def plot_zetares(time1, zeta1, time2, zeta2,show=True, tidecon=None):
"""
Plot timeseries and residual.
"""
ittide=True
try:
import ttide
except ImportError:
print("ttide is not installed, please install tide.")
ittide=False
f,ax=plt.subplots(2,1,sharex=True)
ax[0].plot(time1, zeta1,'r',lw=2)
ax[0].plot(time2, zeta2,'b',lw=.5)
if ittide:
if tidecon is None:
out1=ttide.t_tide(zeta1,stime=time1[0],dt=24*(time1[1]-time1[0]))
out2=ttide.t_tide(zeta2,stime=time2[0],dt=24*(time2[1]-time2[0]))
else:
out1=ttide.t_tide(zeta1,stime=time1[0],dt=24*(time1[1]-time1[0]),constitnames=tidecon)
out2=ttide.t_tide(zeta2,stime=time2[0],dt=24*(time2[1]-time2[0]),constitnames=tidecon)
ax[1].plot(time1, zeta1-out1(time1),'r',lw=2)
ax[1].plot(time2, zeta2-out2(time2),'b',lw=.5)
if show==True:
f.show()
return f,ax,out1,out2
else:
return f,ax,out1,out2
def run_ttide(time,zeta,lat,constitnames=None):
if constitnames is None:
out=ttide.t_tide(zeta,stime=time[0],dt=np.diff(time)[0]*24,synth=-1,out_style=None,lat=lat)
else:
out=ttide.t_tide(zeta,stime=time[0],dt=np.diff(time)[0]*24,synth=-1,out_style=None,lat=lat,constitnames=constitnames)
return out
def process_point(i):
try:
e = interp(nhours,hours,ncv['elev'][:,i])
tides = tt.t_tide(e,dt=dthours,out_style=None)
except:
tides=None
return tides
def process_timeseries_parallel(elev):
try:
e = interp(nhours,hours,elev)
tides = tt.t_tide(e,dt=dthours,out_style=None)
except:
tides=None
return tides
def process_parallel(i):
try:
nnc = netCDF4.Dataset('schout_2.nc')
e = interp(nhours,hours,nnc.variables['elev'][:,i])
nnc.close()
tides = tt.t_tide(e,dt=dthours,out_style=None)
except:
tides=None
return tides
def process_chunk_parallel(elev):
tides=[]
for i in range(elev.shape[1]):
try:
e = interp(nhours,hours,elev[:,i])
tides.append(tt.t_tide(e,dt=dthours,out_style=None))
except:
tides.append(None)
return tides
def run_ttide(time, zeta, lat, constitnames=None):
if constitnames is None:
out = ttide.t_tide(zeta,
stime=time[0],
dt=np.diff(time)[0] * 24,
synth=-1,
out_style=None,
lat=lat)
else:
out = ttide.t_tide(zeta,
stime=time[0],
dt=np.diff(time)[0] * 24,
synth=-1,
out_style=None,
lat=lat,
constitnames=constitnames)
return out
def ttide_filt(utest, vtest):
#Subtract TIDES
if sum(~isnan(utest)) < 3:
ufilt = utest
vfilt = vtest
else:
tfit_u = tt.t_tide(utest + 1j * vtest, dt=1)
tidepart = tfit_u(arange(0,
len(utest) / 2, 0.5))
ufilt = utest - tidepart.real
vfilt = vtest - tidepart.imag
return ufilt, vfilt
def station_ttide(zeta_da, grid, lat_t, lon_t, stime, constit_list):
zeta_flat = zeta_da.stack(etaxi=('eta_rho', 'xi_rho'))
grid_flat = grid.stack(etaxi=('eta_rho', 'xi_rho'))
lat_s = grid_flat.lat_rho.values[grid_flat.mask_rho.values == True]
lon_s = grid_flat.lon_rho.values[grid_flat.mask_rho.values == True]
zeta_s = zeta_flat.values[:, grid_flat.mask_rho.values == True]
etaxi_s = zeta_flat.etaxi.values[grid_flat.mask_rho.values == True]
points = np.column_stack((lat_s, lon_s))
tree = KDTree(points)
target = np.column_stack((lat_t, lon_t))
dist, ind = tree.query(target)
#dist=dist*10.0
tmp = {}
tmp['roms_signal'] = zeta_s[:, ind].squeeze()
tmp['roms_ind'], tmp['dist_to ATG'] = ind, dist
lat_r = lat_s[ind]
lon_r = lon_s[ind]
eta_rho, xi_rho = np.fromstring(str(etaxi_s[ind])[2:-2],
sep=', ',
dtype=int)
#print('atg lat(lon): %.2f,%.2f'%(lat_t,lon_t))
#print('roms lat(lon): %.2f,%.2f'%(lat_r,lon_r))
dist = haversine(lon_t, lat_t, lon_r, lat_r)
try:
tmp['t_tide'] = tt.t_tide(tmp['roms_signal'],
dt=1,
stime=stime,
lat=lat_r,
out_style=None)
except TypeError:
for const in constit_list:
tmp[const] = [np.nan, np.nan, np.nan, np.nan]
return eta_rho, xi_rho, dist, tmp
for const in constit_list:
tide_con_ind = list(tmp['t_tide']['nameu']).index(
str.encode(const + ' '))
tmp[const] = tmp['t_tide']['tidecon'][tide_con_ind]
#print(eta_rho,xi_rho)
return eta_rho, xi_rho, dist, tmp
def ttideAnalyse(self):
'''T Tide Analysis processing'''
input_dict1 = self.inputDict1()
input_dict2 = self.inputDict2()
ad = input_dict1['depth']
at = input_dict1['time']
latitude = input_dict2['latitude']
time_diff = input_dict1['interval'] / 60
time_num = date2num(at.to_pydatetime())
coef = t_tide(ad,
dt=time_diff,
stime=time_num[0],
lat=latitude,
synth=0)
return coef
def grid_ttide(da, grid_ds, stime, constit_list, res=50):
ana_list = ['amp', 'amp_err', 'phase', 'phase_err']
print('setting up the new fields ', ana_list, ' for ', constit_list)
dummy = np.empty((da.eta_rho.size, da.xi_rho.size))
dummy[:, :] = np.nan
for const in constit_list:
for ana in ana_list:
#print(const+'_'+ana)
grid_ds[const + '_' + ana] = (('eta_rho', 'xi_rho'), dummy.copy())
print("applying t_tide to every ", res, "th cell ...")
