3bt06dKF0aNHM2vWLJpnfF2m06RJE9asWZNjPSkpKaSkZCb1iouLK5QdJcmqVUo3+siRypLBH39Aly65qyAD/P670rKdk3J0XpSEKG9eQsaQu3J0Xvs9CCHj3K5Z7vdBRZcurlhbK3X41Byd7ZjKrKnQLIOK76bPmDKfNZXRNRWbi+1WVh6mJH4OkbbUASIi9mVJ6JdL11R+NyM38lJWLsbNsNSoaeLtRBNvJ/CxhtRk9n42hJsJhkyByrtxTBr6EbN2L6Pen9/gV6UO/Xq9ye2vjmFloab/yHlMmTkXj1deJbXFo1j9+htWhVGV1mphzRolys/actKli/LHVdT9CqEqnfhvGLE7AxCpRtTqBFw1C7HV/A1tX4GeH4NlMQakSST5IIQgJTHRrAvn/i6dhKgo0vTZM3bfj0qlxt7VFUc3JShx9PDIEqR44uDujr2LK2q1eety4H//EBsWmt22Mmp9KVSG3e7du+dciUrFwYMHi2RAcnIyMTExDB8+HBsbG6pXr05qairr1683lVmzZg3z588nICAg2/5z5sxh7ty52daXVobdwECoU0d51pUHyrMob3kVMi6qqjSAEAZSUsJymBZ+F12KEvQYDOaDPm3u6ek0PJgDBzI1bDQae3MZBut0WQZb5f/W1lVKtmuqlG+G0Si4FaUobl/OkD8I0RKvy/6VZ6lR0bCqI828nel17Rg+rrZUe2Vk6QtUFhaVCuPVAGIvpJH0rzJLy1pzGTeLz9HYAf2+gUbPla2NkkpBSlKS0jISqXTjZG05yejS0afkkrrgPuycXcxaTDIXTxw9PLB3cUNTyKEfQgh+ev8twm4G5PzyU6moWrc+wz9ZXKzWlxJXlc7g0KFDRTYqN2xtbbG1teWzzz7j8ccfZ/LkycTGxpqVSUpKMrXU3M97773HW2+9ZfodFxdHzZo1S9zO8kp5FeUtz0LGxUGl0uQ6awoyu6YyxTPvkqbyBRbh6Ng8S9dUYj5dUxZY36c1lbWLqlBdUw/gZqjVKup5OlDP04HnW1YHlIAmOCbJpOWUoesUm6THNyQO35A46h/cz2udhpE8ey8NqjjQ1FuZtt28uiJQaW9dtqmootb5kmJ0BYw4WWzEUbMFVb0noN9KcCo9QVZJ5UGfokvvvokkPjpLd06WLp3U5ILNcrNxdDIPRtw9cHL3wCGj1cTNHYtiTqbJCUNaGvGREbl/tQtBfGQkhrS0Ujl+ThRa22jXrl2sWrWKwMBAqlWrxtixYxlWyJGjJ0+eZMyYMVy8eBGr9PwVx44d46mnnuKrr75i6dKlXLlyxVR+4sSJJCQk8OOPP+Zbd2lrG2W0vNjbK91A/fqV+CEkpciDUJXOxn1OY+jz7H0tNuaimikpoQXSmrK0dDcba3O/oKaFhXO505oSQnA3NtmsdebyXS2RCdmbu1UZApXpA4ObpQtUOpWyQKXYsQOGj0CVnETohM3gaoObxQKsLa4reVs6vA7qyq/ALsmfNL0+vWUkIvtYk/RFlxBfoLqs7e0zW0xMXTpZxpy4e2BpVXZpHuIiI0iO0+a6XWnx8ch1e4GOUVrCjBs3bmTSpEm8+uqr1KlTh4CAAFavXs2iRYsYl3VqTT4kJCTQpEkTBg4cyIIFCwgNDWXw4MG0bt2ajz/+mPr165umZR8/fpznn3+eXbt25dptlRUpzCip6Jh3Td0f5ITk2DWVE+ZdU97YWJsHOSXeNVVEhBCExaWkt9CkLyFawuJyFqis42FP0/SApnm68raLnVWOZQuLMUlP9C/+6PzSlaDVZ3C1XIrGo6qSKde7ZYkcR1L+MaSlkRAdld5aEpllhk76GJPoKJK0sQWqy9LGNluLyf3dOVY2ctxUqQUvjz76KEuXLjULIg4dOsRrr72Gn59foYz08/Nj6tSpnDt3DmdnZ0aMGMEHH3yAtbU1f//9N1OmTOHSpUt4enrywQcfMOr+zJm5IIMXSWUno2sqI5BRuqhCSc4yyFivj863npy6psy6qIoza6oECI/X4Xs3LnNgcEgcd2OTcyxb082WZt7OphaaZt5OuDsUzvaUoDiif05XgiYNZ4vvcdD8iqrVS/DsArAuWA4YSfnHPJdJemASHWXWapIYG1OgwY0Wllbpg17NB75mbTGxtrMvd62g5ZFSC15cXFyIiYkxuwlGoxEXF5cyneWTFRm8SCRgMOgyW21S7pdkCEmfNZX/lMny1jUVnZhqapnJ6Hq6HZ1zK5S3sw1NqzvTzNuZ5jWcaObtTBWn7ImIhFEQf+QOcfsCwQga1T3cLedjZRcJfb6Epv1L+awkJUluuUyyzsopaC4TjYVF+niS9IAkY/pwlhk6Ng6OMjApIUptwG6NGjU4evQoXbMIxxw9elTqGkkk5QyNxgZ7+7rY29fNcXtBu6b0+ij0+iji4i/mchx786CmlLum3OyteKKhJ080zMxgq03S4xuiBDSX7sbhe1fLzchEQrQ6QrQ69vmFmcpWcbQ2a51p6myH1Z9BpATEAmCrPoyr5XLUtR6DF3aBy8Mz+L8iYMplkhGY5NClkxBd9FwmpsAkfcyJnZMzKjm+qVxSqJaX7777jrfffptXX32VunXrcuPGDVatWsWSJUsYPTp7zoyyQLa8SCTFR+ma0pqCmuxdUyHo9VH51qN0TXnl2HKjdE1VQ6MpwbTM6cTr9PiFpMsfpI+juRGRgDHL064tGj7AFjfUQAquFiuwsThEQvtpOD09A5WmbGc6PWzknMvEvEun0LlMMga/emTv0rFzccmWy0RStpRatxHA2rVr+eGHHwgLC6N27dqMGzeOgQMHFsvgkkQGLxLJgyH3rqnQLAn9Ct41lZmp2Hz8TUl1TSWlKgKVvsFaHP8Op+095SVooQrE3XIB90hhiv41/hUNcba1pFn19FlO6WNparnZoVbL7oGikpKUlG0mTtbfCUXNZeKRvUunKLlMJGVPqQYv5R0ZvEgk5YMcu6ZSzJP7FW7WVPG7ptKidURvukrqbWX6qr3md1ws1hDo/TRrXV/nnzAD1+7Fozdkfyw6WlvQJF2gMqPrqY6HPRoZ0JjnMonKuUunoLlMbB2dTJles+YyMQUmrqWTy0RS9pRa8HLz5k0+/fRTAgMDMd432KmoGXZLGhm8SCQVg/u7pjKCnKwJ/orbNZWRuVijsSH5croStC4NFYm4Wn6Fne1F+N8ieHSIqb6UNAP+YQmZU7dD4rgSGkdqWvYBnnZWGppUy8xD07y6M/U87RWBykpCWmoq8dHm4n33d+kUKZdJDtOGyzqXiaRsKbUBu0OHDsXKyooePXqgloOYJBJJMVCpVFhaumBp6YKjY86ihqauqRxmTGXtmtLp7qDT3cn1WBZGFyziXbFo5I6NToVz6t8kOnhg6Po9NlXbYSGEqWvK2kJjCkYy0BuMBIQnpGs5KQGNX0gcSakG/g6K4e+gGFNZaws1j1TLaKFRApsGVRyxsih/z0xTLpNs3TmZAUpeicmykmMuEw/zAEXmMpGUFIVqeXF0dCQ8PBxb2/LrgLLlRSJ5eFC6psLNg5qUzCAnOfkORmPOuWGykr1rytusJcfaumq2rimDUXAzIsEkf3A5RAlsElOzZ0e20qhp5OWYHhQpgU3Dqo6lqudkNBhIjL0vl0lGYBJdyFwmVtb3BSb3zdLx8MTazr7UzkXycFBq3UadO3fmhx9+oEGDBsU2srSQwYtEIhFCkPRPODG7/DGIeAy2QVg7/IDRNQZd06dJtqIYXVPV7uuWqm7qmjIaBYFRiaakepfuaHMVqLRQpwtUVs/sdnrEywlbq/wDGmE0kqiNNRPvu1/QLzEmumi5THIIUGQuE8mDoNSCl3///Zd+/foxYMAAXF1dzbZ9+OGHRbO2hJHBi0TycGNMSSN25w2SzqcrQasv4Ga5EE2TztDnK7BzMytvMOhISTHPUJxT11R+WFq63yeiWc2UqTgq2R2/e4LLoZlTt2OS9NnqUKuggacDzT0taGhvoKZ1Cm4iiRRttFmXTkFzmag1mvRcJh44uJnnMnFKD0xsHZ1kLhNJuaDUgpe+ffty/PhxmjVrhkaT+XWgUqnkgF2JRFLmpN5NIHrjFdKidIABJ4ufcLT5HdVz86HVy4raYyHJr2tKpwvBYEjMtx6Nxg4ba28sLauiNrqQmGhLeISa8HtpRIToiQ9PxloXj4MhEYsCCHOa5TLJpcVE5jKRVCRKLXhxcHDgxo0bVK1atdhGlhalGbycCjnFgrMLeLfdu3Tw7lCidUskkqIjhCDhZAjaPbfAINAQjpvVQqy9LRRBRc+GpXrsjFlT8dpbaGP8SYwPJDn5Lvq0cIyqaFQW+Y+7EQZITbREn2BJaoIlumQ74pIciUh0JkLnTlhKFbQqF+ItHEiwsMeziidNargq8gfpApWu9iUjUCmRlAWlNtuoevXq2NiUfDbMioAQgi///ZKb2pt8+e+XtK/WXvYBSyTlAEOinphfrqO7oohR2qhP4Wb5JeqOo+DJD8GiZKbe6nU6ZaBr1lwm983SyZ7LxAlwQqUxYuWgx9JBj5WjHjs3DXZuKqyc9FjYJPP/9u47PKpqa+Dw70xJnUw6oSWA9A6CYAEFFL1gFxUEBbwWsGH5wGvnYqGoiHoteFFEr2ABFQVBRAEpFhCQqpTQS0J6z7Szvz8mGTLJpAwkJIH1+uRJ5syZPfskmLOyy1qYctGMOoFWB4HW4umkLBpznHYlWit0WUkriOR4XgTpBZGkZUbxw/EoPl0XSVphFJGWaDo1jvBaRxPjZ4FKIeoDv4KX8ePHc8MNNzBu3DiioqK8bt6XXnpptXeuLvnl2C/sSNsBwI60Hfxy7BcuaXJJLfdKiHObbX8W6Z/9jSvLDjiIMH1AqHU92k0fQ6vLq9xOcS6T4sDEa/tw0S6dwrzcKrUVFGopsQC27JSOr1wm5U9NnUzq53LlEWTMpoklmyaWgz7fu9AZQFphFOmpkfx0OIovCiNxaXFEhiUQH9uCdo1b0KlpFHE+ClQKUZ/4NW1UXm4XTdNwuSqfoz0TamLaSCnFtV9fy8Gck78wgoxBtI9qT0hACMHGYIJMQQSZggg2BRNkLPpsKvHZ6P04yBREiCmEIKP760BjoIzkCFFFSlfkrDxM9o8HQYFJO0qUeRoBbVvD9W+D5WThRpfTQW56eqlREu8Rk6rmMgkIDvYEI54FsGcgl8nJqSnf+W6qumvKpRvIsEWQ7YgGQ0NCQ5oQE96M5nHn0Ti6BcFBjTEa624qDHF2k/IA1Ry8rDu6jrE/jq2WtsqjoXkFP6UDnWBjsNfjIGMQIeaTwU/x+aWDJE8wZQ4mwBAgAZKo91zZdtI//xtbojvgMGurcKhPyetwCzmRXcsEKHlZmaeWyyQm5mRhv3qQy6TkrqnibeC5eUfIyDlEYeExjCoFg1b5H5kOFY7R3Iiw0KZEhcUTFNzEq/aUyRQhv0dEjZDgpRqDF6UUt313G3+l/4WuTuZM0NBoGtaUezvfi81lo9BVSIGzgAJnAYXOQvdjRwEFrqLHTvfzxecVP3boZbdL1pSSAVLJIMnXsTLBT+lAyux9bvHzEiCJ6lI6l0lOaiqug3nEHo7DrAJw6nY2pv3AgdwdlbZlNJs9QUjZnCbuwCQo1HJW/9stnprKzD3E/qR9HE8/QFbuYZz24wQaUogOSifIZKu0HaMhpCiQKVGGoUQxTV8J/YSoihpbsHsuKrnWpSSF4nDOYWJDYk9r7YtTd2Jz2bwDn+Lgp9SxAufJYMjruMvHa0sETsUBkkJ52qwpBs3gHQCVDnz8DJxKHw82BWM2mM/qm8y5QClFQXZWOdM4KSVymbhHCgwY6BR5Ke0jegOQYUvm15RvyXGke+Uy8ZrSKZnLxFo9lanrM00zEhTUiIZBjWgY09vruUKHi7+PZ7Pj6FEOnEgkOeMAhQVHiQjMIDoonajgdKKDMggPzMGl55Ofv5f8/L3lvo9XranA0kGOTE2J0ycjLxUoHnXZmbYTRdlvk4ZGh+gOfHr1p3X6F6NTd3oFRF7BTyVBUslzKwqcnHrlCbOqS+kAqcojRuUEQ8XTcCVfKwHSqVNKUZiX65WOPje9bHp6l6PyUUdNMxATFU9P65VYcSeXy3esBPNCwgaOI6z7tZLLpIbYnTq7k3OKilO6SyDsTU4l1JReFNC4A5vo4AxigzOIC80kzJxepakpszmqzIhNyQR/MjV1bpKRl2ri0B0k5SX5DFzAPZKRlJeEQ3cQYKy7+RVMBhOWAAsWLDX2Hg7dgc1pK3e6rNzAx+GeSqtK4FQcIOlKJ9+ZT76z9LbU6mPQDBUGOqcyYlTytSGmEEwGU738BW3Lz/NRXbjo66JdOk5b5dMPAKERJ5OsWbzq5bg/G4/oZH69F2XT0cglyvwGTTsEwg3fgbVRDV/puS3AZPBZoDIxJZdtR9zlD7YfzeKHXdkUONwBi4ZORGAWUUEZNLRk0jY2j+YROcQEZxBiTAVXEi5XHg5HOg5HOjk523y+t9EYQmBgyakp79GbgIAGGAxy+zqXVWnkZdeuXbRt29bzeP369SxYsIDg4GBGjRrFeeedV6Od9Ed1r3lJyksivTC93OejgqJoGNrwtN9HVM6hO05OjfmYLvOMILlKBT6l1hr5nGorOs9ZhTTw1cWoGX2O+vhauF16hMjrsbn8HW7+BkiOwkKyS9TL8TWlYy+o2rRjcJjVe+FrcWBSNKVjiYrGaDL7fK1yuMhcvI+835MACND+IipwBqaB98FFD4Kks68zXLpif2qJApVFdZ1ybWX/XzIboUtjI+c3tdEmOo/48GzCA9Jw2o+7d1DZjmG3p1b6nl5TU4GNS43gNJGpqXqq2hfsWq1WsrOzAfjuu+8YMmQIV1xxBTabjV9//ZWlS5fSt2/f6un9aZLyAOJ0lAyQKgqGSh4vdBaS78z3TMNVNNVW4CzAVYXU79XFqBk9wUwIQVjtgVhtgYQWGAkuMBCYrzDnK0y5Tgy5dvBRQNCXgNBQLFHRWGNii9aVxHrv0omKwRRwaqORjuQ80ub9hTO5ANAJMy7A2uBXtFtmQePup9SmOLN0XXEwPd9doPJoVlFgk0V2OQUqW8eF0amxO7Fex0aBnBeVB65kzzbw4t1ThYXHsNmOV7HWVFSZERuZmqrbqj14CQsLIycnB4BevXrxyCOPMHz4cABmz57Nf//7X3777bdq6Prpk+BF1HUO3XFKI0alj5cMnGy2QsizYcxxYMxzElxgILTQSGiByf250ESQvWrrQuwmnbwgJ/lBLvKCneQFucgLcn/OL3rsNJ38tWHSTN55jnzlNSo9YuQrJ5IxiPC/jYSuKEBzakAmIQHTCTu/A+ZBL0NgzU17ipqnlOJIRoEnkNlWSYHKVg0sdGocTscm7vIHHRpbsQSa3Lum7CnukZqConw3Nu/8N1WtNVU8NVW8DVympmpXjY68xMbGcvz4cUwm9w/V5XIRERHhCW5qmwQv4myju1zkZqR778Q5xVwmhgAz5nALpvBQtLBgVFgAusWM02LCEWqgIFinwOgod4F3yYCqZOqA0xXiCuLBpNvon30BAJtC/+LVxh+RYXL/3jFpJu88R+XkNfIVJBVPrZVOJln6sUluVGecUorjWYVeIzTbjmaTmlt2zZSmQYuYUDo1DveUP+jYOJzwYHOZNp3O7FLJ/LyDHL+mpgLLLiqWqamaUe0Ldl0uF0eOHKFp06Z07NiRQ4cOeda5HDp0iPDw8EpaEEL4onSdvMwMd2r6kgtf004ugM1LT0dVIVAomcukOHeJpdTj6splopTCqTu9pssqCnQq2uEWlRHKzTsuJaYwAhcuFkR/w4KYFeSXWNbiVE5yHDnkOGrujySTweQzE7avQKe8rNmlk0mWzokkAZI3TdNoHBFM44hgrup4cu1gcnahe5fT0Wx3YHMsi+NZhexLyWNfSh7fbjnmObdZdEjRCI2Vzk3C6dQ4nMjQcMzmcMLCOvh8X5fLhs3mnaHY19RUcQBE1gaf7XhPTZX6HNgYszlSpqZqSJVGXtq3b8+ePXuIjo4mODiYXr168cUXX7BixQoefPBBBg0axPTp089EfyslIy+iriiZyyQ7LYWc1KItw+XkMqmIr1wmpb+ub7lMlFLkrjtG1pJ9oFNUCXo6gZffCH0fQ2kGzxRbycDHa41RBdv9qxI4FTgLyt1NWBNMBpPvESNfWbTLCZwqSg1wNgdIqbk2z2LgbUfc27ePZPhePN4kItg9OtM4nE5N3QFNbFjVC1SWmZqyHS9TlsHlqrzWlcEQXBTQNPKstQkqka1Ypqa81UiG3ZycHDZu3MiGDRvQNI3x48czffp0Dh8+zCuvvILZ7HvXwJkmwYs4E3zlMikzpeNHLpPQqCjvVPSldumEhkegnUU7bFx5DjLm76Lw7wwAggy/EBXzJYZb3oL4XmesH0op7Lr9lEaMqhI4FR8/kwGS2WAuNxN2lUeMKkkmaawjeXUy8uzuLdvHsjxTTwfSfKdQaGgNolMTKx0bu9fQdGoSTpz11GrKeU9NlVhvUyLIqfLUVECcz0XF5+LUlJQHkOBFnCZbft7JwMTXlE56atVymWgaoeERZUZJLCWzwUZGYTDWjZvBmWDbl0X6pztw5bgAOxGm9wntFop2zXQIOvumoEsHSP6WEKnq6NKZDpDKy2tUYa6jKiaTPJ0AKavAwc6iHDTu5HpZ7EvN87kkLMYSSKei6aaORWtpmkQEV8sIZumpqZJTVKe+a6rE56K1OGfT1FS1By/vv/8+d999t+fx9OnT+fzzzwkODmbs2LHcdtttp9/raiLBi6hMyVwmOUXTOZ4ssP7mMrGGl5jCKTulY4mKKjeXyblG6YqcFYfI/ukgKA2TdpiokLcIuO5h6Dq0trtXryml3DXWygt0fJQQyXfkl5sgsrzRpTMpwBBQYSbsqiaEDDYFo/QAjmU42X/CTuIJO7uO2Ug8YUNXZUczI0PMnuR8xYuDE6JCqj1A8JqaKjyGr4rhVZ+a8t4GHlji6/o0NVWju41efvll3nzzTR555BEKCwt58803eeGFFxgzZkz19P40SfBybnPYbUXTNmneC1+L09SnpVKYV/kvBICgUItnoevJYCS6WnKZnGtcWTbSPt2J/YD7ex9iXE5EwnoMt7wDUXUnyaUoX8kAqaolRHwmiCwnNUDxeWeS2RCAkUBQZpxOE3aHCV03gx6AUkWfdTMBhkBiQsNoZLXSNMJKs8gImoaHExIQUmm5EYN2atO9J6emSk5LeQc5pzY11ahMQU1/p6bW7pnFsQMzaNz8Ufq0vueUrs+XGs3z0q5dO/73v/9xwQXuLY2//vorI0eOZM+ePdXQ9dMnwcvZy+V0kJue5pm+yS45WlJ0rCAnu0ptBQSHlDNicjIwMQcF1fAVnRsK/k4n47Md6IWgUUCE+R1C+/WAfk+AUUalxElKKe/RIB+ZsPOdPkaMqhAklQymzqRAY6DvvEZVLT/iIwVA8ecATQNnBnbPWpuSu6eOU2g7jlKVr7s7OTVVYr1NiczFJaemdF3n85+60sCYzwlXCEMv34Khmtbj1ejIS8OGDTl69CjGEnP0JYOb2ibBS/3kzmWSVnbEpMSUTlVzmZgCA0tN35QNUAJDQmr+os5xyqmT9f1+cte6t7WatUSiIudgvvVFaN6nlnsnzlW60r1GkCocMXIUkOco4Fh2FseysknOySEtP5dsWz46NjA40DQHGOxoBjuawYHB4EBplQcM1ankeqOSgU6wKZBwI0QYdawGBxbNTjAFBKl8AlQORlcWBlX52r2SU1OZhVno+Vs9z5mbjufSNvdVy3VUe54Xu93OJ598Qo8ePejbty+//PKLpxzAypUradKkyen3Wpy1dN1FfmZmiSmcslM6eRkZp5zLxGtKpxpzmYhT50wrIO2TbTiOu38xWozfEt4pGe36ryAkqpZ7J85lxUVXg03BRBJ5Sm04XTqJKXmeLMHbj2ax80g2efbitAc6aE40gx2z2UXLBmbOaxBAQoyZJpFGosM0nMrmM2u2r9QAvqbabK6TQUehy30uVauHWvK7QbAWTKRJEWnUiTIpIo2KSJMiyqiINOlYjaDrBeTnJ5Kfn+j1al3B0YPvoLcaU22jL1VVpZGX++67jz/++INt27bhcDjo378/P/74I//973957LHHmDFjBvfcU33zXqdDRl7OLF+5TE7mMCkeNalqLhNTiVwmPkZMYmIJDrNKYFLH5W9JIWPBXyiHhkYOUUHvEnztzXD+SHeaVCHOQu4ClXmeYGbb0Sx2Hssmx2eBSo02cWHuXU5F5Q/aNQwjyFz1HVa60sskiCwv0Cm9rqi8ciOlX2tz2TChiCgKZtoHu+gf5uN6qmn0pca2StvtdrZs2UJWVhZXXHEF33zzDUopbrjhhtPtc7WR4KX6KKUozM0pkccktcyISW5aKi5n5dv9vHKZFAUjVs+W4bMzl8m5Rre7yPp2N3l/uBcRBmg7iGqyCNPQGRDbppZ7J8SZV1ygsuS27e1Hs8kqKDutZDRotG5gKdrlZKVz03DaN7ISElB7O4Vcugubyz1ClO/IZ83vg4g1FGAo8TeIriBVr561L2ckz0teXh4BAQGnlJxuy5YtjB8/no0bNxIQEMCVV17Ja6+9RkxMDPfddx+zZ8/2ave1117j3nvvrVLbErxUnVcuE59TOqk47VXMZRIRSVjxTpxSUzqWqHMvl8m5xpGUR9rHW3Cmu3BXgv4Ca59ItIHPgqnqmU2FONsVF6gsDmi2HXXnpEnPs5c516BBy1hLUR0nq6dAZVjQmV/ovnr3uziOvFru89Ux+lLtwcuoUaP46KOPAMjMzOSOO+5g6dKlGAwG7