def plot_cross_section(cube, pv, theta, theta_e, z_bl, rh, ax, n, ylims):
# Make the plot
if cube.units == 'K':
im = iplt.contourf(cube,
even_cscale(20),
coords=coords,
cmap='coolwarm',
extend='both')
elif cube.units == 'PVU':
im = iplt.contourf(cube,
even_cscale(2),
coords=coords,
cmap='coolwarm',
extend='both')
else:
print(cube.units)
iplt.contour(pv, [2], colors='k', coords=coords)
cs_theta = iplt.contour(theta,
theta_levs,
coords=coords,
colors='k',
linewidths=1,
linestyles='-')
cs_theta_e = iplt.contour(theta_e,
theta_levs,
coords=coords,
colors='w',
linewidths=1,
linestyles='-')
iplt.plot(z_bl.coord('grid_longitude'), z_bl, color='y')
iplt.contour(rh, [0.8], coords=coords, colors='grey')
iplt.contourf(rh, [0.8, 2], coords=coords, colors='None', hatches=['.'])
plt.title(second_analysis.all_diagnostics[cube.name()].symbol)
if n == 0:
plt.clabel(cs_theta, fmt='%1.1f')
plt.clabel(cs_theta_e, fmt='%1.1f')
ax.set_ylim(*ylims)
if n < (nrows - 1) * ncols:
ax.set_xticks([])
if n % ncols != 0:
ax.set_yticks([])
return im
def cross_sections(tracers, theta, rh, z_bl, fig):
# Plot each cube separately
for n, cube in enumerate(tracers):
row = n / ncols
col = n - row * ncols
ax = plt.subplot2grid((nrows, ncols), (row, col))
# Interpolate along the cross section
cube = cs_cube(cube, xs, xf, ys, yf)
coords = ['grid_longitude', 'altitude']
# Make the plot
im = iplt.contourf(cube,
even_cscale(2),
coords=coords,
cmap='coolwarm',
extend='both')
cs = iplt.contour(theta,
theta_levs,
coords=coords,
colors='k',
linewidths=2)
iplt.plot(z_bl.coord('grid_longitude'), z_bl, color='y')
iplt.contour(rh, [0.8], coords=coords, colors='w')
iplt.contourf(rh, [0.8, 2],
coords=coords,
colors='None',
hatches=['.'])
plt.title(second_analysis.all_diagnostics[cube.name()].symbol)
if n == 0:
plt.clabel(cs, fmt='%1.0f', colors='k')
ax.set_ylim(0, 7)
if n < 4:
ax.set_xticks([])
# Add letter labels to panels
for n, ax in enumerate(fig.axes):
multilabel(ax, n)
# Add colorbar at bottom of figure
cbar = plt.colorbar(im,
ax=fig.axes,
orientation='horizontal',
fraction=0.05,
spacing='proportional')
cbar.set_label('PVU')
cbar.set_ticks(np.linspace(-2, 2, 9))
fig.text(0.075, 0.58, 'Height (km)', va='center', rotation='vertical')
fig.text(0.5, 0.2, 'Grid Longitude', ha='center')
return
def main(**kwargs):
# Initialise the plot
fig = plt.figure(figsize=(18, 15))
for n, coord in enumerate(coords):
for m, subdomain in enumerate(['ridges', 'troughs']):
cubes = second_analysis.get_data(coord, subdomain)
cube = convert.calc(name, cubes)
cube.coord(coord).convert_units('km')
mean, std_err = second_analysis.extract_statistics(
cube, 'forecast_index')
ax = plt.subplot2grid((2, 2), (n, m))
im = iplt.contourf(mean, even_cscale(0.18), cmap='coolwarm')
# X-axis - Same for both columns
ax.set_xticks([0, 12, 24, 36, 48, 60])
if n == 0:
ax.get_xaxis().set_ticklabels([])
ax.set_title(subdomain.capitalize())
# Y-axis - Same for both rows
ax.set_ylim(-2, 2)
ax.set_yticks([-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2])
