# X, Y = np.meshgrid(shearbins,areabins/1000)
#
# Zm = ma.masked_where(np.isnan(outarea),outarea)
#
# cmapp = uplot.discrete_cmap(10, base_cmap='RdBu')
# ax5 = fig.add_subplot(33)
# mappable = ax5.pcolormesh(X, Y, Zm.T, cmap=cmapp, vmin=30, vmax=40) # viridis_r
#
# ax5.set_ylabel('Area (10$^3$km$^2$)')
# ax5.set_xlabel('Shear')
# cbar = fig.colorbar(mappable, ticks=np.linspace(30,45,11))
# # cbar.set_label('90th centile')
X, Y = np.meshgrid(shearbins, qbins)
cmapp = uplot.discrete_cmap(10, base_cmap='RdBu')
ax5 = fig.add_subplot(339)
#outperc[outval<30] = np.nan
Zm = ma.masked_where(np.isnan(outperc), outperc)
mappable = ax5.pcolormesh(X, Y, Zm.T, cmap=cmapp, vmin=40,
vmax=70) # viridis_r
ax5.set_ylabel('Max. q925hPa')
ax5.set_xlabel('Max. u925hPa (equally populated)')
ax5.set_title('')
cbar = fig.colorbar(mappable,
ticks=np.linspace(40, 70,
11)) # ticks=np.linspace(30,45,11)
cbar.set_label('90th centile max. rain')
# ax5 = fig.add_subplot(235)
def wav_checkDry():
start_time = time.time()
# smfile = '/users/global/cornkle/data/OBS/AMSRE/day_aqua/amsre_monthly_anomaly.nc'
# sm = xr.open_dataset(smfile)
# sm = sm.sel(lon=slice(-10, 10), lat=slice(10, 18))
file = '/users/global/cornkle/data/OBS/MSG_LSTA/lsta_netcdf/lsta_daily_' + str(
DATE['year']) + str(DATE['month']).zfill(2) + str(
DATE['day']).zfill(2) + '.nc'
ds = xr.open_dataset(file)
ds = ds.sel(lon=slice(-10, 10), lat=slice(10, 18))
lsta2 = ds['LSTA'][0, :, :]
scales, power, lsta, inter, cells, lsta_raw, wll = run_waveletDry()
print('Scales:', scales)
latmin, latmax = (np.min(lsta['lat']), np.max(lsta['lat']))
lonmin, lonmax = (np.min(lsta['lon']), np.max(lsta['lon']))
daystring = str(DATE['year']) + '-' + str(
DATE['month']).zfill(2) + '-' + str(DATE['day']).zfill(2)
wllmean0 = wll
xv, yv = np.meshgrid(lsta['lon'], lsta['lat'])
cmap = u_plot.discrete_cmap(24, base_cmap='gist_ncar')
f = plt.figure()
ax1 = f.add_subplot(2, 2, 1)
# cmap= plt.get_cmap('RdBu_r')
# cmap.set_bad(color='gray', alpha=1.)
# plt.pcolormesh(lsta['lon'], lsta['lat'], lsta_raw, vmin=-8, vmax=8, cmap=cmap)
#
# plt.colorbar()
map = lsta.salem.get_map()
xl, yl = map.grid.transform(xv, yv)
map.set_data(lsta_raw)
map.set_plot_params(cmap='RdBu_r', vmin=-8, vmax=8, extend='both')
map.visualize(ax=ax1, addcbar=False)
cax = ax1.scatter(xl, yl, c=cells, cmap=cmap, s=5)
cbar = plt.colorbar(cax, ticks=np.arange(0, 24))
cbar.set_ticklabels(np.array(np.arange(0, 24), dtype=str))
plt.title(str(pd.to_datetime(lsta['time'].values)) + ' LSTA')
ax2 = f.add_subplot(2, 2, 2)
map.set_data(wllmean0)
minscales = [np.flip(np.array(scales) * -1, axis=0), np.array(scales)]
map.set_plot_params(cmap='RdBu_r', vmin=-200, vmax=200)
map.visualize(ax=ax2, addcbar=True)
#plt.contourf(day_small['lon'], day_small['lat'], day_small)
# plt.pcolormesh(lsta['lon'], lsta['lat'], wllmean0, cmap='RdBu_r', vmin=-200, vmax=200)
# plt.colorbar()
ax2.scatter(xl, yl, c=cells, cmap=cmap, s=5)
plt.title('Wavelet powers < 42km')
ax3 = f.add_subplot(2, 2, 3)
map.set_data(power)
map.set_plot_params(cmap='RdBu_r', vmin=-10, vmax=10)
map.visualize(ax=ax3, addcbar=True)