xi_values = np.linspace(da.xi_rho[0].values,
da.xi_rho.size - 1,
res,
dtype=int,
endpoint=True)
eta_values = np.linspace(da.eta_rho[0].values,
da.eta_rho.size - 1,
res,
dtype=int,
endpoint=True)
for xi in log_progress(xi_values, name='xi'):
for eta in eta_values:
da_sl = da.isel(eta_rho=eta, xi_rho=xi)
grd_sl = grid_ds.isel(eta_rho=eta, xi_rho=xi)
if da_sl.isnull().values.any():
for const in constit_list:
for ana in ana_list:
grid_ds[const + '_' + ana][eta, xi] = np.NaN
else:
signal = da_sl.values
latitude = grd_sl.lat_rho.values
try:
ttide_out = tt.t_tide(signal,
stime=stime,
lat=latitude,
out_style=None)
tt_ind = {}
for const in constit_list:
tt_ind[const] = list(ttide_out['nameu']).index(
str.encode(const + ' '))
for ana, tt_ana in zip(
ana_list, ttide_out['tidecon'][tt_ind[const]]):
grid_ds[const + '_' + ana][eta, xi] = tt_ana
except TypeError:
for const in constit_list:
for ana in ana_list:
grid_ds[const + '_' + ana][eta, xi] = np.NaN
print('interpolating intermediate cells and mask land')
for con in constit_list:
for ana in ana_list:
grid_ds[con + '_' + ana].values = NDinterp(grid_ds[con + '_' +
ana].values)
grid_ds[con + '_' + ana] = grid_ds[con + '_' + ana].where(
grid_ds.mask_rho, 0.0)
return grid_ds
from aux_funcs import *
#############################################################################
################### Start with AQD data, then add ADCP and interp############
#############################################################################
import ttide as tt
moorname = 'CF1'
aqdlist = glob.glob(datadir + 'OSNAP2016recovery/AQD_Data_CF/OS_OSNAP-' +
moorname + '*.nc')
dat = Dataset(aqdlist[0], 'r')
utest = array([float(tt) for tt in dat.variables['UCUR'][:].flatten()])
vtest = array([float(tt) for tt in dat.variables['VCUR'][:].flatten()])
tfit_u = tt.t_tide(utest + 1j * vtest, dt=0.5)
tidepart = tfit_u(arange(0, len(utest) / 2, 0.5))
ufilt = utest - tidepart.real
vfilt = vtest - tidepart.imag
figure(figsize=(15, 5))
plot(utest)
plot(tidepart.real)
figure(figsize=(15, 5))
plot(vtest)
plot(tidepart.imag)
mean(vtest)
mean(vfilt)
# def extrapsurf(afield,datevec,prsvec,pdall):
# panpiv=pd.DataFrame(index=datevec,
# columns=prsvec)
'/home/mif001/scratch/sjh_hr_v3/test_bfric2/{}/output/'.format(name),
singlename=grid + '_0001.nc')
print('done load')
savepath = '{}/{}_{}/ttide/{}/'.format(datapath, grid, datatype, name)
if not os.path.exists(savepath): os.makedirs(savepath)
dt = np.diff(data['time'])[0] * 24
out = pymp.shared.array((len(data['nodell'][:, 0]), 35, 4))
with pymp.Parallel(48) as p:
for j in p.range(len(data['nodell'][:, 0])):
print(j)
out[j, ] = t_tide(data['zeta'][starttime:endtime, j],
stime=data['time'][starttime],
lat=data['lat'][j],
synth=-1,
out_style=None,
dt=.25)['tidecon']
outall = t_tide(data['zeta'][starttime:endtime, j],
stime=data['time'][starttime],
lat=data['lat'][j],
synth=-1,
out_style=None,
dt=.25)
np.save(
'{}{}_{}_{}_ttide_grid_tidecon_el_all_pymp.npy'.format(
savepath, name, starttime, endtime), out)
np.save(
'{}{}_{}_{}_ttide_grid_singleout_el_all_pymp.npy'.format(
datetime.datetime(2012, 1, 1, 0, 0, 0) +
datetime.timedelta(day) for day in days
]),
constituents=cons)
for c in mod_tide.model:
# if c['constituent'].name in ['M2','M4','S2']:
print(' model %s: %0.2f m' % (c['constituent'].name, c[1]))
if use_ttide:
print('%s' % name)
ostart, ostop = abs(ncdays -
days[0]).argmin(), abs(ncdays -
days[-1]).argmin()
try:
obs_tide = tt.t_tide(ncelev[ostart:ostop],
dt=obsdt,
out_style=None)
e = interp(nhours, hours, elevs[name])
mod_tide = tt.t_tide(e, dt=nmodeldt, out_style=None)
m2idx = list(mod_tide['nameu']).index(b'M2 ')
m4idx = list(mod_tide['nameu']).index(b'M4 ')
s2idx = list(mod_tide['nameu']).index(b'S2 ')
modtext = 'modelled M2: %0.3f, S2: %0.3f' % (
mod_tide['tidecon'][m2idx][0],
mod_tide['tidecon'][s2idx][0])
obstext = 'observed M2: %0.3f, S2: %0.3f' % (
obs_tide['tidecon'][m2idx][0],
obs_tide['tidecon'][s2idx][0])
print(modtext)
print(obstext)
except:
return asarray(nvmask)
inum = len(nc.dimensions['nSCHISM_hgrid_node'])
m2_amp = zeros((inum, ))
m2_pha = zeros((inum, ))
m4_amp = zeros((inum, ))
m4_pha = zeros((inum, ))
ut = netcdftime(ncv['time'].units)
hours = ncv['time'][:] / 3600.
hours = hours - hours[0]
dthours = hours[1] - hours[0]
for i in range(inum):
tides = tt.t_tide(ncv['elev'][:, i], dt=dthours, out_style=None)
m2idx = tides['nameu'].index(b'M2 ')
m4idx = tides['nameu'].index(b'M4 ')
m2_amp[i] = tides['tidevcon'][m2idx][0]
m4_pha[i] = tides['tidevcon'][m2idx][2]
m4_amp[i] = tides['tidevcon'][m4idx][0]
m2_pha[i] = tides['tidevcon'][m4idx][2]
if False:
figure()
tripcolor(x, y, nv, m2_amp, cmap=cm.jet, rasterized=True)
proj.drawcoastlines()
proj.fillcontinents((0.9, 0.9, 0.8))
cb = colorbar()
cb.ax.set_title('M2 [m]\n', size=10.)
savefig('m2_amp.jpg' % (varname, varname, tidx), dpi=600)
oz=ipt.interp1d(obs['rtime'],obs['rzeta'],timeshift)
mz=ipt.interp1d(mod['rtime'],mod['rzeta'],timeshift)
nidx=np.isnan(oz)
mz[nidx]=np.nan
oz=oz-np.nanmean(oz)
mz=mz-np.nanmean(mz)
r1z=residual_stats(mz[~nidx],oz[~nidx])
print('T_tide zeta obs')
oout=ttide.t_tide(oz,dt=60.0/3600.0,stime=timeshift[0],lat=adcp['lat'],out_style=None)
osnr=0
clist=oout['nameu'][oout['snr']>2]
nsnr=len(clist)
while(osnr!=nsnr):
#print('looping')
oout2=ttide.t_tide(oz,dt=60.0/3600.0,stime=timeshift[0],lat=adcp['lat'],constitnames=clist,out_style=None)
osnr=nsnr
clist=oout2['nameu'][oout2['snr']>2]
nsnr=len(clist)
print('T_tide zeta mod')
mout=ttide.t_tide(mz,dt=60.0/3600.0,stime=timeshift[0],lat=model['lat'],out_style=None)
osnr=0
clist=mout['nameu'][mout['snr']>2]
name5 = 'sjh_lr_v1_year_coare3_wu_mixing'
data1 = loadnc(
'/home/moflaher/Desktop/workspace_python/dataout/sjh_lr_v1_sub/tg/' +
name1, tgname + '_fvcom.nc', False)
data2 = loadnc(
'/home/moflaher/Desktop/workspace_python/dataout/sjh_lr_v1_sub/tg/' +
name2, tgname + '_fvcom.nc', False)
data4 = loadnc(
'/home/moflaher/Desktop/workspace_python/dataout/sjh_lr_v1/tg/' + name4,
tgname + '_fvcom.nc', False)
data5 = loadnc(
'/home/moflaher/Desktop/workspace_python/dataout/sjh_lr_v1/tg/' + name5,
tgname + '_fvcom.nc', False)
obs = loadnc('/mnt/drive_1/obs/east/all', tgname + '.nc', False)
