Orbit stability during van der Meer scan

The goal of this presentation is to share a (preliminary) analysis of the orbit stability of one of the 2018 van der Meer scan (FILL 6868).

From the analysis carried out from the experiments, there are evidences of hysteresis effects during the scan.

I fact, the scan consists in varying, using special orbit bumps, the beams positions at the IP (namely the H/V separation of the two beams). To do so the current of special orbit correctors is varied and this can potentially produce hysteretic behaviour of the magnets themselves.

In the following we will show the approach we used to verify and assess the hysteretics behaviour of the magnets.

Analysis can be run from /eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/OrbitStabilityVdM.ipynb (stack 95py3).

From https://i.redd.it/c2xhv8qhn0h01.jpg

# Using the stack 95py3
mySource='/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/OrbitStabilityVdM.ipynb'
import cl2pd # do "pip install --user git+https://github.com/sterbini/cl2pd.git@py3" to install
from cl2pd import importData
from cl2pd import plotFunctions
from cl2pd import dotdict
from cl2pd import bbFunctions
from matplotlib import dates as dates

dotdict=dotdict.dotdict
pd=importData.pd     # is the pandas package
np=importData.np     # is the numpy package
cals=importData.cals # pytimber log class

import matplotlib.pyplot as plt
get_ipython().magic('matplotlib inline')
%config InlineBackend.figure_format = 'retina' # retina display
myTitle='FILL 6868, 30th June 2018'
myFill=6868

Fill 6868:

This is the usual cycle overview.

# Analysis of the fill
fillDF=importData.LHCFillsByNumber([myFill])
fillDF[fillDF['mode']=='FILL']

	mode	startTime	endTime	duration
6868	FILL	2018-06-30 08:25:45.239000082+00:00	2018-07-01 08:02:33.473999977+00:00	23:36:48.234999

params = {'legend.fontsize': 'x-large',
          'figure.figsize': (15, 5),
         'axes.labelsize': 'x-large',
         'axes.titlesize':'x-large',
         'xtick.labelsize':'x-large',
         'ytick.labelsize':'x-large'}
plt.rcParams.update(params)

# use the "ignore" warning with care
import warnings
warnings.filterwarnings("ignore")
plotFunctions.plotLHCFill(fillDF,myTitle,pd.Timestamp('2018-06-30 08:25:45.239000082'))
plotFunctions.setSourcePlot(plt.gca(), mySource,color='k')

A very long fill: protons travelled more than Voyager 1, the farthest man-crafted object (see https://voyager.jpl.nasa.gov/).

Beam intensities

myDF=importData.LHCCals2pd(['LHC.BCTFR.A6R4.B%:BEAM_INTENSITY'],[myFill])
myFigIntensityDecay=plt.figure()
plt.plot(myDF['LHC.BCTFR.A6R4.B1:BEAM_INTENSITY'].dropna()/1e13, color='b')
(myDF['LHC.BCTFR.A6R4.B2:BEAM_INTENSITY'].dropna()/1e13).plot( color='r')
plt.grid('on')
plt.xlabel('UTC time [month-day hh]');
plt.ylabel('Intensity [1e13 p]'); 
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='INJPHYS'], color='y',alpha=.3)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='PRERAMP'], color='m',alpha=.3)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='RAMP'], color='c',alpha=.3)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='FLATTOP'], color='g',alpha=.3)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='ADJUST'], color='orange',alpha=.3)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='STABLE'], color='r',alpha=.3)

t1=dates.date2num(pd.Timestamp('2018-06-30 20:25'))
if 1:
    for i,j,k in zip(['INJPHYS','PRERAMP','RAMP','FLATTOP','ADJUST','STABLE'],np.arange(0,6),
                     ['y','m','c','g','orange','r']):
        plotFunctions.setArrowLabel(plt.gca(), label=i,
                                arrowPosition=(t1, .1+j*.12),
                                labelPosition=(t1, .1+j*.12), 
                                myColor=k, arrowArc_rad=-0.2);
    plotFunctions.setSourcePlot(plt.gca(), mySource,color='k')
plt.title(myTitle);

Filling scheme

# BB Matrix, considering 25 encouters
nbLR = 25
fillingSchemeDF=importData.LHCCals2pd(['LHC.BCTFR.A6R4.B%:BUNCH_FILL_PATTERN'], myFill, 'RAMP', flag='last')

print(f"Number of bunches in B1: \
{len(np.where(fillingSchemeDF['LHC.BCTFR.A6R4.B1:BUNCH_FILL_PATTERN'].iloc[0])[0])}")
print(f"Number of bunches in B2: \
{len(np.where(fillingSchemeDF['LHC.BCTFR.A6R4.B1:BUNCH_FILL_PATTERN'].iloc[0])[0])}")

Number of bunches in B1: 140
Number of bunches in B2: 140

BBMatrixLHC=bbFunctions.computeBBMatrix(numberOfLRToConsider=nbLR)
# We plot it
B1_bunches=np.where(fillingSchemeDF.iloc[0]['LHC.BCTFR.A6R4.B1:BUNCH_FILL_PATTERN'])[0]
B2_bunches=np.where(fillingSchemeDF.iloc[0]['LHC.BCTFR.A6R4.B2:BUNCH_FILL_PATTERN'])[0]
plotFunctions.plot_BBMatrix(BBMatrixLHC, B1_bunches, B2_bunches,alpha=1,width=1)
plt.title(myTitle);
plotFunctions.setSourcePlot(plt.gca(),mySource,color='w')

B1CollisionSchedule=bbFunctions.B1CollisionScheduleDF(
    fillingSchemeDF['LHC.BCTFR.A6R4.B1:BUNCH_FILL_PATTERN'].iloc[0],
    fillingSchemeDF['LHC.BCTFR.A6R4.B2:BUNCH_FILL_PATTERN'].iloc[0],
    16,
)

B1CollisionSchedule.head(2)

	HO partner in ALICE	# of LR in ALICE	BB partners in ALICE	Positions in ALICE	HO partner in ATLAS/CMS	# of LR in ATLAS/CMS	BB partners in ATLAS/CMS	Positions in ATLAS/CMS	HO partner in LHCB	# of LR in LHCB	BB partners in LHCB	Positions in LHCB
12	NaN	2	[896.0, 917.0]	[-7.0, 14.0]	12.0	0	[12.0]	[0.0]	NaN	0	[]	[]
33	NaN	2	[917.0, 938.0]	[-7.0, 14.0]	33.0	0	[33.0]	[0.0]	NaN	0	[]	[]

B2CollisionSchedule=bbFunctions.B2CollisionScheduleDF(
    fillingSchemeDF['LHC.BCTFR.A6R4.B1:BUNCH_FILL_PATTERN'].iloc[0],
    fillingSchemeDF['LHC.BCTFR.A6R4.B2:BUNCH_FILL_PATTERN'].iloc[0],
    16,
)

B1CollisionSchedule.head(2)

	HO partner in ALICE	# of LR in ALICE	BB partners in ALICE	Positions in ALICE	HO partner in ATLAS/CMS	# of LR in ATLAS/CMS	BB partners in ATLAS/CMS	Positions in ATLAS/CMS	HO partner in LHCB	# of LR in LHCB	BB partners in LHCB	Positions in LHCB
12	NaN	2	[896.0, 917.0]	[-7.0, 14.0]	12.0	0	[12.0]	[0.0]	NaN	0	[]	[]
33	NaN	2	[917.0, 938.0]	[-7.0, 14.0]	33.0	0	[33.0]	[0.0]	NaN	0	[]	[]

print(f"Collision in ALICE: {len(B1CollisionSchedule['HO partner in ALICE'].dropna())}")
print(f"Collision in ATLAS/CMS: {len(B1CollisionSchedule['HO partner in ATLAS/CMS'].dropna())}")
print(f"Collision in LHCB: {len(B1CollisionSchedule['HO partner in LHCB'].dropna())}")

Collision in ALICE: 32
Collision in ATLAS/CMS: 124
Collision in LHCB: 23

# Selecting the B1 bunches 
# 1. colliding HO in ATLAS and CMS 
# 2. without HO collision in ALICE
# 3. without HO collision and LHCB 
# 4. without BBLR in ALICE 

B1CollisionSchedule[(~np.isnan(B1CollisionSchedule['HO partner in ATLAS/CMS'])) & \
                   (np.isnan(B1CollisionSchedule['HO partner in ALICE'])) & \
                   (np.isnan(B1CollisionSchedule['HO partner in LHCB'])) & \
                   (B1CollisionSchedule['# of LR in ALICE']==0)
                   ]

	HO partner in ALICE	# of LR in ALICE	BB partners in ALICE	Positions in ALICE	HO partner in ATLAS/CMS	# of LR in ATLAS/CMS	BB partners in ATLAS/CMS	Positions in ATLAS/CMS	HO partner in LHCB	# of LR in LHCB	BB partners in LHCB	Positions in LHCB
1779	NaN	0	[]	[]	1779.0	0	[1779.0]	[0.0]	NaN	1	[896.0]	[11.0]
1800	NaN	0	[]	[]	1800.0	0	[1800.0]	[0.0]	NaN	2	[896.0, 917.0]	[-10.0, 11.0]
1821	NaN	0	[]	[]	1821.0	0	[1821.0]	[0.0]	NaN	2	[917.0, 938.0]	[-10.0, 11.0]
1842	NaN	0	[]	[]	1842.0	0	[1842.0]	[0.0]	NaN	2	[938.0, 959.0]	[-10.0, 11.0]
1863	NaN	0	[]	[]	1863.0	0	[1863.0]	[0.0]	NaN	2	[959.0, 980.0]	[-10.0, 11.0]
1884	NaN	0	[]	[]	1884.0	0	[1884.0]	[0.0]	NaN	2	[980.0, 1001.0]	[-10.0, 11.0]
1905	NaN	0	[]	[]	1905.0	0	[1905.0]	[0.0]	NaN	2	[1001.0, 1022.0]	[-10.0, 11.0]
1926	NaN	0	[]	[]	1926.0	0	[1926.0]	[0.0]	NaN	2	[1022.0, 1043.0]	[-10.0, 11.0]
1947	NaN	0	[]	[]	1947.0	0	[1947.0]	[0.0]	NaN	2	[1043.0, 1064.0]	[-10.0, 11.0]
3032	NaN	0	[]	[]	3032.0	0	[3032.0]	[0.0]	NaN	2	[2128.0, 2149.0]	[-10.0, 11.0]
3053	NaN	0	[]	[]	3053.0	0	[3053.0]	[0.0]	NaN	2	[2149.0, 2170.0]	[-10.0, 11.0]
3074	NaN	0	[]	[]	3074.0	0	[3074.0]	[0.0]	NaN	2	[2170.0, 2191.0]	[-10.0, 11.0]
3095	NaN	0	[]	[]	3095.0	0	[3095.0]	[0.0]	NaN	2	[2191.0, 2212.0]	[-10.0, 11.0]

