dt_list_hr = timelib.dt_range(dt1, dt2, dt_int)
dt_list_hr_o = timelib.dt2o(dt_list_hr)
dt_list_hr_mat = [timelib.dt2mat(dt) for dt in dt_list_hr]
print "Running tide model for %i timestamps" % len(dt_list_hr_mat)
tide_a_hr = matlab.tmd_tide_pred('%s' % model, dt_list_hr_mat, lat, lon, 'z')
print "Writing out"
out = zip(dt_list_hr, dt_list_hr_o, tide_a_hr)
out_fn = os.path.splitext(out_fn)[0]+'_hr.csv'
with open(out_fn, 'w') as f:
f.write('date,date_o,tide\n')
f.write('\n'.join('%s,%0.6f,%0.2f' % x for x in out))
import re
#idx = np.array([bool(re.search('atm|lvis|glas',fn)) for fn in fn_list])
idx = np.array([bool(re.search('glas',fn)) for fn in fn_list])
fig, ax = plt.subplots()
ax.plot_date(dt_list[~idx], tide_a[~idx], color='b', label='WV, SPIRIT, ATM, LVIS')
ax.plot_date(dt_list[idx], tide_a[idx], color='b', fillstyle='none', label='GLAS')
pltlib.fmt_date_ax(ax)
pltlib.pad_xaxis(ax)
ax.axhline(0, color='k')
ax.set_ylabel('Tide z (m)')
ax.set_title('CATS2008A Tidal Amplitude for PIG shelf DEMs (%s, %s)' % (lat, lon))
plt.legend()
outfig_fn = os.path.splitext(out_fn)[0]+'.pdf'
plt.savefig(outfig_fn)
#plt.show()
axa[1].set_ylabel('Inv. Barometer (m)')
axa[2].plot_date(tide_full['date_o'],
combined,
marker=None,
linestyle='-',
linewidth=0.5,
color='r',
alpha=0.5)
axa[2].plot_date(ib['date_o'],
tide['tide'] + ib['IB'],
marker='o',
markersize=4,
color='r')
axa[2].set_ylabel('Combined (m)')
axa[0].set_xlim(ib['date_o'].min(), ib['date_o'].max())
pltlib.fmt_date_ax(axa[0])
pltlib.fmt_date_ax(axa[1])
pltlib.fmt_date_ax(axa[2])
pltlib.pad_xaxis(axa[2])
plt.savefig(out_fn, dpi=300)
plt.show()
#pairs, out = find_pairs(dem_stack, tide['tide']+ib['IB'], max_dt=timedelta(180))
#Want to subtract this from WGS84 elevation
#mdt = -1.1
mdt = 0
geoid = 0
ds = dem_stack.get_ds()
geoid_fn = os.path.splitext(dem_stack_fn)[0] + '_geoidoffset.tif'