def ll_to_lonilati(ll, coastal_ds):
"""
Match lon/lat coordinates to a cell in the coastal_ds
dataset.
ll: [lon,lat]
coastal_ds: xarray dataset, assumed to be lon/lat grid, rectilinear.
"""
loni = utils.nearest(snap_lon % 360, g_map_ll[c, 0] % 360)
lati = utils.nearest(snap_lat, g_map_ll[c, 1])
err_km = utils.haversine([snap_lon[loni], snap_lat[lati]], g_map_ll[c, :])
def add_src_time(a_to_b,
a_to_a,
shift_us=0,
drift_us_per_day=0,
t0=np.datetime64("1970-01-01")):
"""
for each a_to_b, find the nearest in time a_to_a,
and add a column for its time, named time_src.
shift_us: constant time offset
drift_us_per_day: linear drift, starting at 0 at t0 (defined
just above), in microseconds per day.
originally this would record the src time
"""
days = (a_to_a.time.values - t0) / np.timedelta64(86400, 's')
shift = (shift_us + drift_us_per_day * days).astype(np.int64)
a_to_a_times = a_to_a.time.values
a_to_a_times_shifted = a_to_a_times + shift * np.timedelta64(1, 'us')
near = utils.nearest(a_to_a_times_shifted, a_to_b.time.values)
# choice here whether to use the shifted or unshifted time.
# now trying the unshifted
a_to_b['time_src'] = ('index', ), a_to_a_times[near]
# but save the shifted version, too
a_to_b['time_src_shifted'] = ('index', ), a_to_a_times_shifted[near]
# and calculate trav_seconds with the shifted data, because it
# is used to weed out bad matches
trav_secs = (a_to_b.time - a_to_b.time_src_shifted) / np.timedelta64(
1, 'us') * 1e-6
# some crazy numbers are throwing off the averages
trav_secs[np.abs(trav_secs) > 1000] = np.nan
# note that this can be negative
a_to_b['trav_secs'] = ('index', ), trav_secs
def depth_avg(run_i, t):
ug = ug_for_run(run_i)
time_idx = utils.nearest(ug.nc.time.values, t)
# have to transpose as the velocities have Nk first, but
# this defaults to Nk second
return ug.vertical_averaging_weights(time_slice=time_idx,
ztop=0,
zbottom=0).T
def quantize_time(t):
run_i=np.searchsorted(run_starts,t)-1
ug=ug_for_run(run_i)
if use_ptm_output:
time_idx=np.searchsorted(ug.nc.time.values,t)
else:
time_idx=utils.nearest(ug.nc.time.values, t)
return run_i,time_idx
def plot_circulation(rxs, ds_tot, num=1):
ds_ab = effective_clock_offset([rxs[0], rxs[1]], ds_tot)
ds_bc = effective_clock_offset([rxs[1], rxs[2]], ds_tot)
ds_ca = effective_clock_offset([rxs[2], rxs[0]], ds_tot)
# Combine
t_common = np.unique(
np.concatenate(
(ds_ab.time.values, ds_bc.time.values, ds_ca.time.values)))
ds_abc = xr.Dataset()
ds_abc['time'] = ('time', ), t_common
ds_abc['offset'] = ('time', ), (
np.interp(t_common, ds_ab.time.values, ds_ab.offset.values) +
np.interp(t_common, ds_bc.time.values, ds_bc.offset.values) +
np.interp(t_common, ds_ca.time.values, ds_ca.offset.values))
error = 0
for ds in [ds_ab, ds_bc, ds_ca]:
near_idx = utils.nearest(ds.time, t_common)
# The accumulated "error" is the error of the nearest value in each source
error = error + ds['error'].values[near_idx]
# *and* the time offset from that value.
error += np.abs(t_common - utils.nearest_val(ds.time.values, t_common))
ds_abc['error'] = ('time', error)
plt.figure(num).clf()
fig, axs = plt.subplots(4, 1, num=num, sharex=True)
for ds in [ds_ab, ds_bc, ds_ca]:
rx_from, rx_to = ds.rx.values
label = "%s-%s" % (rx_from, rx_to)
axs[0].plot(ds.time, ds.offset, label=label)
axs[1].plot(ds.time, ds.transit, label=label)
axs[0].set_ylabel('Offset')
axs[1].set_ylabel('Transit')
axs[2].plot(ds_abc.time, 1e6 * ds_abc.offset, label="Circ")
axs[2].set_ylabel('Circulation (us)')
axs[3].plot(ds_abc.time, ds_abc.error, label="Error")
[ax.legend(loc='upper right') for ax in axs]
return fig, ds_abc, [ds_ab, ds_bc, ds_ca]
def add_src_time(a_to_b, a_to_a, shift_us=0, shift_sec_per_day=0):
"""
for each a_to_b, find the nearest in time a_to_a,
and add a column for its time, named time_src.
