def test_invalid_type_attribute(self):
with pytest.raises(ValueError):
cs_test = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
cs_test.type = 'invalid_type'
assert AveragedCrossspectrum._make_crossspectrum(cs_test,
self.lc1,
self.lc2)
def test_invalid_type_attribute_with_multiple_lcs(self):
acs_test = AveragedCrossspectrum([self.lc1, self.lc2], [self.lc2, self.lc1], segment_size=1)
acs_test.type = "invalid_type"
with pytest.raises(ValueError):
assert AveragedCrossspectrum._make_crossspectrum(
acs_test, lc1=[self.lc1, self.lc2], lc2=[self.lc2, self.lc1]
)
def test_high_coherence(self):
t = np.arange(1280)
a = np.random.poisson(100, len(t))
lc = Lightcurve(t, a)
c = AveragedCrossspectrum(lc, lc, 128)
coh, _ = c.coherence()
np.testing.assert_almost_equal(np.mean(coh).real, 1.0)
def test_timelag(self):
from ..simulator.simulator import Simulator
dt = 0.1
simulator = Simulator(dt, 10000, rms=0.4, mean=200)
test_lc1 = simulator.simulate(2)
test_lc2 = Lightcurve(test_lc1.time,
np.array(np.roll(test_lc1.counts, 2)),
err_dist=test_lc1.err_dist,
dt=dt)
cs = AveragedCrossspectrum(test_lc1, test_lc2, segment_size=10,
norm="none")
time_lag, time_lag_err = cs.time_lag()
assert np.all(np.abs(time_lag[:10] - 0.1) < 3 * time_lag_err[:10])
def setup_class(self):
tstart = 0.0
tend = 1.0
dt = np.longdouble(0.0001)
time = np.arange(tstart + 0.5*dt, tend + 0.5*dt, dt)
counts1 = np.random.poisson(0.01, size=time.shape[0])
counts2 = np.random.negative_binomial(1, 0.09, size=time.shape[0])
self.lc1 = Lightcurve(time, counts1, gti=[[tstart, tend]], dt=dt)
self.lc2 = Lightcurve(time, counts2, gti=[[tstart, tend]], dt=dt)
self.cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
def setup_class(self):
tstart = 0.0
tend = 1.0
dt = 0.0001
time = np.linspace(tstart, tend, int((tend - tstart)/dt))
counts1 = np.random.poisson(0.01, size=time.shape[0])
counts2 = np.random.negative_binomial(1, 0.09, size=time.shape[0])
self.lc1 = Lightcurve(time, counts1)
self.lc2 = Lightcurve(time, counts2)
self.cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
def test_with_iterable_of_lightcurves(self):
def iter_lc(lc, n):
"Generator of n parts of lc."
t0 = int(len(lc) / n)
t = t0
i = 0
while (True):
lc_seg = lc[i:t]
yield lc_seg
if t + t0 > len(lc):
break
else:
i, t = t, t + t0
cs = AveragedCrossspectrum(iter_lc(self.lc1, 1),
iter_lc(self.lc2, 1),
segment_size=1)
def test_different_tseg(self):
time2 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
counts2_test = np.random.poisson(1000, size=len(time2))
test_lc2 = Lightcurve(time2, counts2_test)
time1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
counts1_test = np.random.poisson(1000, size=len(time1))
test_lc1 = Lightcurve(time1, counts1_test)
assert test_lc2.dt == test_lc1.dt
assert test_lc2.tseg != test_lc1.tseg
with pytest.warns(UserWarning) as record:
AveragedCrossspectrum(test_lc1, test_lc2, segment_size=5)
assert np.any(["same tseg" in r.message.args[0]
for r in record])
def test_with_zero_counts(self):
nbins = 100
x = np.linspace(0, 10, nbins)
ycounts1 = np.random.normal(loc=10, scale=0.5, size=int(0.4 * nbins))
ycounts2 = np.random.normal(loc=10, scale=0.5, size=int(0.4 * nbins))
yzero = np.zeros(int(0.6 * nbins))
y1 = np.hstack([ycounts1, yzero])
y2 = np.hstack([ycounts2, yzero])
lc1 = Lightcurve(x, y1)
lc2 = Lightcurve(x, y2)
with pytest.warns(UserWarning) as record:
acs = AveragedCrossspectrum(lc1,
lc2,
segment_size=5.0,
norm="leahy")
assert acs.m == 1
assert np.any(
["No counts in interval" in r.message.args[0] for r in record])
class TestAveragedCrossspectrumEvents(object):
def setup_class(self):
tstart = 0.0
tend = 1.0
self.dt = np.longdouble(0.0001)
times1 = np.sort(np.random.uniform(tstart, tend, 1000))
times2 = np.sort(np.random.uniform(tstart, tend, 1000))
gti = np.array([[tstart, tend]])
self.events1 = EventList(times1, gti=gti)
self.events2 = EventList(times2, gti=gti)
self.cs = Crossspectrum(self.events1, self.events2, dt=self.dt)
self.acs = AveragedCrossspectrum(self.events1,
self.events2,
segment_size=1,
dt=self.dt)
self.lc1, self.lc2 = self.events1, self.events2
def test_it_works_with_events(self):
lc1 = self.events1.to_lc(self.dt)
lc2 = self.events2.to_lc(self.dt)
lccs = Crossspectrum(lc1, lc2)
assert np.allclose(lccs.power, self.cs.power)
def test_no_segment_size(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, dt=self.dt)
def test_init_with_norm_not_str(self):
with pytest.raises(TypeError):
cs = AveragedCrossspectrum(self.lc1,
self.lc2,
segment_size=1,
norm=1,
dt=self.dt)
def test_init_with_invalid_norm(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1,
self.lc2,
segment_size=1,
norm='frabs',
dt=self.dt)
def test_init_with_inifite_segment_size(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1,
self.lc2,
segment_size=np.inf,
dt=self.dt)
def test_coherence(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
coh = self.acs.coherence()
assert len(coh[0]) == 4999
assert len(coh[1]) == 4999
assert issubclass(w[-1].category, UserWarning)
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1,
self.lc2,
segment_size=1,
norm="wrong",
dt=self.dt)
def test_failure_when_power_type_not_recognized(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1,
self.lc2,
segment_size=1,
power_type="wrong",
dt=self.dt)
def test_rebin(self):
new_cs = self.acs.rebin(df=1.5)
assert new_cs.df == 1.5
new_cs.time_lag()
def test_rebin_factor(self):
new_cs = self.acs.rebin(f=1.5)
assert new_cs.df == self.acs.df * 1.5
new_cs.time_lag()
def test_rebin_log(self):
# For now, just verify that it doesn't crash
new_cs = self.acs.rebin_log(f=0.1)
assert type(new_cs) == type(self.acs)
new_cs.time_lag()
def test_rebin_log_returns_complex_values(self):
# For now, just verify that it doesn't crash
new_cs = self.acs.rebin_log(f=0.1)
assert np.iscomplexobj(new_cs.power[0])
def test_rebin_log_returns_complex_errors(self):
# For now, just verify that it doesn't crash
new_cs = self.acs.rebin_log(f=0.1)
assert np.iscomplexobj(new_cs.power_err[0])
def createAvgCspec(lc1, lc2, seg):
