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ConditionalStatistics.pyx
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ConditionalStatistics.pyx
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#!python
#cython: boundscheck=False
#cython: wraparound=False
#cython: initializedcheck=False
#cython: cdivision=True
from scipy.fftpack import fft, ifft
cimport Grid
cimport ReferenceState
cimport DiagnosticVariables
cimport PrognosticVariables
cimport ParallelMPI
from NetCDFIO cimport NetCDFIO_CondStats
import cython
cimport numpy as np
import numpy as np
from libc.math cimport sqrt, ceil
from thermodynamic_functions cimport thetas_c
include "parameters.pxi"
cdef class ConditionalStatistics:
def __init__(self, namelist):
self.CondStatsClasses = []
cpdef initialize(self, namelist, Grid.Grid Gr, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV, NetCDFIO_CondStats NC, ParallelMPI.ParallelMPI Pa):
try:
conditional_statistics = namelist['conditional_stats']['classes']
except:
conditional_statistics = ['Null']
#Convert whatever is in twodimensional_statistics to list if not already
if not type(conditional_statistics) == list:
conditional_statistics = [conditional_statistics]
#Build list of twodimensional statistics class instances
if 'Spectra' in conditional_statistics:
self.CondStatsClasses.append(SpectraStatistics(Gr,PV, DV, NC, Pa))
if 'Null' in conditional_statistics:
self.CondStatsClasses.append(NullCondStats())
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV, NetCDFIO_CondStats NC, ParallelMPI.ParallelMPI Pa):
#loop over class instances and class stats_io
for _class in self.CondStatsClasses:
_class.stats_io(Gr, RS, PV, DV, NC, Pa)
return
cdef class NullCondStats:
def __init__(self) :
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV, NetCDFIO_CondStats NC, ParallelMPI.ParallelMPI Pa):
return
cdef class SpectraStatistics:
def __init__(self, Grid.Grid Gr, PrognosticVariables.PrognosticVariables PV, DiagnosticVariables.DiagnosticVariables DV,
NetCDFIO_CondStats NC, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t ii, i, jj, j
double xi, yj
# Set up the wavenumber vectors
self.nwave = int( np.ceil(np.sqrt(2.0) * (Gr.dims.n[0] + 1.0) * 0.5 ) + 1.0)
self.dk = 2.0 * pi/(Gr.dims.n[0]*Gr.dims.dx[0])
self.wavenumbers = np.arange(self.nwave, dtype=np.double) * self.dk
self.kx = np.zeros(Gr.dims.nl[0],dtype=np.double,order='c')
self.ky = np.zeros(Gr.dims.nl[1],dtype=np.double,order='c')
for ii in xrange(Gr.dims.nl[0]):
i = Gr.dims.indx_lo[0] + ii
if i <= (Gr.dims.n[0])/2:
xi = np.double(i)
else:
xi = np.double(i - Gr.dims.n[0])
self.kx[ii] = xi * self.dk
for jj in xrange(Gr.dims.nl[1]):
j = Gr.dims.indx_lo[1] + jj
if j <= Gr.dims.n[1]/2:
yj = np.double(j)
else:
yj = np.double(j-Gr.dims.n[1])
self.ky[jj] = yj * self.dk
NC.create_condstats_group('spectra','wavenumber', self.wavenumbers, Gr, Pa)
# set up the names of the variables
NC.add_condstat('energy_spectrum', 'spectra', 'wavenumber', Gr, Pa)
if 's' in PV.name_index:
NC.add_condstat('s_spectrum', 'spectra', 'wavenumber', Gr, Pa)
if 'qt' in PV.name_index:
NC.add_condstat('qt_spectrum', 'spectra', 'wavenumber', Gr, Pa)
