savepath='figures/png/' + grid + '_' + '/eluv_for_ttide_at_element/'
if not os.path.exists(savepath): os.makedirs(savepath)
base_dir = os.path.dirname(__file__)
cons=np.array([['M2 '],['M4 ']])
for i in range(0,len(elements)):
j=elements[i]
print 'Element: ' +("%d"%j)
f,ax=plt.subplots(nrows=3,ncols=1,sharex=True)
[nameu, freq, tidecon_uv, xout]=t_tide(data['ua'][starttime:,j]+1j*data['va'][starttime:,j],stime=data['time'][starttime],lat=data['uvnodell'][j,1],output=False,constitnames=cons)
tpre=t_predic(data['time'][starttime:(starttime+plotlength)],nameu,freq,tidecon_uv)
[nameu, freq, tidecon_uv2, xout]=t_tide(data2['ua'][starttime:,j]+1j*data2['va'][starttime:,j],stime=data['time'][starttime],lat=data['uvnodell'][j,1],output=False,constitnames=cons)
tpre2=t_predic(data['time'][starttime:(starttime+plotlength)],nameu,freq,tidecon_uv2)
uvel1=(data['zeta'][starttime:,data['nv'][j,0]] + data['zeta'][starttime:,data['nv'][j,1]] + data['zeta'][starttime:,data['nv'][j,2]]) / 3.0
[nameu, freq, tidecon_el, xout]=t_tide(uvel1,stime=data['time'][starttime],lat=data['uvnodell'][j,1],output=False,constitnames=cons)
tpre_el=t_predic(data['time'][starttime:(starttime+plotlength)],nameu,freq,tidecon_el)
uvel2=(data['zeta'][starttime:,data['nv'][j,0]] + data['zeta'][starttime:,data['nv'][j,1]] + data['zeta'][starttime:,data['nv'][j,2]]) / 3.0
[nameu, freq, tidecon_el2, xout]=t_tide(uvel2,stime=data['time'][starttime],lat=data['uvnodell'][j,1],output=False,constitnames=cons)
tpre_el2=t_predic(data['time'][starttime:(starttime+plotlength)],nameu,freq,tidecon_el2)
f.savefig(savepath + grid +'_'+regionname+ '_ebb_vectors_at_' + ("%04d" %(starttime+offset+ebb)) + '.png',dpi=600)
plt.close(f)
resu=np.empty((len(eidx),len(data['time'][starttime:])))
resv=np.empty((len(eidx),len(data['time'][starttime:])))
resu2=np.empty((len(eidx),len(data['time'][starttime:])))
resv2=np.empty((len(eidx),len(data['time'][starttime:])))
for j in range(0,len(eidx)):
print j
i=eidx[j]
[nameu, freq, tidecon_uv, xout]=t_tide(data['ua'][starttime:,i]+1j*data['va'][starttime:,i],stime=data['time'][starttime],lat=data['uvnodell'][i,1],output=False,constitnames=np.array([['M2 '],['N2 '],['S2 '],['K1 '],['O1 ']]))
resu[j,:]=data['ua'][starttime:,i]-np.real(t_predic(data['time'][starttime:],nameu,freq,tidecon_uv)).flatten()
resv[j,:]=data['va'][starttime:,i]-np.imag(t_predic(data['time'][starttime:],nameu,freq,tidecon_uv)).flatten()
[nameu, freq, tidecon_uv, xout]=t_tide(data2['ua'][starttime:,i]+1j*data2['va'][starttime:,i],stime=data['time'][starttime],lat=data['uvnodell'][i,1],output=False,constitnames=np.array([['M2 '],['N2 '],['S2 '],['K1 '],['O1 ']]))
resu2[j,:]=data2['ua'][(starttime+offset):,i]-np.real(t_predic(data2['time'][(starttime+offset):],nameu,freq,tidecon_uv)).flatten()
resv2[j,:]=data2['va'][(starttime+offset):,i]-np.imag(t_predic(data2['time'][(starttime+offset):],nameu,freq,tidecon_uv)).flatten()
f=plt.figure()
ax_res=f.add_axes([.125,.1,.8,.775])
ax_res.triplot(data['trigrid'],lw=.5)
axsub1lw=1
for i in cages:
tnodes=data['nv'][i,:]
ax_res.plot(data['nodell'][tnodes[[0,1]],0],data['nodell'][tnodes[[0,1]],1],cagecolor,lw=axsub1lw,label='Drag')
