def cs_contrib(a,b,bins,subsamp=10):
from metobs.generic import get_dims
'''
a,b: raw time series (not perturbations)
Compute the contribution of coherent structures to the total flux of a'b'.
bins: list of arrays that contain N.arange(start_index,stop_index) for CS events
the list is returned from metobs.vis.wavelet_plt() as the first member.
subsamp: number of samples in original time series for each sample in subsampled
timeseres (if timeseries was subsampled to calculate wavelet).
'''
sensors,obs,blks=get_dims(a,b)
a=a.reshape((sensors,obs,blks))
b=a.reshape((sensors,obs,blks))
cohf_n=[]
tcoh=[]
ap=get_pert(a)
bp=get_pert(b)
for i in range(0,len(bins[0])):
tcoh.append(len(bins[0][i])*subsamp)
cohf_n.append(tcoh[i]*N.average(
ap[0,bins[0][i][0]*subsamp:bins[0][i][-1]*subsamp+subsamp-1,0]*
bp[0,bins[0][i][0]*subsamp:bins[0][i][-1]*subsamp+subsamp-1,0]))
F_coh=N.array(cohf_n).squeeze().sum()/(a.shape[1]*turb_covar(a[0,:,0],b[0,:,0]))
TE=F_coh/((1./a.shape[1])*N.array(tcoh).sum())
return (F_coh,TE,tcoh)
def ogive(coPxx,delf):
sensors,obs,blks = get_dims(coPxx)
coPxx=coPxx.reshape(sensors,obs,blks)
og = np.zeros((sensors,obs-1,blks))
ogt=cumtrapz(coPxx[:,::-1,:],dx=delf,axis=1)
og=ogt[:,::-1,:]
return og
def turb_covar(a,b,remove_mean=True):
'''
Compute a turbulence covariance term (i.e. u'w', w'T'...)
'''
sensors,obs,blks = get_dims(a,b)
a=a.reshape(sensors,obs,blks)
b=b.reshape(sensors,obs,blks)
t_cov = N.ones((sensors,blks))
ax = 1
for blk in range(0,blks):
for sen in range(0,sensors):
if remove_mean:
t_cov[sen,blk] = N.average(a[sen,:,blk]*b[sen,:,blk])-\
N.average(a[sen,:,blk])*N.average(b[sen,:,blk])
else:
t_cov[sen,blk] = N.average(a[sen,:,blk]*b[sen,:,blk])
return t_cov
def two_three_rot(ui,vi,wi,common=None,return_basis=False,unrotate = False,use_basis=None):
'''
Perform 2-3 rotation (only apply first two rotations)
ui,vi,wi: input u,v, and w compoenents of velocity
common: use `common` basis to perform rotation/unrotation (int)
return_basis: Return the new basis vectors (True/False)
unrotate: undo rotation (True/False)
use_basis: list of basis vectors to use in the rotation [i(nx3),j(nx3),k(nx3)]
'''
sensors,obs,blks = get_dims(ui,vi,wi)
#
# initilize arrays to force three dimensions
#
u=N.reshape(ui,(sensors,obs,blks))
v=N.reshape(vi,(sensors,obs,blks))
w=N.reshape(wi,(sensors,obs,blks))
meanu1=N.ones((sensors,obs,blks))
meanu2=N.ones((sensors,obs,blks))
u1=N.ones((sensors,blks))
v1=N.ones((sensors,blks))
w1=N.ones((sensors,blks))
data_rot_u = N.ones((sensors,obs,blks))
data_rot_v = N.ones((sensors,obs,blks))
data_rot_w = N.ones((sensors,obs,blks))
avg_axis = 1
zeros = N.zeros((sensors,blks))
sindex = range(0,sensors)
#
# define cartesian unit vectors
#
i1t = N.array([1,0,0])
j1t = N.array([0,1,0])
k1t = N.array([0,0,1])
i1=i1t
j1=j1t
k1=k1t
if sensors != 1:
for i in range(0,sensors-1):
i1=N.c_[i1,i1t]
j1=N.c_[j1,j1t]
k1=N.c_[k1,k1t]
i1=i1.transpose()
j1=j1.transpose()
k1=k1.transpose()
#
# Compute mean 2d and 3d wind vectors
#
u1 = N.average(u,axis=avg_axis) # mean u comp
v1 = N.average(v,axis=avg_axis) # mean v comp
w1 = N.average(w,axis=avg_axis) # mean w comp
meanu1 = N.array([u1,v1,zeros]) # mean 2D wind vector
meanu2 = N.array([u1,v1,w1]) # mean 3D wind vector
for blk in range(0,blks):
#
# define 2-3 rotation unit vectors
#
if use_basis is None:
# first rotation
# i2 is aligned with mean horizontal wind
i2_temp = meanu1[:,:,blk]/N.power(meanu1[0,:,blk]*meanu1[0,:,blk]+\
meanu1[1,:,blk]*meanu1[1,:,blk]+\
meanu1[2,:,blk]*meanu1[2,:,blk],0.5)
# k2 is the same as k1 in original cartesian coordinate system
# j2 is the cross product between k1 and the new i (i2)
j2 = N.cross(k1[sindex,:],i2_temp.transpose()[sindex,:])
#
# second rotation
#
# now define i2 to be aligned with the mean vector wind
i2 = N.transpose(meanu2[:,:,blk]/N.power(meanu2[0,:,blk]*meanu2[0,:,blk]+\
meanu2[1,:,blk]*meanu2[1,:,blk]+\
meanu2[2,:,blk]*meanu2[2,:,blk],0.5))
# j2 is the result of the first rotation
# k2 is the cross product between i2 and j2
k2 = N.cross(i2[sindex,:],j2[sindex,:])
