def make_runfile(output_dir, row_len=60, num_rows=60, data_rate=60,
num_rows_reported=60):
"""
Make the runfile
"""
#S.log('Making pysmurf object')
S = pysmurf.SmurfControl(cfg_file=os.path.join(os.path.dirname(__file__),
'..', 'cfg_files' , cfg_filename), smurf_cmd_mode=True, setup=False)
S.log('Making Runfile')
# 20181119 dB, modified to use the correct format runfile.
#with open(os.path.join(os.path.dirname(__file__),"runfile/runfile_template.txt")) as f:
with open(os.path.join(os.path.dirname(__file__),
"runfile/runfile.default.bicep53")) as f:
lines = f.readlines()
line_holder = []
for l in lines:
# A bunch of replacements
if "ctime=<replace>" in l:
timestamp = S.get_timestamp()
S.log('Adding ctime {}'.format(timestamp))
l = l.replace("ctime=<replace>", "ctime={}".format(timestamp))
elif "Date:<replace>" in l:
time_string = time.strftime("%a %b %d %H:%M:%S %Y",
time.localtime())
S.log('Adding date {}'.format(time_string))
l = l.replace('Date:<replace>', "Date: {}".format(time_string))
elif "row_len : <replace>" in l:
S.log("Adding row_len {}".format(row_len))
l = l.replace('row_len : <replace>',
'row_len : {}'.format(row_len))
elif "num_rows : <replace>" in l:
S.log("Adding num_rows {}".format(num_rows))
l = l.replace('num_rows : <replace>',
'num_rows : {}'.format(num_rows))
elif "num_rows_reported : <replace>" in l:
S.log("Adding num_rows_reported {}".format(num_rows_reported))
l = l.replace('num_rows_reported : <replace>',
'num_rows_reported : {}'.format(num_rows_reported))
elif "data_rate : <replace>" in l:
S.log("Adding data_rate {}".format(data_rate))
l = l.replace('data_rate : <replace>',
'data_rate : {}'.format(data_rate))
line_holder.append(l)
full_path = os.path.join(output_dir,
'smurf_status_{}.txt'.format(S.get_timestamp()))
#20181119 mod by dB to dump content of runfile, not path of runfile
#print(full_path)
for line in line_holder:
print(line)
with open(full_path, "w") as f1:
f1.writelines(line_holder)
S.log("Writing to {}".format(full_path))
sys.stdout.writelines(line_holder)
import os
import pysmurf
import matplotlib.pylab as plt
import numpy as np
import sys
config_file_path = '/data/pysmurf_cfg/'
slot = int(sys.argv[1])
epics_prefix = 'smurf_server_s%d' % slot
if slot == 5:
config_file = 'experiment_fp29_srv03_dspv3_cc02-03.cfg'
else:
assert False, "There isn't a SMuRF carrier in slot %d right now!" % slot
config_file = os.path.join(config_file_path, config_file)
S = pysmurf.SmurfControl(epics_root=epics_prefix,
cfg_file=config_file,
setup=False,
make_logfile=False)
import numpy as np
import scipy.signal as signal
import matplotlib.pyplot as plt
import os
import seaborn as sns
import glob
import pysmurf
datadir = '/data/smurf_data/20190216/1550347814/outputs'
datafile = os.path.join(datadir, '1550349384.dat')
S = pysmurf.SmurfControl(make_logfile=False,
epics_root='test_epics',
cfg_file='/usr/local/controls/Applications/'+\
'smurf/pysmurf/pysmurf/cfg_files/'+\
'experiment_fp28_smurfsrv04.cfg',
no_dir=True)
t, d, m = S.read_stream_data(datafile)
# Extract useful values
_, filename = os.path.split(datafile)
timestamp = filename.split('.')[0]
# Channels with IV curves
ivch = np.array([