Longitudinal sigma

if 1:
    rawDF=pd.read_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/BQM.pickle')
else:
    rawDF=importData.LHCCals2pd(['LHC.BQM.B%:BUNCH_LENGTH_M%'],[myFill],'STABLE')
    rawDF.to_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/BQM.pickle')

plt.plot(rawDF['LHC.BQM.B1:BUNCH_LENGTH_MEAN'].dropna()*1e9,'b.')
(rawDF['LHC.BQM.B1:BUNCH_LENGTH_MEAN'].dropna()*1e9).plot(marker='.',color='k',ls='none')
(rawDF['LHC.BQM.B1:BUNCH_LENGTH_MAX'].dropna()*1e9).plot(marker='.',color='r',ls='none')
(rawDF['LHC.BQM.B1:BUNCH_LENGTH_MIN'].dropna()*1e9).plot(marker='.',color='b',ls='none')

plt.xlabel('time [hh:mm]')
plt.ylabel('mean bunch length (4$\sigma$) [ns]')

plt.grid('on')
plt.xlabel('UTC time [month-day hh]');
plt.ylabel('[arb. units]'); 
plt.legend(loc=1)
plt.ylim(0,1.9)
plt.xticks(rotation=45)
if 0:
    plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='INJPHYS'], color='y',alpha=.3)
    plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='PRERAMP'], color='m',alpha=.3)
    plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='RAMP'], color='c',alpha=.3)
    plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='FLATTOP'], color='g',alpha=.3)
    plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='SQUEEZE'], color='orange',alpha=.3)
    plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='ADJUST'], color='r',alpha=.3)
plotFunctions.setSourcePlot(plt.gca(), mySource,color='k')
plt.title(myTitle);

plt.plot(rawDF['LHC.BQM.B2:BUNCH_LENGTH_MEAN'].dropna()*1e9,'b.')
(rawDF['LHC.BQM.B2:BUNCH_LENGTH_MEAN'].dropna()*1e9).plot(marker='.',color='k',ls='none')
(rawDF['LHC.BQM.B2:BUNCH_LENGTH_MAX'].dropna()*1e9).plot(marker='.',color='r',ls='none')
(rawDF['LHC.BQM.B2:BUNCH_LENGTH_MIN'].dropna()*1e9).plot(marker='.',color='b',ls='none')

plt.xlabel('time [hh:mm]')
plt.ylabel('mean bunch length (4$\sigma$) [ns]')

plt.grid('on')
plt.xlabel('UTC time [month-day hh]');
plt.ylabel('[arb. units]'); 
plt.legend(loc=1)
plt.ylim(0,1.9)
plt.xticks(rotation=45)
if 0:
    plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='INJPHYS'], color='y',alpha=.3)
    plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='PRERAMP'], color='m',alpha=.3)
    plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='RAMP'], color='c',alpha=.3)
    plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='FLATTOP'], color='g',alpha=.3)
    plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='SQUEEZE'], color='orange',alpha=.3)
    plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='ADJUST'], color='r',alpha=.3)
plotFunctions.setSourcePlot(plt.gca(), mySource,color='k')
plt.title(myTitle);

Intensity decay

if 1:
    myDF=pd.read_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/BCTFR.pickle')
else:
    myDF=importData.LHCCals2pd(['LHC.BCTFR.A6R4.B%:BUNCH_INTENSITY'],[myFill],['STABLE'])
    myDF.to_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/BCTFR.pickle')

for i in np.where(np.array(myDF['LHC.BCTFR.A6R4.B1:BUNCH_INTENSITY'].iloc[0])>.2e11)[0]:
    plt.plot(myDF['LHC.BCTFR.A6R4.B1:BUNCH_INTENSITY'].apply(lambda x:x[i]))

plt.grid(True)
plt.xlabel('UTC time [month-day hh]');
plt.ylabel('[ppb]');
plt.xticks(rotation=45);
plotFunctions.setSourcePlot(plt.gca(), mySource,color='k')

The problematic bunches are [1600, 1642, 1684, 1726, 1747, 3148, 3190, 3211, 3232, 3253, 3274, 3295, 3316, 3337] in B1.

for i in [1600, 1642, 1684, 1726, 1747, 3148, 3190, 3211, 3232, 3253, 3274, 3295, 3316, 3337]:
    plt.plot(myDF['LHC.BCTFR.A6R4.B1:BUNCH_INTENSITY'].apply(lambda x:x[i]))

plt.grid(True)
plt.xlabel('UTC time [month-day hh]');
plt.ylabel('[ppb]');
plt.xticks(rotation=45);
plotFunctions.setSourcePlot(plt.gca(), mySource,color='k')

for i in np.where(np.array(myDF['LHC.BCTFR.A6R4.B2:BUNCH_INTENSITY'].iloc[0])>.2e11)[0]:
    plt.plot(myDF['LHC.BCTFR.A6R4.B2:BUNCH_INTENSITY'].apply(lambda x:x[i]))

plt.grid('on')
plt.xlabel('UTC time [month-day hh]');
plt.ylabel('[ppb]');
plt.xticks(rotation=45);
plotFunctions.setSourcePlot(plt.gca(), mySource,color='k')

aux=importData.LHCCals2pd(['LHC.BCTFR.A6R4.B1:BUNCH_INTENSITY'],[myFill],['STABLE'],flag='next',offset=pd.Timedelta('0m'))
plt.plot(aux['LHC.BCTFR.A6R4.B1:BUNCH_INTENSITY'].iloc[0],'.g',label='start STABLE')

aux=importData.LHCCals2pd(['LHC.BCTFR.A6R4.B1:BUNCH_INTENSITY'],[myFill],['STABLE'],flag='next',offset=pd.Timedelta('1200m'))
plt.plot(aux['LHC.BCTFR.A6R4.B1:BUNCH_INTENSITY'].iloc[0],'.r',label='start STABLE + 20 h')
plt.xlim([0,4000])
plt.grid('on')
plt.xlabel('bunch number');
plt.ylabel('Beam 1 [ppb]');
plt.legend(loc='best')
plt.title(myTitle)
plotFunctions.setSourcePlot(plt.gca(), mySource,color='k')

aux=importData.LHCCals2pd(['LHC.BCTFR.A6R4.B2:BUNCH_INTENSITY'],[myFill],['STABLE'],flag='next',offset=pd.Timedelta('0m'))
plt.plot(aux['LHC.BCTFR.A6R4.B2:BUNCH_INTENSITY'].iloc[0],'.g',label='start STABLE')

aux=importData.LHCCals2pd(['LHC.BCTFR.A6R4.B2:BUNCH_INTENSITY'],[myFill],['STABLE'],flag='next',offset=pd.Timedelta('1200m'))
plt.plot(aux['LHC.BCTFR.A6R4.B2:BUNCH_INTENSITY'].iloc[0],'.r',label='start STABLE + 20 h')
plt.xlim([0,4000])
plt.grid('on')
plt.xlabel('bunch number');
plt.ylabel('Beam 2 [ppb]');
plt.legend(loc='best')
plt.title(myTitle)
plotFunctions.setSourcePlot(plt.gca(), mySource,color='k')

plt.figure(figsize=(15,5))
aux=importData.LHCCals2pd(['LHC.BCTFR.A6R4.B%:BUNCH_INTENSITY'],myFill, 'FLATTOP',flag='next')
plt.plot(aux['LHC.BCTFR.A6R4.B1:BUNCH_INTENSITY'].dropna().values[0],'b',label='B1')
plt.plot(aux['LHC.BCTFR.A6R4.B2:BUNCH_INTENSITY'].dropna().values[0],'r',label='B2')
plt.xlim([0,4000])
plt.title('Fill '+str(myFill)+', start of FLATTOP')
plt.xlabel('bunch slot')
plt.ylabel('[ppb]')
plt.ylim(-0.05e11, 1.4e11)
plt.grid('on')
plotFunctions.setSourcePlot(plt.gca(), mySource,color='k')
plt.legend(loc='best')

<matplotlib.legend.Legend at 0x7fe80059f0f0>

Octupoles

importData.LHCCals2pd(['RPMBB.RR17.ROF.A12B1:I_MEAS', 'RPMBB.RR17.ROF.A12B2:I_MEAS'],myFill, ['STABLE'], flag='last')

	RPMBB.RR17.ROF.A12B1:I_MEAS	RPMBB.RR17.ROF.A12B2:I_MEAS
2018-06-30 09:52:45.160000086+00:00	376.65	376.65

plt.figure(figsize=(12,5))
fillDF=importData.LHCFillsByNumber(myFill)

aux=importData.LHCCals2pd(['RPMBB.RR17.ROF.A12B1:I_MEAS', 'RPMBB.RR17.ROF.A12B2:I_MEAS'],myFill)
plt.plot(aux['RPMBB.RR17.ROF.A12B1:I_MEAS'].dropna(),'b',label='B1 octupoles')
plt.plot(aux['RPMBB.RR17.ROF.A12B2:I_MEAS'].dropna(),'r',label='B2 octupoles')
plt.xticks(rotation=45);
plt.xlabel('time [day hh:mm]')
plt.ylabel('[A]')
plt.title('FILL '+str(myFill))
plt.grid()
plt.legend(loc='best')
plt.ylim(-30,560)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='INJPHYS'], color='y',alpha=.3)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='PRERAMP'], color='m',alpha=.3)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='RAMP'], color='c',alpha=.3)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='FLATTOP'], color='g',alpha=.3)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='SQUEEZE'], color='orange',alpha=.3)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='ADJUST'], color='r',alpha=.3)
plotFunctions.setSourcePlot(plt.gca(), mySource,color='k')

