def pad_stack(s, dt_offset=timedelta(365.25)):
o = s.ma_stack.shape
new_ma_stack = np.ma.vstack((s.ma_stack[0:1], s.ma_stack, s.ma_stack[-1:]))
new_date_list = np.ma.hstack((s.date_list[0:1] - dt_offset, s.date_list,
s.date_list[-1:] + dt_offset))
new_date_list_o = timelib.dt2o(new_date_list)
return new_ma_stack, new_date_list_o
def make_test():
nt = 30
shp = (nt,40,60)
res = 2000
test = np.ma.masked_all(shp)
init = np.full((shp[1], shp[2]), 300)
#Some spatial distribution of dhdt
dhdt = np.full((shp[1], shp[2]), -2/365.25)
ti_min = timelib.dt2o(datetime(2008,1,1))
ti_max = timelib.dt2o(datetime(2015,4,1))
ti = np.sort(np.random.random(nt) * (ti_max - ti_min))
for i,t in enumerate(ti):
test[i] = init + dhdt * ti[i]
tilt_factor = (110000/res)
i = nt/2
dx = 3./tilt_factor
dy = 1./tilt_factor
dz = -0.5
print i, dx, dy, dz
test[i] = apply_tilt(test[i], -dx, -dy, -dz)
i = 2
dx = -1.5/tilt_factor
dy = 0.1/tilt_factor
dz = 0.4
print i, dx, dy, dz
test[i] = apply_tilt(test[i], -dx, -dy, -dz)
i = -2
dx = 0.1/tilt_factor
dy = 0.1/tilt_factor
dz = 2.5
print i, dx, dy, dz
test[i] = apply_tilt(test[i], -dx, -dy, -dz)
#x,y,z = malib.get_xyz(test[nt/2])
#d = (dx*x + dy*y + dz).reshape((shp[1], shp[2]))
#test[nt/2] += d
#Need to add t_min back to ti
return test, (ti + ti_min), res
#This is approx PIG shelf center
lat=-75.08865
lon=-100.38043
#dem_list_fn = '/scr/pig_stack_20151201_tworows_highcount/shelf_clip/20021204_1925_atm_mean-adj_20150223_1531_1040010008556800_104001000855E100-DEM_32m_trans-adj_stack_203_fn_list.txt'
#dem_list_fn = '/scr/pig_stack_20151201_tworows_highcount/shelf_clip/20071020_1438_glas_mean-adj_20150223_1531_1040010008556800_104001000855E100-DEM_32m_trans-adj_stack_189_fn_list.txt'
#dem_list_fn = '/scr/pig_stack_20151201_tworows_highcount/shelf_clip/20071020_1438_glas_mean-adj_20150223_1531_1040010008556800_104001000855E100-DEM_32m_trans-adj_stack_189_fn_list.txt'
#This includes individual GLAS orbits, individual ATM/LVIS flights, all WV+SPIRIT over shelf
#dem_list_fn = '/scr/pig_stack_20160307_tworows_highcount/shelf_clip/20021128_2050_atm_256m-DEM_20150223_1531_1040010008556800_104001000855E100-DEM_32m_trans_stack_417_fn_list.txt'
#dem_list_fn = '/scr/pig_stack_20160307_tworows_highcount/full_extent/20021128_2050_atm_256m-DEM_20150406_1519_103001003F89B700_1030010040D68600-DEM_32m_trans_stack_730_fn_list.txt'
dem_list_fn = sys.argv[1]
out_fn = os.path.splitext(dem_list_fn)[0]+'_tides.csv'
fn_list = [os.path.split(line.strip())[-1] for line in open(dem_list_fn, 'r')]
dt_list = np.array([timelib.fn_getdatetime(fn) for fn in fn_list])
dt_list_o = timelib.dt2o(dt_list)
dt_list_mat = [timelib.dt2mat(dt) for dt in dt_list]
print "Running tide model for %i timestamps" % len(dt_list_mat)
tide_a = matlab.tmd_tide_pred('%s' % model, dt_list_mat, lat, lon, 'z')
print "Writing out"
out = zip(fn_list, dt_list, dt_list_o, tide_a)
with open(out_fn, 'w') as f:
f.write('fn,date,date_o,tide\n')
f.write('\n'.join('%s,%s,%0.6f,%0.2f' % x for x in out))
#Get continuous tidal record
dt1 = datetime(2002,1,1)
dt2 = datetime(2016,1,1)
dt_int = timedelta(days=1/24.)
