def fill_data(da):
valid = np.isfinite(da.values)
valid_times = da.time[valid]
dt = np.median(np.diff(da.time))
dt_gap = np.diff(da.time[valid]).max()
if dt_gap > dt:
log.warning("%s: gaps up to %.1f minutes" %
(da.name, dt_gap / np.timedelta64(60, 's')))
da.values = utils.fill_invalid(da.values)
def lp(x):
x = utils.fill_invalid(x)
dn = utils.to_dnum(t)
# cutoff for low pass filtering, must be 2 * cutoff days after start or before end of datenums
cutoff = 36 / 24.
x_lp = filters.lowpass(x, dn, cutoff=cutoff)
mask = (dn < dn[0] + 2 * cutoff) | (dn > dn[-1] - 2 * cutoff)
x_lp[mask] = np.nan
return x_lp
def samples_from_usgs_erddap(run_start, field='salinity'):
"""
DEPRECATED. Better to use the dynamically cached data from
samples_from_usgs().
--
Pull some USGS transect data and return a set of 2D salinity
samples appropriate for the given date.
Caveats: This is currently using ERDDAP behind the scenes, which
does not have the most recent data. It will use a prior year
if the date cannot be matched within 30 days.
field: name of the field to pull from usgs_crusies.
'salinity','temperature'
"""
# This copy of the USGS data ends early:
usgs_data_end = np.datetime64('2016-04-28')
usgs_pad = np.timedelta64(30, 'D')
usgs_target = run_start
# so we may have to grab a previous years cruise and pretend
while usgs_target + usgs_pad > usgs_data_end:
usgs_target -= np.timedelta64(365, 'D')
usgs_cruises = usgs_sfbay.cruise_dataset(usgs_target - usgs_pad,
usgs_target + usgs_pad)
# lame filling
scal3d = usgs_cruises[field]
scal2d = scal3d.mean(dim='prof_sample')
assert scal2d.dims[0] == 'date'
scal2d_fill = utils.fill_invalid(scal2d.values, axis=0)
scal_f = interp1d(utils.to_dnum(scal2d.date.values),
scal2d_fill,
axis=0,
bounds_error=False)(utils.to_dnum(usgs_target))
usgs_init_scal = np.c_[scal2d.x.values, scal2d.y.values, scal_f]
return usgs_init_scal
def load_cimis(start_date,end_date):
union_city=cimis.cimis_fetch_to_xr(stations=171,
start_date=start_date,
end_date=end_date,
cache_dir=common.cache_dir)
# union_city=xr.open_dataset('/opt/data/cimis/union_city-hourly-2001-2016.nc')
# https://cals.arizona.edu/azmet/et1.htm
# which says cool period, divide ETO by 0.7 to get pan evaporation,
# warm period, divide by 0.6.
temps=utils.fill_invalid(union_city.HlyAirTmp.values)
temp_zscore=((temps-temps.mean()) / temps.std()).clip(-1,1)
# score of 1 means warm temperature
factors=np.interp(temp_zscore,
[-1,1],[0.7,0.6])
union_city['HlyEvap']=union_city.HlyEto/factors
union_city.time.values += np.timedelta64(8,'h')
return union_city
def load_cimis(start_date, end_date):
# undo this change rusty made because don't want to deal with putting cimis key in local environment
# but also change the name to not limit to 2001-2016 and check that dates are included ([email protected])
#union_city=cimis.cimis_fetch_to_xr(stations=171,
# start_date=start_date,
# end_date=end_date,
# cache_dir=common.cache_dir)
# union_city=xr.open_dataset('/opt/data/cimis/union_city-hourly-2001-2016.nc')
union_city = xr.open_dataset('/opt/data/cimis/union_city-hourly.nc')
# add a check that the date range is ok
uc_start = (np.datetime64(union_city.Date.values[0][0:10]) +
np.timedelta64(union_city.Date.values[0][11:13], 'h') +
np.timedelta64(union_city.Date.values[0][13:], 'm'))
uc_end = (np.datetime64(union_city.Date.values[-1][0:10]) +
np.timedelta64(union_city.Date.values[-1][11:13], 'h') +
np.timedelta64(union_city.Date.values[-1][13:], 'm'))
if uc_start > start_date:
raise Exception(
'/opt/data/cimis/union_city-hourly.nc start date is after simulation start date'
)
if uc_end < end_date:
raise Exception(
'/opt/data/cimis/union_city-hourly.nc end date is before simulation end date'
)
