def pft_to_cartesian(Shat, T, N, pf_grid):
xnodesr = pf_grid['xnodesr']
n_psi = pf_grid['n_psi']
quad_wts = pf_grid['quad_wts']
ngridr = xnodesr.shape[0]
n_templates = Shat.shape[0]
dx = T / N
dy = T / N
wx = pf_grid['wx']
wy = pf_grid['wy']
templates1 = np.zeros((n_templates, N, N))
# Again, Fortran ordering is necessary for FINUFFT.
gxx = np.empty((N, N), dtype=np.complex128, order='F')
upsampfac = 1.25
for k in range(n_templates):
fcc1 = Shat[k, :] * quad_wts
isign = 1
eps = 1e-6
finufftpy.nufft2d1(wx * dx,
wy * dy,
fcc1,
isign,
eps,
N,
N,
gxx,
upsampfac=upsampfac)
gxx = gxx * dx * dy / (4 * np.pi**2)
templates1[k, :, :] = np.real(gxx.transpose((1, 0)))
return templates1
def vel_convolution_nufft(scalar, source_strenth, num_modes, L, eps=1e-8, method=1, *args, **kwargs):
if('kernel_hat' in kwargs):
kernel_hat = kwargs['kernel_hat']
assert isinstance(kernel_hat, np.ndarray)
else:
raise Exception(
'kernel_hat, fourier transform of kernel should be given')
Nshape = scalar.shape
dim = Nshape[0]
assert dim == 2, "Dim should be 2!"
nx, ny = num_modes[:]
if method == 1:
xj, yj = scalar
nj = len(xj)
xmin = xj.min()
xmax = xj.max()
ymin = yj.min()
ymax = yj.max()
Lx, Ly = L
assert ((xmax-xmin) <= Lx) & ((ymax-ymin) <= Ly), "All particles should be limieted to given [Lx,Ly] box!"
# double zero-padding the delta function and scale it into [-pi,pi)
xj = (xj-(xmax+xmin)/2)/Lx*np.pi
yj = (yj-(ymax+ymin)/2)/Ly*np.pi
fk = np.zeros(num_modes, dtype=np.complex128, order='F')
# particle locations change at every iteration, no need to reuse fftw object
ret = finufftpy.nufft2d1(xj, yj, source_strenth, -1, eps, nx, ny, fk, modeord=1)
assert ret == 0, "NUFFT not successful!"
v_hat = np.zeros((nx, ny, 2), order='F', dtype=np.complex128)
v_hat[:, :, 0] = fk * kernel_hat[0]
v_hat[:, :, 1] = fk * kernel_hat[1]
v = np.zeros((nj, 2), order='F', dtype=np.complex128)
ret = finufftpy.nufft2d2many(xj, yj, v, 1, eps, v_hat, modeord=1)
assert ret == 0, "NUFFT not successful!"
v = np.real(v)/(2*Lx*2*Ly) # Real?
return v
u = u_function(x, y, t)
v = v_function(x, y, t)
ux, uy = gradient(u)
vx, vy = gradient(v)
# get arrival points
xn = x + dt*u
yn = y + dt*v
# compute weights
Jxx = 1 + dt*ux
Jxy = dt*uy
Jyx = dt*vx
Jyy = 1 + dt*vy
det = Jxx*Jyy - Jxy*Jyx
cw = c*det
ch = np.zeros([n,n], dtype=complex, order='F')
finufftpy.nufft2d1(xn, yn, cw.astype(complex), -1, 1e-14, n, n, ch, modeord=1)
c = np.fft.ifft2(ch).real
t += dt
print(' t = {:0.3f}'.format(t), max_time, '\r', end='')
c_arrival = c.copy()
time_arrival = time.time() - st
################################################################################
# Evaluate
print('\n')
print('Err, true vs. euler {:0.1e}'.format(np.abs(c_true-c_eulerian).max()))
print('Err, eul vs. dep {:0.1e}'.format(np.abs(c_true-c_nonlinear_departure).max()))
print('Err, true vs. lin dep {:0.1e}'.format(np.abs(c_true-c_linear_departure).max()))
print('Err, true vs. arrival {:0.1e}'.format(np.abs(c_true-c_arrival).max()))
def accuracy_speed_tests(num_nonuniform_points,num_uniform_points,eps):
nj,nk = int(num_nonuniform_points),int(num_nonuniform_points)
iflag=1
num_samples=int(np.minimum(5,num_uniform_points*0.5+1)) # number of outputs used for estimating accuracy; is small for speed
print('Accuracy and speed tests for %d nonuniform points and eps=%g (error estimates use %d samples per run)' % (num_nonuniform_points,eps,num_samples))
# for doing the error estimates
Xest=np.zeros(num_samples,dtype=np.complex128)
Xtrue=np.zeros(num_samples,dtype=np.complex128)
