def f4():
plt.figure(figsize=(6.88,6.74))
#geographic coordinates
ax1, projection1 = gcc.create_map(
2, 2, 1, 'pol', 90, 50, 0, coastlines=False,useLT=True,
dlat=10, lonticklabel=(1, 1, 1, 1))
ax1.plot([45,45],[50,90], 'k--',transform=ccrs.PlateCarree())
ax1.plot([225,225],[50,90], 'k--',transform=ccrs.PlateCarree())
ax1.plot([105,105],[50,90], 'k--',transform=ccrs.PlateCarree())
ax1.plot([285,285],[50,90], 'k--',transform=ccrs.PlateCarree())
ax1.scatter(
0, 90, color='r', transform=ccrs.PlateCarree(), zorder=10,
label='North Pole')
ax1.text(0,1,'(a)', transform = plt.gca().transAxes)
plt.legend(loc=[0.5,1.1])
ax2, projection2 = gcc.create_map(
2, 2, 2, 'pol', -50, -90, 0, coastlines=False,useLT=True,
dlat=10, lonticklabel=(1, 1, 1, 1))
ax2.scatter(
0, -90, color='b', transform=ccrs.PlateCarree(),label='South Pole')
ax2.text(0,1,'(b)', transform = plt.gca().transAxes)
plt.legend(loc=[-0.1,1.1])
#geomagnetic coordinates
ax3, projection3 = gcc.create_map(
2, 2, 3, 'pol', 90, 50, 0, coastlines=False,useLT=True,
dlat=10, lonticklabel=(1, 1, 1, 1))
mlatn,mlonn = convert(90,0,0,date=dt.date(2002,3,21))
for k in range(24):
mltn = convert_mlt(mlonn[0],dtime=dt.datetime(2003,3,21,k))
ax3.scatter(mltn*15,mlatn[0],color='r',transform=ccrs.PlateCarree())
ax3.scatter(180,75,s=50,c='k',marker='x',transform=ccrs.PlateCarree())
ax3.text(0,1,'(c)', transform = plt.gca().transAxes)
ax4, projection4 = gcc.create_map(
2, 2, 4, 'pol', -50, -90, 0, coastlines=False,useLT=True,
dlat=10, lonticklabel=(1, 1, 1, 1))
mlats,mlons = convert(-90,0,0,date=dt.date(2002,3,21))
for k in range(24):
mlts = convert_mlt(mlons[0],dtime=dt.datetime(2003,3,21,k))
ax4.scatter(mlts*15,mlats[0],color='b',transform=ccrs.PlateCarree())
ax4.scatter(180,-75,s=50,c='k',marker='x',transform=ccrs.PlateCarree())
ax4.text(0,1,'(d)', transform = plt.gca().transAxes)
plt.savefig(
'/Users/guod/Documents/Pole_Density_MLT_Change/Figures/'
'000_Pole_Feature.pdf')
return
def plot_ion_drift(show=True,save=True):
apex = Apex(date=2003)
qlat, qlon = apex.convert(-90, 0, source='apex', dest='geo', height=400)
g = g2a
plt.close('all')
plt.figure(figsize=(7.26, 9.25))
for ialt, alt in enumerate([130, 200, 300, 400, 500, 600]):
alt_ind = np.argmin(np.abs(g['Altitude'][0, 0, :]/1000-alt))
alt_str = '%6.2f' % (g['Altitude'][0, 0, alt_ind]/1000)
ax, projection = gcc.create_map(
3, 2, ialt+1, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g, 'polar', useLT=True),
coastlines=False)
lon0, lat0, ewind, nwind = g3ca.vector_data(g, 'ion', alt=alt)
lon0, lat0, ewind, nwind = g3ca.convert_vector(
lon0, lat0, ewind, nwind, plot_type='polar',
projection=projection)
hq = ax.quiver(
lon0, lat0, ewind, nwind, scale=1500, scale_units='inches',
regrid_shape=20, headwidth=5)
ax.quiverkey(hq, 0.93, -0.1, 1000, '1000 m/s')
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
plt.title('%s km' % alt_str, y=1.05)
plt.text(0.5, 0.95, 'Time: '+tstring, fontsize=15,
horizontalalignment='center', transform=plt.gcf().transFigure)
if show:
plt.show()
if save:
plt.savefig(spath+'03_ion_drift_%s%s.pdf' % (tstrday,tstring))
return
def plot_polar_contour_vector_diff(nrow,
ncol,
iax,
g1,
g2,
nlat,
slat,
alt,
contour=True,
zstr='Rho',
vector=True,
neuion='neu',
useLT=True,
coastlines=False,
N=21,
levels=None):
# g1 and g2 have the same time, the same grid
centrallon = g3ca.calculate_centrallon(g1, 'pol', useLT)
ax, projection = gcc.create_map(nrow,
ncol,
iax,
'pol',
nlat,
slat,
centrallon,
coastlines=coastlines,
dlat=10,
useLT=useLT,
lonticklabel=(1, 1, 1, 1))
hc = []
hq = []
# contour
if contour:
lon0, lat0, zdata1 = g3ca.contour_data(zstr, g1, alt=alt)
lon0, lat0, zdata2 = g3ca.contour_data(zstr, g2, alt=alt)
cb = N if levels is None else levels
hc.append(
ax.contourf(lon0,
lat0,
100 * (zdata2 - zdata1) / zdata1,
cb,
transform=ccrs.PlateCarree(),
cmap='jet',
extend='both'))
# vector
if vector:
lon0, lat0, ewind1, nwind1 = g3ca.vector_data(g1, neuion, alt=alt)
lon0, lat0, ewind2, nwind2 = g3ca.vector_data(g2, neuion, alt=alt)
lon0, lat0, ewind0, nwind0 = g3ca.convert_vector(
lon0, lat0, ewind2 - ewind1, nwind2 - nwind1, 'pol', projection)
hq.append(
ax.quiver(lon0,
lat0,
ewind0,
nwind0,
scale=1500,
scale_units='inches',
regrid_shape=20))
return ax, projection, hc, hq
def plot_polar_contour_vector_diff(
nrow, ncol, iax, g1, g2, nlat, slat, alt, contour=True, zstr='Rho',
vector=True, neuion='neu', useLT=True, coastlines=False, N=21, levels=None):
# g1 and g2 have the same time, the same grid
centrallon = g3ca.calculate_centrallon(g1, 'pol', useLT)
ax, projection = gcc.create_map(
nrow, ncol, iax, 'pol', nlat, slat, centrallon,
coastlines=coastlines, dlat=10,
useLT=useLT, lonticklabel=(1, 1, 1, 1))
hc = []
hq = []
# contour
if contour:
lon0, lat0, zdata1 = g3ca.contour_data(zstr, g1, alt=alt)
lon0, lat0, zdata2 = g3ca.contour_data(zstr, g2, alt=alt)
cb = N if levels is None else levels
hc.append(ax.contourf(lon0, lat0, 100*(zdata2-zdata1)/zdata1, cb,
transform=ccrs.PlateCarree(),cmap='jet', extend='both'))
# vector
if vector:
lon0, lat0, ewind1, nwind1 = g3ca.vector_data(g1,neuion,alt=alt)
lon0, lat0, ewind2, nwind2 = g3ca.vector_data(g2,neuion,alt=alt)
lon0, lat0, ewind0, nwind0 = g3ca.convert_vector(
lon0, lat0, ewind2-ewind1, nwind2-nwind1, 'pol', projection)
hq.append(ax.quiver(
lon0,lat0,ewind0,nwind0,scale=1500,scale_units='inches',
regrid_shape=20))
return ax, projection, hc, hq
def plot_temperature():
apex = Apex(date=2003)
qlat, qlon = apex.convert(-90, 0, source='apex', dest='geo', height=400)
plt.close('all')
plt.figure(figsize=(7.26, 9.25))
g = g2a
alts = [130, 400]
for ialt, alt in enumerate(alts):
alt_ind = np.argmin(np.abs(g['Altitude'][0, 0, 2:-2]/1000-alt))+2
alt_str = '%6.2f' % (g['Altitude'][0, 0, alt_ind]/1000)
ax, projection = gcc.create_map(
1, 2, ialt+1, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g, 'polar', useLT=True),
coastlines=False)
lon0, lat0, zdata0 = g3ca.contour_data('Temperature', g, alt=alt)
fp = (lat0[:,0]>slat) & (lat0[:,0]<nlat)
lon0, lat0, zdata0 = lon0[fp, :], lat0[fp,:], zdata0[fp,:]
hc = ax.contourf(lon0, lat0, zdata0, 21,
transform=ccrs.PlateCarree(), cmap='jet',
extend='both')
hc = plt.colorbar(hc, pad=0.17)
hc.set_label('Temperature (K)')
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
plt.title('%s km' % alt_str, y=1.05)
plt.text(0.5, 0.95, 'Time: '+titletime, fontsize=15,
horizontalalignment='center', transform=plt.gcf().transFigure)
plt.show()
return
def animate_vert_divv(i):
g1 = gitm.GitmBin(fn1[i])
g2 = gitm.GitmBin(fn2[i])
alt_ind = np.argmin(np.abs(g1['Altitude'][0, 0, 2:-2]/1000-which_alt))+2
# create axis
ax = list(range(2))
projection = ax.copy()
for ins in range(2):
nlat, slat = [90, 40] if ins==0 else [-40, -90]
ax[ins], projection[ins] = gcc.create_map(
1, 2, 1+ins, 'polar', nlat=nlat, slat=slat,
dlat=10, centrallon=g3ca.calculate_centrallon(
g1, 'polar', useLT=True),
coastlines=False)
alt1 = np.array(g1['Altitude'])
uwind1 = np.array(g1['V!Dn!N (up)'])
divv1 = cr.calc_div_vert(alt1,uwind1)
alt2 = np.array(g2['Altitude'])
uwind2 = np.array(g2['V!Dn!N (up)'])
divv2 = cr.calc_div_vert(alt2,uwind2)
g1['divv'] = divv1-divv2
lon0,lat0,divv = g3ca.contour_data('divv',g1,alt=which_alt)
hc = ax[0].contourf(
lon0, lat0, divv, np.linspace(-1,1,21)*1e-4,
transform=ccrs.PlateCarree(), cmap='seismic', extend='both')
ax[0].set_title(g1['time'].strftime('%d-%b-%y %H:%M')+' UT', y=1.05)
hc = ax[1].contourf(
lon0, lat0, divv, np.linspace(-1,1,21)*1e-4,
transform=ccrs.PlateCarree(), cmap='seismic', extend='both')
ax[1].set_title(g1['time'].strftime('%d-%b-%y %H:%M')+' UT', y=1.05)
# low density center
lon3, lat3, zdata3 = g3ca.contour_data('Rho', g1, alt=which_alt)
lon4, lat4, zdata4 = g3ca.contour_data('Rho', g2, alt=which_alt)
diffrho = 100*(zdata4-zdata3)/zdata3
hc = [ax[k].contour(
lon0, lat0, diffrho, [-10], transform=ccrs.PlateCarree(),
colors='g',linestyles='-') for k in [0, 1]]
# wind difference
lon1, lat1, ewind1, nwind1 = g3ca.vector_data(g1, 'neutral', alt=which_alt)
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2, 'neutral', alt=which_alt)
lon0, lat0, ewind, nwind = (
lon1.copy(), lat1.copy(), ewind2-ewind1, nwind2-nwind1)
for ins in [0, 1]:
lon0t, lat0t, ewindt, nwindt = g3ca.convert_vector(
lon0, lat0, ewind, nwind, plot_type='polar',
projection=projection[ins])
hq = ax[ins].quiver(
lon0t, lat0t, ewindt, nwindt, scale=1500,
scale_units='inches', color='k', regrid_shape=20)
def plot_vert_vgradrho_rho_diff(show=True, save=True):
rho1 = np.array(g1a['Rho'])
vgradrho1 = \
g1a['V!Dn!N (up)'] \
* cr.calc_rusanov_alts_ausm(g1a['Altitude'],rho1)/g1a['Rho']
rho2 = np.array(g2a['Rho'])
vgradrho2 = \
g2a['V!Dn!N (up)']\
* cr.calc_rusanov_alts_ausm(g2a['Altitude'],rho2)/g2a['Rho']
g1a['vgradrho_diff'] = vgradrho1-vgradrho2
apex = Apex(date=2003)
qlat, qlon = apex.convert(-90, 0, source='apex', dest='geo', height=400)
plt.close('all')
plt.figure(figsize=(7.26, 9.25))
for ialt, alt in enumerate([130, 200, 300, 400, 500, 600]):
alt_ind = np.argmin(np.abs(g1a['Altitude'][0, 0, :]/1000-alt))
alt_str = '%6.2f' % (g1a['Altitude'][0, 0, alt_ind]/1000)
ax, projection = gcc.create_map(
3, 2, ialt+1, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g1a, 'polar', useLT=True),
coastlines=False)
lon0, lat0, zdata0 = g3ca.contour_data('vgradrho_diff', g1a, alt=alt)
fp = (lat0[:,0]>slat) & (lat0[:,0]<nlat)
lon0, lat0, zdata0 = lon0[fp, :], lat0[fp,:], zdata0[fp,:]
hc = ax.contourf(
lon0, lat0, zdata0, np.linspace(-1,1,21)*1e-4,
transform=ccrs.PlateCarree(), cmap='seismic', extend='both')
hcb = plt.colorbar(hc, pad=0.17)
hcb.formatter.set_powerlimits((0,0))
hcb.update_ticks()
hcb.set_label(r'$-\vec{u}\cdot\frac{\nabla\rho}{\rho}$ (up)')
# wind difference
lon1, lat1, ewind1, nwind1 = g3ca.vector_data(g1a, 'neutral', alt=alt)
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2a, 'neutral', alt=alt)
lon0, lat0 = lon1, lat1
lon0, lat0, ewind0, nwind0 = g3ca.convert_vector(
lon0, lat0, ewind2-ewind1, nwind2-nwind1, plot_type='polar',
projection=projection)
hq = ax.quiver(
lon0, lat0, ewind0, nwind0, scale=1000, scale_units='inches',
regrid_shape=20, headwidth=5)
ax.quiverkey(hq, 0.93, -0.1, 500, '500 m/s')
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
plt.title('%s km' % alt_str, y=1.05)
plt.text(0.5, 0.95, 'Time: '+tstring, fontsize=15,
horizontalalignment='center', transform=plt.gcf().transFigure)
if show:
plt.show()
if save:
plt.savefig(spath+'06_vert_vgradrho_rho_diff_%s%s.pdf' % (tstrday,tstring))
return
def plot_vert_vgradrho_rho_diff(show=True, save=True):
rho1 = np.array(g1a['Rho'])
vgradrho1 = \
g1a['V!Dn!N (up)']\