tt1 = ttide.t_tide(data1['zeta'], lat=data1['lat'], dt=1)
tt2 = ttide.t_tide(data2['zeta'], lat=data2['lat'], dt=1)
tt4 = ttide.t_tide(data4['zeta'], lat=data4['lat'], dt=1)
tt5 = ttide.t_tide(data5['zeta'], lat=data5['lat'], dt=1)
t1, d1, d2 = interp_clean_common(data2['time'], data2['zeta'], obs['time'],
obs['zeta'], 500, -500)
time = np.arange(t1[0], t1[-1] + 1 / 24.0, 1 / 24.0)
testz = ipt.interp1d(t1, d2, time)
tt3 = ttide.t_tide(testz, lat=obs['lat'], dt=1)
FFT1 = sp.fft(tt1['xres'])
FFT2 = sp.fft(tt2['xres'])
FFT3 = sp.fft(tt3['xres'])
FFT4 = sp.fft(tt4['xres'])
from datetime import datetime
import numpy as np
from ttide import t_tide
from scipy.interpolate import interp1d
"""
功能：处理验潮站数据，并画图
"""
# %% 数据准备
gauge_path = r'E:\gauge_data\h632a93.dat' # 376表示厦门，632表示坎门
gauge_data = ReadData(gauge_path, types='gauge')
gauge_position = (121.169, 28.053) # 厦门地理坐标(118.04, 24.27)，坎门的为(121.169, 28.053)
# %% 调和分析
elev_anomaly = gauge_data.data.elev / 1000 - 3.87 # 3.281表示厦门的海平参考面，坎门的为3.87
xout = t_tide(elev_anomaly, stime=gauge_data.data.time[0],
lat=gauge_position[1])
gauge_zeta = elev_anomaly - xout(gauge_data.data.time)
# %% 筛选数据
select_time = pd.date_range('1993-09-28 00:00:00',
'1993-10-12 00:00:00',
freq='H')
select_time_stamp = date2num(select_time)
select_start_indx = np.where(gauge_data.data.time == select_time_stamp[0])[0][0]
select_end_indx = np.where(gauge_data.data.time == select_time_stamp[-1])[0][0]
select_gauge_zeta = gauge_zeta[select_start_indx:select_end_indx + 1]
# %% 验潮站相对于高度计过境时间时的增水和前期最大增水）
alti_pass_time = datetime(1993, 10, 8, 8, 32)
alti_pass_date = alti_pass_time.strftime('%m-%dT%H:%M')
alti_pass_stime = date2num(alti_pass_time)
zeta_func = interp1d(select_time_stamp, select_gauge_zeta)
gauge_zeta_alti = zeta_func(alti_pass_stime)
dist = np.sqrt((data['x'] - xloc)**2 + (data['y'] - yloc)**2)
asort = np.argsort(dist)
close = 0
#while np.sum(data['wet_nodes'][:,asort[close]])<len(data['time']):
# close+=1
node = asort[close]
if dist[node] > 10000:
continue
zetac = data['zeta'][starttime:endtime, node]
timec = data['time'][starttime:endtime]
print(np.diff(data['time'])[0] * 24)
out = ttide.t_tide(zetac,
stime=timec[0] - 4 / 24.0,
dt=np.diff(data['time'])[0] * 24,
synth=-1,
out_style=None,
lat=data['lat'][node])
idxw = np.array([], dtype=int)
idx = np.array([], dtype=int)
for j, name in enumerate(ttide.t_utils.fourpad(wlev[key]['name'])):
tidx = np.argwhere(out['nameu'] == name)
if len(tidx) > 0:
idxw = np.append(idxw, j)
idx = np.append(idx, tidx[0])
print('=' * 80)
print(wlev[key]['snum'])
print(wlev[key]['sname'])
print(dist[node])
def Interp_AQD_ADCP(moornum):
moorname = 'CF' + str(moornum)
if moorname == 'CF8':
aqdlist = hstack(
(glob.glob(datadir + 'NOC_M1/nocm1_01_2014/nor/*.edt'),
glob.glob(datadir + 'NOC_M1/nocm1_02_2015/nor/*.edt')))
else:
aqdlist = glob.glob(datadir + 'AQD_Data_CF/OS_OSNAP-' + moorname +
'*.nc')
figure(figsize=(12, 10))
time = array([])
u = array([])
v = array([])
prs = array([])
meanprs = array([])
date = array([])
for dd in aqdlist:
print(dd)
if moorname == 'CF8':
dat = pd.read_csv(dd, header=12, sep='\s*')
prs_tmp = hrly_ave(dat.ix[:, 5], hrcon)
date_tmp = unique([
datetime.datetime(int(dat.ix[ii, 0]), int(dat.ix[ii, 1]),
int(dat.ix[ii, 2])) for ii in range(len(dat))
])[:len(prs_tmp)]
time_tmp = array(
[datetime.datetime.toordinal(adate) for adate in date_tmp])
utest = array(dat.ix[:, 6] / 100)
vtest = array(dat.ix[:, 7] / 100)
tfit_u = tt.t_tide(utest)
tfit_v = tt.t_tide(vtest)
ufilt = utest - tfit_u(arange(len(utest)))
vfilt = vtest - tfit_v(arange(len(vtest)))
u_tmp = hrly_ave(ufilt, hrcon)
v_tmp = hrly_ave(vfilt, hrcon)
mprs_tmp = mean(dat.ix[:, 5]) * ones(len(date_tmp))
else:
dat = Dataset(dd, 'r')
utest = array(
[float(tt) for tt in dat.variables['UCUR'][:].flatten()])
vtest = array(
[float(tt) for tt in dat.variables['VCUR'][:].flatten()])
tfit_u = tt.t_tide(utest + 1j * vtest, dt=0.5)
tidepart = tfit_u(arange(0, len(utest) / 2, 0.5))
ufilt = utest - tidepart.real
vfilt = vtest - tidepart.imag
u_tmp = hrly_ave(ufilt, hrcon * 2)
v_tmp = hrly_ave(vfilt, hrcon * 2)
prs_tmp = hrly_ave(list(dat.variables['PRES'][:].flatten()),
hrcon * 2)
time_tmp = list(dat.variables['TIME'][:])[::hrcon *
2][:len(prs_tmp)]
mprs_tmp = nanmean(prs_tmp) * ones(len(time_tmp))
date_tmp = array([
datetime.datetime(1950, 1, 1) +
datetime.timedelta(days=int(tt)) for tt in time_tmp
])
u_tmp = u_tmp[:len(date_tmp)]
v_tmp = v_tmp[:len(date_tmp)]
prs_tmp = prs_tmp[:len(date_tmp)]
figure(figsize=(14, 6))
subplot(211)
title(str(int(nanmean(prs_tmp))))
plot(utest)
plot(tidepart.real)
subplot(212)
plot(vtest)
plot(tidepart.imag)
prs = hstack((prs, prs_tmp))
u = hstack((u, u_tmp))
v = hstack((v, v_tmp))
meanprs = hstack((meanprs, mprs_tmp))
date = hstack((date, date_tmp))
time = hstack((time, time_tmp))
##### Choose time and pressure bins
#units are db
pstep = 2
##### Now add ADCP data
lat = 60
ua = array([])
va = array([])
prsa = array([])
meanprs = array([])
datea = array([])
if moorname == 'CF8':
adcplist = hstack(
(glob.glob(datadir + 'NOC_M1/nocm1_01_2014/adcp/*.edt'),
glob.glob(datadir + 'NOC_M1/nocm1_02_2015/adcp/*.edt')))
for dd in adcplist:
dat = pd.read_csv(dd, header=12, sep='\s*')
prs_tmp = hrly_ave(gsw.p_from_z(-dat.ix[:, 4], lat), hrcon)
datea_tmp = unique([
datetime.datetime(int(dat.ix[ii, 0]), int(dat.ix[ii, 1]),
int(dat.ix[ii, 2])) for ii in range(len(dat))
])[:len(prs_tmp)]
utest = array(dat.ix[:, 6] / 100)
vtest = array(dat.ix[:, 7] / 100)
utest[utest < -90] = NaN
vtest[vtest < -90] = NaN
tfit_u = tt.t_tide(utest)
tfit_v = tt.t_tide(vtest)
ufilt = utest - tfit_u(arange(len(utest)))
vfilt = vtest - tfit_v(arange(len(vtest)))
ua_tmp = hrly_ave(ufilt, hrcon)
va_tmp = hrly_ave(vfilt, hrcon)
prsa = hstack((prsa, prs_tmp))
ua = hstack((ua, ua_tmp))
va = hstack((va, va_tmp))
datea = hstack((datea, datea_tmp))
plot(datea_tmp, ua_tmp)
plot(datea_tmp, va_tmp)
else:
dat = io.loadmat(datadir + 'ADCP_Data_CF/OSNAP_cf' + str(moornum) +
'_Final1_ilebras.mat')
shapmat = shape(dat['u'][1:, :])
shapmat1 = int(shape(dat['u'])[1] / 24) + 1
datea_tmp = unique([
datetime.datetime(int(tt[0]), int(tt[1]), int(tt[2]))
for tt in dat['time']
])[:shapmat1]
