def test_gets_thermal_with_correlated(self):
"""Checks that the part of the freq_modes code that compensates the
thermal for mode subtraction works."""
self.data *= sp.sqrt(self.bw * 2) # Makes thermal unity.
# Need to add something correlated so the modes arn't just the
# channels.
correlated_overf = noise_power.generate_overf_noise(1,
-2, 0.5, self.dt, self.data.shape[0])
correlated_overf += (rand.normal(size=(self.data.shape[0],))
* sp.sqrt((self.bw * 2) * 0.3))
self.data += correlated_overf[:,None,None,None] / sp.sqrt(self.nf)
Blocks = self.make_blocks()
# Mask a channel out completly.
for Data in Blocks:
Data.data[:,:,:,3] = ma.masked
model = 'freq_modes_over_f_4' # Take out 20% of the thermal power.
parameters = mn.measure_noise_parameters(Blocks,
[model])
right_ans = sp.ones(self.nf)
right_ans[3] = T_infinity**2
for p in parameters.itervalues():
pars = p[model]
thermal = pars['thermal']
self.assertTrue(sp.allclose(thermal,
right_ans, rtol=0.3))
mean_thermal = sp.mean(thermal[right_ans==1])
self.assertTrue(sp.allclose(mean_thermal, 1, rtol=0.05))
self.assertTrue(sp.allclose(pars['over_f_mode_0']['thermal'],
0.3, atol=0.1))
def test_few_masked(self):
Blocks = self.make_blocks()
for Data in Blocks:
Data.data[:,:,:,6] = ma.masked
Data.data[:,:,:,13] = ma.masked
model_name = 'freq_modes_over_f_' + str(3)
parameters = mn.measure_noise_parameters(Blocks, [model_name])
for pol, pol_params in parameters.iteritems():
for ii in range(3):
mode_noise = pol_params[model_name]['over_f_mode_' + str(ii)]
#self.assertTrue(sp.allclose(mode_noise['thermal'], self.dt,
# rtol=0.5))
self.assertTrue(sp.allclose(mode_noise['mode'][6], 0,
atol=1.e-10))
self.assertTrue(sp.allclose(mode_noise['mode'][13], 0,
atol=1.e-10))
expected = sp.ones(self.nf, dtype=float) * self.dt
expected[6] = T_infinity**2
expected[13] = T_infinity**2
thermal = pol_params[model_name]['thermal']
# weak test since the above modes may have favoured a few channels.
#print thermal, expected
self.assertTrue(sp.allclose(thermal, expected, rtol=0.8))
measured_general_ind = pol_params[model_name]['all_channel_index']
measured_corner = pol_params[model_name]['all_channel_corner_f']
self.assertTrue(measured_corner < 4. / self.dt / self.nt / self.nb)
def test_few_masked(self):
Blocks = self.make_blocks()
for Data in Blocks:
Data.data[:, :, :, 6] = ma.masked
Data.data[:, :, :, 13] = ma.masked
model_name = 'freq_modes_over_f_' + str(3)
parameters = mn.measure_noise_parameters(Blocks, [model_name])
for pol, pol_params in parameters.iteritems():
for ii in range(3):
mode_noise = pol_params[model_name]['over_f_mode_' + str(ii)]
#self.assertTrue(sp.allclose(mode_noise['thermal'], self.dt,
# rtol=0.5))
self.assertTrue(
sp.allclose(mode_noise['mode'][6], 0, atol=1.e-10))
self.assertTrue(
sp.allclose(mode_noise['mode'][13], 0, atol=1.e-10))
expected = sp.ones(self.nf, dtype=float) * self.dt
expected[6] = T_infinity**2
expected[13] = T_infinity**2
thermal = pol_params[model_name]['thermal']
# weak test since the above modes may have favoured a few channels.
#print thermal, expected
self.assertTrue(sp.allclose(thermal, expected, rtol=0.8))
measured_general_ind = pol_params[model_name]['all_channel_index']
measured_corner = pol_params[model_name]['all_channel_corner_f']
self.assertTrue(measured_corner < 4. / self.dt / self.nt / self.nb)
def test_gets_thermal_with_correlated(self):
"""Checks that the part of the freq_modes code that compensates the
thermal for mode subtraction works."""
self.data *= sp.sqrt(self.bw * 2) # Makes thermal unity.