rzzTv7zn/8QUMUtowUFBbRs2ZJ77rmHp59+mpycHEaOHInBYGDRokX07NmThx56iFGjRlWpvdIkeHGzFxa4p3DSvRe+lpzS8T+Xie9FsJLL5NyllCJvfRKZ3+wG3YCBdKKsHxB06zhodUVtd0+IeqG4QKU7oMn2TD2dyPH9x2OLmNCTIzTFBSpDau53cPEOoxhDvteoi+f5ahp9qdHdRmPGjGHPnj28/PLLFBYW8vjjj9O7d29mzJhRpc7t2rWLRx55hMWLF3t2LH377bfccccdnDhxAqvVyqZNm+jYsWOV2ivtXAheTuYyKd4mnHIyC2xRgGLLq7wkPECQJcw9YlJi4atXFljJZSLKoRc6yfhiBwU73b8bAg0biWqzGePNr4IltpZ7J0T9dyK70D06c8RdAmHH0SyOZfne6p0QFVKm/EFUaPX87i505LJsVXcsxvI3VeS6DFzVbzNBZsspv0+N5nlJSEjgl19+oWnTpoC7qnSvXr1ISko65Q6PHDmSI0eOMHXqVC688EJuvPFG1q5dS3h4OHfddRcTJkwoN5qz2WzYSqRpz87OJj4+vt4GL75ymRQHJLlFUzp+5zKJiS01pSO5TMTpsR/OIe3jzbhyNMBJuHkelqsvQOt9jyzKFaIGpeba3PWcinc6HcvicHr5BSo7NrbSqWhRcMcmVhqEndrv/MPpW0jPO1ju81GhzYmP6nJKbRer9q3SJXd3GAwGYmJiPI+bNGlCQRWnH0pTSvHss8+yaNEiVq9eTVJSEv369WPcuHF89tlnbN68mRtvvBGDwcCECRN8tjFlyhQmTZp0Su/vr4Nb/2TFnPcYMHoMzbp08/v1nlwmxTty0tPKTOnkZ2VWqa2yuUxKT+lILhNR/ZSuyF19kKxlB0EZMGpJRMXOJ3DERIg7tdFSIUTVxVgCuaxNLJe1OTm6mZnvLlBZvHV7x7Fs9qfmcTSzgKOZBfywM9lzbpw1kE6NT+5y6tTESkNrUKW7OOOjuhIf1bXGrstfVRp5MZvNjBw5kp49e/Ldd99x8803M3r0aABeffVVvv76a9atW+fXG2dnZ3PnnXeyceNGFi1aROfOnX2e98orr/D555/zxx9/+Hz+TI28KKWY+/RjJCfuIa5la0a89JrXD7tsLpOyC2D9ymVSHJD4nNKJJTA0VLYMizPKlWsnY+5mCve7FxYGG9YS2TsLw+CJYD53Kt8KUR9kF5YoUFm0dbv8ApUBJaab3FNPTSPLL1C5dk8q/160g39f25E+rWN8nnNKfa7ukZe5c+eyYcMGPv/8czZt2kRubi6jR4/mySef5K233mLJkiV+dTAxMZHBgweTkJDAH3/84RnJWbhwIcnJyV51kmw2G8HB5f9iDAwMJDCw5ncz7N3wK8mJ7hIIyYl7WDRjKgaj0ROk5GWkn1ouk9JTOpLLRNRBhXszSP/kT/RCE2AjImQeobfcjNZ+cG13TQjhgzXIzIXnRXPhedGeY3k2J38dLx6hcQc2e07kkJpr5+fdKfy8O8VzbkSIuagwpTug6dQ4nGbR7tH8l5f9zd4Tuby87G8uaXVJrdyv/N4qrZQiJSWFBg0asG3bNqKiovzKsJuRkUG3bt0YMGAAH3zwgddalq+//poRI0awaNEiBgwYwG+//cZ1113HjBkzuP3226vUfk0s2FVK8cG4u8k6kVzheeXlMin+2hIdI7lMRL2iXIrsZbvIWX0C0DBph4hu/hPm214Ca6Pa7p4Q4jQV2F38nZTtyUGz7WgWu5NzcOplQ4OwIBNNIoL4O+lkcduP/tnLawrrdFT7yEtJmqYRHBzMkiVLCA4OpkOHDn69/sMPP+TQoUN88cUXzJ8/3+u53NxcZsyYwf3338+RI0do2LAhkyZNqnLgUlMObtnkM3DpPOBKmnXpfjLJWkSk5DIRZw1nlo30OeuxHwfQCDH+SMSVMRj6vg8SgAtxVggOMNI9IZLuCSdLJdicLnYn5bpHaIqS6/19PIecQqdX4GLQYPoPu7i0dcwZH32p0sjLeeedx759+wD466+/GDhwIDk5OTgcDs477zyWLl1KfHx8jXe2Kqp75KV4rcuJ/Yko/eR6Fc1goEGLlmXWvghxNijYfoKMz7ejO8xo5BMZsYCQkfdD4+613TUhRC1wuHQ+XX+I577ZUea56hp98ef+XaU/n1JSTs6DTZgwgVtuuYXMzEyysrK45JJLGDdu3On1uA47uGUTyYl7vAIXAKXrJCfu4eCWTbXUMyGqn3LqZM7fRNonu9AdZszaXuK6rSTk0RkSuAhxDjMZNBZsPFImSV3x6MspJus/9f5U5aSSIwvr16/n888/R9M0zGYz06dP9+R8OdsopVj7xSfuvBW+fjCaxtovPqFZ1/Nl9EXUe87UAtI++AVHhjuxlSVwKeFDLkDrMrWWeyaEqG2r96Sy9UhWmeO6gq1Hsli9J7Xa1r5Uhd9rXuLi4rDZbISGhgLutMGnUt+oPnA5neSkpvgOXACUIifVXZjQdJZ+D8S5IX/9ATIW7kPpARjIJrLx9wSPfBwi6sZ0sBCi9iilmP7Dror+jj/ja1+qFLzk5ubSsmVLevbsicViYdq0aUybNo1Dhw4xYcIELrvsspruZ60wmc2MmDyDguyy0WaxkPAICVxEvaXbXWR++hv5f+mAmQDDDqIuLcR05etgkMXnQgiwu3SOZRZU9Hc8xzMLsbt0Ak1n5vdGlYKXpKQkNmzYwIYNG8jNzeXo0aMA/Oc//2H37t18++23NdrJ2mSNicUaI3VaxNnHcSybtA9+xZkXAuiEhS7FescNaM0vrO2uCSHqkECTkW8f7OOz8nWxaEvAGQtc4BTyvJSUm5uLxXLqRZhqwrlQmFGI06GUIm/VTjJ/SAZlxkAaUW3/IOi2CRAUXtvdE0Kco6o9z8uuXbto27at5/H69etZsGABwcHBjB49mhYtWpxej4UQZ4Re4CTjw1UUHAoEzASZNhN5bSOMvV+s7a4JIUSVVWmr9AUXXOD5+rvvvuPSSy9l586d/PLLL3Tu3Jk1a9bUWAeFENXDlphM8tTlRYGLk/DoZUQ/ejXG3rfUdteEEMIvVRp5KTmzNGnSJGbPns3w4cMBmD17NhMmTOC3336rmR4KIU6L0hW5360na10BYMGoJRF9wRECrp8IRllsLoSof6q05sVqtZKdnQ1AbGwsx48fx2Ryxz0ul4uIiAhycnJqtqdVJGtehDjJlV1I+qyfsKW4/18IDtxA5G0XYGjXt5Z7JoQQ3qo9w67L5eLIkSMAdOzYkUOHDnmeO3ToEOHhsshPiLqmcOs+kl9eWRS42IiI/5mox0dJ4CKEqPeqFLwkJCTQvHlz4uLiOHDgAE888QQAK1as4Oqrr2bo0KE12kkhRNUplyJr3k+kzjuM7rRgMhwm7soTWO5/Gi00ura7J4QQp61Ka17++usvcnJy2LhxIxs2bPBk0Nu8eTNXXnklU6dK+nAh6gJnSibps1Zhz3YHKaGW9YT/8xoMjdtW8kohhKg/TivPS10ka17Euargl82kLz6B0kPclaA77CZk+L1gCqztrgkhRKWqPc+LEKLuUg4XWR8uJndfFBCC2bif6CGNMZ3/UG13TQghaoQEL0LUY45DR0mf/RuOwgYAWGI2EX7PbWjhDWq5Z0IIUXMkeBGinspbupLMn50oGmAgi8iLsgi+bpy7xKsQQpzFqrTb6PXXX+fgwYM13RchRBXoefmkv/4ZGT+bUAQRGLiXuLsTCL5+hAQuQohzQpVGXlJSUhg0aBDBwcHcdNNN3HTTTbRv376m+yaEKMW+cyfp8/7G6WwCuLAm7CbsrtvRAkNqu2tCCHHG+LXb6K+//uKrr77iq6++Ij8/nxtvvJGbbrqJnj171mQf/SK7jcTZSOk6efO/IXOzFQjAqKUTdVUQgf2uqu2uCSFEtfDn/n3KW6UPHjzIl19+yVdffcXRo0fZv3//KXW2uknwIs42eloK6f9dSmGWu3p7kGUvkXcPxNiwaS33TAghqs8ZCV5KSk5OJi4u7nSbqRYSvIizie23NaR/m4ZLjwYchHdMwjJ8KJrRWNtdE0KIanXG87zUlcBFiLOFctjJ+ehTsvc2A6IxGlOIHhJPwPkDartrQghR62SrtBB1jOvwXtJnr8VW0BKA4OiDRN57LQYpgCqEEIAEL0LUKYXfLyT9ZyO6aomGjYgL7YRcP8JTT0wIIcQpBC8ulwtj0Xz70qVLiYmJ4YILLqj2jglxLlF5WWTPmktOUkfAgCngBNEju2Ju1aq2uyaEEHVOlZLUFVu0aBGNGzcG4MUXX+Smm27isssuY9asWTXSOSHOBc4d60mZupCcpM6AgdD4JOKevlYCFyGEKIdfu4169+7N3XffzV133UWjRo2YM2cODRo0YOjQoezdu7cm+1llsttI1Bu6i4L5H5K+uQkKC5pWQOTAMEIGXFTbPRNCiDOuxnYbJSYmcs8997B582by8/MZOHAgJpOJ5OTk0+qwEOcalXqQzFlfk5fVAwBzaArRd/fF1Ci2lnsmhBB1n1/BS0hICCdOnGDRokX06dMHk8nE1q1biY6Orqn+CXHWcfzyLemLs3Ho7sDF0i6H8NuvRzNJ7hYhhKgKv4KXf/7zn3Tv3p2MjAy+/PJLNm7cyD/+8Q/Gjx9fU/0T4uxhzyPvo3fJTOyCIhKDIZeom5oR1LNvbfdMCCHqFb8z7K5atYqgoCAuvPBCDh8+zIYNG7jppptqqn9+kzUvoi7SD2wm86OV5Be4d+YFRmUQdc/lGCNDa7lnQghRN9TYmpfrr7+eb775xvM4Pj6e+Ph4LrvsMn7++edT660QZzNdx/79B6SvseJUFwA61gsMhN14LZpBcrcIIcSpqDR4OXDgAB9//DEAy5Yt4/nnn/d6Pisri61bt9ZM74Sox1T2cfJm/5fMpL6AGaM5l6jhnQhsH1/bXRNCiHqt0jwvCQkJbN++nZUrV+J0Olm5cqXXx86dO3nnnXf8etMtW7YwcOBAoqKiaNiwISNHjiQ1NRWA33//nd69e2OxWGjRogUffPDBqV2ZELVI37qUtFc+JzNpAGAmqHEeDf51uQQuQghRDfxa83LPPfecdkK6goICWrZsyT333MPTTz9NTk4OI0eOxGAw8PHHH9OqVSuef/55xowZw+rVq7nhhhv48ccf6dWrV5XalzUvolY5CrDNn0H61ra4aAA4Ce8fieXKrpLiXwghKuDP/dvvBbt2u50TJ06g67rX8YSEhCq9fteuXTzyyCMsXrzYU2bg22+/5Y477mD69Om8/PLL7N6923P+fffdR35+Ph999FGV2pfgRdQWdXw7OXPmkZ11JWDEFJxH1J29CEiIqu2uCSFEnVdjC3YXLFjAPffcQ3Z2tueYUgpN03C5XFVqo23btixdurRMuz169GDHjh107tzZ67kOHTpUOHVks9mw2WyexyX7JsQZoRSu1R+SvqwAmz4IgJCWLiJGXoEhUGqfCiFEdfPrN+tzzz3Hgw8+yKhRozCbzaf95kopnn32WRYtWsTq1at54403CA313joaEhJCbm5uuW1MmTKFSZMmnXZfhDgleakUfjKN9P2XohOBpjmIuCaBkItbyjSREELUEL+Cl8OHDzNx4kRMptP/azI7O5s777yTjRs3snr1ajp37kxoaCiZmZle5+Xn5xMWFlZuO08++SSPPfaYV7vx8bIoUtQ8tWcFWfN+JLfgOgDM1kKi7roYc5zkbhFCiJrkVxRy/vnns3PnTrp06XJab5qYmMjgwYNJSEjgjz/+ICYmBoBOnTrxww8/eJ27c+dOOnXqVG5bgYGBBAYGnlZ/hPCL04Zz8Suk/x6HXbmniUK7BBJxy8VoZknxL4QQNc2v4OWSSy7h8ssv55ZbbqFhw4Zezz333HNVaiMjI4MBAwYwYMAAPvjgAwyGk7u1b7rpJh5//HFef/11HnjgAdauXcvcuXO9EuMJUatS95D/0WtkpFznrgRtshN1c3uCuzWu7Z4JIcQ5w6/dRv379/fdiKaxYsWKKrXx2muv8X//93+EhISUWROQm5vLH3/8wcMPP8y2bduIjY3l2WefZfTo0VXtouw2EjVDKdSG/5G5aA95jisBCIh1EXXnhZiigmq5c0IIUf/V6Fbpuk6CF1Ht8tNxzJ9I+t8X4FAtAAi7KALrNR3RjJXmeRRCCFEFNbZVurhMgC8jR470pykh6gW1fw358z4iM2coiiAMAQ6iRnQlqG10bXdNCCHOWX6NvLRo0cLrcXp6Onl5efTp04dVq1ZVd99OiYy8iGrhcqD/9CoZq5wU6P0ACIw3EDXyAoxhAbXbNyGEOAvV2MjL/v37vR4rpZg2bRrp6en+91KIuip9P/Z5z5F+9BqcqjGgY728CWGXt5RK0EIIUQec9poXl8tFkyZNSEpKqq4+nRYZeRGnQ/35Oblf/0iW7TbAjDHURdTI8wlsJv+WhBCiJtXYyIsvu3btkkyiov4rzMb1zVNkbGlBoe5evxXUOpio27piCDn9bNJCCCGqj1/BS//+/b0CFbvdztatW7n99turvWNCnDGHN2D7dArp6XfgIgY0nYhrWhJ6cRMJzIUQog7yK3jp16+f12Oj0cijjz7KDTfcUI1dEuIM0V2o1a+R8+M+sp3jACOmCIga2YOAxpba7p0QQohynPKalxMnThAVFVUtdY6qk6x5EVWSdQTXF4+Rvv8ybLq73EVI10gibmqPIVBS/AshxJnmz/3brwxbDoeDRx99FIvFQqNGjbBardx7773YbLbT6rAQZ9SOhRS8eR/JiSOx6V3QTDqRt7Qm6rZOErgIIUQ94Ffw8sILL7By5Urmz5/Pjh07+OKLL/j999959tlna6p/QlQfex5q4cNkzvuZtLzH0QnH3MBMg4cvILRHw8pfL4QQok7wa9qoZcuWLF++nPPOO89zLDExkUsvvZSjR4/WSAf9JdNGwqdjf+L8/HHSTgzFodoAEHphHBFXt0IzS4p/IYSobTW2VTo9PZ2EhASvYwkJCeTn5/vfSyHOBF2HX98if9mPZNgfQxGKFghRt7YnuGNMbfdOCCHEKfDrT84uXbowc+ZMr2MzZ86kc+fO1dopIapFThL6x7eSsSSZdPsEFKEExIcQ9+gFErgIIUQ95tfIy4svvsiVV17JJ598wnnnnUdiYiI7d+5k2bJlNdU/IU7Nru9xfPkSaTljcKpmgCKsXzzWgc3RjJK7RQgh6jO/t0rv3r2buXPnkpycTPPmzbntttto1qxZTfXPb7Lm5RznKED98Bz5vx4g03mvuxJ0iIGo2zoQ1DqytnsnhBCiHDW2VRrg559/ZsyYMcycOZOmTZuyfPnyU+6oENUqeSf6e4NJXxdBhnMciiACW1mJe/QCCVyEEOIs4te00cSJE5kzZ44n067VauW5554jIyODCRMm1ET/hKicUrDhfexLZpNmexSXagSawnpVC8IubSqVoIUQ4izj17RR06ZNWb16dZmt0gMGDODgwYM10kF/ybTROSYvFbXwAXL/CiLLOQowYww3ETW8o1SCFkKIeqTGtkpnZ2f73Cqdm5vrfy+FOF2JK3B9OZ6MzNsp1C8AILhTNJFD2mAIrltlK4QQQlQfv37D9+jRg6lTp/LMM894jr366qt069atuvslRPmcdljxPIVrVpHueA6daDBCxLWtCO3dUCpBnwO0SRoh5hA0NDRNw2wwM6j1IN4e/DYRQRG13T0hRA3za9po06ZNXHnllYSEhBAfH8/hw4dxOBwsW7aMLl261GQ/q0ymjc5yqXtQC+4m+3AHclzDAAOmmCCiRnQgoFFobfdOnCHaJI39D++neURzALJt2dzw2Q3EWeL4dMintds5IcQpqbFpo/PPP589e/awaNEijh8/Tnx8PFdffTXh4eGn1WEhKqUUbP4fzu+mkZ7/AHblTowY0iOOiOtbYgiQgornMmuglSHth/DuH+8C0PGdjhzMLLsOL/cpmeIW4mzg98KAyMhIRo4cWRN9EcK3ggxY9DAF246R4ZiGTjhagEbkjW0I6d6gtnsn6oCDmQeZu20u/Zr3A2DH/Ttqt0NCiBolqxpF3XZgHerL+8hKH0iu6x4AzI1DiRreHnNMcC13TtSmLu92waAZUCisgVauankVUy6fUtvdEkKcARK8iLrJ5YCfp+FcNZc0xwQcqjUAlksaEz6oBZpJKkGf67bet9Wz5qW0Lu924VDWoTLHM5/IrNlOCSHOCAleRN2Tvh++uof8g8FkOF5HEYIh2EjkLW0J7hBd270T9cDW+7bWdheEEDVIghdRt2z9An3Rk2TmDyffdSUAAc2tRA1rhykisJY7J4QQoi6Q4EXUDYXZsGQ8jj9/I83xPE7lToYYNiAe6+XNpBK0EEIIDwleRO07vAG14C7y0tqT6XwNCMQQZiZqWDuCWkbUdu9EHaQmVjk9lRDiLCTBi6g9ugvWvoa+4k0y7PdRoPcFILBNJFG3tsFoCajlDgohhKiLJHgRtSPrCHx1L7b9KaQ7XsOlGoIBwv/RAkufJlIJWgghRLkkeBFn3o6FqG8fJjdvAFnOCYAJY2Qg0cPbExAfVtu9E0IIUcdJ8CLOHHsefP8Ero3fkO54FJveE4DgzjFEDmmNIUj+OQohhKicZPoSZ8axP+G9Sync8CfJtjfdgYtJI+LGVkQNbyeBi6iyA5kH0CZpWCZbWPj3Qp/nTF07lbhX44icFsljyx5DV3qFbX7111f0/6i/17Ftydu46IOLCJ8aTsd3OrJkz5Iqve7nAz/T/b3uhE0Jo8PbHVi0a5F/F+iDrnQe+f4Rol+OpsErDZi2dlq55/5+5He6v9ed0Mmh9P2wL4npiZ7nvtz5Je3eaodlsoULZl3Ar4d/LfP6nSk7CXoxiAOZBzzHEtMTufzjy7FMttD2rbYs3bP0lK5jT9oe+n7YF8tkC91mduOPY3+Ue25VfobPrniWfnP6+Xx96ecGzR2EZbLF8xH4YiABLwSw+fhmLJMtGCYZWHVg1Sldl6gFqpadOHFCtWzZUq1cudJzbOzYsSogIECFhoZ6Pt57770qtZeVlaUAlZWVVUM9Fn5xuZRa96bS/x2rMp+6Xx3+1yp1+F+r1fHpfyj78dza7p2oh/Zn7Ff8u/xfXQv/WqhavtFSHcw8qJJyklSP93qo9ze+7/Ncl+5Sb/z2hgp6MUhd9uFlXs91fbermrVxllJKqcW7FqvgF4NVvj2/wtfl2nJV+JRw9c3f3yillPpp308q+MVglV2YXeXrG/X1KPXh5g+9jr3+6+uq96zeKi0/Te1J26Oav95c/Zj4Y5nXFjgKVKNXG6m5W+cqm9OmJq2apPrM7qOUcn/fQl8KVb8d/k0ppdTsTbNV09eaer3e4XKoXrN6Kf6N2p+x33Otnd7ppF78+UXl0l3q+z3fK8tki8qx5VT5mop1n9ldTV0zVdmddjV702zVbEYz5XQ5y5xXlZ/hhqMbVMALAWV+bpU9p5RSdqdd9Z7VW729/m3PsWYzmqmV+1f6fU2i+vhz/67VkZd169Zx0UUXkZiY6HV8w4YN/Pe//yU3N9fzce+999ZSL8Upy0mCT27Cuex1UgqfJ8c1DDAQ0jOOBg92w9wwtLZ7KM5C87bPY2zPsSSEJxBniePJPk8ya9Msn+c+/dPTfL7jcx698NEyz+1N34vD5UBXOkaDkSBTUKWvCw0I5chjR7iu7XXk2fNIyk3CGmjFbDSf9jWNv3g8UcFRtIpqxYMXPOjzmlbuX0lUcBTDOw8nwBjA032fZvuJ7fyd+jfNI5qTND6J3k17k1WYRWp+KtHB3hmrp6yZQp/4Pl7Hfj38KwWOAp7q+xQGzcBVra5izZ1rMBn8Gy39K+UvEjMSGX/xeMxGM3d2v5OwwDCW71vu83or+hnanDbu/vZuxvQYU+a1FT1XbPqv0wkLDOP+C+736xpE3VFrwctHH33E8OHDeemll7yO22w2tm3bRs+ePWupZ6Ja7Poe3r2Ygj15nLD9B7vqiBZoJGpYW6JuboMhwFjbPRRnqb9T/6ZdTDvP47YxbdmZstPnueN6j2PdP9fRJrpNmef+dcm/eHDpgwS8EMC1n17Lh9d/SLA5uNLXWQIsZNuysU61cvtXtzPtimlegY8vh7IOETE1goipEczbNo/7v7ufiKkRXDPvGr+uqfR5RoORlpEtPedaAixsS95G1MtRPLPyGV4Z+Irn3C1JW/h8x+e8OOBFrzb/TPqTDrEdeGDJAzR4pQHnv3c+ufbcSq/JV99aR7XGaDj5/37b6KpdR+nrfXbls1zb5lrOb3R+mddW9BxAekE6U9ZO4Y1/vOFX/0XdUmvBy1VXXUViYiJDhw71Or5lyxYcDgfPPfcccXFxtGnThmnTpqHrvuesbTYb2dnZXh+iFjkKYMkE1LwRZGYNIc3xHDphmJtaiBvXnZBuDWq7h+Isl2fPI8Qc4nkcYg4h35Hv89xGYY3KbcegGfjfjf8j/+l8Ph3yKf/89p8czT5a6esAQs2hFDxdwNIRS7nvu/vYfmJ7hecnhCeQ+UQmmU9kMrzzcN65+h0yn8hk8fDFfl1TnsP7PF/ntotpR+HThcy8eiZDvhhCSl4KdpedO7+5k/euec8ToBXLKMxgyZ4ldI3rypHHjvD4JY9z/WfXk16QXuE1nUrfPOdWcL2/Hv6V5fuW8+xlz5Z5XUXPFZv5x0wub3E5HWI7+NV/UbfUWvDSsGFDTKayw45ZWVn069ePcePGceTIET755BPefPNNpk+f7rOdKVOmEB4e7vmIj4+v6a6L8iTvhFkDcPy2mBP2V8l1XQeApU8TGoztiik6uJIGhPDPoaxDXosw1xxcQ4g5hAJHgeecfEc+lgCLX+2uP7qeedvneaZfbu5wMz0b9+TLv76s0uuNBiMBxgCuanUVl593Ocv2LvPr/Uur6jWFmEMocBZ4HSt9rtlo9kzbNItoxuqDq3n+5+fp17wflyRcUqbNAGMAzSKaMabnGAKMAQzrNIwmYU345fAvFfZ57OKxnp/LoLmDqtS3yq63wFHA3Yvu5oPrPiDA6J3EsqLnSvrf1v9xV/e7Kuy7qPvq3BaPgQMHMnDgQM/jXr168cgjj/D5558zYcKEMuc/+eSTPPbYY57H2dnZEsCcaUrBhvdh2dPk2y8iwzEJRTCGEBORt7QhuL1UghY1IyE8gdyncr2OtYtpx+603VzN1QDsSt1F25i2frV7JPsIdpfd65jZYMZsqHjtyl8pf3HrglvZMnYLBs39t6HNaSMiKMKv9y+t+Jo6NugIlH9N7WLa8dGWjzyPXbqLvel7aRvdlh/3/ci0ddNYfsfJNSZ2l52IoAgW7FzA8dzjzN482/Ncl3e7MPOambSJbkO2zXtE26Vcle7gmnnNTGZeM9PzeGfKThLTE9GV7vne7Erb5TOQKO9n+MexP9ifsZ8BHw3w9N/ushMxNYJFty0q97nMJzIB2J+xnyPZRxjYcmCZ9xT1S53bKr1w4ULee+89r2M2m43gYN9/tQcGBmK1Wr0+xBmUlwqfDkP/7mnSC8eS7hiPIpiAFuHEPXy+BC7ijBvWaRhvbXiLfRn7OJF3gilrpzCi8wi/2rg4/mKSc5N5a/1b6Epn2d5lrD20lsGtB1f4ulZRrch35PPqL6/i0l0s/Hshm45v4po211T5vefcMIfR3UaXuaap66ZyIu8EiemJvLXhLZ/X1L95f5Jzk/l4y8fYXXZeWvMSLaNa0j62Pd0admPjsY18tv0znLqTt9a/hVN3cnH8xfz94N9kPZHlmboC2HrfVoZ3Hs7A8wZi1IxMWTMFXel8svUTknKT6N+8f5n3r0iH2A7Eh8czZc0U7C47c/6cQ1p+Gv1blG2nvJ9h32Z9yX8639PPd65+hz4Jfch8IrPC54qtP7qe8xudX+HIjKgf6lzwopTi0Ucf5aeffkIpxa+//sobb7zBmDHlrxwXtSRxBbx7Mfa/d3HC/gb5roGggfWKBGLv6YwxPLC2eyjOQTe1v4kxPcZw2ZzLaP92ey5tdikP9noQgDUH12CZXPkUUkNLQxbdtog5f84hclokj//4OAtuXUCziGYVvs5sNPPtsG9ZtHsRUS9HMXnNZL4b/h1xljjAnWtk8prJfl/TuN7j6JvQly7vduHi2RcztsdYrmvrnpadu3UuHd9xj8gEm4P5bvh3/Gf9f4h+OZrl+5bzxc1fABATEsO3t33LtHXTiHk5hq/++oolw5eUWeNSWmhAKCtHrWRZ4jIip0Uybd00Fg5dSFigOxt2x3c6Mnfr3Cpdx1e3fsWP+38k5uUY3vj9Db4Z9o1n4W/J701FP8PTcTDrII0sFa9XEvWDppSq9fKsmqaxcuVK+vXrB8B7773Ha6+9xpEjR2jYsCH/93//x/33V21LW3Z2NuHh4WRlZckoTE1x2mHF86h1/yHPNZhM5z2AGYM1gOhhbQk8L6K2eyjOYgcyD9DijRb1srL0hqMbWHlgJY9f8nhtd6XazN8xH4Xi1o631nZXTkvz15sz54Y59Gver7a7cs7y5/5dJ9a8lI6fxowZIyMtdVXqXvjyn+jH9pLheJIC3b3AL6hdFJG3tMEYenr5LIQ4my3du5Q7u91Z292oVr8d+Y2J/SbWdjfEOabOTRuJOkop2PQxvNcX21EbyY633IGLUSP86vOIHtVBAhdxRlVUHqCueu6y54gPP7s2FEy/ajrWwPo7yl1cHuBQ1qHa7orwQ52YNqpOMm1UAwoyYNHDqB3fkuMaQrZzJGDAGBVE9PB2BDSVStBCCCFOjz/3bxl5ERU7sA7e7YNrx8+kOp4n2zkaMBDcNZa4cd0lcBFnXFUKM5a0ZM8S2r7VltDJoVz76bWk5KX4PM+lu3h8+ePEvhJLzMsxPPL9I57twJmFmdwy/xaipkXR5LUmXoURt5/YjvF5o1e+mS93uvPBJOUmcePnNxI5LZK2b7X1HD9dc/6cQ8KMBMKmhDHy65FeOVF8WX90Pa3/09rrWEpeCoPnDiZsShit/9Oa7/d+X+Z1BY4C2r7Vljl/zgHK5tWxTLZgfN7IvYv8L99S4ChgxFcjiJgaQfyMeD7686NKXzNu6Tj+verfXscq+vne/MXNBL8U7Olrj//28Dy3YOcCWr3ZivCp4fT/qD970vYA0PDVhphfMJd5H1HH1GSRpdoghRmridOu1E8vKPXvCFXwTF919MnF6vC/Vqsjz6xVueuPK13Xa7uH4hxVXmHGlftXlinEdzznuLJOsaqV+1eqAkeBuvubu9XtX93us91pa6epXrN6qZS8FHUi94Tq/E5nT4HEh5c+rEYvHK1sTptKTE9UDV5p4Cni98mWT9R1n17ns82BHw9UwxYMU7m2XLX5+GYVPS1abTy2scrX+uHmD9Wor0d5Hdt8fLOKeTlGbU3aqrILs9XguYPVMz89U24bc7fOVZFTI1WzGc28jt/w2Q3qge8eUDanTS3ds1RFTo1UGQUZXuc8vPRhZZhkKFMostiGoxtU4+mN1cHMg1W+pmKPLH1EXf/p9SrPnqc2HN2goqdFq12pu3yem1mQqe799l7Fv1ETV070HK/s59vqzVZq07FNZdpLzk1W4VPC1ZakLcrpcqp/Lf+X17+dUV+P8nofcWbUm8KMoo5K3w8fDkL9PJ0s++2kOl5E162Y4kJo8GA3Qi9oiKZptd1LISr19V9f0zehL/2a9yPIFMTUK6byxY4vyiRdA5i1aRavDHyFmJAYYkNjWTx8MVe2vBJwF2l06e7EbBoaRu1kocYtyVvoGte1THt59jx+3Pcjrw58ldCAULo17MbQjkP535b/ndY1fbrtU4Z2HErnuM6EBYbxfL/neX/z+z7Pfe+P93hpzUs80ecJr+O59lwW7VrExMsmEmAM4B+t/sElCZfw2fbPPOesPria3478xsXxF/ts26W7uPObO3n5ipdJCE/w/zq2f8ozlz5DiDmEno17clun27yS5JU0eN5gbC4bN7W/yet4RT/fPHseh7IO+SwDcCjrEIXOQpy6EwCjZqx0y7ioW+rEbiNRh2ydD4sfxWkLIt35KnaXu/BcaK+GhF9znhRUFHXONfOuYe2htTh1J4XOQiKmRgDuJGulC/xFh0QTFhDG3vS9XoX7cu257E3fy76Mfdy76F5y7bnc1f0u/t3v3wA81Oshbp5/M/O2zcOlXIy/aDwXNr0QcAcvdpedZq83Q0NjTI8xPNn3SXSlo1BeNXqMBiOJGYmVXlOXd7twKOsQdpcdp+70TI9lPpHJ32l/M/C8kxli28a0JSk3ifSCdKKCo7zauaHdDdzT4x5WH1zNOxve8Rzfk7aHiKAIYkNjT7ZTokhirj2XsYvHsuDWBdz/ne80FR9s/gBLgIURXfxLAAiQUZBBcl5ymeKLPyT+4PP8BbcsoFFYI0YvHO11vKKfr81pI9QcyuB5g9mavJWucV15a/BbtItpx/mNzqd/i/70+G8PjJqRmJAYfrmr4nIHom6RkRfhVpgNX90LX91NQUEnkh3vYHe1cVeCHt6OyJtaS+Ai6qTFwxd7ihgWZ1TNfCKThPCEKhcDzCzMBGD+zvn8ctcvrP3nWj7b8Zkn1b5Dd/DYhY+R9UQWf475k0+3f8qiXYsAiA6O5prW17Dz/p0sGbGEWZtmMefPOYQFhnFps0t58qcnKXAUsCVpC/N3zqfQWVjpNW29b6snS+zwzsO9Mt/6KloI+CxwGGeJ86TiL6my78uEHyYwsuvIcosXKqWY/ut0nuzzZKXX4kueI8+r76Xfv7TyCmFWdB15jjwubHoh/xn0Hw4/ephL4i/huk+vw+FyUOAooEVEC3676zdyn8rl9i63M2zBsDJpO0TdJcGLgMMbYGYf1JavyHDcR5rjaZQefLISdJfYytsQog4qXeAPfBcDLE4X/8QlTxAVHEXziOaM6TGGxbsXY3fZuf2r23mw14OEBoTStWFXHrjgAT7880MA5g2Zx/9d/H+EBoTSIbYDD1zwAN/u+haAT278hENZh4ifEc+E5RMY3XU04UHh1XpNxTd8f4pPVlQkcXnicjYlbWLCxWVryRVbf3Q9WYVZXN366iq939ytc70W+RYHHKdbQLOin+8V513BkhFL6BDbgSBTEP/u92+S85L5K/Uv3t7wNhoavZv29kw37UjZwbYT2/x6f1F7ZNroXKa7YO1rsHIKDldD0vU3cbjcOSgslzYh/MrmaCaJb0X91S6mHcsST1Z0Ts1PJbMwk1ZRrbzOiw2JJSIogixbludY8RqXXHsuWbYsr0KNxZWZCxwFPLfyOZ659BlPUGJz2TzrYdIK0vhq6Feex8O/HO5zfYy/17Q7bbfn8a7UXcSFxvlV/LFVVCsyCzO9ppp2pe1icKvBzN85n50pO4l+2V2XLNeey/qj61l/dD3vXO2eelqyZwnXt70eo6Fqo7EjuowoM70UGxLL7rTddG/U3XMdbaP9K6BZ0c938e7F5NpzGdZpGOAuJunUnQSZgsoU3jRoBoyasdLCm6LukDvTuSrrCHx0Lax4kTznZZxwvoXDGY8h1ET0nR2JGHyeBC6iXunXvB+rRq/yOnZ92+v5+cDPLE9cTqGzkKd+eorr2l5X5i98TdO4vfPtvLD6BTIKMjiYeZCZG2cypP0QooKjuKDxBfzrx39R6Cxkb/pe3t7wNje3v5lgczDLEpfx/M/P43A52H5iO29veJs7utwBwENLH+LN399EVzrf7/2eJXuWMLLryCpf0+huo5lzwxyvY0M7DmXe9nlsOr6JHFsOz616juGdh/v1vbIGWhnUahBP//Q0hc5Clu1dxpqDa7ix/Y3899r/kvNkjmeqqk9CH965+h1P4AKw/th6z5qfUzWs0zAmrppIji2Hjcc2Mm/7PG7rfJtfbVT087W77Dz8/cP8lfIXNqeNJ398ks4NOtMmug1XtbyKz3Z8xi+Hf8GpO3nh5xdoHtGcNtFtTuuaxJkjd6dz0c5v4N1L0A9sJN31OBmOx1C6mcDziipBt42qvA0h6oEm1iZ8fvPnPPz9wzR4pQFHc47y32v/63neMtnCmoNrAHj1yle5sMmFdHynIz1n9eSOLnd4Rgvm3zKfLFsWjac35oqPr2Bcr3Hc0vEWAL689Uu2nthKzCsxDJo7iKf6PMWg1oMAmHXtLBb+vZDwqeH868d/8eWtX3p25kxeM5lBcwf5fU09GvfgtStf45b5t9B0RlOigqN4acBLwMk8LFXJFvv+de9zPPc4DV9tyLjvx/HZzZ/R0NKwSn04mHnQ5zqUsYvHMnbx2Cq1MeXyKUSHRNPijRbc+PmNzLhqBt0adgOq/r2p6Od7U/ubGH/ReK785EqiX45me8p25t8yH4Cr21zNy1e8zB1f30HsK7H8cuQXFg5bWOWRJFH7JMPuucSeB98/CZs+wq63IF1NxOmIKaoE3Yyw/vFoBtkCLeq2+lyYsSSn7uSOr+/g0yGf1nZXqk1KXgoTV030GqWpj0YvHE3ziOae3WbizKh3hRnFGXB8Cyy4C5W6hzzXNWS67gVlwBgeQNSwdgS2OL1FhEII/yz8eyGju46u7W5Uq/k753PP+ffUdjfEOUCCl7OdrsNvb8OPk9BdgaSrFyh0uhfIBbWPIvJmqQQt6ifLZAuf3PQJN7S7oba7ckpu7nBzbXeh2t1/ge+cMPVJw1cbklaQxtN9n67trogKyLTR2SwnCb4eC/tWYtPbk66ew+UIc1eCHtQCyyWNJVOuEEKIOkGmjQTs+h6+uR+Vl0GOGk624zZQGqboIKJuk0rQQggh6i/ZbXS2cRTCkgnw6VBceS5StdfItg8HpRHcLZYGUgla1HO1UVW6dCVl0/Mmrvyfu+7RkewjXPfpdURNi6Lpa0154ecXPG1WVHHaH7rSeeT7R4h+OZoGrzTwqmpd2u9Hfqf7e90JnRxK3w/7kph+shzBlzu/pN1b7bBMtnDBrAv49fCvXt+nju90xDrFyuC5gzmSfaRKbfpjT9oe+n7YF8tkC91mduOPY39UeL7NaaPXrF6sOrDKc2zQ3EFe38/AFwMJeCHA8/zUtVOJezWOyGmRPLbsMc/PEOCTrZ/Q4o0WWKdYuXre1aTkpbD5+GYsky0YJhm83kfUcTVaIrIWnNNVpZN2KPX2hUpNtKqCZy5TR59ZfrIS9AapBC3ODrVRVbqkAxkHVKNXG6k/jv6hlHJXjh63ZJyyOW1qf8Z+1fKNlmru1rlKqYorTvvj9V9fV71n9VZp+WlqT9oe1fz15urHxB/LnFfgKFCNXm2k5m6dq2xOm5q0apLqM7uPUsr9fQt9KVT9dvg3pZRSszfNVk1fa6qUUioxPVGFvBSiFuxYoOxOu3rx5xdV95ndla7rFbbpr+4zu6upa6Yqu9OuZm+arZrNaKacLqfPc3el7lJ9ZvdR/BtPBe/S7E676j2rt3p7/dtKKaUW/rVQtXyjpTqYeVAl5SSpHu/1UO9vfF8ppdRvh39TMS/HqE3HNimb06ZGfT3Kq2J3sxnNyn0fcWZIVelzjVKwfhbM6o9K3kUW95HqeAHdEYi5YQgNHupOaE+pBC3OPdVVVbqksd+N5YELHqBH4x7oSifYHMxTfZ8iwBhA84jmXN/2es+IRnkVp/01b/s8xl88nqjgKFpFteLBCx5k1qZZZc5buX8lUcFRDO88nABjAE/3fZrtJ7bzd+rfNI9oTtL4JHo37U1WYRap+alEB7uz6H6/93sujr+YIR2GYDaaebLvk+xJ38PW5K0VtumPv1L+IjEjkfEXj8dsNHNn9zsJCwxj+b7lZc7NKszi4g8u5to211ZYsXr6r9MJCwzzLBSet30eY3uOJSE8gThLHE/2edLzfXp/0/s81OshujfqToAxgNf/8fop12YStU+CFz/k/fILiVdfQ94vdaj6aF4afHobLBmP024lxTCTnEJ3vZHQCxvR4IFumBuEVNKIEPXXNfOuIWJqhKe6dMTUCCKmRnAo61CFVYdLKllVut1b7Wj6WlM+2PQBjSzeidh+SPyBnSk7mXCJu+6PQTPwzbBviLPEAeBwOfhh3w90jusMuIOXNYfW0Oz1ZjR/vTlT1kw5pWssfR1tY05WgK7oPKPBSMvIlp5zLQEWtiVvI+rlKJ5Z+QyvDHwFcE+ZlSxwqKFh0AwkZiRW2qY/19A6qrVXIriSlaxLCjGHsOvBXTx+yeNo+P6jK70gnSlrp/DGP94o9/pLfp/+TP4Ts8HMJbMvocErDbj/u/s9PzdR/0jwUkVKKU68NgN7YiInXptRN6qPJq6Edy+G3UvJV5eSrGZhL2iEFmQkakQ7Im9ohWaWjJHi7HYmqkoXe/WXV/m/i/7PU8ixJJfuYvQ3owk0BjKq6yig/IrT/vJVSdpXBeaqXG+7mHYUPl3IzKtnMuSLIaTkpXBlyyv5ad9PLNu7DIfLwSu/vEK+I59CZ2GVv4eVXoMf7ZiNZqJDoitsb+YfM7m8xeVela8r+j5lFGQwa9Ms3r/2fRLHJWJz2Xjk+0f8ugZRd0jwUkV5a9dRuH07AIXbt5O3dl3tdcZphx+egf/dgMpJJ8P4JOm2x1EOIwEJYcSNO5+QzlIJWojqqCpd7HjOcVYfXO2pW1RSnj2P6z67jl2pu/j+9u8JNAUCFVecLs+ag2u8FqQeyjrks5K0rwrMFVWLLlZcVPLO7nfSLKIZqw+upm1MWz664SMeWfYI8TPisTltdIjtQHhgeJXa9GXs4rGeaxg0d9Apt1Oe/239H3d1v8vrWEXfpwBjAHd2u5P2se0JCwzj6b5Pe/18Rf0iW6WrQClF0ksveR07+sgjhF7aF2NEBMbwcIzWcPfniKLP4eEYrO7HhsDA6utM6l748p9wfAsOvSnppqk48iIACLusKdYrm6EZJSYVAqqnqnSx7/d+z2XNLyMyONLrtRkFGQz830AahTXi59E/ExoQClBpxeny9G3Wl9yncstcx+603XRs0BEoqsAcU7YCc7uYdl6jRS7dxd70vbSNbsuP+35k2rppLL/j5BoTu8tORFAE2bZs2se2568H/gIg25bN5LWT6dqwK5qmldtmRWZeM5OZ18z0PN6ZspPE9ER0pWPQ3L+jdqXtKhOAVMX+jP0cyT7CwJYDy1z/7rTdXI176rzk96lNdJsKf76ifpHgpQry1q7DceCA1zE9L4+cpd9X6fVaUJAnoDGGh2MItxZ9HVHiuLXouaLjEeEYQkNPLrJVCjZ/AksfR9nzyTdeR6bzHlS+hiHUTNTQtgS1iay4I0KcxcqrKv3UT0+xPHE5fZv1rVJV6UviLyHbls3MjTN5vt/znnPWH13PhU3KVlIeumAoCeEJzL9lvtd6juKK07rSmXrFVHal7eLtDW/z/rXv+31twzoNY+q6qVyScAk5thze2vCW11qPYv2b9yc5N5mPt3zsfs3aqbSMakn72PbEhsay8dhGPtv+GTd3uJmZf8zEqTu5OP5ijmQf4ZLZl7D+7vU0DmvM48sfp3/z/jS1NiU6OLrcNv3RIbYD8eHxTFkzhQmXTGDetnmk5afRv0V/v78f64+u5/xG55eZvhvWaRgTlk/g+nbXYwmwMGXtFE8V7zu63MG9i+9lVNdRnBd5Hi+sfoEh7Yf4/d6ibpDgpRJKKVLeeAMMBneq/WKahqlhQ8JvvBE9OxtXVhaurExcWVnomVm4io6h66jCQpyFhTiTk/17c6MRo9WK0RqG0ZWGwZWGIciK1uoJVOj57lPCHYSer6PyD2Lbl+ke+bFa0UzyoxWiZNXhI9lH6NusLx/dcHIUwTLZwtIRS+nbrC+vXvkqjy9/nI7vdMShO3jgggc8VaUBDmYdpGtD751D209sZ/m+5QSZggiferI+2O1dbmfmNTP58tYvuX/J/cS8EoM10OpVcXrymsmsObSGpSOWVnod43qP41jOMbq82wWF4rELH+O6ttcBMHfrXCavncyO+3cQbA7mu+HfuXdELXmAbg278cXNXwAQExLDt7d9y0NLH2Ls4rGc3+h8lgxfQrA5mNbRrXn5ipcZ8PEAcmw5DGw5kE9u+gSgwjYBOr7Tkaf6POX1vSrPV7d+xd2L7mbaumm0jGrJN8O+8YxEDZo7iL4JfXmq71OVtnMw62CZxdTgriS9N30vl825jHxHPqO6juLBXg8CMKTDENIK0rjpi5tIzk1mcOvBTL9qeqXvJeomKQ9Qidw1azl8T/mFxuJnzcLSt4/P55Suo+fluQObTHdw4wl0MrOKAp4sXNlFAU/x48xMlN1epj1DeALBF9yDwRKH0l3Y//4W++7vgbI/QoPFUmqkJ8Jr9McYEY7BWmIEqGi6yxBU8ZC2ELVNqkrXLfN3zEehuLXjrbXdldPS/PXmzLlhDv2a96vtrpyzpDxANfGMumiae9qmNE0j5Y03CO1zic8cKprBgDEsDGNYGDRt6td763m5uJZNwbXmv7hsUBB8G4XmIYABDHaMAVsJbKkwx1zoDoSKgiI9J8f9+txc9NxcHEeP+vW+WkDAyeCm5NSW1eq9nqd4nU9x0GOxoBlkrY0QVXW2VJX+7chvTOw3sba7Ic4xErxUQDkcOI4f9x24ACiFIykJ5XCgBZTdOnnKMg5g+PIeDEfWY7CGkRvyKoUZTQAI6hBN1M2tMYRc7rtLTieunBxcmSVGeUqN9OjZpUZ+ij5wuVB2O86UFJwpvlOol8tgcAc4nnU74VUa6TFarWhmqWot/CdVpeuG+j71svn4Zvp+2Nfvrd+idsm0USUcx4/jTE8v93lTdDTmhg1P+308ts6HxY+CPQebsRfp6mlc+UYwakRcfR6hFzWqkUy5Sin0vHz0onU7Jz9KrecpDoRKBEaqoKDyN6iAISQEQ0SpBcwlRno8wVCJkR5jeDhacLBkDRZCiLOETBtVI3OjRpgbuReGJSYmsnTpUgYNGkTLli2r940Ks90FFbd+hlIGckLHk53RDxSYYoLdlaCbnFo+hKrQNA2jJRSjJRRzkyZ+vVa32U4GNmXW9BSNAPkY6dFzckAp9Px89Px8nMeO+9dns9kd9FgrGumJ8N7CbrViCAtDM0ryPiGEqK8keKkipRQ//fQTqamp/PTTT5x33nnV91f/kT/gy7sg4wAuYkgPfR1begQAId0bEHFDSwyBdfdHZQgMxNCgATRo4NfrlMuFnpPjY6Sn1ChPlvdIjysrCxwOlMOBKyUVV0qqfx3WtBJTWN4jPSenvCI829dLrvMxVOf0oBBC1EOH/0pnzee76Tu0DfHto2qlD3X3jljH7Nq1i2PHjgFw7Ngx/v77b1q1aoXRaETTtFMLZHQXrJ0BKyeDclEQ9A8ybA+ip4MWYCDi+laE9jh7a29oRqM7yV9EhF+vU0qh8vNPBjUlRnnca3rKGenJykLPz3eP9hQ9dvjb55AQz9qekqM8nqSEpY4ZrVYM4REYQkNkiksIUe8ppfhtYSIZSfn8tjCRpu0ia+V3mwQvVaCU4ocffvA69vnnn3s9NhgMGAwGjEaj5+sKHysnxrQ9GGwZGNR16OY26HnhaGzDZDUTfF4kpqNpGI772a6Px9Vxbl268WqahhYaiiE0FHPjxn69Vtnt3qM4mUVb1UsvbC6Ru6c4bw9FQZMzPx9nUpJ/nTaZTmmkx2i1yhSXEKLOOLwznRMH3btaTxzM4fDOdBI6VlyHqiZI8FIFiYmJpFewaBdA13V0XcfpdPrRstX