ax2.scatter(xl, yl, c=cells, cmap=cmap, s=5)
plt.title('Wavelet powers < 42km')
def wav_checkDry():
lsta, cells = run_waveletDry()
xv, yv = np.meshgrid(lsta['lon'], lsta['lat'])
cmap = u_plot.discrete_cmap(24, base_cmap='gist_ncar')
era = xr.open_dataset(constants.ERA5 + 'ERA5_2008_12UTCpl.nc')
eday = era.sel(time=str(DATE['year']) + str(DATE['month']).zfill(2) +
str(DATE['day']).zfill(2),
level=[950, 700])
era_on_lsta = lsta.salem.transform(eday)
shear = era_on_lsta['u'].sel(level=700).values - era_on_lsta['u'].sel(
level=950).values
era_on_lsta = era_on_lsta.sel(level=950)
f = plt.figure()
ax1 = f.add_subplot(2, 2, 1)
map = lsta.salem.get_map()
xl, yl = map.grid.transform(xv, yv)
st = 20
xquiv = xl[4::st, 4::st]
yquiv = yl[4::st, 4::st]
map.set_data(lsta)
map.set_plot_params(cmap='RdBu_r', vmin=-8, vmax=8, extend='both')
map.visualize(ax=ax1, addcbar=False)
cax = ax1.scatter(xl, yl, c=cells, cmap=cmap, s=5)
#cbar = plt.colorbar(cax, ticks=np.arange(0,24))
#cbar.set_ticklabels(np.array(np.arange(0,24), dtype=str))
plt.title(str(pd.to_datetime(lsta['time'].values)) + ' LSTA')
ax2 = f.add_subplot(2, 2, 2)
# plt.contourf(night['lon'], night['lat'], night)
tera = shear #era_on_lsta['t'].squeeze()-273.15
map.set_data((tera))
map.set_plot_params(cmap='jet', extend='both', vmin=-25,
vmax=-10) #vmin=-0.1, vmax=0.1,
#map.set_plot_params(cmap='RdBu_r', vmin=-8, vmax=8, extend='both')
map.visualize(ax=ax2, addcbar=True)
plt.scatter(xl, yl, c=cells, cmap=cmap, s=5)
u = era_on_lsta['u'].squeeze()[4::st, 4::st]
v = era_on_lsta['v'].squeeze()[4::st, 4::st]
qu = ax2.quiver(xquiv, yquiv, u.values, v.values, scale=120)
cax = ax2.scatter(xl, yl, c=cells, cmap=cmap, s=5)
# qk = plt.quiverkey(qu, 0.7, 0.95, 3, '3 m s$^{-1}$',
# labelpos='E', coordinates='figure')
ax2 = f.add_subplot(2, 2, 3)
# plt.contourf(night['lon'], night['lat'], night)
tera = era_on_lsta['d'].squeeze() * 1000
map.set_data((tera))
map.set_plot_params(cmap='RdBu', extend='both', vmin=-0.1,
vmax=0.1) # vmin=-0.1, vmax=0.1,
# map.set_plot_params(cmap='RdBu_r', vmin=-8, vmax=8, extend='both')
map.visualize(ax=ax2, addcbar=True)
plt.scatter(xl, yl, c=cells, cmap=cmap, s=5)
u = era_on_lsta['u'].squeeze()[4::st, 4::st]
v = era_on_lsta['v'].squeeze()[4::st, 4::st]
qu = ax2.quiver(xquiv, yquiv, u.values, v.values, scale=120)
cax = ax2.scatter(xl, yl, c=cells, cmap=cmap, s=5)
print('bigger than',shl )
print('smaller than', shearbins[issh+1])
try:
cmean = np.percentile(pp[poss], 90)
except IndexError:
cmean = np.nan
prob = np.sum(pp[poss]>=30) / np.sum(pp[poss]>=1)
outprob[issh,isq] = prob
outperc[issh,isq] = cmean
outval[issh,isq] = len(poss[0])
X, Y = np.meshgrid(shearbins,qbins)
cmapp = uplot.discrete_cmap(10, base_cmap='RdBu')
ax5 = fig.add_subplot(224)
#outperc[outval<30] = np.nan
Zm = ma.masked_where(np.isnan(outperc),outperc)
mappable = ax5.pcolormesh(X, Y, Zm.T, cmap=cmapp) # viridis_rvmin=20, vmax=40
ax5.set_ylabel('Max. q925hPa')
ax5.set_xlabel('Max. u925hPa (equally populated)')
ax5.set_title('')
cbar = fig.colorbar(mappable) # ticks=np.linspace(30,45,11) , ticks=np.linspace(20,40,11)