# Note that adcp "depths" from Dan Torres are also in db...
prsa_tmp = zeros((shapmat[0], shapmat1))
ua_tmp = zeros((shapmat[0], shapmat1))
va_tmp = zeros((shapmat[0], shapmat1))
for ii in range(shapmat[0]):
prsa_tmp[ii, :] = hrly_ave(dat['z'][ii + 1, :], hrcon)
utest = array(dat['u'][ii + 1, :])
vtest = array(dat['v'][ii + 1, :])
# if sum(~isnan(utest))>len(utest)/2:
# # print(utest)
# tfit_u = tt.t_tide(utest+1j*vtest)
# tidepart=tfit_u(arange(len(utest)))
# ufilt=utest-tidepart.real
# vfilt=vtest-tidepart.imag
# figure(figsize=(14,6))
# subplot(211)
# title(str(int(nanmean(prsa_tmp[ii,:]))))
# plot(utest)
# plot(tidepart.real)
# subplot(212)
# plot(vtest)
# plot(tidepart.imag)
# ua_tmp[ii,:]=hrly_ave(utest,hrcon)
# va_tmp[ii,:]=hrly_ave(vtest,hrcon)
# title(str(int(nanmean(vtest)*100)/100)+'/'+str(int(nanmean(va_tmp[ii,:])*100)/100))
# else:
ua_tmp[ii, :] = hrly_ave(utest, hrcon)
va_tmp[ii, :] = hrly_ave(vtest, hrcon)
ua = ua_tmp.flatten()
va = va_tmp.flatten()
prsa = prsa_tmp.flatten()
datea = tile(datea_tmp, [1, shapmat[0]]).flatten()
pdall = pd.DataFrame({
'pressure': hstack((prsa, prs)),
'u': hstack((ua, u)),
'v': hstack((va, v)),
'date bin': hstack((datea, date))
})
pdall['u'][pdall['u'] < -2] = NaN
pdall['v'][pdall['v'] < -2] = NaN
if moornum == 4:
plim = 40
else:
plim = 0
pdall = pdall[pdall['pressure'] >= plim]
common_mindate = max(min(datea), min(date))
common_maxdate = min(max(datea), max(date))
# datevec=sort(unique(pdall['date bin']))
prsvec = arange(0, int(max(pdall['pressure'])) + 1, 2)
dmin = min(pdall['date bin'])
dmax = max(pdall['date bin'])
dlen = int(divmod((dmax - dmin).total_seconds(), 60 * 60 * 24)[0]) + 1
datevec = array(
[dmin + datetime.timedelta(days=x) for x in range(0, dlen)])
u_interp = fillsurf('u', datevec, prsvec, pdall)
v_interp = fillsurf('v', datevec, prsvec, pdall)
u_interp[(u_interp < -2.5) | (u_interp > 2)] = nan
v_interp[(v_interp < -2.5) | (v_interp > 2)] = nan
versname = '1808tidecomp'
figure(figsize=(12, 4))
subplot(121)
plot(u_interp, prsvec)
plot(pdall['u'], pdall['pressure'], 'k.')
gca().invert_yaxis()
title('u, zonal velocity (m/s)')
ylabel('pressure (db)')
subplot(122)
plot(v_interp, prsvec)
plot(pdall['v'], pdall['pressure'], 'k.')
gca().invert_yaxis()
title('v, meridional velocity (m/s)')
savefig('../figures/interpolation/VEL/' + moorname + '_uv_measinterp_' +
versname + '.png',
bbox_inches='tight')
plotcontour(u_interp, cm.RdBu_r, -2, 2, moorname)
savefig('../figures/interpolation/VEL/' + moorname + '_ucontour_' +
versname + '.png',
bbox_inches='tight')
plotcontour(v_interp, cm.RdBu_r, -2, 2, moorname)
savefig('../figures/interpolation/VEL/' + moorname + '_vcontour_' +
versname + '.png',
bbox_inches='tight')
pickle.dump([u_interp, v_interp],
open(
'../pickles/VELinterp/' + moorname + '_uvinterp_' +
versname + '.pickle', 'wb'))
nvmask.append(False)
return asarray(nvmask)
inum = len(nc.dimensions['nSCHISM_hgrid_node'])
m2_amp = zeros((inum,))
m2_pha = zeros((inum,))
m4_amp = zeros((inum,))
m4_pha = zeros((inum,))
ut = netcdftime(ncv['time'].units)
hours = ncv['time'][:]/3600.
hours = hours - hours[0]
dthours = hours[1]-hours[0]
for i in range(inum):
tides = tt.t_tide(ncv['elev'][:,i],dt=dthours,out_style=None)
m2idx = tides['nameu'].index(b'M2 ')
m4idx = tides['nameu'].index(b'M4 ')
m2_amp[i] = tides['tidevcon'][m2idx][0]
m4_pha[i] = tides['tidevcon'][m2idx][2]
m4_amp[i] = tides['tidevcon'][m4idx][0]
m2_pha[i] = tides['tidevcon'][m4idx][2]
if False:
figure()
tripcolor(x,y,nv,m2_amp,cmap=cm.jet,rasterized=True)
proj.drawcoastlines()
proj.fillcontinents((0.9,0.9,0.8))
cb=colorbar()
cb.ax.set_title('M2 [m]\n',size=10.)
savefig('m2_amp.jpg'%(varname,varname,tidx),dpi=600)
tgzeta=tg65[idx,1]-np.nanmean(tg65[idx,1])
tgtime=tgtime[~np.isnan(tgzeta)]
tgzeta=tgzeta[~np.isnan(tgzeta)]
mtgzeta=ipt.interp1d(tgtime.flatten(),tgzeta.flatten(),time)
n=55468
m2amp=np.empty((5000,))
m2amp_tg=np.empty((5000,))
m2amp_cur=np.empty((5000,))
for i in range(5000):
print(i)
out=ttide.t_tide(data['zeta'][starttime+i:endtime+i,n],stime=time[starttime+i],dt=np.diff(data['time'])[0]*24,synth=-1,out_style=None,lat=data['lat'][n])#,constitnames=np.array(['M2 ']))
idx=np.argwhere(out['nameu']=='M2 ')
amp=out['tidecon'][idx,0]
m2amp[i]=amp
out=ttide.t_tide(mtgzeta[starttime+i:endtime+i],stime=time[starttime+i],dt=np.diff(data['time'])[0]*24,synth=-1,out_style=None,lat=data['lat'][n])#,constitnames=np.array(['M2 ']))
idx=np.argwhere(out['nameu']=='M2 ')
amp=out['tidecon'][idx,0]
m2amp_tg[i]=amp
out=ttide.t_tide(data['ua'][starttime+i:endtime+i,n]+1j*data['va'][starttime+1:endtime+1,n],stime=time[starttime+i],dt=np.diff(data['time'])[0]*24,synth=-1,out_style=None,lat=data['lat'][n])#,constitnames=np.array(['M2 ']))
idx=np.argwhere(out['nameu']=='M2 ')
amp=out['tidecon'][idx,0]
m2amp_cur[i]=amp
def __init__(self,
filename,
time1=datetime.datetime(2000, 1, 1, 00, 0),
time2=datetime.datetime(2018, 10, 16, 5, 0)):
self.data = {}
self.year = {}
self.month = {}
self.day = {}
for s in xlrd.open_workbook(filename=filename).sheet_names():
try:
self.tide_sheet = pandas.read_excel(filename, sheetname=s)
if 'tide' in self.tide_sheet.columns.values:
if 'time' in self.tide_sheet.columns.values:
pass
else:
say_out('Please Check Your Format of the Sheet')
except:
say_out('Please Check Your sheets name of the File')
temp_data = self.tide_sheet
t = []
if isinstance(temp_data.time[1], pandas.tslib.Timestamp):
t = temp_data.time
else:
for x in temp_data.time:
t.append(dateutil.parser.parse(x))
temp_data['format_time'] = t
temp_data['tide_init'] = temp_data.tide / 100
if t[0] + (t[1] - t[0]) * (len(t) - 1) != t[len(t) - 1]:
say_out(str(s) + '时间间隔有问题，请检查时间序列')
deltatime = t[1] - t[0]
num_time = np.linspace(0,
len(t) * deltatime.total_seconds() / 3600,
len(t))
self.tide_info = tt.t_tide(temp_data['tide_init'].values,
dt=deltatime.total_seconds() / 3600)
#temp_data.tide = self.tide_info(num_time)
temp_data[
'tide_init'] = temp_data.tide #后续调和导出、根据调和分析对数据进行去噪-需要处理###
if len(temp_data) % 2 == 1:
temp_data = temp_data.ix[0:(len(temp_data) - 2)]
temp_data.tide = process(temp_data.tide)
temp_data.index = temp_data['format_time']
temp_data = temp_data.ix[temp_data.format_time <= time2]
temp_data = temp_data.ix[temp_data.format_time >= time1]
temp_data['if_min'] = np.r_[
True, temp_data.tide.values[1:] < temp_data.tide.