9oeGuV2Qs2uFjB/72b4tzTdM07YwESWckGAsOxhAairFpUwJKPF8RpevuKa4qj/Rkomdlu3P2OBzgdOJKS8OVlub3994QFlZ2PU/JkhReoz/Wkxmaq7MshRDitCilULpC1xW6S6EUKJf7cfFxX5+VTtH5Ra8r59zic9xtUkmbJ/tQUZuez0Vt6i6do7szPNekafD7t/uI7xB1xv/AleClEkopVqxYgaZpXpWkNU0jLi6OO+64wxO4FH+4XC6fX+v2fFzrZ6MnrkDHQEHwZeTkXYTu0lFmjeDusRjign2/1s/Hp3Nued8Hl8uFy+U6U9/6M+6Ug6SQEIxhYRiaJfg+Vyk0hwMcDjSHA81uR7PbwWZDKyiEwqKPgnwoKIC8PMhzf21QOpquMLicaKmpGE6cQFMKg657PnvOKf5c9JwxIABTWBjmsDBMXnl6/ChLIUQVKFXiZuy5gZZzM3QV3ZAruWlX5QbvdePW3X9EVtS2u3+gXHrR57Jt+QwKfLTp8wZf1KavQOPs2tfrplTtjb7UevCSkpLCRRddxPvvv0+/fv0A+P333xk3bhw7duwgNjaWZ555hrvu8r94V3VITEz0rHUpSSlFUlISx48fp1WrVj5eWcrxLbD4Lkjbg1KBZMa8Sd7RJsQBAc2sRA1riymy9rPbKqUqDMZOJ0iqzgDrdF5bXnaAioK3GmXQICTY/VGDNF13BzZZmRgy0ssEQZ4ASCmMmgGD0YDBYMRgMmI0mdwfZjMGsxlTQACGwEB3gBQUhDEoCFNwMAaj8YxPaZb8qCnl32Sr9lex0otG7/y8cVf2V7H766r9Be/VZon++n5N0TW7Kg4GTl7bWXhnPoM0g4bBoKEZKPqsYTC611IWf9aM2snnDCVfc/Jz6deVabP4PKOGQSv1uahNX+0aDBposOXHQ+Rk2LySutfW6EutBi/r1q1j1KhRJCYmeo5lZGQwePBgnn/+ecaMGcPq1au54YYb6Ny5M7169Tqj/SsedanIihUraNmyZfk/NF2H396BH/8NugNHcA/StH/jPOr+xxB2WTzWgQl1phK0pmkYi25AZ6uaCs7OVCBX2WvLHT0zGHCdzg1eKbDb3R95eafeTg3SMKAV/YdW4mvl/hrP46Kviz6jSnwUP9aLjwFKQ8Pgec5XG97HDN7tl3qv4j6VfJ3X6320RdEz9Y2mlbrRlrwZa5S6Kfu+cRuMvm/CXjdtQ8k2DeXeuCtru7ybt1ZBMFBemxW2VfQ90DTqxUjnoR1p5KTbyhyvrdGXWgtePvroI5577jlefvllhg0b5jn+5ZdfEh0dzQMPPADAgAEDGDFiBG+//bbP4MVms2GznfyGZmdnV1sfXS4XWVlZFZ6TnZ2Ny+XC5KsQYk4yLBwLiStQCvIbjCfzeH+UU2GwFFWCbi2VoGtS5cPZoOsGlK6hdCPKpdCUQnMpNF1h1FXRVIz7fKOfw9ml/zKv8nB26bZUURvFn1EoQPd8XfKzdyCju3T3eboLpYqOqeJRqOKW3K9FK/q61GfQT36tnTy3+LUlj1HcO813m+6vde9jPttyB2Gec0u0Vd493P1d8Dwon1bqcz2hYSja3ej+bNAMaBTdqDWD+7Fm8CyyNxgMGDQjBoPmHkXTij6XHLEyFo1kFR83GjEaT45uGQ0GjCYjBoMRo8mA0WBwj8YV/ZFjNLnPMxUdM5mMGEwGz+PiEbKqjKrVh5v4uUgpxe/f7nP//6LAHpBBrjURS3ZLAuyRUAujL7UWvFx11VWMGDECk8nkFbzs2LGDzp07e53boUMHPvjgA5/tTJkyhUmTJtVIH00mE/feey95RX9l/vjhTtKP5xHVKJQr7uwAQGhoqO/AZfcyWHg/5KeiG6PIiHqbgsNhgCKwdQRRt7bFGHb6OUOqbThbr+gGf5rD2f60WdXhbL2SNmU42wdj0Yf7d1BVxta0En9pVjicrYGGAuXC4P7Hh6a7wOVEczrdn13udT847eCwg90GDhua7kJTerkfoKMplzuoLPqMckFRgKMFB2IICoDgIAzBgWhBgUXH3J+LH2vBQRDkPlcLDACTEb2oHaWKwr+iz3rxY734cVGgVzytWvS4pkbZypvadPeRous/+1T3xoC6uKmgPtKdipz0QlDuPyPyLPtxmfLJs+zHnB6BpjRyM2zoToXRfJYHLw3LSamfk5NDaGio17GQkBByc3N9nv/kk0/y2GOPeR5nZ2cTHx9fbf0MDw8nPDycHWuOknPYgJkwcg7D1kVpWKKCUHouup508obpdKIf/ROVmojOaIICGxFvakvgYfcfgkctASRl2tHf21bhjbvcgMTrL3VV8V+XokK1Npx9im1XNAStaRSdr1UYaNS14WzfZSmyvXZtuXP05JU6XgNlKYrz95QsS2H13s11pspSlFx7Vt+nMct7XN51y8aAuhmMDRjTAkehTlLyMX5a474fOwNyuWhkAxKaNic4zIzRfOaCs1pfsFtaaGgomZmZXsfy8/MJCwvzeX5gYCCBNbwlVCnF5h8OeR1L3FxR4cImQBPOCzRwHgYMLsjXFX/kucjI9Dct2qnz3Nh8LQar8MbpfSP0vklW/cbt3fbJNit9f4P7f/Aq97e8m7HXOWW/BzJEXfuqpSyFrwKk2aUyNGedzN+jF+XsOa2yFL6Kj4aHYwi3eu/oijgZFPlTlqLk2jPzWVq49GwPzurcxoAaoGkaG7f/yvkXdZKt0p06dSqTEG7nzp106tSplnrkTsqTlVL2r7xWPWIJb+DOnGpI2oS29wcMug1DQCRWyw0EZLt/UTkahuDq1oDzg0yV/rXrXvHt343bZ1sGuTGLs1v1laWovPhombIUqam4Uk+zLEWJkZ7yio+ezWUpiv+q9zntfhYovYi+LgZY/p7rdDpxOE7+Aa6U4tixYyQmJlZt1201qnP/am666SYef/xxXn/9dR544AHWrl3L3Llz+eabb2qlP8ULlTQNr336mgbZqYVceVsjtG8fhGNLIQRsjUaRljoUPdsJpqJK0BfWTCVoIYT/TqssRUFB2VGe4uKjPkd6stAzq6EsRXBw5cVHS4/0SFmKWlWf17j4opRi1qxZHD9+vEzOs0p33daAOhe8REdHs3z5ch5++GGee+45YmNjefPNN+nfv3+t9KdkKuSSPNvDpt9NglqJMgSRk/A22bviQDkxxRZVgm5cc5WghRBnjqZpaCEhGEJCPJXmq8q7LEWp4qM+R3pKlaUoKMBZUHB6ZSn8GOmRshSitIpyntXG6EudCF5Kzw327NmTdevW1VJvTiq9Pawsnd9TBtG4VS7pxuex/+1eZBbSI46I61tiCJD/+YUQoAUEYIqJwRQT49frlK6j5+ZWeaRHzy46VlNlKcoZ6fHK0CxlKc461ZLzrJrVieClriq5Pcw3A0Hm5iRnPY8qcKEFGIm4sRWh3f2bgxdCCF80g8E9YmK1gh+7KJVSqMLCKo70FD1XFAjpRakh9Jwc9JwcHEeO+NfnoCCvkR5DybIUXmUqSpSlCLdisFhkiquOOu2cZzVAU+Utia6nsrOzCQ8PJysrC6vVetrt5aQXUpjrgEO/w9IJnuPKZMHVaCKuA+6/MsyNQ4m6rR3m2JDTfk8hhKgtyuHAlZNTNKpTheKjJRc0n84uGqPx5NRWcdBjLW+dz8mRHqPVinaWLvqtS7Kysjw5z3wJDQ0lPDz8tN7Dn/u3BC9VoRS81oHCzFgynWMIM84n13ArDrv7LyHLxY0JH9wCzXT2LM4SQgh/KF1Hz8s7OYVVMtDxjPR4j/IUb19XhYWn9d4Gi8U9fVVp8dGTIz3G8HC0oCAZ7alD/Ll/S7haFYk/obKPkeUcj1MlkOF8BDCiBSqihnYkuMOZraYphBB1jWYwYAwLwxgWBk2b+vVavbDQM72lFwU03iM9xdNe3iM9eo57M4Wem4uemws+FpRW2OeAAO9FzFbvUR5jeKl1PsXHLBa0s2gnUX0kwUtllIIVL1KgX4hDtSk6aMSkHSAmbj6m9rWzhVsIIc4WhqAgDEFBmOP8zNnjdOLKyfEe5fGM9JQYASo10uPKygKnE2W340xJwZlSUdJRXx12B2peGZqrMNJjtFrRzsKcPbVBgpfKJP6EOrqZTMecEgd1NOwYk3+GxJ+g1RW11TshhDhnaSYTpshIiPSvwG2ZshRVHOnxlKXQdc9jB4cqf8MSvMpSlExaWAfKUtQnErxUpGjUxab3QKfkFkcDDtUGm96DoBUvQsvL3VnrhBBC1HmnVZbCbj+lkZ7TLUuB2eyzJIUx3FpqlOfk6I+/ZSmqKu+XX0h6aTINn36K0Isvrta2q0qCl4q47KjMo2Q5nwBceNfhdZHlHEFg1lQ0lx1MkttACCHOdoaAAAyxsZhiY/16nVdZCq+RnpLb2MuO9BSXpaA6ylKUGukpb5SnorIUSilOvDYDe2IiJ16bQfOLLqqVESEJXipiCsQ28Dscnyf7eNLoHn25YglBErgIIYSoQLWXpcgulbfHa6Qns8bKUrgK8incvh2Awu3byVu7DkvfPn62fPokeKmAUoqsdXnlZ9jVIGtdLoHdlMxFCiGEqHY1WpaizEhPUVmKnBzQ9crLUhgMpLzxBqF9LpGq0nWKS+HKtJWfYVfhft6lwCTBixBCiLqjOstS6NlZ5G/cSMbceSdP1PVaG32R4KUCmslAgwe7o+eVP9hmsJglOZ0QQoizhq+yFEop0mZ/CAaDdyblWhp9keClEqaIQIiQNS1CCCHOXXlr13nWunippdEXGTIQQgghRLmUUqS88Ub5KUE0jZQ33uBMVhuS4EUIIYQQ5VIOB47jx925z3yeoHAkJaEc/u5nOnUybSSEEEKIchkCAmixYD7O9PRyzzFFR/vMC1NTJHgRQgghRIXMjRr5vVW7Jsm0kRBCCCHqFQlehBBCCFGvSPAihBBCiHpFghchhBBC1CsSvAghhBCiXpHgRQghhBD1igQvQgghhKhXJHgRQgghRL1y1iWpK66tkJ2dXcs9EUIIIURVFd+3q1Ij6awLXnJycgCILyrjLYQQQoj6Iycnh/Dw8ArP0dSZLAN5Bui6zrFjxwgLC0MrrwLmKcrOziY+Pp7Dhw9jtVqrtW0hhBCiPqipe6FSipycHBo3bozBUPGqlrNu5MVgMNC0adMafQ+r1SrBixBCiHNaTdwLKxtxKSYLdoUQQghRr0jwIoQQQoh6RYIXPwQGBjJx4kQCAwNruytCCCFEragL98KzbsGuEEIIIc5uMvIihBBCiHpFghchhBBC1CsSvAghhBCiXpHgRQghhBD1igQvFfjzzz8ZMGAA4eHhxMTEcMcdd5CWllbu+V9++SXdunXDarXSvHlzJk2ahK7rZ7DHQgghxOlbsWIFvXv3xmq10rBhQx566CEKCgp8njtz5kzatm1LWFgYbdq04Z133qnx/knwUg673c7gwYPp378/aWlp7N27l+PHj/PYY4/5PH/jxo3ccccdvPjii2RmZrJ06VLmzJnDjBkzznDPhRBCiFOXkpLC1VdfzX333UdmZiabN29m1apVTJ06tcy5Cxcu5Mknn+Sjjz4iOzubjz76iKeffpovv/yyRvt41pUHqC4BAQHs2bOH4OBgDAYDGRkZ5OXlERsb6/P8AwcOMHbsWK655hoA2rdvz4033sjq1av5v//7vzPZdSGEEOKUxcbGcuLECcLCwlBKkZaWRmFhoc/737Fjx3jiiSe48MILAbjooovo378/q1evZsiQITXWx3M6z0tBQQFHjx71+VyjRo0IDQ0F4JJLLuGXX36hQ4cOrFixgri4uCq13a1bN0aMGMFzzz1Xrf0WQgghzoSmTZty9OhR+vbty9KlSz33xfKcOHGCDh068NprrzFy5Mga69c5HbysWrWK/v37+3zu66+/5oYbbgDcgUhhYSH33XcfO3bs4M8//8RoNJbbbk5ODjfffDPJycmsWbOGsLCwmui+EEIIUaMKCgrIyMhgxIgRBAUFsXTp0nLPTUpK4uqrryYmJobvvvsOk6nmJnfO6eDFXydOnCAuLo5NmzbRvXt3n+fs2rWLIUOGEBcXx4IFC4iMjDzDvRRCCCGq1/r16+nduzfp6ek+72u//fYbt9xyC3379uXDDz+s8dIBsmC3HAcOHKBFixYcP37cc8xmswEQFRXl8zVLliyhV69e/OMf/2DZsmUSuAghhKh3fvnlF9q1a4fdbvccs9lsBAQE+Jw2mj17NpdffjmPPPII8+bNOyM1j2TkpRxKKXr27Enr1q15//33KSws5M4778TlcrFkyZIy5//2229cdtllvPvuu/zzn/+shR4LIYQQpy83N5cOHTpw8803M3XqVI4fP86tt95Kjx49ymyD/vLLLxk+fDjffvstV1111Rnro4y8lEPTNL755hscDgfNmjWja9euJCQk8Omnn3rO6dixI5MnTwZg8uTJOBwOxo0bh8Vi8XwMGjSoti5BCCGE8JvFYuH7779n+/btxMXFcdlllzFw4EBP6g+LxcLcuXMBmDRpEk6nkyFDhnjd+8aOHVujfZSRFyGEEELUKzLyIoQQQoh6RYIXIYQQQtQrErwIIYQQol6R4EUIIYQQ9YoEL0IIIYSoVyR4EUIIIUS9IsGLEEIIIeoVCV6EEHXC3r17a7sLQoh6ol4ELxkZGdx///3Ex8cTGhpKo0aNGDVqFEeOHPGcM3r0aMxms1eGv+IPl8tVi70Xou46cOAAmqZx4MCBWu3HhAkTePHFF2u1D9XloYce8srEDfDdd9+haRoPP/xwmfMnT57Ma6+9dqa6J8QZ069fP+Lj40lLS/M6Xvr3jq7rvPzyy3To0AGLxUJkZCT/+Mc/+PXXXz2vmTNnDgaDwXNfrxfBy9ChQ0lNTWXDhg3k5eXx559/YrPZGDhwIE6n03PeiBEjyM3NLfNhNBprsfdCiMqkpKTUdheqxU8//cTmzZu57bbbvI6/9dZb3HfffcyePZuMjAyv58aPH8+sWbP466+/zmRXhTgjjhw5wsiRI6komf/UqVOZM2cOCxYsICcnh6NHjzJgwAAGDBjgNSKbkJDgua/Xi+Bl7dq13HjjjTRs2BCAuLg4Xn/9dXr37l3mF4EQonw5OTmMGjWK8PBwmjRpwv/+9z+v5999911atmxJREQEXbp04f33369Su3PmzKFPnz48/PDDxMbG0qBBA+6++24cDgfgHhkdPXq012s0TWPVqlW88MILzJ07l7lz59K1a9cqvd/s2bPp0aMH0dHRhIWFcc0113gCoH//+9/ceuutjBo1CqvVStOmTZk/fz4vvPACcXFxREVFeY3yaJrGK6+8QkJCgqetY8eOeb5fw4YNIyYmhoYNG3LVVVdVGGQ8+eSTPPTQQ17H9u7dy4oVK5g4cSJdunThvffe83o+ICCA0aNHM3HixCpduxD1yciRI1m3bh2vvPJKueesXbuWvn370qFDBzRNIyQkhMcff5x7772X5ORk3y9S9cA///lPZbVa1X333ac+//xzdeDAgTLnjBo1So0aNerMd06IeuSOO+5QF110kUpOTlapqalq4MCBClD79+9XiYmJKjAwUP39999KKaW+//57FRQUpI4dO1Zpux9++KEC1Isvvqjsdrtav369Cg0NVZ9++qlSyvf/n4BauXJluc+X5/fff1chISHq999/V0opdfjwYdWmTRv1zDPPKKWUmjhxotI0Tc2fP1/puq7+9a9/KaPRqMaPH6/sdrtauHChAjy/RwDVsWNHtW/fPpWZmamuu+461adPH6WUUs8995waNGiQys/PV4WFhWrUqFHquuuu89mv4mvOz8/3Ov7www+rYcOGKaWU+vTTT1Xjxo2VzWbzOufgwYPKYDCo5OTkKn0PhKgPLrvsMjVx4kS1YMECZTKZ1Lp165RSSu3fv9/ze0cppT766CNlNBrVkCFD1KxZs9TWrVuVy+XyauvDDz9UzZo18zyuFyMvs2bN4u233+bQoUPce++9NG/enFatWnmqWhabN28eERERXh/Lli2rpV4LUbfYbDa++OILJk2aRIMGDYiOjvb6a8hkMqGUYubMmaxbt47LL7+cvLw8GjVqVKX2g4ODeeqppzCbzVxwwQV07dqV3bt3V/t1dO7cmR07dtCrVy8yMjI4duwYsbGxHD161HNOhw4duPnmm9E0jYEDB+JyuTx9u/baawE4ePCg5/wXXniBFi1aEB4eziuvvMLatWs5cOAAwcHBbNmyhY8//phjx44xe/ZsvvnmG5/9WrFiBd27dyc4ONhzLC8vjzlz5vDII48AcPPNN2MwGMqsiUlISKBRo0asXLmyur5NQtQZQ4YMYcyYMQwbNoz09PQyz48cOZKVK1cSEhLCpEmT6NKlC7GxsTz11FNeS0MOHTrkubfXi+DFYDBw++23s3jxYjIyMtixYwe33HILd9xxBz/99JPnvOHDh5OZmen1cdVVV9Viz4WoO9LS0rDZbCQkJHiOtWzZ0vN1QkICq1at4sCBA1xzzTVERUXx6KOPUlhYWKX2GzRogKZpnsdmsxld16vvAooYjUbeeOMNGjRoQI8ePZg8eTLZ2dle7xUdHe11PkBkZCTg/n0CeJ3funVrz9fF35/jx4/zr3/9iwkTJvDBBx/QunVr2rdvz1dffeWzX4cOHaJJkyZexz7++GOysrK4+uqrPVNPJ06cYPr06WVe37RpUw4fPuzX90KI+mL69OnExsYyatQon+tf+vbty8cff8zhw4c5cuQIU6ZM4Z133mHSpEmecxISEjz39jofvCxbtgyLxeKJ1jRNo0OHDkyZMoXu3buzefPmWu6hEPVDTEwMQUFB7Nu3z3Os5I69EydO4HK5+Prrr0lLS2PJkiV89tlnfPDBB6f93kajEbvd7nmcmpp6ym3NmDGDH374gW3btrFv3z4WLlxIs2bNvM4pGURVRclRm/379wPuX5Rbt27l2muvZf369aSlpTF69GiGDh1KVlZWmTYMBkOZYO3tt9/mhRde4M8///R8/P777+zcuZMffvjB61yn0ymbC8RZKzAwkM8//5zVq1d7Be+5ubmEhoayePFiz7EmTZpw7733ctddd5V7j6/zwcull15KXFwcd955J9u2bcPhcJCTk8PcuXPZs2cPV199dW13UYh6ISAggJEjR/Lss89y5MgRsrKyePzxxz3PHzp0iIEDB7JixQoMBgONGzcG3EHP6Wrfvj1r1qzh6NGjFBQUMGnSJK8AIygoyGdA4EtWVhZms5mAgACcTieffPIJ33//vVdw5K9JkyaRlJREZmYm48eP5x//+AdNmjTh/fffZ+TIkZw4cQKr1Up4eDgWi4XAwMAybTRr1swrCFqxYgW7d+/m3nvvpWnTpp6Pbt26MXjwYF599VWv1x87dsxrVEyIs02rVq3473//y9tvv+05ZrFYuP7665kwYQKrVq0iLy8Pu93OunXrWLBgAUOGDPHZVp0PXoKDg1m7di0NGzbk2muvJTw8nPj4eD755BOWL19O+/bta7uLQtQbb7zxBr169aJz5860bt2a3r17e57r2bMnb7/9NmPHjsVisXDppZdy//33c+utt572+44ZM4aLL76Yrl270qpVKxISErxu1EOHDmXdunVVunmPHz+e+Ph4mjVrRuPGjfnkk0944IEH2LZt2yn3r0ePHvTp04fmzZsTGRnpWU83ZcoUWrVqRceOHQkLC+PDDz/km2++ISgoqEwbV155JRs3bvRMs7311lsMHjyYBg0alDl3zJgxLF++nK1btwKwb98+UlNTufzyy0/5GoSoD4YOHcqYMWO8jn344YeMGDGChx56yLMj8JFHHuGll17izjvv9NmOpnxNPgkhxDlC0zRWrlxJv379Trutnj17MmHCBIYOHerX66ZMmcKWLVv47LPPTrsPQpwL6vzIixBC1BdTpkzh9ddf9+s1NpuNDz74gOeff75mOiXEWUiCFyFEhTZs2OCz7EbxR8eOHavtvU6cOFHhe1kslmp7r5owcOBAunfvXiaNQ0VeeeUVxowZQ5s2bWqwZ0KcXWTaSAghhBD1ioy8CCGEEKJekeBFCCGEEPWKBC9CCCGEqFckeBFCCCFEvSLBixBCCCHqFQlehBBCCFGvSPAihBBCiHpFghchhBBC1Cv/D6/2nPtYwx+tAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#def polamat([scn_Pzz,scn_Pzx,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy])\n",
-    "\n",
-    "plt.figure()\n",
-    "#plt.title('(0-12)_Ni2InSbO6-R [scan 128:136] T=2.1 K')\n",
-    "plabel=['Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz']\n",
-    "P = np.full((3,3), np.nan)\n",
-    "u=[]\n",
-    "d=[]\n",
-    "u1=[]\n",
-    "d1=[]\n",
-    "i=0\n",
-    "for n in [190,191,192,193,194,195,196,197,198]:    \n",
-    "    try:\n",
-    "        data=read_data(n)\n",
-    "        plt.plot(data['col_ds_nut'],55555*data['col_detector']/data['col_monitor'],'^-', label='scan'+str(n)+':'+plabel[i])\n",