# Need to add something correlated so the modes arn't just the
# channels.
correlated_overf = noise_power.generate_overf_noise(
1, -2, 0.5, self.dt, self.data.shape[0])
correlated_overf += (rand.normal(size=(self.data.shape[0], )) *
sp.sqrt((self.bw * 2) * 0.3))
self.data += correlated_overf[:, None, None, None] / sp.sqrt(self.nf)
Blocks = self.make_blocks()
# Mask a channel out completly.
for Data in Blocks:
Data.data[:, :, :, 3] = ma.masked
model = 'freq_modes_over_f_4' # Take out 20% of the thermal power.
parameters = mn.measure_noise_parameters(Blocks, [model])
right_ans = sp.ones(self.nf)
right_ans[3] = T_infinity**2
for p in parameters.itervalues():
pars = p[model]
thermal = pars['thermal']
self.assertTrue(sp.allclose(thermal, right_ans, rtol=0.3))
mean_thermal = sp.mean(thermal[right_ans == 1])
self.assertTrue(sp.allclose(mean_thermal, 1, rtol=0.05))
self.assertTrue(
sp.allclose(pars['over_f_mode_0']['thermal'], 0.3, atol=0.1))
def test_gets_thermal_no_correlated(self):
"""Just check that the part of the freq_modes code that measures
thermal is sane."""
self.data *= sp.sqrt(self.bw * 2)
Blocks = self.make_blocks()
# Mask a channel out completly.
for Data in Blocks:
Data.data[:, :, :, 3] = ma.masked
parameters = mn.measure_noise_parameters(Blocks,
['freq_modes_over_f_0'])
right_ans = sp.ones(self.nf)
right_ans[3] = T_infinity**2
for p in parameters.itervalues():
thermal = p['freq_modes_over_f_0']['thermal']
self.assertTrue(sp.allclose(thermal, right_ans, rtol=0.3))
mean_thermal = sp.mean(thermal[right_ans == 1])
self.assertTrue(sp.allclose(mean_thermal, 1, rtol=0.05))
def test_gets_thermal_no_correlated(self):
"""Just check that the part of the freq_modes code that measures
thermal is sane."""
self.data *= sp.sqrt(self.bw * 2)
Blocks = self.make_blocks()
# Mask a channel out completly.
for Data in Blocks:
Data.data[:,:,:,3] = ma.masked
parameters = mn.measure_noise_parameters(Blocks,
['freq_modes_over_f_0'])
right_ans = sp.ones(self.nf)
right_ans[3] = T_infinity**2
for p in parameters.itervalues():
thermal = p['freq_modes_over_f_0']['thermal']
self.assertTrue(sp.allclose(thermal,
right_ans, rtol=0.3))
mean_thermal = sp.mean(thermal[right_ans==1])
self.assertTrue(sp.allclose(mean_thermal, 1, rtol=0.05))
def test_all_masked(self):
Blocks = self.make_blocks()
for Data in Blocks:
Data.data[...] = ma.masked
model_name = 'freq_modes_over_f_' + str(3)
parameters = mn.measure_noise_parameters(Blocks, [model_name])
for pol, pol_params in parameters.iteritems():
for ii in range(3):
mode_noise = pol_params[model_name]['over_f_mode_' + str(ii)]
self.assertTrue(sp.allclose(mode_noise['amplitude'], 0))
self.assertTrue(sp.allclose(mode_noise['index'], 0))
self.assertTrue(sp.allclose(mode_noise['thermal'],
T_infinity**2))
thermal = pol_params[model_name]['thermal']
self.assertTrue(sp.allclose(thermal, T_infinity**2))
measured_general_ind = pol_params[model_name]['all_channel_index']
measured_corner = pol_params[model_name]['all_channel_corner_f']
self.assertTrue(sp.allclose(measured_general_ind, 0))
self.assertTrue(sp.allclose(measured_corner, 0))
def test_get_variance(self):
nf = self.nf
nt = self.nt
dt = self.dt
bw = self.bw
nb = self.nb
self.data *= sp.arange(1, nf + 1)
Blocks = self.make_blocks()