AveragedCrossspectrum(lc1, lc2, seg, silent=True)
def callerFunction(bench_msg):
# bench_msg = input("Enter the changes made, if none put a '-': ")
func_dict = {
'Lightcurve': [
'Time_MakeLightcurve', 'Mem_MakeLightcurve', 'Time_InitNoParam',
'Mem_InitNoParam', 'Time_InitParam', 'Mem_InitParam',
'Time_ChangeMJDREF', 'Mem_ChangeMJDREF', 'Time_Rebin_Sum',
'Mem_Rebin_Sum', 'Time_Rebin_Mean_Avg', 'Mem_Rebin_Mean_Avg',
'Time_AddLC', 'Mem_AddLC', 'Time_SubLC', 'MemSubLC', 'Time_EqLC',
'Mem_EqLC', 'Time_NegLC', 'Mem_NegLC', 'Time_Trunc_Index',
'Mem_Trunc_Index', 'Time_Trunc_Time', 'Mem_Trunc_Time',
'Time_SplitLC', 'Mem_SplitLC', 'Time_Sort_Time', 'Mem_Sort_Time',
'Time_Sort_Counts', 'Mem_Sort_Counts', 'Time_Analyze_Chunks',
'Mem_Analyze_Chunks', 'Time_Est_Chunk_Len', 'Mem_Est_Chunk_Len',
'Time_JoinLC', 'Mem_JoinLC'
],
'Crossspectrum': [
'Time_Init', 'Mem_Init', 'Time_Rebin_Linear', 'Mem_Rebin_Linear',
'Time_Coherence', 'Mem_Coherence', 'Time_Tlag', 'Mem_Tlag'
],
'AveragedCrossspectrum': [
'Time_Init', 'Mem_Init', 'Time_Coher', 'Mem_Coher', 'Time_Tlag',
'Mem_Tlag'
],
'Powerspectrum': [
'Time_Init', 'Mem_Init', 'Time_Rebin', 'Mem_Rebin', 'Time_RMS',
'Mem_RMS', 'Time_Class_Sign', 'Mem_Class_Sign'
],
'AveragedPowerspectrum': ['Time_Init', 'Mem_Init']
}
wall_time = [[
f'{datetime.utcfromtimestamp(int(time.time())).strftime("%Y-%m-%d %H:%M:%S")}',
f'{bench_msg}',
] for i in range(int(sum([len(x) for x in func_dict.values()]) / 2))]
mem_use = [[
f'{datetime.utcfromtimestamp(int(time.time())).strftime("%Y-%m-%d %H:%M:%S")}',
f'{bench_msg}',
] for i in range(int(sum([len(x) for x in func_dict.values()]) / 2))]
for size in [10**i for i in range(5, 7)]:
num_func = 0
times = np.arange(size)
counts = np.random.rand(size) * 100
time1, mem1 = benchCode(makeLCFunc, times)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(createLc, times, counts)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(createLcP, times, counts)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
lc = Lightcurve(times, counts, dt=1.0, skip_checks=True)
time1, mem1 = benchCode(lcMJD, lc)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(rebinSum, lc, 2.0)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(rebinMean, lc, 2.0)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
lc_other = Lightcurve(times,
counts * np.random.rand(size),
dt=1.0,
skip_checks=True)
time1, mem1 = benchCode(addLC, lc, lc_other)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(subLC, lc, lc_other)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(eqLC, lc, lc_other)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
del lc_other
time1, mem1 = benchCode(negLC, lc)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(indexTrunc, lc)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(tTrunc, lc)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
times2 = np.arange(0, size, np.random.randint(4, 9))
counts2 = np.random.rand(len(times)) * 100
lc_temp = Lightcurve(times, counts, dt=1.0, skip_checks=True)
time1, mem1 = benchCode(splitLc, lc_temp, 4)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
del times2, counts2, lc_temp
time1, mem1 = benchCode(sortLcTime, lc)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(sortLcCount, lc)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(chunkAnlyze, lc, 100000, lambda x: np.mean(x))
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(chunkLen, lc, 10000, 10000)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
lc_other = Lightcurve(times,
counts * np.random.rand(size),
dt=1.0,
skip_checks=True)
time1, mem1 = benchCode(joinLc, lc, lc_other)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(createCspec, lc, lc_other)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
cspec = Crossspectrum(lc, lc_other, dt=1.0)
time1, mem1 = benchCode(rebinCspec, cspec)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(coherCspec, cspec)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(TlagCspec, cspec)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
del cspec
time1, mem1 = benchCode(createAvgCspec, lc, lc_other, 10000)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
avg_cspec = AveragedCrossspectrum(lc, lc_other, 10000, silent=True)
time1, mem1 = benchCode(coherAvgCspec, avg_cspec)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(TlagAvgCspec, avg_cspec)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
del avg_cspec, lc_other
time1, mem1 = benchCode(createPspec, lc)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
pspec = Powerspectrum(lc)
time1, mem1 = benchCode(rebinPspec, pspec)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
time1, mem1 = benchCode(pspecRMS, pspec)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
temp_pspec = Powerspectrum(lc, norm='leahy')
time1, mem1 = benchCode(classSign, temp_pspec)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
del pspec, temp_pspec
time1, mem1 = benchCode(createAvgPspec, lc, 10000)
wall_time[num_func].append(time1)
mem_use[num_func].append(mem1)
num_func += 1
del lc, time1, mem1
CSVWriter(func_dict, wall_time, mem_use)
del func_dict, wall_time, mem_use
def test_rebin_with_valid_type_attribute(self):
new_df = 2
with pytest.warns(UserWarning) as record:
aps = AveragedCrossspectrum(lc1=self.lc1, lc2=self.lc2,
segment_size=1, norm='leahy')
assert aps.rebin(df=new_df)
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
self.cs = AveragedCrossspectrum(self.lc1, self.lc2,
segment_size=1,
norm="wrong")
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
self.cs = AveragedCrossspectrum(self.lc1, self.lc2,
segment_size=1,
norm="wrong")
class TestAveragedCrossspectrum(object):
def setup_class(self):
tstart = 0.0
tend = 1.0
dt = 0.0001
time = np.linspace(tstart, tend, int((tend - tstart) / dt))
counts1 = np.random.poisson(0.01, size=time.shape[0])
counts2 = np.random.negative_binomial(1, 0.09, size=time.shape[0])
self.lc1 = Lightcurve(time, counts1)
self.lc2 = Lightcurve(time, counts2)
self.cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