*cr.calc_rusanov_alts_ausm(g1a['Altitude'],rho1)/g1a['Rho']
rho2 = np.array(g2a['Rho'])
vgradrho2 = \
g2a['V!Dn!N (up)']\
*cr.calc_rusanov_alts_ausm(g2a['Altitude'],rho2)/g2a['Rho']
g1a['vgradrho_diff'] = vgradrho1-vgradrho2
apex = Apex(date=2003)
qlat, qlon = apex.convert(-90, 0, source='apex', dest='geo', height=400)
plt.close('all')
#plt.figure(figsize=(7.26, 9.25))
plt.figure(figsize=(8.97, 3.45))
alts = [130, 400]
for ialt, alt in enumerate(alts):
alt_ind = np.argmin(np.abs(g1a['Altitude'][0, 0, :]/1000-alt))
alt_str = '%6.2f' % (g1a['Altitude'][0, 0, alt_ind]/1000)
ax, projection = gcc.create_map(
1, 2, ialt+1, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g1a, 'polar', useLT=True),
coastlines=False)
lon0, lat0, zdata0 = g3ca.contour_data('vgradrho_diff', g1a, alt=alt)
fp = (lat0[:,0]>slat) & (lat0[:,0]<nlat)
lon0, lat0, zdata0 = lon0[fp, :], lat0[fp,:], zdata0[fp,:]
hc = ax.contourf(
lon0, lat0, zdata0, np.linspace(-1,1,21)*1e-4,
transform=ccrs.PlateCarree(), cmap='seismic', extend='both')
hcb = plt.colorbar(hc, pad=0.17)
hcb.formatter.set_powerlimits((0,0))
hcb.update_ticks()
hcb.set_label(r'$-\vec{u}\cdot\frac{\nabla\rho}{\rho}$ (up)')
# lon1, lat1, ewind1, nwind1 = g3ca.vector_data(g1a, 'neutral', alt=alt)
# lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2a, 'neutral', alt=alt)
# lon0, lat0 = lon1, lat1
# lon0, lat0, ewind0, nwind0 = g3ca.convert_vector(
# lon0, lat0, ewind2-ewind1, nwind2-nwind1, plot_type='polar',
# projection=projection)
# hq = ax.quiver(
# lon0, lat0, ewind0, nwind0, scale=1000, scale_units='inches',
# regrid_shape=20, headwidth=5)
# ax.quiverkey(hq, 0.93, -0.1, 300, '500 m/s')
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
plt.title('%s km' % alt_str, y=1.05)
plt.text(0.5, 0.95, 'Time: '+titletime, fontsize=15,
horizontalalignment='center', transform=plt.gcf().transFigure)
plt.show()
return
def plot_temperature_diff(show=True, save=True):
apex = Apex(date=2003)
qlat, qlon = apex.convert(-90, 0, source='apex', dest='geo', height=400)
plt.close('all')
plt.figure(figsize=(7.26, 9.25))
for ialt, alt in enumerate([130, 200, 300, 400, 500, 600]):
alt_ind = np.argmin(np.abs(g1a['Altitude'][0, 0, :]/1000-alt))
alt_str = '%6.2f' % (g1a['Altitude'][0, 0, alt_ind]/1000)
ax, projection = gcc.create_map(
3, 2, ialt+1, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g1a, 'polar', useLT=True),
coastlines=False)
# temperature diff
lon1, lat1, zdata1 = g3ca.contour_data('Temperature', g1a, alt=alt)
lon2, lat2, zdata2 = g3ca.contour_data('Temperature', g2a, alt=alt)
fp = (lat1[:,0]>slat) & (lat1[:,0]<nlat)
lon0, lat0, zdata0 = lon1[fp, :], lat1[fp,:], (zdata2-zdata1)[fp,:]
hc = ax.contourf(
lon0, lat0, zdata0, np.linspace(-80,80,21),
transform=ccrs.PlateCarree(), cmap='seismic', extend='both')
hc = plt.colorbar(hc, pad=0.17)
hc.set_label(r'$T_2-T_1$ (K)')
# geomagnetic pole
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
# wind difference
lon1, lat1, ewind1, nwind1 = g3ca.vector_data(g1a, 'neutral', alt=alt)
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2a, 'neutral', alt=alt)
lon0, lat0 = lon1, lat1
lon0, lat0, ewind0, nwind0 = g3ca.convert_vector(
lon0, lat0, ewind2-ewind1, nwind2-nwind1, plot_type='polar',
projection=projection)
hq = ax.quiver(
lon0, lat0, ewind0, nwind0, scale=1000, scale_units='inches',
regrid_shape=20, headwidth=5)
ax.quiverkey(hq, 0.93, -0.1, 500, '500 m/s')
# rho difference
#lon3, lat3, zdata3 = g3ca.contour_data('Rho', g1a, alt=alt)
#lon4, lat4, zdata4 = g3ca.contour_data('Rho', g2a, alt=alt)
#diffrho = 100*(zdata4-zdata3)/zdata3
#hc = ax.contour(
# lon4, lat4, diffrho, [-10], transform=ccrs.PlateCarree(),
# colors='g',linestyles='-')
plt.title('%s km' % alt_str, y=1.05)
plt.text(0.5, 0.95, 'Time: '+tstring, fontsize=15,
horizontalalignment='center', transform=plt.gcf().transFigure)
if show:
plt.show()
if save:
plt.savefig(spath+'02_temperature_diff_%s%s.pdf' % (tstrday,tstring))
return
def plot_den_win_diff():
apex = Apex(date=2003)
qlat, qlon = apex.convert(-90, 0, source='apex', dest='geo', height=400)
plt.close('all')
#plt.figure(figsize=(7.26, 9.25))
plt.figure(figsize=(8.97, 3.45))
alts = [130, 400]
for ialt, alt in enumerate(alts):
alt_ind = np.argmin(np.abs(g1a['Altitude'][0, 0, :]/1000-alt))
alt_str = '%6.2f' % (g1a['Altitude'][0, 0, alt_ind]/1000)
ax, projection = gcc.create_map(
1, 2, ialt+1, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g1a, 'polar', useLT=True),
coastlines=False)
# density difference
lon1, lat1, zdata1 = g3ca.contour_data('Rho', g1a, alt=alt)
lon2, lat2, zdata2 = g3ca.contour_data('Rho', g2a, alt=alt)
fp = (lat1[:,0]>slat) & (lat1[:,0]<nlat)
lon0, lat0, zdata0 = lon1[fp, :], lat1[fp,:], \
(100*(zdata2-zdata1)/zdata1)[fp,:]
hc = ax.contourf(lon0, lat0, zdata0, np.linspace(-20,20,21),
transform=ccrs.PlateCarree(), cmap='seismic',
extend='both')
hc = plt.colorbar(hc, pad=0.17)
hc.set_label(r'$100*\frac{\rho2-\rho1}{\rho1}$ (%)')
# wind difference
lon1, lat1, ewind1, nwind1 = g3ca.vector_data(g1a, 'neutral', alt=alt)
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2a, 'neutral', alt=alt)
lon0, lat0 = lon1, lat1
lon0, lat0, ewind0, nwind0 = g3ca.convert_vector(
lon0, lat0, ewind2-ewind1, nwind2-nwind1, plot_type='polar',
projection=projection)
hq = ax.quiver(
lon0, lat0, ewind0, nwind0, scale=500, scale_units='inches',
regrid_shape=20,headwidth=5)
ax.quiverkey(hq, 0.93, -0.1, 500, '500m/s')
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
# rho difference
# lon3, lat3, zdata3 = g3ca.contour_data('Rho', g1a, alt=alt)
# lon4, lat4, zdata4 = g3ca.contour_data('Rho', g2a, alt=alt)
# diffrho = 100*(zdata4-zdata3)/zdata3
# hc = ax.contour(
# lon4, lat4, diffrho, [-10], transform=ccrs.PlateCarree(),
# colors='g',linestyles='-')
plt.title('%s km' % alt_str, y=1.05)
plt.text(0.5, 0.95, 'Time: '+titletime, fontsize=15,
horizontalalignment='center', transform=plt.gcf().transFigure)
plt.show()
return
def plot_den_win(show=True, save=True):
apex = Apex(date=2003)
qlat, qlon = apex.convert(-90, 0, source='apex', dest='geo', height=400)
plt.close('all')
plt.figure(figsize=(7.26, 9.25))
g = g2a
for ialt, alt in enumerate([130, 200, 300, 400, 500, 600]):
alt_ind = np.argmin(np.abs(g['Altitude'][0, 0, 2:-2]/1000-alt))+2
alt_str = '%6.2f' % (g['Altitude'][0, 0, alt_ind]/1000)
ax, projection = gcc.create_map(
3, 2, ialt+1, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g, 'polar', useLT=True),
coastlines=False)
# density
lon0, lat0, zdata0 = g3ca.contour_data('Rho', g, alt=alt)
fp = (lat0[:,0]>slat) & (lat0[:,0]<nlat)
lon0, lat0, zdata0 = lon0[fp, :], lat0[fp,:], zdata0[fp,:]
hc = ax.contourf(lon0, lat0, zdata0, 21,
transform=ccrs.PlateCarree(), cmap='jet',
extend='both')
hc = plt.colorbar(hc, pad=0.17)
hc.set_label(r'$\rho$ (kg/m$^3$)')
# wind
lon0, lat0, ewind, nwind = g3ca.vector_data(g, 'neutral', alt=alt)
lon0, lat0, ewind, nwind = g3ca.convert_vector(
lon0, lat0, ewind, nwind, plot_type='polar',
projection=projection)
hq = ax.quiver(
lon0, lat0, ewind, nwind, scale=1500, scale_units='inches',
regrid_shape=20)
ax.quiverkey(hq, 0.93, -0.1, 1000, '1000 m/s')
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
# rho difference
# lon3, lat3, zdata3 = g3ca.contour_data('Rho', g1a, alt=alt)
# lon4, lat4, zdata4 = g3ca.contour_data('Rho', g2a, alt=alt)
# diffrho = 100*(zdata4-zdata3)/zdata3
# hc = ax.contour(
# lon4, lat4, diffrho, [-10], transform=ccrs.PlateCarree(),
# colors='g',linestyles='-')
plt.title('%s km' % alt_str, y=1.05)
plt.text(0.5, 0.95, 'Time: '+tstring, fontsize=15,
horizontalalignment='center', transform=plt.gcf().transFigure)
if show:
plt.show()
if save:
plt.savefig(spath+'01_den_win_run2_%s%s.pdf' %(tstrday,tstring))
return
def plot_ion_drift(show=True, save=True):
apex = Apex(date=2003)
qlat, qlon = apex.convert(-90, 0, source='apex', dest='geo', height=400)
g = g2a
plt.close('all')
plt.figure(figsize=(7.26, 9.25))
for ialt, alt in enumerate([130, 200, 300, 400, 500, 600]):
alt_ind = np.argmin(np.abs(g['Altitude'][0, 0, :] / 1000 - alt))
alt_str = '%6.2f' % (g['Altitude'][0, 0, alt_ind] / 1000)
ax, projection = gcc.create_map(3,
2,
ialt + 1,
'polar',
nlat=nlat,
slat=slat,
dlat=10,
centrallon=g3ca.calculate_centrallon(
g, 'polar', useLT=True),
coastlines=False)
lon0, lat0, ewind, nwind = g3ca.vector_data(g, 'ion', alt=alt)
lon0, lat0, ewind, nwind = g3ca.convert_vector(lon0,
lat0,
ewind,
nwind,
plot_type='polar',
projection=projection)
hq = ax.quiver(lon0,
lat0,
ewind,
nwind,
scale=1500,
scale_units='inches',
regrid_shape=20,
headwidth=5)
ax.quiverkey(hq, 0.93, -0.1, 1000, '1000 m/s')
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
plt.title('%s km' % alt_str, y=1.05)
plt.text(0.5,
0.95,
'Time: ' + tstring,
fontsize=15,
horizontalalignment='center',
transform=plt.gcf().transFigure)
if show:
plt.show()
if save:
plt.savefig(spath + '03_ion_drift_%s%s.pdf' % (tstrday, tstring))
return
def plot_temperature_diff():
apex = Apex(date=2003)
qlat, qlon = apex.convert(-90, 0, source='apex', dest='geo', height=400)
plt.close('all')
#plt.figure(figsize=(7.26, 9.25))
plt.figure(figsize=(10.3,4.3))
alts = [130, 400]
for ialt, alt in enumerate(alts):
alt_ind = np.argmin(np.abs(g1a['Altitude'][0, 0, :]/1000-alt))
alt_str = '%6.2f' % (g1a['Altitude'][0, 0, alt_ind]/1000)
ax, projection = gcc.create_map(
1, 2, ialt+1, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g1a, 'polar', useLT=True),
coastlines=False)
# temperature diff
lon1, lat1, zdata1 = g3ca.contour_data('Temperature', g1a, alt=alt)
lon2, lat2, zdata2 = g3ca.contour_data('Temperature', g2a, alt=alt)
fp = (lat1[:,0]>slat) & (lat1[:,0]<nlat)
lon0, lat0, zdata0 = lon1[fp, :], lat1[fp,:], (zdata2-zdata1)[fp,:]
hc = ax.contourf(lon0, lat0, zdata0, np.linspace(-80,80,21),
transform=ccrs.PlateCarree(), cmap='seismic',
extend='both')
hc = plt.colorbar(hc, pad=0.17)
hc.set_label(r'$T_2-T_1$ (K)')
# geomagnetic pole
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
# wind difference
# lon1, lat1, ewind1, nwind1 = g3ca.vector_data(g1a, 'neutral', alt=alt)
# lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2a, 'neutral', alt=alt)
# lon0, lat0 = lon1, lat1
# lon0, lat0, ewind0, nwind0 = g3ca.convert_vector(
# lon0, lat0, ewind2-ewind1, nwind2-nwind1, plot_type='polar',
# projection=projection)
# hq = ax.quiver(
# lon0, lat0, ewind0, nwind0, scale=1000, scale_units='inches',
# regrid_shape=20, headwidth=5)
# ax.quiverkey(hq, 0.93, -0.1, 300, '500 m/s')
# rho difference
plt.title('%s km' % alt_str, y=1.05)
plt.text(0.5, 0.95, 'Time: '+titletime, fontsize=15,
horizontalalignment='center', transform=plt.gcf().transFigure)
plt.show()
return
