dolfyn.adv.turbulence.TurbBinner

class dolfyn.adv.turbulence.TurbBinner(n_bin, fs, n_fft=None, n_fft_coh=None)

Computes various averages and turbulence statistics from cleaned ADV data.

Parameters

n_binint

The length of bin s, in number of points, for this averaging operator.

n_fftint (optional, default: n_fft = n_bin)

The length of the FFT for computing spectra (must be < n_bin)

__init__(self, n_bin, fs, n_fft=None, n_fft_coh=None)

Initialize an averaging object.

Parameters

n_binint

the number of data points to include in a ‘bin’ (average).

n_fftint

the number of data points to use for fft (n_fft`<=`n_bin). Default: n_fft`=`n_bin

n_fft_cohint

the number of data points to use for coherence and cross-spectra ffts (n_fft_coh`<=`n_bin). Default: n_fft_coh`=`n_bin/6

Methods

__init__(self, n_bin, fs[, n_fft, n_fft_coh])	Initialize an averaging object.
calc_Lint(self, corr_vel, U_mag[, fs])	Calculate integral length scales.
calc_acov(self, indat[, n_bin])	Calculate the auto-covariance of the raw-signal indat.
calc_epsilon_LT83(self, spec, omega, U_mag)	Calculate the dissipation rate from the spectrum.
calc_epsilon_SF(self, veldat, umag[, fs, …])	Calculate epsilon using the “structure function” (SF) method.
calc_epsilon_TE01(self, advbin, advraw[, …])	Calculate the dissipation according to TE01.
calc_lag(self[, npt, one_sided])
calc_omega(self[, fs, coh])	Calculate the radial-frequency vector for the psd’s.
calc_stress(self, veldat)	Calculate the stresses (cross-covariances of u,v,w).
calc_tke(self, veldat[, noise])	Calculate the tke (variances of u,v,w).
calc_vel_cpsd(self, veldat[, rotate_u, fs, …])	Calculate the cross-spectra of velocity components.
calc_vel_psd(self, veldat[, rotate_u, fs, …])	Calculate the psd of velocity.
calc_xcov(self, indt1, indt2[, npt, n_bin1, …])	Calculate the cross-covariance between arrays indt1 and indt2 for each bin.
cohere(self, dat1, dat2[, window, debias, …])	Calculate coherence between dat1 and dat2.
cpsd(self, dat1, dat2[, fs, window, n_fft, …])	Calculate the ‘cross power spectral density’ of dat.
demean(self, dat[, n_pad, n_bin])	Reshape the array dat and remove the mean from each ensemble.
detrend(self, dat[, n_pad, n_bin])	Reshape the array dat and remove the best-fit trend line.
do_avg(self, rawdat[, outdat, names, n_time])	Parameters
do_cross_spec(self, indat[, out, names])
do_spec(self, indat[, out, names])
do_tke(self, indat[, out])
do_var(self, rawdat[, outdat, names, suffix])	Calculate the variance of data attributes.
mean(self, dat[, axis, n_bin, mask_thresh])	Average an array object.
mean_angle(self, dat[, axis, units, n_bin, …])	Average an angle array.
phase_angle(self, dat1, dat2[, window, …])	Calculate the phase difference between two signals as a function of frequency (complimentary to coherence).
psd(self, dat[, fs, window, noise, n_bin, …])	Calculate ‘power spectral density’ of dat.
reshape(self, arr[, n_pad, n_bin])	Reshape the array arr to shape (…,n,n_bin+n_pad).
std(self, dat[, n_bin])
up_angle(self, Uh_complex)	Calculate the angle of the turbulence fluctuations.
var(self, dat[, n_bin])