cbar.set_label('90th centile max. rain')
plt.show()
def wav_checkDry():
start_time = time.time()
# smfile = '/users/global/cornkle/data/OBS/AMSRE/day_aqua/amsre_monthly_anomaly.nc'
# sm = xr.open_dataset(smfile)
# sm = sm.sel(lon=slice(-10, 10), lat=slice(10, 18))
file = '/users/global/cornkle/data/OBS/MSG_LSTA/lsta_netcdf/lsta_daily_' + str(
DATE['year']) + str(DATE['month']).zfill(2) + str(
DATE['day']).zfill(2) + '.nc'
ftopo = '/users/global/cornkle/data/pythonWorkspace/proj_CEH/topo/gtopo_1min_afr.nc'
msg_t = '/users/global/cornkle/MCSfiles/blob_map_MCSs_-50_JJAS_points_dominant.nc'
ds = xr.open_dataset(file)
top = xr.open_dataarray(ftopo)
msg = xr.open_dataarray(msg_t)
msg.name = 'msg'
ds = ds.sel(lon=slice(-10, 10), lat=slice(10, 20))
top = top.sel(lon=slice(-10, 10), lat=slice(10, 20))
msg = msg.sel(lon=slice(-10, 10),
lat=slice(10, 20),
time=slice('2006-06-28T17:00:00', '2006-06-29T15:00:00'))
msg_mins = msg.min(dim='time')
lsta2 = ds['LSTA'][0, :, :]
wll, scales, power, lsta, inter, cells, lsta_raw = run_waveletDry()
daystring = str(DATE['year']) + '-' + str(
DATE['month']).zfill(2) + '-' + str(DATE['day']).zfill(2)
wllmean0 = wll
ppower_small = np.sum(power[0:4, :, :], axis=0)
ppower_mid = np.sum(power[4:7, :, :], axis=0)
ppower_big = np.sum(power[7::, :, :], axis=0)
print('Small', scales[0:4])
print('Mid', scales[4:7])
print('Big', scales[7::])
map = lsta.salem.get_map()
xv, yv = np.meshgrid(lsta['lon'], lsta['lat'])
xl, yl = map.grid.transform(xv, yv)
cmap = u_plot.discrete_cmap(8, base_cmap='gnuplot2') #gist_ncar
cmap_back = u_plot.discrete_cmap(8, base_cmap='Greys')
# era = xr.open_dataset(constants.ERA_DAILY_PL)
# era = era.sel(latitude=slice(None, None, -1))
#
# eday = era.sel(
# time=str(DATE['year']) + str(DATE['month']).zfill(2) + str(DATE['day']).zfill(2), level=850)
#
# u = eday['u'].squeeze()
# v = eday['v'].squeeze()
# div = eday['d'].squeeze()
#
# u_plot.quick_map(div*1000, vmin=-0.01, vmax=0.01, cmap='RdBu')
#
#
# # ws, wd = u_met.u_v_to_ws_wd(u.values,v.values)
# # eday['ws10'] = (('time', 'latitude', 'longitude'), ws[None,...])
# # eday['wd10'] = (('time', 'latitude', 'longitude'), wd[None, ...])
# ws_on_lsta = ds.salem.transform(div)*1000
# u_on_lsta = ds.salem.transform(u)
# u_on_lsta = u_on_lsta[4::50, 4::50]
# v_on_lsta = ds.salem.transform(v)
# v_on_lsta = v_on_lsta[4::50, 4::50]
# xe, ye = np.meshgrid(u_on_lsta['lon'], u_on_lsta['lat'])
# xx, yy = map.grid.transform(xe, ye, crs=u.salem.grid.proj)
# transform their coordinates to the map reference system and plot the arrows
xi, yi = np.where(np.isfinite(cells))
topo_on_lsta = ds.salem.lookup_transform(top)
grad = np.gradient(topo_on_lsta.values)
gradsum = abs(grad[0]) + abs(grad[1])
msg_on_lsta = ds.salem.transform(msg_mins)
topo_on_lsta.values[gradsum > 30] = np.nan
topo_on_lsta.values[topo_on_lsta > 400] = np.nan
for rh in [3, 6, 9, 12, 15, 18, 21, 24]:
pos = np.where((cells <= rh) & (cells > rh - 3))
if rh == 24:
cells[pos] = 0
else:
cells[pos] = rh
cmap2 = plt.get_cmap('RdBu_r')
cmap2.set_bad('Grey', 1.)
wavelet = plt.get_cmap('PuRd')
wavelet.set_bad('Grey', 1.)
