def test_dbstore(tmp_db_store, data_files, random_samples):
from wwb_scanner.file_handlers import BaseImporter
from wwb_scanner.scan_objects import Spectrum
from wwb_scanner.utils.dbstore import db_store
spec = {}
for fkey, d in data_files.items():
for skey, fn in d.items():
spectrum = BaseImporter.import_file(fn)
spectrum.save_to_dbstore()
assert spectrum.eid is not None
spec[spectrum.eid] = spectrum
rs = 1.024e6
nsamp = 256
step_size = 0.5e6
freq_range = [572e6, 636e6]
spectrum = Spectrum()
fc = freq_range[0]
while True:
freqs, sig, Pxx = random_samples(n=nsamp, rs=rs, fc=fc)
spectrum.add_sample_set(frequency=freqs, iq=Pxx)
if spectrum.sample_data['frequency'].max() >= freq_range[1] / 1e6:
break
fc += step_size
spectrum.save_to_dbstore()
assert spectrum.eid is not None
spec[spectrum.eid] = spectrum
for eid, spectrum in spec.items():
db_spectrum = Spectrum.from_dbstore(eid=spectrum.eid)
for attr in Spectrum._serialize_attrs:
assert getattr(spectrum, attr) == getattr(db_spectrum, attr)
assert np.array_equal(spectrum.sample_data, db_spectrum.sample_data)
db_data = db_store.get_all_scans()
assert set(db_data.keys()) == set(spec.keys())
def on_sweep_processed(self, **kwargs):
freqs = kwargs.get('frequencies')
powers = kwargs.get('powers')
fc = kwargs.get('sample_set').center_frequency / 1e6
fc = float(fc)
spectrum = self.current_spectrum
new_spectrum = False
if spectrum is None or fc != self.scan_controls.current_freq:
spectrum = Spectrum()
new_spectrum = True
try:
self.scan_controls.current_freq = fc
except:
print(type(fc), repr(fc))
self.cancel_scan()
raise
spectrum.add_sample_set(frequency=freqs, iq=powers, center_frequency=fc)
if new_spectrum:
self.current_spectrum = spectrum
elif self.scan_controls.live_view_visible:
sg = self.scan_controls.live_spectrum_graph
plot = sg.spectrum_plot_container.children[0]
plot.update_data()
def test_io(tmpdir, random_samples):
from wwb_scanner.scan_objects import Spectrum
rs = 1.024e6
nsamp = 256
step_size = 0.5e6
freq_range = [572e6, 636e6]
def build_data(fc):
freqs, sig, Pxx = random_samples(n=nsamp, rs=rs, fc=fc)
return freqs, Pxx
spectrum = Spectrum(step_size=step_size)
spectrum.color['r'] = 1.
fc = freq_range[0]
while True:
freqs, ff = build_data(fc)
spectrum.add_sample_set(frequency=freqs, iq=ff)
if spectrum.sample_data['frequency'].max() >= freq_range[1] / 1e6:
break
fc += step_size
dB = np.around(spectrum.sample_data['dbFS'], decimals=1)
p = tmpdir.mkdir('test_io')
for ext in ['csv', 'sdb2']:
fn = p.join('foo.{}'.format(ext))
spectrum.export_to_file(filename=str(fn))
imp_spectrum = Spectrum.import_from_file(str(fn))
assert np.allclose(spectrum.sample_data['frequency'],
imp_spectrum.sample_data['frequency'])
imp_dB = np.around(imp_spectrum.sample_data['dbFS'], decimals=1)
assert np.allclose(dB, imp_dB)
def test_io(tmpdir, random_samples):
from wwb_scanner.scan_objects import Spectrum
rs = 1.024e6
nsamp = 256
step_size = 0.5e6
freq_range = [572e6, 636e6]
def build_data(fc):
freqs, sig, Pxx = random_samples(n=nsamp, rs=rs, fc=fc)
return freqs, Pxx
spectrum = Spectrum(step_size=step_size)
spectrum.color['r'] = 1.
