cu_inj Live¶

In [1]:

# Useful for debugging
%load_ext autoreload
%autoreload 2

%config InlineBackend.figure_format = 'retina'
%matplotlib inline

# Useful for debugging %load_ext autoreload %autoreload 2 %config InlineBackend.figure_format = 'retina' %matplotlib inline

In [37]:

from lcls_live.datamaps import get_datamaps
from lcls_live.archiver import lcls_archiver_restore

from lcls_live.tools import isotime

import matplotlib.pyplot as plt
import numpy as np

import os

from lcls_live.datamaps import get_datamaps from lcls_live.archiver import lcls_archiver_restore from lcls_live.tools import isotime import matplotlib.pyplot as plt import numpy as np import os

In [3]:

BEAMPATH = 'cu_spec'

BEAMPATH = 'cu_spec'

Datamaps, and all PVs needed¶

In [4]:

DM = get_datamaps(BEAMPATH)

# Hack in 10 Hz
df = DM['bpms'].data
DM['bpms'].data['pvname'] = [name[:-2]+'TH' for name in df['pvname']]

DM.keys()

DM = get_datamaps(BEAMPATH) # Hack in 10 Hz df = DM['bpms'].data DM['bpms'].data['pvname'] = [name[:-2]+'TH' for name in df['pvname']] DM.keys()

Out[4]:

dict_keys(['bpms', 'correctors', 'quad', 'linac'])

In [5]:

# datamaps to exclude
DENYLIST = ['correctors', 'linac'] #, 'quad']

# datamaps to exclude DENYLIST = ['correctors', 'linac'] #, 'quad']

In [6]:

# PVs needed
PVLIST =  []
for name, dm in DM.items():
    if name in DENYLIST:
        continue
    PVLIST.extend(dm.pvlist)
PVLIST = list(set(PVLIST))
len(PVLIST)

# PVs needed PVLIST = [] for name, dm in DM.items(): if name in DENYLIST: continue PVLIST.extend(dm.pvlist) PVLIST = list(set(PVLIST)) len(PVLIST)

Out[6]:

46

EPICS¶

In [8]:

import epics
from time import sleep, time

import epics from time import sleep, time

In [9]:

MONITOR = {pvname:epics.PV(pvname) for pvname in PVLIST}
sleep(5) # Wait for all to connect

MONITOR = {pvname:epics.PV(pvname) for pvname in PVLIST} sleep(5) # Wait for all to connect

In [10]:

def get_pvdata():   
    itime = isotime()
    pvdata =  {k:MONITOR[k].get() for k in MONITOR}
    return pvdata
PVDATA = get_pvdata()

def get_pvdata(): itime = isotime() pvdata = {k:MONITOR[k].get() for k in MONITOR} return pvdata PVDATA = get_pvdata()

In [11]:

# Check for bad PVs
bad = set()
for k, v in PVDATA.items():
    if v is None:
        bad.add(k.split(':')[2])
        print(k)
assert len(bad) == 0

# Check for bad PVs bad = set() for k, v in PVDATA.items(): if v is None: bad.add(k.split(':')[2]) print(k) assert len(bad) == 0

Tao conveniences¶

In [12]:

def tao_commands(pvdata):
    cmds = []
    for name, dm in DM.items():
        cmds.extend(dm.as_tao(pvdata))
    return cmds

def tao_commands(pvdata): cmds = [] for name, dm in DM.items(): cmds.extend(dm.as_tao(pvdata)) return cmds

In [13]:

def save_cmds(cmds, filename='cmds.tao'): # Write to file for running with vanilla Tao
    with open(filename, 'w') as f:
        f.write('set global lattice_calc_on = F\n')
        f.write('set global plot_on = F\n')    
        for cmd in CMDS:
            f.write(cmd+'\n')
        f.write('set global lattice_calc_on = T\n')        
        f.write('set global plot_on = T\n')

def save_cmds(cmds, filename='cmds.tao'): # Write to file for running with vanilla Tao with open(filename, 'w') as f: f.write('set global lattice_calc_on = F\n') f.write('set global plot_on = F\n') for cmd in CMDS: f.write(cmd+'\n') f.write('set global lattice_calc_on = T\n') f.write('set global plot_on = T\n')

