def plot_pd_heat_fn(inp_fol, years):
data=heatbudg_zon_pd_fn(inp_fol, years,[0,72])
pd_dic = pentad_dic(1)
#data = rundata.mean(('lon'))
def plot_heat_var(var,lev,levels):
var_dic = {'heat_eddy': 'Eddy advective terms',
'heat_mean': 'Mean state advective terms'}
plot_data = data.data_vars[var]
plot_data = plot_data*86400.
g=plot_data[:,lev,:].plot.contourf(x='pentad', y='lat', add_label = False, add_colorbar=False)
plt.colorbar(g)
tickspace = range(10,80,10)
labels = [pd_dic[k] for k in tickspace]
plt.ylim(0,90)
plt.xlim(1,73)
plt.xlabel('Pentad')
plt.xticks(tickspace,labels,rotation=25)
plt.ylabel('Latitude')
plt.plot([float(data.pentad[36]),float(data.pentad[36])],[0.,90.],'k--')
plt.title(var_dic[var])
plt.savefig('/scratch/rg419/plots/mom_budg_work/'+inp_fol+'/'+var+'.png')
plt.close()
vars = ['heat_eddy','heat_mean']
for var in vars:
plot_heat_var(var,2,np.arange(-2.,2.1,0.2))
def plot_pd_mom_fn(inp_fol):
data_file = '/scratch/rg419/plots/monsoon_analysis/'+inp_fol+'/mombudg_data.nc'
data= xr.open_dataset( data_file)
print 'data loaded, starting plotting'
land_file = '/scratch/rg419/GFDL_model/GFDLmoistModel/exp/topo_10m/input/land.nc'
land = xr.open_dataset( land_file)
land_plot = xr.DataArray(land.land_mask.values, [('lat', data.lat), ('lon', data.lon)])
pd_dic = pentad_dic(1)
def plot_mom_var(var,levels,qscale):
if var == 'fv_mn_imb':
plot_data = data.data_vars['fv_av'] + data.data_vars['mom_mean']
else:
plot_data = data.data_vars[var]
plot_data = plot_data*10000.
for mnth in range(0,12):
g=plot_data[mnth*6 : mnth*6+6,:,:].plot.contourf(x='lon', y='lat',levels=levels, col='pentad', col_wrap=3)
plt.ylim(0,90)
#plt.xlim(60,180)
for i, ax in enumerate(g.axes.flat):
land_plot.plot.contour(x='lon', y='lat',levels=np.arange(0.,2.,1.), colors='k',add_colorbar=False,add_labels=False,ax=ax)
ax.quiver(data.lon[::3], data.lat[::1], data.u_av[mnth*6 +i,::1,::3], data.v_av[mnth*6+i,::1,::3], scale=qscale,headwidth=5)
ax.set_title(pd_dic[mnth*6+i+1])
plt.savefig('/scratch/rg419/plots/monsoon_analysis/'+ inp_fol + '/' + var + str(mnth + 1) + '.png')
plt.close()
vars = ['dphidx_av']#,'fv_av','mom_eddy','mom_mean','mom_sum','fv_mn_imb']
for var in vars:
plot_mom_var(var,np.arange(-2.,2.1,0.2),500.)
def plot_pd_mom_fn(inp_fol, years, trange):
data=mom_adv_terms_fn(inp_fol, years, trange)
print 'data loaded, starting plotting'
land_file = '/scratch/rg419/GFDL_model/GFDLmoistModel/exp/topo_10m/input/land.nc'
land = xr.open_dataset( land_file)
land_plot = xr.DataArray(land.land_mask.values, [('lat', data.lat), ('lon', data.lon)])
pd_dic = pentad_dic(1)
data.to_netcdf(path='/scratch/rg419/plots/monsoon_analysis/'+inp_fol+'/mom_adv_data.nc', mode='w')
def plot_mom_var(var,levels,qscale):
plot_data = -1.*data.data_vars[var]
plot_data = plot_data*10000.