# Mask a channel out completly.
for Data in Blocks:
Data.data[:, :, :, 3] = ma.masked
parameters = mn.measure_noise_parameters(Blocks, ['channel_var'])
for ii in [1, 3]:
self.assertTrue(parameters.has_key(ii))
for p in parameters.itervalues():
variance = p['channel_var']
right_ans = sp.arange(1, nf + 1)**2
right_ans[3] = T_infinity**2
self.assertTrue(sp.allclose(variance, right_ans, rtol=0.1))
def test_all_masked(self):
Blocks = self.make_blocks()
for Data in Blocks:
Data.data[...] = ma.masked
model_name = 'freq_modes_over_f_' + str(3)
parameters = mn.measure_noise_parameters(Blocks, [model_name])
for pol, pol_params in parameters.iteritems():
for ii in range(3):
mode_noise = pol_params[model_name]['over_f_mode_' + str(ii)]
self.assertTrue(sp.allclose(mode_noise['amplitude'], 0))
self.assertTrue(sp.allclose(mode_noise['index'], 0))
self.assertTrue(
sp.allclose(mode_noise['thermal'], T_infinity**2))
thermal = pol_params[model_name]['thermal']
self.assertTrue(sp.allclose(thermal, T_infinity**2))
measured_general_ind = pol_params[model_name]['all_channel_index']
measured_corner = pol_params[model_name]['all_channel_corner_f']
self.assertTrue(sp.allclose(measured_general_ind, 0))
self.assertTrue(sp.allclose(measured_corner, 0))
def test_get_variance(self) :
nf = self.nf
nt = self.nt
dt = self.dt
bw = self.bw
nb = self.nb
self.data *= sp.arange(1, nf+1)
Blocks = self.make_blocks()
# Mask a channel out completly.
for Data in Blocks:
Data.data[:,:,:,3] = ma.masked
parameters = mn.measure_noise_parameters(Blocks, ['channel_var'])
for ii in [1,3]:
self.assertTrue(parameters.has_key(ii))
for p in parameters.itervalues():
variance = p['channel_var']
right_ans = sp.arange(1, nf+1)**2
right_ans[3] = T_infinity**2
self.assertTrue(sp.allclose(variance,
right_ans, rtol=0.1))
def test_gets_modes_by_scan(self):
nf = self.nf
nt = self.nt
dt = self.dt
bw = self.bw
nb = self.nb
# Give every channel a different thermal noise floor.
thermal_norm = 1.0 + 1.0/nf*sp.arange(nf) # K**2/Hz
self.data *= sp.sqrt(thermal_norm * bw * 2)
n_time = self.data.shape[0]
# Now make a 1/f like noise component in a few frequency modes.
n_modes = 3
index = -0.8 * (2.0 - sp.arange(n_modes, dtype=float)/n_modes)
amp = 1.2 * (3.**(n_modes - 1.
- sp.arange(n_modes, dtype=float))) # K**2/Hz
f_0 = 1.0 # Hz
modes = sp.empty((n_modes, nf))
for ii in range(n_modes):
correlated_overf = noise_power.generate_overf_noise(amp[ii],
index[ii], f_0, dt, n_time)
# Generate the frequency mode. They should all be orthonormal.
mode = sp.sin(2.*sp.pi*(ii + 1)*sp.arange(nf, dtype=float)/nf
+ 6.4 * (ii + 3))
mode /= sp.sqrt(sp.sum(mode**2))
modes[ii] = mode
self.data += correlated_overf[:,None,None,None] * mode
# Add a subdominant general 1/f noise to all channels.
general_amp = 0.1
general_index = -0.9
general_cross_over = f_0 * general_amp**(-1./general_index)
for ii in range(nf):
tmp_a = general_amp * thermal_norm[ii]
correlated_overf = noise_power.generate_overf_noise(tmp_a,
general_index, f_0, dt, n_time)
self.data[:,0,:,ii] += correlated_overf[:,None]
