df1 = df_1.loc[i1]
if switch == 0:
phi0 = df0.azim.unique()
phi1 = df1.azim.unique()
r0 = df0.range_gate.iloc[0].values
r1 = df1.range_gate.iloc[0].values
r_0, phi_0 = np.meshgrid(r0, np.pi -
np.radians(phi0)) # meshgrid
r_1, phi_1 = np.meshgrid(r0, np.pi -
np.radians(phi1)) # meshgrid
tree, tri, w0, neigh0, index0, w1, neigh1, index1 = wr.grid_over2(
(r_0, phi_0), (r_1, phi_1), -d)
switch = 1
u, v, grd, s = wr.direct_wf_rec(df0.astype(np.float32),
df1.astype(np.float32),
tri,
d,
N_grid=512)
U_out.append(u), V_out.append(v), su.append(s)
U_out = [item for sublist in U_out for item in sublist]
V_out = [item for sublist in V_out for item in sublist]
su = [item for sublist in su for item in sublist]
#filename = file_out_path_u_field+'/U_rec_'+dy+'.pkl'
#joblib.dump((U_out, V_out, grd, su), filename)
#
#with open(file_out_path_u_field+'/U_rec_'+dy+'.pkl', 'wb') as field:
# pickle.dump((U_out, V_out, grd, su),field)
#### Vorticity
indt0 = [x for x in indt0 if np.sum(x) > 0]
indt1 = [x for x in indt1 if np.sum(x) > 0]
if (len(indt0)>0)&(len(indt1)>0):
for i0,i1 in zip(indt0,indt1):
df0 = df_0.loc[i0]
df1 = df_1.loc[i1]
if switch == 0:
phi0 = df0.azim.unique()
phi1 = df1.azim.unique()
r0 = df0.range_gate.iloc[0].values
r1 = df1.range_gate.iloc[0].values
r_0, phi_0 = np.meshgrid(r0, np.pi/2-np.radians(phi0)) # meshgrid
r_1, phi_1 = np.meshgrid(r0, np.pi/2-np.radians(phi1)) # meshgrid
tree,tri, w0, neigh0, index0, w1, neigh1, index1 = wr.grid_over2((r_0, phi_0),(r_1,phi_1), -d)
switch = 1
uo, vo, grdu, so = wr.direct_wf_rec(df0.astype(np.float32), df1.astype(np.float32), tri, d, N_grid = chunk)
ulist.append(uo)
vlist.append(vo)
ulist = [item for sublist in ulist for item in sublist]
vlist = [item for sublist in vlist for item in sublist]
dr0 = np.array(np.r_[-d/2,np.ones(1)]).T
dr1 = np.array(np.r_[d/2,np.ones(1)]).T
fig0, ax0 = plt.subplots(figsize=(8, 8))
ax0.set_aspect('equal')
ax0.use_sticky_edges = False
ax0.margins(0.07)
for j in range(len(ulist)):
areatriangles(tri_t1)) + 2 * np.std(areatriangles(tri_t1))
mask1 = maskt | maska
tri_t1.set_mask(mask1)
pts_in_1 = tri_t1.get_trifinder()(x_i, y_i) != -1
tri_t1.set_mask(None)
##################
start = 8500
end = 8750
ind0 = (df_0.scan >= start) & (df_0.scan <= end)
ind1 = (df_1.scan >= start) & (df_1.scan <= end)
U_out, V_out, grd, _ = wf.direct_wf_rec(df_0.loc[ind0],
df_1.loc[ind1],
tri,
d,
N_grid=512)
x = grd[0][0, :]
y = grd[1][:, 0]
k_1_r = []
k_2_r = []
Su_u_r = []
Sv_v_r = []
Su_v_r = []
for scan_i in range(len(U_out)):
print(scan_i)
ku, kv, Su, Sv, Suv = sc.spatial_spec_sq(x,
times_scan0 = scan_init_0 + times0
times_scan1 = scan_init_1 + times1
if len(times0) <= len(times0):
times = times0
else:
times = times1
UV = pd.DataFrame()
for i in range(len(times) - 1):
print(times[i], times[i + 1])
loc0 = (chunk0.scan >= times[i]) & (chunk0.scan <
times[i + 1])
loc1 = (chunk1.scan >= times[i]) & (chunk1.scan <
times[i + 1])
U_out, V_out, grd, _ = wr.direct_wf_rec(
chunk0.loc[loc0].astype(np.float32),
chunk1.loc[loc1].astype(np.float32),
tri,
d,
N_grid=512)
init = scan_init_0 + times[i]
end = scan_init_0 + times[i + 1]
scan = np.arange(init, end)
time = np.array([
str(date + timedelta(seconds=chunk0.loc[loc0].loc[
chunk0.loc[loc0].scan == s].stop_time.min()))[:-7]
for s in chunk0.loc[loc0].scan.unique()
])
vel = np.vstack([
np.c_[grd[0][~np.isnan(u)], grd[1][~np.isnan(u)],
u[~np.isnan(u)], v[~np.isnan(u)],
np.asarray(~np.isnan(u)).nonzero()[0],
np.asarray(~np.isnan(u)).nonzero()[1],