# https://cals.arizona.edu/azmet/et1.htm
# which says cool period, divide ETO by 0.7 to get pan evaporation,
# warm period, divide by 0.6.
temps = utils.fill_invalid(union_city.HlyAirTmp.values)
temp_zscore = ((temps - temps.mean()) / temps.std()).clip(-1, 1)
# score of 1 means warm temperature
factors = np.interp(temp_zscore, [-1, 1], [0.7, 0.6])
union_city['HlyEvap'] = union_city.HlyEto / factors
union_city.time.values += np.timedelta64(8, 'h')
return union_city
def build_data(ping_xyt_template, ds_strong, xyt0, rx_xy):
matrix = ds_strong.matrix.values
temps = ds_strong.temp.values
tx_beacon = np.zeros(matrix.shape[0], np.int32)
for p, tag in enumerate(ds_strong.tag.values):
tx_beacon[p] = 1 + np.nonzero(ds_strong.rx_beacon.values == tag)[0][0]
rx_c = seawater.svel(0, ds_strong['temp'], 0) / time_scale
# occasional missing data...
rx_c = utils.fill_invalid(rx_c, axis=1)
data = dict(
Np=matrix.shape[0],
Nb=matrix.shape[1],
rx_t=(matrix - xyt0[2]) * time_scale,
rx_c=rx_c,
rx_x=rx_xy[:, 0] - xyt0[0],
rx_y=rx_xy[:, 1] - xyt0[1],
tx_x=ping_xyt_template[:, 0] - xyt0[0],
tx_y=ping_xyt_template[:, 1] - xyt0[1],
# tx_t=ping_xyt_template[:,2]-xyt0[2],
tx_beacon=tx_beacon,
sigma_t=0.0005 * time_scale, # 0.5ms timing precision
)
# explicitly pass in the indexing for matrix to linear
Nb = data['Nb']
dist_k = np.zeros((Nb, Nb), np.int32)
for ai in range(Nb):
for bi in range(ai + 1, Nb):
a = ai + 1
b = bi + 1
k = (Nb * (Nb - 1) / 2) - (Nb - a + 1) * (Nb - a) / 2 + b - a
dist_k[ai, bi] = dist_k[bi, ai] = k
data['dist_k'] = dist_k
data['Ndist'] = (Nb * (Nb - 1)) // 2
return data
missing=np.nan*np.ones(ntimes)
for traj_i in utils.progress(range(len(trimmed_with_hits))):
traj,hits,T = trimmed_with_hits[traj_i]
rec=dict(traj=traj,
hits=hits,
T=T)
# Additional data:
# some trajectories have no hits,
# some with only a single hit or cluster of hits
measured=np.nonzero(hits>=0)[0]
# This will remain all nan if there is no valid data in T
rec['Tfill']=utils.fill_invalid(T)
if len(measured)==0:
# Save on memory when there is no data.
rec['fill_dist']=missing[:len(T)]
rec['dTdt']=missing[:len(T)]
else:
# trajectory time in seconds
t_s=(traj['t']-traj['t'][0])/np.timedelta64(1,'s')
# just the times that were observed
t_measured=t_s[measured]
# second between each point and the nearest observation
fill_dist=t_s - utils.nearest_val(t_measured,t_s)
# that's signed seconds offset from the nearest in time measurement
rec['fill_dist']=fill_dist
if len(measured)>1:
# Final formulae:
# u_lagoon=0.307*u_haf - 0.517*v_haf
# v_lagoon=0.097*u_haf + 0.673*v_haf
##
# Generate a single csv with the predicted values:
ds_pred=xr.Dataset()
ds_pred['time']=('time',), ds_haf.time.values
u_coeff_u= 0.307
u_coeff_v=-0.517
v_coeff_u= 0.097
v_coeff_v= 0.673
ds_pred['u_wind']=('time',),utils.fill_invalid( (u_coeff_u*ds_haf['u10'] + u_coeff_v*ds_haf['v10']).values )
ds_pred['u_wind'].attrs['units']='m s-1'
ds_pred['u_wind'].attrs['desc']=f'extrapolated wind from ASOS HAF station, {u_coeff_u}*u10 + {u_coeff_v}*v10'
ds_pred['v_wind']=('time',),utils.fill_invalid( (v_coeff_u*ds_haf['u10'] + v_coeff_v*ds_haf['v10']).values )
ds_pred['v_wind'].attrs['units']='m s-1'
ds_pred['u_wind'].attrs['desc']=f'extrapolated wind from ASOS HAF station, {v_coeff_u}*u10 + {v_coeff_v}*v10'
ds_pred['T']=('time',), utils.fill_invalid(ds_haf['Ta'].values)
ds_pred['T'].attrs.update(ds_haf['Ta'].attrs)
ds_pred['rh']=('time',), utils.fill_invalid(ds_haf['rh'].values)
ds_pred['rh'].attrs.update(ds_haf['rh'].attrs)
##
ds_pred.to_netcdf('lagoon-met-updated.nc',mode='w')
# get the data into a monthly time series before trying to fit seasonal cycle
valid = np.isfinite(fld_in.values)
absmonth_mean=bin_mean(absmonth[valid],fld_in.values[valid])
month_mean=bin_mean(month[valid],fld_in.values[valid])
if np.sum(np.isfinite(month_mean)) < 12:
print("Insufficient data for seasonal trends - will fill with sample mean")
trend_and_season=np.nanmean(month_mean) * np.ones(len(dns))
t_and_s_flag=FLAG_MEAN
else:
# fit long-term trend and a stationary seasonal cycle
# this removes both the seasonal cycle and the long-term mean,
# leaving just the trend
trend_hf=fld_in.values - month_mean[month]
lp = filters.lowpass_fir(trend_hf,lowpass_days,nan_weight_threshold=0.01)
trend = utils.fill_invalid(lp)
# recombine with the long-term mean and monthly trend
# to get the fill values.
trend_and_season = trend + month_mean[month]
t_and_s_flag=FLAG_SEASONAL_TREND
# long gaps are mostly filled by trend and season
gaps=mark_gaps(dns,valid,shortgap_days,include_ends=True)
fld_in.values[gaps] = trend_and_season[gaps]
fld_flag.values[gaps] = t_and_s_flag
still_missing=np.isnan(fld_in.values)
fld_in.values[still_missing] = utils.fill_invalid(fld_in.values)[still_missing]
fld_flag.values[still_missing] = FLAG_INTERP
# Make sure all flows are nonnegative
def effective_clock_offset(rxs, ds_tot):
ds = ds_tot.sel(rx=rxs)
# weird. sometimes adds an extra array(..,dtype=object) layer
ds['rx_beacon'] = ('rx', ), [
ds_tot.rx_beacon.sel(rx=rx).item() for rx in rxs
]
beacon_to_xy = dict([(ds.rx_beacon.values[i], (ds.rx_x.values[i],
ds.rx_y.values[i]))
for i in range(ds.dims['rx'])])
# both beacons heard it
is_multirx = (np.isfinite(ds.matrix).sum(axis=1) > 1).values
# it came from one of them.
is_selfaware = np.zeros_like(is_multirx)
for i, tag in enumerate(ds.tag.values):
if tag not in beacon_to_xy:
continue
else:
# And did the rx see itself?
local_rx = np.nonzero(ds.rx_beacon.values == tag)[0]
if local_rx.size and np.isfinite(ds.matrix.values[i, local_rx[0]]):
is_selfaware[i] = True
sel_pings = is_multirx & is_selfaware
ds_strong = ds.isel(index=sel_pings)
matrix = ds_strong.matrix.values
rx_xy = np.c_[ds_strong.rx_x.values, ds_strong.rx_y.values]
temps = ds_strong.temp.values
tx_beacon = np.zeros(matrix.shape[0], np.int32)
for p, tag in enumerate(ds_strong.tag.values):
tx_beacon[p] = np.nonzero(ds_strong.rx_beacon.values == tag)[0][0]
rx_c = seawater.svel(0, ds_strong['temp'], 0) / time_scale
# occasional missing data...
rx_c = utils.fill_invalid(rx_c, axis=1)
# do the calculation manually for two rxs:
ab_mask = (tx_beacon == 0)
ba_mask = (tx_beacon == 1)
assert np.all(ab_mask | ba_mask) # somebody has to hear it
# how much 'later' b saw it than a
t = matrix[:, 0] # take rx 0 as the reference
# without rx_c, transit time varies from 0.051970 to 0.051945
# for a variation of 0.48ppt
# with rx_c, transit time distance varies 0.07721 to 0.07710
# for a variation of 1.4ppt, and it introduces some step changes.
# so for this application it's better to have the smaller variation
# that also changes smoothly, rather than correct back to a precise
# distance
deltas = (matrix[:, 1] - matrix[:, 0]) # * rx_c.mean(axis=1)
# partial time series
dt_ab = deltas[ab_mask]
dt_ba = deltas[ba_mask]
dt_ab_dense = np.interp(t, t[ab_mask], deltas[ab_mask])
dt_ba_dense = np.interp(t, t[ba_mask], deltas[ba_mask])
# proxy for the uncertainty. Not scaled!
dt_ab_dense_std = np.abs(t - utils.nearest_val(t[ab_mask], t))
dt_ba_dense_std = np.abs(t - utils.nearest_val(t[ba_mask], t))
dt_offset = 0.5 * (dt_ab_dense + dt_ba_dense
) # sum of clock offset and travel asymmetry
dt_transit = 0.5 * (dt_ab_dense - dt_ba_dense) # transit time
dt_std = dt_ab_dense_std + dt_ba_dense_std
ds = xr.Dataset()
ds['time'] = ('time', ), t
ds['offset'] = ('time', ), dt_offset
ds['transit'] = ('time', ), dt_transit
ds['error'] = ('time', ), dt_std
ds['c'] = ('time', ), rx_c.mean(axis=1)
ds['rx'] = ('rx', ), rxs
return ds
def fill_and_flag(ds,fld,site,
lowpass_days=3*365,
shortgap_days=45 # okay to interpolate a little over a month?
):
"""
Update a single field for a single site in ds, by
extracting long-term trends, seasonal cycle, and
interpolating between these and measured data
"""
# first, create mapping from time index to absolute month
dts=utils.to_datetime(dns)
absmonth = [12*dt.year + (dt.month-1) for dt in dts]
absmonth = np.array(absmonth) - dts[0].year*12
month=absmonth%12
fld_in=ds[fld].sel(site=site)
orig_values=fld_in.values
fld_flag=ds[fld+'_flag'].sel(site=site)
prefilled=fld_flag.values & (FLAG_SEASONAL_TREND | FLAG_INTERP | FLAG_MEAN)
fld_in.values[prefilled]=np.nan # resets the work of this loop in case it's run multiple times
n_valid=np.sum(~fld_in.isnull())
if n_valid==0:
msg=" --SKIPPING--"
else:
msg=""
print(" field: %s %d/%d valid input points %s"%(fld,n_valid,len(fld_in),msg))
if n_valid==0:
return
# get the data into a monthly time series before trying to fit seasonal cycle
valid = np.isfinite(fld_in.values)
absmonth_mean=bin_mean(absmonth[valid],fld_in.values[valid])
month_mean=bin_mean(month[valid],fld_in.values[valid])
if np.sum(np.isfinite(month_mean)) < 12:
print("Insufficient data for seasonal trends - will fill with sample mean")
trend_and_season=np.nanmean(month_mean) * np.ones(len(dns))
t_and_s_flag=FLAG_MEAN
else:
# fit long-term trend and a stationary seasonal cycle
# this removes both the seasonal cycle and the long-term mean,
# leaving just the trend
trend_hf=fld_in.values - month_mean[month]
lp = filters.lowpass_fir(trend_hf,lowpass_days,nan_weight_threshold=0.01)
trend = utils.fill_invalid(lp)