# Now put the data into the form of the real data.
Blocks = self.make_blocks()
# Measure all the noise parameters.
model_name = 'freq_modes_over_f_' + str(n_modes)
parameters = mn.measure_noise_parameters(Blocks, [model_name],
split_scans=True)
for pol, pol_params in parameters.iteritems():
for ii in range(n_modes):
mode_noise = pol_params[model_name]['over_f_mode_' + str(ii)]
self.assertTrue(sp.allclose(mode_noise['amplitude'], amp[ii],
rtol=0.5))
self.assertTrue(sp.allclose(mode_noise['index'], index[ii],
atol=0.2))
#thermal_proj = sp.sum(thermal_norm * modes[ii,:]**2)
#self.assertTrue(sp.allclose(mode_noise['thermal'], thermal_proj,
# rtol=0.5))
self.assertTrue(abs(sp.dot(mode_noise['mode'], modes[ii,:]))
> 0.90)
thermal = pol_params[model_name]['thermal']
loss = float(nf - n_modes) / nf
self.assertTrue(sp.allclose(thermal, thermal_norm * loss,
rtol=0.4))
measured_general_ind = pol_params[model_name]['all_channel_index']
measured_corner = pol_params[model_name]['all_channel_corner_f']
if pol == 1:
self.assertTrue(sp.allclose(measured_general_ind,
general_index, atol=0.4))
#Only need logarithmic accuracy on the corner.
self.assertTrue(sp.allclose(sp.log(measured_corner),
sp.log(general_cross_over),
atol=1.5))
elif pol == 3:
self.assertTrue(measured_corner < 4. / dt / nt)
def test_gets_modes_by_scan(self):
nf = self.nf
nt = self.nt
dt = self.dt
bw = self.bw
nb = self.nb
# Give every channel a different thermal noise floor.
thermal_norm = 1.0 + 1.0 / nf * sp.arange(nf) # K**2/Hz
self.data *= sp.sqrt(thermal_norm * bw * 2)
n_time = self.data.shape[0]
# Now make a 1/f like noise component in a few frequency modes.
n_modes = 3
index = -0.8 * (2.0 - sp.arange(n_modes, dtype=float) / n_modes)
amp = 1.2 * (3.**(n_modes - 1. - sp.arange(n_modes, dtype=float))
) # K**2/Hz
f_0 = 1.0 # Hz
modes = sp.empty((n_modes, nf))
for ii in range(n_modes):
correlated_overf = noise_power.generate_overf_noise(
amp[ii], index[ii], f_0, dt, n_time)
# Generate the frequency mode. They should all be orthonormal.
mode = sp.sin(2. * sp.pi *
(ii + 1) * sp.arange(nf, dtype=float) / nf + 6.4 *
(ii + 3))
mode /= sp.sqrt(sp.sum(mode**2))
modes[ii] = mode
self.data += correlated_overf[:, None, None, None] * mode
# Add a subdominant general 1/f noise to all channels.
general_amp = 0.1
general_index = -0.9
general_cross_over = f_0 * general_amp**(-1. / general_index)
for ii in range(nf):
tmp_a = general_amp * thermal_norm[ii]
correlated_overf = noise_power.generate_overf_noise(
tmp_a, general_index, f_0, dt, n_time)
self.data[:, 0, :, ii] += correlated_overf[:, None]
# Now put the data into the form of the real data.
Blocks = self.make_blocks()
# Measure all the noise parameters.
model_name = 'freq_modes_over_f_' + str(n_modes)
parameters = mn.measure_noise_parameters(Blocks, [model_name],
split_scans=True)
for pol, pol_params in parameters.iteritems():
for ii in range(n_modes):
mode_noise = pol_params[model_name]['over_f_mode_' + str(ii)]
self.assertTrue(
sp.allclose(mode_noise['amplitude'], amp[ii], rtol=0.5))
self.assertTrue(
sp.allclose(mode_noise['index'], index[ii], atol=0.2))
#thermal_proj = sp.sum(thermal_norm * modes[ii,:]**2)
#self.assertTrue(sp.allclose(mode_noise['thermal'], thermal_proj,
# rtol=0.5))
self.assertTrue(
abs(sp.dot(mode_noise['mode'], modes[ii, :])) > 0.90)
thermal = pol_params[model_name]['thermal']
loss = float(nf - n_modes) / nf
self.assertTrue(sp.allclose(thermal, thermal_norm * loss,
rtol=0.4))
measured_general_ind = pol_params[model_name]['all_channel_index']
measured_corner = pol_params[model_name]['all_channel_corner_f']
if pol == 1:
self.assertTrue(
sp.allclose(measured_general_ind, general_index, atol=0.4))
#Only need logarithmic accuracy on the corner.
self.assertTrue(
sp.allclose(sp.log(measured_corner),
sp.log(general_cross_over),
atol=1.5))
elif pol == 3:
self.assertTrue(measured_corner < 4. / dt / nt)