savepath='figures/png/' + grid + '_' + datatype + '/eluv_for_ttide_at_element/'
if not os.path.exists(savepath): os.makedirs(savepath)
base_dir = os.path.dirname(__file__)
cons=np.array([['M2 '],['M4 ']])
for i in range(0,len(elements)):
j=elements[i]
print 'Element: ' +("%d"%j)
f,ax=plt.subplots(nrows=3,ncols=1,sharex=True)
[nameu, freq, tidecon_uv, xout]=t_tide(data['ua'][starttime:,j]+1j*data['va'][starttime:,j],stime=data['time'][starttime],lat=data['uvnodell'][j,1],output=False,constitnames=cons)
tpre=t_predic(data['time'][starttime:(starttime+plotlength)],nameu,freq,tidecon_uv)
[nameu, freq, tidecon_uv2, xout]=t_tide(data2['ua'][starttime:,j]+1j*data2['va'][starttime:,j],stime=data['time'][starttime],lat=data['uvnodell'][j,1],output=False,constitnames=cons)
tpre2=t_predic(data['time'][starttime:(starttime+plotlength)],nameu,freq,tidecon_uv2)
uvel1=(data['zeta'][starttime:,data['nv'][j,0]] + data['zeta'][starttime:,data['nv'][j,1]] + data['zeta'][starttime:,data['nv'][j,2]]) / 3.0
[nameu, freq, tidecon_el, xout]=t_tide(uvel1,stime=data['time'][starttime],lat=data['uvnodell'][j,1],output=False,constitnames=cons)
tpre_el=t_predic(data['time'][starttime:(starttime+plotlength)],nameu,freq,tidecon_el)
uvel2=(data['zeta'][starttime:,data['nv'][j,0]] + data['zeta'][starttime:,data['nv'][j,1]] + data['zeta'][starttime:,data['nv'][j,2]]) / 3.0
[nameu, freq, tidecon_el2, xout]=t_tide(uvel2,stime=data['time'][starttime],lat=data['uvnodell'][j,1],output=False,constitnames=cons)
tpre_el2=t_predic(data['time'][starttime:(starttime+plotlength)],nameu,freq,tidecon_el2)
f.savefig(savepath + grid +'_'+regionname+ '_ebb_vectors_at_' + ("%04d" %(starttime+offset+ebb)) + '.png',dpi=600)
plt.close(f)
resu=np.empty((len(eidx),len(data['time'][starttime:])))
resv=np.empty((len(eidx),len(data['time'][starttime:])))
resu2=np.empty((len(eidx),len(data['time'][starttime:])))
resv2=np.empty((len(eidx),len(data['time'][starttime:])))
for j in range(0,len(eidx)):
print j
i=eidx[j]
[nameu, freq, tidecon_uv, xout]=t_tide(data['ua'][starttime:,i]+1j*data['va'][starttime:,i],stime=data['time'][starttime],lat=data['uvnodell'][i,1],output=False,constitnames=np.array([['M2 '],['N2 '],['S2 '],['K1 '],['O1 ']]))
resu[j,:]=data['ua'][starttime:,i]-np.real(t_predic(data['time'][starttime:],nameu,freq,tidecon_uv)).flatten()
resv[j,:]=data['va'][starttime:,i]-np.imag(t_predic(data['time'][starttime:],nameu,freq,tidecon_uv)).flatten()
[nameu, freq, tidecon_uv, xout]=t_tide(data2['ua'][starttime:,i]+1j*data2['va'][starttime:,i],stime=data['time'][starttime],lat=data['uvnodell'][i,1],output=False,constitnames=np.array([['M2 '],['N2 '],['S2 '],['K1 '],['O1 ']]))
resu2[j,:]=data2['ua'][(starttime+offset):,i]-np.real(t_predic(data2['time'][(starttime+offset):],nameu,freq,tidecon_uv)).flatten()
resv2[j,:]=data2['va'][(starttime+offset):,i]-np.imag(t_predic(data2['time'][(starttime+offset):],nameu,freq,tidecon_uv)).flatten()
f=plt.figure()
ax_res=f.add_axes([.125,.1,.8,.775])
ax_res.triplot(data['trigrid'],lw=.5)
axsub1lw=1
for i in cages:
tnodes=data['nv'][i,:]
ax_res.plot(data['nodell'][tnodes[[0,1]],0],data['nodell'][tnodes[[0,1]],1],cagecolor,lw=axsub1lw,label='Drag')