# topobad = plt.get_cmap('topo')
# topobad.set_bad('Grey', 1.)
f = plt.figure(figsize=(13, 7))
ax1 = f.add_subplot(2, 2, 1)
masked = np.ma.masked_invalid(lsta_raw)
map.set_data(masked, interp='linear')
map.set_plot_params(cmap=cmap2,
levels=[-6, -4, -2, -1, -0.5, 0.5, 1, 2, 4, 6])
#map.set_lonlat_contours(interval=0)
map.visualize(
ax=ax1,
cbar_title='K',
addcbar=True,
title='28-06-2006: 0630-1600UTC LSTA & afternoon/nighttime cores')
cax = ax1.scatter(xl, yl, c=cells, cmap=cmap_back, s=15)
cax = ax1.scatter(xl, yl, c=cells, cmap=cmap, s=6)
cbar = plt.colorbar(cax,
ticks=[0, 3, 6, 9, 12, 15, 18, 21],
fraction=0.07,
pad=0.5,
orientation='horizontal')
cbar.set_ticklabels(
['22-0', '1-3', '4-6', '7-9', '10-12', '13-15', '16-18', '19-21'])
cbar.set_label('Hours of day')
ax1.get_xaxis().set_visible(False)
# print('Topo min', np.min(topo_on_lsta))
ax2 = f.add_subplot(2, 2, 2)
map.set_plot_params(cmap='topo',
vmin=np.nanmin(topo_on_lsta),
vmax=1000,
extend='both')
masked = np.ma.masked_invalid(topo_on_lsta)
map.set_data(masked)
map.set_contour(topo_on_lsta,
levels=np.arange(400, 1000, 25),
cmap='Greys_r')
map.set_contour(msg_on_lsta, levels=np.arange(-80, -40, 5), cmap='jet_r')
map.visualize(ax=ax2, addcbar=True)
map.visualize(ax=ax2,
cbar_title='m',
title='Domain topography, total height difference: 342m')
#cax = ax2.scatter(xl, yl, c=cells, cmap=cmap_back, s=20)
#cax = ax2.scatter(xl, yl, c=cells, cmap=cmap, s=10)
#plt.scatter(xv, yv, c=cells, cmap='jet', s=5)
map.set_contour()
ax3 = f.add_subplot(2, 2, 3)
map.set_plot_params(cmap=wavelet, vmin=0, vmax=5)
map.set_data(ppower_small)
map.visualize(ax=ax3,
cbar_title='Wavelet power',
title='Surface scales 9 - 30km')
#ax3.scatter(xl, yl, c=cells, cmap=cmap, s=5)
#plt.scatter(xv, yv, c=cells, cmap='jet', s=5)
ax4 = f.add_subplot(2, 2, 4)
map.set_plot_params(cmap=wavelet, vmin=0, vmax=5)
map.set_data(ppower_big)
map.visualize(ax=ax4,
cbar_title='Wavelet power',
title='Surface scales 80 - 250km')
#ax4.scatter(xl, yl, c=cells, cmap=cmap, s=5)
plt.tight_layout()
def plot_colormesh():
dic = pkl.load( open(cnst.network_data + "figs/LSTA-bullshit/scales/new/dominant_scales_save/scales.p", "rb"))
bin = np.array(dic['bin'])
bin = bin[3:-3]
center = bin[0:-1] + (bin[1::]-bin[0:-1])/2
data = (dic['blob']- dic['scale'])/(dic['scale'])#dic['scale']#(np.sum(dic['blobc'], axis=0)/np.sum(dic['blobc']))## (np.sum(dic['blobc'], axis=0)/np.sum(dic['blobc'])) #dic['scale'] #(np.sum(dic['blobc'], axis=0)/np.sum(dic['blobc']))
data = data[:,3:-3]*100
# data[0:3,-5::]= np.abs(data[0:3,-5::])
# data[3:7, -9:-4] = np.abs(data[3:7, -9:-4])
f = plt.figure()
plt.imshow(data, origin='lower')
db = dic['blobc']
filler = np.zeros_like(db)
for i in range(db.shape[0]):
for j in range(db.shape[1]):
low , up = prop.proportion_confint(db[i,j], np.nansum(db[i,:]))
unrange = (db[i,j] / np.nansum(db[i,:])) - low
filler[i,j] = unrange
#mask = np.zeros_like(db)
#mask[filler[:,3:-3]>(np.abs(dic['blob'][:,3:-3]- dic['scale'][:,3:-3])+0.004)] = 1
#mask = mask[:,-3:3]
#data[np.where(mask)] = 0
#data[np.where(data > 0)] += 1
data[np.where(np.abs(data*100)<8)]=0
levels=[-40,-30,-20,-10,-5,5,10,20,30,40]