16, 32, 64, 165, 171, 179, 197, 203, 213, 222, 256, 389, 395, 398, 415,
421, 427, 447
])
d = d[m[2][ivch]]
import pysmurf
import numpy as np
import matplotlib.pyplot as plt
S = pysmurf.SmurfControl(setup=False,
cfg_file='/usr/local/controls/Applications/smurf/pysmurf/pysmurf/cfg_files/experiment_kx_mapodaq.cfg',make_logfile=False)
tones = np.arange(5,16)
freq = 5193.799997711181
resp = np.zeros((len(tones), 201), dtype=complex)
for i, t in enumerate(tones):
print('Tone {}'.format(t))
f, resp[i], eta = S.eta_estimator(2, freq, t, f_sweep_half=.3,
df_sweep=.003)
fig, ax = plt.subplots(3, sharex=True, figsize=(5,8))
cm = plt.get_cmap('viridis')
for i, t in enumerate(tones):
color = cm(i/len(tones))
ax[0].plot(f, np.abs(resp[i]), color=color, label='{:02}'.format(t))
ax[1].plot(f, np.real(resp[i]), color=color)
ax[2].plot(f, np.imag(resp[i]), color=color)
ax[0].legend(loc='upper left', ncol=2)
ax[0].set_ylabel('Abs')
ax[1].set_ylabel('Real')
ax[2].set_ylabel('Imag')
ax[2].set_xlabel('Freq [MHz]')
plt.tight_layout()
import pysmurf
import matplotlib.pyplot as plt
import scipy.signal as signal
import numpy as np
import os
plt.ioff()
config_file = '/usr/local/src/pysmurf/cfg_files/experiment_fp28_docker.cfg'
S = pysmurf.SmurfControl(make_logfile=False,
epics_root='test_epics2',
cfg_file=config_file,
offline=True)
datadir = '/data/smurf_data/20190418/1555618703/outputs/'
plotdir = datadir.replace('outputs', 'plots')
#filename = '1555622545.dat'
filename = '1555628193.dat'
fn = filename.split('.')[0]
datafile = os.path.join(datadir, filename)
t, d, m = S.read_stream_data(datafile, n_samp=10000)
_, n_res = np.shape(np.where(m != -1))
d *= S.pA_per_phi0 / (2 * np.pi) # phase to pA
# Take SVD
u, s, vh = S.noise_svd(d, m)
import pysmurf S = pysmurf.SmurfControl(make_logfile=False,setup=False,epics_root='test_epics2',cfg_file='/usr/local/controls/Applications/smurf/pysmurf/pysmurf/cfg_files/experiment_fp29_smurfsrv04.cfg')
import pysmurf
import numpy as np
import matplotlib.pyplot as plt
S = pysmurf.SmurfControl(make_logfile=False, setup=True)
S.set_noise_select(3, 1, write_log=True)
S.get_noise_select(3, write_log=True)
n_sample = 2**19
n = 4
dat = np.zeros((n, n_sample), dtype=complex)
for i in np.arange(n):
dat[i] = S.read_adc_data(band, n_samples, hw_trigger=True)
time.sleep(.5)
S.set_noise_select(3, 0, write_log=True)
S.get_noise_select(3, write_log=True)
fig, ax = plt.subplots(1)
end = 250
for i in np.arange(n):
ax.plot(dat[n, :end])
plt.show()
import numpy as np
import pysmurf
import matplotlib.pyplot as plt
# datafile = '/home/common/data/cpu-b000-hp01/cryo_data/data2/20180921/1537561706/outputs/1537562122.dat'
# datafile = '/home/common/data/cpu-b000-hp01/cryo_data/data2/20180921/1537569911/outputs/1537569951.dat'
datafile = '/home/common/data/cpu-b000-hp01/cryo_data/data2/20180924/1537818448/outputs/1537818492.dat'
data_dir = '/home/common/data/cpu-b000-hp01/cryo_data/data2/20180924/1537818448/outputs/'
S = pysmurf.SmurfControl(make_logfile=False, setup=False, data_dir=data_dir)
print('Loading data')
timestamp, I, Q = S.read_stream_data(datafile)
# ch = 0
# chs = np.array([0, 16, 32, 64, 139, 147, 179, 203, 245, 288, 395, 398, 415,
# 427, 447, 459])
chs = np.array([16])