In [14]:

def toggle_beam():
    tao.cmd('set global track_type = beam') 
    tao.cmd('set global track_type = single')

def toggle_beam(): tao.cmd('set global track_type = beam') tao.cmd('set global track_type = single')

Form commands using PVDATA and datamaps¶

In [15]:

tao_commands(PVDATA)[0:5]

tao_commands(PVDATA)[0:5]

Out[15]:

['set data orbit.x[1]|meas  = 0.001 * 0.18149997293949127',
 'set data orbit.x[2]|meas  = 0.001 * 0.3294964134693146',
 'set data orbit.x[3]|meas  = 0.001 * 2.2582709789276123e-05',
 'set data orbit.x[4]|meas  = 0.001 * -0.008475333452224731',
 'set data orbit.x[5]|meas  = 0.001 * -0.03837699070572853']

Start Tao¶

In [16]:

from pytao import Tao

from pytao import Tao

In [18]:

MODEL_ROOT = f'$LCLS_LATTICE/bmad/models/{BEAMPATH}/'
init = f'-init {MODEL_ROOT}/tao.init'
tao = Tao(init)
tao.cmd('set ele quad::* field_master = T')
init

MODEL_ROOT = f'$LCLS_LATTICE/bmad/models/{BEAMPATH}/' init = f'-init {MODEL_ROOT}/tao.init' tao = Tao(init) tao.cmd('set ele quad::* field_master = T') init

Out[18]:

'-init $LCLS_LATTICE/bmad/models/cu_spec//tao.init'

In [19]:

%%time
# Turn on the beam
tao.cmd('set beam_init n_particle = 10000')
toggle_beam()

%%time # Turn on the beam tao.cmd('set beam_init n_particle = 10000') toggle_beam()

CPU times: user 1.39 s, sys: 146 ms, total: 1.54 s
Wall time: 536 ms

In [27]:

%%tao
place floor none
place top2 bpm_orbit
place middle2 bunch_sigma_xy 

sc top2 -2 2

set graph middle2 y%label = "\gs\fn\dx\u, \gs\fn\dy\u (\gmm)"
set curve middle2.g.x y_axis_scale_factor = 1e6
set curve middle2.g.y y_axis_scale_factor = 1e6
sc middle2 ! 0 600


x-a all s

%%tao place floor none place top2 bpm_orbit place middle2 bunch_sigma_xy sc top2 -2 2 set graph middle2 y%label = "\gs\fn\dx\u, \gs\fn\dy\u (\gmm)" set curve middle2.g.x y_axis_scale_factor = 1e6 set curve middle2.g.y y_axis_scale_factor = 1e6 sc middle2 ! 0 600 x-a all s

-------------------------
Tao> place floor none
-------------------------
Tao> place top2 bpm_orbit
-------------------------
Tao> place middle2 bunch_sigma_xy 
-------------------------
Tao> 
-------------------------
Tao> sc top2 -2 2
-------------------------
Tao> 
-------------------------
Tao> set graph middle2 y%label = "\gs\fn\dx\u, \gs\fn\dy\u (\gmm)"
-------------------------
Tao> set curve middle2.g.x y_axis_scale_factor = 1e6
-------------------------
Tao> set curve middle2.g.y y_axis_scale_factor = 1e6
-------------------------
Tao> sc middle2 ! 0 600
-------------------------
Tao> 
-------------------------
Tao> 
-------------------------
Tao> x-a all s 
-------------------------
Tao> 

In [28]:

#tao.cmd(f'call {MODEL_ROOT}/scripts/SC.tao')

#tao.cmd(f'call {MODEL_ROOT}/scripts/SC.tao')

Continuous loop¶

In [29]:

def run1():
    #sleep(.001)
    t1 = time()
    pvdata = get_pvdata()
    cmds = tao_commands(pvdata)
    tao.cmd('set global plot_on = F;set global lattice_calc_on = F')
    tao.cmds(cmds); # Apply

    tao.cmd('set global lattice_calc_on = T')
    tao.cmd('set global plot_on = T')
    toggle_beam()
    
    dt = time()-t1
    #print(dt)

def run1(): #sleep(.001) t1 = time() pvdata = get_pvdata() cmds = tao_commands(pvdata) tao.cmd('set global plot_on = F;set global lattice_calc_on = F') tao.cmds(cmds); # Apply tao.cmd('set global lattice_calc_on = T') tao.cmd('set global plot_on = T') toggle_beam() dt = time()-t1 #print(dt)