def test(g, alt=400, contour=True, zstr='Rho',levels=None, vector=True,
neuion='neu', scale=500, useLT=True):
import gitm_create_coordinate as gcc
import matplotlib.pyplot as plt
plt.close('all')
fig = plt.figure(figsize=[8.38,8.12])
# Tested parameters
polar = [True, True, False]
nrow = [2,2,2]
ncol = [2,2,1]
nax = [1,2,2]
nlat = [90, -30, 90]
slat = [30, -90, -90]
dlat = [10, 10, 30]
for k in range(3):
ipolar = polar[k]
pr = 'pol' if ipolar else 'rec'
centrallon = calculate_centrallon(g, pr, useLT)
ax, projection = gcc.create_map(
nrow[k],ncol[k],nax[k], pr, nlat[k], slat[k], centrallon,
coastlines=False, dlat=dlat[k],
useLT=useLT, lonticklabel=(1, 1, 1, 1))
ialt = np.argmin(np.abs(g['Altitude'][0,0,:]/1000-alt))
# contour
if contour:
lon0, lat0, zdata0 = contour_data(zstr, g, ialt=ialt)
fplat = (lat0[:,0]>=slat[k]) & (lat0[:,0]<=nlat[k])
lon0, lat0, zdata0 = (k[fplat,:] for k in [lon0, lat0, zdata0])
hc = ax.contourf(lon0, lat0, zdata0, 21 if levels is None else levels,
transform=ccrs.PlateCarree(),cmap='jet', extend='both')
print(np.max(zdata0), np.min(zdata0))
# vector
if vector:
lon0, lat0, ewind0, nwind0 = vector_data(g,neuion,alt=alt)
lon0, lat0, ewind0, nwind0 = convert_vector(
lon0, lat0, ewind0, nwind0, pr, projection)
hq = ax.quiver(
lon0,lat0,ewind0,nwind0,scale=scale,scale_units='inches',
regrid_shape=20)
# title
plt.title(zstr+' at '+'%.2f km' % (g['Altitude'][0,0,ialt]/1000),y=1.05)
plt.show()
return fig, hc
def plot_den_win():
apex = Apex(date=2003)
qlat, qlon = apex.convert(-90, 0, source='apex', dest='geo', height=400)
plt.close('all')
plt.figure(figsize=(7.26, 9.25))
g = g2a
alts = [130, 400]
for ialt, alt in enumerate(alts):
alt_ind = np.argmin(np.abs(g['Altitude'][0, 0, 2:-2]/1000-alt))+2
alt_str = '%6.2f' % (g['Altitude'][0, 0, alt_ind]/1000)
ax, projection = gcc.create_map(
1, 2, ialt+1, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g, 'polar', useLT=True),
coastlines=False)
# density
lon0, lat0, zdata0 = g3ca.contour_data('Rho', g, alt=alt)
fp = (lat0[:,0]>slat) & (lat0[:,0]<nlat)
lon0, lat0, zdata0 = lon0[fp, :], lat0[fp,:], zdata0[fp,:]
hc = ax.contourf(lon0, lat0, zdata0, 21,
transform=ccrs.PlateCarree(), cmap='jet',
extend='both')
hc = plt.colorbar(hc, pad=0.17)
hc.set_label(r'$\rho$ (kg/m$^3$)')
# wind
lon0, lat0, ewind, nwind = g3ca.vector_data(g, 'neutral', alt=alt)
lon0, lat0, ewind, nwind = g3ca.convert_vector(
lon0, lat0, ewind, nwind, plot_type='polar',
projection=projection)
hq = ax.quiver(
lon0, lat0, ewind, nwind, scale=1500, scale_units='inches',
regrid_shape=20)
ax.quiverkey(hq, 0.93, -0.1, 1000, '1000 m/s')
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
plt.title('%s km' % alt_str, y=1.05)
plt.text(0.5, 0.95, 'Time: '+titletime, fontsize=15,
horizontalalignment='center', transform=plt.gcf().transFigure)
plt.show()
return
def animate_vgradrho_rho(i):
g1 = gitm.GitmBin(fn1[i])
g2 = gitm.GitmBin(fn2[i])
alt_ind = np.argmin(
np.abs(g1['Altitude'][0, 0, 2:-2] / 1000 - which_alt)) + 2
# create axis
ax = list(range(2))
projection = ax.copy()
for ins in range(2):
nlat, slat = [90, 40] if ins == 0 else [-40, -90]
ax[ins], projection[ins] = gcc.create_map(
1,
2,
1 + ins,
'polar',
nlat=nlat,
slat=slat,
dlat=10,
centrallon=g3ca.calculate_centrallon(g1, 'polar', useLT=True),
coastlines=False)
lon1 = np.array(g1['Longitude'])
lat1 = np.array(g1['Latitude'])
alt1 = np.array(g1['Altitude'])
nwind1 = np.array(g1['V!Dn!N (north)'])
RR = 6371 * 1000 + alt1
omega = (2 * np.pi) / (24 * 3600)
ewind1 = np.array(g1['V!Dn!N (east)']) + omega * RR * np.cos(lat1)
uwind1 = np.array(g1['V!Dn!N (up)'])
rho1 = np.array(g1['Rho'])
vgradrho1 = uwind1 * cr.calc_rusanov_alts_ausm(alt1, rho1) / rho1
lon2 = np.array(g2['Longitude'])
lat2 = np.array(g2['Latitude'])
alt2 = np.array(g2['Altitude'])
nwind2 = np.array(g2['V!Dn!N (north)'])
RR = 6371 * 1000 + alt2
omega = (2 * np.pi) / (24 * 3600)
ewind2 = np.array(g2['V!Dn!N (east)']) + omega * RR * np.cos(lat2)
uwind2 = np.array(g2['V!Dn!N (up)'])
rho2 = np.array(g2['Rho'])
vgradrho2 = uwind2 * cr.calc_rusanov_alts_ausm(alt2, rho2) / rho2
g1['vgradrho'] = vgradrho1 - vgradrho2
lon0, lat0, vgradrho = g3ca.contour_data('vgradrho', g1, alt=which_alt)
hc = ax[0].contourf(lon0,
lat0,
vgradrho,
np.linspace(-1, 1, 21) * 1e-4,
transform=ccrs.PlateCarree(),
cmap='seismic',
extend='both')
ax[0].set_title(g1['time'].strftime('%d-%b-%y %H:%M') + ' UT', y=1.05)
hc = ax[1].contourf(lon0,
lat0,
vgradrho,
np.linspace(-1, 1, 21) * 1e-4,
transform=ccrs.PlateCarree(),
cmap='seismic',
extend='both')
ax[1].set_title(g1['time'].strftime('%d-%b-%y %H:%M') + ' UT', y=1.05)
# low density center
lon3, lat3, zdata3 = g3ca.contour_data('Rho', g1, alt=which_alt)
lon4, lat4, zdata4 = g3ca.contour_data('Rho', g2, alt=which_alt)
diffrho = 100 * (zdata4 - zdata3) / zdata3
hc = [
ax[k].contour(lon0,
lat0,
diffrho, [-10],
transform=ccrs.PlateCarree(),
colors='g',
linestyles='-') for k in [0, 1]
]
# wind difference
lon1, lat1, ewind1, nwind1 = g3ca.vector_data(g1,
'neutral',
alt=which_alt)
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2,
'neutral',
alt=which_alt)
lon0, lat0, ewind, nwind = (lon1.copy(), lat1.copy(), ewind2 - ewind1,
nwind2 - nwind1)
for ins in [0, 1]:
lon0t, lat0t, ewindt, nwindt = g3ca.convert_vector(
lon0,
lat0,
ewind,
nwind,
plot_type='polar',
projection=projection[ins])
hq = ax[ins].quiver(lon0t,
lat0t,
ewindt,
nwindt,
scale=1500,
scale_units='inches',
color='k',
regrid_shape=20)
def animate_vert_divv(i):
g1 = gitm.GitmBin(fn1[i])
g2 = gitm.GitmBin(fn2[i])
alt_ind = np.argmin(
np.abs(g1['Altitude'][0, 0, 2:-2] / 1000 - which_alt)) + 2
# create axis
ax = list(range(2))
projection = ax.copy()
for ins in range(2):
nlat, slat = [90, 40] if ins == 0 else [-40, -90]
ax[ins], projection[ins] = gcc.create_map(
1,
2,
1 + ins,
'polar',
nlat=nlat,
slat=slat,
dlat=10,
centrallon=g3ca.calculate_centrallon(g1, 'polar', useLT=True),
coastlines=False)
alt1 = np.array(g1['Altitude'])
uwind1 = np.array(g1['V!Dn!N (up)'])
divv1 = cr.calc_div_vert(alt1, uwind1)
alt2 = np.array(g2['Altitude'])
uwind2 = np.array(g2['V!Dn!N (up)'])
divv2 = cr.calc_div_vert(alt2, uwind2)
g1['divv'] = divv1 - divv2
lon0, lat0, divv = g3ca.contour_data('divv', g1, alt=which_alt)
hc = ax[0].contourf(lon0,
lat0,
divv,
np.linspace(-1, 1, 21) * 1e-4,
transform=ccrs.PlateCarree(),
cmap='seismic',
extend='both')
ax[0].set_title(g1['time'].strftime('%d-%b-%y %H:%M') + ' UT', y=1.05)
hc = ax[1].contourf(lon0,
lat0,
divv,
np.linspace(-1, 1, 21) * 1e-4,
transform=ccrs.PlateCarree(),
cmap='seismic',
extend='both')
ax[1].set_title(g1['time'].strftime('%d-%b-%y %H:%M') + ' UT', y=1.05)
# low density center
lon3, lat3, zdata3 = g3ca.contour_data('Rho', g1, alt=which_alt)
lon4, lat4, zdata4 = g3ca.contour_data('Rho', g2, alt=which_alt)
diffrho = 100 * (zdata4 - zdata3) / zdata3
hc = [
ax[k].contour(lon0,
lat0,
diffrho, [-10],
transform=ccrs.PlateCarree(),
colors='g',
linestyles='-') for k in [0, 1]
]
# wind difference
lon1, lat1, ewind1, nwind1 = g3ca.vector_data(g1,
'neutral',
alt=which_alt)
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2,
'neutral',
alt=which_alt)
lon0, lat0, ewind, nwind = (lon1.copy(), lat1.copy(), ewind2 - ewind1,
nwind2 - nwind1)
for ins in [0, 1]:
lon0t, lat0t, ewindt, nwindt = g3ca.convert_vector(
lon0,
lat0,
ewind,
nwind,
plot_type='polar',
projection=projection[ins])
hq = ax[ins].quiver(lon0t,
lat0t,
ewindt,
nwindt,
scale=1500,
scale_units='inches',
color='k',
regrid_shape=20)
def animate_den_wind(i):
g1, g2 = [gitm.GitmBin(k[i]) for k in [fn1, fn2]]
# create axis
ax = [1, 2, 3, 4, 5, 6, 7, 8, 9]
projection = ax.copy()
for ialts in range(3):
for ird in range(2): # run2 or diff
centrallon = g3ca.calculate_centrallon(g1, 'polar', useLT=True)
ax[ialts * 2 + ird], projection[ialts * 2 +
ird] = gcc.create_map(
3,
2,
1 + ialts * 2 + ird,
'polar',
nlat=nlat,
slat=slat,
dlat=10,
centrallon=centrallon,
coastlines=False)
ax[ialts * 2 + ird].scatter(qlon,
qlat,
color='k',
transform=ccrs.PlateCarree())
ax[0].set_title(g1['time'].strftime('%d-%b-%y %H:%M') + ' UT', y=1.05)
ax[1].set_title(g1['time'].strftime('%d-%b-%y %H:%M') + ' UT', y=1.05)
#ax[2].set_title(g1['time'].strftime('%d-%b-%y %H:%M')+' UT', y=1.05)
# Density and diff
for ialt, alt in enumerate(alts):
lon1, lat1, zdata1 = g3ca.contour_data('Rho', g1, alt=alt)
lon2, lat2, zdata2 = g3ca.contour_data('Rho', g2, alt=alt)
diffzdata = 100 * (zdata2 - zdata1) / zdata1
hc = ax[ialt * 2].contourf(lon1,
lat1,
zdata2,
transform=ccrs.PlateCarree(),
levels=rholevels[ialt],
cmap='jet',
extend='both')
hc = ax[ialt * 2 + 1].contourf(lon2,
lat2,
diffzdata,
transform=ccrs.PlateCarree(),
levels=np.linspace(-20, 20, 21),
cmap='seismic',
extend='both')
# density change rate
# for ialt, alt in enumerate(alts):
# lon1 = np.array(g1['Longitude'])
# lat1 = np.array(g1['Latitude'])
# alt1 = np.array(g1['Altitude'])
# Re = 6371*1000 # Earth radius, unit: m
# RR = Re+alt1
# omega = 2*np.pi/(24*3600)
# rho1 = np.array(g1['Rho'])
# nwind1 = np.array(g1['V!Dn!N (north)'])
# ewind1 = np.array(g1['V!Dn!N (east)']) + omega*RR*np.cos(lat1)
# uwind1 = np.array(g1['V!Dn!N (up)'])
# div_rhov1 = \
# (cr.calc_div_hozt(lon1, lat1, alt1, rho1*nwind1, rho1*ewind1)\
# +cr.calc_div_vert(alt1, rho1*uwind1))/rho1
# lon2 = np.array(g2['Longitude'])
# lat2 = np.array(g2['Latitude'])
# alt2 = np.array(g2['Altitude'])
# Re = 6371*1000 # Earth radius, unit: m
# RR = Re+alt2
# omega = 2*np.pi/(24*3600)
# rho2 = np.array(g2['Rho'])
# nwind2 = np.array(g2['V!Dn!N (north)'])
# ewind2 = np.array(g2['V!Dn!N (east)']) + omega*RR*np.cos(lat2)
# uwind2 = np.array(g2['V!Dn!N (up)'])
# div_rhov2 = \
# (cr.calc_div_hozt(lon2, lat2, alt2, rho2*nwind2, rho2*ewind2)\
# +cr.calc_div_vert(alt2, rho2*uwind2))/rho2
# g1['divrhov_diff'] = div_rhov1-div_rhov2
# lon2, lat2, zdata2 = g3ca.contour_data('divrhov_diff', g1, alt=alt)
# hc = ax[ialt*3+2].contourf(
# lon2, lat2, zdata2, transform=ccrs.PlateCarree(),
# levels=np.linspace(-1,1,21)*1e-4, cmap='seismic', extend='both')
# wind
for ialt, alt in enumerate(alts):
lon1, lat1, ewind1, nwind1 = \
g3ca.vector_data(g1, 'neutral', alt=alt)
lon2, lat2, ewind2, nwind2 = \
g3ca.vector_data(g2, 'neutral', alt=alt)
lon0, lat0, ewind, nwind = (lon2.copy(), lat2.copy(),
ewind2.copy(), nwind2.copy())
lon0, lat0, ewind, nwind = g3ca.convert_vector(
lon0,
lat0,
ewind,
nwind,
plot_type='polar',
projection=projection[ialt * 2])
hq = ax[ialt * 2].quiver(lon0,
lat0,
ewind,
nwind,
scale=1500,
scale_units='inches',
color='k',
regrid_shape=20)
lon0, lat0, ewind, nwind = (lon1.copy(), lat1.copy(),
ewind2 - ewind1, nwind2 - nwind1)
lon0, lat0, ewind, nwind = g3ca.convert_vector(
lon0,
lat0,
ewind,
nwind,