Beam stability

if 1:
    myDF=pd.read_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/BBQ.pickle')
else:    
    myDF=importData.LHCCals2pd(['LHC.BQBBQ.CONTINUOUS_HS.B%:EIGEN_AMPL%'],[myFill],['FLATTOP','ADJUST','STABLE'])
    myDF.to_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/BBQ.pickle')

aux=myDF#.between_time('10:08','10:27')
plt.figure(figsize=(15,5))
plt.plot(aux['LHC.BQBBQ.CONTINUOUS_HS.B1:EIGEN_AMPL_1'].dropna(),'b.',alpha=.05)
plt.plot(aux['LHC.BQBBQ.CONTINUOUS_HS.B1:EIGEN_AMPL_2'].dropna(),'r.',alpha=.05)
plt.grid(True)
plt.xlabel('UTC time [month-day hh]');
plt.ylabel('[arb. units]'); 
plt.legend(loc='best')
plt.ylim(-.001,.004)
plt.xticks(rotation=45)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='INJPHYS'], color='y',alpha=.3)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='PRERAMP'], color='m',alpha=.3)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='RAMP'], color='c',alpha=.3)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='FLATTOP'], color='g',alpha=.3)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='SQUEEZE'], color='orange',alpha=.3)
plotFunctions.shadedDF(plt.gca(), fillDF[fillDF['mode']=='ADJUST'], color='r',alpha=.3)
plotFunctions.setSourcePlot(plt.gca(), mySource,color='k')

Crossing angle configuration

importData.LHCCals2pd('LHC.RUNCONFIG:IP%-XING-%-MURAD',myFill, ['STABLE'],flag='last').describe().loc['mean']

LHC.RUNCONFIG:IP1-XING-V-MURAD      0.0
LHC.RUNCONFIG:IP2-XING-V-MURAD    145.0
LHC.RUNCONFIG:IP5-XING-H-MURAD      0.0
LHC.RUNCONFIG:IP8-XING-H-MURAD   -300.0
Name: mean, dtype: float64

importData.LHCCals2pd('LHC.STATS:BETA_STAR_%',myFill, ['STABLE'],flag='last').describe().loc['mean']

LHC.STATS:BETA_STAR_ALICE    19.0
LHC.STATS:BETA_STAR_ATLAS    19.0
LHC.STATS:BETA_STAR_CMS      19.0
LHC.STATS:BETA_STAR_LHCB     24.0
Name: mean, dtype: float64

Status of the feedbacks

(importData.LHCCals2pd('LHC.BOFSU:%FB%STATE',myFill, ['RAMP'],flag='last').describe()).loc['mean']

LHC.BOFSU:CHROMA_FB_B1_STATE      0.0
LHC.BOFSU:CHROMA_FB_B2_STATE      0.0
LHC.BOFSU:COUPLING_FB_B1_STATE    0.0
LHC.BOFSU:COUPLING_FB_B2_STATE    0.0
LHC.BOFSU:ENERGY_FB_STATE         1.0
LHC.BOFSU:ORBIT_FB_STATE          1.0
LHC.BOFSU:RADIALLOOP_FB_STATE     1.0
LHC.BOFSU:TUNE_FB_B1_STATE        1.0
LHC.BOFSU:TUNE_FB_B2_STATE        1.0
Name: mean, dtype: float64

(importData.LHCCals2pd('LHC.BOFSU:%FB%STATE',myFill, ['STABLE'],flag='last').describe()).loc['mean']

LHC.BOFSU:CHROMA_FB_B1_STATE      0.0
LHC.BOFSU:CHROMA_FB_B2_STATE      0.0
LHC.BOFSU:COUPLING_FB_B1_STATE    0.0
LHC.BOFSU:COUPLING_FB_B2_STATE    0.0
LHC.BOFSU:ENERGY_FB_STATE         0.0
LHC.BOFSU:ORBIT_FB_STATE          0.0
LHC.BOFSU:RADIALLOOP_FB_STATE     0.0
LHC.BOFSU:TUNE_FB_B1_STATE        0.0
LHC.BOFSU:TUNE_FB_B2_STATE        0.0
Name: mean, dtype: float64

Orbit analysis

In LHC there are 544 BPMs x 2 planes x 2 beams (2176 BPMs in total).

The way this information is stored in CALS is optimized for data space but can be problematic in terms of post-processing.

You can retrieve the name, status and mask of all the BPMS from CALS.

The s-position of the BPM is not available in CALS and has to be derived from MAD-X.

if 1:
    myBPM=pd.read_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/myBPM.pickle')
else:
    myBPM=importData.LHCCals2pd(['LHC.BOFSU:BPM_MASK_%', 'LHC.BOFSU:BPM_NAMES_%', 'LHC.BOFSU:BPM_STATUS_%'],myFill, ['STABLE'],flag='last')
    myBPM.to_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/myBPM.pickle')

# There are 1088 H BPMs in total and 544 per beam
# There are 1088 V BPMs in total and 544 per beam
for i in ['LHC.BOFSU:BPM_NAMES_H', 'LHC.BOFSU:BPM_NAMES_V']:
    aux=myBPM[i].dropna().iloc[0]
    len([s for s in aux if 'B2' in s])
    print(f'Number of {i} is {len(aux)}.')

Number of LHC.BOFSU:BPM_NAMES_H is 1088.
Number of LHC.BOFSU:BPM_NAMES_V is 1088.

# There are 1088 H BPMs in total and 544 per beam
# There are 1088 V BPMs in total and 544 per beam

for i in ['LHC.BOFSU:BPM_STATUS_H', 'LHC.BOFSU:BPM_STATUS_V']:
    aux=myBPM[i].dropna().iloc[0]
    print(f'Number of {i} is {len(aux)}.')

Number of LHC.BOFSU:BPM_STATUS_H is 1088.
Number of LHC.BOFSU:BPM_STATUS_V is 1088.

# There are 1088 H BPMs in total and 544 per beam
# There are 1088 V BPMs in total and 544 per beam
for i in ['LHC.BOFSU:BPM_MASK_H', 'LHC.BOFSU:BPM_MASK_V']:
    aux=myBPM[i].dropna().iloc[0]
    print(f'Number of {i} is {len(aux)}.')

Number of LHC.BOFSU:BPM_MASK_H is 1088.
Number of LHC.BOFSU:BPM_MASK_V is 1088.

if 1:
    myBPMreadings=pd.read_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/myBPMreadings.pickle')
else:
    myBPMreadings=importData.LHCCals2pd('LHC.BOFSU:%POSITIONS_%',myFill, ['STABLE'],split=8)
    myBPMreadings.to_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/myBPMreadings.pickle')

# There are 1088 H BPMs in total and 544 per beam
# There are 1088 V BPMs in total and 544 per beam
for i in ['LHC.BOFSU:POSITIONS_H', 'LHC.BOFSU:POSITIONS_V']:
    aux=myBPMreadings[i].dropna().iloc[0]
    print(f'Number of {i} is {len(aux)}.')

Number of LHC.BOFSU:POSITIONS_H is 1088.
Number of LHC.BOFSU:POSITIONS_V is 1088.

# Now we need to correlate the LHC.BOFSU:BPM_NAMES_H with the s-position
b1DF=pd.read_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/b1DF.pickle')
b2DF=pd.read_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/b2DF.pickle')

myS_B1_H=[]
for i in range(544):
    aux=b1DF[b1DF['name'].str.contains(str.lower(myBPM['LHC.BOFSU:BPM_NAMES_H'].dropna().iloc[0][i]))]['s'].unique()
    if len(aux)!=1:
        print(myBPM['LHC.BOFSU:BPM_NAMES_H'].dropna().iloc[0][0])
    else:
        myS_B1_H.append(aux[0])

myS_B1_V=[]
for i in range(544):
    aux=b1DF[b1DF['name'].str.contains(str.lower(myBPM['LHC.BOFSU:BPM_NAMES_V'].dropna().iloc[0][i]))]['s'].unique()
    if len(aux)!=1:
        print(myBPM['LHC.BOFSU:BPM_NAMES_V'].dropna().iloc[0][0])
    else:
        myS_B1_V.append(aux[0])

# the same for B2
myS_B2_H=[]
for i in range(544,1088):
    aux=b2DF[b2DF['name'].str.contains(str.lower(myBPM['LHC.BOFSU:BPM_NAMES_H'].dropna().iloc[0][i]))]['s'].unique()
    if len(aux)!=1:
        print(myBPM['LHC.BOFSU:BPM_NAMES_H'].dropna().iloc[0][i])
    else:
        myS_B2_H.append(aux[0])

myS_B2_V=[]
for i in range(544,1088):
    aux=b2DF[b2DF['name'].str.contains(str.lower(myBPM['LHC.BOFSU:BPM_NAMES_V'].dropna().iloc[0][i]))]['s'].unique()
    if len(aux)!=1:
        print(myBPM['LHC.BOFSU:BPM_NAMES_V'].dropna().iloc[0][i])
    else:
        myS_B2_V.append(aux[0])

# Let us consider B1 and the H/V plane
df_B1_H=pd.DataFrame([myBPM['LHC.BOFSU:BPM_NAMES_H'].dropna().iloc[0][0:544], myS_B1_H,myBPM['LHC.BOFSU:BPM_MASK_H'].dropna().iloc[0][0:544]],index=['BPM name','s [m]','mask']).transpose()
df_B1_V=pd.DataFrame([myBPM['LHC.BOFSU:BPM_NAMES_V'].dropna().iloc[0][0:544], myS_B1_V,myBPM['LHC.BOFSU:BPM_MASK_V'].dropna().iloc[0][0:544]],index=['BPM name','s [m]','mask']).transpose()
display(df_B1_H.head())
display(df_B1_V.head())