f = plt.figure(figsize=(7,5))
ax = plt.subplot(111)
pmap = ax.contourf(center, np.arange(0,24), data, levels=[-30, -20, -15, -10,-5,5,10,15, 20, 30], cmap='PuOr_r', extend='both')
plt.axvline(0, c='k', linestyle='--')
ax.set_xticks(center, minor=False)
ax.set_xticklabels(np.array(center, dtype=int)) # center
X, Y = np.meshgrid(center,np.arange(0,24))
cmapp = uplot.discrete_cmap(9, base_cmap='RdBu_r')
Zm = ma.masked_where(np.isnan(data),data)
mappable = ax.pcolormesh(X, Y, Zm, cmap=cmapp, vmin=-35, vmax=35) # viridis_rvmin=20, vmax=40
ax.set_ylabel('Time of day')
ax.set_xlabel('Scale of pos/neg. amplitude (km)')
cbar = f.colorbar(mappable, ticks=np.linspace(-35,35,10)) # ticks=np.linspace(30,45,11) , ticks=np.linspace(20,40,11)
cbar.set_label('Difference in surface-scale frequency (%)')
# cbar = plt.colorbar(pmap)
# cbar.set_label('Difference in scale frequency | Blobs')
# ax.set_yticks(np.arange(0,24), minor=False)
# ax.set_yticklabels(np.arange(0,24))
# ax.set_xlabel('Scales of pos/neg. amplitude (km)')
# ax.set_ylabel('Time of day')
ax1 = ax.twinx()
ax1.set_yticks(np.arange(dic['blob'].shape[0]), minor=False)
ax1.set_yticklabels(dic['nblobs'])
plt.tight_layout()
f.subplots_adjust(right=0.81)
cax = f.add_axes([0.89, 0.15, 0.02, 0.75])
# cbar = f.colorbar(pmap, cax)
# cbar.ax.tick_params(labelsize=11)
# cbar.set_label('Difference in surface-scale frequency (%)', fontsize=11)
plt.show()
def plot():
dic = pkl.load(
open(
"/users/global/cornkle/figs/LSTA-bullshit/scales/new/dominant_scales_save/scatter_scales.p",
"rb"))
cmap = u_plot.discrete_cmap(24, base_cmap='jet')
f = plt.figure()
ax = plt.subplot(111)
chourly = []
shourly = []
smhourly = []
cmhourly = []
sstd = []
cstd = []
hh = []
for h in range(0, 24, 1):
c = np.mean(
dic['cell'][(dic['hour'] == h) & (dic['cell'] >= 15) &
(dic['surface'] > 9)]) #& (dic['surface'] < 130)
s = np.mean(
np.abs(dic['surface'][(dic['hour'] == h) & (dic['cell'] >= 15) &
(dic['surface'] > 9)]))
sm = np.mean(dic['surface'][(dic['hour'] == h) & (dic['cell'] >= 15) &
(dic['surface'] < -9)])
cm = np.mean(
dic['cell'][(dic['hour'] == h) & (dic['cell'] >= 15) &
(dic['surface'] < -9)]) # & (dic['surface'] < 130)
chourly.append(c)
shourly.append(s)
smhourly.append(sm)
cmhourly.append(cm)
hh.append(h)
plt.scatter(chourly, shourly, c=hh, cmap=cmap)
# plt.errorbar(chourly, shourly, cstd, sstd)
plt.colorbar()
clouds = []
hhh = []
for h in range(0, 24, 1):
c = np.mean(dic['cell'][(dic['hour'] == h) & (dic['cell'] >= 15)])
clouds.append(c)
hhh.append(h)
f = plt.figure(figsize=(4, 10))
ax = plt.subplot(311)
plt.scatter(hh, chourly, c=hhh, cmap=cmap, edgecolors='k', s=50)
plt.xlabel('Time of day')
plt.ylabel('Average cloud core scale (km)')
ax = plt.subplot(312)
mpl = plt.scatter(shourly, chourly, c=hh, cmap=cmap, edgecolors='k', s=50)
plt.xlabel('Average surface scale (km)')
plt.ylabel('Average cloud core scale (km)')
plt.title('Warm features', fontsize=10)
ax = plt.subplot(313)
mpl = plt.scatter(smhourly,
cmhourly,
c=hh,
cmap=cmap,
edgecolors='k',
s=50)
plt.xlabel('Average surface scale (km)')
plt.ylabel('Average cloud core scale (km)')
plt.title('Cold features', fontsize=10)