if 'theta_rho' in DV.name_index:
NC.add_condstat('theta_rho_spectrum', 'spectra', 'wavenumber', Gr, Pa)
if 'thetali' in DV.name_index:
NC.add_condstat('thetali_spectrum', 'spectra', 'wavenumber', Gr, Pa)
if 'theta' in DV.name_index:
NC.add_condstat('theta_spectrum', 'spectra', 'wavenumber', Gr, Pa)
if 'qt_variance' in DV.name_index:
NC.add_condstat('qtvar_spectrum', 'spectra', 'wavenumber', Gr, Pa)
if 'qt_variance_clip' in DV.name_index:
NC.add_condstat('qtvarclip_spectrum', 'spectra', 'wavenumber', Gr, Pa)
if 's_variance' in DV.name_index:
NC.add_condstat('svar_spectrum', 'spectra', 'wavenumber', Gr, Pa)
if 'covariance' in DV.name_index:
NC.add_condstat('covar_spectrum', 'spectra', 'wavenumber', Gr, Pa)
if 's' in PV.name_index and 'qt' in PV.name_index:
NC.add_condstat('s_qt_cospectrum', 'spectra', 'wavenumber', Gr, Pa)
#Instantiate classes used for Pencil communication/transposes
self.X_Pencil = ParallelMPI.Pencil()
self.Y_Pencil = ParallelMPI.Pencil()
#Initialize classes used for Pencil communication/tranposes (here dim corresponds to the pencil direction)
self.X_Pencil.initialize(Gr,Pa,dim=0)
self.Y_Pencil.initialize(Gr,Pa,dim=1)
return
cpdef stats_io(self, Grid.Grid Gr, ReferenceState.ReferenceState RS, PrognosticVariables.PrognosticVariables PV,
DiagnosticVariables.DiagnosticVariables DV, NetCDFIO_CondStats NC, ParallelMPI.ParallelMPI Pa):
cdef:
Py_ssize_t i, j, k, ijk, var_shift
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t ishift
Py_ssize_t jshift
Py_ssize_t u_shift = PV.get_varshift(Gr, 'u')
Py_ssize_t v_shift = PV.get_varshift(Gr, 'v')
Py_ssize_t w_shift = PV.get_varshift(Gr, 'w')
complex [:] data_fft= np.zeros(Gr.dims.npg,dtype=np.complex,order='c')
complex [:] data_fft_s= np.zeros(Gr.dims.npg,dtype=np.complex,order='c')
double [:] uc = np.zeros(Gr.dims.npg,dtype=np.double,order='c')
double [:] vc = np.zeros(Gr.dims.npg,dtype=np.double,order='c')
double [:] wc = np.zeros(Gr.dims.npg,dtype=np.double,order='c')
Py_ssize_t npg = Gr.dims.npg
Py_ssize_t gw = Gr.dims.gw
double [:,:] spec_u, spec_v, spec_w, spec
#Interpolate to cell centers
with nogil:
for i in xrange(1, Gr.dims.nlg[0]):
ishift = i * istride
for j in xrange(1, Gr.dims.nlg[1]):
jshift = j * jstride
for k in xrange(1, Gr.dims.nlg[2]):
ijk = ishift + jshift + k
uc[ijk] = 0.5 * (PV.values[u_shift + ijk - istride] + PV.values[u_shift + ijk])
vc[ijk] = 0.5 * (PV.values[v_shift + ijk - jstride] + PV.values[v_shift + ijk])
wc[ijk] = 0.5 * (PV.values[w_shift + ijk - 1] + PV.values[w_shift + ijk])
self.fluctuation_forward_transform(Gr, Pa, uc[:], data_fft[:])
spec_u = self.compute_spectrum(Gr, Pa, data_fft[:])
self.fluctuation_forward_transform(Gr, Pa, vc[:], data_fft[:])
spec_v = self.compute_spectrum(Gr, Pa, data_fft[:])
self.fluctuation_forward_transform(Gr, Pa, wc[:], data_fft[:])
spec_w = self.compute_spectrum(Gr, Pa, data_fft[:])
spec = np.add(np.add(spec_u,spec_v), spec_w)
NC.write_condstat('energy_spectrum', 'spectra', spec[:,:], Pa)
if 's' in PV.name_index:
var_shift = PV.get_varshift(Gr, 's')
self.fluctuation_forward_transform(Gr, Pa, PV.values[var_shift:var_shift+npg], data_fft_s[:])