for mnth in range(0,12):
g=plot_data[mnth*6 : mnth*6+6,:,:].plot.contourf(x='lon', y='lat',levels=levels, col='pentad', col_wrap=3)
plt.ylim(-45,45)
plt.xlim(60,180)
for i, ax in enumerate(g.axes.flat):
land_plot.plot.contour(x='lon', y='lat',levels=np.arange(0.,2.,1.), colors='k',add_colorbar=False,add_labels=False,ax=ax)
ax.quiver(data.lon[::3], data.lat[::1], data.u_av[mnth*6 +i,::1,::3], data.v_av[mnth*6+i,::1,::3], scale=qscale,headwidth=5)
ax.set_title(pd_dic[mnth*6+i+1])
plt.savefig('/scratch/rg419/plots/monsoon_analysis/'+ inp_fol + '/' + var + str(mnth + 1) + '.png')
plt.close()
vars = ['ududx_av','vdudy_av','wdudp_av','duueddx_av','duveddy_av','duweddp_av']
for var in vars:
plot_mom_var(var,np.arange(-2.,2.1,0.2),500.)
def plot_pd_gp_fn(inp_fol, nproc, years, trange):
phi = load_phi(inp_fol, years, 'pentad', trange)
land_file = '/scratch/rg419/GFDL_model/GFDLmoistModel/exp/flat_10m/input/land.nc'
land = xr.open_dataset(land_file)
land_plot = xr.DataArray(land.land_mask.values, [('lat', phi.lat),
('lon', phi.lon)])
pd_dic = pentad_dic(1)
def plot_gp_var(lev, levels):
g = phi[:, lev, :, :].plot.contourf(x='lon',
y='lat',
levels=levels,
col='pentad',
col_wrap=5)
plt.ylim(0, 45)
plt.xlim(60, 180)
for i, ax in enumerate(g.axes.flat):
if not 'aquaplanet' in inp_fol:
land_plot.plot.contour(x='lon',
y='lat',
levels=np.arange(0., 2., 1.),
colors='k',
add_colorbar=False,
add_labels=False,
ax=ax)
ax.set_title(pd_dic[trange[0] + i + 1])
plt.savefig('/scratch/rg419/plots/' + inp_fol + '/pd_phi_' +
str(int(phi.pfull[lev].values)) + '.png')
plt.close()
plot_gp_var(2, np.arange(1., 1.6, 0.025))
plot_gp_var(9, np.arange(12., 14.1, 0.1))
def plot_pd_mom_fn(inp_fol, years, trange):
data = mombudg_lev_pd_fn(inp_fol, years, trange)
land_file = '/scratch/rg419/GFDL_model/GFDLmoistModel/exp/topo_10m/input/land.nc'
land = xr.open_dataset(land_file)
land_plot = xr.DataArray(land.land_mask.values, [('lat', data.lat),
('lon', data.lon)])
pd_dic = pentad_dic(1)
def plot_mom_var(var, levels, qscale):
var_dic = {
'fv_av': 'fv',
'mom_eddy': 'Eddy advective terms',
'mom_mean': 'Mean state advective terms',
'mom_sum': 'Residual',
'dphidx_av': 'Geopotential gradient'
}
plot_data = data.data_vars[var]
plot_data = plot_data * 10000.
g = plot_data.plot.contourf(x='lon',
y='lat',
levels=levels,
col='pentad',
col_wrap=5)
plt.ylim(-45, 45)
plt.xlim(60, 180)
for i, ax in enumerate(g.axes.flat):
land_plot.plot.contour(x='lon',
y='lat',
levels=np.arange(0., 2., 1.),
colors='k',
add_colorbar=False,
add_labels=False,
ax=ax)
ax.quiver(data.lon[::3],
data.lat[::1],
data.u_av[i, ::1, ::3],
data.v_av[i, ::1, ::3],
scale=qscale,
headwidth=5)
ax.set_title(pd_dic[trange[0] + i + 1])
plt.savefig('/scratch/rg419/plots/mom_budg_work/' + inp_fol +
'/mom_ll_' + var + '.png')
plt.close()
vars = ['dphidx_av', 'fv_av', 'mom_eddy', 'mom_mean', 'mom_sum']
for var in vars:
plot_mom_var(var, np.arange(-5., 5.1, 0.5), 500.)