region = regions(regionname)
savepath = 'figures/png/' + grid + '_' + '/ellipses/' + name + '_' + regionname + '/'
if not os.path.exists(savepath): os.makedirs(savepath)
idx = equal_vectors(data, region, spacing)
tidecon_uv = np.empty([len(idx), 5, 8])
print len(idx)
for i in range(0, len(idx)):
j = idx[i]
[nameu, freq, tidecon_uv[i, ],
xout] = t_tide(data['ua'][starttime:, j] + 1j * data['va'][starttime:, j],
stime=data['time'][starttime],
lat=data['uvnodell'][j, 1],
output=False,
constitnames=np.array([['M2 '], ['N2 '], ['S2 '],
['K1 '], ['O1 ']]))
print('T_tide Finished')
idxl = np.where(tidecon_uv[:, 3, 2] >= 0)[0]
idxr = np.where(tidecon_uv[:, 3, 2] < 0)[0]
ellsleft = [
Ellipse(xy=(data['uvnodell'][idx[idxl[i]],
0], data['uvnodell'][idx[idxl[i]], 1]),
width=np.fabs(tidecon_uv[idxl[i], 3, 2]) / divider,
height=tidecon_uv[idxl[i], 3, 0] / divider,
angle=tidecon_uv[idxl[i], 3, 4] + 90,
fc='None',
#[nameu, fu, tidecon, xout]=tt.t_tide(data['zeta'][199:-1,118836],dt=1.555) print print '/\ Scalar test (errcalc=linear)' print #[nameu, fu, tidecon, xout]=tt.t_tide(data['ua'][199:-1,149999]+1j*data['va'][199:-1,149999],errcalc='linear') print print '/\ Complex test (errcalc=linear)' print #kill [nameu, fu, tidecon, xout]=tt.t_tide(data['zeta'][199:-1,118836],errcalc='wboot') print print '/\ Scalar test (errcalc=wboot)' print [nameu, fu, tidecon, xout]=tt.t_tide(data['ua'][199:-1,149999]+1j*data['va'][199:-1,149999],errcalc='wboot') print print '/\ Complex test (errcalc=wboot)' print [nameu, fu, tidecon, xout]=tt.t_tide(data['zeta'][199:-1,118836],stime=data['time'][0],lat=data['nodell'][118836,1],constitnames=np.array([['M2 ']])) print print '/\ Scalar test (stime,lat,con=m2)' print [nameu, fu, tidecon, xout]=tt.t_tide(data['ua'][199:-1,149999]+1j*data['va'][199:-1,149999],stime=data['time'][0],lat=data['uvnodell'][149999,1],constitnames=np.array([['M2 ']]))
print('done load')
data = ncdatasort(data,trifinder=True)
print('done sort')
savepath='figures/png/' + grid + '_' + datatype + '/misc/'
if not os.path.exists(savepath): os.makedirs(savepath)
loc=[-58.9161,44.1942]
ua=interpE_at_loc(data,'ua',loc)
va=interpE_at_loc(data,'va',loc)
[nameu, freq, tidecon_uv, xout]=t_tide(ua[starttime:endtime]+1j*va[starttime:endtime],stime=data['time'][starttime],lat=loc[1],output=True,constitnames=['M2','S2','N2','K1','O1'],dt=np.diff(data['time'])[0]*24)
dist=np.sqrt(ua**2+va**2)
f=plt.figure()
ax=f.add_axes([.125,.1,.775,.8])
scb=ax.scatter(ua,va,s=25,c=dist,edgecolors='None')
cb=plt.colorbar(scb)
ax.set_xlabel(r'depth averaged u-velocity (m/s)')
ax.set_ylabel(r'depth averaged v-velocity (m/s)')
cb.set_label(r'depth averaged speed (m/s)')
f.savefig(savepath + grid + '_' +name+ '_john_scatter.png',dpi=600)
plt.close(f)
endtime=2785
### load the .nc file #####
data = loadnc('runs/'+grid+'/'+name+'/output/',singlename=grid + '_0001.nc')
print('done load')
data = ncdatasort(data)
print('done sort')
dt=np.diff(data['time'])[0]*24
tidecon_uv=np.empty([len(data['uvnodell'][:,0]),29,8])
for i in range(0,len(data['uvnodell'][:,0])):
print(i)
[nameu, freq, tidecon_uv[i,], xout]=t_tide(data['ua'][starttime:endtime,i]+1j*data['va'][starttime:endtime,i],stime=data['time'][starttime],lat=data['uvnodell'][i,1],output=False,synth=-1,dt=dt)
