def precip_psi_plot(run, ax):
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run +
'.nc')
f1 = precip_mse_plot(data, ax, plot_type='precip', precip_contour=None)
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-500., 0., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
linestyles='--')
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(0., 510., 100.),
add_labels=False,
colors='0.7',
linewidths=2)
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-500., 510., 500.),
add_labels=False,
colors='0.5',
linewidths=2)
return f1
def plot_multiple(data,
plotname,
do_xlabels=False,
sharex=True,
var_dic=var_dic):
n = len(data)
rcParams['figure.figsize'] = 6, 10 / 3 * n
fig, axes = plt.subplots(n, sharex=sharex)
axes = axes.tolist()
f1 = precip_mse_plot(data[0], axes[0], **var_dic)
for i in range(1, n - 1):
precip_mse_plot(data[i], axes[i], **var_dic)
precip_mse_plot(data[n - 1], axes[n - 1], do_xlabels=True, **var_dic)
plt.subplots_adjust(left=0.2, right=0.95, top=0.95, bottom=0., hspace=0.2)
#Colorbar
cb1 = fig.colorbar(f1,
ax=axes,
use_gridspec=True,
orientation='horizontal',
fraction=0.15,
pad=0.07,
aspect=30)
if var_dic['plot_type'] == 'mse':
cb1.set_label('Moist static energy, kJ/kg')
else:
cb1.set_label('Precipitation, mm/day')
if var_dic['plot_type'] == None:
plt.savefig(plot_dir + 'precip_mse_hm_' + plotname + '.pdf',
format='pdf')
elif var_dic['plot_type'] == 'precip':
plt.savefig(plot_dir + 'precip_hm_' + plotname + '.pdf', format='pdf')
elif var_dic['plot_type'] == 'mse':
plt.savefig(plot_dir + 'mse_hm_' + plotname + '.pdf', format='pdf')
plt.close()
def precip_psi_plot(run, ax, label='a)', p_cent=True):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
psi = make_sym(psi, asym=True)
data['precipitation'] = make_sym(data.precipitation)
f1 = precip_mse_plot(data,
ax,
plot_type='precip',
precip_contour=None,
p_cent=p_cent)
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-500., 0., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
linestyles='--',
alpha=0.7)
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(0., 510., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
alpha=0.7)
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-1000., 1010., 1000.),
add_labels=False,
colors='0.5',
linewidths=2,
alpha=0.7)
ax.text(-10, 60, label)
return f1
def local_pcent_plots(run,
regions=[[350, 10], [80, 100], [170, 190], [260, 280]],
do_make_sym=True):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
if do_make_sym:
data['precipitation'] = make_sym(data.precipitation)
data = precip_centroid_ll(data, lat_bound=30.)
def get_lons(lonin, data):
if lonin[1] > lonin[0]:
lons = [
data.lon[i] for i in range(len(data.lon))
if data.lon[i] >= lonin[0] and data.lon[i] < lonin[1]
]
else:
lons = [
data.lon[i] for i in range(len(data.lon))
if data.lon[i] >= lonin[0] or data.lon[i] < lonin[1]
]
return lons
lons_1 = get_lons(regions[0], data)
lons_2 = get_lons(regions[1], data)
lons_3 = get_lons(regions[2], data)
lons_4 = get_lons(regions[3], data)
dpcentdt_1 = gr.ddt(data.p_cent.sel(lon=lons_1).mean('lon')) * 86400.
dpcentdt_2 = gr.ddt(data.p_cent.sel(lon=lons_2).mean('lon')) * 86400.
dpcentdt_3 = gr.ddt(data.p_cent.sel(lon=lons_3).mean('lon')) * 86400.
dpcentdt_4 = gr.ddt(data.p_cent.sel(lon=lons_4).mean('lon')) * 86400.
# Set figure parameters
rcParams['figure.figsize'] = 10, 7
rcParams['font.size'] = 14
# Start figure with 4 subplots
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
ax1.plot(data.p_cent.sel(lon=lons_1).mean('lon'),
dpcentdt_1,
'kx',
mew=2,
ms=10,
alpha=0.5)
ax1.plot(data.p_cent.sel(lon=lons_1).mean('lon'),
dpcentdt_1,
alpha=0.3,
color='k',
linewidth=2)
ax1.set_title('West coast')
ax2.plot(data.p_cent.sel(lon=lons_2).mean('lon'),
dpcentdt_2,
'kx',
mew=2,
ms=10,
alpha=0.5)
ax2.plot(data.p_cent.sel(lon=lons_2).mean('lon'),
dpcentdt_2,
alpha=0.3,
color='k',
linewidth=2)
ax2.set_title('Land')
ax3.plot(data.p_cent.sel(lon=lons_3).mean('lon'),
dpcentdt_3,
'kx',
mew=2,
ms=10,
alpha=0.5)
ax3.plot(data.p_cent.sel(lon=lons_3).mean('lon'),
dpcentdt_3,
alpha=0.3,
color='k',
linewidth=2)
ax3.set_title('East coast')
ax4.plot(data.p_cent.sel(lon=lons_4).mean('lon'),
dpcentdt_4,
'kx',
mew=2,
ms=10,
alpha=0.5)
ax4.plot(data.p_cent.sel(lon=lons_4).mean('lon'),
dpcentdt_4,
alpha=0.3,
color='k',
linewidth=2)
ax4.set_title('Ocean')
ax3.set_xlabel('ITCZ latitude')
ax4.set_xlabel('ITCZ latitude')
ax1.set_ylabel('ITCZ migration rate')
ax3.set_ylabel('ITCZ migration rate')
for ax in [ax1, ax2, ax3, ax4]:
ax.grid(True, linestyle=':')
ax.set_xlim(-25, 25)
ax.set_ylim(-1.0, 1.0)
plt.subplots_adjust(left=0.1,
right=0.97,
top=0.95,
bottom=0.1,
hspace=0.3,
wspace=0.3)
# Save as a pdf
plt.savefig(plot_dir + 'bowties_' + run + '.pdf', format='pdf')
plt.close()
# Start figure with 4 subplots
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
precip_mse_plot(data, ax1, lonin=regions[0], lat_bound=30.)