shift_us: constant time offset
shift_sec_per_day: linear drift, starting at 0 at t0 (defined
just above)
"""
a_to_a_times = a_to_a.time.values + shift_us * np.timedelta64(1, 'us')
days = (a_to_a.time.values - t0) / np.timedelta64(86400, 's')
a_to_a_times += np.timedelta64(
1, 'us') * (1e6 * shift_sec_per_day * days).astype(np.int32)
near = utils.nearest(a_to_a_times, a_to_b.time.values)
a_to_b['time_src'] = ('index', ), a_to_a_times[near]
trav_secs = (a_to_b.time - a_to_b.time_src) / np.timedelta64(1,
'us') * 1e-6
# some crazy numbers are throwing off the averages
trav_secs[np.abs(trav_secs) > 1000] = np.nan
a_to_b['trav_secs'] = ('index', ), trav_secs
def snap_for_time(run_i, t):
map_ds = map_for_run(run_i)
time_idx = utils.nearest(map_ds.time.values, t)
return map_ds.isel(time=time_idx)
def process_period(start_time_string,
end_time_string,
outfileprefix,
force=False):
nc_fn = outfileprefix + ".nc"
print("Processing %s to %s output to %s" %
(start_time_string, end_time_string, nc_fn))
if (not force) and os.path.exists(nc_fn):
print("File %s exists - skipping" % nc_fn)
return
# pick interpolation method (natural neighbor or linear is recommended):
# 'nearest' = nearest neighbor
# 'linear' = linear
# 'cubic' = cubic spline
# 'natural' = natural neighbor
interp_method = 'natural'
# specify comment string (one line only, i.e., no '\n') for *.amu/*.amv files
commentstring = 'Prepared by Allie King, SFEI, times are in PST (ignore the +00:00), adapted by Rusty Holleman'
# specify properties of the wind grid -- this one was used for CASCaDE and sfb_dfm
bounds = [340000, 610000, 3980000, 4294000]
dx = 1500.
dy = 1500.
#--------------------------------------------------------------------------------------#
# Main Program
#--------------------------------------------------------------------------------------#
n_cols = int(round(1 + (bounds[1] - bounds[0]) / dx))
n_rows = int(round(1 + (bounds[3] - bounds[2]) / dy))
x_llcorner = bounds[0]
y_llcorner = bounds[2]
start_date = np.datetime64(start_time_string)
end_date = np.datetime64(end_time_string)
# specify directory containing the compiled wind observation data and station
# coordinates (SFB_hourly_U10_2011.csv, SFB_hourly_V10_2011.csv, etc...)
windobspath = os.path.join(basedir, 'Compiled_Hourly_10m_Winds/data')
# convert start and end time to datetime object
start_dt = utils.to_datetime(start_date)
end_dt = utils.to_datetime(end_date)
# create a meshgrid corresponding to the CASCaDE wind grid
x_urcorner = x_llcorner + dx * (n_cols - 1)
y_urcorner = y_llcorner + dy * (n_rows - 1)
x = np.linspace(x_llcorner, x_urcorner, n_cols)
# RH: orient y the usual way, not the arcinfo/dfm wind way (i.e. remove flipud)
y = np.linspace(y_llcorner, y_urcorner, n_rows)
xg, yg = np.meshgrid(x, y)
# read the observed wind data
tz_offset = dt.timedelta(hours=8)
try:
# start_time,end_time are in UTC, so remove the offset when requesting data
# from wlib which expects PST
time_days, station_names, U10_obs = wlib.read_10m_wind_data_from_csv(
os.path.join(windobspath, 'SFB_hourly_U10_'), start_dt - tz_offset,
end_dt - tz_offset)
time_days, station_names, V10_obs = wlib.read_10m_wind_data_from_csv(
os.path.join(windobspath, 'SFB_hourly_V10_'), start_dt - tz_offset,
end_dt - tz_offset)
except FileNotFoundError:
print("Okay - probably beyond the SFEI data")
U10_obs = V10_obs = None
if U10_obs is not None:
# note that time_days is just decimal days after start, so it doesn't need to be adjusted for timezone.
# read the coordinates of the wind observation stations
df = pd.read_csv(os.path.join(windobspath, 'station_coordinates.txt'))
station_names_check = df['Station Organization-Name'].values
x_obs = df['x (m - UTM Zone 10N)'].values
y_obs = df['y (m - UTM Zone 10N)'].values
Nstations = len(df)
for snum in range(Nstations):
if not station_names[snum] == station_names_check[snum]:
raise (
'station names in station_coordinates.txt must match headers in SFB_hourly_U10_YEAR.csv and SFB_hourly_V10_YEAR.csv files'
)
else:
x_obs = np.zeros(0, np.float64)
y_obs = np.zeros(0, np.float64)
Nstations = 0
# Fabricate time_days
all_times = []
t = start_dt
interval = dt.timedelta(hours=1)
while t <= end_dt:
all_times.append(t)
t = t + interval
all_dt64 = np.array([utils.to_dt64(t) for t in all_times])
time_days = (all_dt64 - all_dt64[0]) / np.timedelta64(1, 's') / 86400.
# zip the x, y coordinates for use in the griddata interpolation
points = np.column_stack((x_obs, y_obs))
# loop through all times, at each time step find all the non-nan data, and
# interpolate it onto the model grid, then compile the data from all times
# into a dimension-3 matrix. keep track of which stations were non-nan ('good')
# at each time step in the matrix igood
coamps_ds = None # handled on demand below
coamps_xy = None # ditto
# drops COAMPS data points within buffer dist of a good observation
buffer_dist = 30e3
for it in range(len(time_days)):
if it % 10 == 0:
print("%d/%d steps" % (it, len(time_days)))
#-- augment with COAMPS output
target_time = start_date + np.timedelta64(int(time_days[it] * 86400),
's')
if (coamps_ds is None) or (target_time > coamps_ds.time.values[-1]):
coamps_ds = coamps.coamps_dataset(bounds,
target_time,
target_time +
np.timedelta64(1, 'D'),
cache_dir=cache_dir,
fields=['wnd_utru', 'wnd_vtru'])
# reduce dataset size -- out in the ocean really don't need too many points
coamps_ds = coamps_ds.isel(x=slice(None, None, 2),
y=slice(None, None, 2))
coamps_X, coamps_Y = np.meshgrid(coamps_ds.x.values,
coamps_ds.y.values)
coamps_xy = np.c_[coamps_X.ravel(), coamps_Y.ravel()]
print("COAMPS shape: ", coamps_X.shape)
# seems that the coamps dataset is not entirely consistent in its shape?
# not sure what's going on, but best to redefine this each time to be
# sure.
@memoize.memoize()
def mask_near_point(xy):
dists = utils.dist(xy, coamps_xy)
return (dists > buffer_dist)
coamps_time_idx = utils.nearest(coamps_ds.time, target_time)
coamps_sub = coamps_ds.isel(time=coamps_time_idx)
# Which coamps points are far enough from good observations. there are
# also some time where coamps data is missing
# mask=np.ones(len(coamps_xy),np.bool8)
mask = np.isfinite(coamps_sub.wind_u.values.ravel())
# find all non-nan data at this time step
if U10_obs is not None:
igood = np.logical_and(~np.isnan(U10_obs[it, :]),
~np.isnan(V10_obs[it, :]))
obs_xy = np.c_[x_obs[igood], y_obs[igood]]
for xy in obs_xy:
mask = mask & mask_near_point(xy)
input_xy = np.concatenate([obs_xy, coamps_xy[mask]])
input_U = np.concatenate(
[U10_obs[it, igood],
coamps_sub.wind_u.values.ravel()[mask]])
input_V = np.concatenate(
[V10_obs[it, igood],
coamps_sub.wind_v.values.ravel()[mask]])
else:
# No SFEI data --
input_xy = coamps_xy[mask]
input_U = coamps_sub.wind_u.values.ravel()[mask]
input_V = coamps_sub.wind_v.values.ravel()[mask]
if np.any(np.isnan(input_U)) or np.any(np.isnan(input_V)):
import pdb
pdb.set_trace()
Ngood = len(input_xy)