def test_invalid_type_attribute(self):
with pytest.raises(ValueError):
cs_test = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
cs_test.type = 'invalid_type'
assert AveragedCrossspectrum._make_crossspectrum(
cs_test, self.lc1, self.lc2)
def test_invalid_type_attribute_with_multiple_lcs(self):
acs_test = AveragedCrossspectrum([self.lc1, self.lc2],
[self.lc2, self.lc1],
segment_size=1)
acs_test.type = 'invalid_type'
with pytest.raises(ValueError):
assert AveragedCrossspectrum._make_crossspectrum(
acs_test, lc1=[self.lc1, self.lc2], lc2=[self.lc2, self.lc1])
def test_different_dt(self):
time1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
counts1_test = np.random.poisson(0.01, size=len(time1))
test_lc1 = Lightcurve(time1, counts1_test)
time2 = [2, 4, 6, 8, 10]
counts2_test = np.random.negative_binomial(1, 0.09, size=len(time2))
test_lc2 = Lightcurve(time2, counts2_test)
assert test_lc1.tseg == test_lc2.tseg
assert test_lc1.dt != test_lc2.dt
with pytest.raises(ValueError):
assert AveragedCrossspectrum(test_lc1, test_lc2, segment_size=1)
def test_different_tseg(self):
time2 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
counts2_test = np.random.poisson(0.01, size=len(time2))
test_lc2 = Lightcurve(time2, counts2_test)
time1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
counts1_test = np.random.negative_binomial(1, 0.09, size=len(time1))
test_lc1 = Lightcurve(time1, counts1_test)
assert test_lc2.dt == test_lc1.dt
assert test_lc2.tseg != test_lc1.tseg
with pytest.raises(ValueError):
assert AveragedCrossspectrum(test_lc1, test_lc2, segment_size=1)
def test_rebin_with_invalid_type_attribute(self):
new_df = 2
aps = AveragedPowerspectrum(lc=self.lc1, segment_size=1, norm='leahy')
aps.type = 'invalid_type'
with pytest.raises(AttributeError):
assert aps.rebin(df=new_df)
def test_rebin_with_valid_type_attribute(self):
new_df = 2
aps = AveragedPowerspectrum(lc=self.lc1, segment_size=1, norm='leahy')
assert aps.rebin(df=new_df)
def test_init_with_norm_not_str(self):
with pytest.raises(TypeError):
cs = AveragedCrossspectrum(self.lc1,
self.lc2,
segment_size=1,
norm=1)
def test_init_with_invalid_norm(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1,
self.lc2,
segment_size=1,
norm='frabs')
def test_init_with_inifite_segment_size(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=np.inf)
def test_with_iterable_of_lightcurves(self):
def iter_lc(lc, n):
"Generator of n parts of lc."
t0 = int(len(lc) / n)
t = t0
i = 0
while (True):
lc_seg = lc[i:t]
yield lc_seg
if t + t0 > len(lc):
break
else:
i, t = t, t + t0
cs = AveragedCrossspectrum(iter_lc(self.lc1, 1),
iter_lc(self.lc2, 1),
segment_size=1)
def test_coherence(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
coh = self.cs.coherence()
assert len(coh[0]) == 4999
assert len(coh[1]) == 4999
assert len(w) == 1
assert issubclass(w[-1].category, UserWarning)
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
self.cs = AveragedCrossspectrum(self.lc1,
self.lc2,
segment_size=1,
norm="wrong")
def test_init_with_invalid_norm(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1,
norm='frabs')
class TestAveragedCrossspectrum(object):
def setup_class(self):
tstart = 0.0
tend = 1.0
dt = np.longdouble(0.0001)
time = np.arange(tstart + 0.5*dt, tend + 0.5*dt, dt)
counts1 = np.random.poisson(0.01, size=time.shape[0])
counts2 = np.random.negative_binomial(1, 0.09, size=time.shape[0])
self.lc1 = Lightcurve(time, counts1, gti=[[tstart, tend]], dt=dt)
self.lc2 = Lightcurve(time, counts2, gti=[[tstart, tend]], dt=dt)
self.cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
def test_invalid_type_attribute(self):
with pytest.raises(ValueError):
cs_test = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
cs_test.type = 'invalid_type'
assert AveragedCrossspectrum._make_crossspectrum(cs_test,
self.lc1,
self.lc2)
def test_invalid_type_attribute_with_multiple_lcs(self):
acs_test = AveragedCrossspectrum([self.lc1, self.lc2],
[self.lc2, self.lc1],
segment_size=1)
acs_test.type = 'invalid_type'
with pytest.raises(ValueError):
assert AveragedCrossspectrum._make_crossspectrum(acs_test,
lc1=[self.lc1,
self.lc2],
lc2=[self.lc2,
self.lc1])
def test_different_dt(self):
time1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
counts1_test = np.random.poisson(0.01, size=len(time1))
test_lc1 = Lightcurve(time1, counts1_test)
time2 = [2, 4, 6, 8, 10]
counts2_test = np.random.negative_binomial(1, 0.09, size=len(time2))
test_lc2 = Lightcurve(time2, counts2_test)
assert test_lc1.tseg == test_lc2.tseg
assert test_lc1.dt != test_lc2.dt
with pytest.raises(ValueError):
assert AveragedCrossspectrum(test_lc1, test_lc2, segment_size=1)
def test_different_tseg(self):
time2 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
counts2_test = np.random.poisson(0.01, size=len(time2))
test_lc2 = Lightcurve(time2, counts2_test)
time1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
counts1_test = np.random.negative_binomial(1, 0.09, size=len(time1))
test_lc1 = Lightcurve(time1, counts1_test)
assert test_lc2.dt == test_lc1.dt
assert test_lc2.tseg != test_lc1.tseg
with pytest.raises(ValueError):
assert AveragedCrossspectrum(test_lc1, test_lc2, segment_size=1)
def test_rebin_with_invalid_type_attribute(self):
new_df = 2
aps = AveragedPowerspectrum(lc=self.lc1, segment_size=1,
norm='leahy')
aps.type = 'invalid_type'
with pytest.raises(AttributeError):
assert aps.rebin(df=new_df)
def test_rebin_with_valid_type_attribute(self):
new_df = 2
aps = AveragedPowerspectrum(lc=self.lc1, segment_size=1,
norm='leahy')
assert aps.rebin(df=new_df)
def test_init_with_norm_not_str(self):
with pytest.raises(TypeError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1,
norm=1)
def test_init_with_invalid_norm(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1,
norm='frabs')
def test_init_with_inifite_segment_size(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=np.inf)
def test_with_iterable_of_lightcurves(self):
def iter_lc(lc, n):
"Generator of n parts of lc."