	BPM name	s [m]	mask
0	BPMSW.1R1.B1	21.564	1
1	BPMWF.A1R1.B1	21.724	0
2	BPMS.2R1.B1	31.529	1
3	BPMSY.4R1.B1	58.3145	1
4	BPMWB.4R1.B1	151.094	1

	BPM name	s [m]	mask
0	BPMSW.1R1.B1	21.564	1
1	BPMWF.A1R1.B1	21.724	0
2	BPMS.2R1.B1	31.529	1
3	BPMSY.4R1.B1	58.3145	1
4	BPMWB.4R1.B1	151.094	1

# Let us consider B2 and the H/V plane
df_B2_H=pd.DataFrame([myBPM['LHC.BOFSU:BPM_NAMES_H'].dropna().iloc[0][544:1088], myS_B2_H , myBPM['LHC.BOFSU:BPM_MASK_H'].dropna().iloc[0][544:1088]],index=['BPM name','s [m]','mask']).transpose()
df_B2_V=pd.DataFrame([myBPM['LHC.BOFSU:BPM_NAMES_V'].dropna().iloc[0][544:1088], myS_B2_V, myBPM['LHC.BOFSU:BPM_MASK_V'].dropna().iloc[0][544:1088]],index=['BPM name','s [m]','mask']).transpose()
display(df_B2_H.head())
display(df_B2_V.head())

	BPM name	s [m]	mask
0	BPMSW.1R1.B2	21.564	1
1	BPMWF.A1R1.B2	21.724	0
2	BPMS.2R1.B2	31.529	1
3	BPMSY.4R1.B2	58.3145	1
4	BPMWB.4R1.B2	151.17	1

	BPM name	s [m]	mask
0	BPMSW.1R1.B2	21.564	1
1	BPMWF.A1R1.B2	21.724	0
2	BPMS.2R1.B2	31.529	1
3	BPMSY.4R1.B2	58.3145	1
4	BPMWB.4R1.B2	151.17	1

# IMPORTANT: from array to pandas columns
# B1H
myBPM_B1_H=pd.DataFrame(myBPMreadings['LHC.BOFSU:POSITIONS_H'].copy())
myBPM_B1_H.head()
aux=df_B1_H[df_B1_H['mask']==1]['BPM name']
for i,j in zip(aux.values, aux.index):
    myBPM_B1_H[i]=myBPM_B1_H['LHC.BOFSU:POSITIONS_H'].apply(lambda x: x[j])
del myBPM_B1_H['LHC.BOFSU:POSITIONS_H']

# IMPORTANT: from array to pandas columns
# B1V
myBPM_B1_V=pd.DataFrame(myBPMreadings['LHC.BOFSU:POSITIONS_V'].copy())
myBPM_B1_V.head()
aux=df_B1_H[df_B1_H['mask']==1]['BPM name']
for i,j in zip(aux.values, aux.index):
    myBPM_B1_V[i]=myBPM_B1_V['LHC.BOFSU:POSITIONS_V'].apply(lambda x: x[j])
del myBPM_B1_V['LHC.BOFSU:POSITIONS_V']

# IMPORTANT: from array to pandas columns
# B2H
myBPM_B2_H=pd.DataFrame(myBPMreadings['LHC.BOFSU:POSITIONS_H'].copy())
myBPM_B2_H.head()
aux=df_B2_H[df_B2_H['mask']==1]['BPM name']
for i,j in zip(aux.values, aux.index):
    myBPM_B2_H[i]=myBPM_B2_H['LHC.BOFSU:POSITIONS_H'].apply(lambda x: x[j+544])
del myBPM_B2_H['LHC.BOFSU:POSITIONS_H']

# IMPORTANT: from array to pandas columns
# B2V
myBPM_B2_V=pd.DataFrame(myBPMreadings['LHC.BOFSU:POSITIONS_V'].copy())
myBPM_B2_V.head()
aux=df_B2_V[df_B2_V['mask']==1]['BPM name']
for i,j in zip(aux.values, aux.index):
    myBPM_B2_V[i]=myBPM_B2_V['LHC.BOFSU:POSITIONS_V'].apply(lambda x: x[j+544])
del myBPM_B2_V['LHC.BOFSU:POSITIONS_V']

Important results

We have now four tables for the time vatiation of the H/V plane in B½.

for i in [myBPM_B1_H,myBPM_B1_V,myBPM_B2_H,myBPM_B2_V]:
    display(i.head(3))

	BPMSW.1R1.B1	BPMS.2R1.B1	BPMSY.4R1.B1	BPMWB.4R1.B1	BPMYA.4R1.B1	BPM.5R1.B1	BPMR.6R1.B1	BPMSX.7R1.B1	BPM.8R1.B1	BPM.9R1.B1	...	BPM.9L1.B1	BPM.8L1.B1	BPMR.7L1.B1	BPM.6L1.B1	BPMR.5L1.B1	BPMYA.4L1.B1	BPMWB.4L1.B1	BPMSY.4L1.B1	BPMS.2L1.B1	BPMSW.1L1.B1
2018-06-30 09:52:46.567000151+00:00	-506.99	-191.17	-379.43	426.54	137.41	138.58	16.29	-131.38	438.92	-72.87	...	269.33	-377.80	490.24	315.39	222.96	-67.14	297.23	-1053.89	66.77	22.62
2018-06-30 09:52:47.526999950+00:00	-507.76	-190.33	-380.41	425.07	136.62	137.36	14.53	-130.32	439.50	-74.21	...	268.83	-377.71	489.68	314.90	223.15	-67.16	296.13	-1054.74	67.28	20.67
2018-06-30 09:52:48.526999950+00:00	-506.38	-192.56	-379.26	426.79	136.76	138.20	15.56	-129.11	439.75	-75.11	...	268.82	-380.25	490.24	314.18	223.29	-67.59	298.66	-1054.54	68.91	21.14

3 rows × 518 columns

	BPMSW.1R1.B1	BPMS.2R1.B1	BPMSY.4R1.B1	BPMWB.4R1.B1	BPMYA.4R1.B1	BPM.5R1.B1	BPMR.6R1.B1	BPMSX.7R1.B1	BPM.8R1.B1	BPM.9R1.B1	...	BPM.9L1.B1	BPM.8L1.B1	BPMR.7L1.B1	BPM.6L1.B1	BPMR.5L1.B1	BPMYA.4L1.B1	BPMWB.4L1.B1	BPMSY.4L1.B1	BPMS.2L1.B1	BPMSW.1L1.B1
2018-06-30 09:52:46.567000151+00:00	-149.91	-160.70	-306.87	-74.58	752.21	-901.88	-104.03	911.58	242.89	-478.25	...	-228.21	251.39	-375.76	600.20	-569.59	711.48	152.80	438.11	-208.22	-257.47
2018-06-30 09:52:47.526999950+00:00	-150.36	-161.56	-309.88	-75.68	749.99	-905.23	-102.46	908.94	243.82	-477.88	...	-227.20	251.49	-374.02	601.05	-568.82	712.39	155.27	438.42	-207.95	-258.24
2018-06-30 09:52:48.526999950+00:00	-149.17	-162.95	-308.38	-76.76	749.51	-903.49	-103.59	909.42	242.95	-476.18	...	-227.08	252.49	-372.03	602.15	-568.59	715.00	158.12	438.50	-204.34	-257.92

3 rows × 518 columns

	BPMSW.1R1.B2	BPMS.2R1.B2	BPMSY.4R1.B2	BPMWB.4R1.B2	BPMYA.4R1.B2	BPMR.5R1.B2	BPM.6R1.B2	BPMRA.7R1.B2	BPM.8R1.B2	BPM.9R1.B2	...	BPM.10L1.B2	BPM.9L1.B2	BPM.8L1.B2	BPM.7L1.B2	BPMR.6L1.B2	BPM.5L1.B2	BPMWB.4L1.B2	BPMSY.4L1.B2	BPMS.2L1.B2	BPMSW.1L1.B2
2018-06-30 09:52:46.567000151+00:00	-241.29	38.00	62.46	-1095.68	155.71	163.05	14.85	66.14	-30.09	-12.40	...	-618.24	132.72	16.05	569.03	182.15	-27.24	124.67	-1626.10	-37.14	9.27
2018-06-30 09:52:47.526999950+00:00	-241.70	38.37	64.46	-1095.65	156.91	162.70	15.61	64.58	-31.28	-10.85	...	-619.22	132.62	16.98	569.32	183.79	-28.06	125.15	-1625.55	-36.74	8.89
2018-06-30 09:52:48.526999950+00:00	-242.74	36.43	63.62	-1094.91	157.67	161.81	14.85	65.68	-31.44	-11.90	...	-617.91	131.87	16.73	569.41	184.00	-27.66	123.23	-1624.02	-37.89	7.87

3 rows × 523 columns

	BPMSW.1R1.B2	BPMS.2R1.B2	BPMSY.4R1.B2	BPMWB.4R1.B2	BPMYA.4R1.B2	BPMR.5R1.B2	BPM.6R1.B2	BPMRA.7R1.B2	BPM.8R1.B2	BPM.9R1.B2	...	BPM.8L1.B2	BPM.7L1.B2	BPMSX.7L1.B2	BPMR.6L1.B2	BPM.5L1.B2	BPMYA.4L1.B2	BPMWB.4L1.B2	BPMSY.4L1.B2	BPMS.2L1.B2	BPMSW.1L1.B2
2018-06-30 09:52:46.567000151+00:00	257.17	190.90	-139.11	-235.79	133.49	-155.30	-201.60	66.57	438.31	-157.35	...	248.56	103.21	95.01	297.41	-291.00	-166.64	-377.31	-406.43	194.84	282.24
2018-06-30 09:52:47.526999950+00:00	257.85	191.66	-138.02	-235.30	135.28	-157.55	-202.23	67.39	439.00	-159.81	...	249.39	102.72	97.34	297.08	-294.39	-164.71	-376.83	-407.38	195.38	283.36
2018-06-30 09:52:48.526999950+00:00	257.74	193.08	-134.43	-235.48	134.45	-156.23	-203.00	67.01	437.74	-159.99	...	249.02	102.78	94.92	298.63	-294.53	-164.25	-379.24	-407.70	194.14	281.54