ax.axhline(color='k')
if m > 0:
ax.get_yaxis().set_ticklabels([])
add_labels(fig)
fig.text(0.5, 0.2, 'Forecast Lead Time (hours)', ha='center')
for n, axis in enumerate(fig.axes):
multilabel(axis, n)
cbar = plt.colorbar(im,
ax=fig.axes,
orientation='horizontal',
fraction=0.05,
spacing='proportional')
cbar.set_ticks([-0.18, -0.09, 0, 0.09, 0.18])
cbar.set_label('PVU')
plt.savefig(plotdir + 'inconsistency.pdf')
plt.show()
return
def plot_overview(mslp, pv):
iplt.contourf(pv,
even_cscale(2),
cmap='coolwarm',
spacing='proportional',
extend='both')
add_map()
cbar = plt.colorbar(orientation='horizontal',
fraction=0.05,
spacing='proportional')
cbar.set_label('PVU')
cbar.set_ticks(np.linspace(-2, 2, 9))
cs = iplt.contour(mslp, plevs, colors='k', linewidths=1)
plt.clabel(cs, fmt='%1.0f', colors='k')
plt.plot(track[:, 0], track[:, 1], '-y')
plt.plot(track[time, 0], track[time, 1], 'yx', markersize=15)
return
def main(cubes):
# Set up cross section plotters
pv = convert.calc('ertel_potential_vorticity', cubes)
adv = convert.calc('advection_only_pv', cubes)
T = convert.calc('air_temperature', cubes)
dtdz = calculus.multidim(T, 'altitude', 'z')
for cube in [pv, adv, dtdz]:
cube.coord('altitude').convert_units('km')
cube.coord('atmosphere_hybrid_height_coordinate').convert_units('km')
cube.coord('surface_altitude').convert_units('km')
plotters = []
for name in names:
cube = convert.calc(name, cubes)
cube.coord('altitude').convert_units('km')
cube.coord('atmosphere_hybrid_height_coordinate').convert_units('km')
cube.coord('surface_altitude').convert_units('km')
plotter1 = CSP(True, qplt.contourf, cube, even_cscale(5),
cmap='coolwarm', extend='both')
plotter2 = CSP(False, iplt.contour, pv, [2], colors='k')
plotter3 = CSP(False, iplt.contour, adv, [2], colors='k',
linestyles='--')
plotter4 = CSP(False, iplt.contour, dtdz, [-0.002], colors='k',
linestyles=':')
plotters.append(plotter1)
plotters.append(plotter2)
plotters.append(plotter3)
plotters.append(plotter4)
onselect = CS(plotters)
# Plot theta on 2-PVU to identify regions
theta = convert.calc('air_potential_temperature', cubes,
levels=('ertel_potential_vorticity', [2]))[0]
theta.data = np.ma.masked_where(theta.data > 340, theta.data)
plot.contourf(theta, np.linspace(280, 340, 13))
# Create Cross-sections
selector = RectangleSelector(plt.gca(), onselect, drawtype='line')
plt.show()
return
def main(dt):
forecast = case_studies.iop5b.copy()
cubes_f = forecast.set_lead_time(hours=dt)
analysis = case_studies.iop5_analyses.copy()
cubes_a = analysis.set_lead_time(hours=dt)
# Mask land
z_0 = convert.calc('altitude', cubes_f)[0]
make_plots(cubes_f, cubes_a, 'air_potential_temperature',
('air_pressure', [90000]), 'K',
even_cscale(10, 11),
np.linspace(270, 300, 13))
plt.savefig(plotdir + 'iop5_T_error.pdf')
"""
# Low-level pressure
fig = plt.figure(figsize=(18, 8))
ax = plt.subplot2grid((1, 2), (0, 0))
make_plot(cubes_f, cubes_a, 'air_pressure', None, 'hPa',