###### 1-d cases ........................................................
ms=int(num_uniform_points)
xj=np.random.rand(nj)*2*math.pi-math.pi
cj=np.random.rand(nj)+1j*np.random.rand(nj);
fk=np.zeros([ms],dtype=np.complex128)
timer=time.time()
ret=finufftpy.nufft1d1(xj,cj,iflag,eps,ms,fk)
elapsed=time.time()-timer
k=np.arange(-np.floor(ms/2),np.floor((ms-1)/2+1))
for ii in np.arange(0,num_samples):
Xest[ii]=np.sum(cj * np.exp(1j*k[ii]*xj))
Xtrue[ii]=fk[ii]
print_report('finufft1d1',elapsed,Xest,Xtrue,nj)
xj=np.random.rand(nj)*2*math.pi-math.pi
cj=np.zeros([nj],dtype=np.complex128);
fk=np.random.rand(ms)+1j*np.random.rand(ms);
timer=time.time()
ret=finufftpy.nufft1d2(xj,cj,iflag,eps,fk)
elapsed=time.time()-timer
k=np.arange(-np.floor(ms/2),np.floor((ms-1)/2+1))
for ii in np.arange(0,num_samples):
Xest[ii]=np.sum(fk * np.exp(1j*k*xj[ii]))
Xtrue[ii]=cj[ii]
print_report('finufft1d2',elapsed,Xest,Xtrue,nj)
x=np.random.rand(nj)*2*math.pi-math.pi
c=np.random.rand(nj)+1j*np.random.rand(nj);
s=np.random.rand(nk)*2*math.pi-math.pi
f=np.zeros([nk],dtype=np.complex128)
timer=time.time()
ret=finufftpy.nufft1d3(x,c,iflag,eps,s,f)
elapsed=time.time()-timer
for ii in np.arange(0,num_samples):
Xest[ii]=np.sum(c * np.exp(1j*s[ii]*x))
Xtrue[ii]=f[ii]
print_report('finufft1d3',elapsed,Xest,Xtrue,nj+nk)
###### 2-d cases ....................................................
ms=int(np.ceil(np.sqrt(num_uniform_points)))
mt=ms
xj=np.random.rand(nj)*2*math.pi-math.pi
yj=np.random.rand(nj)*2*math.pi-math.pi
cj=np.random.rand(nj)+1j*np.random.rand(nj)
fk=np.zeros([ms,mt],dtype=np.complex128,order='F')
timer=time.time()
ret=finufftpy.nufft2d1(xj,yj,cj,iflag,eps,ms,mt,fk)
elapsed=time.time()-timer
Ks,Kt=np.mgrid[-np.floor(ms/2):np.floor((ms-1)/2+1),-np.floor(mt/2):np.floor((mt-1)/2+1)]
for ii in np.arange(0,num_samples):
Xest[ii]=np.sum(cj * np.exp(1j*(Ks.ravel()[ii]*xj+Kt.ravel()[ii]*yj)))
Xtrue[ii]=fk.ravel()[ii]
print_report('finufft2d1',elapsed,Xest,Xtrue,nj)
## 2d1many:
ndata = 5 # how many vectors to do
cj=np.array(np.random.rand(nj,ndata)+1j*np.random.rand(nj,ndata),order='F')
fk=np.zeros([ms,mt,ndata],dtype=np.complex128,order='F')
timer=time.time()
ret=finufftpy.nufft2d1many(xj,yj,cj,iflag,eps,ms,mt,fk)
elapsed=time.time()-timer
dtest = ndata-1 # which of the ndata to test (in 0,..,ndata-1)
for ii in np.arange(0,num_samples):
Xest[ii]=np.sum(cj[:,dtest] * np.exp(1j*(Ks.ravel(order='F')[ii]*xj+Kt.ravel(order='F')[ii]*yj))) # note fortran-ravel-order needed throughout - mess.