-    "        \n",
-    "        u.append(data['col_detector'][0]/data['col_monitor'][0])\n",
-    "        d.append(data['col_detector'][1]/data['col_monitor'][1])\n",
-    "\n",
-    "        u1.append(data['col_detector'][0])\n",
-    "        d1.append(data['col_detector'][1])\n",
-    "        i=i+1\n",
-    "        #print(n)\n",
-    "    except:\n",
-    "        pass\n",
-    "        print(n)\n",
-    "plt.xlabel('SF                                                  ds_nut_amps (A)                                              NSF')\n",
-    "plt.xticks([-3.2,3.2])\n",
-    "plt.ylabel('counts / 55555 mon [~ 1 min]')\n",
-    "plt.legend(fontsize=9, ncol=3)\n",
-    "plt.title(data['subtitle'][:-4]+', T ='+str('%6.2f'%data['sample'])+' K'+', time ='+str('%6.2f'%data['time'])+' sec')\n",
-    "up=np.zeros([3,3])\n",
-    "dn=np.zeros([3,3])\n",
-    "  \n",
-    "up[2,2],up[2,0],up[2,1],up[0,0],up[0,1],up[1,1],up[1,0],up[0,2],up[1,2]=u[0],u[1],u[2],u[3],u[4],u[5],u[6],u[7],u[8]\n",
-    "dn[2,2],dn[2,0],dn[2,1],dn[0,0],dn[0,1],dn[1,1],dn[1,0],dn[0,2],dn[1,2]=d[0],d[1],d[2],d[3],d[4],d[5],d[6],d[7],d[8]\n",
-    "\n",
-    "up1=np.zeros([3,3])\n",
-    "dn1=np.zeros([3,3])\n",
-    "   \n",
-    "up1[2,2],up1[2,0],up1[2,1],up1[0,0],up1[0,1],up1[1,1],up1[1,0],up1[0,2],up1[1,2]=u1[0],u1[1],u1[2],u1[3],u1[4],u1[5],u1[6],u1[7],u1[8]\n",
-    "dn1[2,2],dn1[2,0],dn1[2,1],dn1[0,0],dn1[0,1],dn1[1,1],dn1[1,0],dn1[0,2],dn1[1,2]=d1[0],d1[1],d1[2],d1[3],d1[4],d1[5],d1[6],d1[7],d1[8]\n",
-    "\n",
-    "P=(up1-dn1)/(up1+dn1)\n",
-    "\n",
-    "txt_up='NSF=\\n'\\\n",
-    " '['+str('%5d'%up1[0,0])+', '+str('%5d'%up1[0,1])+', '+str('%5d'%up1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[1,0])+', '+str('%5d'%up1[1,1])+', '+str('%5d'%up1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[2,0])+', '+str('%5d'%up1[2,1])+', '+str('%5d'%up1[2,2])+']'\n",
-    "\n",
-    "txt_dn='SF=\\n'\\\n",
-    " '['+str('%5d'%dn1[0,0])+', '+str('%5d'%dn1[0,1])+', '+str('%5d'%dn1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[1,0])+', '+str('%5d'%dn1[1,1])+', '+str('%5d'%dn1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[2,0])+', '+str('%5d'%dn1[2,1])+', '+str('%5d'%dn1[2,2])+']'\n",
-    "\n",
-    "txt_P='P=\\n'\\\n",
-    " '['+str('%+5.4f'%P[0,0])+', '+str('%+5.4f'%P[0,1])+', '+str('%+5.4f'%P[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[1,0])+', '+str('%+5.4f'%P[1,1])+', '+str('%+5.4f'%P[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[2,0])+', '+str('%+5.4f'%P[2,1])+', '+str('%+5.4f'%P[2,2])+']'\n",
-    "\n",
-    "\n",
-    "plt.text(-3.2, 0.65*55555*np.max(u), txt_up, fontsize=9, color='blue')\n",
-    "plt.text(-1.2, 0.65*55555*np.max(u), txt_dn, fontsize=9, color='red')\n",
-    "plt.text(0, 0.25*55555*np.max(u), txt_P, fontsize=9, color='green')\n",
-    "\n",
-    "plt.savefig('(011)_+q_Ni2InSbO6-R_T2K_count3660sec.jpg', dpi=600)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 639,
-   "id": "3cc5bc2b-2d6a-4b8b-a283-7b64aabe1b97",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#def polamat([scn_Pzz,scn_Pzx,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy])\n",
-    "def scan2pol(scn_Pzz,scn_Pzx,scn_Pzy,scn_Pxx,scn_Pxy,scn_Pyy,scn_Pyx,scn_Pxz,scn_Pyz):\n",
-    "    plabel=['Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz']\n",
-    "    P = np.full((3,3), np.nan)\n",
-    "    u=[]\n",
-    "    d=[]\n",
-    "    u1=[]\n",
-    "    d1=[]\n",
-    "    i=0\n",
-    "    for n in [scn_Pzz,scn_Pzx,scn_Pzy,scn_Pxx,scn_Pxy,scn_Pyy,scn_Pyx,scn_Pxz,scn_Pyz]:    \n",
-    "        try:\n",
-    "            data=read_data(n)\n",
-    "            plt.plot(data['col_ds_nut'],55555*data['col_detector']/data['col_monitor'],'^-', label='scan'+str(n)+':'+plabel[i])\n",
-    "            \n",
-    "            u.append(data['col_detector'][0]/data['col_monitor'][0])\n",
-    "            d.append(data['col_detector'][1]/data['col_monitor'][1])\n",
-    "    \n",
-    "            u1.append(data['col_detector'][0])\n",
-    "            d1.append(data['col_detector'][1])\n",
-    "            i=i+1\n",
-    "            #print(n)\n",
-    "        except:\n",
-    "            pass\n",
-    "            print(n)\n",
-    "    plt.xlabel('SF                                                  ds_nut_amps (A)                                              NSF')\n",
-    "    plt.xticks([-3.2,3.2])\n",
-    "    plt.ylabel('counts / 55555 mon [~ 1 min]')\n",
-    "    plt.legend(fontsize=9, ncol=3)\n",
-    "    plt.title(data['subtitle'][:-4]+', T ='+str('%6.2f'%data['sample'])+' K'+', time ='+str('%6.2f'%data['time'])+' sec')\n",
-    "    up=np.zeros([3,3])\n",
-    "    dn=np.zeros([3,3])\n",
-    "      \n",
-    "    up[2,2],up[2,0],up[2,1],up[0,0],up[0,1],up[1,1],up[1,0],up[0,2],up[1,2]=u[0],u[1],u[2],u[3],u[4],u[5],u[6],u[7],u[8]\n",
-    "    dn[2,2],dn[2,0],dn[2,1],dn[0,0],dn[0,1],dn[1,1],dn[1,0],dn[0,2],dn[1,2]=d[0],d[1],d[2],d[3],d[4],d[5],d[6],d[7],d[8]\n",
-    "    \n",
-    "    up1=np.zeros([3,3])\n",
-    "    dn1=np.zeros([3,3])\n",
-    "       \n",
-    "    up1[2,2],up1[2,0],up1[2,1],up1[0,0],up1[0,1],up1[1,1],up1[1,0],up1[0,2],up1[1,2]=u1[0],u1[1],u1[2],u1[3],u1[4],u1[5],u1[6],u1[7],u1[8]\n",
-    "    dn1[2,2],dn1[2,0],dn1[2,1],dn1[0,0],dn1[0,1],dn1[1,1],dn1[1,0],dn1[0,2],dn1[1,2]=d1[0],d1[1],d1[2],d1[3],d1[4],d1[5],d1[6],d1[7],d1[8]\n",
-    "    \n",
-    "    P=(up1-dn1)/(up1+dn1)\n",
-    "    \n",
-    "    txt_up='NSF=\\n'\\\n",
-    "     '['+str('%5d'%up1[0,0])+', '+str('%5d'%up1[0,1])+', '+str('%5d'%up1[0,2])+']'+'\\n'\\\n",
-    "    +'['+str('%5d'%up1[1,0])+', '+str('%5d'%up1[1,1])+', '+str('%5d'%up1[1,2])+']'+'\\n'\\\n",
-    "    +'['+str('%5d'%up1[2,0])+', '+str('%5d'%up1[2,1])+', '+str('%5d'%up1[2,2])+']'\n",
-    "    \n",
-    "    txt_dn='SF=\\n'\\\n",
-    "     '['+str('%5d'%dn1[0,0])+', '+str('%5d'%dn1[0,1])+', '+str('%5d'%dn1[0,2])+']'+'\\n'\\\n",
-    "    +'['+str('%5d'%dn1[1,0])+', '+str('%5d'%dn1[1,1])+', '+str('%5d'%dn1[1,2])+']'+'\\n'\\\n",
-    "    +'['+str('%5d'%dn1[2,0])+', '+str('%5d'%dn1[2,1])+', '+str('%5d'%dn1[2,2])+']'\n",
-    "\n",
-    "    txt_P='P=\\n'\\\n",
-    "     '['+str('%+5.4f'%P[0,0])+', '+str('%+5.4f'%P[0,1])+', '+str('%+5.4f'%P[0,2])+']'+'\\n'\\\n",
-    "    +'['+str('%+5.4f'%P[1,0])+', '+str('%+5.4f'%P[1,1])+', '+str('%+5.4f'%P[1,2])+']'+'\\n'\\\n",
-    "    +'['+str('%+5.4f'%P[2,0])+', '+str('%+5.4f'%P[2,1])+', '+str('%+5.4f'%P[2,2])+']'\n",
-    "    \n",
-    "    \n",
-    "    plt.text(-3.2, 0.65*55555*np.max(u), txt_up, fontsize=9, color='blue')\n",
-    "    plt.text(-1.2, 0.65*55555*np.max(u), txt_dn, fontsize=9, color='red')\n",
-    "    plt.text(0, 0.25*55555*np.max(u), txt_P, fontsize=9, color='green')\n",
-    "    \n",
-    "    figname=data['subtitle'][:-4]+'_T'+str(int(data['sample']))+'K'+'count'+str(int(data['time']))+'sec.jpg'\n",
-    "    \n",
-    "    print('saved as:',figname)\n",
-    "    plt.savefig(figname, dpi=600)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 640,
-   "id": "9c31a1cc-d213-4a58-9c7f-a2eb122d613d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "saved as: (011)_+q_Ni2InSbO6-R_T2Kcount3600sec.jpg\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "saved as: (011)_-q_Ni2InSbO6-R_T2Kcount3600sec.jpg\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "scn_Pzz,scn_Pzx,scn_Pzy,scn_Pxx,scn_Pxy,scn_Pyy,scn_Pyx,scn_Pxz,scn_Pyz=190,191,192,193,194,195,196,197,198\n",
-    "scan2pol(scn_Pzz,scn_Pzx,scn_Pzy,scn_Pxx,scn_Pxy,scn_Pyy,scn_Pyx,scn_Pxz,scn_Pyz)\n",
-    "scn_Pzz,scn_Pzx,scn_Pzy,scn_Pxx,scn_Pxy,scn_Pyy,scn_Pyx,scn_Pxz,scn_Pyz=172,173,174,175,176,177,178,179,180\n",
-    "scan2pol(scn_Pzz,scn_Pzx,scn_Pzy,scn_Pxx,scn_Pxy,scn_Pyy,scn_Pyx,scn_Pxz,scn_Pyz)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c12c5531-600e-4a3b-a9a7-61155224f834",
-   "metadata": {},
-   "source": [
-    "# Extra"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 550,
-   "id": "94a59f73-c6d8-49f7-93f9-66cadb607972",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'raw_input' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[550], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m hosts \u001b[38;5;241m=\u001b[39m raw_input(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124menter hosts (separated by a comma):\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      2\u001b[0m scn_Pzz,scn_Pzx,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28mint\u001b[39m, hosts\u001b[38;5;241m.\u001b[39msplit())\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'raw_input' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "hosts = raw_input(\"enter hosts (separated by a comma):\")\n",
-    "scn_Pzz,scn_Pzx,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy = map(int, hosts.split())   # assigns integer input values to variables a, b and c"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 453,
-   "id": "391fe6b7-321e-4a40-a89a-6d1b405f9590",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'raw_input' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[453], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m host_string \u001b[38;5;241m=\u001b[39m raw_input(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124menter hosts (separated by a comma):\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      2\u001b[0m hosts \u001b[38;5;241m=\u001b[39m hosts_string\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'raw_input' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "host_string = raw_input(\"enter hosts (separated by a comma):\")\n",
-    "hosts = hosts_string.split(',')\n",
-    "if "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 476,
-   "id": "64409709-b1ff-4300-b679-f28209cbb63a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "enter file# (separated by a comma) in the following order:\n",
-      "'Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz'\n",
-      " 1,2,3,4,5,6,7,8,9\n"
-     ]
-    }
-   ],
-   "source": [
-    "hosts = input(\"enter file#s (separated by a comma) in the following order:\\n'Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz'\\n\").split(',')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 464,
-   "id": "5a01fed9-94fc-40f7-bc51-a51db32cc7b4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scn_Pzz,scn_Pzx,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy = hosts.split(',') "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 465,
-   "id": "ac572e04-20f7-4599-bcd4-32b46ac14e81",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('1', '2', '9', '9', '9', '9', '9', '9', '9')"
-      ]
-     },
-     "execution_count": 465,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scn_Pzz,scn_Pzx,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 469,
