Python anom示例

示例#1

Q_ek = Q_ek.transpose('time', 'lat', 'lon')
Q_ek.values[:, eqi, :] = 0

#w_ek_mean = w_ek.mean(dim='time')
#
Q_ek_mean = Q_ek.mean(dim='time')

#Q_ek_v_mean = Q_ek_v.mean(dim='time')

#Q_ek_f_mean = Q_ek_f.mean(dim='time')
#
#Q_ek = Q_ek.where(np.abs(lats) > 0)

# Compute monthly anomaly
if anom_flag:
    Q_net_surf, Q_net_surf_clim = st.anom(Q_net_surf)
    thf, thf_clim = st.anom(thf)
    sst, sst_clim = st.anom(sst)
    Q_ek, Q_ek_clim = st.anom(Q_ek)
    #Q_ek_f,Q_ek_f_clim = st.anom(Q_ek_f)

# Remove linear trend
if detr:
    sst = sst.fillna(0.)
    sst = xr.DataArray(signal.detrend(sst, axis=0),
                       dims=sst.dims,
                       coords=sst.coords)

    # h = h.fillna(0.)
    # h = xr.DataArray(signal.detrend(h, axis=0), dims=h.dims, coords=h.coords)

示例#2

u_ek = xr.DataArray(u_ek, dims=sst.dims, coords=sst.coords)
v_ek = xr.DataArray(v_ek, dims=sst.dims, coords=sst.coords)

Q_ek = -Cbar * (u_ek * dSSTdx + v_ek * dSSTdy)

#eqi = np.where(lats==0)
#Q_ek = Q_ek.transpose('time','lat','lon')
#Q_ek.values[:,eqi,:] = 0

Q_ek_mean = Q_ek.mean(dim='time')

Q_ek = Q_ek.where(np.abs(lats) > 0)

# Compute monthly anomaly
if anom_flag:
    Q_net_surf, Q_net_surf_clim = st.anom(Q_net_surf)
    thf, thf_clim = st.anom(thf)
    sst, sst_clim = st.anom(sst)
    Q_ek, Q_ek_clim = st.anom(Q_ek)
    sst_ECCO, sst_ECCO_clim = st.anom(sst_ECCO)
    Tmxl_ECCO, Tmxl_ECCO_clim = st.anom(Tmxl_ECCO)
    h, h_clim = st.anom(h)

# Remove linear trend
if detr:
    sst = sst.fillna(0.)
    sst = xr.DataArray(signal.detrend(sst, axis=0),
                       dims=sst.dims,
                       coords=sst.coords)

    # h = h.fillna(0.)

示例#3

# v_ek = -(1/(rho*f3D*hbar3D))*taux

# u_ek =  xr.DataArray(u_ek, dims=sst.dims, coords=sst.coords) 
# v_ek =  xr.DataArray(v_ek, dims=sst.dims, coords=sst.coords) 

# Q_ek = -Cbar*(u_ek*dSSTdx + v_ek*dSSTdy)

# #eqi = np.where(lats==0)
# #Q_ek = Q_ek.transpose('time','lat','lon')
# #Q_ek.values[:,eqi,:] = 0

# Q_ek_mean = Q_ek.mean(dim='time')

# Q_ek = Q_ek.where(np.abs(lats) > 0)

h_anom, h_clim = st.anom(h)

h_clim_std = h_clim.std(dim='month')
h_bar = h_clim.mean(dim='month')


# Compute monthly anomaly
if anom_flag:
    Qs,Qs_clim = st.anom(Qs)
    #thf,thf_clim = st.anom(thf)
    sst,sst_clim = st.anom(sst)
    #Q_ek,Q_ek_clim= st.anom(Q_ek)
    # sst_ECCO,sst_ECCO_clim= st.anom(sst_ECCO)
    # Tmxl_ECCO,Tmxl_ECCO_clim= st.anom(Tmxl_ECCO)

示例#4

c_p = 3850
dt = 30 * 3600 * 24
#C = rho*c_p*h

#C_anom, C_clim = st.anom(C)

#Cbar = C_clim.mean(dim='month')

Cbar = rho * c_p * hbar

#Cbar = C.mean(dim='time')

#if dataname == 'ECCO':
#    Cbar = rho*c_p*delz_sum

h_anom, h_clim = st.anom(h)

h_clim_std = h_clim.std(dim='month')
h_bar = h_clim.mean(dim='month')

# Compute monthly anomaly
if anom_flag:
    Q_net_surf, Q_net_surf_clim = st.anom(Q_net_surf)
    thf, thf_clim = st.anom(thf)
    sst, sst_clim = st.anom(sst)
    ta, ta_clim = st.anom(ta)
    #Q_ek,Q_ek_clim= st.anom(Q_ek)
    sst_ECCO, sst_ECCO_clim = st.anom(sst_ECCO)
    Tmxl_ECCO, Tmxl_ECCO_clim = st.anom(Tmxl_ECCO)

# Remove linear trend

示例#5

# v_ek =  xr.DataArray(v_ek, dims=sst.dims, coords=sst.coords)

# Q_ek = -Cbar*(u_ek*dSSTdx + v_ek*dSSTdy)

# #eqi = np.where(lats==0)
# #Q_ek = Q_ek.transpose('time','lat','lon')
# #Q_ek.values[:,eqi,:] = 0

# Q_ek_mean = Q_ek.mean(dim='time')

# Q_ek = Q_ek.where(np.abs(lats) > 0)

# Compute monthly anomaly
if anom_flag:
    #Q_net_surf,Q_net_surf_clim = st.anom(Q_net_surf)
    thf, thf_clim = st.anom(thf)
    sst, sst_clim = st.anom(sst)
    #Q_ek,Q_ek_clim= st.anom(Q_ek)
    sst_ECCO, sst_ECCO_clim = st.anom(sst_ECCO)
    Tmxl_ECCO, Tmxl_ECCO_clim = st.anom(Tmxl_ECCO)