Xtrue[ii]=fk.ravel(order='F')[ii + dtest*ms*mt] # hack the offset in fk array - has to be better way
print_report('finufft2d1many',elapsed,Xest,Xtrue,ndata*nj)
# 2d2
xj=np.random.rand(nj)*2*math.pi-math.pi
yj=np.random.rand(nj)*2*math.pi-math.pi
cj=np.zeros([nj],dtype=np.complex128);
fk=np.random.rand(ms,mt)+1j*np.random.rand(ms,mt);
timer=time.time()
ret=finufftpy.nufft2d2(xj,yj,cj,iflag,eps,fk)
elapsed=time.time()-timer
Ks,Kt=np.mgrid[-np.floor(ms/2):np.floor((ms-1)/2+1),-np.floor(mt/2):np.floor((mt-1)/2+1)]
for ii in np.arange(0,num_samples):
Xest[ii]=np.sum(fk * np.exp(1j*(Ks*xj[ii]+Kt*yj[ii])))
Xtrue[ii]=cj[ii]
print_report('finufft2d2',elapsed,Xest,Xtrue,nj)
# 2d2many (using same ndata and dtest as 2d1many; see above)
cj=np.zeros([nj,ndata],order='F',dtype=np.complex128);
fk=np.array(np.random.rand(ms,mt,ndata)+1j*np.random.rand(ms,mt,ndata),order='F')
timer=time.time()
ret=finufftpy.nufft2d2many(xj,yj,cj,iflag,eps,fk)
elapsed=time.time()-timer
for ii in np.arange(0,num_samples):
Xest[ii]=np.sum(fk[:,:,dtest] * np.exp(1j*(Ks*xj[ii]+Kt*yj[ii])))
Xtrue[ii]=cj[ii,dtest]
print_report('finufft2d2many',elapsed,Xest,Xtrue,ndata*nj)
# 2d3
x=np.random.rand(nj)*2*math.pi-math.pi
y=np.random.rand(nj)*2*math.pi-math.pi
c=np.random.rand(nj)+1j*np.random.rand(nj);
s=np.random.rand(nk)*2*math.pi-math.pi
t=np.random.rand(nk)*2*math.pi-math.pi
f=np.zeros([nk],dtype=np.complex128)
timer=time.time()
ret=finufftpy.nufft2d3(x,y,c,iflag,eps,s,t,f)
elapsed=time.time()-timer
for ii in np.arange(0,num_samples):
Xest[ii]=np.sum(c * np.exp(1j*(s[ii]*x+t[ii]*y)))
Xtrue[ii]=f[ii]
print_report('finufft2d3',elapsed,Xest,Xtrue,nj+nk)