tidesave={}
tidesave['nameu']=nameu
tidesave['freq']=freq
tidesave['tidecon']=tidecon_uv
np.save('data/ttide/'+grid+'_'+name+'_'+datatype+'_uv_all.npy',tidesave)
tidecon_el=np.empty([len(data['nodell'][:,0]),29,4])
for j in range(0,len(data['nodell'][:,0])):
print(j)
[nameu, freq, tidecon_el[j,], xout]=t_tide(data['zeta'][starttime:endtime,j],stime=data['time'][starttime],lat=data['nodell'][j,1],synth=-1,output=False,dt=dt)
tidesave={}
tidesave['nameu']=nameu
# Build an M2 tidal series with 5m elevation
ein = 5*np.cos(((2*np.pi/12.42))*np.arange(1001))
# Build an M2 tidal series for current
# Use to 2m/s horizontal currents and .5m/s vertical currents
uin = 2*np.cos(((2*np.pi/12.42))*np.arange(1001))
vin = 0.5*np.cos(((2*np.pi/12.42))*np.arange(1001))
uvin = uin+1j*vin
########################################################################
# Elevation Test Cases
########################################################################
# Basic test case
[nameu, freq, tidecon, xout] = t_tide(ein)
print()
print('*' * 80)
print()
# No output test case
[nameu, freq, tidecon, xout] = t_tide(ein, output=False)
print()
print('*' * 80)
print()
# Pandas output case
[nameu, freq, tidecon, xout] = t_tide(ein, out_style='pandas')
#testel=np.genfromtxt('kiti_118836.csv', dtype=None, delimiter=',')
#print np.absolute(data['zeta'][199:-1,118836]-testel).min()
#print np.absolute(data['zeta'][199:-1,118836]-testel).max()
#[nameu, fu, tidecon, xout]=tt.t_tide(data['zeta'][199:-1,118836],dt=1.555)
print
print '/\ Scalar test (errcalc=linear)'
print
#[nameu, fu, tidecon, xout]=tt.t_tide(data['ua'][199:-1,149999]+1j*data['va'][199:-1,149999],errcalc='linear')
print
print '/\ Complex test (errcalc=linear)'
print
#kill
[nameu, fu, tidecon, xout] = tt.t_tide(data['zeta'][199:-1, 118836],
errcalc='wboot')
print
print '/\ Scalar test (errcalc=wboot)'
print
[nameu, fu, tidecon, xout
] = tt.t_tide(data['ua'][199:-1, 149999] + 1j * data['va'][199:-1, 149999],
errcalc='wboot')
print
print '/\ Complex test (errcalc=wboot)'
print
[nameu, fu, tidecon, xout] = tt.t_tide(data['zeta'][199:-1, 118836],
stime=data['time'][0],
lat=data['nodell'][118836, 1],
constitnames=np.array([['M2 ']]))
print
print('done load')
data = ncdatasort(data, trifinder=True)
print('done sort')
savepath = 'figures/png/' + grid + '_' + datatype + '/misc/'
if not os.path.exists(savepath): os.makedirs(savepath)
loc = [-58.9161, 44.1942]
ua = ipt.interpE_at_loc(data, 'ua', loc)
va = ipt.interpE_at_loc(data, 'va', loc)
[nameu, freq, tidecon_uv,
xout] = t_tide(ua[starttime:endtime] + 1j * va[starttime:endtime],
stime=data['time'][starttime],
lat=loc[1],
output=True,
constitnames=['M2', 'S2', 'N2', 'K1', 'O1'],
dt=np.diff(data['time'])[0] * 24)
dist = np.sqrt(ua**2 + va**2)
f = plt.figure()
ax = f.add_axes([.125, .1, .775, .8])
scb = ax.scatter(ua, va, s=25, c=dist, edgecolors='None')
cb = plt.colorbar(scb)
ax.set_xlabel(r'depth averaged u-velocity (m/s)')
ax.set_ylabel(r'depth averaged v-velocity (m/s)')
cb.set_label(r'depth averaged speed (m/s)')
f.savefig(savepath + grid + '_' + name + '_john_scatter.png', dpi=600)
plt.close(f)