t0 = int(len(lc) / n)
t = t0
i = 0
while(True):
lc_seg = lc[i:t]
yield lc_seg
if t + t0 > len(lc):
break
else:
i, t = t, t + t0
cs = AveragedCrossspectrum(iter_lc(self.lc1, 1), iter_lc(self.lc2, 1),
segment_size=1)
def test_coherence(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
coh = self.cs.coherence()
assert len(coh[0]) == 4999
assert len(coh[1]) == 4999
assert issubclass(w[-1].category, UserWarning)
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
self.cs = AveragedCrossspectrum(self.lc1, self.lc2,
segment_size=1,
norm="wrong")
def test_rebin(self):
new_cs = self.cs.rebin(df=1.5)
assert new_cs.df == 1.5
new_cs.time_lag()
def test_rebin_factor(self):
new_cs = self.cs.rebin(f=1.5)
assert new_cs.df == self.cs.df * 1.5
new_cs.time_lag()
def test_rebin_log(self):
# For now, just verify that it doesn't crash
new_cs = self.cs.rebin_log(f=0.1)
assert type(new_cs) == type(self.cs)
new_cs.time_lag()
def test_timelag(self):
from ..simulator.simulator import Simulator
dt = 0.1
simulator = Simulator(dt, 10000, rms=0.4, mean=200)
test_lc1 = simulator.simulate(2)
test_lc2 = Lightcurve(test_lc1.time,
np.array(np.roll(test_lc1.counts, 2)),
err_dist=test_lc1.err_dist,
dt=dt)
cs = AveragedCrossspectrum(test_lc1, test_lc2, segment_size=10,
norm="none")
time_lag, time_lag_err = cs.time_lag()
assert np.all(np.abs(time_lag[:10] - 0.1) < 3 * time_lag_err[:10])
def test_errorbars(self):
time = np.arange(10000) * 0.1
test_lc1 = Lightcurve(time, np.random.poisson(200, 10000))
test_lc2 = Lightcurve(time, np.random.poisson(200, 10000))
cs = AveragedCrossspectrum(test_lc1, test_lc2, segment_size=10,
norm="leahy")
assert np.allclose(cs.power_err, np.sqrt(2/cs.m))
def test_invalid_type_attribute(self):
with pytest.raises(ValueError):
cs_test = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
cs_test.type = 'invalid_type'
assert AveragedCrossspectrum._make_crossspectrum(
cs_test, self.lc1, self.lc2)
class TestAveragedCrossspectrum(object):
def setup_class(self):
tstart = 0.0
tend = 1.0
dt = np.longdouble(0.0001)
time = np.arange(tstart + 0.5 * dt, tend + 0.5 * dt, dt)
counts1 = np.random.poisson(0.01, size=time.shape[0])
counts2 = np.random.negative_binomial(1, 0.09, size=time.shape[0])
self.lc1 = Lightcurve(time, counts1, gti=[[tstart, tend]], dt=dt)
self.lc2 = Lightcurve(time, counts2, gti=[[tstart, tend]], dt=dt)
with pytest.warns(UserWarning) as record:
self.cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
def test_lc_keyword_deprecation(self):
cs1 = AveragedCrossspectrum(data1=self.lc1,
data2=self.lc2,
segment_size=1)
with pytest.warns(DeprecationWarning) as record:
cs2 = AveragedCrossspectrum(lc1=self.lc1,
lc2=self.lc2,
segment_size=1)
assert np.any(['lcN keywords' in r.message.args[0] for r in record])
assert np.allclose(cs1.power, cs2.power)
assert np.allclose(cs1.freq, cs2.freq)
def test_make_empty_crossspectrum(self):
cs = AveragedCrossspectrum()
assert cs.freq is None
assert cs.power is None
assert cs.df is None
assert cs.nphots1 is None
assert cs.nphots2 is None
assert cs.m == 1
assert cs.n is None
assert cs.power_err is None
def test_no_counts_warns(self):
newlc = copy.deepcopy(self.lc1)
newlc.counts[:newlc.counts.size // 2] = \
0 * newlc.counts[:newlc.counts.size // 2]
with pytest.warns(UserWarning) as record:
ps = AveragedCrossspectrum(newlc, self.lc2, segment_size=0.2)
assert np.any(["No counts in " in r.message.args[0] for r in record])
def test_no_segment_size(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2)
def test_invalid_type_attribute(self):
with pytest.raises(ValueError):
cs_test = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
cs_test.type = 'invalid_type'
assert AveragedCrossspectrum._make_crossspectrum(
cs_test, self.lc1, self.lc2)
def test_invalid_type_attribute_with_multiple_lcs(self):
with pytest.warns(UserWarning) as record:
acs_test = AveragedCrossspectrum([self.lc1, self.lc2],
[self.lc2, self.lc1],
segment_size=1)
acs_test.type = 'invalid_type'
with pytest.raises(ValueError) as excinfo:
assert AveragedCrossspectrum._make_crossspectrum(
acs_test, [self.lc1, self.lc2], [self.lc2, self.lc1])
assert "Type of spectrum not recognized" in str(excinfo.value)
def test_different_dt(self):
time1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
counts1_test = np.random.poisson(0.01, size=len(time1))
test_lc1 = Lightcurve(time1, counts1_test)
time2 = [2, 4, 6, 8, 10]
counts2_test = np.random.negative_binomial(1, 0.09, size=len(time2))
test_lc2 = Lightcurve(time2, counts2_test)
assert test_lc1.tseg == test_lc2.tseg
assert test_lc1.dt != test_lc2.dt
with pytest.raises(ValueError):
assert AveragedCrossspectrum(test_lc1, test_lc2, segment_size=1)
def test_different_tseg(self):
time2 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
counts2_test = np.random.poisson(1000, size=len(time2))
test_lc2 = Lightcurve(time2, counts2_test)
time1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
counts1_test = np.random.poisson(1000, size=len(time1))
test_lc1 = Lightcurve(time1, counts1_test)
assert test_lc2.dt == test_lc1.dt
assert test_lc2.tseg != test_lc1.tseg
with pytest.warns(UserWarning) as record:
AveragedCrossspectrum(test_lc1, test_lc2, segment_size=5)
assert np.any(["same tseg" in r.message.args[0] for r in record])
def test_with_zero_counts(self):
nbins = 100
x = np.linspace(0, 10, nbins)