3 rows × 528 columns

Plotting the machine Closed Orbit

We average it in time.

plt.plot(df_B1_H[df_B1_H['mask']==1]['s [m]'].values, myBPM_B1_H.mean()/1000,'b',label='x-plane')
plt.plot(df_B1_H[df_B1_H['mask']==1]['s [m]'].values, myBPM_B1_V.mean()/1000,'r',label='y-plane')
plt.xlabel('s [m]')
plt.ylabel('[mm]')
plt.grid(True)
plt.legend(loc='best')

<matplotlib.legend.Legend at 0x7fea87a68630>

Let us focus during the IP1 H-scan

smallDF_B1=myBPM_B1_H.iloc[900:2200].copy()
plt.plot(smallDF_B1['BPMSW.1R1.B1']/1000,'b',lw=3)
smallDF_B2=myBPM_B2_H.iloc[900:2200].copy()
plt.plot(smallDF_B2['BPMSW.1R1.B2']/1000,'r',lw=3)

plt.grid(True)
plt.xlabel('time [month hh:mm]')
plt.ylabel('[mm]')
plt.grid(True)
plt.legend(loc='best')

<matplotlib.legend.Legend at 0x7fe8b2d80048>

Stability consideration during the scan

if 1:
    myDF=pd.read_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/BBQ.pickle')
else:    
    myDF=importData.LHCCals2pd(['LHC.BQBBQ.CONTINUOUS_HS.B%:EIGEN_AMPL%'],[myFill],['FLATTOP','ADJUST','STABLE'])
    myDF.to_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/BBQ.pickle')

aux=myDF.between_time('10:07:47','10:29:26')
plt.figure(figsize=(15,10))
plt.plot(aux['LHC.BQBBQ.CONTINUOUS_HS.B1:EIGEN_AMPL_1'].dropna(),'b.-',alpha=.5)
plt.plot(aux['LHC.BQBBQ.CONTINUOUS_HS.B1:EIGEN_AMPL_2'].dropna(),'r.-',alpha=.5)
plt.grid(True)
plt.xlabel('UTC time [month-day hh]');
plt.ylabel('[arb. units]'); 
plt.legend(loc='best')

ax2 = plt.gca().twinx()   # instantiate a second axes that shares the same x-axis

color = 'm'
ax2.set_ylabel('BPMSW.1R1.B1 [mm]', color=color)  # we already handled the x-label with ax1
ax2.plot(smallDF_B1['BPMSW.1R1.B1']/1000,'m',lw=3)
ax2.tick_params(axis='y', labelcolor=color)

#plt.ylim(-.001,.004)
plotFunctions.setSourcePlot(plt.gca(), mySource,color='k')

It is interesting to observe that the activity in B1 is increasing, as expected, when the two beams are not HO.

Singular value decomposition

Given the orbit matrix (time $\times$ s-position) we perform a SVD to establish a hierarchy of the modes in the CO evolution. This is a standard procedure and is only one application of the SVD algorithm. To have an interesting overview of SVD potential, I suggest this collection of examples here.

The SVD is a generatization of the diagonal decomposition. The latter can consider only for square matrices but it is not always possible. The SVD can be done for square and rectangular matrices and is always possible.

# Example
myMatrix=np.array([[1,1],[0,1]])
U,S,V_star=np.linalg.svd(myMatrix)

print('===============')
print(f"myMatrix size is {np.shape(myMatrix)}")
display(myMatrix)


print('===============')
print(f"U size is {np.shape(U)}")
display(U)

print('===============')
print(f"S size is {np.shape(S)}")
display(S)

print('===============')
print(f"V_star size is {np.shape(V_star)}")
display(V_star)

print('===============')
print(f"Sanity check: {np.allclose(myMatrix,U * S @ V_star)}")

===============
myMatrix size is (2, 2)



array([[1, 1],
       [0, 1]])


===============
U size is (2, 2)



array([[ 0.85065081, -0.52573111],
       [ 0.52573111,  0.85065081]])


===============
S size is (2,)



array([1.61803399, 0.61803399])


===============
V_star size is (2, 2)



array([[ 0.52573111,  0.85065081],
       [-0.85065081,  0.52573111]])


===============
Sanity check: True

D,A=np.linalg.eig(myMatrix)
print('===============')
print(f"D size is {np.shape(D)}")
display(D)

print('===============')
print(f"A size is {np.shape(A)}")
display(A)

print('===============')
print(f"Sanity check: {np.allclose(myMatrix,A @ np.diag(D) @ np.linalg.inv(A))}")

===============
D size is (2,)



array([1., 1.])


===============
A size is (2, 2)



array([[ 1.00000000e+00, -1.00000000e+00],
       [ 0.00000000e+00,  2.22044605e-16]])


===============
Sanity check: False

The algorithm to compute the SVD of $M$ is based on the diagonal form of $MM^{*}$ and $M^{*}M$. The SV are the square root of the $MM^{*}$ ($M^{*}M$ or since they are identical). The SVD modes are related to the eigenvecotors of $MM^{*}$ and $M^{*}M$.

# just to check
np.linalg.eig(myMatrix.transpose()@myMatrix)

(array([0.38196601, 2.61803399]), array([[-0.85065081, -0.52573111],
        [ 0.52573111, -0.85065081]]))

# just to check
np.linalg.eig(myMatrix@myMatrix.transpose())

(array([2.61803399, 0.38196601]), array([[ 0.85065081, -0.52573111],
        [ 0.52573111,  0.85065081]]))

A grephical representation of the SVD can be found on https://en.wikipedia.org

The diagonal form associated with a linear endomorphism defines a basis that is algebrically transformed, The SVD defines an orho-normal basis $V$ that is algebrically transformed in the orho-normal basis $U$.

Since the SV are ordered and since the matrices $U$ and $V$ are orthogonal (i.e., bases of orthonormal vectors), a metric hierarchy is established.

If the matrix $M$ represents the evolution of the orbits (s-direction) in time (t-direction), we can decompose the orbit of the machine in s-dipendent modes (V_star rows) that vary in time (U columns). The modes will be order by their metric.

# we remove the mean orbit
del U, S, V, aux

aux=smallDF_B1-smallDF_B1.mean()
# we decompose the matrix
U,S,V=np.linalg.svd(aux.values,full_matrices=False)
plt.semilogy(S,'.-b')
#plt.xlim([0,40])
plt.xlabel('Singular Values number')
plt.ylabel('Singular Values amplitude');
plt.grid(True)
# sanity check    
print(f'Sanity check: {np.allclose(aux.values,U * S @ V, equal_nan=True)}')

def plotMode_B1(mode=0):
    plt.figure(figsize=(20,10))
    plt.subplot(2,1,1)
    plt.plot(aux.index,U[:,mode]*S[mode],'b',lw=2)
    plt.grid(True)
    plt.ylabel(f'U[:,{mode}]*S[{mode}]')
    plt.title(f'MODE {mode}')
    plt.subplot(2,1,2)
    plt.plot(df_B1_H[df_B1_H['mask']==1]['s [m]'].values,V[mode,:],'k',lw=3)
    plt.grid(True)
    plt.ylabel(f'V[{mode},:]')
    plt.xlabel('s [m]')

Sanity check: False

for i in range(10):
    plotMode_B1(mode=i)

# we remove the mean orbit
aux=smallDF_B2-smallDF_B2.mean()

# we decompose the matrix
U,S,V=np.linalg.svd(aux.values,full_matrices=False)
plt.semilogy(S,'.-b')
#plt.xlim([0,40])
plt.xlabel('Singular Values number')
plt.ylabel('Singular Values amplitude');
plt.grid(True)
# sanity check    
print(f'Sanity check: {np.allclose(aux.values,U * S @ V)}')

def plotMode_B2(mode=0):
    plt.figure(figsize=(20,10))
    plt.subplot(2,1,1)
    plt.plot(aux.index,U[:,mode]*S[mode],'b',lw=2)
    plt.grid(True)
    plt.ylabel(f'U[:,{mode}]*S[{mode}]')
    plt.title(f'Mode {mode}')
    plt.subplot(2,1,2)
    plt.plot(df_B2_H[df_B2_H['mask']==1]['s [m]'].values,V[mode,:],'k',lw=3)
    plt.grid(True)
    plt.ylabel(f'V[{mode},:]')
    plt.xlabel('s [m]')

Sanity check: False

for i in range(10):
    plotMode_B2(mode=i)

Tracking the temperature of the BPM

I started without much success (expert in holidays) to look to correlation between the temperature of the BPM and the beam oscillations.

cals.search('LHC.BPTUH.%.B1:%TEMP%')

['LHC.BPTUH.A4L1.B1:HW_TEMP_ADC',
 'LHC.BPTUH.A4L1.B1:HW_TEMP_ANALOG',
 'LHC.BPTUH.A4L1.B1:HW_TEMP_FPGA',
 'LHC.BPTUH.A4L2.B1:HW_TEMP_ADC',
 'LHC.BPTUH.A4L2.B1:HW_TEMP_ANALOG',
 'LHC.BPTUH.A4L2.B1:HW_TEMP_FPGA',
 'LHC.BPTUH.A4L5.B1:HW_TEMP_ADC',
 'LHC.BPTUH.A4L5.B1:HW_TEMP_ANALOG',
 'LHC.BPTUH.A4L5.B1:HW_TEMP_FPGA',
 'LHC.BPTUH.A4L8.B1:HW_TEMP_ADC',
 'LHC.BPTUH.A4L8.B1:HW_TEMP_ANALOG',
 'LHC.BPTUH.A4L8.B1:HW_TEMP_FPGA',
 'LHC.BPTUH.A4R6.B1:HW_TEMP_ADC',
 'LHC.BPTUH.A4R6.B1:HW_TEMP_ANALOG',
 'LHC.BPTUH.A4R6.B1:HW_TEMP_FPGA',
 'LHC.BPTUH.C6L7.B1:HW_TEMP_ADC',
 'LHC.BPTUH.C6L7.B1:HW_TEMP_ANALOG',
 'LHC.BPTUH.C6L7.B1:HW_TEMP_FPGA']

plt.plot(importData.cals2pd('LHC.BPTUH.C6L7.B1:HW_TEMP_ANALOG',pd.Timestamp(2018,6,30,10,8),pd.Timestamp(2018,6,30,10,38)),'.')