fc = freq_range[0]
while True:
freqs, ff = build_data(fc)
spectrum.add_sample_set(frequency=freqs, iq=ff)
if spectrum.sample_data['frequency'].max() >= freq_range[1] / 1e6:
break
fc += step_size
dB = np.around(spectrum.sample_data['dbFS'], decimals=1)
p = tmpdir.mkdir('test_io')
for ext in ['csv', 'sdb2']:
fn = p.join('foo.{}'.format(ext))
spectrum.export_to_file(filename=str(fn))
imp_spectrum = Spectrum.import_from_file(str(fn))
assert np.allclose(spectrum.sample_data['frequency'], imp_spectrum.sample_data['frequency'])
imp_dB = np.around(imp_spectrum.sample_data['dbFS'], decimals=1)
assert np.allclose(dB, imp_dB)
def test_add_sample_set(random_samples):
from wwb_scanner.scan_objects import Spectrum
rs = 2.048e6
def build_data(fc):
freqs, sig, Pxx = random_samples(n=256, rs=rs, fc=fc)
return freqs, Pxx
fc = 600e6
spectrum = Spectrum()
freqs, ff = build_data(fc)
spectrum.add_sample_set(frequency=freqs, iq=ff, center_frequency=fc, force_lower_freq=True)
assert np.array_equal(spectrum.sample_data['frequency'], freqs)
assert np.array_equal(spectrum.sample_data['iq'], ff)
assert np.array_equal(spectrum.sample_data['magnitude'], np.abs(ff))
assert np.array_equal(spectrum.sample_data['dbFS'], 10 * np.log10(np.abs(ff)))
fc += 1e6
freqs2, ff2 = build_data(fc)
assert np.any(np.in1d(freqs, freqs2))
print('in1d: ', np.nonzero(np.in1d(spectrum.sample_data['frequency'], freqs2)))
print('spectrum size: ', spectrum.sample_data['frequency'].size)
spectrum.add_sample_set(frequency=freqs2, iq=ff2, center_frequency=fc, force_lower_freq=True)
print('spectrum size: ', spectrum.sample_data['frequency'].size)
assert np.unique(spectrum.sample_data['frequency']).size == spectrum.sample_data['frequency'].size
for freq, val in zip(freqs2, ff2):
sample = spectrum.samples[freq]
ix = sample.spectrum_index
iq = spectrum.sample_data['iq'][ix]
m = spectrum.sample_data['magnitude'][ix]
dB = spectrum.sample_data['dbFS'][ix]
assert iq == val == sample.iq
assert m == np.abs(val) == sample.magnitude
assert dB == 10 * np.log10(np.abs(val)) == sample.dbFS
fc = 800e6
freqs3, ff3 = build_data(fc)
assert not np.any(np.in1d(spectrum.sample_data['frequency'], freqs3))
spectrum.add_sample_set(frequency=freqs3, iq=ff3, center_frequency=fc, force_lower_freq=True)
print('spectrum size: ', spectrum.sample_data['frequency'].size)
assert np.unique(spectrum.sample_data['frequency']).size == spectrum.sample_data['frequency'].size
assert spectrum.sample_data['frequency'].size == spectrum.sample_data['iq'].size
assert spectrum.sample_data['frequency'].size == spectrum.sample_data['magnitude'].size
for freq, val in zip(freqs3, ff3):
sample = spectrum.samples[freq]
ix = sample.spectrum_index
iq = spectrum.sample_data['iq'][ix]
m = spectrum.sample_data['magnitude'][ix]
dB = spectrum.sample_data['dbFS'][ix]
assert iq == val == sample.iq
assert m == np.abs(val) == sample.magnitude
assert dB == 10 * np.log10(np.abs(val)) == sample.dbFS
def test_add_sample_set(random_samples):
from wwb_scanner.scan_objects import Spectrum
rs = 2.048e6
def build_data(fc):
freqs, sig, Pxx = random_samples(n=256, rs=rs, fc=fc)
return freqs, Pxx