plot_type='polar',
projection=projection[ialt * 2 + 1])
hq = ax[ialt * 2 + 1].quiver(lon0,
lat0,
ewind,
nwind,
scale=1500,
scale_units='inches',
color='k',
regrid_shape=20)
return
def animate_vert_wind(i):
g1, g2 = [gitm.GitmBin(k[i]) for k in [fn1, fn2]]
# create axis
ax = list(range(6))
projection = ax.copy()
for ins in range(2):
nlat, slat = [90, 40] if ins == 0 else [-40, -90]
for irun in range(3):
ax[ins +
irun * 2], projection[ins + irun * 2] = gcc.create_map(
3,
2,
1 + ins + irun * 2,
'polar',
nlat=nlat,
slat=slat,
dlat=10,
centrallon=g3ca.calculate_centrallon(g1,
'polar',
useLT=True),
coastlines=False)
# vertical wind
lon1, lat1, zdata1 = g3ca.contour_data('V!Dn!N (up)',
g1,
alt=which_alt)
lon2, lat2, zdata2 = g3ca.contour_data('V!Dn!N (up)',
g2,
alt=which_alt)
hc = [
ax[k].contourf(
lon1,
lat1,
zdata1,
21,
transform=ccrs.PlateCarree(),
levels=np.linspace(-20, 20, 21),
#levels=np.linspace(15e-13, 25e-13, 21),
cmap='seismic',
extend='both') for k in [0, 1]
]
ax[0].set_title(g1['time'].strftime('%d-%b-%y %H:%M') + ' UT', y=1.05)
ax[1].set_title(g1['time'].strftime('%d-%b-%y %H:%M') + ' UT', y=1.05)
hc = [
ax[k].contourf(lon2,
lat2,
zdata2,
21,
transform=ccrs.PlateCarree(),
levels=np.linspace(-20, 20, 21),
cmap='seismic',
extend='both') for k in [2, 3]
]
diffzdata = zdata2 - zdata1
hc = [
ax[k].contourf(lon2,
lat2,
diffzdata,
21,
transform=ccrs.PlateCarree(),
levels=np.linspace(-20, 20, 21),
cmap='seismic',
extend='both') for k in [4, 5]
]
# low density center
lon3, lat3, zdata3 = g3ca.contour_data('Rho', g1, alt=which_alt)
lon4, lat4, zdata4 = g3ca.contour_data('Rho', g2, alt=which_alt)
diffrho = 100 * (zdata4 - zdata3) / zdata3
hc = [
ax[k].contour(lon2,
lat2,
diffrho, [-10],
transform=ccrs.PlateCarree(),
colors='g',
linestyles='-') for k in [4, 5]
]
# wind
lon1, lat1, ewind1, nwind1 = g3ca.vector_data(g1,
'neutral',
alt=which_alt)
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2,
'neutral',
alt=which_alt)
for iax in range(6):
if iax == 0 or iax == 1:
lon0, lat0, ewind, nwind = (lon1.copy(), lat1.copy(),
ewind1.copy(), nwind1.copy())
lon0, lat0, ewind, nwind = g3ca.convert_vector(
lon0,
lat0,
ewind,
nwind,
plot_type='polar',
projection=projection[iax])
elif iax == 2 or iax == 3:
lon0, lat0, ewind, nwind = (lon2.copy(), lat2.copy(),
ewind2.copy(), nwind2.copy())
lon0, lat0, ewind, nwind = g3ca.convert_vector(
lon0,
lat0,
ewind,
nwind,
plot_type='polar',
projection=projection[iax])
elif iax == 4 or iax == 5:
lon0, lat0, ewind, nwind = (lon1.copy(), lat1.copy(),
ewind2 - ewind1, nwind2 - nwind1)
lon0, lat0, ewind, nwind = g3ca.convert_vector(
lon0,
lat0,
ewind,
nwind,
plot_type='polar',
projection=projection[iax])
hq = ax[iax].quiver(lon0,
lat0,
ewind,
nwind,
scale=1500,
scale_units='inches',
color='k',
regrid_shape=20)
# ax.quiverkey(hq, 0.93, 0, 1000, '1000 m/s')
# hc = plt.colorbar(hc, ticks=np.arange(3, 7)*1e-12)
# hc.set_label(r'$\rho$ (kg/m$^3$)')
# ax[iax].scatter(
# qlonn, qlatn, color='k', transform=ccrs.PlateCarree())
# ax[iax].scatter(
# qlons, qlats, color='k', transform=ccrs.PlateCarree())
return
#END
if __name__ == '__main__':
fn = '/home/guod/big/raid4/guod/run_imfby/run2c/data/3DALL_t100323_042000.bin'
gdata = gitm.GitmBin(fn)
calc_vorticity(gdata, 'neu', name='nvorticity', component='radial')
alt = 200
nlat = 90
slat = 50
centrallon = g3ca.calculate_centrallon(gdata, 'polar', useLT=True)
ax, projection = gcc.create_map(1,
1,
1,
plot_type='polar',
nlat=nlat,
slat=slat,
centrallon=centrallon,
coastlines=False,
dlat=10)
lon0, lat0, nvort0 = g3ca.contour_data('nvorticity', gdata, alt=alt)
lon, lat, ewind, nwind = g3ca.vector_data(gdata, 'neu', alt=alt)
lon, lat, ewind, nwind = g3ca.convert_vector(lon, lat, ewind, nwind,
'polar', projection)
hc = ax.contourf(lon0,
lat0,
nvort0,
transform=ccrs.PlateCarree(),
cmap='seismic')
ax.quiver(lon,
lat,
dglon = np.array(g2['dLon'][2:-2, 0, 0])
zdata2, dglon1 = add_cyclic_point(zdata2[2:-2, 2:-2, alt_ind].T,
coord=dglon,
axis=1)
zdata22, dglon1 = add_cyclic_point(zdata22[2:-2, 2:-2, alt_ind].T,
coord=dglon,
axis=1)
dglon, dglat = np.meshgrid(dglon1, dglat)
gt = g1['time']
centrallon = (0 - (gt.hour + gt.minute / 60 + gt.second / 3600)) * 15
ax1, projection = gcc.create_map(1,
2,
1,
'polar',
nlat=-40,
slat=-90,
centrallon=centrallon,
useLT=True,
dlat=10)
ax2, projection = gcc.create_map(1,
2,
2,
'polar',
nlat=-40,
slat=-90,
centrallon=centrallon,
useLT=True,
dlat=10)
hct = ax1.contourf(np.array(dglon),
np.array(dglat),
def figure5_7_refer(alt=300):
times = pd.to_datetime([
'2003-03-22 00:00:00', '2003-03-22 00:30:00', '2003-03-22 01:00:00',
'2003-03-22 02:00:00', '2003-03-22 03:00:00', '2003-03-22 06:00:00'])
plt.close('all')
plt.figure(figsize=(3, 9.25))
ylabel = times.strftime('%H:%M')
qlat, qlon = convert(-90, 0, 0, date=dt.date(2003,3,22), a2g=True)
for itime, time in enumerate(times):
strtime = time.strftime('%y%m%d_%H%M')
fn1 = glob.glob(
'/home/guod/simulation_output/momentum_analysis/'\
'run_no_shrink_iondrift_4_1/data/3DALL_t%s*.bin' % strtime)
fn2 = glob.glob(
'/home/guod/simulation_output/momentum_analysis/'\
'run_shrink_iondrift_4_c1/data/3DALL_t%s*.bin' % strtime)
g1 = gitm.read(fn1[0])
g2 = gitm.read(fn2[0])
lon1, lat1, alt1, T1 = \
g1['Longitude'], g1['Latitude'], g1['Altitude'], g1['Temperature']
lon2, lat2, alt2, T2 = \
g2['Longitude'], g2['Latitude'], g2['Altitude'], g2['Temperature']
alt_ind = np.argmin(np.abs(alt1[0, 0, :]/1000-alt))
alt_str = '%6.0f' % (alt1[0, 0, alt_ind]/1000)
lonticklabel=[0, 1, 0, 1]
if itime == 0:
lonticklabel[0] = 1
if itime == 5:
lonticklabel[2] = 1
ax, projection = gcc.create_map(
6, 1, itime+1, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g1, 'polar', useLT=True),
coastlines=False, lonticklabel=lonticklabel)
g1['temp'] = T1-T2
lon0, lat0, zdata0 = g3ca.contour_data('temp', g1, alt=alt)
fp = (lat0[:,0]>slat) & (lat0[:,0]<nlat)
lon0, lat0, zdata0 = lon0[fp, :], lat0[fp,:], zdata0[fp,:]
hc = ax.contourf(
lon0, lat0, zdata0, levels=np.linspace(-60, 60, 21),
transform=ccrs.PlateCarree(), cmap='seismic', extend='both')
# wind difference
lon1, lat1, ewind1, nwind1 = g3ca.vector_data(
g1, 'neutral', alt=alt)
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(
g2, 'neutral', alt=alt)
lon0, lat0 = lon1, lat1
lon0, lat0, ewind0, nwind0 = g3ca.convert_vector(
lon0, lat0, ewind1-ewind2, nwind1-nwind2,
plot_type='polar', projection=projection)
hq = ax.quiver(
lon0, lat0, ewind0, nwind0, scale=1500,
scale_units='inches', regrid_shape=20, headwidth=5)
ax.scatter(
qlon, qlat, s=10, color='k', transform=ccrs.PlateCarree())
ax.scatter(
0, -90, color='w', s=10, transform=ccrs.PlateCarree(),
zorder=1000)
plt.text(
-0.3, 0.5, ylabel[itime], rotation=90,
transform=ax.transAxes, verticalalignment='center')
if itime==0:
plt.title(r'$T_d$ (K)', y=1.15)
if itime==5:
axp = ax.get_position()
cax = [axp.x0, axp.y0-0.03, axp.x1-axp.x0, 1/20*(axp.y1-axp.y0)]
cax = plt.axes(cax)
ticks = np.arange(-60, 61, 20)
hcb = plt.colorbar(
hc, cax=cax, extendrect=True, orientation='horizontal',
ticks=ticks)
plt.subplots_adjust(wspace=0, hspace=0)
plt.show()
return
from aacgmv2 import convert
from aacgmv2 import convert_mlt
import datetime as dt
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import gitm_create_coordinate as gcc
import numpy as np
plt.figure(figsize=(6.88,6.74))
#geographic coordinates
ax1, projection1 = gcc.create_map(
2, 2, 1, 'pol', 90, 50, 0, coastlines=False,useLT=True,
dlat=10, lonticklabel=(1, 1, 1, 1))
ax1.plot([45,45],[50,90], 'k--',transform=ccrs.PlateCarree())
ax1.plot([225,225],[50,90], 'k--',transform=ccrs.PlateCarree())
ax1.plot([105,105],[50,90], 'k--',transform=ccrs.PlateCarree())
ax1.plot([285,285],[50,90], 'k--',transform=ccrs.PlateCarree())
ax1.scatter(0, 90, color='r', transform=ccrs.PlateCarree(), zorder=10)
ax2, projection2 = gcc.create_map(
2, 2, 2, 'pol', -50, -90, 0, coastlines=False,useLT=True,
dlat=10, lonticklabel=(1, 1, 1, 1))
ax2.scatter(0, -90, color='b', transform=ccrs.PlateCarree())
#geomagnetic coordinates
ax3, projection3 = gcc.create_map(
2, 2, 3, 'pol', 90, 50, 0, coastlines=False,useLT=True,
dlat=10, lonticklabel=(1, 1, 1, 1))
mlatn,mlonn = convert(90,0,0,date=dt.date(2002,3,21))
for k in range(24):
mltn = convert_mlt(mlonn[0],dtime=dt.datetime(2003,3,21,k))
ax3.scatter(mltn*15,mlatn[0],color='r',transform=ccrs.PlateCarree())
def figure2_3(run=1):
plt.close('all')
plt.figure(figsize=(8.28, 9.25))
if run==1:
g = g1
else:
g = g2
rholevels = [np.linspace(8, 16, 21)*1e-10, np.linspace(1,4,21)*1e-11,
np.linspace(0.5, 8,21)*1e-13]
rhoticks = [np.arange(8, 17, 2)*1e-10, np.arange(1,5,1)*1e-11,
np.arange(1, 9, 2)*1e-13]
templevels = [np.linspace(600, 800, 21), np.linspace(900,1500,21),
np.linspace(900, 1500,21)]
tempticks = [np.arange(600, 900, 100), np.arange(900,1600,200),
np.arange(900, 1600, 200)]
labels =[('(a)','(b)','(c)'), ('(d)','(e)','(f)'), ('(g)','(h)','(i)')]
qlat, qlon = convert(-90, 0, 0, date=dt.date(2003,3,22), a2g=True)
for ialt, alt in enumerate([150, 300, 600]):
alt_ind = np.argmin(np.abs(g['Altitude'][0, 0, 2:-2]/1000-alt))+2
alt_str = '%6.0f' % (g['Altitude'][0, 0, alt_ind]/1000)
ax, projection = gcc.create_map(
3, 3, ialt+1, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g, 'polar', useLT=True),
coastlines=False, lonticklabel=[1,1,1,1])
# density
lon0, lat0, zdata0 = g3ca.contour_data('Rho', g, alt=alt)
fp = (lat0[:,0]>slat) & (lat0[:,0]<nlat)
lon0, lat0, zdata0 = lon0[fp, :], lat0[fp,:], zdata0[fp,:]
hc = ax.contourf(
lon0, lat0, zdata0, rholevels[ialt], transform=ccrs.PlateCarree(),
cmap='jet', extend='both')
gcapos = ax.get_position()
axcb = [
gcapos.x0, gcapos.y0+0.028, gcapos.width, gcapos.height/20]
axcb = plt.axes(axcb)
hcb = plt.colorbar(
hc, cax=axcb, extendrect=True, ticks=rhoticks[ialt],
orientation='horizontal')
# wind
lon0, lat0, ewind, nwind = g3ca.vector_data(g, 'neutral', alt=alt)
lon0, lat0, ewind, nwind = g3ca.convert_vector(
lon0, lat0, ewind, nwind, plot_type='polar', projection=projection)
hq = ax.quiver(
lon0, lat0, ewind, nwind, scale=1000, scale_units='inches',
regrid_shape=20)
ax.quiverkey(hq, 0.97, 0.92, 500, '500 m/s')
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
ax.scatter(
0, -90, color='w', s=30, transform=ccrs.PlateCarree(), zorder=1000)
ax.set_title('%s km' % alt_str, y=1.08)
if ialt == 0:
plt.text(
-0.2, 0.5, r'$\rho$ and wind', rotation=90,
transform=ax.transAxes, verticalalignment='center')
plt.text(0, 0.9, labels[0][ialt],transform=ax.transAxes,color='r')
#----------------------------------------------------------------------
ax, projection = gcc.create_map(
3, 3, ialt+4, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g, 'polar', useLT=True),
