dpi=100,
facecolor=(0.5, 0.5, 0.5, 1),
edgecolor=None,
linewidth=0.0,
frameon=False, # Don't draw a frame
subplotpars=None,
tight_layout=None)
fig.set_frameon(False)
# Attach a canvas
canvas = FigureCanvas(fig)
# Projection for plotting
cs = iris.coord_systems.RotatedGeogCS(args.pole_latitude, args.pole_longitude,
args.npg_longitude)
wind_pc = plot_cube(0.2, -180 / args.zoom, 180 / args.zoom, -90 / args.zoom,
90 / args.zoom)
rw = iris.analysis.cartography.rotate_winds(u10m, v10m, cs)
u10m = rw[0].regrid(wind_pc, iris.analysis.Linear())
v10m = rw[1].regrid(wind_pc, iris.analysis.Linear())
seq = (dte - datetime.datetime(2000, 1, 1)).total_seconds() / 3600
z = make_wind_seed(resolution=0.4, seed=0)
wind_noise_field = wind_field(u10m,
v10m,
z,
sequence=int(seq * 5),
epsilon=0.01)
# Define an axes to contain the plot. In this case our axes covers
# the whole figure
ax = fig.add_axes([0, 0, 1, 1])
ax.set_axis_off() # Don't want surrounding x and y axis
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--epoch",
help="Epoch",
type=int,
required=False,
default=10)
args = parser.parse_args()
# Function to do the multivariate plot
lsmask = iris.load_cube("%s/fixed_fields/land_mask/opfc_global_2019.nc" %
os.getenv('DATADIR'))
# Random field for the wind noise
z = lsmask.regrid(plot_cube(0.5), iris.analysis.Linear())
(width, height) = z.data.shape
z.data = numpy.random.rand(width, height)
def three_plot(ax, t2m, u10m, v10m, prmsl):
ax.set_xlim(-180, 180)
ax.set_ylim(-90, 90)
ax.set_aspect('auto')
ax.set_axis_off() # Don't want surrounding x and y axis
ax.add_patch(
Rectangle((0, 0),
1,
1,
facecolor=(0.6, 0.6, 0.6, 1),
fill=True,
def three_plot(ax, t2m, u10m, v10m, prmsl):
ax.set_xlim(-180, 180)
ax.set_ylim(-90, 90)
ax.set_aspect('auto')
ax.set_axis_off() # Don't want surrounding x and y axis
ax.add_patch(
Rectangle((0, 0),
1,
1,
facecolor=(0.6, 0.6, 0.6, 1),
fill=True,
zorder=1))
# Draw lines of latitude and longitude
draw_lat_lon(ax)
# Add the continents
mask_pc = plot_cube(0.05)
lsmask = iris.load_cube("%s/fixed_fields/land_mask/opfc_global_2019.nc" %
os.getenv('SCRATCH'))
lsmask = lsmask.regrid(mask_pc, iris.analysis.Linear())
lats = lsmask.coord('latitude').points
lons = lsmask.coord('longitude').points
mask_img = ax.pcolorfast(lons,
lats,
lsmask.data,
cmap=matplotlib.colors.ListedColormap(
((0.4, 0.4, 0.4, 0), (0.4, 0.4, 0.4, 1))),
vmin=0,
vmax=1,
alpha=1.0,
zorder=20)
# Calculate the wind noise
wind_pc = plot_cube(0.5)
cs = iris.coord_systems.RotatedGeogCS(90, 180, 0)
rw = iris.analysis.cartography.rotate_winds(u10m, v10m, cs)
u10m = rw[0].regrid(wind_pc, iris.analysis.Linear())
v10m = rw[1].regrid(wind_pc, iris.analysis.Linear())
wind_noise_field = wind_field(u10m, v10m, z, sequence=None, epsilon=0.01)
# Plot the temperature
t2m_pc = plot_cube(0.05)
t2m = t2m.regrid(t2m_pc, iris.analysis.Linear())
t2m = quantile_normalise_t2m(t2m)
# Adjust to show the wind
wscale = 200
s = wind_noise_field.data.shape
wind_noise_field.data = qcut(
wind_noise_field.data.flatten(),
wscale,
labels=False,
duplicates='drop').reshape(s) - (wscale - 1) / 2
# Plot as a colour map
wnf = wind_noise_field.regrid(t2m, iris.analysis.Linear())
t2m_img = ax.pcolorfast(lons,
lats,
t2m.data * 200 + wnf.data,
cmap='RdYlBu_r',
alpha=0.8,
zorder=100)
# Plot the prmsl
prmsl_pc = plot_cube(0.25)
prmsl = prmsl.regrid(prmsl_pc, iris.analysis.Linear())
lats = prmsl.coord('latitude').points
lons = prmsl.coord('longitude').points
lons, lats = numpy.meshgrid(lons, lats)
CS = ax.contour(lons,
lats,
prmsl.data * 0.01,
colors='black',
linewidths=0.5,
alpha=1.0,
levels=numpy.arange(870, 1050, 10),
zorder=200)