#plt.colorbar(mpl)
plt.tight_layout()
f.subplots_adjust(right=0.83)
cax = f.add_axes([0.85, 0.33, 0.035, 0.35])
cbar = f.colorbar(mpl, cax)
cbar.ax.tick_params(labelsize=10)
cbar.set_ticklabels(np.arange(0, 24))
cbar.set_label('Time of day', fontsize=10)
def plot():
dic = pkl.load(
open("/users/global/cornkle/figs/LSTA-bullshit/scales/new/dominant_scales_save/scatter_scales.p", "rb"))
cmap = u_plot.discrete_cmap(24, base_cmap='jet')
f = plt.figure()
ax = plt.subplot(111)
chourly = []
shourly = []
smhourly = []
cmhourly = []
sstd = []
cstd = []
hh = []
for h in range(0,24,1):
c = np.mean(dic['cell'][(dic['hour']==h) & (dic['cell']>=15) & (dic['surface'] > 9)]) #& (dic['surface'] < 130)
s = np.mean(np.abs(dic['surface'][(dic['hour']==h) & (dic['cell']>=15)& (dic['surface'] > 9)]))
sm = np.mean(dic['surface'][(dic['hour'] == h) & (dic['cell'] >= 15) & (dic['surface'] < -9)])
cm = np.mean(
dic['cell'][(dic['hour'] == h) & (dic['cell'] >= 15) & (dic['surface'] < -9)]) # & (dic['surface'] < 130)
chourly.append(c)
shourly.append(s)
smhourly.append(sm)
cmhourly.append(cm)
hh.append(h)
plt.scatter(chourly, shourly, c=hh, cmap=cmap)
# plt.errorbar(chourly, shourly, cstd, sstd)
plt.colorbar()
clouds = []
hhh = []
for h in range(0,24,1):
c = np.mean(dic['cell'][(dic['hour']==h) & (dic['cell']>=15)])
clouds.append(c)
hhh.append(h)
f = plt.figure(figsize=(4,10))
ax = plt.subplot(311)
plt.scatter(hh, chourly, c=hhh, cmap=cmap, edgecolors='k', s=50)
plt.xlabel('Time of day')
plt.ylabel('Average cloud core scale (km)')
ax = plt.subplot(312)
mpl = plt.scatter(shourly, chourly, c=hh, cmap=cmap, edgecolors='k', s=50)
plt.xlabel('Average surface scale (km)')
plt.ylabel('Average cloud core scale (km)')
plt.title('Warm features', fontsize=10)
ax = plt.subplot(313)
mpl = plt.scatter(smhourly, cmhourly, c=hh, cmap=cmap, edgecolors='k', s=50)
plt.xlabel('Average surface scale (km)')
plt.ylabel('Average cloud core scale (km)')
plt.title('Cold features', fontsize=10)
#plt.colorbar(mpl)
plt.tight_layout()
f.subplots_adjust(right=0.83)
cax = f.add_axes([0.85, 0.33, 0.035, 0.35])
cbar = f.colorbar(mpl, cax)
cbar.ax.tick_params(labelsize=10)
cbar.set_ticklabels(np.arange(0,24))
cbar.set_label('Time of day', fontsize=10)
def wav_checkDry():
start_time = time.time()
# smfile = '/users/global/cornkle/data/OBS/AMSRE/day_aqua/amsre_monthly_anomaly.nc'
# sm = xr.open_dataset(smfile)
# sm = sm.sel(lon=slice(-10, 10), lat=slice(10, 18))
file = '/users/global/cornkle/data/OBS/MSG_LSTA/lsta_netcdf/lsta_daily_'+str(DATE['year'])+str(DATE['month']).zfill(2)+str(DATE['day']).zfill(2)+'.nc'
ftopo = '/users/global/cornkle/data/pythonWorkspace/proj_CEH/topo/gtopo_1min_afr.nc'
msg_t = '/users/global/cornkle/MCSfiles/blob_map_MCSs_-50_JJAS_points_dominant.nc'
ds = xr.open_dataset(file)
top = xr.open_dataarray(ftopo)
msg = xr.open_dataarray(msg_t)
msg.name = 'msg'
ds = ds.sel(lon=slice(-10,10), lat=slice(10,20))
top = top.sel(lon=slice(-10, 10), lat=slice(10, 20))
msg = msg.sel(lon=slice(-10, 10), lat=slice(10, 20), time=slice('2006-06-28T17:00:00','2006-06-29T15:00:00'))
msg_mins = msg.min(dim='time')
lsta2 = ds['LSTA'][0,:,:]
