rss_ringoccs.calibration.calc_tau_thresh module¶

Purpose:

Compute $\tau_{thresh}$ for use as a proxy for maximum reliable value of optical depth within the diffraction-limited or diffraction-reconstructed profile. This follows [MTR1986] Equations 24 and 26, which define, respectively, the power of the thermal noise

$\hat{P}_N = \frac{\dot{\rho}_0} {\mathrm{SNR}_0 \Delta\rho_0}$

and the threshold optical depth

$\tau_{thresh} = -\sin(B) \ln\left(\frac{1}{2}C_{\alpha}\hat{P}_N\right)$

Dependencies:

numpy
matplotlib
scipy

class rss_ringoccs.calibration.calc_tau_thresh.calc_tau_thresh(rsr_inst, geo_inst, cal_inst, res_km=1.0, Calpha=2.41)¶

Bases: object

Purpose:	Compute threshold optical depth following

Arguments

rsr_inst (object):
	object instance of the RSRReader class
geo_inst (object):
	object instance of the Geometry class
cal_inst (object):
	object instance of the Calibration class

Keyword Arguments

res_km (float):	Reconstruction resolution in km
Calpha (float):	Constant for scaling Bandwidth/SNR ratio. Default is 2.41 for 70% confidence (see [MTR1986])

Attributes

snr (np.ndarray):
	Signal-to-noise ratio SNR0 over the entire occultation. This changes over the occultation because the signal power fluctuates.
tau_thresh (np.ndarray):
	threshold optical depth computed using [MTR1986]
spm_vals (np.ndarray):
	Observed event time array from cal_inst
rho_vals (np.ndarray):
	Ring intercept point array interpolated to spm_vals

find_noise(spm, IQ, df)¶

Locate the additive receiver noise within the data set. This is done by computing a spectrogram of the raw complex signal, filtering out the spacecraft signal, and averaging over the frequency and time domains.

Arguments

spm (np.ndarray):
	raw SPM in seconds
IQ (np.ndarray):
	measured complex signal
df (float):	sampling frequency in Hz of the IQ

Returns

p_noise (np.ndarray):
	noise power