-   "id": "9fdd93fd-c821-44e5-99ca-e0af76cb4a24",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['1', '2', '3', '4', '5', '6', '7', '8', '9']"
-      ]
-     },
-     "execution_count": 469,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hosts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 472,
-   "id": "dce8261a-8332-4bad-8e0f-9fa64aead009",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": 472,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "int(hosts[8])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 480,
-   "id": "4ff368bd-e020-450e-9a7b-e02e575a209c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "enter file#s (separated by a comma) in the following order:\n",
-      "'Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz'\n",
-      " 511\n"
-     ]
-    },
-    {
-     "ename": "SyntaxError",
-     "evalue": "'continue' not properly in loop (1871636291.py, line 6)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;36m  Cell \u001b[1;32mIn[480], line 6\u001b[1;36m\u001b[0m\n\u001b[1;33m    continue\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m 'continue' not properly in loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "files = input(\"enter file#s (separated by a comma) in the following order:\\n'Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz'\\n\").split(',')\n",
-    "if len(files) < 9 or len(files)>9:\n",
-    "    print('Numebr of files must be 9!!')    \n",
-    "    files = input(\"enter file#s (separated by a comma) in the following order:\\n'Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz'\\n\").split(',')\n",
-    "    \n",
-    "#def polamat([scn_Pzz,scn_Pzx,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy,scn_Pzy])\n",
-    "\n",
-    "plt.figure()\n",
-    "#plt.title('(0-12)_Ni2InSbO6-R [scan 128:136] T=2.1 K')\n",
-    "plabel=['Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz']\n",
-    "P = np.full((3,3), np.nan)\n",
-    "up = np.full((3,3), np.nan)\n",
-    "dn = np.full((3,3), np.nan)\n",
-    "up1 = np.full((3,3), np.nan)\n",
-    "dn1 = np.full((3,3), np.nan)\n",
-    "\n",
-    "u=[]\n",
-    "d=[]\n",
-    "u1=[]\n",
-    "d1=[]\n",
-    "i=0\n",
-    "for n in range(163,172):    \n",
-    "    try:\n",
-    "        data=read_data(n)\n",
-    "        plt.plot(data['col_ds_nut'],55555*data['col_detector']/data['col_monitor'],'^-', label='scan'+str(n)+':'+plabel[i])\n",
-    "        \n",
-    "        u.append(data['col_detector'][0]/data['col_monitor'][0])\n",
-    "        d.append(data['col_detector'][1]/data['col_monitor'][1])\n",
-    "\n",
-    "        u1.append(data['col_detector'][0])\n",
-    "        d1.append(data['col_detector'][1])\n",
-    "        i=i+1\n",
-    "        #print(n)\n",
-    "    except:\n",
-    "        pass\n",
-    "        print(n)\n",
-    "plt.xlabel('SF                                                  ds_nut_amps (A)                                              NSF')\n",
-    "plt.xticks([-3.2,3.2])\n",
-    "plt.ylabel('counts / 55555 mon [~ 1 min]')\n",
-    "plt.legend(fontsize=9, ncol=3)\n",
-    "plt.title(data['subtitle'][:-4]+', T ='+str('%6.2f'%data['sample'])+' K'+', time ='+str('%6.2f'%data['time'])+' sec')\n",
-    "up=np.zeros([3,3])\n",
-    "dn=np.zeros([3,3])\n",
-    "  \n",
-    "up[2,2],up[2,0],up[2,1],up[0,0],up[0,1],up[1,1],up[1,0],up[0,2],up[1,2]=u[0],u[1],u[2],u[3],u[4],u[5],u[6],u[7],u[8]\n",
-    "dn[2,2],dn[2,0],dn[2,1],dn[0,0],dn[0,1],dn[1,1],dn[1,0],dn[0,2],dn[1,2]=d[0],d[1],d[2],d[3],d[4],d[5],d[6],d[7],d[8]\n",
-    "\n",
-    "up1=np.zeros([3,3])\n",
-    "dn1=np.zeros([3,3])\n",
-    "   \n",
-    "up1[2,2],up1[2,0],up1[2,1],up1[0,0],up1[0,1],up1[1,1],up1[1,0],up1[0,2],up1[1,2]=u1[0],u1[1],u1[2],u1[3],u1[4],u1[5],u1[6],u1[7],u1[8]\n",
-    "dn1[2,2],dn1[2,0],dn1[2,1],dn1[0,0],dn1[0,1],dn1[1,1],dn1[1,0],dn1[0,2],dn1[1,2]=d1[0],d1[1],d1[2],d1[3],d1[4],d1[5],d1[6],d1[7],d1[8]\n",
-    "\n",
-    "P=(up1-dn1)/(up1+dn1)\n",
-    "\n",
-    "txt_up='NSF=\\n'\\\n",
-    " '['+str('%5d'%up1[0,0])+', '+str('%5d'%up1[0,1])+', '+str('%5d'%up1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[1,0])+', '+str('%5d'%up1[1,1])+', '+str('%5d'%up1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[2,0])+', '+str('%5d'%up1[2,1])+', '+str('%5d'%up1[2,2])+']'\n",
-    "\n",
-    "txt_dn='SF=\\n'\\\n",
-    " '['+str('%5d'%dn1[0,0])+', '+str('%5d'%dn1[0,1])+', '+str('%5d'%dn1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[1,0])+', '+str('%5d'%dn1[1,1])+', '+str('%5d'%dn1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[2,0])+', '+str('%5d'%dn1[2,1])+', '+str('%5d'%dn1[2,2])+']'\n",
-    "\n",
-    "txt_P='P=\\n'\\\n",
-    " '['+str('%+5.4f'%P[0,0])+', '+str('%+5.4f'%P[0,1])+', '+str('%+5.4f'%P[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[1,0])+', '+str('%+5.4f'%P[1,1])+', '+str('%+5.4f'%P[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[2,0])+', '+str('%+5.4f'%P[2,1])+', '+str('%+5.4f'%P[2,2])+']'\n",
-    "\n",
-    "print(55555*np.max(u))\n",
-    "plt.text(-3.2, 0.65*55555*np.max(u), txt_up, fontsize=9, color='blue')\n",
-    "plt.text(-1.2, 0.65*55555*np.max(u), txt_dn, fontsize=9, color='red')\n",
-    "plt.text(0, 0.25*55555*np.max(u), txt_P, fontsize=9, color='green')\n",
-    "\n",
-    "plt.savefig('(011)_-q_Ni2InSbO6-R_T2K_count60sec.jpg', dpi=600)\n",
-    "plt.show()\n",
-    "    \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 488,
-   "id": "3f3bc3ee-6264-40e9-b838-5aaba09f5e4f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "file_path='C:/Users/num/Documents/cycle506/exp943/Datafiles/'\n",
-    "file_number=137\n",
-    "file=file_path+'HB1_exp0943_scan'+str('%04d'%file_number)+'.dat'\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 486,
-   "id": "c06b2930-d158-4cbb-9539-c24bf8734262",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "lst = input(\"enter file#s (separated by a comma) in the following order:\\n'Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz'\\n\").split(',')\n",
-    "if len(lst) < 9 or len(lst)>9:\n",
-    "    print('Numebr of files must be 9!!')  \n",
-    "\n",
-    "file_number_list=[]\n",
-    "for l in range(len(lst)):\n",
-    "    file_number_list.append(int(lst[l]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 526,
-   "id": "bd57034a-95b1-410c-8a4f-0a46fe57ab51",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Edit this part \n",
-    "#-----------------------------------------------------------#\n",
-    "file_path='C:/Users/num/Documents/cycle506/exp943/Datafiles/'\n",
-    "file_number_list=[146,147,148,149,150,151,152,153,154]\n",
-    "title='(011)_-q_Ni2InSbO6-R_T2K_count60sec'\n",
-    "#---------------------------------------------------------#\n",
-    "\n",
-    "#  code\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "d=np.loadtxt(file)\n",
-    "P = np.full((3,3), np.nan)\n",
-    "up = np.full((3,3), np.nan)\n",
-    "dn = np.full((3,3), np.nan)\n",
-    "up1 = np.full((3,3), np.nan)\n",
-    "dn1 = np.full((3,3), np.nan)\n",
-    "u =np.full(len(file_number_list), np.nan)\n",
-    "d =np.full(len(file_number_list), np.nan)\n",
-    "u1=np.full(len(file_number_list), np.nan)\n",
-    "u1=np.full(len(file_number_list), np.nan)\n",
-    "\n",
-    "plabel=['Pzz','Pzx','Pzy','Pxx','Pxy','Pyy','Pyx','Pxz','Pyz']\n",
-    "\n",
-    "i=0\n",
-    "for file_number in file_number_list:    \n",
-    "    try:\n",
-    "        file=file_path+'HB1_exp0943_scan'+str('%04d'%file_number)+'.dat'\n",
-    "        data=np.loadtxt(file)\n",
-    "\n",
-    "        # plot SF and NSf counts from scans\n",
-    "        plt.plot(data[:,1],data[:,3],'o-', label='scan'+str(n)+':'+plabel[i])\n",
-    "\n",
-    "        # normalized SF and NSf counts from scans\n",
-    "        u[i]=(data[:,3][0]/data[:,4][0])\n",
-    "        d[i]=(data[:,3][1]/data[:,4][1])\n",
-    "\n",
-    "        #  SF and NSf counts from scans\n",
-    "        u1[i]=(data[:,3][0])\n",
-    "        d1[i]=(data[:,3][1])\n",
-    "        i=i+1\n",
-    "    except ValueError:\n",
-    "        pass\n",
-    "\n",
-    "plt.xlabel('SF                                                  ds_nut_amps (A)                                              NSF')\n",
-    "plt.xticks([-3.2,3.2])\n",
-    "plt.ylabel('counts')\n",
-    "plt.legend(fontsize=9, ncol=3)\n",
-    "\n",
-    "# Calculation of NSF, SF and P matrices\n",
-    "#--------------------------------------------------------------------------------\n",
-    "up=np.zeros([3,3])\n",
-    "dn=np.zeros([3,3])\n",
-    "up[2,2],up[2,0],up[2,1],up[0,0],up[0,1],up[1,1],up[1,0],up[0,2],up[1,2]=u[0],u[1],u[2],u[3],u[4],u[5],u[6],u[7],u[8]\n",
-    "dn[2,2],dn[2,0],dn[2,1],dn[0,0],dn[0,1],dn[1,1],dn[1,0],dn[0,2],dn[1,2]=d[0],d[1],d[2],d[3],d[4],d[5],d[6],d[7],d[8]\n",
-    "\n",
-    "up1=np.zeros([3,3])\n",
-    "dn1=np.zeros([3,3])\n",
-    "up1[2,2],up1[2,0],up1[2,1],up1[0,0],up1[0,1],up1[1,1],up1[1,0],up1[0,2],up1[1,2]=\\\n",
-    "u1[0],u1[1],u1[2],u1[3],u1[4],u1[5],u1[6],u1[7],u1[8]\n",
-    "dn1[2,2],dn1[2,0],dn1[2,1],dn1[0,0],dn1[0,1],dn1[1,1],dn1[1,0],dn1[0,2],dn1[1,2]=\\\n",
-    "d1[0],d1[1],d1[2],d1[3],d1[4],d1[5],d1[6],d1[7],d1[8]\n",
-    "P=(up1-dn1)/(up1+dn1)\n",
-    "\n",
-    "\n",
-    "# printing NSF, SF and P matrices on plot\n",
-    "#------------------------------------------------------------------------------------\n",
-    "txt_up='NSF=\\n'\\\n",
-    " '['+str('%5d'%up1[0,0])+', '+str('%5d'%up1[0,1])+', '+str('%5d'%up1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[1,0])+', '+str('%5d'%up1[1,1])+', '+str('%5d'%up1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%up1[2,0])+', '+str('%5d'%up1[2,1])+', '+str('%5d'%up1[2,2])+']'\n",
-    "txt_dn='SF=\\n'\\\n",
-    " '['+str('%5d'%dn1[0,0])+', '+str('%5d'%dn1[0,1])+', '+str('%5d'%dn1[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[1,0])+', '+str('%5d'%dn1[1,1])+', '+str('%5d'%dn1[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%5d'%dn1[2,0])+', '+str('%5d'%dn1[2,1])+', '+str('%5d'%dn1[2,2])+']'\n",
-    "txt_P='P=\\n'\\\n",
-    " '['+str('%+5.4f'%P[0,0])+', '+str('%+5.4f'%P[0,1])+', '+str('%+5.4f'%P[0,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[1,0])+', '+str('%+5.4f'%P[1,1])+', '+str('%+5.4f'%P[1,2])+']'+'\\n'\\\n",
-    "+'['+str('%+5.4f'%P[2,0])+', '+str('%+5.4f'%P[2,1])+', '+str('%+5.4f'%P[2,2])+']'\n",
-    "\n",
-    "plt.text(-3.2, 0.75*np.max(u1), txt_up, fontsize=9, color='blue')\n",
-    "plt.text(-1.2, 0.75*np.max(u1), txt_dn, fontsize=9, color='red')\n",
-    "plt.text(0,    0.25*np.max(u1), txt_P,  fontsize=9, color='green')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 654,
-   "id": "0be13d4b-705c-4143-93cd-ad794f36bb0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([267., 268., 269., 270., 271., 272., 273., 274., 275.])"
