import logging
logger = logging.getLogger(name=__name__)
from pyrpl.attributes import *
from pyrpl import CurveDB
from pyrpl.test.test_base import TestPyrpl
[docs]class TestPidNaIq(TestPyrpl):
[docs] def setup(self):
self.extradelay = 0.6 * 8e-9 # no idea where this comes from
# shortcut
self.pyrpl.na = self.pyrpl.networkanalyzer
self.na = self.pyrpl.networkanalyzer
[docs] def teardown(self):
self.na.stop()
[docs] def test_na(self):
error_threshold = 0.03 # (relative error, dominated by phase error)
if self.r is None:
return
else:
r = self.r
extradelay = self.extradelay
# shortcuts and na configuration
na = self.pyrpl.na
for iq in [r.iq0, r.iq1, r.iq2]:
na._iq = iq
na.setup(start_freq=3000,
stop_freq=10e6,
points=101,
# I reduced points from 1001 to 101, is it normal that
# it was taking ages ? -> no, should not take more than 1
# second with rbw=1000
rbw=1000,
average_per_point=1,
trace_average=1,
amplitude=0.1, input=na.iq, output_direct='off',
acbandwidth=1000, logscale=True)
data= na.curve()
f = na.data_x
theory = np.array(f * 0 + 1.0,
dtype=np.complex)
# obsolete since na data now comes autocorrected:
# theory = na.transfer_function(f, extradelay=extradelay)
relerror = np.abs((data - theory) / theory)
maxerror = np.max(relerror)
if maxerror > error_threshold:
print(maxerror)
c = CurveDB.create(f, data, name='test_na-failed-data')
c.add_child(CurveDB.create(f, theory,
name='test_na-failed-theory'))
c.add_child(CurveDB.create(f, relerror,
name='test_na-failed-relerror'))
assert False, maxerror
[docs] def test_pid_na1(self):
# setup a pid module with a bunch of different settings and measure
# its transfer function, and compare it to the model.
error_threshold = 0.03 # (relative error)
# Let's check the transfer function of the pid module with the
# integrated NA
if self.r is None:
return
else:
r = self.r
plotdata = []
# shortcuts and na configuration
na = self.pyrpl.na
for pid in self.pyrpl.pids.all_modules:
na.setup(start_freq=1000,
stop_freq=1000e3,
# points 101->11, it was taking ages
points=11,
rbw=100,
average_per_point=1,
trace_average=1,
amplitude=0.1,
input=pid,
output_direct='off',
acbandwidth=0,
logscale=True)
# setup pid: input is the network analyzer output.
pid.input = na.iq
pid.setpoint = 0
# specify extradelay for theory. 3.6 cycles is empirical, but not
# far from what expects for NA delay (2 cycles for output, 2 for input)
extradelay = self.extradelay
# proportional gain of 1, no inputfilter
pid.p = 1.0
pid.i = 0
pid.d = 0
pid.ival = 0
pid.inputfilter = 0
data= na.curve()
f = na.data_x
plotdata.append((f, data, 'p=1'))
theory = pid.transfer_function(f, extradelay=extradelay)
relerror = np.abs((data - theory) / theory)
maxerror = np.max(relerror)
if maxerror > error_threshold:
print(maxerror)
c = CurveDB.create(f, data, name='test_na_pid-failed-data')
c.add_child(CurveDB.create(f, theory,
name='test_na_pid-failed-theory'))
c.add_child(CurveDB.create(f, relerror,
name='test_na_pid-failed-relerror'))
assert False, maxerror
[docs] def test_pid_na2(self):
# setup a pid module with a bunch of different settings and measure
# its transfer function, and compare it to the model.
error_threshold = 0.04 # (relative error)
# Let's check the transfer function of the pid module with the integrated NA
plotdata = []
# shortcuts and na configuration
na = self.pyrpl.na
for pid in self.pyrpl.pids.all_modules:
na.setup(start_freq=1000,
stop_freq=1000e3,
# 101 points, 1 av->11 points, 7 av (taking ages)
points=11,
rbw=100,
average_per_point=1,
trace_average=1,
amplitude=0.1, input=pid, output_direct='off',
acbandwidth=0, logscale=True)
# setup pid: input is the network analyzer output.
pid.input = na.iq
pid.setpoint = 0
# specify extradelay for theory. 3.6 cycles is empirical, but not
# far from what one expects for NA delay (2 cycles for output,
# 2 for input)
extradelay = self.extradelay
# proportional gain of 0.01, integral = 1 kHz
pid.p = 0.025
pid.i = 250
pid.d = 0
pid.ival = 0
pid.inputfilter = 0
data = na.curve()
f = na.data_x
plotdata.append((f, data, 'p=%.1e, i=%.1e' % (pid.p, pid.i)))
theory = pid.transfer_function(f, extradelay=extradelay)
relerror = np.abs((data - theory) / theory)
maxerror = np.max(relerror)
if maxerror > error_threshold:
print(maxerror)
c = CurveDB.create(f, data, name='test_na_pid-failed-data')
c.add_child(CurveDB.create(f, theory,
name='test_na_pid-failed-theory'))
c.add_child(CurveDB.create(f, relerror,
name='test_na_pid-failed-relerror'))
assert False, maxerror
# check that no saturation has occured
print ("Integral value after measurement: ", pid.ival)
if abs(pid.ival) >= 1.0:
print("Saturation has occured. Data not reliable.")