In [30]:

%%time
run1()

%%time run1()

CPU times: user 2.57 s, sys: 274 ms, total: 2.85 s
Wall time: 727 ms

In [ ]:

while True:
    run1()

while True: run1()

In [32]:

%%time
#toggle on and off
tao.cmd('set global track_type = beam', raises=False) 
tao.cmd('set global track_type = single')

%%time #toggle on and off tao.cmd('set global track_type = beam', raises=False) tao.cmd('set global track_type = single')

CPU times: user 1.33 s, sys: 113 ms, total: 1.44 s
Wall time: 382 ms

Out[32]:

[]

In [33]:

%%tao
place top2 beambeta
place middle2 bunch_sigma_xy
x-a all s
sc

%%tao place top2 beambeta place middle2 bunch_sigma_xy x-a all s sc

-------------------------
Tao> place top2 beambeta
-------------------------
Tao> place middle2 bunch_sigma_xy
-------------------------
Tao> x-a all s
-------------------------
Tao> sc
-------------------------
Tao> 

Get particles¶

In [34]:

from pmd_beamphysics import ParticleGroup

from pmd_beamphysics import ParticleGroup

In [35]:

P = ParticleGroup(data=tao.bunch_data('OTR2'))
# Select out live particles
P = P[P.status==1]
P.drift_to_z()

P = ParticleGroup(data=tao.bunch_data('OTR2')) # Select out live particles P = P[P.status==1] P.drift_to_z()

In [38]:

P.plot('delta_t', 'energy')
plt.title('OTR2')

P.plot('delta_t', 'energy') plt.title('OTR2')

Out[38]:

Text(0.5, 1.0, 'OTR2')

In [39]:

P.plot('x', 'y')

P.plot('x', 'y')

In [40]:

P.plot('t', 'x')

P.plot('t', 'x')

In [41]:

# Make a function for this
def get_beam(ele):
    P = ParticleGroup(data=tao.bunch_data(ele))
    # Select out live particles
    P = P[P.status==1]    
    
    return P

# Make a function for this def get_beam(ele): P = ParticleGroup(data=tao.bunch_data(ele)) # Select out live particles P = P[P.status==1] return P

In [42]:

P1 = get_beam('OTR2')
P1.plot('delta_t', 'delta_energy')

P1 = get_beam('OTR2') P1.plot('delta_t', 'delta_energy')

In [43]:

P2 = get_beam('OTR2')
P2.plot('x', 'y')

P2 = get_beam('OTR2') P2.plot('x', 'y')

Get giant table of bunch stats¶

In [44]:

import pandas as pd

import pandas as pd

In [45]:

# Get list of indices
IX = tao.lat_list('*', 'ele.ix_ele')

# Stop before the dump
S_STOP = tao.ele_head('OTR2')['s']

stats = []
for ele in IX:
    d = tao.bunch_params(ele)
    # Skipl ones with no beam
    if d['charge_live'] == 0:
        continue
    
    if d['s'] > S_STOP:
        break
        
    stats.append(d)

df = pd.DataFrame(stats)#.set_index('ix_ele')
df

# Get list of indices IX = tao.lat_list('*', 'ele.ix_ele') # Stop before the dump S_STOP = tao.ele_head('OTR2')['s'] stats = [] for ele in IX: d = tao.bunch_params(ele) # Skipl ones with no beam if d['charge_live'] == 0: continue if d['s'] > S_STOP: break stats.append(d) df = pd.DataFrame(stats)#.set_index('ix_ele') df