precip_mse_plot(data, ax2, lonin=regions[1], lat_bound=30.)
precip_mse_plot(data, ax3, lonin=regions[2], lat_bound=30.)
precip_mse_plot(data, ax4, lonin=regions[3], lat_bound=30.)
ax1.set_title('West coast')
ax2.set_title('Land')
ax3.set_title('East coast')
ax4.set_title('Ocean')
ax3.set_xlabel('Pentad')
ax4.set_xlabel('Pentad')
ax1.set_ylabel('Latitude')
ax3.set_ylabel('Latitude')
for ax in [ax1, ax2, ax3, ax4]:
ax.grid(True, linestyle=':')
plt.subplots_adjust(left=0.1,
right=0.97,
top=0.95,
bottom=0.1,
hspace=0.3,
wspace=0.3)
# Save as a pdf
plt.savefig(plot_dir + 'precip_' + run + '.pdf', format='pdf')
plt.close()
data.close()
rcParams['figure.figsize'] = 5, 10
rcParams['font.size'] = 14
data_ctrl = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/control_qflux.nc')
data_noam = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/no_americas.nc')
data_notp = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/no_TIP.nc')
plot_dir = '/scratch/rg419/plots/egu_2018_talk_plots/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
f1 = precip_mse_plot(data_ctrl, ax1, lonin=[60.,150.])
ax1.set_title('Control')
precip_mse_plot(data_notp, ax2, lonin=[60.,150.])
ax2.set_title('No Tibetan Plateau')
precip_mse_plot(data_noam, ax3, lonin=[60.,150.])
ax3.set_title('No America')
plt.xticks([0.,12.,24.,36.,48.,60.,72.])
plt.subplots_adjust(left=0.2, right=0.95, top=0.95, bottom=0., hspace=0.2)
#Colorbar
cb1=fig.colorbar(f1, ax=(ax1, ax2, ax3), use_gridspec=True, orientation = 'horizontal',fraction=0.15, pad=0.07, aspect=30)
cb1.set_label('Precipitation, mm/day')
plt.savefig(plot_dir+'precip_hms.pdf', format='pdf')
plt.close()
data_sn1_zs = xr.open_dataset(
'/scratch/rg419/Data_moist/climatologies/sn_1_sst_zs.nc')
data_sine = xr.open_dataset(
'/scratch/rg419/Data_moist/climatologies/sine_sst_10m.nc')
data_sine_zs = xr.open_dataset(
'/scratch/rg419/Data_moist/climatologies/sine_sst_10m_zs.nc')
plot_dir = '/scratch/rg419/plots/paper_2_figs/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
rcParams['figure.figsize'] = 5, 10
rcParams['font.size'] = 14
fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, sharex=True)
f1 = precip_mse_plot(data_sn1, ax1, plot_type='precip')
ax1.set_title('sn1 SSTs')
precip_mse_plot(data_sn1_zs, ax2, plot_type='precip')
ax2.set_title('sn1 SSTs, zonally symmetric')
precip_mse_plot(data_sine, ax3, plot_type='precip')
ax3.set_title('sinusoidal SSTs')
precip_mse_plot(data_sine_zs, ax4, plot_type='precip')
ax4.set_title('sinusoidal SSTs, zonally symmetric')
for ax in [ax1, ax2, ax3, ax4]:
ax.set_xticks([12., 24., 36., 48., 60., 72.])
ax.set_xlim([1, 72])
ax.fill_between([32., 36.], -60, 60, facecolor='k', alpha=0.3)
ax.fill_between([39., 43.], -60, 60, facecolor='k', alpha=0.3)
ax.fill_between([46., 50.], -60, 60, facecolor='k', alpha=0.3)
data_050 = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/rt_0.500.nc')
data_075 = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/rt_0.750.nc')
data_100 = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/sn_1.000.nc')
data_150 = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/rt_1.500.nc')
data_200 = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/rt_2.000.nc')
plot_dir = '/scratch/rg419/plots/paper_2_figs/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
rcParams['figure.figsize'] = 5, 12
rcParams['font.size'] = 14
fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5, sharex=True)
f1 = precip_mse_plot(data_050, ax1)
ax1.set_title('0.5xf')
precip_mse_plot(data_075, ax2)
ax2.set_title('0.75xf')
precip_mse_plot(data_100, ax3)
ax3.set_title('1xf')
precip_mse_plot(data_150, ax4)
ax4.set_title('1.5xf')
precip_mse_plot(data_200, ax5, do_xlabels=True)
ax5.set_title('2xf')
plt.subplots_adjust(left=0.2, right=0.95, top=0.95, bottom=0., hspace=0.2)
#Colorbar
cb1=fig.colorbar(f1, ax=(ax1, ax2, ax3, ax4, ax5), use_gridspec=True, orientation = 'horizontal',fraction=0.15, pad=0.07, aspect=30)
cb1.set_label('Precipitation, mm/day')
plt.savefig(plot_dir+'precip_mse_hm_rotation.pdf', format='pdf')