spec = self.compute_spectrum(Gr, Pa, data_fft_s[:])
NC.write_condstat('s_spectrum', 'spectra', spec[:,:], Pa)
if 'qt' in PV.name_index:
var_shift = PV.get_varshift(Gr, 'qt')
self.fluctuation_forward_transform(Gr, Pa, PV.values[var_shift:var_shift+npg], data_fft[:])
spec = self.compute_spectrum(Gr, Pa, data_fft[:])
NC.write_condstat('qt_spectrum', 'spectra', spec[:,:], Pa)
if 's' in PV.name_index and 'qt' in PV.name_index:
spec = self.compute_cospectrum(Gr, Pa, data_fft_s[:], data_fft[:])
NC.write_condstat('s_qt_cospectrum', 'spectra', spec[:,:], Pa)
if 'theta_rho' in DV.name_index:
var_shift = DV.get_varshift(Gr, 'theta_rho')
self.fluctuation_forward_transform(Gr, Pa, DV.values[var_shift:var_shift+npg], data_fft[:])
spec = self.compute_spectrum(Gr, Pa, data_fft[:])
NC.write_condstat('theta_rho_spectrum', 'spectra', spec[:,:], Pa)
if 'thetali' in DV.name_index:
var_shift = DV.get_varshift(Gr, 'thetali')
self.fluctuation_forward_transform(Gr, Pa, DV.values[var_shift:var_shift+npg], data_fft[:])
spec = self.compute_spectrum(Gr, Pa, data_fft[:])
NC.write_condstat('thetali_spectrum', 'spectra', spec[:,:], Pa)
if 'theta' in DV.name_index:
var_shift = DV.get_varshift(Gr, 'theta')
self.fluctuation_forward_transform(Gr, Pa, DV.values[var_shift:var_shift+npg], data_fft[:])
spec = self.compute_spectrum(Gr, Pa, data_fft[:])
NC.write_condstat('theta_spectrum', 'spectra', spec[:,:], Pa)
if 'qt_variance' in DV.name_index:
var_shift = DV.get_varshift(Gr, 'qt_variance')
self.fluctuation_forward_transform(Gr, Pa, DV.values[var_shift:var_shift+npg], data_fft[:])
spec = self.compute_spectrum(Gr, Pa, data_fft[:])
NC.write_condstat('qtvar_spectrum', 'spectra', spec[:,:], Pa)
if 'qt_variance_clip' in DV.name_index:
var_shift = DV.get_varshift(Gr, 'qt_variance_clip')
self.fluctuation_forward_transform(Gr, Pa, DV.values[var_shift:var_shift+npg], data_fft[:])
spec = self.compute_spectrum(Gr, Pa, data_fft[:])
NC.write_condstat('qtvarclip_spectrum', 'spectra', spec[:,:], Pa)
if 's_variance' in DV.name_index:
var_shift = DV.get_varshift(Gr, 's_variance')
self.fluctuation_forward_transform(Gr, Pa, DV.values[var_shift:var_shift+npg], data_fft[:])
spec = self.compute_spectrum(Gr, Pa, data_fft[:])
NC.write_condstat('svar_spectrum', 'spectra', spec[:,:], Pa)
if 'covariance' in DV.name_index:
var_shift = DV.get_varshift(Gr, 'covariance')
self.fluctuation_forward_transform(Gr, Pa, DV.values[var_shift:var_shift+npg], data_fft[:])
spec = self.compute_spectrum(Gr, Pa, data_fft[:])
NC.write_condstat('covar_spectrum', 'spectra', spec[:,:], Pa)
return
cpdef forward_transform(self, Grid.Grid Gr,ParallelMPI.ParallelMPI Pa, double [:] data, complex [:] data_fft):
cdef:
double [:,:] x_pencil
complex [:,:] x_pencil_fft, y_pencil, y_pencil_fft
#Do fft in x direction
x_pencil = self.X_Pencil.forward_double(&Gr.dims, Pa, &data[0])
x_pencil_fft = fft(x_pencil,axis=1)
self.X_Pencil.reverse_complex(&Gr.dims, Pa, x_pencil_fft, &data_fft[0])
#Do fft in y direction
y_pencil = self.Y_Pencil.forward_complex(&Gr.dims, Pa, &data_fft[0])
y_pencil_fft = fft(y_pencil,axis=1)
self.Y_Pencil.reverse_complex(&Gr.dims, Pa, y_pencil_fft, &data_fft[0])
return