coastlines=False, lonticklabel=[1,1,1,1])
# temperature
lon0, lat0, zdata0 = g3ca.contour_data('Temperature', g, alt=alt)
fp = (lat0[:,0]>slat) & (lat0[:,0]<nlat)
lon0, lat0, zdata0 = lon0[fp, :], lat0[fp,:], zdata0[fp,:]
hc = ax.contourf(
lon0, lat0, zdata0, templevels[ialt], transform=ccrs.PlateCarree(),
cmap='jet', extend='both')
gcapos = ax.get_position()
axcb = [
gcapos.x0, gcapos.y0-0.017, gcapos.width, gcapos.height/20]
axcb = plt.axes(axcb)
hcb = plt.colorbar(
hc, cax=axcb, extendrect=True, ticks=tempticks[ialt],
orientation='horizontal')
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
ax.scatter(
0, -90, color='w', s=30, transform=ccrs.PlateCarree(), zorder=1000)
if ialt == 0:
plt.text(
-0.2, 0.5, 'Temperature (K)', rotation=90,
transform=ax.transAxes, verticalalignment='center')
plt.text(0, 0.9, labels[1][ialt],transform=ax.transAxes,color='r')
#----------------------------------------------------------------------
ax, projection = gcc.create_map(
3, 3, ialt+7, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g, 'polar', useLT=True),
coastlines=False, lonticklabel=[1,1,1,1])
# ion drift
lon0, lat0, ewind, nwind = g3ca.vector_data(g, 'ion', alt=alt)
lon0, lat0, ewind, nwind = g3ca.convert_vector(
lon0, lat0, ewind, nwind, plot_type='polar', projection=projection)
if run == 2:
ewind, nwind = 0.1*ewind, 0.1*nwind
hq = ax.quiver(
lon0, lat0, ewind, nwind, scale=1000, scale_units='inches',
regrid_shape=20)
ax.quiverkey(hq, 0.97, 0.92, 500, '500 m/s')
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
ax.scatter(
0, -90, color='w', s=30, transform=ccrs.PlateCarree(), zorder=1000)
if ialt == 0:
plt.text(
-0.2, 0.5, 'Ion Drift', rotation=90,
transform=ax.transAxes, verticalalignment='center')
plt.text(0, 0.9, labels[2][ialt],transform=ax.transAxes,color='r')
plt.subplots_adjust(wspace=0.25, hspace=0.25, top=0.95, bottom=0.05)
if run==1:
plt.savefig(savepath+'Figure2.eps')
plt.savefig(savepath+'Figure2.jpeg')
else:
plt.savefig(savepath+'Figure3.eps')
plt.savefig(savepath+'Figure3.jpeg')
return
from aacgmv2 import convert
from aacgmv2 import convert_mlt
import datetime as dt
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import gitm_create_coordinate as gcc
import numpy as np
plt.figure(figsize=(6.88, 6.74))
#geographic coordinates
ax1, projection1 = gcc.create_map(2,
2,
1,
'pol',
90,
50,
0,
coastlines=False,
useLT=True,
dlat=10,
lonticklabel=(1, 1, 1, 1))
ax1.plot([45, 45], [50, 90], 'k--', transform=ccrs.PlateCarree())
ax1.plot([225, 225], [50, 90], 'k--', transform=ccrs.PlateCarree())
ax1.plot([105, 105], [50, 90], 'k--', transform=ccrs.PlateCarree())
ax1.plot([285, 285], [50, 90], 'k--', transform=ccrs.PlateCarree())
ax1.scatter(0, 90, color='r', transform=ccrs.PlateCarree(), zorder=10)
ax2, projection2 = gcc.create_map(2,
2,
2,
'pol',
-50,
def animate_den_wind(i):
g1, g2 = [gitm.GitmBin(k[i]) for k in [fn1, fn2]]
# create axis
ax = [1,2,3,4,5,6,7,8,9]
projection = ax.copy()
for ialts in range(3):
for ird in range(2): # run2 or diff
centrallon=g3ca.calculate_centrallon(g1, 'polar', useLT=True)
ax[ialts*2+ird], projection[ialts*2+ird] = gcc.create_map(
3, 2, 1+ialts*2+ird, 'polar', nlat=nlat, slat=slat,
dlat=10, centrallon=centrallon, coastlines=False)
ax[ialts*2+ird].scatter(
qlon, qlat, color='k', transform=ccrs.PlateCarree())
ax[0].set_title(g1['time'].strftime('%d-%b-%y %H:%M')+' UT', y=1.05)
ax[1].set_title(g1['time'].strftime('%d-%b-%y %H:%M')+' UT', y=1.05)
#ax[2].set_title(g1['time'].strftime('%d-%b-%y %H:%M')+' UT', y=1.05)
# Density and diff
for ialt, alt in enumerate(alts):
lon1, lat1, zdata1 = g3ca.contour_data('Rho', g1, alt=alt)
lon2, lat2, zdata2 = g3ca.contour_data('Rho', g2, alt=alt)
diffzdata = 100*(zdata2-zdata1)/zdata1
hc = ax[ialt*2].contourf(
lon1, lat1, zdata2, transform=ccrs.PlateCarree(),
levels=rholevels[ialt], cmap='jet', extend='both')
hc = ax[ialt*2+1].contourf(
lon2, lat2, diffzdata, transform=ccrs.PlateCarree(),
levels=np.linspace(-20, 20, 21),
cmap='seismic', extend='both')
# density change rate
# for ialt, alt in enumerate(alts):
# lon1 = np.array(g1['Longitude'])
# lat1 = np.array(g1['Latitude'])
# alt1 = np.array(g1['Altitude'])
# Re = 6371*1000 # Earth radius, unit: m
# RR = Re+alt1
# omega = 2*np.pi/(24*3600)
# rho1 = np.array(g1['Rho'])
# nwind1 = np.array(g1['V!Dn!N (north)'])
# ewind1 = np.array(g1['V!Dn!N (east)']) + omega*RR*np.cos(lat1)
# uwind1 = np.array(g1['V!Dn!N (up)'])
# div_rhov1 = \
# (cr.calc_div_hozt(lon1, lat1, alt1, rho1*nwind1, rho1*ewind1)\
# +cr.calc_div_vert(alt1, rho1*uwind1))/rho1
# lon2 = np.array(g2['Longitude'])
# lat2 = np.array(g2['Latitude'])
# alt2 = np.array(g2['Altitude'])
# Re = 6371*1000 # Earth radius, unit: m
# RR = Re+alt2
# omega = 2*np.pi/(24*3600)
# rho2 = np.array(g2['Rho'])
# nwind2 = np.array(g2['V!Dn!N (north)'])
# ewind2 = np.array(g2['V!Dn!N (east)']) + omega*RR*np.cos(lat2)
# uwind2 = np.array(g2['V!Dn!N (up)'])
# div_rhov2 = \
# (cr.calc_div_hozt(lon2, lat2, alt2, rho2*nwind2, rho2*ewind2)\
# +cr.calc_div_vert(alt2, rho2*uwind2))/rho2
# g1['divrhov_diff'] = div_rhov1-div_rhov2
# lon2, lat2, zdata2 = g3ca.contour_data('divrhov_diff', g1, alt=alt)
# hc = ax[ialt*3+2].contourf(
# lon2, lat2, zdata2, transform=ccrs.PlateCarree(),
# levels=np.linspace(-1,1,21)*1e-4, cmap='seismic', extend='both')
# wind
for ialt, alt in enumerate(alts):
lon1, lat1, ewind1, nwind1 = \
g3ca.vector_data(g1, 'neutral', alt=alt)
lon2, lat2, ewind2, nwind2 = \
g3ca.vector_data(g2, 'neutral', alt=alt)
lon0, lat0, ewind, nwind = (
lon2.copy(), lat2.copy(), ewind2.copy(), nwind2.copy())
lon0, lat0, ewind, nwind = g3ca.convert_vector(
lon0, lat0, ewind, nwind, plot_type='polar',
projection=projection[ialt*2])
hq = ax[ialt*2].quiver(
lon0, lat0, ewind, nwind, scale=1500, scale_units='inches',
color='k', regrid_shape=20)
lon0, lat0, ewind, nwind = (
lon1.copy(), lat1.copy(), ewind2-ewind1, nwind2-nwind1)
lon0, lat0, ewind, nwind = g3ca.convert_vector(
lon0, lat0, ewind, nwind, plot_type='polar',
projection=projection[ialt*2+1])
hq = ax[ialt*2+1].quiver(
lon0, lat0, ewind, nwind, scale=1500, scale_units='inches',
color='k', regrid_shape=20)
return
def animate_vert_wind(i):
g1, g2 = [gitm.GitmBin(k[i]) for k in [fn1, fn2]]
# create axis
ax = list(range(6))
projection = ax.copy()
for ins in range(2):
nlat, slat = [90, 40] if ins==0 else [-40, -90]
for irun in range(3):
ax[ins+irun*2], projection[ins+irun*2] = gcc.create_map(
3, 2, 1+ins+irun*2, 'polar', nlat=nlat, slat=slat,
dlat=10, centrallon=g3ca.calculate_centrallon(
g1, 'polar', useLT=True),
coastlines=False)
# vertical wind
lon1, lat1, zdata1 = g3ca.contour_data('V!Dn!N (up)', g1, alt=which_alt)
lon2, lat2, zdata2 = g3ca.contour_data('V!Dn!N (up)', g2, alt=which_alt)
hc = [ax[k].contourf(
lon1, lat1, zdata1, 21, transform=ccrs.PlateCarree(),
levels=np.linspace(-20, 20, 21),
#levels=np.linspace(15e-13, 25e-13, 21),
cmap='seismic', extend='both') for k in [0, 1]]
ax[0].set_title(g1['time'].strftime('%d-%b-%y %H:%M')+' UT', y=1.05)
ax[1].set_title(g1['time'].strftime('%d-%b-%y %H:%M')+' UT', y=1.05)
hc = [ax[k].contourf(
lon2, lat2, zdata2, 21,transform=ccrs.PlateCarree(),
levels=np.linspace(-20, 20, 21),
cmap='seismic', extend='both') for k in [2, 3]]
diffzdata = zdata2-zdata1
hc = [ax[k].contourf(
lon2, lat2, diffzdata, 21, transform=ccrs.PlateCarree(),
levels=np.linspace(-20, 20, 21), cmap='seismic',
extend='both') for k in [4, 5]]
# low density center
lon3, lat3, zdata3 = g3ca.contour_data('Rho', g1, alt=which_alt)
lon4, lat4, zdata4 = g3ca.contour_data('Rho', g2, alt=which_alt)
diffrho = 100*(zdata4-zdata3)/zdata3
hc = [ax[k].contour(
lon2, lat2, diffrho, [-10], transform=ccrs.PlateCarree(),
colors='g',linestyles='-') for k in [4, 5]]
# wind
lon1, lat1, ewind1, nwind1 = g3ca.vector_data(g1, 'neutral', alt=which_alt)
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2, 'neutral', alt=which_alt)
for iax in range(6):
if iax == 0 or iax == 1:
lon0, lat0, ewind, nwind = (
lon1.copy(), lat1.copy(), ewind1.copy(), nwind1.copy())
lon0, lat0, ewind, nwind = g3ca.convert_vector(
lon0, lat0, ewind, nwind, plot_type='polar',
projection=projection[iax])
elif iax == 2 or iax == 3:
lon0, lat0, ewind, nwind = (
lon2.copy(), lat2.copy(), ewind2.copy(), nwind2.copy())
lon0, lat0, ewind, nwind = g3ca.convert_vector(
lon0, lat0, ewind, nwind, plot_type='polar',
projection=projection[iax])
elif iax == 4 or iax == 5:
lon0, lat0, ewind, nwind = (
lon1.copy(), lat1.copy(), ewind2-ewind1, nwind2-nwind1)
lon0, lat0, ewind, nwind = g3ca.convert_vector(
lon0, lat0, ewind, nwind, plot_type='polar',
projection=projection[iax])
hq = ax[iax].quiver(
lon0, lat0, ewind, nwind, scale=1500, scale_units='inches',
color='k', regrid_shape=20)
# ax.quiverkey(hq, 0.93, 0, 1000, '1000 m/s')
# hc = plt.colorbar(hc, ticks=np.arange(3, 7)*1e-12)
# hc.set_label(r'$\rho$ (kg/m$^3$)')
# ax[iax].scatter(
# qlonn, qlatn, color='k', transform=ccrs.PlateCarree())
# ax[iax].scatter(
# qlons, qlats, color='k', transform=ccrs.PlateCarree())
return
def plot_divrhov_rho_diff(show=True, save=True):
# density change (shrink)
lon1 = np.array(g1a['Longitude'])
lat1 = np.array(g1a['Latitude'])
alt1 = np.array(g1a['Altitude'])
Re = 6371 * 1000 # Earth radius, unit: m
RR = Re + alt1
omega = 2 * np.pi / (24 * 3600)
rho1 = np.array(g1a['Rho'])
nwind1 = np.array(g1a['V!Dn!N (north)'])
ewind1 = np.array(g1a['V!Dn!N (east)']) + omega * RR * np.cos(lat1)
uwind1 = np.array(g1a['V!Dn!N (up)'])
div_rhov1 = \
(cr.calc_div_hozt(lon1, lat1, alt1, rho1*nwind1, rho1*ewind1)\
+cr.calc_div_vert(alt1, rho1*uwind1))/rho1
# density change (no shrink)
lon2 = np.array(g2a['Longitude'])
lat2 = np.array(g2a['Latitude'])
alt2 = np.array(g2a['Altitude'])
Re = 6371 * 1000 # Earth radius, unit: m
RR = Re + alt2
omega = 2 * np.pi / (24 * 3600)
rho2 = np.array(g2a['Rho'])
nwind2 = np.array(g2a['V!Dn!N (north)'])
ewind2 = np.array(g2a['V!Dn!N (east)']) + omega * RR * np.cos(lat2)
uwind2 = np.array(g2a['V!Dn!N (up)'])
div_rhov2 = \
(cr.calc_div_hozt(lon2, lat2, alt2, rho2*nwind2, rho2*ewind2)\
+cr.calc_div_vert(alt2, rho2*uwind2))/rho2
g1a['divrhov_diff'] = div_rhov1 - div_rhov2
apex = Apex(date=2003)
qlat, qlon = apex.convert(-90, 0, source='apex', dest='geo', height=400)
plt.close('all')
plt.figure(figsize=(7.26, 9.25))
for ialt, alt in enumerate([130, 200, 300, 400, 500, 600]):
alt_ind = np.argmin(np.abs(g1a['Altitude'][0, 0, :] / 1000 - alt))
alt_str = '%6.2f' % (g1a['Altitude'][0, 0, alt_ind] / 1000)
ax, projection = gcc.create_map(3,
2,
ialt + 1,
'polar',
nlat=nlat,
slat=slat,
dlat=10,
centrallon=g3ca.calculate_centrallon(
g1a, 'polar', useLT=True),