###### 3-d cases ............................................................
ms=int(np.ceil(num_uniform_points**(1.0/3)))
mt=ms
mu=ms
xj=np.random.rand(nj)*2*math.pi-math.pi
yj=np.random.rand(nj)*2*math.pi-math.pi
zj=np.random.rand(nj)*2*math.pi-math.pi
cj=np.random.rand(nj)+1j*np.random.rand(nj);
fk=np.zeros([ms,mt,mu],dtype=np.complex128,order='F')
timer=time.time()
ret=finufftpy.nufft3d1(xj,yj,zj,cj,iflag,eps,ms,mt,mu,fk)
elapsed=time.time()-timer
Ks,Kt,Ku=np.mgrid[-np.floor(ms/2):np.floor((ms-1)/2+1),-np.floor(mt/2):np.floor((mt-1)/2+1),-np.floor(mu/2):np.floor((mu-1)/2+1)]
for ii in np.arange(0,num_samples):
Xest[ii]=np.sum(cj * np.exp(1j*(Ks.ravel()[ii]*xj+Kt.ravel()[ii]*yj+Ku.ravel()[ii]*zj)))
Xtrue[ii]=fk.ravel()[ii]
print_report('finufft3d1',elapsed,Xest,Xtrue,nj)
xj=np.random.rand(nj)*2*math.pi-math.pi
yj=np.random.rand(nj)*2*math.pi-math.pi
zj=np.random.rand(nj)*2*math.pi-math.pi
cj=np.zeros([nj],dtype=np.complex128);
fk=np.random.rand(ms,mt,mu)+1j*np.random.rand(ms,mt,mu);
timer=time.time()
ret=finufftpy.nufft3d2(xj,yj,zj,cj,iflag,eps,fk)
elapsed=time.time()-timer
Ks,Kt,Ku=np.mgrid[-np.floor(ms/2):np.floor((ms-1)/2+1),-np.floor(mt/2):np.floor((mt-1)/2+1),-np.floor(mu/2):np.floor((mu-1)/2+1)]
for ii in np.arange(0,num_samples):
Xest[ii]=np.sum(fk * np.exp(1j*(Ks*xj[ii]+Kt*yj[ii]+Ku*zj[ii])))
Xtrue[ii]=cj[ii]
print_report('finufft3d2',elapsed,Xest,Xtrue,nj)
x=np.random.rand(nj)*2*math.pi-math.pi
y=np.random.rand(nj)*2*math.pi-math.pi
z=np.random.rand(nj)*2*math.pi-math.pi
c=np.random.rand(nj)+1j*np.random.rand(nj);
s=np.random.rand(nk)*2*math.pi-math.pi
t=np.random.rand(nk)*2*math.pi-math.pi
u=np.random.rand(nk)*2*math.pi-math.pi
f=np.zeros([nk],dtype=np.complex128)
timer=time.time()
ret=finufftpy.nufft3d3(x,y,z,c,iflag,eps,s,t,u,f)
elapsed=time.time()-timer
for ii in np.arange(0,num_samples):
Xest[ii]=np.sum(c * np.exp(1j*(s[ii]*x+t[ii]*y+u[ii]*z)))
Xtrue[ii]=f[ii]
print_report('finufft3d3',elapsed,Xest,Xtrue,nj+nk)
def accuracy_speed_tests(num_nonuniform_points, num_uniform_points, eps):
nj, nk = int(num_nonuniform_points), int(num_nonuniform_points)
iflag = 1
num_samples = int(np.minimum(20, num_uniform_points * 0.5 +
1)) #for estimating accuracy
print(
'Accuracy and speed tests for %d nonuniform points and eps=%g (error estimates use %d samples per run)'
% (num_nonuniform_points, eps, num_samples))
# for doing the error estimates
Xest = np.zeros(num_samples, dtype=np.complex128)
Xtrue = np.zeros(num_samples, dtype=np.complex128)
###### 1-d
ms = int(num_uniform_points)
xj = np.random.rand(nj) * 2 * math.pi - math.pi
cj = np.random.rand(nj) + 1j * np.random.rand(nj)
fk = np.zeros([ms], dtype=np.complex128)
timer = time.time()
ret = finufftpy.nufft1d1(xj, cj, iflag, eps, ms, fk)
elapsed = time.time() - timer
k = np.arange(-np.floor(ms / 2), np.floor((ms - 1) / 2 + 1))
for ii in np.arange(0, num_samples):
Xest[ii] = np.sum(cj * np.exp(1j * k[ii] * xj))
Xtrue[ii] = fk[ii]
print_report('finufft1d1', elapsed, Xest, Xtrue, nj)