# Define the figure (page size, background color, resolution, ...
fig = Figure(
figsize=(19.2, 10.8), # Width, Height (inches)
dpi=100,
facecolor=(0.5, 0.5, 0.5, 1),
edgecolor=None,
linewidth=0.0,
frameon=False, # Don't draw a frame
subplotpars=None,
tight_layout=None)
fig.set_frameon(False)
# Attach a canvas
canvas = FigureCanvas(fig)
wind_pc = plot_cube(0.2, -180, 180, -90, 90)
cs = iris.coord_systems.RotatedGeogCS(90, 180, 0)
rw = iris.analysis.cartography.rotate_winds(u10m, v10m, cs)
u10m = rw[0].regrid(wind_pc, iris.analysis.Linear())
v10m = rw[1].regrid(wind_pc, iris.analysis.Linear())
z = make_wind_seed(resolution=0.4)
wind_noise_field = wind_field(u10m, v10m, z, sequence=None, epsilon=0.01)
# Define an axes to contain the plot. In this case our axes covers
# the whole figure
ax = fig.add_axes([0, 0, 1, 1])
ax.set_axis_off() # Don't want surrounding x and y axis
# Lat and lon range (in rotated-pole coordinates) for plot
ax.set_xlim(-180, 180)
ax.set_ylim(-90, 90)
def three_plot(ax,t2m,u10m,v10m,prmsl,ls):
ax.set_xlim(-180,180)
ax.set_ylim(-90,90)
ax.set_aspect('auto')
ax.set_axis_off() # Don't want surrounding x and y axis
ax.add_patch(Rectangle((0,0),1,1,facecolor=(0.6,0.6,0.6,1),
fill=True,zorder=1))
# Draw lines of latitude and longitude
draw_lat_lon(ax)
# Add the continents
mask_pc = plot_cube(0.05)
lsmask = iris.load_cube("%s/fixed_fields/land_mask/opfc_global_2019.nc" % os.getenv('SCRATCH'))
lsmask = lsmask.regrid(mask_pc,iris.analysis.Linear())
lats = lsmask.coord('latitude').points
lons = lsmask.coord('longitude').points
mask_img = ax.pcolorfast(lons, lats, lsmask.data,
cmap=matplotlib.colors.ListedColormap(
((0.4,0.4,0.4,0),
(0.4,0.4,0.4,1))),
vmin=0,
vmax=1,
alpha=1.0,
zorder=20)
# Calculate the wind noise
wind_pc=plot_cube(0.2)
cs=iris.coord_systems.RotatedGeogCS(90,180,0)
rw=iris.analysis.cartography.rotate_winds(u10m,v10m,cs)
u10m = rw[0].regrid(wind_pc,iris.analysis.Linear())
v10m = rw[1].regrid(wind_pc,iris.analysis.Linear())
seq=(dte-datetime.datetime(2000,1,1)).total_seconds()/3600
wind_noise_field=wind_field(u10m,v10m,z,sequence=int(seq*5),epsilon=0.01)
# Plot the temperature
t2m=quantile_normalise_t2m(t2m)
t2m_pc=plot_cube(0.05)
t2m = t2m.regrid(t2m_pc,iris.analysis.Linear())
# Adjust to show the wind
wscale=200
s=wind_noise_field.data.shape
wind_noise_field.data=qcut(wind_noise_field.data.flatten(),wscale,
labels=False,
duplicates='drop').reshape(s)-(wscale-1)/2
# Plot as a colour map
wnf=wind_noise_field.regrid(t2m,iris.analysis.Linear())
t2m_img = ax.pcolorfast(lons, lats, t2m.data*1000+wnf.data,
cmap='RdYlBu_r',
alpha=0.8,
vmin=-100,
vmax=1100,
zorder=100)
# Plot the prmsl
prmsl_pc=plot_cube(0.25)
prmsl = prmsl.regrid(prmsl_pc,iris.analysis.Linear())
lats = prmsl.coord('latitude').points
lons = prmsl.coord('longitude').points
lons,lats = numpy.meshgrid(lons,lats)
CS=ax.contour(lons, lats, prmsl.data*0.01,
colors='black',
linewidths=0.5,
alpha=1.0,
levels=numpy.arange(870,1050,10),
zorder=200)
# Overlay the latent-space representation in the SE Pacific
x=numpy.linspace(-160,-120,10)
y=numpy.linspace(-75,-75+(40*16/18),10)
latent_img = ax.pcolorfast(x,y,ls.reshape(10,10),
cmap='viridis',
alpha=1.0,
vmin=-3,
vmax=3,
zorder=1000)
# Label with the date
ax.text(180/args.zoom-(360/args.zoom)*0.009,
90/args.zoom-(180/args.zoom)*0.016,
"%04d-%02d-%02d" % (args.year,args.month,args.day),
horizontalalignment='right',
verticalalignment='top',
color='black',
bbox=dict(facecolor=(0.6,0.6,0.6,0.5),
edgecolor='black',
boxstyle='round',
pad=0.5),
size=14,
clip_on=True,
zorder=500)