# Remove linear trend
if detr:
    sst = sst.fillna(0.)
    sst = xr.DataArray(signal.detrend(sst, axis=0),
                       dims=sst.dims,
                       coords=sst.coords)

    # h = h.fillna(0.)
    # h = xr.DataArray(signal.detrend(h, axis=0), dims=h.dims, coords=h.coords)

示例#6

# v_ek = -(1/(rho*f3D*hbar3D))*taux

# u_ek =  xr.DataArray(u_ek, dims=sst.dims, coords=sst.coords)
# v_ek =  xr.DataArray(v_ek, dims=sst.dims, coords=sst.coords)

# Q_ek = -Cbar*(u_ek*dSSTdx + v_ek*dSSTdy)

# #eqi = np.where(lats==0)
# #Q_ek = Q_ek.transpose('time','lat','lon')
# #Q_ek.values[:,eqi,:] = 0

# Q_ek_mean = Q_ek.mean(dim='time')

# Q_ek = Q_ek.where(np.abs(lats) > 0)

h_anom, h_clim = st.anom(h)

h_clim_std = h_clim.std(dim='month')
h_bar = h_clim.mean(dim='month')

# Compute monthly anomaly
if anom_flag:
    Qs, Qs_clim = st.anom(Qs)
    #thf,thf_clim = st.anom(thf)
    sst, sst_clim = st.anom(sst)
    tendT_mxl, tendT_mxl_clim = st.anom(tendT_mxl)
    #Q_ek,Q_ek_clim= st.anom(Q_ek)
    # sst_ECCO,sst_ECCO_clim= st.anom(sst_ECCO)
    # Tmxl_ECCO,Tmxl_ECCO_clim= st.anom(Tmxl_ECCO)

# Remove linear trend

示例#7

文件： plot_index_field_regr_xr.py 项目： cpatrizio88/ocean_atmosphere

# #Q_ek = Q_ek.transpose('time','lat','lon')
# #Q_ek.values[:,eqi,:] = 0

# Q_ek_mean = Q_ek.mean(dim='time')

# Q_ek = Q_ek.where(np.abs(lats) > 0)

# h_anom, h_clim = st.anom(h)

# h_clim_std = h_clim.std(dim='month')
# h_bar = h_clim.mean(dim='month')


# Compute monthly anomaly
if anom_flag:
    Q_net_surf,Q_net_surf_clim = st.anom(Q_net_surf)
    thf,thf_clim = st.anom(thf)
    sst,sst_clim = st.anom(sst)
    #Q_ek,Q_ek_clim= st.anom(Q_ek)
    ps,ps_clim= st.anom(ps)
    sst_ECCO,sst_ECCO_clim= st.anom(sst_ECCO)
    Tmxl_ECCO,Tmxl_ECCO_clim= st.anom(Tmxl_ECCO)

# Remove linear trend
if detr: 
 sst = sst.fillna(0.)    
 sst = xr.DataArray(signal.detrend(sst, axis=0), dims=sst.dims, coords=sst.coords)   

 #ps = ps.fillna(0.)    
 #ps = xr.DataArray(signal.detrend(ps, axis=0), dims=ps.dims, coords=ps.coords)   
 

示例#8

#C = rho*c_p*h

#C_anom, C_clim = st.anom(C)

#Cbar = C_clim.mean(dim='month')

Cbar = rho * c_p * hbar

# h_anom, h_clim = st.anom(h)

# h_clim_std = h_clim.std(dim='month')
# h_bar = h_clim.mean(dim='month')

# Compute monthly anomaly
if anom_flag:
    Qs, Qs_clim = st.anom(Qs)
    #thf,thf_clim = st.anom(thf)
    sst, sst_clim = st.anom(sst)
    #tendT_mxl, tendT_mxl_clim = st.anom(tendT_mxl)
    #Q_ek,Q_ek_clim= st.anom(Q_ek)
    # sst_ECCO,sst_ECCO_clim= st.anom(sst_ECCO)
    # Tmxl_ECCO,Tmxl_ECCO_clim= st.anom(Tmxl_ECCO)

# Remove linear trend
if detr:
    sst = sst.fillna(0.)
    sst = xr.DataArray(signal.detrend(sst, axis=0),
                       dims=sst.dims,
                       coords=sst.coords)

    # h = h.fillna(0.)

示例#9

# #Q_ek.values[:,eqi,:] = 0

# Q_ek_mean = Q_ek.mean(dim='time')

# Q_ek = Q_ek.where(np.abs(lats) > 0)

# h_anom, h_clim = st.anom(h)

# h_clim_std = h_clim.std(dim='month')
# h_bar = h_clim.mean(dim='month')

# Q_ek = Q_ek.transpose('time', 'lat', 'lon')

# Compute monthly anomaly
if anom_flag:
    Q_net_surf, Q_net_surf_clim = st.anom(Q_net_surf)
    thf, thf_clim = st.anom(thf)
    sst, sst_clim = st.anom(sst)
    err_sst, err_sst_clim = st.anom(err_sst)
    err_thf, err_thf_clim = st.anom(err_thf)
    #Q_ek,Q_ek_clim= st.anom(Q_ek)
    sst_ECCO, sst_ECCO_clim = st.anom(sst_ECCO)
    Tmxl_ECCO, Tmxl_ECCO_clim = st.anom(Tmxl_ECCO)

# Remove linear trend
if detr:
    sst = sst.fillna(0.)
    sst = xr.DataArray(signal.detrend(sst, axis=0),
                       dims=sst.dims,
                       coords=sst.coords)