xj = np.random.rand(nj) * 2 * math.pi - math.pi
cj = np.zeros([nj], dtype=np.complex128)
fk = np.random.rand(ms) + 1j * np.random.rand(ms)
timer = time.time()
ret = finufftpy.nufft1d2(xj, cj, iflag, eps, fk)
elapsed = time.time() - timer
k = np.arange(-np.floor(ms / 2), np.floor((ms - 1) / 2 + 1))
for ii in np.arange(0, num_samples):
Xest[ii] = np.sum(fk * np.exp(1j * k * xj[ii]))
Xtrue[ii] = cj[ii]
print_report('finufft1d2', elapsed, Xest, Xtrue, nj)
x = np.random.rand(nj) * 2 * math.pi - math.pi
c = np.random.rand(nj) + 1j * np.random.rand(nj)
s = np.random.rand(nk) * 2 * math.pi - math.pi
f = np.zeros([nk], dtype=np.complex128)
timer = time.time()
ret = finufftpy.nufft1d3(x, c, iflag, eps, s, f)
elapsed = time.time() - timer
for ii in np.arange(0, num_samples):
Xest[ii] = np.sum(c * np.exp(1j * s[ii] * x))
Xtrue[ii] = f[ii]
print_report('finufft1d3', elapsed, Xest, Xtrue, nj + nk)
###### 2-d
ms = int(np.ceil(np.sqrt(num_uniform_points)))
mt = ms
xj = np.random.rand(nj) * 2 * math.pi - math.pi
yj = np.random.rand(nj) * 2 * math.pi - math.pi
cj = np.random.rand(nj) + 1j * np.random.rand(nj)
fk = np.zeros([ms, mt], dtype=np.complex128, order='F')
timer = time.time()
ret = finufftpy.nufft2d1(xj, yj, cj, iflag, eps, ms, mt, fk)
elapsed = time.time() - timer
Ks, Kt = np.mgrid[-np.floor(ms / 2):np.floor((ms - 1) / 2 + 1),
-np.floor(mt / 2):np.floor((mt - 1) / 2 + 1)]
for ii in np.arange(0, num_samples):
Xest[ii] = np.sum(
cj * np.exp(1j * (Ks.ravel()[ii] * xj + Kt.ravel()[ii] * yj)))
Xtrue[ii] = fk.ravel()[ii]
print_report('finufft2d1', elapsed, Xest, Xtrue, nj)
xj = np.random.rand(nj) * 2 * math.pi - math.pi
yj = np.random.rand(nj) * 2 * math.pi - math.pi
cj = np.zeros([nj], dtype=np.complex128)
fk = np.random.rand(ms, mt) + 1j * np.random.rand(ms, mt)
timer = time.time()
ret = finufftpy.nufft2d2(xj, yj, cj, iflag, eps, fk)
elapsed = time.time() - timer
Ks, Kt = np.mgrid[-np.floor(ms / 2):np.floor((ms - 1) / 2 + 1),
-np.floor(mt / 2):np.floor((mt - 1) / 2 + 1)]
for ii in np.arange(0, num_samples):
Xest[ii] = np.sum(fk * np.exp(1j * (Ks * xj[ii] + Kt * yj[ii])))
Xtrue[ii] = cj[ii]
print_report('finufft2d2', elapsed, Xest, Xtrue, nj)
x = np.random.rand(nj) * 2 * math.pi - math.pi
y = np.random.rand(nj) * 2 * math.pi - math.pi
c = np.random.rand(nj) + 1j * np.random.rand(nj)
s = np.random.rand(nk) * 2 * math.pi - math.pi
t = np.random.rand(nk) * 2 * math.pi - math.pi
f = np.zeros([nk], dtype=np.complex128)
timer = time.time()
ret = finufftpy.nufft2d3(x, y, c, iflag, eps, s, t, f)
elapsed = time.time() - timer
for ii in np.arange(0, num_samples):
Xest[ii] = np.sum(c * np.exp(1j * (s[ii] * x + t[ii] * y)))
Xtrue[ii] = f[ii]
print_report('finufft2d3', elapsed, Xest, Xtrue, nj + nk)
###### 3-d
ms = int(np.ceil(num_uniform_points**(1.0 / 3)))
mt = ms
mu = ms
xj = np.random.rand(nj) * 2 * math.pi - math.pi
yj = np.random.rand(nj) * 2 * math.pi - math.pi
zj = np.random.rand(nj) * 2 * math.pi - math.pi