even_cscale(5, 11), np.linspace(950, 1050, 11),
mask=z_0.data != 20)
plt.title('(a)'.ljust(30) + 'p(20 m)'.ljust(35))
# Geopotential height
ax = plt.subplot2grid((1, 2), (0, 1))
make_plot(cubes_f, cubes_a, 'altitude', ('air_pressure', [50000]), 'm',
even_cscale(50, 11), np.linspace(5000, 6000, 11))
plt.title('(b)'.ljust(30) + 'z(500 hPa)'.ljust(35))
plt.savefig(plotdir + 'iop8_errors.pdf')
plt.show()
"""
return
import matplotlib.pyplot as plt
import iris.plot as iplt
from irise import convert
from irise.plot.util import add_map, even_cscale
from myscripts.models.um import case_studies
levels = ('air_potential_temperature', [320])
clevs = even_cscale(2)
cmap = 'coolwarm'
def main(cubes):
pv = convert.calc('ertel_potential_vorticity', cubes, levels=levels)[0]
adv = convert.calc('advection_only_pv', cubes, levels=levels)[0]
for n, name in enumerate([
'sum_of_physics_pv_tracers', 'epsilon',
'dynamics_tracer_inconsistency', 'residual_pv'
]):
cube = convert.calc(name, cubes, levels=levels)[0]
m = n / 2
ax = plt.subplot2grid((2, 2), (n - 2 * m, m))
iplt.contourf(cube, clevs, cmap=cmap)
add_map()
iplt.contour(pv, [2], colors='k', linestyles='-')
iplt.contour(adv, [2], colors='k', linestyles='-')
plt.show()
if __name__ == '__main__':
import numpy as np
import matplotlib.pyplot as plt
import iris.plot as iplt
from irise import convert, plot
from irise.plot.util import add_map, even_cscale
from myscripts.models.um import case_studies
from myscripts.projects.thesis.case_studies import plotdir
levels = ('altitude', [1000])
levs = np.linspace(0, 50, 21)
errlevs = even_cscale(25, levels=21)
def main(dt):
fig = plt.figure(figsize=(18, 15))
# Load data
forecast = case_studies.iop8.copy()
cubes_f = forecast.set_lead_time(hours=24)
analysis = case_studies.iop8_analyses.copy()
cubes_a = analysis.set_lead_time(hours=24)
# Forecast
plt.subplot2grid((25, 4), (0, 0), colspan=2, rowspan=10)
wind_speed(cubes_f, levs, cmap='magma_r')
plt.title('(a)'.ljust(28) + 'Forecast'.ljust(35))
# Analysis
plt.subplot2grid((25, 4), (0, 2), colspan=2, rowspan=10)
im = wind_speed(cubes_a, levs, cmap='magma_r')
plt.title('(b)'.ljust(28) + 'Analysis'.ljust(35))
iplt.contour(mask, [0.5], colors='k', linestyles='--')
add_map()
plt.title(second_analysis.all_diagnostics[name].symbol)
iplt.contour(theta, [300], colors='k', linewidths=2)
iplt.contour(rh, [0.8], colors='w', linewidths=2)
iplt.contourf(rh, [0.8, 2], colors='None', hatches=['.'])
for n, ax in enumerate(fig.axes):
multilabel(ax, n)
cbar = plt.colorbar(im, ax=fig.axes, orientation='horizontal',
fraction=0.05, spacing='proportional')
cbar.set_label('PVU')
cbar.set_ticks(np.linspace(-2, 2, 17)[::2])
plt.savefig(plotdir + 'iop5_pv_tracers_24h_' +
str(levels[1][0])[0:3] + 'hpa.pdf')
# plt.show()
return
if __name__ == '__main__':
forecast = case_studies.iop5b.copy()
cubes = forecast.set_lead_time(hours=24)
for p in [60000, 75000, 90000]:
levels = ('air_pressure', [p])
main(cubes, levels, even_cscale(2), cmap='coolwarm',
extend='both')