fc = 600e6
spectrum = Spectrum()
freqs, ff = build_data(fc)
spectrum.add_sample_set(frequency=freqs,
iq=ff,
center_frequency=fc,
force_lower_freq=True)
assert np.array_equal(spectrum.sample_data['frequency'], freqs)
assert np.array_equal(spectrum.sample_data['iq'], ff)
assert np.array_equal(spectrum.sample_data['magnitude'], np.abs(ff))
assert np.array_equal(spectrum.sample_data['dbFS'],
10 * np.log10(np.abs(ff)))
fc += 1e6
freqs2, ff2 = build_data(fc)
assert np.any(np.in1d(freqs, freqs2))
print('in1d: ',
np.nonzero(np.in1d(spectrum.sample_data['frequency'], freqs2)))
print('spectrum size: ', spectrum.sample_data['frequency'].size)
spectrum.add_sample_set(frequency=freqs2,
iq=ff2,
center_frequency=fc,
force_lower_freq=True)
print('spectrum size: ', spectrum.sample_data['frequency'].size)
assert np.unique(spectrum.sample_data['frequency']
).size == spectrum.sample_data['frequency'].size
for freq, val in zip(freqs2, ff2):
sample = spectrum.samples[freq]
ix = sample.spectrum_index
iq = spectrum.sample_data['iq'][ix]
m = spectrum.sample_data['magnitude'][ix]
dB = spectrum.sample_data['dbFS'][ix]
assert iq == val == sample.iq
assert m == np.abs(val) == sample.magnitude
assert dB == 10 * np.log10(np.abs(val)) == sample.dbFS
fc = 800e6
freqs3, ff3 = build_data(fc)
assert not np.any(np.in1d(spectrum.sample_data['frequency'], freqs3))
spectrum.add_sample_set(frequency=freqs3,
iq=ff3,
center_frequency=fc,
force_lower_freq=True)
print('spectrum size: ', spectrum.sample_data['frequency'].size)
assert np.unique(spectrum.sample_data['frequency']
).size == spectrum.sample_data['frequency'].size
assert spectrum.sample_data['frequency'].size == spectrum.sample_data[
'iq'].size
assert spectrum.sample_data['frequency'].size == spectrum.sample_data[
'magnitude'].size
for freq, val in zip(freqs3, ff3):
sample = spectrum.samples[freq]
ix = sample.spectrum_index
iq = spectrum.sample_data['iq'][ix]
m = spectrum.sample_data['magnitude'][ix]
dB = spectrum.sample_data['dbFS'][ix]
assert iq == val == sample.iq
assert m == np.abs(val) == sample.magnitude
assert dB == 10 * np.log10(np.abs(val)) == sample.dbFS
def test_add_sample_set(random_samples):
from wwb_scanner.scan_objects import Spectrum
from wwb_scanner.scan_objects import SampleArray
rs = 2.000e6
def build_data(fc):
freqs, sig, Pxx = random_samples(n=256, rs=rs, fc=fc)
return freqs, Pxx
def build_struct_data(freqs, ff):
data = np.zeros(freqs.size, dtype=SampleArray.dtype)
data['frequency'] = freqs
data['iq'] = ff
data['magnitude'] = np.abs(ff)
data['dbFS'] = 10 * np.log10(np.abs(ff))
return data
def get_overlap_arrays(data1, data2):
freqs1 = data1['frequency']
freqs2 = data2['frequency']
overlap_data1 = data1[np.flatnonzero(np.in1d(freqs1, freqs2))]
overlap_data2 = data2[np.flatnonzero(np.in1d(freqs2, freqs1))]
avg_data = np.zeros(overlap_data1.size, dtype=overlap_data1.dtype)
avg_data['frequency'] = overlap_data1['frequency']
for key in ['iq', 'magnitude', 'dbFS']:
avg_data[key] = np.mean([overlap_data1[key], overlap_data2[key]], axis=0)
non_overlap_data2 = data2[np.flatnonzero(np.in1d(freqs2, freqs1, invert=True))]