-      ]
-     },
-     "execution_count": 654,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.linspace(267,275,9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 655,
-   "id": "86c3b0a3-093a-4735-b4c4-dd51d8923c2f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([276., 277., 278., 279., 280., 281., 282., 283., 284.])"
-      ]
-     },
-     "execution_count": 655,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.linspace(276,284,9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "afe8f3a0-b395-48c9-ad97-3f98b76c96e3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "276., 277., 278., 286., 280., 281., 282., 283., 284."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 656,
-   "id": "f1d2c3d6-09a9-4f9f-9676-8fd13bc7e043",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([289., 290., 291., 292., 293., 294., 295., 296., 297.])"
-      ]
-     },
-     "execution_count": 656,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.linspace(289,297,9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d72a8dae-5b20-46a0-890d-4b3c31025d1b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/test_data/exp932/jupynb/SNP944.ipynb b/test_data/exp932/jupynb/SNP944.ipynb
deleted file mode 100644
index bfd4b111..00000000
--- a/test_data/exp932/jupynb/SNP944.ipynb
+++ /dev/null
@@ -1,322 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ed9ca272-c658-431a-ac2b-e21f18146245",
-   "metadata": {},
-   "source": [
-    "# Alignment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a078108d-eca9-42e0-92e3-372003133957",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[30mIt doens't reset! \u001b[30m\n",
-      "\u001b[31mIt doens't reset! \u001b[31m\n",
-      "\u001b[32mIt doens't reset! \u001b[32m\n",
-      "\u001b[33mIt doens't reset! \u001b[33m\n",
-      "\u001b[34mIt doens't reset! \u001b[34m\n",
-      "\u001b[35mIt doens't reset! \u001b[35m\n",
-      "\u001b[36mIt doens't reset! \u001b[36m\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "from IPython.display import Image\n",
-    "import ufit\n",
-    "import numpy as np\n",
-    "from ufit.lab import *\n",
-    "from lmfit.models import LinearModel, GaussianModel, LorentzianModel, PseudoVoigtModel, Model\n",
-    "set_datatemplate('C:/Users/num/Documents/cycle506/exp943/Datafiles/HB1_exp0943_scan%04d.dat')\n",
-    "ufit.set_dataformat('simple')\n",
-    "\n",
-    "def gauss_fit(x,y,\\\n",
-    "        center1,center1_vary,center1_min, center1_max,\\\n",
-    "        fwhm1,fwhm1_vary, fwhm1_min, fwhm1_max,\\\n",
-    "        amplitude1,amplitude1_vary, amplitude1_min, amplitude1_max,\\\n",
-    "        results):\n",
-    "    erry =np.sqrt(y)\n",
-    "\n",
-    "\n",
-    "    pgvt1 = GaussianModel(prefix='g1_')\n",
-    "    pars = pgvt1.guess(y, x=x)\n",
-    "    pars['g1_center'].set(value=center1,\n",
-    "                           vary=center1_vary,\n",
-    "                           min=center1_min,\n",
-    "                           max=center1_max)\n",
-    "    pars['g1_sigma'].set(value=fwhm1 / 2.,\n",
-    "                          vary=fwhm1_vary,\n",
-    "                          min=fwhm1_min,\n",
-    "                          max=fwhm1_max)\n",
-    "    pars['g1_amplitude'].set(value=amplitude1,\n",
-    "                              vary=amplitude1_vary,\n",
-    "                              min=amplitude1_min,\n",
-    "                              max=amplitude1_max)\n",
-    "\n",
-    "    mod1 =pgvt1\n",
-    "    init = mod1.eval(pars, x=x)\n",
-    "    out = mod1.fit(y, pars, x=x)\n",
-    "    print_fit_report = results\n",
-    "    if print_fit_report == 1:\n",
-    "        print((out.fit_report()))\n",
-    "    if print_fit_report == 0:\n",
-    "        #print('#--------peak1---------#')\n",
-    "        print(('g1_center:', out.params['g1_center'].value))\n",
-    "        print(('g1_fwhm:', out.params['g1_fwhm'].value))\n",
-    "        print(('g1_amplitude:', out.params['g1_amplitude'].value))\n",
-    "        print('chisqr, redchisqr', out.chisqr, out.redchi)\n",
-    "    if print_fit_report == -1:\n",
-    "        pass\n",
-    "\n",
-    "    #--------------------------------------\n",
-    "    #plt.figure()\n",
-    "    plt.errorbar(x, y, erry, xerr=None, fmt='', marker='o', ms= 3, mfc='none', mec=None,\\\n",
-    "                 ecolor=None, elinewidth=0.5, capsize=0,capthick=0,lw=0,\\\n",
-    "                 label='cen:'+str('%10.3f'%out.params['g1_center'].value)+'\\nfwhm :'+str('%10.3f'%out.params['g1_fwhm'].value))\n",
-    "    plt.plot(x, out.best_fit, '--r', linewidth=0.5)\n",
-    "\n",
-    "\n",
-    "\n",
-    "def lgauss_fit(x,y,\\\n",
-    "        slope,slope_vary,slope_min,slope_max,\\\n",
-    "        intercept,intercept_vary, intercept_min, intercept_max,\\\n",
-    "        center1,center1_vary,center1_min, center1_max,\\\n",
-    "        fwhm1,fwhm1_vary, fwhm1_min, fwhm1_max,\\\n",
-    "        amplitude1,amplitude1_vary, amplitude1_min, amplitude1_max,\\\n",
-    "        results):\n",
-    "    erry =np.sqrt(y)\n",
-    "\n",
-    "    lin_mod = LinearModel(prefix='l1_')\n",
-    "    pars = lin_mod.guess(y, x=x)\n",
-    "    pars['l1_slope'].set(value=slope,\n",
-    "                          vary=slope_vary,\n",
-    "                          min=slope_min,\n",
-    "                          max=slope_max)\n",
-    "    pars['l1_intercept'].set(value=intercept,\n",
-    "                              vary=intercept_vary,\n",
-    "                              min=intercept_min,\n",
-    "                              max=intercept_min)    \n",
-    "\n",
-    "    pgvt1 = GaussianModel(prefix='g1_')\n",
-    "    pars.update(pgvt1.make_params())\n",
-    "    pars['g1_center'].set(value=center1,\n",
-    "                           vary=center1_vary,\n",
-    "                           min=center1_min,\n",
-    "                           max=center1_max)\n",
-    "    pars['g1_sigma'].set(value=fwhm1 / 2.,\n",
-    "                          vary=fwhm1_vary,\n",
-    "                          min=fwhm1_min,\n",
-    "                          max=fwhm1_max)\n",
-    "    pars['g1_amplitude'].set(value=amplitude1,\n",
-    "                              vary=amplitude1_vary,\n",
-    "                              min=amplitude1_min,\n",
-    "                              max=amplitude1_max)\n",
-    "\n",
-    "    mod1 =lin_mod+pgvt1\n",
-    "    init = mod1.eval(pars, x=x)\n",
-    "    out = mod1.fit(y, pars, x=x)\n",
-    "    print_fit_report = results\n",
-    "    if print_fit_report == 1:\n",
-    "        print((out.fit_report()))\n",
-    "    if print_fit_report == 0:\n",
-    "        #print('#--------peak1---------#')\n",
-    "        print('g1_center:', out.params['g1_center'].value, '+-', out.params['g1_center'].stderr)\n",
-    "        print('g1_fwhm:', out.params['g1_fwhm'].value, '+-', out.params['g1_fwhm'].stderr)\n",
-    "        print('g1_amplitude:', out.params['g1_amplitude'].value, '+-', out.params['g1_amplitude'].stderr)\n",
-    "        print('chisqr, redchisqr', out.chisqr, out.redchi)\n",
-    "    if print_fit_report == -1:\n",
-    "        pass\n",
-    "\n",
-    "    #--------------------------------------\n",
-    "    #plt.figure()\n",
-    "    plt.errorbar(x, y, erry, xerr=None, fmt='', marker='o', ms= 3, mfc='none', mec=None,\\\n",
-    "                 ecolor=None, elinewidth=0.5, capsize=0,capthick=0,lw=0,\\\n",
-    "                 label='cen:'+str('%10.3f'%out.params['g1_center'].value)+'\\nfwhm :'+str('%10.3f'%out.params['g1_fwhm'].value))\n",
-    "    plt.plot(x, out.best_fit, '--r', linewidth=0.5)\n",
-    "\n",
-    "\n",
-    "\n",
-    "def lgauss2_fit(x,y,\\\n",
-    "        slope,slope_vary,slope_min,slope_max,\\\n",
-    "        intercept,intercept_vary, intercept_min, intercept_max,\\\n",
-    "        amplitude1,amplitude1_vary, amplitude1_min, amplitude1_max,\\\n",
-    "        center1,center1_vary,center1_min, center1_max,\\\n",
-    "        fwhm1,fwhm1_vary, fwhm1_min, fwhm1_max,\\\n",
-    "        amplitude2,amplitude2_vary, amplitude2_min, amplitude2_max,\\\n",
-    "        center2,center2_vary,center2_min, center2_max,\\\n",
-    "        fwhm2,fwhm2_vary, fwhm2_min, fwhm2_max,\\\n",
-    "        results):\n",
-    "    erry =np.sqrt(y)\n",
-    "\n",
-    "    lin_mod = LinearModel(prefix='l1_')\n",
-    "    pars = lin_mod.guess(y, x=x)\n",
-    "    pars['l1_slope'].set(value=slope,\n",
-    "                          vary=slope_vary,\n",
-    "                          min=slope_min,\n",
-    "                          max=slope_max)\n",
-    "    pars['l1_intercept'].set(value=intercept,\n",
-    "                              vary=intercept_vary,\n",
-    "                              min=intercept_min,\n",
-    "                              max=intercept_min)    \n",
-    "\n",
-    "    pgvt1 = GaussianModel(prefix='g1_')\n",
-    "    pars.update(pgvt1.make_params())\n",
-    "    pars['g1_center'].set(value=center1,\n",
-    "                           vary=center1_vary,\n",
-    "                           min=center1_min,\n",
-    "                           max=center1_max)\n",
-    "    pars['g1_sigma'].set(value=fwhm1 / 2.,\n",
-    "                          vary=fwhm1_vary,\n",
-    "                          min=fwhm1_min,\n",
-    "                          max=fwhm1_max)\n",
-    "    pars['g1_amplitude'].set(value=amplitude1,\n",
-    "                              vary=amplitude1_vary,\n",
-    "                              min=amplitude1_min,\n",
-    "                              max=amplitude1_max)\n",
-    "\n",
-    "    pgvt2 = GaussianModel(prefix='g2_')\n",
-    "    pars.update(pgvt2.make_params())\n",
-    "    pars['g2_center'].set(value=center2,\n",
-    "                           vary=center2_vary,\n",
-    "                           min=center2_min,\n",
-    "                           max=center2_max)\n",
-    "    pars['g2_sigma'].set(value=fwhm2 / 2.,\n",
-    "                          vary=fwhm2_vary,\n",
-    "                          min=fwhm2_min,\n",
-    "                          max=fwhm2_max)\n",
-    "    pars['g2_amplitude'].set(value=amplitude2,\n",
-    "                              vary=amplitude2_vary,\n",
-    "                              min=amplitude2_min,\n",
-    "                              max=amplitude2_max)\n",
-    "    \n",
-    "    mod1 =lin_mod+pgvt1+pgvt2\n",
-    "    init = mod1.eval(pars, x=x)\n",
-    "    out = mod1.fit(y, pars, x=x)\n",
-    "    print_fit_report = results\n",
-    "    if print_fit_report == 1:\n",
-    "        print((out.fit_report()))\n",
-    "    if print_fit_report == 0:\n",
-    "        #print('#--------peak1---------#')\n",
-    "        print('g1_center:', out.params['g1_center'].value, '+-', out.params['g1_center'].stderr)\n",
-    "        print('g1_fwhm:', out.params['g1_fwhm'].value, '+-', out.params['g1_fwhm'].stderr)\n",
-    "        print('g1_amplitude:', out.params['g1_amplitude'].value, '+-', out.params['g1_amplitude'].stderr)\n",
-    "        print('chisqr, redchisqr', out.chisqr, out.redchi)\n",
-    "    if print_fit_report == -1:\n",
-    "        pass\n",
-    "\n",
-    "    #--------------------------------------\n",
-    "    #plt.figure()\n",
-    "    plt.errorbar(x, y, erry, xerr=None, fmt='', marker='o', ms= 3, mfc='none', mec=None,\\\n",
-    "                 ecolor=None, elinewidth=0.5, capsize=0,capthick=0,lw=0,\\\n",
-    "                 label='cen:'+str('%10.3f'%out.params['g1_center'].value)+' fwhm :'+str('%10.3f'%out.params['g1_fwhm'].value)+'\\n'+'cen:'+str('%10.3f'%out.params['g2_center'].value)+' fwhm :'+str('%10.3f'%out.params['g2_fwhm'].value))\n",
-    "    plt.plot(x, out.best_fit, '--r', linewidth=0.5)\n",
-    "\n",
-    "def tcal(lamda, h,k,l):\n",
-    "    a,b,c=3.524041, 3.524041, 3.524041\n",
-    "    g=np.sqrt(((h*h)/(a*a))+((k*k)/(b*b))+((l*l)/(c*c)))\n",
-    "    d=1/g\n",
-    "    t=(180/np.pi)*np.arcsin(lamda/(2*d))\n",
-    "    return t\n",
-    "def e2l(e):\n",
-    "    return 9.045/np.sqrt(e)\n",
-    "\n",
-    "def l2e(l):\n",
-    "    return (9.045*9.045)/(l*l)\n",
-    "\n",
-    "def plot_params(x, y, font, lw, ms):\n",
-    "    plt.rcParams['figure.figsize'] = [x, y]\n",
-    "    plt.rcParams.update({'font.size': font})\n",
-    "    plt.rcParams['axes.linewidth'] = lw\n",
-    "    plt.rcParams[\"legend.markerscale\"] = ms\n",
-    "    import matplotlib as mpl\n",
-    "    mpl.rcParams['patch.linewidth'] = 0.0\n",
-    "\n",
-    "\n",
-    "TBLACK =  '\\033[30m'\n",
-    "print (TBLACK + \"It doens't reset!\" , TBLACK)\n",
-    "TRED =  '\\033[31m'\n",
-    "print (TRED + \"It doens't reset!\" , TRED)\n",
-    "TGREEN =  '\\033[32m' # Green Text\n",
-    "print (TGREEN + \"It doens't reset!\" , TGREEN)\n",
-    "TYELO =  '\\033[33m'\n",
-    "print (TYELO + \"It doens't reset!\" , TYELO)\n",
-    "TBLUE =  '\\033[34m'\n",
-    "print (TBLUE + \"It doens't reset!\" , TBLUE)\n",
-    "TPURP =  '\\033[35m'\n",
-    "print (TPURP + \"It doens't reset!\" , TPURP)\n",
-    "TCYAN =  '\\033[36m'\n",
-    "print (TCYAN + \"It doens't reset!\" , TCYAN)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "bc091888-09c5-4b12-8f86-d5757438a1c5",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'C:/Users/num/Documents/cycle506/exp944/LogFile.txt'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m f\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC:/Users/num/Documents/cycle506/exp944/LogFile.txt\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m f:\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mExecuting\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m line:\n",
-      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:324\u001b[0m, in \u001b[0;36m_modified_open\u001b[1;34m(file, *args, **kwargs)\u001b[0m\n\u001b[0;32m    317\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[0;32m    318\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    319\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    320\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    321\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    322\u001b[0m     )\n\u001b[1;32m--> 324\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m io_open(file, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:/Users/num/Documents/cycle506/exp944/LogFile.txt'"
-     ]
-    }
-   ],
-   "source": [
-    "f=open('C:/Users/num/Documents/cycle506/exp944/LogFile.txt','r')\n",
-    "\n",
-    "for line in f:\n",
-    "    if 'Executing' in line:\n",
-    "        print(line,end='')\n",
-    "        #pass\n",
-    "        \n",
-    "    if 'time     detector      monitor          mcu' in line:\n",
-    "        print(line, end='')\n",
-    "        print(next(f), end='')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4974888d-1ed8-4836-996d-798bef1e890e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/test_data/exp932/jupynb/Untitled.ipynb b/test_data/exp932/jupynb/Untitled.ipynb
deleted file mode 100644
index a42c048c..00000000
--- a/test_data/exp932/jupynb/Untitled.ipynb
+++ /dev/null
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6d3cce43-aa4d-40f0-aa8b-46d3dd18e693",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/test_data/exp932/jupynb/Untitled1.ipynb b/test_data/exp932/jupynb/Untitled1.ipynb
deleted file mode 100644
index daa8181c..00000000
--- a/test_data/exp932/jupynb/Untitled1.ipynb
+++ /dev/null
@@ -1,23 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6f172d48-376f-450f-b57d-dc2554a7e1f5",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "",
-   "name": ""
-  },
-  "language_info": {
-   "name": ""
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/test_data/exp932/jupynb/_T100Kcount60sec.jpg b/test_data/exp932/jupynb/_T100Kcount60sec.jpg
deleted file mode 100644
index e45dc18a..00000000
Binary files a/test_data/exp932/jupynb/_T100Kcount60sec.jpg and /dev/null differ
diff --git a/test_data/exp932/jupynb/a1_a2_alignment.jpg b/test_data/exp932/jupynb/a1_a2_alignment.jpg
deleted file mode 100644
index 602597ca..00000000
Binary files a/test_data/exp932/jupynb/a1_a2_alignment.jpg and /dev/null differ
diff --git a/test_data/exp932/jupynb/a1_alignment.jpg b/test_data/exp932/jupynb/a1_alignment.jpg
deleted file mode 100644
index 50a4ca22..00000000
Binary files a/test_data/exp932/jupynb/a1_alignment.jpg and /dev/null differ
diff --git a/test_data/exp932/jupynb/color.txt b/test_data/exp932/jupynb/color.txt
deleted file mode 100644
index dc632d40..00000000
--- a/test_data/exp932/jupynb/color.txt
+++ /dev/null
@@ -1,189 +0,0 @@
- 42                                    scanrel sgu 3 -3 0.5 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 53        scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 54        scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   3.932   0.074 -18.124 | 299 |   [NSF PX]
- 60      scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.123 | 299 |   [NSF PX]
- 73        scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   3.984   0.078 -18.124 | 330 |   [NSF PX]
- 74        scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 330 |   [NSF PX]
- 75      scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 329 |   [NSF PX]
- 88        scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 330 |   [NSF PX]
- 89        scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 329 |   [NSF PX]
- 90      scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.005 | 0.007   3.932   0.079 -18.124 | 330 |   [NSF PX]
-147        scan h 0 k 0 l 0.86 1.12 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.007   0.078 -18.124 | 400 |   [NSF PX]
-148        scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 400 |   [NSF PX]
-149      scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 400 |   [NSF PX]
-163        scan h 0 k 0 l 1.02 1.06 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.087 -18.124 | 400 |   [NSF PX]
-164        scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.020   0.078 -18.124 | 399 |   [NSF PX]
-165      scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.077 -18.124 | 400 |   [NSF PX]
-223                         scan h 0 k 0 l 1.9 2.1 0.01 e 0 | 4.511   0.001   0.004 | 0.007   3.986   0.078 -18.124 | 365 |   [NSF PX]
-281           scan h 0 k 0 l 0.85 1.1 0.01 e 0 preset mcu 2 | 4.511   0.001   0.005 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-282          scan h 0 k 0 l 0.85 1.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   3.997   0.078 -18.124 | 425 |   [NSF PX]
-283         scan h 0 k 0 l 1.04 1.05 0.02 e 0 preset mcu 30 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-284          scan h 0 k 0 l 1.85 2.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   4.002   0.078 -18.124 | 424 |   [NSF PX]