values[:-1]] & np.r_[
temp_data.tide.values[:-1] < temp_data.tide.values[1:],
True]
temp_data['if_max'] = np.r_[
True, temp_data.tide.values[1:] > temp_data.tide.
values[:-1]] & np.r_[
temp_data.tide.values[:-1] > temp_data.tide.values[1:],
True]
temp_data.set_value(temp_data.index.max(), 'if_min', False)
temp_data.set_value(temp_data.index.max(), 'if_max', False)
temp_data.set_value(temp_data.index.min(), 'if_max', False)
temp_data.set_value(temp_data.index.min(), 'if_min', False)
temp_data['diff'] = None
temp_data['raising_time'] = None
temp_data['ebb_time'] = None
for x in temp_data.ix[temp_data.if_max == True].format_time:
try:
temp_data.loc[x, 'raising_time'] = x - temp_data[
(temp_data.if_min == True)
& (temp_data.format_time < x)].format_time.max()
temp_data.loc[x, 'diff'] = temp_data.loc[
x, 'tide'] - temp_data.loc[temp_data[
(temp_data.if_min == True)
& (temp_data.format_time < x)].format_time.max(),
'tide']
except:
pass
for x in temp_data.ix[temp_data.if_min == True].format_time:
try:
temp_data.loc[x, 'ebb_time'] = x - temp_data[
(temp_data.if_max == True)
& (temp_data.format_time < x)].format_time.max()
temp_data.loc[x, 'diff'] = temp_data.loc[temp_data[
(temp_data.if_max == True)
& (temp_data.format_time < x)].format_time.max(
), 'tide'] - temp_data.loc[x, 'tide']
except:
pass
###############筛选极值
#####根据潮差与涨落潮历时筛选
if temp_data['diff'].max() < 50:
temp_data['diff'] = temp_data['diff'] * 100
temp_data.loc[temp_data[temp_data['diff'] < 10].index,
'diff'] = None
temp_data.loc[temp_data[temp_data['diff'] < 10].index,
'if_max'] = False
temp_data.loc[temp_data[temp_data['diff'] < 10].index,
'if_min'] = False
temp_data.loc[temp_data[temp_data['diff'] < 10].index,
'raising_time'] = 0
temp_data.loc[temp_data[temp_data['diff'] < 10].index,
'ebb_time'] = 0
temp_data.loc[temp_data[
temp_data['raising_time'] < datetime.timedelta(hours=2)].index,
'diff'] = None
temp_data.loc[temp_data[
temp_data['raising_time'] < datetime.timedelta(hours=2)].index,
'if_max'] = False
temp_data.loc[temp_data[
temp_data['raising_time'] < datetime.timedelta(hours=2)].index,
'raising_time'] = 0
temp_data.loc[temp_data[temp_data['ebb_time'] < datetime.timedelta(
hours=2)].index, 'diff'] = None
temp_data.loc[temp_data[temp_data['ebb_time'] < datetime.timedelta(
hours=2)].index, 'if_min'] = False
temp_data.loc[temp_data[temp_data['ebb_time'] < datetime.timedelta(
hours=2)].index, 'ebb_time'] = 0
###############根据大小潮个数筛选
switch = temp_data.ix[temp_data.if_min == True].append(
temp_data.ix[temp_data.if_max == True]).sort_index().index
count_filtertime = 0
while (len(temp_data.ix[temp_data.if_min == True]) != len(
temp_data.ix[temp_data.if_max == True])):
print('低潮潮位个数' +
str(len(temp_data.ix[temp_data.if_min == True])))
print('高潮潮位个数' +
str(len(temp_data.ix[temp_data.if_max == True])))
if ((len(temp_data.ix[temp_data.if_min == True]) -
len(temp_data.ix[temp_data.if_max == True]) == 1) or
(len(temp_data.ix[temp_data.if_min == True]) -
len(temp_data.ix[temp_data.if_max == True]) == -1)):
print('极值只多一个，筛选结束')
break
print('进行高低潮筛选————————————————————————————————')
for i in range(1, len(switch) - 1):
## 时刻i是最大值时
if (temp_data.loc[switch[i - 1], 'if_max']
and temp_data.loc[switch[i + 1], 'if_max']) or (
temp_data.loc[switch[i - 1], 'if_min']
and temp_data.loc[switch[i + 1], 'if_min']):
pass
else:
if temp_data.loc[switch[i], 'if_max']: # 自身高潮
if temp_data.loc[switch[i - 1],
'if_max']: # 后一个也是高潮
if temp_data.loc[switch[i],
'tide'] > temp_data.loc[
switch[i - 1],
'tide']: # 前面的i大,i-1为假
temp_data.loc[switch[i - 1],
'if_max'] = False
temp_data.loc[switch[i - 1], 'diff'] = None
temp_data.loc[switch[i - 1],
'raising_time'] = None
else:
temp_data.loc[switch[i], 'if_max'] = False
temp_data.loc[switch[i], 'diff'] = None
temp_data.loc[switch[i],
'raising_time'] = None
else: # 自身低潮
if temp_data.loc[switch[i - 1],
'if_min']: # 后一个也是低潮
if temp_data.loc[switch[i],
'tide'] < temp_data.loc[
switch[i - 1],
'tide']: # 前面的i较小,i-1为假
temp_data.loc[switch[i - 1],
'if_min'] = False
temp_data.loc[switch[i - 1],
'diff'] = False
temp_data.loc[switch[i - 1],
'ebb_time'] = False
else:
temp_data.loc[switch[i], 'if_min'] = False
temp_data.loc[switch[i], 'diff'] = False
temp_data.loc[switch[i],
'ebb_time'] = False
switch = temp_data.ix[temp_data.if_min == True].append(
