def main():
"""Visually test the addition of Noise using add_noise function."""
org_flux = np.ones(500)
for i, snr in enumerate([50, 100, 200, 300, 50000]):
# Test that the standard deviation of the noise is close to the snr level
print("Applying a snr of {}".format(snr))
noisey_flux = add_noise(org_flux, snr)
spec = Spectrum(
flux=copy.copy(org_flux)) # Copy becasue org_flux is mutable.
spec.add_noise(snr)
std = np.std(noisey_flux)
print("Standard deviation of signal = {}".format(std))
snr_est = 1 / std
snr_spectrum = 1. / np.std(spec.flux)
print(
"Estimated SNR from stddev = {}".format(snr_est))
print("Estimated SNR from stddev of Spectrum class = {}".format(
snr_spectrum))
plt.plot(noisey_flux + 0.1 * i, label="snr={}".format(snr))
plt.plot(spec.flux + 0.1 * i,
"--",
label="Spectrum snr={}".format(snr))
# Calculate chisqr from one
chi2 = np.sum((noisey_flux - 1)**2 / 1**2)
print("Chisqr for SNR {} = \t\t{}".format(snr, chi2))
chi2_spectrum = np.sum((spec.flux - 1)**2 / 1**2)
print("Chisqr for snr_spectrum {} = \t\t{}".format(snr, chi2_spectrum))
plt.legend()
plt.show()
def fake_obs(simnum, snr=200, suffix=None, plot=False, mode="iam"):
snr = 200
params1 = [5300, 4.5, 0.0]
params2 = [2500, 4.5, 0.0]
gamma = 5
rv = -3
normalization_limits = [2100, 2180]
mod1_spec = load_starfish_spectrum(params1,
limits=normalization_limits,
hdr=True,
normalize=False,
area_scale=True,
flux_rescale=True)
mod2_spec = load_starfish_spectrum(params2,
limits=normalization_limits,
hdr=True,
normalize=False,
area_scale=True,
flux_rescale=True)
if broadcast:
broadcast_result = inherent_alpha_model(mod1_spec.xaxis,
mod1_spec.flux,
mod2_spec.flux,
rvs=rv,
gammas=gamma)
broadcast_values = broadcast_result(obs_spec.xaxis)
result_spectrum = Spectrum(flux=broadcast_values,
xaxis=mod1_spec.xaxis)
else:
# Manually redo the join
mod2_spec.doppler_shift(rv)
mod_combine = mod1_spec.copy()
mod_combine += mod2_spec
mod_combine.doppler_shift(gamma)
mod_combine.normalize(method="exponential")
mod_combine.interpolate(obs_spec.xaxis)
result_spectrum = mod_combine
# result_spectrum = Spectrum(flux=broadcast_values, xaxis=obs_spec.xaxis)
result_spectrum.add_noise(snr)
# Save as
# Detector limits
dect_limits = [(2112, 2123), (2127, 2137), (2141, 2151), (2155, 2165)]
for ii, dect in enumerate(dect_limits):
spec = result_spectrum.copy()
spec.wav_select(*dect)
spec.resample(1024)
name = "HDsim-{0}_{1}_snr_{2}".format(sim_num, ii, snr)