[<matplotlib.lines.Line2D at 0x7fea87fb8ba8>]

Localization of the error

betx=[]
mux=[]
for i in df_B2_H[df_B2_H['mask']==1]['s [m]'].values:
    betx.append(b2DF[b2DF['s']==i].betx.values[0])
    mux.append(b2DF[b2DF['s']==i].mux.values[0])

plt.figure(figsize=(20,10))
mode=1
plt.plot(mux,V[mode,:]/np.sqrt(betx),'.-k',lw=3)
#plt.xlim(0,10)
plt.xlabel('$\mu$ [2$\pi$]')
plt.ylabel(f'Mode {mode} [arb. units]');

mode=1
plt.plot(mux,V[mode,:]/np.sqrt(betx),'.-k',lw=3)
myIndex=3
plt.plot(mux[myIndex],(V[mode,:]/np.sqrt(betx))[myIndex],'sr',lw=3)
plt.xlim(0,6)
plt.xlabel('$\mu$ [2$\pi$]')
plt.ylabel(f'Mode {mode} [arb. units]');
plt.grid(True)

mode=0
plt.plot(mux,V[mode,:]/np.sqrt(betx),'.-k',lw=3)
myIndex=3
plt.plot(mux[myIndex],(V[mode,:]/np.sqrt(betx))[myIndex],'sr',lw=3)
plt.xlim(0,6)
plt.xlabel('$\mu$ [2$\pi$]')
plt.ylabel(f'Mode {mode} [arb. units]');
plt.grid(True)

SVD filtering

# we remove the mean orbit
aux=smallDF_B1-smallDF_B1.mean()
# we decompose the matrix
U,S,V=np.linalg.svd(aux.values,full_matrices=False)
# sanity check    
print(f'Sanity check: {np.allclose(aux.values,U * S @ V)}')

Sanity check: True

# taking only the first mode
plt.figure(figsize=(20,10))
myS=S.copy()
myS[1:]=0
plt.pcolormesh(U * myS @ V *1e-3)
ax=plt.colorbar()
plt.xlabel('BPM')
plt.ylabel('time [arb. units]')
ax.set_label('B1 H orbit [mm]')

# removing the first singular value
plt.figure(figsize=(20,10))
myS=S.copy()
myS[0]=0
plt.pcolormesh(U * myS @ V *1e-3)
ax=plt.colorbar()
plt.xlabel('BPM')
plt.ylabel('time [arb. units]')
ax.set_label('B1 H orbit [mm]')

# we remove the mean orbit
aux=smallDF_B2-smallDF_B2.mean()
# we decompose the matrix
U,S,V=np.linalg.svd(aux.values,full_matrices=False)
# sanity check    
print(f'Sanity check: {np.allclose(aux.values,U * S @ V)}')

Sanity check: True

# taking only the first singular value
plt.figure(figsize=(20,10))
myS=S.copy()
myS[1:]=0
plt.pcolormesh(U * myS @ V *1e-3)
ax=plt.colorbar()
plt.xlabel('BPM')
plt.ylabel('time [arb. units]')
ax.set_label('B2 H orbit [mm]')

# removing the first singular value
plt.figure(figsize=(20,10))
myS=S.copy()
myS[0]=0
plt.pcolormesh(U * myS @ V *1e-3)
ax=plt.colorbar()
plt.xlabel('BPM')
plt.ylabel('time [arb. units]')
ax.set_label('B2 H orbit [mm]')

Conclusions

• The preliminary analysis of the orbit stability during FILL 6868 is compatible with histeresis effect of the separation bumps (see mode=1).

• After normalization, the mode=1 in B2 seems to reveal a spurious kick within the separation bump.

• The analysis was perfomed only for the first scan (H-plane scan in IP1 and H-plane).

• Some families of BPM show oscillations of their electrical center (clearly non physical).

• The analysis spotted also a very reduced number of problematic BPM (in particular one in B1/H).

• The SVD analysis can combine also additional signal signals (e.g., V plane, DOROS, thermal sensor of BPM crate) to improve the reading.

Optics during the vdm scan

importData.LHCCals2pd(['HX:OPTID'],myFill,beamModeList=['INJPROT','INJPHYS','PRERAMP','RAMP','FLATTOP','ADJUST','STABLE'],beamMode_column=True)

	HX:OPTID	mode
2018-06-30 09:13:12.046000004+00:00	3215.0	RAMP
2018-06-30 09:14:12.046000004+00:00	3215.0	RAMP
2018-06-30 09:15:12.046000004+00:00	3215.0	RAMP
2018-06-30 09:16:32.046000004+00:00	3215.0	RAMP
2018-06-30 09:18:12.046000004+00:00	3215.0	RAMP
2018-06-30 09:19:52.046000004+00:00	3215.0	RAMP
2018-06-30 09:21:32.046000004+00:00	3215.0	RAMP
2018-06-30 09:22:52.046000004+00:00	3215.0	RAMP
2018-06-30 09:24:02.046000004+00:00	3226.0	RAMP
2018-06-30 09:25:02.046000004+00:00	3220.0	RAMP
2018-06-30 09:26:02.046000004+00:00	3223.0	RAMP
2018-06-30 09:27:02.046000004+00:00	3222.0	RAMP
2018-06-30 09:28:02.046000004+00:00	3221.0	RAMP
2018-06-30 09:29:02.046000004+00:00	3225.0	RAMP
2018-06-30 09:31:32.046000004+00:00	3225.0	RAMP
2018-06-30 09:33:22.046000004+00:00	3225.0	RAMP
2018-06-30 09:38:09.472000122+00:00	3225.0	FLATTOP
2018-06-30 09:38:29.472000122+00:00	3224.0	FLATTOP
2018-06-30 09:41:07.421999931+00:00	3224.0	ADJUST
2018-06-30 09:42:27.421999931+00:00	3224.0	ADJUST
2018-06-30 09:43:47.421999931+00:00	3224.0	ADJUST
2018-06-30 09:45:55.582999945+00:00	3224.0	ADJUST

Optics computation

from cpymad.madx import Madx
from matplotlib import pyplot as plt

myString='''
! Sequence definition
Option, -echo,warn,-info;

call, file="/eos/project/a/abpdata/lhc/optics/runII/2015/aperture/const_for_aperture.madx";
call, file="/eos/project/a/abpdata/lhc/optics/runII/2015/lhc_as-built.seq";

! Aperture definition

call, file="/eos/project/a/abpdata/lhc/optics/runII/2015/aperture/aperture_as-built.b1.madx";
call, file="/eos/project/a/abpdata/lhc/optics/runII/2015/aperture/aperture_as-built.b2.madx";
call, file="/eos/project/a/abpdata/lhc/optics/runII/2015/aperture/aper_tol_as-built.b1.madx";
call, file="/eos/project/a/abpdata/lhc/optics/runII/2015/aperture/aper_tol_as-built.b2.madx";
call, file="/eos/project/a/abpdata/lhc/optics/runII/2015/aperture/exp_pipe_model_after_LS1.madx";
call, file="/eos/project/a/abpdata/lhc/optics/runII/2015/aperture/exp_pipe_install_after_LS1.madx";


! Beam definition
beam, sequence=lhcb1, bv= 1,
  particle=proton, charge=1, mass=0.938272046,
  energy= 6500,   npart=1.2e11,kbunch=2748,
  ex=3.608738638461539e-10,ey=3.608738638461539e-10;
beam, sequence=lhcb2, bv=-1,
  particle=proton, charge=1, mass=0.938272046,
  energy= 6500,   npart=1.2e11,kbunch=2748,
  ex=3.608738638461539e-10,ey=3.608738638461539e-10;

! Strength definition

call, file="/eos/project/a/abpdata/lhc/optics/runII/2015/opt_19200_19000_19200_24000_coll.madx";

nrj=beam%lhcb1->pc/beam%lhcb1->charge;

! Cycle
!seqedit,sequence=lhcb1;flatten;cycle,start=s.ds.l1.b1;endedit;
!seqedit,sequence=lhcb2;flatten;cycle,start=s.ds.l1.b2;endedit;

! Post Strength
! Twiss before customized knobs
set,format="22.15e";
select,flag=twiss,clear;
select,flag=twiss, pattern="IP.$",column=name,s,betx,bety,alfx,alfy,dx,dpx,dy,dpy,mux,muy,x,y,px,py;

use, sequence=lhcb1;
twiss, table=twiss_ipb1_flat;

use, sequence=lhcb2;
twiss, table=twiss_ipb2_flat;
'''

myMad = Madx()

  ++++++++++++++++++++++++++++++++++++++++++++
  +     MAD-X 5.04.02  (64 bit, Linux)       +
  + Support: mad@cern.ch, http://cern.ch/mad +
  + Release   date: 2018.10.03               +
  + Execution date: 2019.07.03 09:53:26      +
  ++++++++++++++++++++++++++++++++++++++++++++

myMad.input(myString)