temp_data.ix[temp_data.if_max == True]).sort_index().index
count_filtertime += 1
year_tide = []
month_tide = []
day_tide = []
for i, j in temp_data.groupby(temp_data.index.year): #
year_tide.append(j)
for ii, jj in j.groupby(j.index.month):
month_tide.append(jj)
for iii, jjj in jj.groupby(jj.index.day):
day_tide.append(jjj)
self.data[s] = temp_data
temp_data2 = temp_data
temp_data2['tide'] = temp_data['tide_init']
self.data[s + '原始数据'] = temp_data2
year_tide2 = []
month_tide2 = []
day_tide2 = []
for i, j in temp_data2.groupby(temp_data.index.year): #
year_tide2.append(j)
for ii, jj in j.groupby(j.index.month):
month_tide2.append(jj)
for iii, jjj in jj.groupby(jj.index.day):
day_tide2.append(jjj)
self.year[s] = year_tide
self.year[s + '原始数据'] = year_tide2
self.month[s] = month_tide
self.month[s + '原始数据'] = month_tide2
self.day[s] = day_tide
self.day[s + '原始数据'] = day_tide2
print(s + "站预处理结束")
for idx_lat in range(0, len(lats)):
print('Indexes: ', idx_lon, idx_lat)
# Read the ssh values
ssh_mod = vals[:, idx_lat, idx_lon]
# Extract Amp and Pha
xin = np.array(ssh_mod)
latitudes = lats[idx_lat]
tidal_comp = ['M2', 'S2', 'K1', 'O1', 'N2', 'P1', 'Q1', 'K2']
# run ttide script
harmanOUT = ttide.t_tide(np.array(xin),
dt=1,
stime=time,
lat=latitudes,
constitnames=tidal_comp,
out_style=None,
outfile=None)
tideconout = harmanOUT['tidecon']
# Write values in the arrays
M2_Amp[idx_lat, idx_lon] = tideconout[5][0]
S2_Amp[idx_lat, idx_lon] = tideconout[6][0]
K1_Amp[idx_lat, idx_lon] = tideconout[3][0]
O1_Amp[idx_lat, idx_lon] = tideconout[1][0]
N2_Amp[idx_lat, idx_lon] = tideconout[4][0]
P1_Amp[idx_lat, idx_lon] = tideconout[2][0]
Q1_Amp[idx_lat, idx_lon] = tideconout[0][0]
K2_Amp[idx_lat, idx_lon] = tideconout[7][0]
tgzeta1=tg65[:,1]
tgtime=tgtime[~np.isnan(tgzeta)]
tgzeta=tgzeta[~np.isnan(tgzeta)]
time=np.arange(tgtime.min(),tgtime.max(),60/(24.0*3600))
mtgzeta=ipt.interp1d(tgtime.flatten(),tgzeta.flatten(),time)
num=len(range(0,len(time)-window,np.round(intv/3).astype(int)*24*60))
m2amp=np.empty((num,))
m2phs=np.empty((num,))
m2nans=np.empty((num,))
for j,i in enumerate(range(0,len(time)-window,np.round(intv/3).astype(int)*24*60)):
out=ttide.t_tide(mtgzeta[starttime+i:window+i],stime=time[starttime+i],dt=np.diff(time)[0]*24,synth=-1,out_style=None)
idx=np.argwhere(out['nameu']=='M2 ')
m2amp[j]=out['tidecon'][idx,0]
m2phs[j]=out['tidecon'][idx,2]
m2nans[j]=np.isnan(tgzeta1[starttime+i:window+i]).sum()
f=plt.figure()
ax=f.add_axes([.125,.1,.775,.8])
ax.plot(m2amp,'r',lw=2,label='M2 Amp.')
#ax.set_xlabel('Time (hours)')
ax.set_ylabel('M2 Amp. (m)')
f.suptitle('Min: {} Max: {} Range: {}\n Mean: {} STD: {} '.format(m2amp.min(),m2amp.max(),m2amp.max()-m2amp.min(),m2amp.mean(),np.sqrt(np.var(m2amp))))
f.savefig('{}m2amp_{}.png'.format(savepath,intv),dpi=300)
for i,key in enumerate(wlev):
xloc,yloc = data['proj'](wlev[key]['lon'],wlev[key]['lat'])
dist=np.sqrt((data['x']-xloc)**2+(data['y']-yloc)**2)
asort=np.argsort(dist)
close=0
#while np.sum(data['wet_nodes'][:,asort[close]])<len(data['time']):
# close+=1
node=asort[close]
if dist[node]>10000:
continue
zetac=data['zeta'][starttime:endtime,node]
timec=data['time'][starttime:endtime]
print(np.diff(data['time'])[0]*24)
out=ttide.t_tide(zetac,stime=timec[0]-4/24.0,dt=np.diff(data['time'])[0]*24,synth=-1,out_style=None,lat=data['lat'][node])
idxw=np.array([],dtype=int)
idx=np.array([],dtype=int)
for j,name in enumerate(ttide.t_utils.fourpad(wlev[key]['name'])):
tidx=np.argwhere(out['nameu']==name)
if len(tidx)>0:
idxw=np.append(idxw,j)
idx=np.append(idx,tidx[0])
print('='*80)
print(wlev[key]['snum'])
print(wlev[key]['sname'])
print(dist[node])
num = len(
range(0,
len(time) - window,
np.round(intv / 3).astype(int) * 24 * 60))
m2amp = np.empty((num, ))
m2phs = np.empty((num, ))
m2nans = np.empty((num, ))
for j, i in enumerate(
range(0,
len(time) - window,
np.round(intv / 3).astype(int) * 24 * 60)):
out = ttide.t_tide(mtgzeta[starttime + i:window + i],
stime=time[starttime + i],
dt=np.diff(time)[0] * 24,
synth=-1,
out_style=None)
idx = np.argwhere(out['nameu'] == 'M2 ')
m2amp[j] = out['tidecon'][idx, 0]
m2phs[j] = out['tidecon'][idx, 2]
m2nans[j] = np.isnan(tgzeta1[starttime + i:window + i]).sum()
f = plt.figure()
ax = f.add_axes([.125, .1, .775, .8])
ax.plot(m2amp, 'r', lw=2, label='M2 Amp.')