wll, scales, power, lsta, inter, cells, lsta_raw = run_waveletDry()
daystring = str(DATE['year'])+'-'+str(DATE['month']).zfill(2)+'-'+str(DATE['day']).zfill(2)
wllmean0=wll
ppower_small = np.sum(power[0:4,:,:], axis=0)
ppower_mid = np.sum(power[4:7, :, :], axis=0)
ppower_big = np.sum(power[7::, :, :], axis=0)
print('Small', scales[0:4])
print('Mid', scales[4:7])
print('Big', scales[7::])
map = lsta.salem.get_map()
xv, yv = np.meshgrid(lsta['lon'], lsta['lat'])
xl, yl = map.grid.transform(xv, yv)
cmap = u_plot.discrete_cmap(8, base_cmap='gnuplot2') #gist_ncar
cmap_back = u_plot.discrete_cmap(8, base_cmap='Greys')
# era = xr.open_dataset(constants.ERA_DAILY_PL)
# era = era.sel(latitude=slice(None, None, -1))
#
# eday = era.sel(
# time=str(DATE['year']) + str(DATE['month']).zfill(2) + str(DATE['day']).zfill(2), level=850)
#
# u = eday['u'].squeeze()
# v = eday['v'].squeeze()
# div = eday['d'].squeeze()
#
# u_plot.quick_map(div*1000, vmin=-0.01, vmax=0.01, cmap='RdBu')
#
#
# # ws, wd = u_met.u_v_to_ws_wd(u.values,v.values)
# # eday['ws10'] = (('time', 'latitude', 'longitude'), ws[None,...])
# # eday['wd10'] = (('time', 'latitude', 'longitude'), wd[None, ...])
# ws_on_lsta = ds.salem.transform(div)*1000
# u_on_lsta = ds.salem.transform(u)
# u_on_lsta = u_on_lsta[4::50, 4::50]
# v_on_lsta = ds.salem.transform(v)
# v_on_lsta = v_on_lsta[4::50, 4::50]
# xe, ye = np.meshgrid(u_on_lsta['lon'], u_on_lsta['lat'])
# xx, yy = map.grid.transform(xe, ye, crs=u.salem.grid.proj)
# transform their coordinates to the map reference system and plot the arrows
xi, yi = np.where(np.isfinite(cells))
topo_on_lsta = ds.salem.lookup_transform(top)
grad = np.gradient(topo_on_lsta.values)
gradsum = abs(grad[0]) + abs(grad[1])
msg_on_lsta = ds.salem.transform(msg_mins)
topo_on_lsta.values[gradsum > 30] = np.nan
topo_on_lsta.values[topo_on_lsta>400] = np.nan
for rh in [3,6,9,12,15,18,21,24]:
pos = np.where((cells<=rh) & (cells>rh-3))
if rh == 24:
cells[pos] = 0
else:
cells[pos] = rh
cmap2 = plt.get_cmap('RdBu_r')
cmap2.set_bad('Grey', 1.)
wavelet = plt.get_cmap('PuRd')
wavelet.set_bad('Grey', 1.)
# topobad = plt.get_cmap('topo')
# topobad.set_bad('Grey', 1.)
f= plt.figure(figsize=(13,7))
ax1=f.add_subplot(2,2,1)
masked = np.ma.masked_invalid(lsta_raw)
map.set_data(masked, interp='linear')
map.set_plot_params(cmap=cmap2, levels=[-6,-4,-2,-1,-0.5,0.5,1,2,4,6])
#map.set_lonlat_contours(interval=0)
map.visualize(ax=ax1, cbar_title='K', addcbar=True, title='28-06-2006: 0630-1600UTC LSTA & afternoon/nighttime cores')
cax = ax1.scatter(xl, yl, c=cells, cmap=cmap_back, s=15)
cax = ax1.scatter(xl, yl, c=cells, cmap=cmap, s=6)
cbar = plt.colorbar(cax, ticks=[0,3,6,9,12,15,18,21], fraction=0.07, pad=0.5, orientation='horizontal')
cbar.set_ticklabels([ '22-0','1-3','4-6','7-9','10-12','13-15', '16-18', '19-21'])
cbar.set_label('Hours of day')
ax1.get_xaxis().set_visible(False)
# print('Topo min', np.min(topo_on_lsta))
ax2 = f.add_subplot(2, 2, 2)
map.set_plot_params(cmap='topo', vmin=np.nanmin(topo_on_lsta), vmax=1000, extend='both')
masked = np.ma.masked_invalid(topo_on_lsta)
map.set_data(masked)