rel = np.c_[s_syn[:,0],t0,rel0,s_syn[:,1],t1,rel1,rel]
###############################################################################
loc0 = np.array([0,6322832.3])
loc1 = np.array([0,6327082.4])
d = loc1-loc0
phi0 = df0.azim.unique()
phi1 = df1.azim.unique()
r0 = df0.range_gate.iloc[0].values
r1 = df1.range_gate.iloc[0].values
r_0, phi_0 = np.meshgrid(r0, np.pi/2-np.radians(phi0)) # meshgrid
r_1, phi_1 = np.meshgrid(r0, np.pi/2-np.radians(phi1)) # meshgrid
tree,tri, w0, neigh0, index0, w1, neigh1, index1 = wr.grid_over2((r_0, phi_0),(r_1,phi_1), -d)
chunk = 256
_, _, grdu, so = wr.direct_wf_rec((df0.loc[df0.scan == s_syn[0,0]]).astype(np.float32),
(df1.loc[df1.scan == s_syn[0,1]]).astype(np.float32),
tri, d, N_grid = chunk)
find = np.reshape(tri.get_trifinder()(grdu[0].flatten(), grdu[1].flatten()),grdu[0].shape)
c = len(grdu[0].flatten())/np.sum(find!=-1)
################## 10min periods and averages #############################
for i in range(len(t_arrayhms)-1):
hms1 = t_arrayhms[i]
hms2 = t_arrayhms[i+1]
L_scan = pd.read_hdf(file_name,where = 'name == name_id & hms >= hms1 & hms < hms2', key='L')
if L_scan.empty:
print('empty df')
else:
ind_rel = np.isin(pd.to_datetime(rel[:,1]),L_scan.time.values)
L_scan['relscan'] = rel[ind_rel,-1]
L_scan['area_frac'] = L_scan['area_frac'].values*c
if switch == 0:
phi0 = df0.azim.unique()
phi1 = df1.azim.unique()
r0 = df0.range_gate.iloc[0].values
r1 = df1.range_gate.iloc[0].values
r_0, phi_0 = np.meshgrid(r0, np.pi / 2 -
np.radians(phi0)) # meshgrid
r_1, phi_1 = np.meshgrid(r0, np.pi / 2 -
np.radians(phi1)) # meshgrid
tree, tri, w0, neigh0, index0, w1, neigh1, index1 = wr.grid_over2(
(r_0, phi_0), (r_1, phi_1), -d)
switch = 1
print(t_scan[0])
uo, vo, grdu, so, r_i_0, phi_i_0, r_i_1, phi_i_1, mask = wr.direct_wf_rec(
df0.astype(np.float32),
df1.astype(np.float32),
tri,
d,
N_grid=chunk)
r_i = np.sqrt(grdu[0]**2 + grdu[1]**2)
phi_i = np.arctan2(grdu[1], grdu[0])
ind = tri.get_trifinder()(grdu[0].flatten(), grdu[1].flatten()) != -1
r_i_0, phi_i_0 = wr.translationpolargrid((r_i, phi_i), -d / 2)
phi_i_0 = np.pi * 0.5 - phi_i_0
phi_i_0 = np.where(phi_i_0 < 0, 2 * np.pi + phi_i_0, phi_i_0)
r_i_1, phi_i_1 = wr.translationpolargrid((r_i, phi_i), d / 2)
phi_i_1 = np.pi * 0.5 - phi_i_1
phi_i_1 = np.where(phi_i_1 < 0, 2 * np.pi + phi_i_1, phi_i_1)
plt.figure()
df = None
df_0.drop(columns = labels_CNR,inplace=True)
df_1.drop(columns = labels_CNR,inplace=True)
df_0.columns = labels_short
df_1.columns = labels_short
if switch == 0:
phi0 = df_0.azim.unique()
phi1 = df_1.azim.unique()
r0 = np.array(df_0.iloc[(df_0.azim==min(phi0)).nonzero()[0][0]].range_gate)
r1 = np.array(df_1.iloc[(df_1.azim==min(phi0)).nonzero()[0][0]].range_gate)
r_0, phi_0 = np.meshgrid(r0, np.pi-np.radians(phi0)) # meshgrid
r_1, phi_1 = np.meshgrid(r1, np.pi-np.radians(phi1)) # meshgrid
tree,tri, w0, neigh0, index0, w1, neigh1, index1 = wr.grid_over2((r_0, phi_0),(r_1,phi_1),-d)
switch = 1
U_out, V_out, grd, _ = wr.direct_wf_rec(df_0.astype(np.float32), df_1.astype(np.float32), tri, d,N_grid = 512)
U_mean = np.nanmean(np.array(U_out))
V_mean = np.nanmean(np.array(V_out))
gamma = np.arctan2(V_mean,U_mean)
tau_out = []
eta_out = []
r_u_out = []
r_v_out = []
r_uv_out = []
if len(U_out)>0:
for U,V in zip(U_out, V_out):
tau,eta,r_u,r_v,r_uv,_,_,_ = sc.spatial_autocorr_sq(grd,U,V,
gamma=gamma, transform = False,
transform_r = True,e_lim=.1,refine=32)
tau_out.append(tau.astype(np.float32))
eta_out.append(eta.astype(np.float32))