# set the interpolation method to be used in this time step: interp_method_1.
# ideally, this would just be the user-defined interpolation method:
# interp_method. however, if we do not have enough non-nan data to use the
# user-defined method this time step, temporarily revert to the nearest
# neighbor method
if interp_method == 'natural' or interp_method == 'linear' or interp_method == 'cubic':
if Ngood >= 4:
interp_method_1 = interp_method
else:
interp_method_1 = 'nearest'
elif interp_method == 'nearest':
interp_method_1 = 'nearest'
# if natural neighbor method, interpolate using the pyngl package
if interp_method_1 == 'natural':
U10g = np.transpose(
ngl.natgrid(input_xy[:, 0], input_xy[:, 1], input_U, xg[0, :],
yg[:, 0]))
V10g = np.transpose(
ngl.natgrid(input_xy[:, 0], input_xy[:, 1], input_V, xg[0, :],
yg[:, 0]))
# for other interpolation methods use the scipy package
else:
U10g = griddata(input_xy,
input_U, (xg, yg),
method=interp_method_1)
V10g = griddata(input_xy,
input_V, (xg, yg),
method=interp_method_1)
# since griddata interpolation fills all data outside range with nan, use
# the nearest neighbor method to extrapolate
U10g_nn = griddata(input_xy, input_U, (xg, yg), method='nearest')
V10g_nn = griddata(input_xy, input_V, (xg, yg), method='nearest')
ind = np.isnan(U10g)
U10g[ind] = U10g_nn[ind]
ind = np.isnan(V10g)
V10g[ind] = V10g_nn[ind]
# compile results together over time
# igood_all not updated for COAMPS, omit here.
if it == 0:
U10g_all = np.expand_dims(U10g, axis=0)
V10g_all = np.expand_dims(V10g, axis=0)
# igood_all = np.expand_dims(igood,axis=0)
else:
U10g_all = np.append(U10g_all,
np.expand_dims(U10g, axis=0),
axis=0)
V10g_all = np.append(V10g_all,
np.expand_dims(V10g, axis=0),
axis=0)
# igood_all = np.append(igood_all, np.expand_dims(igood,axis=0), axis=0)
##
# Write netcdf:
ds = xr.Dataset()
ds['time'] = ('time', ), start_date + (time_days * 86400).astype(
np.int32) * np.timedelta64(1, 's')
ds['x'] = ('x', ), x
ds['y'] = ('y', ), y
ds['wind_u'] = ('time', 'y', 'x'), U10g_all
ds['wind_v'] = ('time', 'y', 'x'), V10g_all
os.path.exists(nc_fn) and os.unlink(nc_fn)
ds.to_netcdf(nc_fn)