map.set_contour(topo_on_lsta, levels=np.arange(400,1000,25), cmap='Greys_r')
map.set_contour(msg_on_lsta, levels=np.arange(-80,-40,5), cmap='jet_r')
map.visualize(ax=ax2, addcbar=True )
map.visualize(ax=ax2, cbar_title='m', title='Domain topography, total height difference: 342m')
#cax = ax2.scatter(xl, yl, c=cells, cmap=cmap_back, s=20)
#cax = ax2.scatter(xl, yl, c=cells, cmap=cmap, s=10)
#plt.scatter(xv, yv, c=cells, cmap='jet', s=5)
map.set_contour()
ax3 = f.add_subplot(2, 2, 3)
map.set_plot_params(cmap=wavelet, vmin=0, vmax=5)
map.set_data(ppower_small)
map.visualize(ax=ax3, cbar_title='Wavelet power', title='Surface scales 9 - 30km')
#ax3.scatter(xl, yl, c=cells, cmap=cmap, s=5)
#plt.scatter(xv, yv, c=cells, cmap='jet', s=5)
ax4 = f.add_subplot(2, 2, 4)
map.set_plot_params(cmap=wavelet, vmin=0, vmax=5)
map.set_data(ppower_big)
map.visualize(ax=ax4, cbar_title='Wavelet power', title='Surface scales 80 - 250km')
#ax4.scatter(xl, yl, c=cells, cmap=cmap, s=5)
plt.tight_layout()
def wav_checkDry():
start_time = time.time()
# smfile = '/users/global/cornkle/data/OBS/AMSRE/day_aqua/amsre_monthly_anomaly.nc'
# sm = xr.open_dataset(smfile)
# sm = sm.sel(lon=slice(-10, 10), lat=slice(10, 18))
file = '/users/global/cornkle/data/OBS/MSG_LSTA/lsta_netcdf/lsta_daily_'+str(DATE['year'])+str(DATE['month']).zfill(2)+str(DATE['day']).zfill(2)+'.nc'
ds = xr.open_dataset(file)
ds = ds.sel(lon=slice(-10,10), lat=slice(10,18))
lsta2 = ds['LSTA'][0,:,:]
scales, power, lsta, inter, cells, lsta_raw , wll= run_waveletDry()
print('Scales:', scales)
latmin, latmax = (np.min(lsta['lat']), np.max(lsta['lat']))
lonmin, lonmax = (np.min(lsta['lon']), np.max(lsta['lon']))
daystring = str(DATE['year'])+'-'+str(DATE['month']).zfill(2)+'-'+str(DATE['day']).zfill(2)
wllmean0=wll
xv, yv = np.meshgrid(lsta['lon'], lsta['lat'])
cmap = u_plot.discrete_cmap(24, base_cmap='gist_ncar')
f= plt.figure()
ax1=f.add_subplot(2,2,1)
# cmap= plt.get_cmap('RdBu_r')
# cmap.set_bad(color='gray', alpha=1.)
# plt.pcolormesh(lsta['lon'], lsta['lat'], lsta_raw, vmin=-8, vmax=8, cmap=cmap)
#
# plt.colorbar()
map = lsta.salem.get_map()
xl, yl = map.grid.transform(xv, yv)
map.set_data(lsta_raw)
map.set_plot_params(cmap='RdBu_r', vmin=-8, vmax=8, extend='both')
map.visualize(ax=ax1, addcbar=False )
cax = ax1.scatter(xl, yl, c=cells, cmap=cmap, s=5)
cbar = plt.colorbar(cax, ticks=np.arange(0,24))
cbar.set_ticklabels(np.array(np.arange(0,24), dtype=str))
plt.title(str(pd.to_datetime(lsta['time'].values))+' LSTA')
ax2 = f.add_subplot(2, 2, 2)
map.set_data(wllmean0)
minscales = [np.flip(np.array(scales)*-1, axis=0), np.array(scales)]
map.set_plot_params(cmap='RdBu_r', vmin=-200, vmax=200)
map.visualize(ax=ax2, addcbar=True)
#plt.contourf(day_small['lon'], day_small['lat'], day_small)
# plt.pcolormesh(lsta['lon'], lsta['lat'], wllmean0, cmap='RdBu_r', vmin=-200, vmax=200)
# plt.colorbar()
ax2.scatter(xl, yl, c=cells, cmap=cmap, s=5)
plt.title('Wavelet powers < 42km')
ax3 = f.add_subplot(2, 2, 3)
map.set_data(power)
map.set_plot_params(cmap='RdBu_r', vmin=-10, vmax=10)
map.visualize(ax=ax3, addcbar=True)
ax2.scatter(xl, yl, c=cells, cmap=cmap, s=5)
plt.title('Wavelet powers < 42km')