# correct alg...need to store results in data_rot_u,v and w correctly.
else:
i2=use_basis[0]
j2=use_basis[1]
k2=use_basis[2]
if not unrotate:
i_new = i2
j_new = j2
k_new = k2
i_old = i1
j_old = j1
k_old = k1
else:
i_new = i1
j_new = j1
k_new = k1
i_old = i2
j_old = j2
k_old = k2
if common == None:
for i in range(0,sensors):
data_rot_u[i,:,blk] = u[i,:,blk]*N.dot(i_old[i,:],i_new[i,:])+\
v[i,:,blk]*N.dot(j_old[i,:],i_new[i,:])+\
w[i,:,blk]*N.dot(k_old[i,:],i_new[i,:])
data_rot_v[i,:,blk] = u[i,:,blk]*N.dot(i_old[i,:],j_new[i,:])+\
v[i,:,blk]*N.dot(j_old[i,:],j_new[i,:])+\
w[i,:,blk]*N.dot(k_old[i,:],j_new[i,:])
data_rot_w[i,:,blk] = u[i,:,blk]*N.dot(i_old[i,:],k_new[i,:])+\
v[i,:,blk]*N.dot(j_old[i,:],k_new[i,:])+\
w[i,:,blk]*N.dot(k_old[i,:],k_new[i,:])
else:
for i in range(0,sensors):
data_rot_u[i,:,blk] = u[i,:,blk]*N.dot(i_old[common,:],i_new[common,:])+\
v[i,:,blk]*N.dot(j_old[common,:],i_new[common,:])+\
w[i,:,blk]*N.dot(k_old[common,:],i_new[common,:])
data_rot_v[i,:,blk] = u[i,:,blk]*N.dot(i_old[common,:],j_new[common,:])+\
v[i,:,blk]*N.dot(j_old[common,:],j_new[common,:])+\
w[i,:,blk]*N.dot(k_old[common,:],j_new[common,:])
data_rot_w[i,:,blk] = u[i,:,blk]*N.dot(i_old[common,:],k_new[common,:])+\
v[i,:,blk]*N.dot(j_old[common,:],k_new[common,:])+\
w[i,:,blk]*N.dot(k_old[common,:],k_new[common,:])
if return_basis:
return data_rot_u,data_rot_v,data_rot_w,(i_new,j_new,k_new)
else:
return data_rot_u,data_rot_v,data_rot_w
def two_three_rot(ui,
vi,
wi,
common=None,
return_basis=False,
unrotate=False,
use_basis=None):
'''
Perform 2-3 rotation (only apply first two rotations)
ui,vi,wi: input u,v, and w compoenents of velocity
common: use `common` basis to perform rotation/unrotation (int)
return_basis: Return the new basis vectors (True/False)
unrotate: undo rotation (True/False)
use_basis: list of basis vectors to use in the rotation [i(nx3),j(nx3),k(nx3)]
'''
sensors, obs, blks = get_dims(ui, vi, wi)
#
# initilize arrays to force three dimensions
#
u = N.reshape(ui, (sensors, obs, blks))
v = N.reshape(vi, (sensors, obs, blks))
w = N.reshape(wi, (sensors, obs, blks))
meanu1 = N.ones((sensors, obs, blks))
meanu2 = N.ones((sensors, obs, blks))
u1 = N.ones((sensors, blks))
v1 = N.ones((sensors, blks))
w1 = N.ones((sensors, blks))
data_rot_u = N.ones((sensors, obs, blks))
data_rot_v = N.ones((sensors, obs, blks))
data_rot_w = N.ones((sensors, obs, blks))
avg_axis = 1
zeros = N.zeros((sensors, blks))
sindex = range(0, sensors)
#
# define cartesian unit vectors
#
i1t = N.array([1, 0, 0])
j1t = N.array([0, 1, 0])
k1t = N.array([0, 0, 1])
i1 = i1t
j1 = j1t
k1 = k1t
if sensors != 1:
for i in range(0, sensors - 1):
i1 = N.c_[i1, i1t]
