def main(infile='series_40.npz'):
npz = np.load(infile)
nprof = npz['nprof']
vcm = npz['vcm']
time = npz['time']
lon = npz['lon']
altmin = npz['altmin']
# exchange time and altmin axes
vcm = vcm.swapaxes(0, 1)
# now vcm[altmin,time,lon]
# average year
# find the vector of julian days for a typical year
jdays = []
time_avg = []
for d in time:
if d.year == 2007:
jdays.append((d - datetime(2007, 1, 1)).days)
time_avg.append(d)
nprof_avg = np.zeros([len(jdays), vcm.shape[2]])
vcm_avg = dict()
for ialtmin in range(len(altmin)):
vcm_avg[ialtmin] = np.zeros([len(jdays), vcm.shape[2]])
cf_anom = np.zeros_like(vcm, dtype='float64')
for i, jday in enumerate(jdays):
idx = np.array(
[jday == (date - datetime(date.year, 1, 1)).days for date in time])
print jday, ' - found {} days'.format(idx.sum())
nprof_avg[i, :] = np.sum(nprof[idx, :], axis=0)
for ialtmin in 0, 1, 2:
vcm_avg[ialtmin][i, :] = np.sum(vcm[ialtmin, idx, :], axis=0)
cf_avg = 1. * vcm_avg[ialtmin][i, :] / nprof_avg[i, :]
actual_cf = 1. * vcm[ialtmin, idx, :] / nprof[idx, :]
cf_anom[ialtmin, idx, :] = actual_cf - cf_avg
for ialtmin in 0, 1, 2:
cf = 100. * vcm_avg[ialtmin] / nprof_avg
pcolor_meridhov(
time_avg, lon, cf,
'clouds above %2d km - Average year' % (altmin[ialtmin]))
nice.savefig(infile[:-4] + '_avg_above%02dkm.png' % (altmin[ialtmin]))
pcolor_meridhov(time,
lon,
cf_anom[ialtmin],
'clouds above > %2d km - Anomaly' % (altmin[ialtmin]),
anom=True)
nice.savefig(infile[:-4] + '_anom_above%02dkm.png' % (altmin[ialtmin]))
plt.show()
def main(year=2007):
import glob
year = int(year)
mask = './out/%04d/*nc4' % (year)
grid_files = glob.glob(mask)
grid_files.sort()
show_files(grid_files, '2007')
nice.savefig('zonal_cf_%04d.png' % (year))
plt.show()
def main(year, month):
import glob
year = int(year)
month = int(month)
mask = './out.40/%04d/vcm_zonal_%04d%02d*.nc4' % (year, year, month)
grid_files = glob.glob(mask)
assert len(grid_files) > 0
grid_files.sort()
show_files(grid_files, 'bof')
nice.savefig('zonal_cf_%04d%02d.png' % (year, month))
plt.show()
def main(infile='series_40.npz'):
npz = np.load(infile)
nprof = npz['nprof']
vcm = npz['vcm']
time = npz['time']
lon = npz['lon']
altmin = npz['altmin']
vcm = vcm.swapaxes(0, 1)
pcolor_meridhov(time, lon, nprof, 'Number of profiles')
nice.savefig('%s_nprof.png' % (infile[:-4]))
for ialtmin in 0, 1, 2:
cf = 1. * vcm[ialtmin, ...] / nprof
pcolor_meridhov(time, lon, 100. * cf,
'Above %2d km' % (altmin[ialtmin]))
nice.savefig('%s_above%02dkm.png' % (infile[:-4], altmin[ialtmin]))
plt.show()
def main(infile='tropic_width_40.npz'):
npz = np.load(infile)
tmin, tmax, time = npz['tmin'], npz['tmax'], npz['datetimes']
tmin = np.array(tmin).item()
tmax = np.array(tmax).item()
newdates, tmin_amean, tmax_amean, tsize_amean = average_year(
time, tmin, tmax)
fig = plt.figure()
plt.subplot(2, 1, 1)
for vcm_min in vcm_mins:
tmin = np.ma.masked_invalid(tmin_amean[vcm_min])
tmax = np.ma.masked_invalid(tmax_amean[vcm_min])
plt.fill_between(newdates, tmin, tmax, alpha=0.3)
plt.text(vcm_x[vcm_min], vcm_y[vcm_min], 'CF > %4.2f' % vcm_min)
plt.gca().xaxis.set_major_formatter(fmt)
plt.title('Average 2006-2014')
plt.grid()
fig.autofmt_xdate()
plt.subplot(2, 1, 2)
for vcm_min in vcm_mins:
tsize = np.ma.masked_invalid(tsize_amean[vcm_min])
plt.fill_between(newdates, tsize, alpha=0.3)
plt.gca().xaxis.set_major_formatter(fmt)
plt.title('Average 2006-2014')
plt.grid()
fig.autofmt_xdate()
nice.savefig('width_avg_year.pdf')
plt.show()
def main(infile='tropic_width_40.npz'):
npz = np.load(infile)
tmin, tmax, time = npz['tmin'], npz['tmax'], npz['datetimes']
tmin = np.array(tmin).item()
tmax = np.array(tmax).item()
plt.figure(figsize=[24, 7])
plt.subplot(2, 1, 1)
for vcm_min in vcm_mins:
this_tmin = np.array(tmin[vcm_min])
this_tmin = np.ma.masked_where(this_tmin < -90, this_tmin)
this_tmax = np.array(tmax[vcm_min])
this_tmax = np.ma.masked_where(this_tmax < -90, this_tmax)