++++++ warning: ignored: attempt to redefine constant: l.mbas2
++++++ warning: ignored: attempt to redefine constant: l.mbaw
++++++ warning: ignored: attempt to redefine constant: l.mbcs2
++++++ warning: ignored: attempt to redefine constant: l.mbls2
++++++ warning: ignored: attempt to redefine constant: l.mblw
++++++ warning: ignored: attempt to redefine constant: l.mbwmd
++++++ warning: ignored: attempt to redefine constant: l.mbxwh
++++++ warning: ignored: attempt to redefine constant: l.mbxws
++++++ warning: ignored: attempt to redefine constant: l.mbxwt
++++++ warning: implicit element re-definition ignored: ip1
++++++ warning: implicit element re-definition ignored: mbas2.1r1
++++++ warning: implicit element re-definition ignored: tas.1r1
++++++ warning: implicit element re-definition ignored: bpmwk.1r1
++++++ warning: implicit element re-definition ignored: mqxa.1r1
++++++ warning: implicit element re-definition ignored: mcbxh.1r1
++++++ warning: implicit element re-definition ignored: mcbxv.1r1
++++++ warning: implicit element re-definition ignored: mqxb.a2r1
++++++ warning: implicit element re-definition ignored: mcbxh.2r1
++++++ warning: implicit element re-definition ignored: mcbxv.2r1
++++++ warning: implicit element re-definition ignored: mqxb.b2r1
++++++ warning: implicit element re-definition ignored: tasb.3r1
++++++ warning: implicit element re-definition ignored: mqsx.3r1
++++++ warning: implicit element re-definition ignored: mqxa.3r1
++++++ warning: implicit element re-definition ignored: mcbxh.3r1
++++++ warning: implicit element re-definition ignored: mcbxv.3r1
++++++ warning: implicit element re-definition ignored: mcsx.3r1
++++++ warning: implicit element re-definition ignored: mctx.3r1
++++++ warning: implicit element re-definition ignored: mcosx.3r1
++++++ warning: implicit element re-definition ignored: mcox.3r1
++++++ warning: implicit element re-definition ignored: mcssx.3r1
++++++ warning: implicit element re-definition ignored: dfbxb.3r1
++++++ warning: implicit element re-definition ignored: mbxw.a4r1
++++++ warning: implicit element re-definition ignored: mbxw.b4r1
++++++ warning: implicit element re-definition ignored: mbxw.c4r1
++++++ warning: implicit element re-definition ignored: mbxw.d4r1
++++++ warning: implicit element re-definition ignored: mbxw.e4r1
++++++ warning: implicit element re-definition ignored: mbxw.f4r1
++++++ warning: implicit element re-definition ignored: x1fcr.4r1
++++++ warning: implicit element re-definition ignored: brana.4r1
++++++ warning: implicit element re-definition ignored: x1zdc.a4r1
++++++ warning: implicit element re-definition ignored: tanar.4r1
++++++ warning: implicit element re-definition ignored: x2zdc.4l2
++++++ warning: implicit element re-definition ignored: branb.4l2
++++++ warning: implicit element re-definition ignored: btvst.a4l2
++++++ warning: implicit element re-definition ignored: tcdd.4l2
++++++ warning: implicit element re-definition ignored: mbx.4l2
++++++ warning: implicit element re-definition ignored: dfbxc.3l2
++++++ warning: implicit element re-definition ignored: mcosx.3l2
++++++ warning: implicit element re-definition ignored: mcox.3l2
++++++ warning: implicit element re-definition ignored: mcssx.3l2
++++++ warning: implicit element re-definition ignored: mcbxh.3l2
++++++ warning: implicit element re-definition ignored: mcbxv.3l2
++++++ warning: implicit element re-definition ignored: mcsx.3l2
++++++ warning: implicit element re-definition ignored: mctx.3l2
++++++ warning: implicit element re-definition ignored: mqxa.3l2
++++++ warning: implicit element re-definition ignored: mqsx.3l2
++++++ warning: implicit element re-definition ignored: mqxb.b2l2
++++++ warning: implicit element re-definition ignored: mcbxh.2l2
++++++ warning: implicit element re-definition ignored: mcbxv.2l2
++++++ warning: implicit element re-definition ignored: mqxb.a2l2
++++++ warning: implicit element re-definition ignored: mcbxh.1l2
++++++ warning: implicit element re-definition ignored: mcbxv.1l2
++++++ warning: implicit element re-definition ignored: mqxa.1l2
++++++ warning: implicit element re-definition ignored: mbxwt.1l2
++++++ warning: implicit element re-definition ignored: mbwmd.1l2
++++++ warning: implicit element re-definition ignored: mbls2.1l2
++++++ warning: implicit element re-definition ignored: ip2
++++++ warning: implicit element re-definition ignored: mbls2.1r2
++++++ warning: implicit element re-definition ignored: mbaw.1r2
++++++ warning: implicit element re-definition ignored: mbxwt.1r2
++++++ warning: implicit element re-definition ignored: mqxa.1r2
++++++ warning: implicit element re-definition ignored: mcbxh.1r2
++++++ warning: implicit element re-definition ignored: mcbxv.1r2
++++++ warning: implicit element re-definition ignored: mqxb.a2r2
++++++ warning: implicit element re-definition ignored: mcbxh.2r2
++++++ warning: implicit element re-definition ignored: mcbxv.2r2
++++++ warning: implicit element re-definition ignored: mqxb.b2r2
++++++ warning: implicit element re-definition ignored: mqsx.3r2
++++++ warning: implicit element re-definition ignored: mqxa.3r2
++++++ warning: implicit element re-definition ignored: mcbxh.3r2
++++++ warning: implicit element re-definition ignored: mcbxv.3r2
++++++ warning: implicit element re-definition ignored: mcsx.3r2
++++++ warning: implicit element re-definition ignored: mctx.3r2
++++++ warning: implicit element re-definition ignored: mcosx.3r2
++++++ warning: implicit element re-definition ignored: mcox.3r2
++++++ warning: implicit element re-definition ignored: mcssx.3r2
++++++ warning: implicit element re-definition ignored: dfbxd.3r2
++++++ warning: implicit element re-definition ignored: mbx.4r2
++++++ warning: implicit element re-definition ignored: tclia.4r2
++++++ warning: implicit element re-definition ignored: branb.4r2
++++++ warning: implicit element re-definition ignored: x2zdc.4r2
++++++ warning: implicit element re-definition ignored: ip3
++++++ warning: implicit element re-definition ignored: ip4
++++++ warning: implicit element re-definition ignored: tanc.4l5
++++++ warning: implicit element re-definition ignored: x5zdc.b4l5
++++++ warning: implicit element re-definition ignored: brana.4l5
++++++ warning: implicit element re-definition ignored: x5zdc.a4l5
++++++ warning: implicit element re-definition ignored: x5fcb.a4l5
++++++ warning: implicit element re-definition ignored: x5fca.b4l5
++++++ warning: implicit element re-definition ignored: mbxw.f4l5
++++++ warning: implicit element re-definition ignored: mbxw.e4l5
++++++ warning: implicit element re-definition ignored: mbxw.d4l5
++++++ warning: implicit element re-definition ignored: mbxw.c4l5
++++++ warning: implicit element re-definition ignored: mbxw.b4l5
++++++ warning: implicit element re-definition ignored: mbxw.a4l5
++++++ warning: implicit element re-definition ignored: x5fca.a4l5
++++++ warning: implicit element re-definition ignored: dfbxe.3l5
++++++ warning: implicit element re-definition ignored: mcosx.3l5
++++++ warning: implicit element re-definition ignored: mcox.3l5
++++++ warning: implicit element re-definition ignored: mcssx.3l5
++++++ warning: implicit element re-definition ignored: mcbxh.3l5
++++++ warning: implicit element re-definition ignored: mcbxv.3l5
++++++ warning: implicit element re-definition ignored: mcsx.3l5
++++++ warning: implicit element re-definition ignored: mctx.3l5
++++++ warning: implicit element re-definition ignored: mqxa.3l5
++++++ warning: implicit element re-definition ignored: mqsx.3l5
++++++ warning: implicit element re-definition ignored: tasb.3l5
++++++ warning: implicit element re-definition ignored: mqxb.b2l5
++++++ warning: implicit element re-definition ignored: mcbxh.2l5
++++++ warning: implicit element re-definition ignored: mcbxv.2l5
++++++ warning: implicit element re-definition ignored: mqxb.a2l5
++++++ warning: implicit element re-definition ignored: mcbxh.1l5
++++++ warning: implicit element re-definition ignored: mcbxv.1l5
++++++ warning: implicit element re-definition ignored: mqxa.1l5
++++++ warning: implicit element re-definition ignored: bpmwk.1l5
++++++ warning: implicit element re-definition ignored: tas.1l5
++++++ warning: implicit element re-definition ignored: mbcs2.1l5
++++++ warning: implicit element re-definition ignored: ip5
++++++ warning: implicit element re-definition ignored: mbcs2.1r5
++++++ warning: implicit element re-definition ignored: tas.1r5
++++++ warning: implicit element re-definition ignored: bpmwk.1r5
++++++ warning: implicit element re-definition ignored: mqxa.1r5
++++++ warning: implicit element re-definition ignored: mcbxh.1r5
++++++ warning: implicit element re-definition ignored: mcbxv.1r5
++++++ warning: implicit element re-definition ignored: mqxb.a2r5
++++++ warning: implicit element re-definition ignored: mcbxh.2r5
++++++ warning: implicit element re-definition ignored: mcbxv.2r5
++++++ warning: implicit element re-definition ignored: mqxb.b2r5
++++++ warning: implicit element re-definition ignored: tasb.3r5
++++++ warning: implicit element re-definition ignored: mqsx.3r5
++++++ warning: implicit element re-definition ignored: mqxa.3r5
++++++ warning: implicit element re-definition ignored: mcbxh.3r5
++++++ warning: implicit element re-definition ignored: mcbxv.3r5
++++++ warning: implicit element re-definition ignored: mcsx.3r5
++++++ warning: implicit element re-definition ignored: mctx.3r5
++++++ warning: implicit element re-definition ignored: mcosx.3r5
++++++ warning: implicit element re-definition ignored: mcox.3r5
++++++ warning: implicit element re-definition ignored: mcssx.3r5
++++++ warning: implicit element re-definition ignored: dfbxf.3r5
++++++ warning: implicit element re-definition ignored: x5fca.b4r5
++++++ warning: implicit element re-definition ignored: mbxw.a4r5
++++++ warning: implicit element re-definition ignored: mbxw.b4r5
++++++ warning: implicit element re-definition ignored: mbxw.c4r5
++++++ warning: implicit element re-definition ignored: mbxw.d4r5
++++++ warning: implicit element re-definition ignored: mbxw.e4r5
++++++ warning: implicit element re-definition ignored: mbxw.f4r5
++++++ warning: implicit element re-definition ignored: x5fca.a4r5
++++++ warning: implicit element re-definition ignored: x5fcb.a4r5
++++++ warning: implicit element re-definition ignored: x5zdc.b4r5
++++++ warning: implicit element re-definition ignored: brana.4r5
++++++ warning: implicit element re-definition ignored: x5zdc.a4r5
++++++ warning: implicit element re-definition ignored: tanc.4r5
++++++ warning: implicit element re-definition ignored: ip6
++++++ warning: implicit element re-definition ignored: ip7
++++++ warning: implicit element re-definition ignored: branb.4l8
++++++ warning: implicit element re-definition ignored: tclia.4l8
++++++ warning: implicit element re-definition ignored: mbx.4l8
++++++ warning: implicit element re-definition ignored: dfbxg.3l8
++++++ warning: implicit element re-definition ignored: mcosx.3l8
++++++ warning: implicit element re-definition ignored: mcox.3l8
++++++ warning: implicit element re-definition ignored: mcssx.3l8
++++++ warning: implicit element re-definition ignored: mcbxh.3l8
++++++ warning: implicit element re-definition ignored: mcbxv.3l8
++++++ warning: implicit element re-definition ignored: mcsx.3l8
++++++ warning: implicit element re-definition ignored: mctx.3l8
++++++ warning: implicit element re-definition ignored: mqxa.3l8
++++++ warning: implicit element re-definition ignored: mqsx.3l8
++++++ warning: implicit element re-definition ignored: mqxb.b2l8
++++++ warning: implicit element re-definition ignored: mcbxh.2l8
++++++ warning: implicit element re-definition ignored: mcbxv.2l8
++++++ warning: implicit element re-definition ignored: mqxb.a2l8
++++++ warning: implicit element re-definition ignored: mcbxh.1l8
++++++ warning: implicit element re-definition ignored: mcbxv.1l8
++++++ warning: implicit element re-definition ignored: mqxa.1l8
++++++ warning: implicit element re-definition ignored: mbxws.1l8
++++++ warning: implicit element re-definition ignored: mbxwh.1l8
++++++ warning: implicit element re-definition ignored: ip8
++++++ warning: implicit element re-definition ignored: mblw.1r8
++++++ warning: implicit element re-definition ignored: mbxws.1r8
++++++ warning: implicit element re-definition ignored: mqxa.1r8
++++++ warning: implicit element re-definition ignored: mcbxh.1r8
++++++ warning: implicit element re-definition ignored: mcbxv.1r8
++++++ warning: implicit element re-definition ignored: mqxb.a2r8
++++++ warning: implicit element re-definition ignored: mcbxh.2r8
++++++ warning: implicit element re-definition ignored: mcbxv.2r8
++++++ warning: implicit element re-definition ignored: mqxb.b2r8
++++++ warning: implicit element re-definition ignored: mqsx.3r8
++++++ warning: implicit element re-definition ignored: mqxa.3r8
++++++ warning: implicit element re-definition ignored: mcbxh.3r8
++++++ warning: implicit element re-definition ignored: mcbxv.3r8
++++++ warning: implicit element re-definition ignored: mcsx.3r8
++++++ warning: implicit element re-definition ignored: mctx.3r8
++++++ warning: implicit element re-definition ignored: mcosx.3r8
++++++ warning: implicit element re-definition ignored: mcox.3r8
++++++ warning: implicit element re-definition ignored: mcssx.3r8
++++++ warning: implicit element re-definition ignored: dfbxh.3r8
++++++ warning: implicit element re-definition ignored: mbx.4r8
++++++ warning: implicit element re-definition ignored: tcddm.4r8
++++++ warning: implicit element re-definition ignored: btvst.a4r8
++++++ warning: implicit element re-definition ignored: branb.4r8
++++++ warning: implicit element re-definition ignored: tanal.4l1
++++++ warning: implicit element re-definition ignored: x1zdc.a4l1
++++++ warning: implicit element re-definition ignored: brana.4l1
++++++ warning: implicit element re-definition ignored: x1fcl.4l1
++++++ warning: implicit element re-definition ignored: mbxw.f4l1
++++++ warning: implicit element re-definition ignored: mbxw.e4l1
++++++ warning: implicit element re-definition ignored: mbxw.d4l1
++++++ warning: implicit element re-definition ignored: mbxw.c4l1
++++++ warning: implicit element re-definition ignored: mbxw.b4l1
++++++ warning: implicit element re-definition ignored: mbxw.a4l1
++++++ warning: implicit element re-definition ignored: dfbxa.3l1
++++++ warning: implicit element re-definition ignored: mcosx.3l1
++++++ warning: implicit element re-definition ignored: mcox.3l1
++++++ warning: implicit element re-definition ignored: mcssx.3l1
++++++ warning: implicit element re-definition ignored: mcbxh.3l1
++++++ warning: implicit element re-definition ignored: mcbxv.3l1
++++++ warning: implicit element re-definition ignored: mcsx.3l1
++++++ warning: implicit element re-definition ignored: mctx.3l1
++++++ warning: implicit element re-definition ignored: mqxa.3l1
++++++ warning: implicit element re-definition ignored: mqsx.3l1
++++++ warning: implicit element re-definition ignored: tasb.3l1
++++++ warning: implicit element re-definition ignored: mqxb.b2l1
++++++ warning: implicit element re-definition ignored: mcbxh.2l1
++++++ warning: implicit element re-definition ignored: mcbxv.2l1
++++++ warning: implicit element re-definition ignored: mqxb.a2l1
++++++ warning: implicit element re-definition ignored: mcbxh.1l1
++++++ warning: implicit element re-definition ignored: mcbxv.1l1
++++++ warning: implicit element re-definition ignored: mqxa.1l1
++++++ warning: implicit element re-definition ignored: bpmwk.1l1
++++++ warning: implicit element re-definition ignored: tas.1l1
++++++ warning: implicit element re-definition ignored: mbas2.1l1
++++++ warning: implicit element re-definition ignored: ip1.l1
enter Twiss module