assert abs(pid.ival) <= 1.0, pid.ival
[docs] def test_pid_na3(self):
# setup a pid module with a bunch of different settings and measure
# its transfer function, and compare it to the model.
error_threshold = 0.1 # (relative error)
# Let's check the transfer function of the pid module with the
# integrated NA
if self.r is None:
return
else:
r = self.r
plotdata = []
# shortcuts and na configuration
na = self.pyrpl.na
for pid in self.pyrpl.pids.all_modules:
na.setup(start_freq=1000,
stop_freq=1000e3,
points=11,
rbw=100,
average_per_point=10,
trace_average=1,
amplitude=0.1, input=pid, output_direct='off',
acbandwidth=0, logscale=True)
# setup pid: input is the network analyzer output.
pid.input = na.iq
pid.setpoint = 0
# specify extradelay for theory. 3.6 cycles is empirical, but not
# far from what one expects for NA delay (2 cycles for output,
# 2 for input)
extradelay = self.extradelay
# proportional gain of 10, inputfilter: 2kHz high-pass, 10 kHz
# Lowpass, 50kHz lowpass
pid.p = 10
pid.i = 0
pid.d = 0
pid.ival = 0
pid.inputfilter = [-5e3, -10e3, 150e3, 300e3]
print("Actual inputfilter after rounding: ", pid.inputfilter)
data= na.curve()
f = na.data_x
plotdata.append((f, data, 'p=10 + filter'))
theory = pid.transfer_function(f, extradelay=extradelay)
relerror = np.abs((data - theory) / theory)
maxerror = np.max(relerror)
if maxerror > error_threshold:
print(maxerror)
c = CurveDB.create(f, data, name='test_na_pid-failed-data')
c.add_child(CurveDB.create(f, theory,
name='test_na_pid-failed-theory'))
c.add_child(CurveDB.create(f, relerror,
name='test_na_pid-failed-relerror'))
assert False, maxerror
# reset
pid.setpoint = 0
pid.p = 0
pid.i = 0
pid.d = 0
pid.ival = 0
pid.inputfilter = 0
[docs] def test_iq_na(self):
# sets up a bandpass filter with iq modules and tests its transfer
# function w.r.t. to the predicted one
extradelay = 0
error_threshold = 0.07
# Let's check the transfer function of the pid module with the
# integrated NA
if self.r is None:
return
else:
r = self.r
plotdata = []
# shortcut for na and bpf (bandpass filter)
na = self.pyrpl.networkanalyzer
for bpf in [r.iq0, r.iq2]:
plotdata = []
# setup na for measurement
na.setup(start_freq=300e3,
stop_freq=700e3,
points=51,
rbw=1000,
average_per_point=3,
trace_average=1,
acbandwidth=0,
amplitude=0.2,
input=bpf,
output_direct='off',
logscale=False)
# setup bandpass
bpf.setup(frequency=500e3, # center frequency
bandwidth=5000, # Q=100.0, # the filter quality factor # sorry, I am dropping this...
acbandwidth=500, # ac filter to remove pot. input offsets
phase=0, # nominal phase at center frequency (
# propagation phase lags not accounted for)
gain=1.0, # peak gain = +0 dB
output_direct='off',
output_signal='output_direct',
input=na.iq) # plug filter input to na output...
for phase in [-45, 0, 45, 90]:
bpf.phase = phase
# take transfer function
data = na.curve()
f = na.data_x
theory = bpf.transfer_function(f, extradelay=extradelay)
abserror = np.abs(data - theory)
maxerror = np.max(abserror)
# relerror = np.abs((data - theory) / theory)
# maxerror = np.max(relerror)
if maxerror > error_threshold:
print(maxerror)
c = CurveDB.create(f, data, name='test_iq_na-failed-data')
c.add_child(CurveDB.create(f, theory,
name='test_iq_na-failed-theory'))
c.add_child(CurveDB.create(f, abserror,
name='test_iq_na-failed-relerror'))
# c.add_child(CurveDB.create(f,relerror,name='test_iq_na-failed-abserror'))
assert False, (maxerror, phase)