coastlines=False)
lon0, lat0, zdata0 = g3ca.contour_data('divrhov_diff', g1a, alt=alt)
fp = (lat0[:, 0] > slat) & (lat0[:, 0] < nlat)
lon0, lat0, zdata0 = lon0[fp, :], lat0[fp, :], zdata0[fp, :]
hc = ax.contourf(lon0,
lat0,
zdata0,
np.linspace(-1, 1, 21) * 1e-4,
transform=ccrs.PlateCarree(),
cmap='seismic',
extend='both')
hc = plt.colorbar(hc, pad=0.17)
hc.formatter.set_powerlimits((0, 0))
hc.update_ticks()
hc.set_label(r'$-\frac{\nabla\cdot(\rho\vec{u})}{\rho}$')
# wind difference
lon1, lat1, ewind1, nwind1 = g3ca.vector_data(g1a, 'neutral', alt=alt)
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2a, 'neutral', alt=alt)
lon0, lat0 = lon1, lat1
lon0, lat0, ewind0, nwind0 = g3ca.convert_vector(lon0,
lat0,
ewind2 - ewind1,
nwind2 - nwind1,
plot_type='polar',
projection=projection)
hq = ax.quiver(lon0,
lat0,
ewind0,
nwind0,
scale=1000,
scale_units='inches',
regrid_shape=20,
headwidth=5)
ax.quiverkey(hq, 0.93, -0.1, 500, '500 m/s')
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
plt.title('%s km' % alt_str, y=1.05)
plt.text(0.5,
0.95,
'Time: ' + tstring,
fontsize=15,
horizontalalignment='center',
transform=plt.gcf().transFigure)
if show:
plt.show()
if save:
plt.savefig(spath + '08_divrhov_rho_diff_%s%s.pdf' %
(tstrday, tstring))
return
def figure4():
plt.close('all')
plt.figure(figsize=(8.28, 9.25))
labels =[('(a)','(b)','(c)'), ('(d)','(e)','(f)'), ('(g)','(h)','(i)')]
qlat, qlon = convert(-90, 0, 0, date=dt.date(2003,3,22), a2g=True)
ylim = [[0.6e-9,1.6e-9], [1.5e-11, 4e-11], [1e-13, 9e-13]]
for ialt, alt in enumerate([150, 300, 600]):
alt_ind = np.argmin(np.abs(g1['Altitude'][0, 0, 2:-2]/1000-alt))+2
alt_str = '%6.0f' % (g1['Altitude'][0, 0, alt_ind]/1000)
rho1, rho2 = g1['Rho'], g2['Rho']
rhod = 100*(rho1-rho2)/rho2
lats, lons = g1['Latitude'], g1['Longitude']
#----------------------------------------------------------------------
ax, projection = gcc.create_map(
3, 3, ialt+1, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g1, 'polar', useLT=True),
coastlines=False, lonticklabel=[1,1,1,1])
# density difference
lon1, lat1, zdata1 = g3ca.contour_data('Rho', g1, alt=alt)
lon2, lat2, zdata2 = g3ca.contour_data('Rho', g2, alt=alt)
fp = (lat1[:,0]>slat) & (lat1[:,0]<nlat)
lon0, lat0 = lon1[fp, :], lat1[fp,:]
zdata0 = (100*(zdata1-zdata2)/zdata2)[fp, :]
print(zdata0.min())
hc = ax.contourf(
lon0, lat0, zdata0, np.linspace(-30,30,21),
transform=ccrs.PlateCarree(), cmap='seismic', extend='both')
# colorbar
gcapos = ax.get_position()
axcb = [
gcapos.x0, gcapos.y0+0.035, gcapos.width, gcapos.height/20]
axcb = plt.axes(axcb)
hcb = plt.colorbar(
hc, cax=axcb, extendrect=True, ticks=np.arange(-30, 31, 15),
orientation='horizontal')
# wind difference
lon1, lat1, ewind1, nwind1 = g3ca.vector_data(g1, 'neutral', alt=alt)
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2, 'neutral', alt=alt)
lon0, lat0 = lon1, lat1
lon0, lat0, ewind0, nwind0 = g3ca.convert_vector(
lon0, lat0, ewind1-ewind2, nwind1-nwind2, plot_type='polar',
projection=projection)
hq = ax.quiver(
lon0, lat0, ewind0, nwind0, scale=1000, scale_units='inches',
regrid_shape=20, headwidth=5)
ax.quiverkey(hq, 0.97, 0.92, 500, '500 m/s')
# magnetic pole
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
ax.scatter(
0, -90, color='w', s=30, transform=ccrs.PlateCarree(), zorder=1000)
latt1 = [-90, nlat]
lont1 = [15*(12-ut), 15*(12-ut)]
ax.plot(lont1, latt1, transform = ccrs.PlateCarree(), c='g')
latt1 = [-90, nlat]
lont1 = [15*(0-ut), 15*(0-ut)]
ax.plot(lont1, latt1, transform = ccrs.PlateCarree(), c='g')
ax.set_title('%s km' % alt_str, y=1.07)
if ialt==0:
ax.text(-0.2, 0.5, r'$\delta \rho$ (%)',
transform=ax.transAxes,rotation=90, fontsize=14,
verticalalignment='center', horizontalalignment='center')
plt.text(
0.05, 0.9, labels[0][ialt],transform=ax.transAxes,color='r')
#----------------------------------------------------------------------
# density in run1 and run2
plt.subplot(3,3,ialt+7)
ilat = (lats[0,:,0]/pi*180<nlat) &(lats[0,:,0]/pi*180>slat)
lt = 12
ilt = np.argmin(np.abs(g1['LT'][2:-2,0,0]-lt))+2
plt.plot(
180+lats[ilt,ilat,alt_ind]/pi*180,rho1[ilt,ilat,alt_ind], 'r',
label = 'Run 1')
plt.plot(
180+lats[ilt,ilat,alt_ind]/pi*180,rho2[ilt,ilat,alt_ind], 'b',
label = 'Run 2')
plt.legend(loc='upper center')
lat_1, rho_11, rho_12, rho_1d = \
180+lats[ilt,2,alt_ind]/pi*180, rho1[ilt,2, alt_ind], \
rho2[ilt,2, alt_ind], rhod[ilt,2,alt_ind]
lt = 0
ilt = np.argmin(np.abs(g1['LT'][2:-2,0,0]-lt))+2
plt.plot(-lats[ilt,ilat,alt_ind]/pi*180,rho1[ilt,ilat,alt_ind], 'r')
plt.plot(-lats[ilt,ilat,alt_ind]/pi*180,rho2[ilt,ilat,alt_ind], 'b')
lat_2, rho_21, rho_22, rho_2d = \
-lats[ilt,2,alt_ind]/pi*180, rho1[ilt,2, alt_ind], \
rho2[ilt,2, alt_ind], rhod[ilt,2,alt_ind]
plt.plot([lat_1,lat_2], [rho_11,rho_21], 'r')
plt.plot([lat_1,lat_2], [rho_12,rho_22], 'b')
plt.grid('on')
plt.xlim([-nlat,180+nlat])
plt.ylim(ylim[ialt])
plt.xticks(np.arange(-nlat,181+nlat,30),
np.concatenate(
[np.arange(nlat,-90,-30), np.arange(-90,nlat+1,30)]))
if ialt==0:
plt.ylabel(r'$\rho$ (kg/m$^3$)', fontsize=14)
plt.xlabel('Latitude')
plt.text(
0.05, 0.9, labels[2][ialt],transform=plt.gca().transAxes,color='r')
#----------------------------------------------------------------------
# density difference
plt.subplot(3,3,ialt+4)
lt = 12
ilt = np.argmin(np.abs(g1['LT'][2:-2,0,0]-lt))+2
plt.plot(
180+lats[ilt,ilat,alt_ind]/pi*180,rhod[ilt,ilat,alt_ind], 'k')
lt = 0
ilt = np.argmin(np.abs(g1['LT'][2:-2,0,0]-lt))+2
plt.plot(
-lats[ilt,ilat,alt_ind]/pi*180,rhod[ilt,ilat,alt_ind], 'k')
plt.plot([lat_1,lat_2], [rho_1d,rho_2d], 'k')
plt.grid('on')
plt.xlim([-nlat,180+nlat])
plt.xticks(np.arange(-nlat,181+nlat,30),
np.concatenate(
[np.arange(nlat,-90,-30), np.arange(-90,nlat+1,30)]))
plt.ylim(-35, 5)
plt.yticks(np.arange(-30, 0.1, 10))
if ialt==0:
plt.ylabel(r'$\delta\rho$ (%)', fontsize=14)
plt.xlabel('Latitude')
plt.text(
0.05, 0.9, labels[1][ialt],transform=plt.gca().transAxes,color='r')
plt.subplots_adjust(wspace=0.25, hspace=0.25, top=0.95, bottom=0.05)
plt.savefig(savepath+'Figure4.eps')
plt.savefig(savepath+'Figure4.jpeg')
return
def plot_rho_wind_ut(ns='SH', alt=150):
path = '/home/guod/simulation_output/momentum_analysis/run_no_shrink_igrf_1_1/data'
fn01 = path+'/3DALL_t030323_000000.bin'
fn02 = path+'/3DALL_t030323_030000.bin'
fn03 = path+'/3DALL_t030323_060001.bin'
fn04 = path+'/3DALL_t030323_090002.bin'
fn05 = path+'/3DALL_t030323_120000.bin'
fn06 = path+'/3DALL_t030323_150001.bin'
fn07 = path+'/3DALL_t030323_180000.bin'
fn08 = path+'/3DALL_t030323_210002.bin'
fn09 = path+'/3DALL_t030324_000000.bin'
fn2 = [fn01, fn02, fn03, fn04, fn05, fn06, fn07, fn08, fn09]
if ns=='SH':
nlat, slat = -50, -90
elif ns=='NH':
nlat, slat = 90, 50
glats, glons = convert(-90, 0, 0, date=dt.date(2003,1,1), a2g=True)
glatn, glonn = convert(90, 0, 0, date=dt.date(2003,1,1), a2g=True)
fig1 = plt.figure(1, figsize=(5.28,8.49))
titf = ['(a)','(b)','(c)','(d)','(e)','(f)','(g)','(h)']
for k in range(8):
g2 = gitm.read(fn2[k])
title = 'UT: '+g2['time'].strftime('%H')
lon2, lat2, rho2 = g3ca.contour_data('Rho', g2, alt=alt)
ilat2 = (lat2[:,0]>=slat) & (lat2[:,0]<=nlat)
min_div_mean = \
np.min(rho2[ilat2, :]) / \
(np.mean(rho2[ilat2, :]*np.cos(lat2[ilat2, :]/180*np.pi)) /\
np.mean(np.cos(lat2[ilat2,:]/180*np.pi)))
print(ns,': ',100*(min_div_mean-1))
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2,'neu',alt=alt)
if k ==0:
lonticklabel = [1, 0, 0, 1]
elif k==1:
lonticklabel = [1, 1, 0, 0]
elif k in [2, 4]:
lonticklabel = [0, 0, 0, 1]
elif k in [3, 5]:
lonticklabel = [0, 1, 0, 0]
elif k==6:
lonticklabel = [0, 0, 1, 1]
elif k==7:
lonticklabel = [0, 1, 1, 0]
olat=False if k<7 else True
# No shrink
plt.figure(1)
centrallon = g3ca.calculate_centrallon(g2, 'pol', useLT=True)
ax, projection = gcc.create_map(
4,2,k+1, 'pol', nlat, slat, centrallon, coastlines=False,
dlat=10, useLT=True, lonticklabel=lonticklabel, olat=olat)
hc1 = ax.contourf(lon2, lat2, rho2, np.linspace(0.8e-9, 1.6e-9),
transform=ccrs.PlateCarree(),cmap='jet', extend='both')
lon9, lat9, ewind9, nwind9 = g3ca.convert_vector(
lon2, lat2, ewind2, nwind2, 'pol', projection)
hq1 = ax.quiver(
lon9,lat9,ewind9,nwind9,scale=1500,scale_units='inches',
regrid_shape=20)
ax.scatter(glons, glats, transform=ccrs.PlateCarree(), s=35, c='k')
ax.scatter(glonn, glatn, transform=ccrs.PlateCarree(), s=35, c='k')
ax.scatter(0, 90, transform=ccrs.PlateCarree(), s=35, c='w', zorder=100)
ax.scatter(0, -90, transform=ccrs.PlateCarree(), s=35, c='w', zorder=100)
plt.title(titf[k]+' '+title,x=0,y=0.93)
plt.figure(1)
plt.subplots_adjust(
hspace=0.01, wspace=0.01, left=0.05, right=0.95, top=0.95, bottom=0.12)
cax = plt.axes([0.25,0.07,0.5,0.02])
hcb = plt.colorbar(
hc1,cax=cax,orientation='horizontal',ticks=np.arange(0.8,1.7,0.2)*1e-9)
hcb.set_label(r'$\rho$ (kg/$m^3$)')
plt.quiverkey(hq1, 0.5,0.12, 500, '500m/s',coordinates='figure')
plt.savefig(savepath+'1'+ns+'AllUT.eps')
plt.savefig(savepath+'1'+ns+'AllUT.jpeg')
return
vort = 1.0/(Re+alt)*(
np.gradient(-(r+alt)*nwind, axis=2)/np.gradient(alt, axis=2) +
np.gradient(uwind, axis=1)/np.gradient(lat, axis=1))
gdata[name] = dmarray(vort, attrs={'units':'m/s^2', 'scale':'linear',
'name':neuion+' velocity '+component+' vorticity'})
return
#END
if __name__ == '__main__':
fn = '/home/guod/big/raid4/guod/run_imfby/run2c/data/3DALL_t100323_042000.bin'
gdata = gitm.GitmBin(fn)
calc_vorticity(gdata, 'neu', name='nvorticity', component='radial')
alt = 200
nlat = 90
slat = 50
centrallon = g3ca.calculate_centrallon(gdata, 'polar', useLT=True)
ax, projection = gcc.create_map(
1, 1, 1, plot_type='polar', nlat=nlat, slat=slat,
centrallon=centrallon, coastlines=False, dlat=10)
lon0, lat0, nvort0 = g3ca.contour_data('nvorticity', gdata, alt=alt)
lon, lat, ewind, nwind = g3ca.vector_data(gdata, 'neu', alt=alt)
lon, lat, ewind, nwind = g3ca.convert_vector(
lon, lat, ewind, nwind, 'polar', projection)
hc = ax.contourf(
lon0, lat0, nvort0,
transform=ccrs.PlateCarree(), cmap='seismic')
ax.quiver(lon, lat, ewind, nwind, regrid_shape=50, scale_units='inches', scale=1000)
plt.show()
def figure5_7(alt=150):
times = pd.to_datetime([
'2003-03-22 00:00:00', '2003-03-22 00:30:00', '2003-03-22 01:00:00',
'2003-03-22 02:00:00', '2003-03-22 03:00:00', '2003-03-22 06:00:00'])
plt.close('all')
plt.figure(figsize=(9.5, 9.25))
xtitle = [
r'$-\nabla\cdot w$', r'$-\nabla\cdot u$',
r'$-\frac{w\cdot\nabla\rho}{\rho}$',
r'$-\frac{u\cdot\nabla\rho}{\rho}$',
r'$\frac{\partial \rho}{\rho\partial t}$',
r'$\delta\rho$ (%)']
ylabel = times.strftime('%H:%M')
qlat, qlon = convert(-90, 0, 0, date=dt.date(2003,3,22), a2g=True)
for itime, time in enumerate(times):