# contaminate the lags as much when the times are already
# shifted.
# this choice does not affect the choice of ping pairs,
# as that choice is made solely on destination times.
a2b_time_srcs = a2b_good.time_src_shifted.values
b2a_time_srcs = b2a_good.time_src_shifted.values
for a2b_idx in range(len(a2b_good.index)):
match = {}
match['a2b_idx'] = a2b_idx
match['a2b_time_b'] = a2b_times[a2b_idx]
match['a2b_time_a'] = a2b_time_srcs[a2b_idx]
# Find a good b2a ping:
b2a_idx = utils.nearest(b2a_times, match['a2b_time_b'])
match['b2a_idx'] = b2a_idx
match['b2a_time_b'] = b2a_time_srcs[b2a_idx]
match['b2a_time_a'] = b2a_times[b2a_idx]
match['match_diff'] = match['a2b_time_b'] - match['b2a_time_a']
a_matches.append(match)
##
a_df = pd.DataFrame(a_matches)
# almost the key calculation
a_df['mean_travel'] = 0.5 * (a_df.a2b_time_b - a_df.a2b_time_a +
a_df.b2a_time_a - a_df.b2a_time_b)
# This is what I want:
#a_df['clock_skew']=0.5*(a_df.a2b_time_b+a_df.b2a_time_b) - 0.5*(a_df.a2b_time_a + a_df.b2a_time_a)
if len(idxs) == 0: continue
print("%d samples to pull for %s -- %s" %
(len(idxs), model_day.run_start, model_day.run_stop))
sample_xy = np.c_[segments.xm.values[idxs], segments.ym.values[idxs]]
# These will have the whole day
stations = model_day.extract_station(xy=sample_xy)
# Slim to just the specific timestamps
sample_times = seg_dt64_utc[idxs]
# this gives time errors [0,1800s]
#time_idxs=np.searchsorted( stations.time.values, sample_times )
# this centers the time offsets [-900s,900s]
time_idxs = utils.nearest(stations.time.values, sample_times)
station_dss = [
stations.isel(station=station_i, time=time_idx)
for station_i, time_idx in enumerate(time_idxs)
]
stations_ds = xr.concat(station_dss, dim='station')
# And record the original index so they can be put back together
stations_ds['input_idx'] = ('station', ), idxs
all_stations_ds.append(stations_ds)
##
joined = xr.concat(all_stations_ds, dim='station')
# Make sure we hit every station
assert np.all(np.unique(all_idxs) == np.arange(len(all_idxs)))
def set_ic_from_hycom(model,
hycom_ll_box,
cache_dir,
default_s=None,
default_T=None):
"""
Update model.ic_ds with salinity and temperature from hycom.
hycom_ll_box is like [-124.9, -121.7, 35.9, 39.0], and is specified here
to make best use of cached data (so slightly mis-alignment between models doesn't require
refetching all of the hycom files).
where to save/find cached hycom files
default_s, default_T: when a grid point does not intersect valid hycom data, what
value to use. leave as None in order to leave the value in ic_ds alone.
In the past used:
default_s=33.4
default_T=10.0
"""
fns = hycom.fetch_range(
hycom_ll_box[:2],
hycom_ll_box[2:],
[model.run_start, model.run_start + np.timedelta64(1, 'D')],
cache_dir=cache_dir)
hycom_ic_fn = fns[0]
hycom_ds = xr.open_dataset(hycom_ic_fn)
if 'time' in hycom_ds.dims:
hycom_ds = hycom_ds.isel(time=0)
cc = model.grid.cells_center()
cc_ll = model.native_to_ll(cc)
# Careful - some experiments (such as 92.8) have lon in [0,360],
# while later ones have lon in [-180,180]
# this forces all to be [-180,180]
hycom_ds.lon.values[:] = (hycom_ds.lon.values + 180) % 360.0 - 180.0
dlat = np.median(np.diff(hycom_ds.lat.values))
dlon = np.median(np.diff(hycom_ds.lon.values))
lat_i = utils.nearest(hycom_ds.lat.values, cc_ll[:, 1], max_dx=1.2 * dlat)
lon_i = utils.nearest(hycom_ds.lon.values, cc_ll[:, 0], max_dx=1.2 * dlon)
# make this positive:down to match hycom and make the interpolation
sun_z = -model.ic_ds.z_r.values
assert ('time', 'Nk',
'Nc') == model.ic_ds.salt.dims, "Workaround is fragile"
for scal, hy_var, sun_var, default in [
('s', 'salinity', 'salt', default_s),
('T', 'water_temp', 'temp', default_T)
]:
if scal == 's' and float(model.config['beta']) == 0.0:
continue
if scal == 'T' and float(model.config['gamma']) == 0.0:
continue
for c in utils.progress(range(model.grid.Ncells()),
msg="HYCOM initial condition %s %%s" % scal):
sun_val = default
if lat_i[c] < 0 or lon_i[c] < 0:
print("Cell %d does not overlap HYCOM grid" % c)
else:
# top to bottom, depth positive:down
val_profile = hycom_ds[hy_var].isel(lon=lon_i[c],
lat=lat_i[c]).values
valid = np.isfinite(val_profile)
if not np.any(valid):
# print("Cell %d is dry in HYCOM grid"%c)
pass
else:
# could add bottom salinity if we really cared.
sun_val = np.interp(sun_z, hycom_ds.depth.values[valid],
val_profile[valid])
if sun_val is not None:
model.ic_ds[sun_var].values[0, :, c] = sun_val
if xcoor.ndim == 2:
# some DFM runs add time to coordinates
xcoor = xcoor[0, :]
ycoor = ycoor[0, :]
mll = utm_to_ll(np.c_[xcoor, ycoor]).T
llind = np.zeros(len(ds["latitude"]), np.int32)
for i in range(len(llind)):
dist_ = np.sqrt((mll[0, :] - lon[i])**2 + (mll[1, :] - lat[i])**2)
llind[i] = np.argmin(dist_) # np.where(dist_ == np.min(dist_))[0]
tind = np.zeros(times[:, :, 0].shape, np.int32)
for i in range(tind.shape[0]): # loop over cruises
for j in range(tind.shape[1]): # loop over stations within cruise
ind = utils.nearest(mtimes, times[i, j,
0]) # match on time at top of cast
# if the match is more than a day off, assume the model
# doesn't cover this cruise.
if np.abs(mtimes[ind] - times[i, j, 0]) > 86400:
ind = -1
tind[i, j] = ind
mdates = np.zeros((len(tind[:, 0]), len(tind[0, :]), len(mdepth[0, 0, :])))
msalt_ = np.zeros((len(tind[:, 0]), len(tind[0, :]), len(mdepth[0, 0, :])))
if temp == True:
mtemp_ = np.zeros((len(tind[:, 0]), len(tind[0, :]), len(mdepth[0, 0, :])))
mdepth_ = np.zeros((len(tind[:, 0]), len(tind[0, :]), len(mdepth[0, 0, :])))
for i in range(len(msalt_[:, 0, 0])):
for j in range(len(msalt_[0, :, 0])):
if tind[i, j] >= 0:
def ping_to_solver_data(ping):
ping_tnum=ping.tnum.item() # fish_tnums[i]
ping_rx=ping.matrix.values # matrix[i,:]
offset_idx=utils.nearest(mat_tnums,ping_tnum)
fish_to_offset=np.abs(ping_tnum-mat_tnums[offset_idx])
offsets=offset_mat[offset_idx,:,:]
# total staleness -- include offset to fish time to be conservative.
stales =stale2_mat[offset_idx,:,:] + fish_to_offset
# The shortest path traversing offsets based on staleness.
# this will choose a->b->c over a->c if the collection of
# pings for the former case are "fresher" in time than
# for a->c directly.
dists,preds=shortest_path(stales,return_predecessors=True)
# dists: [N,N] matrix of total staleness
# preds: [N,N] of predecessors
# the specific indices involved in this ping
rxs=np.nonzero(np.isfinite(ping_rx))[0]
# and calculate offsets to the other receivers
# use stales as a distance matrix, and find the
# shortest path
# declare rxs[0] as the reference...
idx0=rxs[0]
best_offsets=offsets[idx0,:].copy()
best_offsets[idx0]=0.0 # declare rxs[0] to be the reference time
for idxn in rxs[1:]:
stale_sum=0.0
offset_sum=0.0
trav=idxn
while trav!=idx0:
new_trav=preds[idx0,trav]
# new_trav-trav is an edge on the shortest path
stale_sum+=stales[new_trav,trav]
offset_sum+=offsets[new_trav,trav]
trav=new_trav
#print(f"{idx0:2} => {idxn:2}: best stale={stale_sum:.2f} offset={offset_sum:.4f}")
#print(f" orig stale={stales[idx0,idxn]:.2f} offset={offsets[idx0,idxn]:.4f}")
best_offsets[idxn]=offset_sum
ping_rx_adj=(ping_rx - best_offsets) - ping_rx[idx0]
times=ping_rx_adj[rxs],
c=ping.c.isel(rx=rxs).values # ds_fish.c.isel(index=i,rx=rxs).values
x=ping.rx_x.isel(rx=rxs).values
y=ping.rx_y.isel(rx=rxs).values
z=ping.rx_z.isel(rx=rxs).values
time_scale=1000.
rx_xy=np.c_[x,y]
xy0=rx_xy.mean(axis=0)
rx_xy-=xy0
data=dict(Nb=len(x),
xy0=xy0,
time_scale=time_scale,
rx_t=time_scale*ping_rx_adj[rxs],
rx_x=rx_xy[:,0],
rx_y=rx_xy[:,1],
sigma_t=0.1,
sigma_x=1000.0,
rx_c=c/time_scale)
return data