#ax.set_xlabel('Time (hours)')
ax.set_ylabel('M2 Amp. (m)')
f.suptitle('Min: {} Max: {} Range: {}\n Mean: {} STD: {} '.format(
m2amp.min(), m2amp.max(),
m2amp.max() - m2amp.min(), m2amp.mean(), np.sqrt(np.var(m2amp))))
# %% 模式数据处理
model_tide_data = ReadData(model_tide_path, types='fvcom', variables=['zeta'])
model_wind_data = ReadData(model_wind_path, types='fvcom', variables=['zeta'])
model_zeta = model_wind_data.data.zeta - model_tide_data.data.zeta
model_start_time = model_tide_data.data.time[0]
model_end_time = model_tide_data.data.time[-1]
gauge_indx = model_tide_data.find_nearest_point('zeta', position_site)
# 减去模式的平均值(减去的平均值是模式结果的整体平均，非个点)
specific_mzeta = model_zeta[:, gauge_indx] - model_zeta.mean()
# %% 验潮站数据处理
gauge_data = ReadData(gauge_path, types='gauge')
# 做调和分析
elev_anomaly = gauge_data.data.elev / 1000 - 3.87 # 3.281表示厦门的海平参考面，坎门的为3.87
xout = ttide.t_tide(elev_anomaly,
stime=gauge_data.data.time[0],
lat=position_site[1])
gauge_zeta = elev_anomaly - xout(gauge_data.data.time)
# 计算模式时间范围的平均值（为了更好的和模式比较）
start_indx = np.where(gauge_data.data.time == model_start_time)[0][0]
end_indx = np.where(gauge_data.data.time == model_end_time)[0][0]
gauge_mean_zeta = np.mean(gauge_zeta[start_indx:end_indx + 1])
# %% 选择研究的时间范围
select_time = pd.date_range('1993-09-28 00:00:00',
'1993-10-12 00:00:00',
freq='H')
select_time_stamp = date2num(select_time)
# 选择时间范围的验潮站数据(减去平均)
select_start_indx = np.where(
gauge_data.data.time == select_time_stamp[0])[0][0]
#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
# __NAME__ = compare_tidal_constituents.py
# __AUTHOR__ = 'QIU ZHOU'
# __TIME__ = 2019/7/16 20:44
"""
比较模式和验潮站的调和常数
"""
from fvcom_tools_packages.read_data import ReadData
from ttide import t_tide
# %% 数据准备
gauge_path = r'E:\gauge_data\h376a93.dat' # 376表示厦门，632表示坎门
gauge_data = ReadData(gauge_path, types='gauge')
gauge_position = (118.04, 24.27) # 厦门地理坐标(118.04, 24.27)，坎门的为(121.169, 28.053)
model_path = r'E:\fvcom\fvcom_output_file\AtideIB_0001.nc'
model_data = ReadData(model_path, types='fvcom', variables=['zeta'])
indx = model_data.find_nearest_point('zeta', gauge_position)
# %% 调和分析
# 验潮站
elev_anomaly = gauge_data.data.elev / 1000 - 3.281 # 3.281表示厦门的海平参考面，坎门的为3.87
gauge_xout = t_tide(elev_anomaly,
stime=gauge_data.data.time[0],
lat=gauge_position[1])
# 模式
xiamen_model_elev = model_data.data.zeta[:, indx].flatten()
fvcom_xout = t_tide(xiamen_model_elev,
stime=model_data.data.time[0],
lat=gauge_position[1])
### load the .nc file #####
data = loadnc('/home/mif001/scratch/sjh_hr_v3/test_bfric2/{}/output/'.format(name),singlename=grid + '_0001.nc')
print('done load')
savepath='{}/{}_{}/ttide/{}/'.format(datapath,grid,datatype,name)
if not os.path.exists(savepath): os.makedirs(savepath)
dt=np.diff(data['time'])[0]*24
out=pymp.shared.array((len(data['nodell'][:,0]),35,4))
with pymp.Parallel(48) as p:
for j in p.range(len(data['nodell'][:,0])):
print(j)
out[j,]=t_tide(data['zeta'][starttime:endtime,j],stime=data['time'][starttime],lat=data['lat'][j],synth=-1,out_style=None,dt=.25)['tidecon']
outall=t_tide(data['zeta'][starttime:endtime,j],stime=data['time'][starttime],lat=data['lat'][j],synth=-1,out_style=None,dt=.25)
np.save('{}{}_{}_{}_ttide_grid_tidecon_el_all_pymp.npy'.format(savepath,name,starttime,endtime),out)
np.save('{}{}_{}_{}_ttide_grid_singleout_el_all_pymp.npy'.format(savepath,name,starttime,endtime),outall)
tgname='tg_00365'
name1='sjh_lr_v1_sub_fit_N4_test_nest_bfric02_taup'
name2='test_1_redo'
name4='sjh_lr_v1_year_wd_gotm-my25_bathy20171109_dt30_calib1_jcool0'
name5='sjh_lr_v1_year_coare3_wu_mixing'
data1=loadnc('/home/moflaher/Desktop/workspace_python/dataout/sjh_lr_v1_sub/tg/'+name1,tgname+'_fvcom.nc',False)
data2=loadnc('/home/moflaher/Desktop/workspace_python/dataout/sjh_lr_v1_sub/tg/'+name2,tgname+'_fvcom.nc',False)
data4=loadnc('/home/moflaher/Desktop/workspace_python/dataout/sjh_lr_v1/tg/'+name4,tgname+'_fvcom.nc',False)
data5=loadnc('/home/moflaher/Desktop/workspace_python/dataout/sjh_lr_v1/tg/'+name5,tgname+'_fvcom.nc',False)
obs=loadnc('/mnt/drive_1/obs/east/all',tgname+'.nc',False)
tt1=ttide.t_tide(data1['zeta'],lat=data1['lat'],dt=1)
tt2=ttide.t_tide(data2['zeta'],lat=data2['lat'],dt=1)
tt4=ttide.t_tide(data4['zeta'],lat=data4['lat'],dt=1)
tt5=ttide.t_tide(data5['zeta'],lat=data5['lat'],dt=1)
t1,d1,d2=interp_clean_common(data2['time'],data2['zeta'],obs['time'],obs['zeta'],500,-500)
time=np.arange(t1[0],t1[-1]+1/24.0,1/24.0)
testz=ipt.interp1d(t1,d2,time)
tt3=ttide.t_tide(testz,lat=obs['lat'],dt=1)
FFT1=sp.fft(tt1['xres'])
FFT2=sp.fft(tt2['xres'])
FFT3=sp.fft(tt3['xres'])
FFT4=sp.fft(tt4['xres'])
tgtime = tgtime[~np.isnan(tgzeta)]
tgzeta = tgzeta[~np.isnan(tgzeta)]
mtgzeta = ipt.interp1d(tgtime.flatten(), tgzeta.flatten(), time)
n = 55468
m2amp = np.empty((5000, ))
m2amp_tg = np.empty((5000, ))
m2amp_cur = np.empty((5000, ))
for i in range(5000):
print(i)
out = ttide.t_tide(data['zeta'][starttime + i:endtime + i, n],
stime=time[starttime + i],
dt=np.diff(data['time'])[0] * 24,
synth=-1,
out_style=None,
lat=data['lat'][n]) #,constitnames=np.array(['M2 ']))
idx = np.argwhere(out['nameu'] == 'M2 ')
amp = out['tidecon'][idx, 0]
m2amp[i] = amp
out = ttide.t_tide(mtgzeta[starttime + i:endtime + i],
stime=time[starttime + i],
dt=np.diff(data['time'])[0] * 24,
synth=-1,
out_style=None,
lat=data['lat'][n]) #,constitnames=np.array(['M2 ']))
idx = np.argwhere(out['nameu'] == 'M2 ')
amp = out['tidecon'][idx, 0]
m2amp_tg[i] = amp
ax.plot(time1,data2-np.mean(data2),'r',lw=.5,label='{}'.format(name))
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(monthsFmt)
ax.legend()
ax.set_ylabel('Elevation (m)')
f.suptitle('Removed TG means - Obs: {} Model: {}\n Bias: {} Std: {} RMSE: {} RAE: {} Corr: {} Skew: {} Skill: {}'.format(np.mean(data1),np.mean(data2),a[0],a[1],a[2],a[3],a[4],a[5],a[6]))
f.savefig(figstr,dpi=600)
if tide:
time=np.arange(time1[0],time1[-1]+1/24.0,1/24.0)
tgm_int=ipt.interp1d(tgm['time'],tgm['zeta'],time)
tgonan=tgo['zeta'][idx]
tgonan[tgonan>500]=np.nan
tgo_int=ipt.interp1d(tgo['time'][idx],tgonan,time)
tgm_tcon_pre=ttide.t_tide(tgm_int,stime=time[0],lat=tgm['lat'],dt=(time[1]-time[0])*24.0,out_style=None)
tgo_tcon_pre=ttide.t_tide(tgo_int,stime=time[0],lat=tgm['lat'],dt=(time[1]-time[0])*24.0,out_style=None)
tgm_tcon=ttide.t_tide(tgm_int,stime=time[0],lat=tgm['lat'],dt=(time[1]-time[0])*24.0,constitnames=tgm_tcon_pre['nameu'][tgm_tcon_pre['snr']>=args.snr],out_style=None)
tgo_tcon=ttide.t_tide(tgo_int,stime=time[0],lat=tgm['lat'],dt=(time[1]-time[0])*24.0,constitnames=tgo_tcon_pre['nameu'][tgo_tcon_pre['snr']>=args.snr],out_style=None)
f=plt.figure(figsize=(15,5));
ax=f.add_axes([.125,.1,.775,.8]);
ax.plot(time[:len(tgo_tcon['xres'])],tgo_tcon['xres']-np.nanmean(tgo_tcon['xres']),'k',label='TG: {:05d}'.format(tgm['tgnumber'][0]))
ax.plot(time[:len(tgm_tcon['xres'])],tgm_tcon['xres']-np.nanmean(tgm_tcon['xres']),'r',lw=.5,label='{}'.format(name))
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(monthsFmt)
ax.legend()
ax.set_ylabel('Residual Elevation (m)')
o,m=remove_common_nan(tgo_tcon['xres']-np.nanmean(tgo_tcon['xres']), tgm_tcon['xres']-np.nanmean(tgm_tcon['xres']))
stats=residual_stats(o,m)
def CalculateM2M4(self, slf, XY, id_wl, id_u, ip_start, ip_end, tf_start,
tf_end, pp_dir):
# calculate M2 and M4 along the channel using ttide_py
M2outfileName = pp_dir + 'M2_along_thalweg.out'
M4outfileName = pp_dir + 'M4_along_thalweg.out'