strtime = time.strftime('%y%m%d_%H%M')
fn1 = glob.glob(
'/home/guod/simulation_output/momentum_analysis/'\
'run_no_shrink_iondrift_4_1/data/3DALL_t%s*.bin' % strtime)
fn2 = glob.glob(
'/home/guod/simulation_output/momentum_analysis/'\
'run_shrink_iondrift_4_c1/data/3DALL_t%s*.bin' % strtime)
g1 = gitm.read(fn1[0])
g2 = gitm.read(fn2[0])
lon1 = g1['Longitude']
lat1 = g1['Latitude']
alt1 = g1['Altitude']
Re = 6371*1000 # Earth radius, unit: m
RR = Re+alt1
omega = 2*pi/(24*3600)
rho1 = np.array(g1['Rho'])
nwind1 = g1['V!Dn!N (north)']
ewind1 = g1['V!Dn!N (east)'] + omega*RR*np.cos(lat1)
uwind1 = g1['V!Dn!N (up)']
div_rhov1 = \
(cr.calc_div_hozt(lon1, lat1, alt1, rho1*nwind1, rho1*ewind1)\
+cr.calc_div_vert(alt1, rho1*uwind1))/rho1
vert_divv1 = cr.calc_div_vert(alt1, uwind1)
hozt_divv1 = cr.calc_div_hozt(lon1, lat1, alt1, nwind1, ewind1)
vert_vgradrho1 = uwind1*cr.calc_rusanov_alts_ausm(alt1,rho1)/rho1
hozt_vgradrho1 = \
(nwind1*cr.calc_rusanov_lats(lat1,alt1,rho1)\
+ewind1*cr.calc_rusanov_lons(lon1,lat1,alt1,rho1))/rho1
dc1 = [
vert_divv1, hozt_divv1, vert_vgradrho1, hozt_vgradrho1,
div_rhov1, rho1]
lon2 = g2['Longitude']
lat2 = g2['Latitude']
alt2 = g2['Altitude']
Re = 6371*1000 # Earth radius, unit: m
RR = Re+alt2
omega = 2*pi/(24*3600)
rho2 = g2['Rho']
nwind2 = g2['V!Dn!N (north)']
ewind2 = g2['V!Dn!N (east)'] + omega*RR*np.cos(lat2)
uwind2 = g2['V!Dn!N (up)']
div_rhov2 = \
(cr.calc_div_hozt(lon2, lat2, alt2, rho2*nwind2, rho2*ewind2)\
+cr.calc_div_vert(alt2, rho2*uwind2))/rho2
vert_divv2 = cr.calc_div_vert(alt2, uwind2)
hozt_divv2 = cr.calc_div_hozt(lon2, lat2, alt2, nwind2, ewind2)
vert_vgradrho2 = uwind2*cr.calc_rusanov_alts_ausm(alt2,rho2)/rho2
hozt_vgradrho2 = \
(nwind2*cr.calc_rusanov_lats(lat2,alt2,rho2)\
+ewind2*cr.calc_rusanov_lons(lon2,lat2,alt2,rho2))/rho2
dc2 = [
vert_divv2, hozt_divv2, vert_vgradrho2, hozt_vgradrho2,
div_rhov2, rho2]
alt_ind = np.argmin(np.abs(alt1[0, 0, :]/1000-alt))
alt_str = '%6.0f' % (alt1[0, 0, alt_ind]/1000)
for idc in range(6):
lonticklabel = [0, 0, 0, 0]
if idc == 0:
lonticklabel[3] = lonticklabel[-1]+1
if idc == 5:
lonticklabel[1] = lonticklabel[-1]+1
if itime == 0:
lonticklabel[0] = lonticklabel[-2]+1
if itime == 5:
lonticklabel[2] = lonticklabel[-2]+1
ax, projection = gcc.create_map(
6, 6, itime*6+idc+1, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g1, 'polar', useLT=True),
coastlines=False, lonticklabel=lonticklabel)
g1['temp'] = -(dc1[idc] - dc2[idc])
if idc==5:
g1['temp'] = 100*(dc1[idc] - dc2[idc])/dc2[idc]
lon0, lat0, zdata0 = g3ca.contour_data('temp', g1, alt=alt)
fp = (lat0[:,0]>slat) & (lat0[:,0]<nlat)
lon0, lat0, zdata0 = lon0[fp, :], lat0[fp,:], zdata0[fp,:]
levels = np.linspace(-8,8,21)*1e-5
if idc==5:
levels = np.linspace(-30,30,21)
hc = ax.contourf(
lon0, lat0, zdata0, levels,
transform=ccrs.PlateCarree(), cmap='seismic', extend='both')
# wind difference
alt_ind = np.argmin(np.abs(alt1[0, 0, :]/1000-alt))
alt_str = '%6.0f' % (alt1[0, 0, alt_ind]/1000)
lon1, lat1, ewind1, nwind1 = g3ca.vector_data(
g1, 'neutral', alt=alt)
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(
g2, 'neutral', alt=alt)
lon0, lat0 = lon1, lat1
lon0, lat0, ewind0, nwind0 = g3ca.convert_vector(
lon0, lat0, ewind1-ewind2, nwind1-nwind2,
plot_type='polar', projection=projection)
hq = ax.quiver(
lon0, lat0, ewind0, nwind0, scale=1500,
scale_units='inches', regrid_shape=20, headwidth=5)
ax.scatter(
qlon, qlat, s=10, color='k', transform=ccrs.PlateCarree())
ax.scatter(
0, -90, color='w', s=10, transform=ccrs.PlateCarree(),
zorder=1000)
if idc==0:
plt.text(
-0.3, 0.5, ylabel[itime], rotation=90,
transform=ax.transAxes, verticalalignment='center')
if itime==0:
plt.title(xtitle[idc], y=1.15, fontsize=14)
texts = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']
plt.text(0.05,1,texts[idc],transform=plt.gca().transAxes,
color='r')
if itime==5:
axp = ax.get_position()
cax = [axp.x0, axp.y0-0.03, axp.x1-axp.x0, 1/20*(axp.y1-axp.y0)]
cax = plt.axes(cax)
ticks = np.arange(-8e-5, 8.1e-5, 4e-5) if idc<5 else \
np.arange(-30, 31, 15)
hcb = plt.colorbar(
hc, cax=cax, extendrect=True, orientation='horizontal',
ticks=ticks)
if idc<5:
hcb.formatter.set_powerlimits((0,0))
hcb.update_ticks()
plt.subplots_adjust(wspace=0, hspace=0)
if alt==300:
plt.savefig(savepath+'Figure5.eps')
plt.savefig(savepath+'Figure5.jpeg')
if alt==600:
plt.savefig(savepath+'Figure7.eps')
plt.savefig(savepath+'Figure7.jpeg')
return
def animate_vgradrho_rho(i):
g1 = gitm.GitmBin(fn1[i])
g2 = gitm.GitmBin(fn2[i])
alt_ind = np.argmin(np.abs(g1['Altitude'][0, 0, 2:-2]/1000-which_alt))+2
# create axis
ax = list(range(2))
projection = ax.copy()
for ins in range(2):
nlat, slat = [90, 40] if ins==0 else [-40, -90]
ax[ins], projection[ins] = gcc.create_map(
1, 2, 1+ins, 'polar', nlat=nlat, slat=slat,
dlat=10, centrallon=g3ca.calculate_centrallon(
g1, 'polar', useLT=True),
coastlines=False)
lon1 = np.array(g1['Longitude'])
lat1 = np.array(g1['Latitude'])
alt1 = np.array(g1['Altitude'])
nwind1 = np.array(g1['V!Dn!N (north)'])
RR = 6371*1000+alt1
omega = (2*np.pi)/(24*3600)
ewind1 = np.array(g1['V!Dn!N (east)'])+omega*RR*np.cos(lat1)
uwind1 = np.array(g1['V!Dn!N (up)'])
rho1 = np.array(g1['Rho'])
vgradrho1 = nwind1*cr.calc_rusanov_lats(lat1,alt1,rho1)/rho1 \
+ewind1*cr.calc_rusanov_lons(lon1,lat1,alt1,rho1)/rho1
lon2 = np.array(g2['Longitude'])
lat2 = np.array(g2['Latitude'])
alt2 = np.array(g2['Altitude'])
nwind2 = np.array(g2['V!Dn!N (north)'])
RR = 6371*1000+alt2
omega = (2*np.pi)/(24*3600)
ewind2 = np.array(g2['V!Dn!N (east)'])+omega*RR*np.cos(lat2)
uwind2 = np.array(g2['V!Dn!N (up)'])
rho2 = np.array(g2['Rho'])
vgradrho2 = nwind2*cr.calc_rusanov_lats(lat2,alt2,rho2)/rho2 \
+ewind2*cr.calc_rusanov_lons(lon2,lat2,alt2,rho2)/rho2
g1['vgradrho'] = vgradrho1-vgradrho2
lon0,lat0,vgradrho = g3ca.contour_data('vgradrho',g1,alt=which_alt)
hc = ax[0].contourf(
lon0, lat0, vgradrho, np.linspace(-1,1,21)*1e-4,
transform=ccrs.PlateCarree(), cmap='seismic', extend='both')
ax[0].set_title(g1['time'].strftime('%d-%b-%y %H:%M')+' UT', y=1.05)
hc = ax[1].contourf(
lon0, lat0, vgradrho, np.linspace(-1,1,21)*1e-4,
transform=ccrs.PlateCarree(), cmap='seismic', extend='both')
ax[1].set_title(g1['time'].strftime('%d-%b-%y %H:%M')+' UT', y=1.05)
# low density center
lon3, lat3, zdata3 = g3ca.contour_data('Rho', g1, alt=which_alt)
lon4, lat4, zdata4 = g3ca.contour_data('Rho', g2, alt=which_alt)
diffrho = 100*(zdata4-zdata3)/zdata3
hc = [ax[k].contour(
lon0, lat0, diffrho, [-10], transform=ccrs.PlateCarree(),
colors='g',linestyles='-') for k in [0, 1]]
# wind difference
lon1, lat1, ewind1, nwind1 = g3ca.vector_data(g1, 'neutral', alt=which_alt)
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2, 'neutral', alt=which_alt)
lon0, lat0, ewind, nwind = (
lon1.copy(), lat1.copy(), ewind2-ewind1, nwind2-nwind1)
for ins in [0, 1]:
lon0t, lat0t, ewindt, nwindt = g3ca.convert_vector(
lon0, lat0, ewind, nwind, plot_type='polar',
projection=projection[ins])
hq = ax[ins].quiver(
lon0t, lat0t, ewindt, nwindt, scale=1500,
scale_units='inches', color='k', regrid_shape=20)
def plot_divrhov_rho_diff(show=True, save=True):
# density change (shrink)
lon1 = np.array(g1a['Longitude'])
lat1 = np.array(g1a['Latitude'])
alt1 = np.array(g1a['Altitude'])
Re = 6371*1000 # Earth radius, unit: m
RR = Re+alt1
omega = 2*np.pi/(24*3600)
rho1 = np.array(g1a['Rho'])
nwind1 = np.array(g1a['V!Dn!N (north)'])
ewind1 = np.array(g1a['V!Dn!N (east)']) + omega*RR*np.cos(lat1)
uwind1 = np.array(g1a['V!Dn!N (up)'])
div_rhov1 = \
(cr.calc_div_hozt(lon1, lat1, alt1, rho1*nwind1, rho1*ewind1)\
+cr.calc_div_vert(alt1, rho1*uwind1))/rho1
# density change (no shrink)
lon2 = np.array(g2a['Longitude'])
lat2 = np.array(g2a['Latitude'])
alt2 = np.array(g2a['Altitude'])
Re = 6371*1000 # Earth radius, unit: m
RR = Re+alt2
omega = 2*np.pi/(24*3600)
rho2 = np.array(g2a['Rho'])
nwind2 = np.array(g2a['V!Dn!N (north)'])
ewind2 = np.array(g2a['V!Dn!N (east)']) + omega*RR*np.cos(lat2)
uwind2 = np.array(g2a['V!Dn!N (up)'])
div_rhov2 = \
(cr.calc_div_hozt(lon2, lat2, alt2, rho2*nwind2, rho2*ewind2)\
+cr.calc_div_vert(alt2, rho2*uwind2))/rho2
g1a['divrhov_diff'] = div_rhov1-div_rhov2
apex = Apex(date=2003)
qlat, qlon = apex.convert(-90, 0, source='apex', dest='geo', height=400)
plt.close('all')
plt.figure(figsize=(7.26, 9.25))
for ialt, alt in enumerate([130, 200, 300, 400, 500, 600]):
alt_ind = np.argmin(np.abs(g1a['Altitude'][0, 0, :]/1000-alt))
alt_str = '%6.2f' % (g1a['Altitude'][0, 0, alt_ind]/1000)
ax, projection = gcc.create_map(
3, 2, ialt+1, 'polar', nlat=nlat, slat=slat, dlat=10,
centrallon=g3ca.calculate_centrallon(g1a, 'polar', useLT=True),
coastlines=False)
lon0, lat0, zdata0 = g3ca.contour_data('divrhov_diff', g1a, alt=alt)
fp = (lat0[:,0]>slat) & (lat0[:,0]<nlat)
lon0, lat0, zdata0 = lon0[fp, :], lat0[fp,:], zdata0[fp,:]
hc = ax.contourf(
lon0, lat0, zdata0, np.linspace(-1,1,21)*1e-4,
transform=ccrs.PlateCarree(), cmap='seismic', extend='both')
hc = plt.colorbar(hc, pad=0.17)
hc.formatter.set_powerlimits((0,0))
hc.update_ticks()
hc.set_label(r'$-\frac{\nabla\cdot(\rho\vec{u})}{\rho}$')
# wind difference
lon1, lat1, ewind1, nwind1 = g3ca.vector_data(g1a, 'neutral', alt=alt)
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2a, 'neutral', alt=alt)
lon0, lat0 = lon1, lat1
lon0, lat0, ewind0, nwind0 = g3ca.convert_vector(
lon0, lat0, ewind2-ewind1, nwind2-nwind1, plot_type='polar',
projection=projection)
hq = ax.quiver(
lon0, lat0, ewind0, nwind0, scale=1000, scale_units='inches',
regrid_shape=20, headwidth=5)
ax.quiverkey(hq, 0.93, -0.1, 500, '500 m/s')
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
plt.title('%s km' % alt_str, y=1.05)
plt.text(0.5, 0.95, 'Time: '+tstring, fontsize=15,
horizontalalignment='center', transform=plt.gcf().transFigure)
if show:
plt.show()
if save:
plt.savefig(spath+'08_divrhov_rho_diff_%s%s.pdf' % (tstrday,tstring))
return
def f4():
plt.figure(figsize=(6.88, 6.74))
#geographic coordinates
ax1, projection1 = gcc.create_map(2,
2,
1,
'pol',
90,
50,
0,
coastlines=False,
useLT=True,
dlat=10,
lonticklabel=(1, 1, 1, 1))
ax1.plot([45, 45], [50, 90], 'k--', transform=ccrs.PlateCarree())
ax1.plot([225, 225], [50, 90], 'k--', transform=ccrs.PlateCarree())
ax1.plot([105, 105], [50, 90], 'k--', transform=ccrs.PlateCarree())
ax1.plot([285, 285], [50, 90], 'k--', transform=ccrs.PlateCarree())
ax1.scatter(0,
90,
color='r',
transform=ccrs.PlateCarree(),
zorder=10,
label='North Pole')
ax1.text(0, 1, '(a)', transform=plt.gca().transAxes)
plt.legend(loc=[0.5, 1.1])
ax2, projection2 = gcc.create_map(2,
2,
2,
'pol',
-50,
-90,
0,
coastlines=False,
useLT=True,
dlat=10,
lonticklabel=(1, 1, 1, 1))
ax2.scatter(0,
