import logging
logger = logging.getLogger(name=__name__)
import numpy as np
from time import sleep
from qtpy import QtCore, QtWidgets
from pyrpl.test.test_base import TestPyrpl
from pyrpl import APP
[docs]class TestClass(TestPyrpl):
[docs] def teardown(self):
""" make 100% sure that specan has stopped """
self.pyrpl.spectrumanalyzer.stop()
[docs] def test_specan_stopped_at_startup(self):
"""
This was so hard to detect, I am making a unit test
"""
# this test is not efficient as nothing guarantees that it will be the first test that is executed
assert(self.pyrpl.spectrumanalyzer.running_state=='stopped')
[docs] def test_no_write_in_config(self):
"""
Make sure the spec an isn't continuously writing to config file,
even in running mode.
:return:
"""
self.pyrpl.spectrumanalyzer.setup_attributes = dict(span=1e5,
input="out1",
running_state='running_continuous')
old = self.pyrpl.c._save_counter
for i in range(10):
sleep(0.01)
APP.processEvents()
new = self.pyrpl.c._save_counter
self.pyrpl.spectrumanalyzer.stop()
assert (old == new), (old, new)
[docs] def test_flatness_baseband(self):
for span in [5e4, 1e5, 5e5, 1e6, 2e6]:
sa = self.pyrpl.spectrumanalyzer
sa.setup(baseband=True,
center=0,
window='flattop',
span=span,
input1_baseband="asg0",
running_state='stopped')
asg = self.pyrpl.rp.asg0
asg.setup(frequency=1e5,
amplitude=1.0,
trigger_source='immediately',
offset=0,
waveform='sin')
freqs = np.linspace(sa.rbw*3, sa.span/2-sa.rbw*3, 11)
points = []
for freq in freqs:
sa._logger.info("Testing flatness for span %f and frequency "
"freq %f...", span, freq)
asg.frequency = freq
curve = self.pyrpl.spectrumanalyzer.curve()[0]
assert(abs(sa.frequencies[np.argmax(curve)] - freq) < sa.rbw), \
(sa.frequencies[np.argmax(curve)], freq, sa.rbw)
points.append(max(curve))
assert abs(max(curve) - asg.amplitude**2) < 0.01, max(curve)
assert abs(max(sa.data_to_unit(curve,
"Vrms^2/Hz",
sa.rbw)*sa.rbw) -
(asg.amplitude**2)/2)<0.01, max(curve)
[docs] def test_white_noise_flatness(self):
"""
Make sure a white noise results in a flat spectrum, with a PSD equal to
<V^2> when integrated from 0 Hz to Nyquist frequency. To make sure
no aliasing problem occurs, a narrowbandpass filter is used.
The test cannot be perfomed for the largest bandwidth because then the
transfer function correction for the scope decimation should not be
applied for internal signals (however, the sinc correction is
applied in practice).
"""
self.asg = self.pyrpl.rp.asg0
# 1. Test flatness for small spans with a bandpass filter to avoid
# aliasing
self.asg = self.pyrpl.rp.asg0
self.asg.setup(amplitude=0.4,
waveform='noise',
trigger_source='immediately')
self.iq = self.pyrpl.rp.iq0
self.iq.setup(input=self.asg,
acbandwidth=0,
gain=1.0,
bandwidth=5e5,
frequency=1e5,
output_signal='output_direct')
self.sa = self.pyrpl.spectrumanalyzer
self.sa.setup(input1_baseband=self.iq,
span=10e6,
trace_average=50)
for freq in np.linspace(self.sa.span/5, self.sa.span/4, 5):
self.iq.frequency = freq # set the bandpass filter
in1, in2, cre, cim = self.sa.single()
# average neighbouring points
in1_av = in1[:-(len(in1) % 1000)].reshape(len(in1) // 1000,
1000).mean(
axis=1)
var_spectrum = max(self.sa.data_to_unit(in1_av,
'Vrms^2/Hz',
self.sa.rbw))*65e6
assert abs(var_spectrum -
self.asg.amplitude**2)/self.asg.amplitude**2<0.1, \
(var_spectrum, self.asg.amplitude**2)
[docs] def test_iq_filter_white_noise(self):
"""
Measure the transfer function of an iq filter by measuring the
cross-spectrum between white-noise input and output
"""
self.asg = self.pyrpl.rp.asg0
self.asg.setup(amplitude=0.4,
waveform='noise',
trigger_source='immediately')
self.iq = self.pyrpl.rp.iq0
# for some reason, the theoretical transfer function calculated with
# the setting below seems wrong...
self.iq.setup(input=self.asg,
acbandwidth=500,
gain=1.0,
bandwidth=5e4,
frequency=1e6,
output_signal='output_direct')
self.iq.setup(frequency=10000e3, # center frequency
bandwidth=300000,
amplitude=0,
# 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=self.asg) # plug filter input to na output...
self.sa = self.pyrpl.spectrumanalyzer
self.sa.setup(input1_baseband=self.asg,
input2_baseband=self.iq,
span=125e6,
trace_average=50)
in1, in2, c_re, c_im = self.sa.single()
cross = c_re + 1j*c_im
exp = cross/in1
theory = self.iq.transfer_function(self.sa.frequencies)
#from pylab import plot, show
assert abs(exp - theory)[1:].max()< 0.05
[docs] def test_flatness_iqmode(self):
return # to be tested in next release
for span in [5e4, 1e5, 5e5, 1e6, 2e6]:
self.pyrpl.spectrumanalyzer.setup(baseband=False,
center=1e5,
span=span,
input="asg0",
running_state='stopped')
asg = self.pyrpl.rp.asg0
asg.setup(frequency=1e5,
amplitude=1,
trigger_source='immediately',
offset=0,
waveform='sin')
freqs = np.linspace(1e5, 9e5)
points = []
for freq in freqs:
asg.frequency = freq
curve = self.pyrpl.spectrumanalyzer.curve()
points.append(max(curve))
assert abs(max(curve) - 1) < 0.01, max(curve)
[docs] def test_save_curve(self):
sa = self.pyrpl.spectrumanalyzer
sa.setup(baseband=True,
center=0,
window='flattop',
span=1e6,
input1_baseband="asg0",
running_state='stopped')
sa.single()
curves = sa.save_curve()
assert (curves[0].data[1]==sa.data_avg[0]).all()
self.curves += curves # curves will be deleted by teardownAll