cj = np.random.rand(nj) + 1j * np.random.rand(nj)
fk = np.zeros([ms, mt, mu], dtype=np.complex128, order='F')
timer = time.time()
ret = finufftpy.nufft3d1(xj, yj, zj, cj, iflag, eps, ms, mt, mu, fk)
elapsed = time.time() - timer
Ks, Kt, Ku = np.mgrid[-np.floor(ms / 2):np.floor((ms - 1) / 2 + 1),
-np.floor(mt / 2):np.floor((mt - 1) / 2 + 1),
-np.floor(mu / 2):np.floor((mu - 1) / 2 + 1)]
for ii in np.arange(0, num_samples):
Xest[ii] = np.sum(cj * np.exp(
1j *
(Ks.ravel()[ii] * xj + Kt.ravel()[ii] * yj + Ku.ravel()[ii] * zj)))
Xtrue[ii] = fk.ravel()[ii]
print_report('finufft3d1', elapsed, Xest, Xtrue, nj)
xj = np.random.rand(nj) * 2 * math.pi - math.pi
yj = np.random.rand(nj) * 2 * math.pi - math.pi
zj = np.random.rand(nj) * 2 * math.pi - math.pi
cj = np.zeros([nj], dtype=np.complex128)
fk = np.random.rand(ms, mt, mu) + 1j * np.random.rand(ms, mt, mu)
timer = time.time()
ret = finufftpy.nufft3d2(xj, yj, zj, cj, iflag, eps, fk)
elapsed = time.time() - timer
Ks, Kt, Ku = np.mgrid[-np.floor(ms / 2):np.floor((ms - 1) / 2 + 1),
-np.floor(mt / 2):np.floor((mt - 1) / 2 + 1),
-np.floor(mu / 2):np.floor((mu - 1) / 2 + 1)]
for ii in np.arange(0, num_samples):
Xest[ii] = np.sum(
fk * np.exp(1j * (Ks * xj[ii] + Kt * yj[ii] + Ku * zj[ii])))
Xtrue[ii] = cj[ii]
print_report('finufft3d2', elapsed, Xest, Xtrue, nj)
x = np.random.rand(nj) * 2 * math.pi - math.pi
y = np.random.rand(nj) * 2 * math.pi - math.pi
z = np.random.rand(nj) * 2 * math.pi - math.pi
c = np.random.rand(nj) + 1j * np.random.rand(nj)
s = np.random.rand(nk) * 2 * math.pi - math.pi
t = np.random.rand(nk) * 2 * math.pi - math.pi
u = np.random.rand(nk) * 2 * math.pi - math.pi
f = np.zeros([nk], dtype=np.complex128)
timer = time.time()
ret = finufftpy.nufft3d3(x, y, z, c, iflag, eps, s, t, u, f)
elapsed = time.time() - timer
for ii in np.arange(0, num_samples):
Xest[ii] = np.sum(c * np.exp(1j * (s[ii] * x + t[ii] * y + u[ii] * z)))
Xtrue[ii] = f[ii]
print_report('finufft3d3', elapsed, Xest, Xtrue, nj + nk)
def FFTgridDSGfile(netCDFfiles):
ds = Dataset(netCDFfiles[1], 'a')
vs = ds.get_variables_by_attributes(
standard_name='sea_water_pressure_due_to_sea_water')
vs = ds.get_variables_by_attributes(long_name='actual depth')
pres_var = vs[0]
pres = pres_var[:]
#plt.plot(pres)
#plt.show()
temp_var = ds.variables["TEMP"]
print("Read and convert time")
time_var = ds.variables["TIME"]
#time = num2date(time_var[:], units=time_var.units, calendar=time_var.calendar)
#first_hour = time[0].replace(minute=0, second=0, microsecond=0)
t = time_var[:] * 24
hours = t
# scale time to -pi to pi
hours_min = np.min(hours)
hours_max = np.max(hours)
mid_hours = (hours_max + hours_min) / 2
print(
"time min, max", hours_min, hours_max,
num2date(hours_max / 24,
units=time_var.units,
calendar=time_var.calendar),
num2date(hours_min / 24,
units=time_var.units,
calendar=time_var.calendar),
num2date(mid_hours / 24,
units=time_var.units,
calendar=time_var.calendar))