- 42                                    scanrel sgu 3 -3 0.5 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 53        scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 54        scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   3.932   0.074 -18.124 | 299 |   [NSF PX]
- 60      scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.123 | 299 |   [NSF PX]
- 73        scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   3.984   0.078 -18.124 | 330 |   [NSF PX]
- 74        scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 330 |   [NSF PX]
- 75      scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 329 |   [NSF PX]
- 88        scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 330 |   [NSF PX]
- 89        scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 329 |   [NSF PX]
- 90      scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.005 | 0.007   3.932   0.079 -18.124 | 330 |   [NSF PX]
-147        scan h 0 k 0 l 0.86 1.12 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.007   0.078 -18.124 | 400 |   [NSF PX]
-148        scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 400 |   [NSF PX]
-149      scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 400 |   [NSF PX]
-163        scan h 0 k 0 l 1.02 1.06 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.087 -18.124 | 400 |   [NSF PX]
-164        scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.020   0.078 -18.124 | 399 |   [NSF PX]
-165      scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.077 -18.124 | 400 |   [NSF PX]
-223                         scan h 0 k 0 l 1.9 2.1 0.01 e 0 | 4.511   0.001   0.004 | 0.007   3.986   0.078 -18.124 | 365 |   [NSF PX]
-281           scan h 0 k 0 l 0.85 1.1 0.01 e 0 preset mcu 2 | 4.511   0.001   0.005 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-282          scan h 0 k 0 l 0.85 1.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   3.997   0.078 -18.124 | 425 |   [NSF PX]
-283         scan h 0 k 0 l 1.04 1.05 0.02 e 0 preset mcu 30 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-284          scan h 0 k 0 l 1.85 2.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   4.002   0.078 -18.124 | 424 |   [NSF PX]
- 42                                    scanrel sgu 3 -3 0.5 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 53        scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 54        scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   3.932   0.074 -18.124 | 299 |   [NSF PX]
- 60      scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.123 | 299 |   [NSF PX]
- 73        scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   3.984   0.078 -18.124 | 330 |   [NSF PX]
- 74        scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 330 |   [NSF PX]
- 75      scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 329 |   [NSF PX]
- 88        scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 330 |   [NSF PX]
- 89        scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 329 |   [NSF PX]
- 90      scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.005 | 0.007   3.932   0.079 -18.124 | 330 |   [NSF PX]
-147        scan h 0 k 0 l 0.86 1.12 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.007   0.078 -18.124 | 400 |   [NSF PX]
-148        scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 400 |   [NSF PX]
-149      scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 400 |   [NSF PX]
-163        scan h 0 k 0 l 1.02 1.06 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.087 -18.124 | 400 |   [NSF PX]
-164        scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.020   0.078 -18.124 | 399 |   [NSF PX]
-165      scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.077 -18.124 | 400 |   [NSF PX]
-223                         scan h 0 k 0 l 1.9 2.1 0.01 e 0 | 4.511   0.001   0.004 | 0.007   3.986   0.078 -18.124 | 365 |   [NSF PX]
-281           scan h 0 k 0 l 0.85 1.1 0.01 e 0 preset mcu 2 | 4.511   0.001   0.005 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-282          scan h 0 k 0 l 0.85 1.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   3.997   0.078 -18.124 | 425 |   [NSF PX]
-283         scan h 0 k 0 l 1.04 1.05 0.02 e 0 preset mcu 30 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-284          scan h 0 k 0 l 1.85 2.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   4.002   0.078 -18.124 | 424 |   [NSF PX]
- 42                                    scanrel sgu 3 -3 0.5 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 53        scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 54        scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   3.932   0.074 -18.124 | 299 |   [NSF PX]
- 60      scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.123 | 299 |   [NSF PX]
- 73        scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   3.984   0.078 -18.124 | 330 |   [NSF PX]
- 74        scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 330 |   [NSF PX]
- 75      scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 329 |   [NSF PX]
- 88        scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 330 |   [NSF PX]
- 89        scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 329 |   [NSF PX]
- 90      scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.005 | 0.007   3.932   0.079 -18.124 | 330 |   [NSF PX]
-147        scan h 0 k 0 l 0.86 1.12 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.007   0.078 -18.124 | 400 |   [NSF PX]
-148        scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 400 |   [NSF PX]
-149      scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 400 |   [NSF PX]
-163        scan h 0 k 0 l 1.02 1.06 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.087 -18.124 | 400 |   [NSF PX]
-164        scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.020   0.078 -18.124 | 399 |   [NSF PX]
-165      scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.077 -18.124 | 400 |   [NSF PX]
-223                         scan h 0 k 0 l 1.9 2.1 0.01 e 0 | 4.511   0.001   0.004 | 0.007   3.986   0.078 -18.124 | 365 |   [NSF PX]
-281           scan h 0 k 0 l 0.85 1.1 0.01 e 0 preset mcu 2 | 4.511   0.001   0.005 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-282          scan h 0 k 0 l 0.85 1.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   3.997   0.078 -18.124 | 425 |   [NSF PX]
-283         scan h 0 k 0 l 1.04 1.05 0.02 e 0 preset mcu 30 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-284          scan h 0 k 0 l 1.85 2.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   4.002   0.078 -18.124 | 424 |   [NSF PX]
- 42                                    scanrel sgu 3 -3 0.5 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 53        scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 54        scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   3.932   0.074 -18.124 | 299 |   [NSF PX]
- 60      scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.123 | 299 |   [NSF PX]
- 73        scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   3.984   0.078 -18.124 | 330 |   [NSF PX]
- 74        scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 330 |   [NSF PX]
- 75      scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 329 |   [NSF PX]
- 88        scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 330 |   [NSF PX]
- 89        scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 329 |   [NSF PX]
- 90      scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.005 | 0.007   3.932   0.079 -18.124 | 330 |   [NSF PX]
-147        scan h 0 k 0 l 0.86 1.12 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.007   0.078 -18.124 | 400 |   [NSF PX]
-148        scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 400 |   [NSF PX]
-149      scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 400 |   [NSF PX]
-163        scan h 0 k 0 l 1.02 1.06 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.087 -18.124 | 400 |   [NSF PX]
-164        scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.020   0.078 -18.124 | 399 |   [NSF PX]
-165      scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.077 -18.124 | 400 |   [NSF PX]
-223                         scan h 0 k 0 l 1.9 2.1 0.01 e 0 | 4.511   0.001   0.004 | 0.007   3.986   0.078 -18.124 | 365 |   [NSF PX]
-281           scan h 0 k 0 l 0.85 1.1 0.01 e 0 preset mcu 2 | 4.511   0.001   0.005 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-282          scan h 0 k 0 l 0.85 1.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   3.997   0.078 -18.124 | 425 |   [NSF PX]
-283         scan h 0 k 0 l 1.04 1.05 0.02 e 0 preset mcu 30 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-284          scan h 0 k 0 l 1.85 2.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   4.002   0.078 -18.124 | 424 |   [NSF PX]
- 42                               scanrel sgu 3 -3 0.5 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 53   scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 54   scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   3.932   0.074 -18.124 | 299 |   [NSF PX]
- 60 scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.123 | 299 |   [NSF PX]
- 73   scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   3.984   0.078 -18.124 | 330 |   [NSF PX]
- 74   scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 330 |   [NSF PX]
- 75 scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 329 |   [NSF PX]
- 88   scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 330 |   [NSF PX]
- 89   scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 329 |   [NSF PX]
- 90 scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.005 | 0.007   3.932   0.079 -18.124 | 330 |   [NSF PX]
-147   scan h 0 k 0 l 0.86 1.12 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.007   0.078 -18.124 | 400 |   [NSF PX]
-148   scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 400 |   [NSF PX]
-149 scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 400 |   [NSF PX]
-163   scan h 0 k 0 l 1.02 1.06 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.087 -18.124 | 400 |   [NSF PX]
-164   scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.020   0.078 -18.124 | 399 |   [NSF PX]
-165 scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.077 -18.124 | 400 |   [NSF PX]
-223                    scan h 0 k 0 l 1.9 2.1 0.01 e 0 | 4.511   0.001   0.004 | 0.007   3.986   0.078 -18.124 | 365 |   [NSF PX]
-281      scan h 0 k 0 l 0.85 1.1 0.01 e 0 preset mcu 2 | 4.511   0.001   0.005 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-282     scan h 0 k 0 l 0.85 1.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   3.997   0.078 -18.124 | 425 |   [NSF PX]
-283    scan h 0 k 0 l 1.04 1.05 0.02 e 0 preset mcu 30 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-284     scan h 0 k 0 l 1.85 2.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   4.002   0.078 -18.124 | 424 |   [NSF PX]
- 42                               scanrel sgu 3 -3 0.5 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 53   scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 54   scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   3.932   0.074 -18.124 | 299 |   [NSF PX]
- 60 scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.123 | 299 |   [NSF PX]
- 73   scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   3.984   0.078 -18.124 | 330 |   [NSF PX]
- 74   scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 330 |   [NSF PX]
- 75 scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 329 |   [NSF PX]
- 88   scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 330 |   [NSF PX]
- 89   scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 329 |   [NSF PX]
- 90 scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.005 | 0.007   3.932   0.079 -18.124 | 330 |   [NSF PX]
-147   scan h 0 k 0 l 0.86 1.12 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.007   0.078 -18.124 | 400 |   [NSF PX]
-148   scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 400 |   [NSF PX]
-149 scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 400 |   [NSF PX]
-163   scan h 0 k 0 l 1.02 1.06 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.087 -18.124 | 400 |   [NSF PX]
-164   scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.020   0.078 -18.124 | 399 |   [NSF PX]
-165 scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.077 -18.124 | 400 |   [NSF PX]
-223                    scan h 0 k 0 l 1.9 2.1 0.01 e 0 | 4.511   0.001   0.004 | 0.007   3.986   0.078 -18.124 | 365 |   [NSF PX]
-281      scan h 0 k 0 l 0.85 1.1 0.01 e 0 preset mcu 2 | 4.511   0.001   0.005 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-282     scan h 0 k 0 l 0.85 1.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   3.997   0.078 -18.124 | 425 |   [NSF PX]
-283    scan h 0 k 0 l 1.04 1.05 0.02 e 0 preset mcu 30 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-284     scan h 0 k 0 l 1.85 2.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   4.002   0.078 -18.124 | 424 |   [NSF PX]
- 42                               scanrel sgu 3 -3 0.5 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 53   scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 54   scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   3.932   0.074 -18.124 | 299 |   [NSF PX]
- 60 scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.123 | 299 |   [NSF PX]
- 73   scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   3.984   0.078 -18.124 | 330 |   [NSF PX]
- 74   scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 330 |   [NSF PX]
- 75 scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 329 |   [NSF PX]
- 88   scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 330 |   [NSF PX]
- 89   scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 329 |   [NSF PX]
- 90 scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.005 | 0.007   3.932   0.079 -18.124 | 330 |   [NSF PX]
-147   scan h 0 k 0 l 0.86 1.12 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.007   0.078 -18.124 | 400 |   [NSF PX]
-148   scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 400 |   [NSF PX]
-149 scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 400 |   [NSF PX]
-163   scan h 0 k 0 l 1.02 1.06 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.087 -18.124 | 400 |   [NSF PX]
-164   scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.020   0.078 -18.124 | 399 |   [NSF PX]
-165 scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.077 -18.124 | 400 |   [NSF PX]
-223                    scan h 0 k 0 l 1.9 2.1 0.01 e 0 | 4.511   0.001   0.004 | 0.007   3.986   0.078 -18.124 | 365 |   [NSF PX]
-281      scan h 0 k 0 l 0.85 1.1 0.01 e 0 preset mcu 2 | 4.511   0.001   0.005 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-282     scan h 0 k 0 l 0.85 1.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   3.997   0.078 -18.124 | 425 |   [NSF PX]
-283    scan h 0 k 0 l 1.04 1.05 0.02 e 0 preset mcu 30 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-284     scan h 0 k 0 l 1.85 2.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   4.002   0.078 -18.124 | 424 |   [NSF PX]
- 42                               scanrel sgu 3 -3 0.5 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 53   scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 299 |   [NSF PX]
- 54   scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   3.932   0.074 -18.124 | 299 |   [NSF PX]
- 60 scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.123 | 299 |   [NSF PX]
- 73   scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   3.984   0.078 -18.124 | 330 |   [NSF PX]
- 74   scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 330 |   [NSF PX]
- 75 scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 329 |   [NSF PX]
- 88   scan h 0 k 0 l 0.96 1.04 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 330 |   [NSF PX]
- 89   scan h 0 k 0 l 0.99 1.01 0.02 e 0 preset mcu 240 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 329 |   [NSF PX]
- 90 scan h 0 k 0 l 0.995 1.005 0.01 e 0 preset mcu 240 | 4.511   0.001   0.005 | 0.007   3.932   0.079 -18.124 | 330 |   [NSF PX]
-147   scan h 0 k 0 l 0.86 1.12 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.007   0.078 -18.124 | 400 |   [NSF PX]
-148   scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.079 -18.124 | 400 |   [NSF PX]
-149 scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 400 |   [NSF PX]
-163   scan h 0 k 0 l 1.02 1.06 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.087 -18.124 | 400 |   [NSF PX]
-164   scan h 0 k 0 l 1.03 1.05 0.02 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.020   0.078 -18.124 | 399 |   [NSF PX]
-165 scan h 0 k 0 l 1.035 1.045 0.01 e 0 preset mcu 120 | 4.511   0.001   0.004 | 0.007   4.019   0.077 -18.124 | 400 |   [NSF PX]
-223                    scan h 0 k 0 l 1.9 2.1 0.01 e 0 | 4.511   0.001   0.004 | 0.007   3.986   0.078 -18.124 | 365 |   [NSF PX]
-281      scan h 0 k 0 l 0.85 1.1 0.01 e 0 preset mcu 2 | 4.511   0.001   0.005 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-282     scan h 0 k 0 l 0.85 1.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   3.997   0.078 -18.124 | 425 |   [NSF PX]
-283    scan h 0 k 0 l 1.04 1.05 0.02 e 0 preset mcu 30 | 4.511   0.001   0.004 | 0.007   4.019   0.078 -18.124 | 424 |   [NSF PX]
-284     scan h 0 k 0 l 1.85 2.15 0.01 e 0 preset mcu 2 | 4.511   0.001   0.004 | 0.007   4.002   0.078 -18.124 | 424 |   [NSF PX]
diff --git a/test_data/exp932/jupynb/extracted/extractor.ini b/test_data/exp932/jupynb/extracted/extractor.ini
deleted file mode 100644
index e69de29b..00000000
diff --git a/test_data/exp932/jupynb/fguide_alignment.jpg b/test_data/exp932/jupynb/fguide_alignment.jpg
deleted file mode 100644
index 959ae580..00000000
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diff --git a/test_data/exp932/jupynb/lamda2.jpg b/test_data/exp932/jupynb/lamda2.jpg
deleted file mode 100644
index ad9e7168..00000000
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diff --git a/test_data/exp932/jupynb/m1-alignment.jpg b/test_data/exp932/jupynb/m1-alignment.jpg
deleted file mode 100644
index 7064ba0e..00000000
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diff --git a/test_data/exp932/jupynb/summary.txt b/test_data/exp932/jupynb/summary.txt
deleted file mode 100644
index e69de29b..00000000
diff --git a/test_data/exp932/jupynb/vguide_alignment.jpg b/test_data/exp932/jupynb/vguide_alignment.jpg
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index 625b3ccf..00000000
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diff --git a/test_data/exp932/jupynb/view.jpg b/test_data/exp932/jupynb/view.jpg
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index 08cf36c3..00000000
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