# chs = np.arange(16)
bias = np.arange(19.9, 0, -.1)
ivs = {}
ivs['bias'] = bias
for ch in chs:
phase = S.iq_to_phase(I[ch], Q[ch]) * 1.443
print('Running IV analysis')
r, rn, idx = S.analyze_slow_iv(bias,
phase,
make_plot=True,
show_plot=True,
# Extract inputs
args = parser.parse_args()
offline = args.offline
# Check for too many commands
n_cmds = (args.log is not None) + args.tes_bias + args.slow_iv + \
args.plc + args.tune + args.start_acq + args.stop_acq + \
args.last_tune + (args.use_tune is not None) + args.overbias_tes + \
args.bias_bump + args.soft_reset + args.make_runfile + args.setup + \
args.flux_ramp_off + args.all_off + args.check_lock + args.status
if n_cmds > 1:
sys.exit(0)
S = pysmurf.SmurfControl(cfg_file=os.path.join(os.path.dirname(__file__),
'..', 'cfg_files' , cfg_filename), smurf_cmd_mode=True, setup=False,
offline=offline)
if args.log is not None:
S.log(args.log)
### Tuning related commands ###
if args.last_tune:
S.log('Loading in last tuning')
S.tune(last_tune=True, make_plot=args.tune_make_plot)
if args.use_tune is not None:
S.log('Loading old tune from file: {}'.format(args.use_tune))
S.tune(tune_file = args.use_tune, make_plot=args.tune_make_plot)
if args.tune:
import matplotlib
matplotlib.use('Agg')
import pysmurf
import numpy as np
import time
import sys
## instead of takedebugdata try relaunch PyRogue, then loopFilterOutputArray, which is
## the integral tracking term with lmsEnable[1..3]=0
S = pysmurf.SmurfControl(
make_logfile=False,
setup=False,
epics_root='smurf_server_s5',
cfg_file=
'/home/cryo/docker/pysmurf/hb-devel-dspv2/pysmurf/cfg_files/experiment_fp29_smurfsrv03_noExtRef_hbOnlyBay0.cfg'
)
#######
hbInBay0 = False
bands = [2, 3]
Npts = 3
bias = None
#wait_time=.05
wait_time = 1.
#bias_low=-0.432
#bias_high=0.432
bias_low = -0.8
bias_high = 0.8
import scipy.io as sio
import os
import sys
sys.path.append('../../../../')
import pysmurf
import numpy as np
S = pysmurf.SmurfControl(epics_root='mitch_epics')
test_filedir = '/home/common/data/cpu-b000-hp01/cryo_data/data2/20180819/'+ \
'1534700282'
eta = np.ravel(
sio.loadmat(os.path.join(test_filedir,
'1534700282mitch_epics_etaOut.mat'))['etaOut'])
# for i in np.arange(len(eta))
for i in np.arange(150, 155):
resp = np.ravel(eta[i][3])
freq = np.ravel(eta[i][4])
if len(resp) > 0:
I, Q, r, resid, eta_est = S.estimate_eta_parameter(freq, resp)
print(I)
print(Q)
print(r)
S.plot_eta_estimate(freq, resp, Ic=I, Qc=Q, r=r, eta=eta_est)
import struct
import numpy as np
import sys
sys.path.append('../../../../')
import pysmurf
# dat_fullpath = '/data/cpu-b000-hp01/cryo_data/data2/20180912/1536771265/'+\
# 'outputs/1536771308.dat'
# # 'outputs/1536771308.dat'
dat_fullpath = '/home/common/data/cpu-b000-hp01/cryo_data/data2/20180912/1536762729.dat'
# with open(dat_fullpath, mode='rb') as file: # b is important -> binary
# fileContent = file.read()
# n = int(len(fileContent)/4)
# print(n)
# x = np.zeros(n, dtype=int)
# for i in np.arange(n):
# # print(fileContent[i*8:(i+1)*8])
# # print(int.from_bytes(fileContent[i*8:(i+1)*8], byteorder='little'))
# x[i] = int.from_bytes(fileContent[i*4:(i+1)*4], byteorder='little')
S = pysmurf.SmurfControl()