X = np.tile(x, t.size)[idx]
Y = np.tile(y, t.size)[idx]
T = np.repeat(t, x.size)[idx]
#These are coords
pts = np.vstack((X,Y,T)).T
#Normalized versions
Xn = rangenorm(X)
Yn = rangenorm(Y)
Tn = rangenorm(T)
ptsn = np.vstack((Xn,Yn,Tn)).T
#Interpoalte at these times
#ti = np.arange(t.min(), t.max(), 90.0)
ti_min = timelib.dt2o(datetime(2008,1,1))
#ti_min = timelib.dt2o(datetime(2012,1,1))
#ti_max = timelib.dt2o(datetime(2009,1,1))
#ti_max = timelib.dt2o(datetime(2014,9,1))
#ti_max = timelib.dt2o(datetime(2015,4,1))
ti_max = timelib.dt2o(datetime(2015,6,1))
#ti_dt = 120
#ti_dt = 121.75
ti_dt = 91.3125
#ti_dt = 365.25
#Interpolate at these times
ti = np.arange(ti_min, ti_max, ti_dt)
#ti = t
#Annual - use for discharge analysis?
#ti = timelib.dt2o([datetime(2008,1,1), datetime(2009,1,1), datetime(2010,1,1), datetime(2011,1,1), datetime(2012,1,1), datetime(2013,1,1), datetime(2014,1,1), datetime(2015,1,1)])
t_scale = t.ptp()*t_factor t_offset = t.min() xn = rangenorm(x, xy_offset, xy_scale) yn = rangenorm(y, xy_offset, xy_scale) tn = rangenorm(t, t_offset, t_scale) X = np.tile(xn, t.size)[idx] Y = np.tile(yn, t.size)[idx] T = np.repeat(tn, x.size)[idx] #These are coords pts = np.vstack((X,Y,T)).T #Interpoalte at these times #ti = np.arange(t.min(), t.max(), 90.0) ti_min = timelib.dt2o(datetime(2008,1,1)) #ti_min = timelib.dt2o(datetime(2006,9,1)) #ti_min = timelib.dt2o(datetime(2012,1,1)) #ti_max = timelib.dt2o(datetime(2009,1,1)) #ti_max = timelib.dt2o(datetime(2014,9,1)) #ti_max = timelib.dt2o(datetime(2015,4,1)) #ti_max = timelib.dt2o(datetime(2015,6,1)) ti_max = timelib.dt2o(datetime(2015,1,1)) #ti_dt = 120 #ti_dt = 121.75 #ti_dt = 91.3125 ti_dt = 365.25 #ti_dt = 365.25/2.0 #Interpolate at these times ti = np.arange(ti_min, ti_max, ti_dt)
def get_dem_list(dem_list_fn):
dem_fn_list = np.array([os.path.split(line.strip())[-1] for line in open(dem_list_fn, 'r')])
dem_dt_list = np.array([timelib.fn_getdatetime(fn) for fn in dem_fn_list])
dem_dt_list_o = timelib.dt2o(dem_dt_list)
return dem_fn_list, dem_dt_list, dem_dt_list_o
#dem_fn_list, dem_dt_list, dem_dt_list_o = get_dem_list(dem_list_fn) #ecmwf_fn = '/Volumes/insar3/dshean/ECMWF/PIG_ECMWF_Pmsl_2012-2015.nc' #ecmwf_fn = '/Volumes/insar3/dshean/ECMWF/PIG_ECMWF_Pmsl_2002-2016.nc' ecmwf_fn = '/Users/dshean/Documents/UW/Shean_iceshelf_paper/PIG_ECMWF_all_1979-2016_t2m_skt.nc' ecmwf_nc = netCDF4.Dataset(ecmwf_fn) lat = -75.0 lon = -101.5 #All dates are days since Jan 1, 1900 refdate=datetime(1900,1,1) lat_i, lon_i = get_closest_idx(lat, lon, ecmwf_nc) ecmwf_dt = [refdate+timedelta(days=i) for i in ecmwf_nc.variables['time'][:]/24.0] ecmwf_dt_o = timelib.dt2o(ecmwf_dt) key = 't2m' ecmwf_Pmsl = ecmwf_nc.variables[key][:] #Pmsl = sample(ecmwf_Pmsl, lat_i, lon_i, pad=0)/100. Pmsl = sample(ecmwf_Pmsl, lat_i, lon_i, pad=0) Pmslref = np.median(Pmsl) print "Median Pmsl: %0.2f" % Pmslref #Note: output will be the expected displacement due to IB #To remove, need to subtract from observation Pmsl_IB = (Pmsl - Pmslref)*-0.01 Pmsl_IB = Pmsl - 273.15 lw = 0.5 f, ax = plt.subplots() ax.axhline(0, color='k', linestyle='--')