ycounts1 = np.random.normal(loc=10, scale=0.5, size=int(0.4 * nbins))
ycounts2 = np.random.normal(loc=10, scale=0.5, size=int(0.4 * nbins))
yzero = np.zeros(int(0.6 * nbins))
y1 = np.hstack([ycounts1, yzero])
y2 = np.hstack([ycounts2, yzero])
lc1 = Lightcurve(x, y1)
lc2 = Lightcurve(x, y2)
acs = AveragedCrossspectrum(lc1, lc2, segment_size=5.0, norm="leahy")
assert acs.m == 1
def test_rebin_with_invalid_type_attribute(self):
new_df = 2
with pytest.warns(UserWarning) as record:
aps = AveragedCrossspectrum(lc1=self.lc1,
lc2=self.lc2,
segment_size=1,
norm='leahy')
aps.type = 'invalid_type'
with pytest.raises(ValueError) as excinfo:
assert aps.rebin(df=new_df, method=aps.type)
assert "Method for summing or averaging not recognized. " in str(
excinfo.value)
def test_rebin_with_valid_type_attribute(self):
new_df = 2
with pytest.warns(UserWarning) as record:
aps = AveragedCrossspectrum(self.lc1,
self.lc2,
segment_size=1,
norm='leahy')
assert aps.rebin(df=new_df)
def test_init_with_norm_not_str(self):
with pytest.raises(TypeError):
cs = AveragedCrossspectrum(self.lc1,
self.lc2,
segment_size=1,
norm=1)
def test_init_with_invalid_norm(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1,
self.lc2,
segment_size=1,
norm='frabs')
def test_init_with_inifite_segment_size(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=np.inf)
def test_with_iterable_of_lightcurves(self):
def iter_lc(lc, n):
"Generator of n parts of lc."
t0 = int(len(lc) / n)
t = t0
i = 0
while (True):
lc_seg = lc[i:t]
yield lc_seg
if t + t0 > len(lc):
break
else:
i, t = t, t + t0
with pytest.warns(UserWarning) as record:
cs = AveragedCrossspectrum(iter_lc(self.lc1, 1),
iter_lc(self.lc2, 1),
segment_size=1)
def test_with_multiple_lightcurves_variable_length(self):
gti = [[0, 0.05], [0.05, 0.5], [0.555, 1.0]]
lc1 = copy.deepcopy(self.lc1)
lc1.gti = gti
lc2 = copy.deepcopy(self.lc2)
lc2.gti = gti
lc1_split = lc1.split_by_gti()
lc2_split = lc2.split_by_gti()
cs = AveragedCrossspectrum(lc1_split,
lc2_split,
segment_size=0.05,
norm="leahy",
silent=True)
def test_coherence(self):
with warnings.catch_warnings(record=True) as w:
coh = self.cs.coherence()
assert len(coh[0]) == 4999
assert len(coh[1]) == 4999
assert issubclass(w[-1].category, UserWarning)
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
self.cs = AveragedCrossspectrum(self.lc1,
self.lc2,
segment_size=1,
norm="wrong")
def test_failure_when_power_type_not_recognized(self):
with pytest.raises(ValueError):
self.cs = AveragedCrossspectrum(self.lc1,
self.lc2,
segment_size=1,
power_type="wrong")
def test_normalize_crossspectrum(self):
cs1 = Crossspectrum(self.lc1, self.lc2, norm="leahy")
cs2 = Crossspectrum(self.lc1, self.lc2, norm="leahy", power_type="all")
cs3 = Crossspectrum(self.lc1,
self.lc2,
norm="leahy",
power_type="real")
cs4 = Crossspectrum(self.lc1,
self.lc2,
norm="leahy",
power_type="absolute")
assert np.all(cs1.power.real == cs3.power)
assert np.all(np.isclose(np.abs(cs2.power), cs4.power, atol=0.0001))
def test_rebin(self):
with warnings.catch_warnings(record=True) as w:
new_cs = self.cs.rebin(df=1.5)
assert new_cs.df == 1.5
new_cs.time_lag()
def test_rebin_factor(self):
with warnings.catch_warnings(record=True) as w:
new_cs = self.cs.rebin(f=1.5)
assert new_cs.df == self.cs.df * 1.5
new_cs.time_lag()
def test_rebin_log(self):
# For now, just verify that it doesn't crash
with warnings.catch_warnings(record=True) as w:
new_cs = self.cs.rebin_log(f=0.1)
assert type(new_cs) == type(self.cs)
new_cs.time_lag()
def test_rebin_log_returns_complex_values(self):
# For now, just verify that it doesn't crash
with warnings.catch_warnings(record=True) as w:
new_cs = self.cs.rebin_log(f=0.1)
assert np.iscomplexobj(new_cs.power[0])
def test_rebin_log_returns_complex_errors(self):
# For now, just verify that it doesn't crash
with warnings.catch_warnings(record=True) as w:
new_cs = self.cs.rebin_log(f=0.1)
assert np.iscomplexobj(new_cs.power_err[0])
def test_timelag(self):
dt = 0.1
simulator = Simulator(dt, 10000, rms=0.2, mean=1000)
test_lc1 = simulator.simulate(2)
test_lc1.counts -= np.min(test_lc1.counts)
test_lc1 = Lightcurve(test_lc1.time,
test_lc1.counts,
err_dist=test_lc1.err_dist,
dt=dt)
test_lc2 = Lightcurve(test_lc1.time,
np.array(np.roll(test_lc1.counts, 2)),
err_dist=test_lc1.err_dist,
dt=dt)
with warnings.catch_warnings(record=True) as w:
cs = AveragedCrossspectrum(test_lc1,
test_lc2,
segment_size=5,
norm="none")
time_lag, time_lag_err = cs.time_lag()
assert np.all(np.abs(time_lag[:6] - 0.1) < 3 * time_lag_err[:6])
def test_errorbars(self):
time = np.arange(10000) * 0.1
test_lc1 = Lightcurve(time, np.random.poisson(200, 10000))
test_lc2 = Lightcurve(time, np.random.poisson(200, 10000))
with warnings.catch_warnings(record=True) as w:
cs = AveragedCrossspectrum(test_lc1,
test_lc2,
segment_size=10,
norm="leahy")
assert np.allclose(cs.power_err, np.sqrt(2 / cs.m))
def test_classical_significances(self):
time = np.arange(10000) * 0.1
np.random.seed(62)
test_lc1 = Lightcurve(time, np.random.poisson(200, 10000))
test_lc2 = Lightcurve(time, np.random.poisson(200, 10000))
with warnings.catch_warnings(record=True) as w:
cs = AveragedCrossspectrum(test_lc1,
test_lc2,
segment_size=10,
norm="leahy")
maxpower = np.max(cs.power)
assert np.all(
np.isfinite(cs.classical_significances(threshold=maxpower / 2.)))