iteration:   1 error:   2.525108E-03 deltap:   0.000000E+00
orbit:  -3.056898E-05  6.269177E-08  4.857955E-06 -1.448609E-04  0.000000E+00  0.000000E+00

iteration:   2 error:   4.315597E-05 deltap:   0.000000E+00
orbit:   1.134273E-09 -7.053445E-10 -2.408938E-10 -1.450000E-04  0.000000E+00  0.000000E+00

iteration:   3 error:   4.337533E-09 deltap:   0.000000E+00
orbit:   2.590299E-11 -5.470202E-10  1.672655E-13 -1.450000E-04  0.000000E+00  0.000000E+00

++++++ table: summ

                length                 orbit5                   alfa                gammatr 
 2.665888319999896e+04 -0.000000000000000e+00  3.202048804239000e-04  5.588381245759998e+01

                    q1                    dq1                betxmax                  dxmax 
 6.430999990172486e+01  3.000000078069948e+00  5.935115009636181e+02  2.720741306485160e+00

                 dxrms                 xcomax                 xcorms                     q2 
 1.344104534674799e+00  1.545439897281311e-02  9.885626081800364e-04  5.932000002666296e+01

                   dq2                betymax                  dymax                  dyrms 
 2.999999805244852e+00  6.098696059736556e+02  1.210194688464986e-01  3.643142661904077e-02

                ycomax                 ycorms                 deltap                synch_1 
 6.986494064697167e-03  6.273649315943959e-04  0.000000000000000e+00  0.000000000000000e+00

               synch_2                synch_3                synch_4                synch_5 
 0.000000000000000e+00  0.000000000000000e+00  0.000000000000000e+00  0.000000000000000e+00

                nflips 
 0.000000000000000e+00 
enter Twiss module

iteration:   1 error:   2.517161E-03 deltap:   0.000000E+00
orbit:   1.769414E-05 -5.593396E-07 -2.801367E-06  1.450025E-04  0.000000E+00  0.000000E+00

iteration:   2 error:   3.438873E-05 deltap:   0.000000E+00
orbit:   2.365727E-09  3.662869E-10 -1.633338E-10  1.450000E-04  0.000000E+00  0.000000E+00

iteration:   3 error:   5.623722E-10 deltap:   0.000000E+00
orbit:   1.323483E-09  3.178942E-10  1.104578E-13  1.450000E-04  0.000000E+00  0.000000E+00

++++++ table: summ

                length                 orbit5                   alfa                gammatr 
 2.665888319999895e+04 -0.000000000000000e+00  3.192054999491873e-04  5.597122565324666e+01

                    q1                    dq1                betxmax                  dxmax 
 6.430999990601576e+01  3.000000009898441e+00  5.909961120775461e+02  2.921792801755544e+00

                 dxrms                 xcomax                 xcorms                     q2 
 1.343382960241845e+00  1.545439614740161e-02  9.994160708666150e-04  5.932000002847266e+01

                   dq2                betymax                  dymax                  dyrms 
 3.000000010147829e+00  6.046420530599705e+02  1.272431476595715e-01  2.218671992885489e-02

                ycomax                 ycorms                 deltap                synch_1 
 6.986494063941867e-03  6.278667310642880e-04  0.000000000000000e+00  0.000000000000000e+00

               synch_2                synch_3                synch_4                synch_5 
 0.000000000000000e+00  0.000000000000000e+00  0.000000000000000e+00  0.000000000000000e+00

                nflips 
 0.000000000000000e+00





True

list(myMad.table)

['summ', 'twiss_ipb1_flat', 'twiss_ipb2_flat']

if 1:
    b1DF=pd.read_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/b1DF.pickle')
else:
    b1DF=myMad.table.twiss_ipb1_flat.dframe()
    b1DF.to_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/b1DF.pickle')

if 1:
    b2DF=pd.read_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/b2DF.pickle')
else:
    b2DF=myMad.table.twiss_ipb2_flat.dframe()
    b2DF.to_pickle('/eos/user/s/sterbini/MD_ANALYSIS/2019/VdMScans/b2DF.pickle')