uz1d = u_z[k, l, :] # data structure is (r, theta, z)
uzF1d = u_zF[k, l, :]
omegaZ1d = omegaZ[k, l, :]
pi1d = pi[k, l, :]
# isolate high-speed/low-speed streaks in the 1d velocity subset
uzp1d = np.where(uz1d > 0, uz1d, 0) # only high-speed, zero elsewhere
uzn1d = np.where(uz1d < 0, uz1d, 0) # only low-speed, zero elsewhere
# isolate forward/backward flux events in the 1d flux subset
pip1d = np.where(pi1d > 0, pi1d, 0) # only forward, zero elsewhere
pin1d = np.where(pi1d < 0, pi1d, 0) # only backward, zero elsewhere
# compute correlations and sum up azimuthal (spatial) and temporal (ensemble) statistics
import crossCorrelation as c
acUz = acUz + c.corr1d(uz1d, uz1d) # auto-correlations
acUzF = acUzF + c.corr1d(uzF1d, uzF1d)
acOmegaZ = acOmegaZ + c.corr1d(omegaZ1d, omegaZ1d)
acPi = acPi + c.corr1d(pi1d, pi1d)
ccUzPi = ccUzPi + c.corr1d(uz1d, pi1d) # cross-correlations full flux
ccUzFPi = ccUzFPi + c.corr1d(uzF1d, pi1d)
ccOmegaZPi = ccOmegaZPi + c.corr1d(omegaZ1d, pi1d)
ccUzPip = ccUzPip + c.corr1d(uz1d, pip1d) # cross-correlations forward flux
ccUzFPip = ccUzFPip + c.corr1d(uzF1d, pip1d)
ccOmegaZPip = ccOmegaZPip + c.corr1d(omegaZ1d, pip1d)
ccUzPin = ccUzPin + c.corr1d(uz1d, pin1d) # cross-correlations backward flux
ccUzFPin = ccUzFPin + c.corr1d(uzF1d, pin1d)
ccOmegaZPin = ccOmegaZPin + c.corr1d(omegaZ1d, pin1d)
ccUzpPi = ccUzpPi + c.corr1d(uzp1d, pi1d) # cross-correlations high-speed streaks
ccUznPi = ccUznPi + c.corr1d(uzn1d, pi1d) # cross-correlations low-speed streaks
if (uz1d[i] < 0) and (ur1d[i] > 0):
q3[i] = ur1d[i] * uz1d[
i] # inward interaction: low-speed fluid towards wall
if (uz1d[i] > 0) and (ur1d[i] > 0):
q4[i] = ur1d[i] * uz1d[
i] # sweep event: high-speed fluid towards wall
ioi = q1 - q3 # unify inward interactions (Q3 being negativ) and outward interactions (Q1 being positive) in one array
see = q2 - q4 # unify sweep events (Q4 being negativ) and ejection events (Q2 being positiv) in one array
# isolate forward/backward flux events in the 1d velocity subset
pip1d = np.where(pi1d > 0, pi1d, 0) # only positive, zero elsewhere
pin1d = np.where(pi1d < 0, pi1d, 0) # only negative, zero elsewhere
# compute correlations and sum up axial (spatial) and temporal (ensemble) statistics
import crossCorrelation as c
acQ1 = acQ1 + c.corr1d(q1, q1) # auto-correlations
acQ2 = acQ2 + c.corr1d(q2, q2)
acQ3 = acQ3 + c.corr1d(q3, q3)
acQ4 = acQ4 + c.corr1d(q4, q4)
acPi = acPi + c.corr1d(pi1d, pi1d)
ccQ1Pi = ccQ1Pi + c.corr1d(q1, pi1d) # cross-correlations full flux
ccQ2Pi = ccQ2Pi + c.corr1d(q2, pi1d)
ccQ3Pi = ccQ3Pi + c.corr1d(q3, pi1d)
ccQ4Pi = ccQ4Pi + c.corr1d(q4, pi1d)
ccQ1Pip = ccQ1Pip + c.corr1d(q1,
pip1d) # cross-correlations forward flux
ccQ2Pip = ccQ2Pip + c.corr1d(q2, pip1d)
ccQ3Pip = ccQ3Pip + c.corr1d(q3, pip1d)
ccQ4Pip = ccQ4Pip + c.corr1d(q4, pip1d)
ccQ1Pin = ccQ1Pin + c.corr1d(q1,
pin1d) # cross-correlations backward flux