-90,
color='b',
transform=ccrs.PlateCarree(),
label='South Pole')
ax2.text(0, 1, '(b)', transform=plt.gca().transAxes)
plt.legend(loc=[-0.1, 1.1])
#geomagnetic coordinates
ax3, projection3 = gcc.create_map(2,
2,
3,
'pol',
90,
50,
0,
coastlines=False,
useLT=True,
dlat=10,
lonticklabel=(1, 1, 1, 1))
mlatn, mlonn = convert(90, 0, 0, date=dt.date(2002, 3, 21))
for k in range(24):
mltn = convert_mlt(mlonn[0], dtime=dt.datetime(2003, 3, 21, k))
ax3.scatter(mltn * 15,
mlatn[0],
color='r',
transform=ccrs.PlateCarree())
ax3.scatter(180, 75, s=50, c='k', marker='x', transform=ccrs.PlateCarree())
ax3.text(0, 1, '(c)', transform=plt.gca().transAxes)
ax4, projection4 = gcc.create_map(2,
2,
4,
'pol',
-50,
-90,
0,
coastlines=False,
useLT=True,
dlat=10,
lonticklabel=(1, 1, 1, 1))
mlats, mlons = convert(-90, 0, 0, date=dt.date(2002, 3, 21))
for k in range(24):
mlts = convert_mlt(mlons[0], dtime=dt.datetime(2003, 3, 21, k))
ax4.scatter(mlts * 15,
mlats[0],
color='b',
transform=ccrs.PlateCarree())
ax4.scatter(180,
-75,
s=50,
c='k',
marker='x',
transform=ccrs.PlateCarree())
ax4.text(0, 1, '(d)', transform=plt.gca().transAxes)
plt.savefig('/Users/guod/Documents/Pole_Density_MLT_Change/Figures/'
'000_Pole_Feature.pdf')
return
def plot_den_win(show=True, save=True):
apex = Apex(date=2003)
qlat, qlon = apex.convert(-90, 0, source='apex', dest='geo', height=400)
plt.close('all')
plt.figure(figsize=(7.26, 9.25))
g = g2a
for ialt, alt in enumerate([130, 200, 300, 400, 500, 600]):
alt_ind = np.argmin(np.abs(g['Altitude'][0, 0, 2:-2] / 1000 - alt)) + 2
alt_str = '%6.2f' % (g['Altitude'][0, 0, alt_ind] / 1000)
ax, projection = gcc.create_map(3,
2,
ialt + 1,
'polar',
nlat=nlat,
slat=slat,
dlat=10,
centrallon=g3ca.calculate_centrallon(
g, 'polar', useLT=True),
coastlines=False)
# density
lon0, lat0, zdata0 = g3ca.contour_data('Rho', g, alt=alt)
fp = (lat0[:, 0] > slat) & (lat0[:, 0] < nlat)
lon0, lat0, zdata0 = lon0[fp, :], lat0[fp, :], zdata0[fp, :]
hc = ax.contourf(lon0,
lat0,
zdata0,
21,
transform=ccrs.PlateCarree(),
cmap='jet',
extend='both')
hc = plt.colorbar(hc, pad=0.17)
hc.set_label(r'$\rho$ (kg/m$^3$)')
# wind
lon0, lat0, ewind, nwind = g3ca.vector_data(g, 'neutral', alt=alt)
lon0, lat0, ewind, nwind = g3ca.convert_vector(lon0,
lat0,
ewind,
nwind,
plot_type='polar',
projection=projection)
hq = ax.quiver(lon0,
lat0,
ewind,
nwind,
scale=1500,
scale_units='inches',
regrid_shape=20)
ax.quiverkey(hq, 0.93, -0.1, 1000, '1000 m/s')
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
# rho difference
# lon3, lat3, zdata3 = g3ca.contour_data('Rho', g1a, alt=alt)
# lon4, lat4, zdata4 = g3ca.contour_data('Rho', g2a, alt=alt)
# diffrho = 100*(zdata4-zdata3)/zdata3
# hc = ax.contour(
# lon4, lat4, diffrho, [-10], transform=ccrs.PlateCarree(),
# colors='g',linestyles='-')
plt.title('%s km' % alt_str, y=1.05)
plt.text(0.5,
0.95,
'Time: ' + tstring,
fontsize=15,
horizontalalignment='center',
transform=plt.gcf().transFigure)
if show:
plt.show()
if save:
plt.savefig(spath + '01_den_win_run2_%s%s.pdf' % (tstrday, tstring))
return
def plot_rho_wind_ut(ns='SH', alt=150):
path = '/home/guod/simulation_output/momentum_analysis/run_no_shrink_igrf_1_1/data'
fn01 = path + '/3DALL_t030323_000000.bin'
fn02 = path + '/3DALL_t030323_030000.bin'
fn03 = path + '/3DALL_t030323_060001.bin'
fn04 = path + '/3DALL_t030323_090002.bin'
fn05 = path + '/3DALL_t030323_120000.bin'
fn06 = path + '/3DALL_t030323_150001.bin'
fn07 = path + '/3DALL_t030323_180000.bin'
fn08 = path + '/3DALL_t030323_210002.bin'
fn09 = path + '/3DALL_t030324_000000.bin'
fn2 = [fn01, fn02, fn03, fn04, fn05, fn06, fn07, fn08, fn09]
if ns == 'SH':
nlat, slat = -50, -90
elif ns == 'NH':
nlat, slat = 90, 50
glats, glons = convert(-90, 0, 0, date=dt.date(2003, 1, 1), a2g=True)
glatn, glonn = convert(90, 0, 0, date=dt.date(2003, 1, 1), a2g=True)
fig1 = plt.figure(1, figsize=(5.28, 8.49))
titf = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)']
for k in range(8):
g2 = gitm.read(fn2[k])
title = 'UT: ' + g2['time'].strftime('%H')
lon2, lat2, rho2 = g3ca.contour_data('Rho', g2, alt=alt)
ilat2 = (lat2[:, 0] >= slat) & (lat2[:, 0] <= nlat)
min_div_mean = \
np.min(rho2[ilat2, :]) / \
(np.mean(rho2[ilat2, :]*np.cos(lat2[ilat2, :]/180*np.pi)) /\
np.mean(np.cos(lat2[ilat2,:]/180*np.pi)))
print(ns, ': ', 100 * (min_div_mean - 1))
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2, 'neu', alt=alt)
if k == 0:
lonticklabel = [1, 0, 0, 1]
elif k == 1:
lonticklabel = [1, 1, 0, 0]
elif k in [2, 4]:
lonticklabel = [0, 0, 0, 1]
elif k in [3, 5]:
lonticklabel = [0, 1, 0, 0]
elif k == 6:
lonticklabel = [0, 0, 1, 1]
elif k == 7:
lonticklabel = [0, 1, 1, 0]
olat = False if k < 7 else True
# No shrink
plt.figure(1)
centrallon = g3ca.calculate_centrallon(g2, 'pol', useLT=True)
ax, projection = gcc.create_map(4,
2,
k + 1,
'pol',
nlat,
slat,
centrallon,
coastlines=False,
dlat=10,
useLT=True,
lonticklabel=lonticklabel,
olat=olat)
hc1 = ax.contourf(lon2,
lat2,
rho2,
np.linspace(0.8e-9, 1.6e-9),
transform=ccrs.PlateCarree(),
cmap='jet',
extend='both')
lon9, lat9, ewind9, nwind9 = g3ca.convert_vector(
lon2, lat2, ewind2, nwind2, 'pol', projection)
hq1 = ax.quiver(lon9,
lat9,
ewind9,
nwind9,
scale=1500,
scale_units='inches',
regrid_shape=20)
ax.scatter(glons, glats, transform=ccrs.PlateCarree(), s=35, c='k')
ax.scatter(glonn, glatn, transform=ccrs.PlateCarree(), s=35, c='k')
ax.scatter(0,
90,
transform=ccrs.PlateCarree(),
s=35,
c='w',
zorder=100)
ax.scatter(0,
-90,
transform=ccrs.PlateCarree(),
s=35,
c='w',
zorder=100)
plt.title(titf[k] + ' ' + title, x=0, y=0.93)
plt.figure(1)
plt.subplots_adjust(hspace=0.01,
wspace=0.01,
left=0.05,
right=0.95,
top=0.95,
bottom=0.12)
cax = plt.axes([0.25, 0.07, 0.5, 0.02])
hcb = plt.colorbar(hc1,
cax=cax,
orientation='horizontal',
ticks=np.arange(0.8, 1.7, 0.2) * 1e-9)
hcb.set_label(r'$\rho$ (kg/$m^3$)')
plt.quiverkey(hq1, 0.5, 0.12, 500, '500m/s', coordinates='figure')
plt.savefig(savepath + '1' + ns + 'AllUT.eps')
plt.savefig(savepath + '1' + ns + 'AllUT.jpeg')
return
def plot_vert_divv_diff(show=True, save=True):
velr = np.array(g1a['V!Dn!N (up)'])
divv1 = cr.calc_div_vert(g1a['Altitude'], velr)
velr = np.array(g2a['V!Dn!N (up)'])
divv2 = cr.calc_div_vert(g2a['Altitude'], velr)
g1a['vert_divv_diff'] = divv1 - divv2
apex = Apex(date=2003)
qlat, qlon = apex.convert(-90, 0, source='apex', dest='geo', height=400)
plt.close('all')
plt.figure(figsize=(7.26, 9.25))
for ialt, alt in enumerate([130, 200, 300, 400, 500, 600]):
alt_ind = np.argmin(np.abs(g1a['Altitude'][0, 0, :] / 1000 - alt))
alt_str = '%6.2f' % (g1a['Altitude'][0, 0, alt_ind] / 1000)
ax, projection = gcc.create_map(3,
2,
ialt + 1,
'polar',
nlat=nlat,
slat=slat,
dlat=10,
centrallon=g3ca.calculate_centrallon(
g1a, 'polar', useLT=True),
coastlines=False)
lon0, lat0, zdata0 = g3ca.contour_data('vert_divv_diff', g1a, alt=alt)
fp = (lat0[:, 0] > slat) & (lat0[:, 0] < nlat)
lon0, lat0, zdata0 = lon0[fp, :], lat0[fp, :], zdata0[fp, :]
hc = ax.contourf(lon0,
lat0,
zdata0,
levels=np.linspace(-1, 1, 21) * 1e-4,
transform=ccrs.PlateCarree(),
cmap='seismic',
extend='both')
hcb = plt.colorbar(hc, pad=0.17)
hcb.set_label(r'$-\nabla\cdot\vec{u}$ (up)')
hcb.formatter.set_powerlimits((-2, 2))
hcb.update_ticks()
# wind difference
lon1, lat1, ewind1, nwind1 = g3ca.vector_data(g1a, 'neutral', alt=alt)
lon2, lat2, ewind2, nwind2 = g3ca.vector_data(g2a, 'neutral', alt=alt)
lon0, lat0 = lon1, lat1
lon0, lat0, ewind0, nwind0 = g3ca.convert_vector(lon0,
lat0,
ewind2 - ewind1,
nwind2 - nwind1,
plot_type='polar',
projection=projection)
hq = ax.quiver(lon0,
lat0,
ewind0,
nwind0,
scale=1000,
scale_units='inches',
regrid_shape=20,
headwidth=5)
ax.quiverkey(hq, 0.93, -0.1, 500, '500 m/s')
ax.scatter(qlon, qlat, color='k', transform=ccrs.PlateCarree())
plt.title('%s km' % alt_str, y=1.05)
plt.text(0.5,
0.95,
'Time: ' + tstring,
fontsize=15,
horizontalalignment='center',
transform=plt.gcf().transFigure)
if show:
plt.show()
if save:
plt.savefig(spath + '04_vert_divv_diff_%s%s.pdf' % (tstrday, tstring))
return
lon0, lat0, gradn0 = g3ca.contour_data('gradn', g, alt=400)
lon0, lat0, grade0 = g3ca.contour_data('grade', g, alt=400)
lon0, lat0, gradr0 = g3ca.contour_data('gradr', g, alt=400)
lon0, lat0, rho0 = g3ca.contour_data('Rho', g, alt=400)
lon0, lat0, div0 = g3ca.contour_data('divergence', g, alt=400)
hadvect = ewind0 * grade0 + nwind0 * gradn0
vadvect = uwind0*gradr0
eee = rho0*div0
plt.close('all')
plt.figure(figsize=(9, 9))
ax, projection = gcc.create_map(
2, 2, 1, 'polar', nlat=90, slat=50,
centrallon=g3ca.calculate_centrallon(g, 'polar', useLT=True),
coastlines=False, dlat=30, useLT=True)
ax.contourf(lon0, lat0, -(hadvect+vadvect+eee), transform=ccrs.PlateCarree(),
levels = np.linspace(-4e-15, 4e-15, 21), cmap='seismic')
ax, projection = gcc.create_map(
2, 2, 2, 'polar', nlat=90, slat=50,
centrallon=g3ca.calculate_centrallon(g, 'polar', useLT=True),
coastlines=False, dlat=30, useLT=True)
ax.contourf(lon0, lat0, -hadvect, transform=ccrs.PlateCarree(),
levels = np.linspace(-4e-15, 4e-15, 21), cmap='seismic')
ax, projection = gcc.create_map(
2, 2, 3, 'polar', nlat=90, slat=50,
centrallon=g3ca.calculate_centrallon(g, 'polar', useLT=True),
coastlines=False, dlat=30, useLT=True)
vele = g2['V!Dn!N (east)']+(2*np.pi)/(24*3600)*(6371*1000+alts)*np.cos(lats)
velu = g2['V!Dn!N (up)']
rho = g2['Rho']
zdata2 = calc_rusanov_lats(lats,alts,veln) - np.tan(lats)*veln/(6371*1000+alts)
zdata22 = gd.calc_divergence_north(g2,veln)
# zdata2 = calc_rusanov_lons(lons,lats,alts,vele)
# zdata22 = gd.calc_divergence_east(g2,vele)
#zdata2 = calc_div_hozt(lons, lats, alts, veln, vele)
#zdata22 = gd.calc_divergence_north(g2,veln)+gd.calc_divergence_east(g2,vele)
dglat = np.array(g2['dLat'][0,2:-2,0])
dglon = np.array(g2['dLon'][2:-2,0,0])
zdata2, dglon1 = add_cyclic_point(zdata2[2:-2,2:-2,alt_ind].T, coord=dglon, axis=1)
zdata22, dglon1 = add_cyclic_point(zdata22[2:-2,2:-2,alt_ind].T, coord=dglon, axis=1)
dglon,dglat = np.meshgrid(dglon1, dglat)
gt = g1['time']
centrallon = (0-(gt.hour+gt.minute/60+gt.second/3600))*15
ax1, projection = gcc.create_map(
1, 2, 1, 'polar', nlat=-40, slat=-90, centrallon=centrallon,
useLT=True, dlat=10)
ax2, projection = gcc.create_map(
1, 2, 2, 'polar', nlat=-40, slat=-90, centrallon=centrallon,
useLT=True, dlat=10)
hct= ax1.contourf(np.array(dglon), np.array(dglat), np.array(zdata22),
levels=np.linspace(-2,2,21)*1e-4,transform=ccrs.PlateCarree(),
cmap='seismic',extend='both')
hct= ax2.contourf(np.array(dglon), np.array(dglat), np.array(zdata2-zdata22),
levels=np.linspace(-2,2,21)*1e-4,transform=ccrs.PlateCarree(),
cmap='seismic',extend='both')
plt.show()