class TestAveragedCrossspectrum(object):
def setup_class(self):
tstart = 0.0
tend = 1.0
dt = 0.0001
time = np.linspace(tstart, tend, int((tend - tstart) / dt))
counts1 = np.random.poisson(0.01, size=time.shape[0])
counts2 = np.random.negative_binomial(1, 0.09, size=time.shape[0])
self.lc1 = Lightcurve(time, counts1)
self.lc2 = Lightcurve(time, counts2)
self.cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
def test_invalid_type_attribute(self):
with pytest.raises(ValueError):
cs_test = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
cs_test.type = "invalid_type"
assert AveragedCrossspectrum._make_crossspectrum(cs_test, self.lc1, self.lc2)
def test_invalid_type_attribute_with_multiple_lcs(self):
acs_test = AveragedCrossspectrum([self.lc1, self.lc2], [self.lc2, self.lc1], segment_size=1)
acs_test.type = "invalid_type"
with pytest.raises(ValueError):
assert AveragedCrossspectrum._make_crossspectrum(
acs_test, lc1=[self.lc1, self.lc2], lc2=[self.lc2, self.lc1]
)
def test_different_dt(self):
time1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
counts1_test = np.random.poisson(0.01, size=len(time1))
test_lc1 = Lightcurve(time1, counts1_test)
time2 = [2, 4, 6, 8, 10]
counts2_test = np.random.negative_binomial(1, 0.09, size=len(time2))
test_lc2 = Lightcurve(time2, counts2_test)
assert test_lc1.tseg == test_lc2.tseg
assert test_lc1.dt != test_lc2.dt
with pytest.raises(ValueError):
assert AveragedCrossspectrum(test_lc1, test_lc2, segment_size=1)
def test_different_tseg(self):
time2 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
counts2_test = np.random.poisson(0.01, size=len(time2))
test_lc2 = Lightcurve(time2, counts2_test)
time1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
counts1_test = np.random.negative_binomial(1, 0.09, size=len(time1))
test_lc1 = Lightcurve(time1, counts1_test)
assert test_lc2.dt == test_lc1.dt
assert test_lc2.tseg != test_lc1.tseg
with pytest.raises(ValueError):
assert AveragedCrossspectrum(test_lc1, test_lc2, segment_size=1)
def test_rebin_with_invalid_type_attribute(self):
new_df = 2
aps = AveragedPowerspectrum(lc=self.lc1, segment_size=1, norm="leahy")
aps.type = "invalid_type"
with pytest.raises(AttributeError):
assert aps.rebin(df=new_df)
def test_rebin_with_valid_type_attribute(self):
new_df = 2
aps = AveragedPowerspectrum(lc=self.lc1, segment_size=1, norm="leahy")
assert aps.rebin(df=new_df)
def test_init_with_norm_not_str(self):
with pytest.raises(TypeError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1, norm=1)
def test_init_with_invalid_norm(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1, norm="frabs")
def test_init_with_inifite_segment_size(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=np.inf)
def test_with_iterable_of_lightcurves(self):
def iter_lc(lc, n):
"Generator of n parts of lc."
t0 = int(len(lc) / n)
t = t0
i = 0
while True:
lc_seg = lc[i:t]
yield lc_seg
if t + t0 > len(lc):
break
else:
i, t = t, t + t0
cs = AveragedCrossspectrum(iter_lc(self.lc1, 1), iter_lc(self.lc2, 1), segment_size=1)
def test_coherence(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
coh = self.cs.coherence()
assert len(coh[0]) == 4999
assert len(coh[1]) == 4999
assert len(w) == 1
assert issubclass(w[-1].category, UserWarning)
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
self.cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1, norm="wrong")
def test_no_segment_size(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2)
class TestAveragedCrossspectrum(object):
def setup_class(self):
tstart = 0.0
tend = 1.0
dt = 0.0001
time = np.linspace(tstart, tend, int((tend - tstart)/dt))
counts1 = np.random.poisson(0.01, size=time.shape[0])
counts2 = np.random.negative_binomial(1, 0.09, size=time.shape[0])
self.lc1 = Lightcurve(time, counts1)
self.lc2 = Lightcurve(time, counts2)
self.cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
def test_init_with_norm_not_str(self):
with pytest.raises(TypeError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1,
norm=1)
def test_init_with_invalid_norm(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1,
norm='frabs')
def test_init_with_inifite_segment_size(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=np.inf)
def test_with_iterable_of_lightcurves(self):
def iter_lc(lc, n):
"Generator of n parts of lc."
t0 = int(len(lc) / n)
t = t0
i = 0
while(True):
lc_seg = lc[i:t]
yield lc_seg
if t + t0 > len(lc):
break
else:
i, t = t, t + t0
cs = AveragedCrossspectrum(iter_lc(self.lc1, 1), iter_lc(self.lc2, 1),
segment_size=1)
def test_coherence(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
coh = self.cs.coherence()
assert len(coh[0]) == 4999
assert len(coh[1]) == 4999
assert len(w) == 1
assert issubclass(w[-1].category, UserWarning)
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
self.cs = AveragedCrossspectrum(self.lc1, self.lc2,
segment_size=1,
norm="wrong")
def test_failure_when_power_type_not_recognized(self):
with pytest.raises(ValueError):
self.cs = AveragedCrossspectrum(self.lc1,
self.lc2,
segment_size=1,
power_type="wrong")
def test_ccf(self):