cpdef fluctuation_forward_transform(self, Grid.Grid Gr,ParallelMPI.ParallelMPI Pa, double [:] data, complex [:] data_fft):
cdef:
double [:,:] x_pencil
complex [:,:] x_pencil_fft, y_pencil, y_pencil_fft
Py_ssize_t i, j, k, ijk
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t ishift
Py_ssize_t jshift
double [:] fluctuation = np.zeros(Gr.dims.npg,dtype=np.double,order='c')
cdef:
double [:] data_mean = Pa.HorizontalMean(Gr, &data[0])
with nogil:
for i in xrange(1, Gr.dims.nlg[0]):
ishift = i * istride
for j in xrange(1, Gr.dims.nlg[1]):
jshift = j * jstride
for k in xrange(1, Gr.dims.nlg[2]):
ijk = ishift + jshift + k
#Compute fluctuations
fluctuation[ijk] = data[ijk] - data_mean[k]
#Do fft in x direction
x_pencil = self.X_Pencil.forward_double(&Gr.dims, Pa, &fluctuation[0])
x_pencil_fft = fft(x_pencil,axis=1)
self.X_Pencil.reverse_complex(&Gr.dims, Pa, x_pencil_fft, &data_fft[0])
#Do fft in y direction
y_pencil = self.Y_Pencil.forward_complex(&Gr.dims, Pa, &data_fft[0])
y_pencil_fft = fft(y_pencil,axis=1)
self.Y_Pencil.reverse_complex(&Gr.dims, Pa, y_pencil_fft, &data_fft[0])
del fluctuation
return
cpdef compute_spectrum(self, Grid.Grid Gr, ParallelMPI.ParallelMPI Pa, complex [:] data_fft ):
cdef:
Py_ssize_t i, j, k, ijk, ik, kg, ishift, jshift
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t gw = Gr.dims.gw
Py_ssize_t nwave = self.nwave
double [:] kx = self.kx
double [:] ky = self.ky
double dk = self.dk
double kmag
double [:,:] spec = np.zeros((Gr.dims.nl[2],self.nwave),dtype=np.double, order ='c')
with nogil:
for i in xrange(Gr.dims.nl[0]):
ishift = (i + gw) * istride
for j in xrange(Gr.dims.nl[1]):
jshift = (j + gw) * jstride
kmag = sqrt(kx[i]*kx[i] + ky[j]*ky[j])
ik = int(ceil(kmag/dk + 0.5) - 1.0)
for k in xrange(Gr.dims.nl[2]):
kg = k + gw
ijk = ishift + jshift + kg
spec[k, ik] += data_fft[ijk].real * data_fft[ijk].real + data_fft[ijk].imag * data_fft[ijk].imag
for k in xrange(Gr.dims.nl[2]):
for ik in xrange(nwave):
spec[k, ik] = Pa.domain_scalar_sum(spec[k,ik])
return spec
cpdef compute_cospectrum(self, Grid.Grid Gr, ParallelMPI.ParallelMPI Pa, complex [:] data_fft_1, complex [:] data_fft_2):
cdef:
Py_ssize_t i, j, k, ijk, ik, kg, ishift, jshift
Py_ssize_t istride = Gr.dims.nlg[1] * Gr.dims.nlg[2]
Py_ssize_t jstride = Gr.dims.nlg[2]
Py_ssize_t gw = Gr.dims.gw
Py_ssize_t nwave = self.nwave
double [:] kx = self.kx
double [:] ky = self.ky
double dk = self.dk
double kmag, R1, R2
double [:,:] spec = np.zeros((Gr.dims.nl[2],self.nwave),dtype=np.double, order ='c')
with nogil:
for i in xrange(Gr.dims.nl[0]):
ishift = (i + gw) * istride
for j in xrange(Gr.dims.nl[1]):
jshift = (j + gw) * jstride
kmag = sqrt(kx[i]*kx[i] + ky[j]*ky[j])
ik = int(ceil(kmag/dk + 0.5) - 1.0)
for k in xrange(Gr.dims.nl[2]):
kg = k + gw
ijk = ishift + jshift + kg
R1 = sqrt(data_fft_1[ijk].real * data_fft_1[ijk].real + data_fft_1[ijk].imag * data_fft_1[ijk].imag)
R2 = sqrt(data_fft_2[ijk].real * data_fft_2[ijk].real + data_fft_2[ijk].imag * data_fft_2[ijk].imag)
spec[k, ik] += R1*R2
for k in xrange(Gr.dims.nl[2]):
for ik in xrange(nwave):
spec[k, ik] = Pa.domain_scalar_sum(spec[k,ik])
return spec