t2pi = 2 * np.pi * (hours - mid_hours) / (hours_max - hours_min)
# scale pressure to -pi to pi
pres_min = np.min(pres)
pres_max = np.max(pres)
pres_mid = (pres_max + pres_min) / 2
print("pres min, max", pres_min, pres_max, pres_mid)
d2pi = 2 * np.pi * (pres - pres_mid) / (pres_max - pres_min)
#plt.plot(t2pi)
#plt.show()
nt_points = int(hours_max - hours_min)
nd_points = 20
print(nt_points, nd_points)
fft = np.full(
[nt_points, nd_points],
np.nan,
dtype=np.complex128,
order='F',
)
print("Calc FFT")
x = t2pi
y = d2pi
c = np.array(temp_var[:])
#c.real = temp_var[:]
#c.imag = 0
print(c)
print("size ", len(x), len(y), len(c))
# finufftpy.nufft2d2(x, y, c, isign, eps, f, debug=0, spread_debug=0, spread_sort=2, fftw=0, modeord=0, chkbnds=1, upsampfac=2.0)
#finufftpy.nufft2d1(x, y, c, isign, eps, ms, mt, f, debug=0, spread_debug=0, spread_sort=2, fftw=0, modeord=0, chkbnds=1, upsampfac=2.0)¶
res = finufftpy.nufft2d1(x,
y,
c,
0,
1e-15,
nt_points,
nd_points,
fft,
debug=1,
spread_debug=1,
spread_sort=0)
print("fft res", res)
print(fft)
for x in range(0, nd_points):
plt.plot(10 * np.log10(abs(fft[:, x])))
plt.grid(True)
plt.show()
F1 = np.fft.ifft2(fft)
f2 = np.fft.fftshift(F1)
print("inverted ", f2.shape, len(hours))
first_hour = num2date(hours_min / 24,
units=time_var.units,
calendar=time_var.calendar).replace(minute=0,
second=0,
microsecond=0)
td = [first_hour + timedelta(hours=x) for x in range(nt_points)]
for x in range(0, nd_points):
plt.plot(td, abs(f2[:, x]) / (len(hours) / (nt_points)))
plt.grid(True)
plt.xticks(fontsize=6)
plt.show()
index_var = ds.variables["instrument_index"]
idx = index_var[:]
instrument_id_var = ds.variables["instrument_id"]
#time = num2date(time_var[:], units=time_var.units, calendar=time_var.calendar)
#print(idx)
i = 0
for x in chartostring(instrument_id_var[:]):
#print (i, x, time[idx == 1], pres[idx == i])
#plt.plot(time[idx == i], pres[idx == i]) # , marker='.'
i += 1
#plt.gca().invert_yaxis()
#plt.grid(True)
# close the netCDF file
ds.close()
plt.show()
ncOut = Dataset("fft-bin.nc", 'w', format='NETCDF4')
# add time
tDim = ncOut.createDimension("TIME", nt_points)
ncTimesOut = ncOut.createVariable("TIME", "d", ("TIME", ), zlib=True)
ncTimesOut.long_name = "time"
ncTimesOut.units = "days since 1950-01-01 00:00:00 UTC"
ncTimesOut.calendar = "gregorian"
ncTimesOut.axis = "T"
ncTimesOut[:] = hours_min / 24
bin_dim = ncOut.createDimension("BIN", nd_points)
bin_var = ncOut.createVariable("BIN", "d", ("BIN", ), zlib=True)
bin_var[:] = range(0, nd_points)
# add variables
nc_var_out = ncOut.createVariable("TEMP",
"f4", ("TIME", "BIN"),
fill_value=np.nan,
zlib=True)
print("shape ", f2.shape, nc_var_out.shape)
nc_var_out[:] = abs(f2)
# add some summary metadata
ncTimeFormat = "%Y-%m-%dT%H:%M:%SZ"
# add creating and history entry
ncOut.setncattr("date_created", datetime.utcnow().strftime(ncTimeFormat))
ncOut.setncattr(
"history",
datetime.utcnow().strftime("%Y-%m-%d") + " created from file " +
netCDFfiles[1])
ncOut.close()