# the computation of the M2 and M4 components of the Telemac data might be expensive.
# as a result, it is first checked if the M2 and M4 components are not already computed and saved to an outputfile
# if this is the case, the M2 and M4 components are obtained from the output file instead of being computed
if (os.path.isfile(M2outfileName)):
M2outputfile = open(M2outfileName, 'r')
M2lineNb = M2outputfile.readline().split('xlength=')[1]
M2lineNb = int(M2lineNb.replace(']', ''))
M2outputfile.close()
if (os.path.isfile(M4outfileName)):
M4outputfile = open(M4outfileName, 'r')
M4lineNb = M4outputfile.readline().split('xlength=')[1]
M4lineNb = int(M4lineNb.replace(']', ''))
M4outputfile.close()
if (os.path.isfile(M2outfileName)) and (
M2lineNb == ip_end - ip_start + 1) and (
os.path.isfile(M4outfileName)) and (M4lineNb == ip_end -
ip_start + 1):
print('all outputfiles exist and have the correct number of lines')
# here there might be some issues between python 3 and python 2
with open(M2outfileName, 'r') as M2outputfile:
M2outputfile.readline()
dataMatrix = list(csv.reader(M2outputfile))
dataMatrix = np.array(dataMatrix)
dataMatrix = dataMatrix.astype(np.float)
#XY=dataMatrix[:, 0:2]
M2_thalweg_WL = dataMatrix[:, 2:6]
M2_thalweg_U = dataMatrix[:, 6:10]
with open(M4outfileName, 'r') as M4outputfile:
M4outputfile.readline()
dataMatrix = list(csv.reader(M4outputfile))
dataMatrix = np.array(dataMatrix)
dataMatrix = dataMatrix.astype(np.float)
#XY=dataMatrix[:, 0:2]
M4_thalweg_WL = dataMatrix[:, 2:6]
M4_thalweg_U = dataMatrix[:, 6:10]
# M2outputfile.close()
# M4outputfile.close()
elif not (os.path.isfile(M2outfileName)
or os.path.isfile(M4outfileName)):
print(
'not all outputfile exist or have the correct number of lines, M2 and M4 are computed'
)
M2_thalweg_WL = np.zeros([ip_end - ip_start + 1, 4])
M4_thalweg_WL = np.zeros([ip_end - ip_start + 1, 4])
M2_thalweg_U = np.zeros([ip_end - ip_start + 1, 4])
M4_thalweg_U = np.zeros([ip_end - ip_start + 1, 4])
# python starts from ip_start-1 to ip_end-1
for iPoint in range(ip_start - 1, ip_end):
i = iPoint - (ip_start - 1)
#i = iPoint - (ip_start - 1)+1
print('Processing the node', str(i))
# read time series at the desired node
slf.readVariablesAtNode(iPoint)
timeseries = slf.getVarValuesAtNode()
# harmonic analysis using t-tide with results every 10 min
tideconout_WL = tt.t_tide(
timeseries[:, id_wl][tf_start:tf_end],
dt=1.0 / 6.0,
constitnames=['M2', 'M4', 'M6', 'M8'],
out_style=None) # id_wl for water level
tideconout_U = tt.t_tide(
timeseries[:, id_u][tf_start:tf_end],
dt=1.0 / 6.0,
constitnames=['M2', 'M4', 'M6', 'M8'],
out_style=None
) # id_u for depth-averaged horizontal velocity
# get M2 and M4 from WL
id_M2_WL = list(tideconout_WL["nameu"]).index(
b'M2 ') # look for M2 constituent
id_M4_WL = list(tideconout_WL["nameu"]).index(
b'M4 ') # look for M4 constituent
M2_thalweg_WL[i, :] = tideconout_WL["tidecon"][
id_M2_WL] # [amp err_amp phase err_phase]
M4_thalweg_WL[i, :] = tideconout_WL["tidecon"][
id_M4_WL] # [amp err_amp phase err_phase]
# get M2 and M4 from U
id_M2_U = list(tideconout_U["nameu"]).index(
b'M2 ') # look for M2 constituent
id_M4_U = list(tideconout_U["nameu"]).index(
b'M4 ') # look for M4 constituent
M2_thalweg_U[i, :] = tideconout_U["tidecon"][
id_M2_U] # [amp err_amp phase err_phase]
M4_thalweg_U[i, :] = tideconout_U["tidecon"][
id_M4_U] # [amp err_amp phase err_phase]
# save the data to text files
M2_out = np.concatenate(
(XY[ip_start - 1:ip_end, :], M2_thalweg_WL, M2_thalweg_U),
axis=1)
M4_out = np.concatenate(
(XY[ip_start - 1:ip_end, :], M4_thalweg_WL, M4_thalweg_U),
axis=1)
if not os.path.exists(pp_dir):
os.mkdir(pp_dir)
np.savetxt(
M2outfileName,
M2_out,
delimiter=',',
header=
'[X,Y,WL_amp,WL_err_amp,WL_phase,WL_err_phase,U_amp,U_err_amp,U_phase,U_err_phase,xlength='
+ str(ip_end - ip_start + 1) + ']')
np.savetxt(
M4outfileName,
M4_out,
delimiter=',',
header=
'[X,Y,WL_amp,WL_err_amp,WL_phase,WL_err_phase,U_amp,U_err_amp,U_phase,U_err_phase,xlength='
+ str(ip_end - ip_start + 1) + ']')
return (M2_thalweg_WL, M4_thalweg_WL, M2_thalweg_U, M4_thalweg_U)
workpath + stations_mod[stn] + '_mod_Tides8_v31.nc', 'r')
xin = fT1_mod.variables['sossheig'][:, 0,
0] * 100.0 # want cm not meters
latitudes = fT1_mod.variables['lat'][0]
# Set the time
if stations_mod[stn] == 'zygi1TG' or stations_mod[
stn] == 'iskeTG' or stations_mod[stn] == 'GinostraTG':
time = datetime(2018, 7, 1, 0, 30, 0)
else:
time = datetime(2017, 7, 1, 0, 30, 0)
print('Extracting.. STZ LAT TIME', stations_mod[stn], latitudes, time)
# Extract Amp and Pha
harmanOUT = ttide.t_tide(np.array(xin),
dt=1.,
stime=time,
lat=latitudes,
constitnames=tidal_comp,
out_style='classic',
outfile=workpath + stations_mod[stn] +
'_mod_tt.txt')
# EMODNET OBS
if flag_modorobs == 1:
# Loop on TG
for stn in range(0, len(stations_obs)):
# Open file and get values
fT1_obs = NC.Dataset(workpath + stations_obs[stn] + 'TG_obs.nc_h.nc',
'r')
xin = fT1_obs.variables["sossheig"][:, 0] * 100.0 # want cm not meters
# Read latitude from model file
fT1_mod = NC.Dataset(
workpath + stations_obs[stn] + 'TG_mod_Tides8_v31.nc', 'r')