# plt.plot(time, this_tmin, colors[vcm_min])
# plt.plot(time, this_tmax, colors[vcm_min])
plt.fill_between(time, this_tmin, this_tmax, alpha=0.3)
plt.ylabel('Latitude')
plt.legend(loc='center right')
plt.grid()
plt.subplot(2, 1, 2)
for vcm_min in vcm_mins:
this_tmin = np.array(tmin[vcm_min])
this_tmin = np.ma.masked_where(this_tmin < -90, this_tmin)
this_tmax = np.array(tmax[vcm_min])
this_tmax = np.ma.masked_where(this_tmax < -90, this_tmax)
# plt.plot(time, this_tmax - this_tmin, colors[vcm_min])
plt.fill_between(time, 0, this_tmax - this_tmin, alpha=0.3)
plt.ylim(0, 50)
plt.ylabel('Tropics meridional height')
plt.grid()
nice.savefig(infile[:-4] + '.pdf')
plt.show()
def main():
npz = np.load('ceilings_40.npz')
dt = npz['datetimes']
nprof = npz['nprof']
cprof = npz['cprof']
lat = npz['lat']
idx = (lat > -82) & (lat < 82)
nprof = nprof[0:len(dt), idx]
cprof = cprof[0:len(dt), idx]
lat = lat[idx]
nprof = nprof.sum(axis=1)
cprof = cprof.sum(axis=1)
plt.figure(figsize=[20, 5])
plt.plot(dt, nprof, label='Total profiles')
plt.plot(dt, cprof, label='Cloudy profiles')
plt.legend()
plt.grid()
cf = 100. * cprof / nprof
badidx = (cf < 57) | (nprof < 1.7e7)
plt.figure(figsize=[24, 4])
plt.plot(dt, cf)
plt.plot(dt[badidx], cf[badidx], 'r*')
plt.grid()
plt.ylabel('Cloud Fraction [%]')
plt.title('2006-2014')
nice.savefig('cf.png')
avgdt, avgnprof = average_year(dt, nprof)
avgdt, avgcprof = average_year(dt, cprof)
avgcf = 100. * avgcprof / avgnprof
plt.figure(figsize=[10, 5])
plt.plot(avgdt, avgcf)
plt.grid()
plt.ylabel('Cloud Fraction [%]')
plt.title('Average 2006-2014')
nice.savefig('cf_avg.png')
plt.gca().xaxis.set_major_formatter(fmt)
cfanom = anomalies(avgdt, avgcf, dt, cf)
cfanom = np.ma.masked_where(badidx, cfanom)
plt.figure(figsize=[24, 4])
plt.plot(dt, cfanom)
plt.fill_between(dt, 0, cfanom, alpha=0.2)
plt.grid()
plt.ylabel('Cloud Fraction Anomalies [%]')
plt.title('2006-2014')
nice.savefig('cf_anom.png')
plt.figure(figsize=[24, 4])
plt.plot(dt, cf, label='Cloud Fraction')
plt.plot(dt, cf - cfanom, color='grey', label='Average 2006-2014')
cfavg = cf - cfanom
cfred = np.ma.masked_where(cfanom < 0, cf)
plt.fill_between(dt, cfred, cfred - cfanom, color='red', alpha=0.2)
cfblue = np.ma.masked_where(cfanom > 0, cf)
plt.fill_between(dt, cfblue, cfblue - cfanom, color='blue', alpha=0.2)
plt.grid()
plt.legend()
plt.ylabel('Cloud Fraction [%]')
plt.title('2006-2014')
nice.savefig('cf_anom_bluered.png')
plt.show()
def main():
npz = np.load('ceilings_40.npz')
dt = npz['datetimes']
nprof = npz['nprof']
cprof = npz['cprof']
lat = npz['lat']
dtnum = mdates.date2num(dt)
nprof = nprof[0:len(dt), :]
cprof = cprof[0:len(dt), :]
nprofl = np.sum(nprof, axis=0)
cfl = 100. * np.sum(cprof, axis=0) / nprofl
plt.figure()
plt.plot(lat, cfl)
nproft = np.sum(nprof, axis=1)
cft = 100. * np.sum(cprof, axis=1) / nproft
badidx = (cft < 57) | (nproft < 1.7e7)
cf = 100. * cprof / nprof
cf[badidx, :] = np.nan
cf = np.ma.masked_where(nprof == 0, cf)
cf = np.ma.masked_invalid(cf)
print dtnum.shape, lat.shape, cf.shape
avgdt, avgcf = average_year(dt, nprof, cprof)
avgdtnum = mdates.date2num(avgdt)
plt.figure(figsize=[24, 4])
plt.pcolormesh(dtnum, lat, cf.T)
plt.xlim(dtnum[0], dtnum[-1])
plt.ylim(-60, 60)
plt.gca().xaxis.axis_date()
plt.grid()
plt.clim(30, 100)
plt.title('Cloud Fraction')
plt.colorbar()
nice.savefig('cfz.png')
plt.figure(figsize=[10, 5])
plt.pcolormesh(avgdtnum, lat, avgcf.T)
plt.xlim(avgdtnum[0], avgdtnum[-1])
plt.ylim(-60, 60)
plt.gca().xaxis.axis_date()
plt.gca().xaxis.set_major_formatter(fmt)
plt.clim(30, 100)
plt.title('Cloud Fraction average 2006-2014')
plt.colorbar()
plt.grid()
nice.savefig('cfz_avg.png')
anom = anomalies(avgdt, avgcf, dt, cf)
plt.figure(figsize=[24, 4])
plt.pcolormesh(dtnum, lat, anom.T, cmap='RdBu_r')
plt.xlim(dtnum[0], dtnum[-1])
plt.ylim(-60, 60)
plt.gca().xaxis.axis_date()
plt.grid()
plt.clim(-10, 10)
plt.title('Cloud Fraction Anomalies [%]')
plt.colorbar()
nice.savefig('cfz_anom.png')
plt.show()