# to make testing faster, fitting is not done.
ref_ps = Powerspectrum(self.ref_lc, norm='abs')
ci_counts_0 = self.ci_counts[0]
ci_times = np.arange(0, self.n_seconds * self.n_seg, self.dt)
ci_lc = Lightcurve(ci_times, ci_counts_0, dt=self.dt)
# rebinning factor used in `rebin_log`
rebin_log_factor = 0.4
acs = AveragedCrossspectrum(lc1=ci_lc,
lc2=self.ref_lc,
segment_size=self.n_seconds,
norm='leahy',
power_type="absolute")
acs = acs.rebin_log(rebin_log_factor)
# parest, res = fit_crossspectrum(acs, self.model, fitmethod="CG")
acs_result_model = self.model
# using optimal filter
optimal_filter = Optimal1D(acs_result_model)
optimal_filter_freq = optimal_filter(acs.freq)
filtered_acs_power = optimal_filter_freq * np.abs(acs.power)
# rebinning power spectrum
new_df = spec.get_new_df(ref_ps, self.n_bins)
ref_ps_rebinned = ref_ps.rebin(df=new_df)
# parest, res = fit_powerspectrum(ref_ps_rebinned, self.model)
ref_ps_rebinned_result_model = self.model
# calculating rms from power spectrum
ref_ps_rebinned_rms = spec.compute_rms(ref_ps_rebinned,
ref_ps_rebinned_result_model,
criteria="optimal")
# calculating normalized ccf
ccf_norm = spec.ccf(filtered_acs_power, ref_ps_rebinned_rms,
self.n_bins)
# calculating ccf error
meta = {
'N_SEG': self.n_seg,
'NSECONDS': self.n_seconds,
'DT': self.dt,
'N_BINS': self.n_bins
}
error_ccf, avg_seg_ccf = spec.ccf_error(self.ref_counts,
ci_counts_0,
acs_result_model,
rebin_log_factor,
meta,
ref_ps_rebinned_rms,
filter_type="optimal")
assert np.all(np.isclose(ccf_norm, avg_seg_ccf, atol=0.01))
assert np.all(
np.isclose(error_ccf, np.zeros(shape=error_ccf.shape), atol=0.01))
# using window function
tophat_filter = Window1D(acs_result_model)
tophat_filter_freq = tophat_filter(acs.freq)
filtered_acs_power = tophat_filter_freq * np.abs(acs.power)
ref_ps_rebinned_rms = spec.compute_rms(ref_ps_rebinned,
ref_ps_rebinned_result_model,
criteria="window")
ccf_norm = spec.ccf(filtered_acs_power, ref_ps_rebinned_rms,
self.n_bins)
error_ccf, avg_seg_ccf = spec.ccf_error(self.ref_counts,
ci_counts_0,
acs_result_model,
rebin_log_factor,
meta,
ref_ps_rebinned_rms,
filter_type="window")
assert np.all(np.isclose(ccf_norm, avg_seg_ccf, atol=0.01))
assert np.all(
np.isclose(error_ccf, np.zeros(shape=error_ccf.shape), atol=0.01))
def test_init_with_norm_not_str(self):
with pytest.raises(TypeError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1,
norm=1)
def test_save_all(self):
cs = AveragedCrossspectrum(self.lc1,
self.lc2,
segment_size=1,
save_all=True)
assert hasattr(cs, 'cs_all')
def test_init_with_inifite_segment_size(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=np.inf)
class TestAveragedCrossspectrum(object):
def setup_class(self):
tstart = 0.0
tend = 1.0
dt = 0.0001
time = np.linspace(tstart, tend, int((tend - tstart)/dt))
counts1 = np.random.poisson(0.01, size=time.shape[0])
counts2 = np.random.negative_binomial(1, 0.09, size=time.shape[0])
self.lc1 = Lightcurve(time, counts1)
self.lc2 = Lightcurve(time, counts2)
self.cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
def test_init_with_norm_not_str(self):
with pytest.raises(TypeError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1,
norm=1)
def test_init_with_invalid_norm(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1,
norm='frabs')
def test_init_with_inifite_segment_size(self):
with pytest.raises(AssertionError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=np.inf)
def test_with_iterable_of_lightcurves(self):
def iter_lc(lc, n):
"Generator of n parts of lc."
t0 = int(len(lc) / n)
t = t0
i = 0
while(True):
lc_seg = lc[i:t]
yield lc_seg
if t + t0 > len(lc):
break
else:
i, t = t, t + t0
cs = AveragedCrossspectrum(iter_lc(self.lc1, 1), iter_lc(self.lc2, 1),
segment_size=1)
def test_coherence(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
coh = self.cs.coherence()
assert len(coh[0]) == 4999
assert len(coh[1]) == 4999
assert len(w) == 1
assert issubclass(w[-1].category, UserWarning)
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
self.cs = AveragedCrossspectrum(self.lc1, self.lc2,
segment_size=1,
norm="wrong")
def test_ccf(self):
# to make testing faster, fitting is not done.
ref_ps = Powerspectrum(self.ref_lc, norm='abs')
ci_counts_0 = self.ci_counts[0]
ci_times = np.arange(0, self.n_seconds * self.n_seg, self.dt)
ci_lc = Lightcurve(ci_times, ci_counts_0, dt=self.dt)
# rebinning factor used in `rebin_log`
rebin_log_factor = 0.4
acs = AveragedCrossspectrum(lc1=ci_lc, lc2=self.ref_lc,
segment_size=self.n_seconds, norm='leahy',
power_type="absolute")
acs = acs.rebin_log(rebin_log_factor)
# parest, res = fit_crossspectrum(acs, self.model, fitmethod="CG")
acs_result_model = self.model
# using optimal filter
optimal_filter = Optimal1D(acs_result_model)
optimal_filter_freq = optimal_filter(acs.freq)
filtered_acs_power = optimal_filter_freq * np.abs(acs.power)
# rebinning power spectrum
new_df = spec.get_new_df(ref_ps, self.n_bins)
ref_ps_rebinned = ref_ps.rebin(df=new_df)
# parest, res = fit_powerspectrum(ref_ps_rebinned, self.model)
ref_ps_rebinned_result_model = self.model
# calculating rms from power spectrum
ref_ps_rebinned_rms = spec.compute_rms(ref_ps_rebinned,
ref_ps_rebinned_result_model,
criteria="optimal")
# calculating normalized ccf
ccf_norm = spec.ccf(filtered_acs_power, ref_ps_rebinned_rms,
self.n_bins)
# calculating ccf error
meta = {'N_SEG': self.n_seg, 'NSECONDS': self.n_seconds, 'DT': self.dt,
'N_BINS': self.n_bins}
error_ccf, avg_seg_ccf = spec.ccf_error(self.ref_counts, ci_counts_0,
acs_result_model,
rebin_log_factor,
meta, ref_ps_rebinned_rms,
filter_type="optimal")
assert np.all(np.isclose(ccf_norm, avg_seg_ccf, atol=0.01))