# recombine with the long-term mean and monthly trend
# to get the fill values.
trend_and_season = trend + month_mean[month]
t_and_s_flag=FLAG_SEASONAL_TREND
# long gaps are mostly filled by trend and season
gaps=mark_gaps(dns,valid,shortgap_days,include_ends=True)
fld_in.values[gaps] = trend_and_season[gaps]
fld_flag.values[gaps] = t_and_s_flag
still_missing=np.isnan(fld_in.values)
fld_in.values[still_missing] = utils.fill_invalid(fld_in.values)[still_missing]
fld_flag.values[still_missing] = FLAG_INTERP
# Make sure all flows are nonnegative
negative=fld_in.values<0.0
fld_in.values[negative]=0.0
fld_flag.values[negative] |= FLAG_CLIPPED
if 0: # illustrative(?) plots
fig,ax=plt.subplots()
ax.plot(dns,orig_values,'m-o',label='Measured %s'%fld)
ax.plot(dns,fld_in,'k-',label='Final %s'%fld,zorder=5)
# ax.plot(dns,month_mean[month],'r-',label='Monthly Clim.')
# ax.plot(dns,trend_hf,'b-',label='Trend w/HF')
ax.plot(dns,trend,'g-',lw=3,label='Trend')
ax.plot(dns,trend_and_season,color='orange',label='Trend and season')
##
lat = np.asarray(ds["latitude"])
lon = np.asarray(ds["longitude"])
salt = np.asarray(ds["salinity"])
temp = np.asarray(ds["temperature"])
depth = np.asarray(ds["depth"])
dist = np.asarray(ds["Distance_from_station_36"])
time = np.asarray(ds["time"])
# These times already come in as UTC. the date math below does everything in
# PST, so take the 8 hours back in
times = -8 * 3600 + (
time - np.datetime64('1970-01-01T00:00:00')) / np.timedelta64(1, 's')
### fix nans in time record and fill in with valid times
tvalid = utils.fill_invalid(times, axis=-1)
tvalid = utils.fill_invalid(tvalid, axis=1)
tvalid = utils.fill_invalid(tvalid, axis=0)
times = tvalid
### Load in his files, define variables, adjust times to same reference time as USGS data
temp = True
his = nc.MFDataset(path + hisfile)
xcoor = his.variables["station_x_coordinate"][:]
ycoor = his.variables["station_y_coordinate"][:]
# get reference time from the mdu:
t_ref, t_start, t_stop = mdu.time_range()
dref = utils.to_unix(t_ref) # dt.datetime.strptime(emdu['time','refdate']
def fill_tidal_data(da,fill_time=True):
"""
Extract tidal harmonics from an incomplete xarray DataArray, use
those to fill in the gaps and return a complete DataArray.
Uses all 37 of the standard NOAA harmonics, may not be stable
with short time series.
A 5-day lowpass is removed from the harmonic decomposition, and added
back in afterwards.
Assumes that the DataArray has a 'time' coordinate with datetime64 values.
The time dimension must be dense enough to extract an exact time step
If fill_time is True, holes in the time coordinate will be filled, too.