j1 = N.c_[j1, j1t]
k1 = N.c_[k1, k1t]
i1 = i1.transpose()
j1 = j1.transpose()
k1 = k1.transpose()
#
# Compute mean 2d and 3d wind vectors
#
u1 = N.average(u, axis=avg_axis) # mean u comp
v1 = N.average(v, axis=avg_axis) # mean v comp
w1 = N.average(w, axis=avg_axis) # mean w comp
meanu1 = N.array([u1, v1, zeros]) # mean 2D wind vector
meanu2 = N.array([u1, v1, w1]) # mean 3D wind vector
for blk in range(0, blks):
#
# define 2-3 rotation unit vectors
#
if use_basis is None:
# first rotation
# i2 is aligned with mean horizontal wind
i2_temp = meanu1[:,:,blk]/N.power(meanu1[0,:,blk]*meanu1[0,:,blk]+\
meanu1[1,:,blk]*meanu1[1,:,blk]+\
meanu1[2,:,blk]*meanu1[2,:,blk],0.5)
# k2 is the same as k1 in original cartesian coordinate system
# j2 is the cross product between k1 and the new i (i2)
j2 = N.cross(k1[sindex, :], i2_temp.transpose()[sindex, :])
#
# second rotation
#
# now define i2 to be aligned with the mean vector wind
i2 = N.transpose(meanu2[:,:,blk]/N.power(meanu2[0,:,blk]*meanu2[0,:,blk]+\
meanu2[1,:,blk]*meanu2[1,:,blk]+\
meanu2[2,:,blk]*meanu2[2,:,blk],0.5))
# j2 is the result of the first rotation
# k2 is the cross product between i2 and j2
k2 = N.cross(i2[sindex, :], j2[sindex, :])
# correct alg...need to store results in data_rot_u,v and w correctly.
else:
i2 = use_basis[0]
j2 = use_basis[1]
k2 = use_basis[2]
if not unrotate:
i_new = i2
j_new = j2
k_new = k2
i_old = i1
j_old = j1
k_old = k1
else:
i_new = i1
j_new = j1
k_new = k1
i_old = i2
j_old = j2
k_old = k2
if common == None:
for i in range(0, sensors):
data_rot_u[i,:,blk] = u[i,:,blk]*N.dot(i_old[i,:],i_new[i,:])+\
v[i,:,blk]*N.dot(j_old[i,:],i_new[i,:])+\
w[i,:,blk]*N.dot(k_old[i,:],i_new[i,:])
data_rot_v[i,:,blk] = u[i,:,blk]*N.dot(i_old[i,:],j_new[i,:])+\
v[i,:,blk]*N.dot(j_old[i,:],j_new[i,:])+\
w[i,:,blk]*N.dot(k_old[i,:],j_new[i,:])
data_rot_w[i,:,blk] = u[i,:,blk]*N.dot(i_old[i,:],k_new[i,:])+\
v[i,:,blk]*N.dot(j_old[i,:],k_new[i,:])+\
w[i,:,blk]*N.dot(k_old[i,:],k_new[i,:])
else:
for i in range(0, sensors):
data_rot_u[i,:,blk] = u[i,:,blk]*N.dot(i_old[common,:],i_new[common,:])+\
v[i,:,blk]*N.dot(j_old[common,:],i_new[common,:])+\
w[i,:,blk]*N.dot(k_old[common,:],i_new[common,:])
data_rot_v[i,:,blk] = u[i,:,blk]*N.dot(i_old[common,:],j_new[common,:])+\
v[i,:,blk]*N.dot(j_old[common,:],j_new[common,:])+\
w[i,:,blk]*N.dot(k_old[common,:],j_new[common,:])
data_rot_w[i,:,blk] = u[i,:,blk]*N.dot(i_old[common,:],k_new[common,:])+\
v[i,:,blk]*N.dot(j_old[common,:],k_new[common,:])+\
w[i,:,blk]*N.dot(k_old[common,:],k_new[common,:])
if return_basis:
return data_rot_u, data_rot_v, data_rot_w, (i_new, j_new, k_new)
else:
return data_rot_u, data_rot_v, data_rot_w