assert np.all(np.isclose(error_ccf, np.zeros(shape=error_ccf.shape),
atol=0.01))
# using window function
tophat_filter = Window1D(acs_result_model)
tophat_filter_freq = tophat_filter(acs.freq)
filtered_acs_power = tophat_filter_freq * np.abs(acs.power)
ref_ps_rebinned_rms = spec.compute_rms(ref_ps_rebinned,
ref_ps_rebinned_result_model,
criteria="window")
ccf_norm = spec.ccf(filtered_acs_power, ref_ps_rebinned_rms,
self.n_bins)
error_ccf, avg_seg_ccf = spec.ccf_error(self.ref_counts, ci_counts_0,
acs_result_model,
rebin_log_factor,
meta, ref_ps_rebinned_rms,
filter_type="window")
assert np.all(np.isclose(ccf_norm, avg_seg_ccf, atol=0.01))
assert np.all(np.isclose(error_ccf, np.zeros(shape=error_ccf.shape),
atol=0.01))
class TestAveragedCrossspectrum(object):
def setup_class(self):
tstart = 0.0
tend = 1.0
dt = np.longdouble(0.0001)
time = np.arange(tstart + 0.5*dt, tend + 0.5*dt, dt)
counts1 = np.random.poisson(0.01, size=time.shape[0])
counts2 = np.random.negative_binomial(1, 0.09, size=time.shape[0])
self.lc1 = Lightcurve(time, counts1, gti=[[tstart, tend]], dt=dt)
self.lc2 = Lightcurve(time, counts2, gti=[[tstart, tend]], dt=dt)
self.cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
def test_make_empty_crossspectrum(self):
cs = AveragedCrossspectrum()
assert cs.freq is None
assert cs.power is None
assert cs.df is None
assert cs.nphots1 is None
assert cs.nphots2 is None
assert cs.m == 1
assert cs.n is None
assert cs.power_err is None
def test_no_segment_size(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2)
def test_invalid_type_attribute(self):
with pytest.raises(ValueError):
cs_test = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1)
cs_test.type = 'invalid_type'
assert AveragedCrossspectrum._make_crossspectrum(cs_test,
self.lc1,
self.lc2)
def test_invalid_type_attribute_with_multiple_lcs(self):
acs_test = AveragedCrossspectrum([self.lc1, self.lc2],
[self.lc2, self.lc1],
segment_size=1)
acs_test.type = 'invalid_type'
with pytest.raises(ValueError):
assert AveragedCrossspectrum._make_crossspectrum(acs_test,
lc1=[self.lc1,
self.lc2],
lc2=[self.lc2,
self.lc1])
def test_different_dt(self):
time1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
counts1_test = np.random.poisson(0.01, size=len(time1))
test_lc1 = Lightcurve(time1, counts1_test)
time2 = [2, 4, 6, 8, 10]
counts2_test = np.random.negative_binomial(1, 0.09, size=len(time2))
test_lc2 = Lightcurve(time2, counts2_test)
assert test_lc1.tseg == test_lc2.tseg
assert test_lc1.dt != test_lc2.dt
with pytest.raises(ValueError):
assert AveragedCrossspectrum(test_lc1, test_lc2, segment_size=1)
def test_different_tseg(self):
time2 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
counts2_test = np.random.poisson(0.01, size=len(time2))
test_lc2 = Lightcurve(time2, counts2_test)
time1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
counts1_test = np.random.negative_binomial(1, 0.09, size=len(time1))
test_lc1 = Lightcurve(time1, counts1_test)
assert test_lc2.dt == test_lc1.dt
assert test_lc2.tseg != test_lc1.tseg
with pytest.raises(ValueError):
assert AveragedCrossspectrum(test_lc1, test_lc2, segment_size=1)
def test_rebin_with_invalid_type_attribute(self):
new_df = 2
aps = AveragedPowerspectrum(lc=self.lc1, segment_size=1,
norm='leahy')
aps.type = 'invalid_type'
with pytest.raises(AttributeError):
assert aps.rebin(df=new_df)
def test_rebin_with_valid_type_attribute(self):
new_df = 2
aps = AveragedPowerspectrum(lc=self.lc1, segment_size=1,
norm='leahy')
assert aps.rebin(df=new_df)
def test_init_with_norm_not_str(self):
with pytest.raises(TypeError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1,
norm=1)
def test_init_with_invalid_norm(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=1,
norm='frabs')
def test_init_with_inifite_segment_size(self):
with pytest.raises(ValueError):
cs = AveragedCrossspectrum(self.lc1, self.lc2, segment_size=np.inf)
def test_with_iterable_of_lightcurves(self):
def iter_lc(lc, n):
"Generator of n parts of lc."
t0 = int(len(lc) / n)
t = t0
i = 0
while(True):
lc_seg = lc[i:t]
yield lc_seg
if t + t0 > len(lc):
break
else:
i, t = t, t + t0
cs = AveragedCrossspectrum(iter_lc(self.lc1, 1), iter_lc(self.lc2, 1),
segment_size=1)
def test_coherence(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
coh = self.cs.coherence()
assert len(coh[0]) == 4999
assert len(coh[1]) == 4999
assert issubclass(w[-1].category, UserWarning)
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
self.cs = AveragedCrossspectrum(self.lc1, self.lc2,
segment_size=1,
norm="wrong")
def test_rebin(self):
new_cs = self.cs.rebin(df=1.5)
assert new_cs.df == 1.5
new_cs.time_lag()
def test_rebin_factor(self):
new_cs = self.cs.rebin(f=1.5)
assert new_cs.df == self.cs.df * 1.5
new_cs.time_lag()
def test_rebin_log(self):
# For now, just verify that it doesn't crash
new_cs = self.cs.rebin_log(f=0.1)
assert type(new_cs) == type(self.cs)
new_cs.time_lag()
def test_timelag(self):
from ..simulator.simulator import Simulator
dt = 0.1
simulator = Simulator(dt, 10000, rms=0.4, mean=200)
test_lc1 = simulator.simulate(2)
test_lc2 = Lightcurve(test_lc1.time,
np.array(np.roll(test_lc1.counts, 2)),
err_dist=test_lc1.err_dist,
dt=dt)
cs = AveragedCrossspectrum(test_lc1, test_lc2, segment_size=10,
norm="none")
time_lag, time_lag_err = cs.time_lag()
assert np.all(np.abs(time_lag[:10] - 0.1) < 3 * time_lag_err[:10])
def test_errorbars(self):
time = np.arange(10000) * 0.1
test_lc1 = Lightcurve(time, np.random.poisson(200, 10000))
test_lc2 = Lightcurve(time, np.random.poisson(200, 10000))
cs = AveragedCrossspectrum(test_lc1, test_lc2, segment_size=10,
norm="leahy")
assert np.allclose(cs.power_err, np.sqrt(2/cs.m))