S.initialize(cfg_file='/home/cryo/pysmurf/cfg_files/experiment_fp28.cfg',
make_logfile=False,
output_dir_only=True)
I, Q = S.read_stream_data(dat_fullpath, 0)
import pysmurf
import numpy as np
import time
import sys
## instead of takedebugdata try relaunch PyRogue, then loopFilterOutputArray, which is
## the integral tracking term with lmsEnable[1..3]=0
S = pysmurf.SmurfControl(make_logfile=False,setup=False,epics_root='smurf_server',cfg_file='/data/pysmurf_cfg/experiment_pc002_smurfsrv08_noExtRef.cfg')
#######
bias=None
wait_time=.0
#bias_low=-0.432
#bias_high=0.432
bias_low=-0.45
bias_high=0.45
bias_step=.0015
quiet=True
if bias is None:
bias = np.arange(bias_low, bias_high, bias_step)
S.log('Staring to flux ramp.', S.LOG_USER)
sys.stdout.write('\rSetting flux ramp bias low at {:4.3f} V\033[K'.format(bias_low))
S.set_fixed_flux_ramp_bias(bias_low,debug=True,do_config=True)
try:
while True:
for b in bias:
import numpy as np
import pysmurf
import matplotlib.pyplot as plt
# datafile = '/home/common/data/cpu-b000-hp01/cryo_data/data2/20180921/1537561706/outputs/1537562122.dat'
# datafile = '/home/common/data/cpu-b000-hp01/cryo_data/data2/20180921/1537569911/outputs/1537569951.dat'
datafile = '/home/common/data/cpu-b000-hp01/cryo_data/data2/20180924/1537818448/outputs/1537818492.dat'
data_dir = '/home/common/data/cpu-b000-hp01/cryo_data/data2/20180924/1537818448/outputs/'
S = pysmurf.SmurfControl(make_logfile=False, output_dir_only=True,
data_dir=data_dir)
print('Loading data')
timestamp, I, Q = S.read_stream_data(datafile)
# ch = 0
# chs = np.array([0, 16, 32, 64, 139, 147, 179, 203, 245, 288, 395, 398, 415,
# 427, 447, 459])
chs = np.array([16])
# chs = np.arange(16)
bias = np.arange(19.9, 0, -.1)
ivs = {}
ivs['bias'] = bias
for ch in chs:
phase = S.iq_to_phase(I[ch], Q[ch]) * 1.443
print('Running IV analysis')
r, rn = S.analyze_slow_iv(bias, phase, make_plot=True,
show_plot=True, save_plot=True, band=3, channel=ch,
import sys
sys.path.append('/home/cryo/')
import pysmurf
import time
epics_root = 'dev_epics'
sd = epics_root + ':AMCc:FpgaTopLevel:AppTop:AppCore:StreamReg:StreamData[{}]'
S = pysmurf.SmurfControl(setup=False,
epics_root=epics_root,
make_logfile=False)
ch = 0
step_size = 2**6
a = -2**15
S._caput(sd.format(ch), 0, write_log=True)
while True:
write_log = False
if a % (step_size * 100) == 0:
write_log = True
S._caput(sd.format(ch), a, write_log=write_log)
a += step_size
time.sleep(.02)
if a > 2**15:
a = -2**15
import numpy as np
import pysmurf
import glob
import os
filedir = '/home/common/data/cpu-b000-hp01/cryo_data/data2/20180926/'+\
'1538008791/outputs'
# filenames = ['1537924144.dat', '1537924208.dat']
bias = np.arange(6, 3 - .5, -.5)
filenames = np.sort(glob.glob(os.path.join(filedir, "*.dat")))
filenames = filenames[-len(bias):]
S = pysmurf.SmurfControl(setup=False, make_logfile=False)
# bias = np.array([8, 7.8])
datafile = np.array([os.path.join(filedir, x) for x in filenames])
print(bias)
print(datafile)
S.analyze_noise_vs_bias(
bias,
datafile,
channel=np.array([0, 16, 32, 64, 139, 147, 179, 203, 395, 415, 427, 459]),
band=3,
fs=4.0E3)