Out[45]:

	beta_x	alpha_x	gamma_x	phi_x	eta_x	etap_x	sigma_x	sigma_p_x	emit_x	norm_emit_x	...	ix_ele	direction	species	location	s	charge_live	n_particle_tot	n_particle_live	n_particle_lost_in_ele	beam_saved
0	17.172295	-4.832192	1.417986	0.0	-0.001708	-0.000506	0.000245	0.000070	3.502335e-09	4.377142e-07	...	62	1	Electron	Downstream_End	4.614539	2.500000e-11	10000	10000	0	True
1	18.282401	-4.992310	1.417929	0.0	-0.001765	-0.000506	0.000253	0.000070	3.502475e-09	4.377317e-07	...	63	1	Electron	Downstream_End	4.727324	2.500000e-11	10000	10000	0	False
2	19.026422	-5.096805	1.417892	0.0	-0.001803	-0.000506	0.000258	0.000070	3.502567e-09	4.377431e-07	...	64	1	Electron	Downstream_End	4.800934	2.500000e-11	10000	10000	0	False
3	23.573059	-39.263591	65.440366	0.0	-0.002009	-0.003367	0.000287	0.000479	3.504147e-09	4.379407e-07	...	65	1	Electron	Downstream_End	4.908934	2.500000e-11	10000	10000	0	False
4	31.566605	-45.416435	65.374550	0.0	-0.002327	-0.003367	0.000333	0.000479	3.507675e-09	4.383816e-07	...	66	1	Electron	Downstream_End	5.003418	2.500000e-11	10000	10000	0	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
82	74.549507	-0.570622	0.017782	0.0	-0.009313	-0.000028	0.000353	0.000005	1.672918e-09	4.413671e-07	...	144	1	Electron	Downstream_End	14.089061	2.500000e-11	10000	10000	0	True
83	74.636433	-0.571975	0.017782	0.0	-0.009315	-0.000028	0.000353	0.000005	1.672918e-09	4.413671e-07	...	145	1	Electron	Downstream_End	14.165045	2.500000e-11	10000	10000	0	False
84	74.636433	-0.571975	0.017782	0.0	-0.009315	-0.000028	0.000353	0.000005	1.672918e-09	4.413671e-07	...	146	1	Electron	Downstream_End	14.165045	2.500000e-11	10000	10000	0	True
85	74.723564	-0.573328	0.017782	0.0	-0.009317	-0.000028	0.000354	0.000005	1.672918e-09	4.413670e-07	...	147	1	Electron	Downstream_End	14.241029	2.500000e-11	10000	10000	0	False
86	74.723564	-0.573328	0.017782	0.0	-0.009317	-0.000028	0.000354	0.000005	1.672918e-09	4.413670e-07	...	148	1	Electron	Downstream_End	14.241029	2.500000e-11	10000	10000	0	True

87 rows × 127 columns

In [46]:

fig, ax = plt.subplots(figsize=(12,5))
plt.plot(df['s'], df['sigma_x']*1e6, label=r'$\sigma_x$')
plt.plot(df['s'], df['sigma_y']*1e6, label=r'$\sigma_y$')
plt.xlabel('s (m)')
plt.ylabel('beam sizes (µm)')
plt.legend();

fig, ax = plt.subplots(figsize=(12,5)) plt.plot(df['s'], df['sigma_x']*1e6, label=r'$\sigma_x$') plt.plot(df['s'], df['sigma_y']*1e6, label=r'$\sigma_y$') plt.xlabel('s (m)') plt.ylabel('beam sizes (µm)') plt.legend();

In [47]:

# Get an array of where the beam is saved
s_saved = np.array(df[df['beam_saved']]['s'])

# Get an array of where the beam is saved s_saved = np.array(df[df['beam_saved']]['s'])

In [48]:

fig, ax = plt.subplots(figsize=(12,5))
plt.plot(df['s'], df['norm_emit_x']*1e6, label=r'$\epsilon_{n,x}$')
plt.plot(df['s'], df['norm_emit_y']*1e6, label=r'$\epsilon_{n,y}$')
plt.xlabel('s (m)')
plt.ylabel('Beam emittance(mm-mrad)')


# This is where the beam is saved
plt.scatter(s_saved, np.ones(len(s_saved))*0, color = 'black', marker = 'x')


plt.legend();

fig, ax = plt.subplots(figsize=(12,5)) plt.plot(df['s'], df['norm_emit_x']*1e6, label=r'$\epsilon_{n,x}$') plt.plot(df['s'], df['norm_emit_y']*1e6, label=r'$\epsilon_{n,y}$') plt.xlabel('s (m)') plt.ylabel('Beam emittance(mm-mrad)') # This is where the beam is saved plt.scatter(s_saved, np.ones(len(s_saved))*0, color = 'black', marker = 'x') plt.legend();