"""
diffs=np.diff(da.time)
dt=np.median(diffs)
if fill_time:
gaps=np.nonzero(diffs>1.5*dt)[0]
pieces=[]
last=0
for gap_i in gaps:
# gap_i=10 means that the 10th diff was too big
# that means the jump from 10 to 11 was too big
# the preceding piece should go through 9, so
# exclusive of gap_i
pieces.append(da.time.values[last:gap_i])
pieces.append(np.arange( da.time.values[gap_i],
da.time.values[gap_i+1],
dt))
last=gap_i+1
pieces.append(da.time.values[last:])
dense_times=np.concatenate(pieces)
dense_values=np.nan*np.zeros(len(dense_times),np.float64)
dense_values[ np.searchsorted(dense_times,da.time.values) ] = da.values
da=xr.DataArray(dense_values,
dims=['time'],coords=[dense_times])
else:
pass
dnums=utils.to_dnum(da.time)
data=da.values
# lowpass at about 5 days, splitting out low/high components
winsize=int( np.timedelta64(5,'D') / dt )
data_lp=filters.lowpass_fir(data,winsize)
data_hp=data - data_lp
valid=np.isfinite(data_hp)
omegas=harm_decomp.noaa_37_omegas() # as rad/sec
harmonics=harm_decomp.decompose(dnums[valid]*86400,data_hp[valid],omegas)
dense=harm_decomp.recompose(dnums*86400,harmonics,omegas)
data_recon=utils.fill_invalid(data_lp) + dense
data_filled=data.copy()
missing=np.isnan(data_filled)
data_filled[missing] = data_recon[missing]
fda=xr.DataArray(data_filled,coords=[da.time],dims=['time'])
return fda
tx_beacon[i]=np.nonzero(ds.rx_beacon.values==tag)[0][0]
# And did the rx see itself?
is_selfaware[i]=np.isfinite(ds.matrix.values[i,tx_beacon[i]])
ds['tx_beacon']=('index',),tx_beacon
is_beacon=tx_beacon>=0
temps=ds.temp.values
# WHOA -- comparisons to the beacon-beacon transits suggest
# that this has some significant error. The inferred and
# calculated speeds of sound are close-ish if temperature is
# offset 4.5 degC.
rx_c=seawater.svel(0,ds['temp']-4.5,0)
# occasional missing data...
rx_c=utils.fill_invalid(rx_c,axis=1)
ds['c']=('index','rx'),rx_c
##
sel_pings=is_multirx&is_selfaware
ds_strong=ds.isel(index=sel_pings)
##
# Calculate time deltas for each ordered pair of receivers
Nping=ds_strong.dims['index']
Nrx=ds_strong.dims['rx']
tx_beacon=ds_strong.tx_beacon.values
A first cut at direct precipitation and evaporation.
"""
import xarray as xr
from stompy import utils
# Last SUNTANS run had used NARR
# it's way way coarse. Seems better to use an in-Bay climatology
# than to use NARR.
##
union_city = xr.open_dataset('/opt/data/cimis/union_city-hourly-2001-2016.nc')
##
temps = utils.fill_invalid(union_city.HlyAirTmp.values)
temp_zscore = ((temps - temps.mean()) / temps.std()).clip(-1, 1)
# score of 1 means warm temperature
factors = np.interp(temp_zscore, [-1, 1], [0.7, 0.6])
union_city['HlyEvap'] = union_city.HlyEto / factors
##
narr_data_dir = "/opt/data/suntans/spinupdated/suntans-spinup/narr-data/data/"
precip = xr.open_dataset(os.path.join(narr_data_dir, "apcp.mon.mean.nc"),
decode_cf=False)
# bug in the nc file
if precip.apcp._FillValue != precip.apcp.missing_value:
precip.apcp.attrs['_FillValue'] = precip.apcp.missing_value
precip = xr.decode_cf(precip)
track['step']=np.cumsum(np.r_[0,dt_steps]).astype(np.int32) track_exp=pd.DataFrame() track_exp['step']=np.arange(expanded_idx.max()+1) track2=track_exp.merge(right=track,on='step', how='left') ## data=track2.loc[:,['x','y']] data.loc[:,'x'] -= data.x.mean() data.loc[:,'y'] -= data.y.mean() # Make life a little simpler and just fill data.loc[:,'x'] = utils.fill_invalid(data.loc[:,'x'].values) data.loc[:,'y'] = utils.fill_invalid(data.loc[:,'y'].values) ## # Sort of following https://www.statsmodels.org/stable/examples/notebooks/generated/tsa_arma_0.html import statsmodels.api as sm from statsmodels.graphics.api import qqplot from statsmodels import tsa data.plot(figsize=(12,8)) ## dta=data['x']