# Define the figure (page size, background color, resolution, ...
fig = Figure(
figsize=(38.4, 21.6), # Width, Height (inches)
dpi=100,
facecolor=(0.5, 0.5, 0.5, 1),
edgecolor=None,
linewidth=0.0,
frameon=False, # Don't draw a frame
subplotpars=None,
tight_layout=None)
fig.set_frameon(False)
# Attach a canvas
canvas = FigureCanvas(fig)
# Make the wind noise
wind_pc = plot_cube(0.2, -180 / args.zoom, 180 / args.zoom, -90 / args.zoom,
90 / args.zoom)
cs = iris.coord_systems.RotatedGeogCS(90.0, 180.0, 0.0)
rw = iris.analysis.cartography.rotate_winds(u10m, v10m, cs)
u10m = rw[0].regrid(wind_pc, iris.analysis.Linear())
v10m = rw[1].regrid(wind_pc, iris.analysis.Linear())
seq = (dte - datetime.datetime(2000, 1, 1)).total_seconds() / 3600
z = make_wind_seed(0.4, seed=1)
wind_noise_field = wind_field(u10m,
v10m,
z,
sequence=int(seq * 5),
epsilon=0.01)
# Define an axes to contain the plot. In this case our axes covers
# the whole figure
ax = fig.add_axes([0, 0, 1, 1])
# Define the figure (page size, background color, resolution, ...
fig = Figure(
figsize=(19.2, 10.8), # Width, Height (inches)
dpi=100,
facecolor=(0.5, 0.5, 0.5, 1),
edgecolor=None,
linewidth=0.0,
frameon=False, # Don't draw a frame
subplotpars=None,
tight_layout=None)
fig.set_frameon(False)
# Attach a canvas
canvas = FigureCanvas(fig)
wind_pc = plot_cube(0.2)
rw = iris.analysis.cartography.rotate_winds(u10m, v10m, cs)
u10m = rw[0].regrid(wind_pc, iris.analysis.Linear())
v10m = rw[1].regrid(wind_pc, iris.analysis.Linear())
seq = (dte - datetime.datetime(1969, 1, 1)).total_seconds() / 3600
z = make_wind_seed(resolution=0.4, seed=0)
wind_noise_field = wind_field(u10m,
v10m,
z,
sequence=int(seq * 5),
epsilon=0.01)
# Define an axes to contain the plot. In this case our axes covers
# the whole figure
ax = fig.add_axes([0, 0, 1, 1])
ax.set_axis_off() # Don't want surrounding x and y axis
fig=Figure(figsize=(38.4,21.6), # Width, Height (inches)
dpi=100,
facecolor=(0.5,0.5,0.5,1),
edgecolor=None,
linewidth=0.0,
frameon=False, # Don't draw a frame
subplotpars=None,
tight_layout=None)
fig.set_frameon(False)
# Attach a canvas
canvas=FigureCanvas(fig)
# Make the wind noise
wind_pc=plot_cube(0.2,-180/args.zoom,180/args.zoom,
-90/args.zoom,90/args.zoom,
pole_latitude=args.pole_latitude,
pole_longitude=args.pole_longitude,
npg_longitude=args.npg_longitude)
cs=iris.coord_systems.RotatedGeogCS(args.pole_latitude,
args.pole_longitude,
args.npg_longitude)
rw=iris.analysis.cartography.rotate_winds(u10m,v10m,cs)
u10m = rw[0].regrid(wind_pc,iris.analysis.Linear())
v10m = rw[1].regrid(wind_pc,iris.analysis.Linear())
seq=(dte-datetime.datetime(2000,1,1)).total_seconds()/3600
z=plot_cube(0.25,-180/args.zoom,180/args.zoom,
-90/args.zoom,90/args.zoom,
pole_latitude=args.pole_latitude,
pole_longitude=args.pole_longitude,
npg_longitude=args.npg_longitude)