def plot_pd_mom_fn(inp_fol, nproc, years, trange):
data = mombudg_2d_pd_fn(inp_fol, nproc, years, trange)
land_file = '/scratch/rg419/GFDL_model/GFDLmoistModel/exp/flat_10m/input/land.nc'
land = xr.open_dataset(land_file)
land_plot = xr.DataArray(land.land_mask.values, [('lat', data.lat),
('lon', data.lon)])
pd_dic = pentad_dic(1)
def plot_mom_var(var, lev, levels, qscale):
g = data.data_vars[var][:, lev, :, :].plot.contourf(x='lon',
y='lat',
levels=levels,
col='pentad',
col_wrap=5)
plt.ylim(0, 45)
plt.xlim(60, 180)
for i, ax in enumerate(g.axes.flat):
if not 'aquaplanet' in inp_fol:
land_plot.plot.contour(x='lon',
y='lat',
levels=np.arange(0., 2., 1.),
colors='k',
add_colorbar=False,
add_labels=False,
ax=ax)
ax.quiver(data.lon[::3],
data.lat[::1],
data.u_av[i, lev, ::1, ::3],
data.v_av[i, lev, ::1, ::3],
scale=qscale,
headwidth=5)
ax.set_title(pd_dic[trange[0] + i + 1])
plt.savefig('/scratch/rg419/plots/' + inp_fol + '/pd_' + var + '_' +
str(int(data.pfull[lev].values)) + '.png')
plt.close()
vars = [
'dphidx_av', 'fv_av', 'mom_eddy', 'mom_mean', 'ddamp_av', 'mom_sum'
]
for var in vars:
plot_mom_var(var, 2, np.arange(-0.0003, 0.00031, 0.00003), 250.)
plot_mom_var(var, 9, np.arange(-0.0005, 0.00051, 0.00005), 500.)
def plot_weather(inp_fol, years):
year = years[0]
rundata = load_year_xr(inp_fol, year)
tot_p = xr.DataArray(np.zeros((72, 64, 128, len(years))),
[('pentad', range(1, 73)), ('lat', rundata.lat),
('lon', rundata.lon), ('year', years)])
tsurf = xr.DataArray(np.zeros((72, 64, 128, len(years))),
[('pentad', range(1, 73)), ('lat', rundata.lat),
('lon', rundata.lon), ('year', years)])
#w = xr.DataArray(np.zeros((72,64,128,len(years))), [('pentad', range(1,73) ), ('lat', rundata.lat), ('lon', rundata.lon), ('year', years)])
if not 'aqua' in inp_fol:
land_file = '/scratch/rg419/GFDL_model/GFDLmoistModel/exp/topo_10m/input/land.nc'
land = xr.open_dataset(land_file)
land_plot = xr.DataArray(land.land_mask.values, [('lat', rundata.lat),
('lon', rundata.lon)])
i = 0
for year in years:
print year
rundata = load_year_xr(inp_fol, year)
rundata.coords['pentad'] = (rundata.time // 5) - 71
conv_p = rundata.convection_rain.groupby('pentad').mean(('time'))
cond_p = rundata.condensation_rain.groupby('pentad').mean(('time'))
tot_p[:, :, :, i] = (conv_p + cond_p) * 86400
tsurf[:, :, :, i] = rundata.t_surf.groupby('pentad').mean(('time'))
#rundata = load_year_xr(inp_fol, year, pinterp=True)
#rundata.coords['pentad'] = (rundata.time // 5) -71
#w[:,:,:,i] = rundata.omega[:,3,:,:].groupby('pentad').mean(('time'))
i = i + 1
tot_p_clim = tot_p.mean(('year'))
tsurf_clim = tsurf.mean(('year'))
#w_clim = w.mean(('year'))
pd_dic = pentad_dic(1)
for p in range(0, 72):
print p
tot_p_clim[p, 15:64, 21:66].plot.contourf(x='lon',
y='lat',
add_labels=False,
levels=range(0, 31, 3))
cs = tsurf_clim[p, 15:64,
21:66].plot.contour(x='lon',
y='lat',
colors='w',
add_labels=False,
add_colorbar=False,
levels=range(250, 321, 5))
if not 'aqua' in inp_fol:
land_plot.plot.contour(x='lon',
y='lat',
levels=np.arange(0., 2., 1.),
colors='w',
add_colorbar=False,
add_labels=False)
plt.clabel(cs, fontsize=15, inline_spacing=-1, fmt='%1.0f')
plt.ylim(0, 90)
plt.xlim(60, 180)
plt.title(pd_dic[p + 1])
plt.tight_layout()
plt.savefig('/scratch/rg419/plots/monsoon_analysis/' + inp_fol +
'/rain_and_t' + str(p) + '.png')
plt.close()