assert np.array_equal(overlap_data1['frequency'], overlap_data2['frequency'])
return avg_data, non_overlap_data2
fc = 600e6
spectrum = Spectrum()
freqs, ff = build_data(fc)
data1 = build_struct_data(freqs, ff)
spectrum.add_sample_set(frequency=freqs, iq=ff, center_frequency=fc, force_lower_freq=True)
assert np.array_equal(spectrum.sample_data['frequency'], freqs)
assert np.array_equal(spectrum.sample_data['iq'], ff)
assert np.array_equal(spectrum.sample_data['magnitude'], np.abs(ff))
assert np.array_equal(spectrum.sample_data['dbFS'], 10 * np.log10(np.abs(ff)))
fc += 1e6
freqs2, ff2 = build_data(fc)
assert np.any(np.in1d(freqs, freqs2))
print('in1d: ', np.nonzero(np.in1d(spectrum.sample_data['frequency'], freqs2)))
print('spectrum size: ', spectrum.sample_data['frequency'].size)
spectrum.add_sample_set(frequency=freqs2, iq=ff2, center_frequency=fc, force_lower_freq=True)
print('spectrum size: ', spectrum.sample_data['frequency'].size)
assert np.unique(spectrum.sample_data['frequency']).size == spectrum.sample_data['frequency'].size
data2 = build_struct_data(freqs2, ff2)
avg_data, non_overlap_data2 = get_overlap_arrays(data1, data2)
assert data2.size == avg_data.size + non_overlap_data2.size
assert spectrum.sample_data.size == data1.size + non_overlap_data2.size
for freq in freqs2:
mask = np.isin([freq], avg_data['frequency'])
if np.any(mask):
val = avg_data[np.searchsorted(avg_data['frequency'], freq)]
else:
val = non_overlap_data2[np.searchsorted(non_overlap_data2['frequency'], freq)]
sample = spectrum.samples[freq]
ix = sample.spectrum_index
iq = spectrum.sample_data['iq'][ix]
m = spectrum.sample_data['magnitude'][ix]
dB = spectrum.sample_data['dbFS'][ix]
assert iq == val['iq'] == sample.iq
assert m == val['magnitude'] == sample.magnitude
assert dB == val['dbFS'] == sample.dbFS
fc = 800e6
freqs3, ff3 = build_data(fc)
assert not np.any(np.in1d(spectrum.sample_data['frequency'], freqs3))
spectrum.add_sample_set(frequency=freqs3, iq=ff3, center_frequency=fc, force_lower_freq=True)
print('spectrum size: ', spectrum.sample_data['frequency'].size)
assert np.unique(spectrum.sample_data['frequency']).size == spectrum.sample_data['frequency'].size
assert spectrum.sample_data['frequency'].size == spectrum.sample_data['iq'].size
assert spectrum.sample_data['frequency'].size == spectrum.sample_data['magnitude'].size
data3 = build_struct_data(freqs3, ff3)
avg_data2, non_overlap_data3 = get_overlap_arrays(data2, data3)
assert data3.size == avg_data2.size + non_overlap_data3.size
assert spectrum.sample_data.size == data1.size + non_overlap_data2.size + non_overlap_data3.size
for freq in freqs3:
mask = np.isin([freq], avg_data2['frequency'])
if np.any(mask):
val = avg_data2[np.searchsorted(avg_data2['frequency'], freq)]
else:
val = non_overlap_data3[np.searchsorted(non_overlap_data3['frequency'], freq)]
sample = spectrum.samples[freq]
ix = sample.spectrum_index
iq = spectrum.sample_data['iq'][ix]
m = spectrum.sample_data['magnitude'][ix]
dB = spectrum.sample_data['dbFS'][ix]
assert iq == val['iq'] == sample.iq
assert m == val['magnitude'] == sample.magnitude
assert dB == val['dbFS'] == sample.dbFS