#
# There are basically two ways of narrowing down the number of files
# processed. You can use the set_time_limits() method of the MultiDBD
# class or you can use DBDPatternSelect. The latter selects files
# according to a pattern, or as a list of files. The select() method
# returns a list of files that match from and until dates, which can
# be used to create a MultiDBD instance using the filenames=[]
# keyword.
# Below this is put to the test.
# open some files, using a pattern
# Note that the default location for cache files ($HOME/.dbdreader) is
# overriden.
dbd=dbdreader.MultiDBD(pattern="../data/amadeus*.[st]bd",
cacheDir='../data/cac')
# print what parameters are available:
print("we the following science parameters:")
for i,p in enumerate(dbd.parameterNames['sci']):
print("%2d: %s"%(i,p))
print("\nand engineering parameters:")
for i,p in enumerate(dbd.parameterNames['eng']):
print("%2d: %s"%(i,p))
# get the measured depth
tm,depth=dbd.get("m_depth")
max_depth=depth.max()
print("\nmax depth found is %f m"%(max_depth))
# use my fast private gsw implementation if available
from fast_gsw import rho as gsw_rho
except ImportError:
# or fall back on the community one, installable via pip, for example.
import gsw
def gsw_rho(C, T, P, lon, lat):
SP = gsw.SP_from_C(C, T, P)
# This particular data set was collected in the Baltic.
SA = gsw.SA_from_SP_Baltic(SP, lon, lat)
# in-situ density
rho = gsw.density.rho_t_exact(SA, T, P)
return rho
dbd = dbdreader.MultiDBD(
pattern="/home/lucas/gliderdata/subex2016/hd/comet-2016-174-05-00?.[de]bd")
_tmp = dbd.get_sync(
"sci_ctd41cp_timestamp",
"sci_water_pressure m_pitch m_ballast_pumped sci_water_cond sci_water_temp"
.split())
_, tctd, P, pitch, buoyancy_drive, C, T = _tmp.compress(_tmp[1] > 0, axis=1)
tilt_correction_factor = 0.86
pitch_offset = -0.004,
pitch = pitch * tilt_correction_factor + pitch_offset
density = gsw_rho(C * 10, T, P * 10, 15, 54)
U_adcp = np.interp(tctd, *np.load("../data/U_kalman_datasetIII.npy"))
U_adcp[np.where(np.logical_or(P * 10 < 12, pitch > 0))[0]] = np.nan
np.save("../data/gliderflight_data.npy",
(tctd, P, pitch, buoyancy_drive, density, U_adcp))
GM.run(dt=1.,
ndtCPU=4,
maxSimulationTime=0.13,
ndtNetCDF=1,
end_on_surfacing=1)
#GM.save()
d = GM.get("m_depth")
tm = GM.get("m_present_time")
b = GM.get("m_ballast_pumped")
p = GM.get("m_pitch")
pc = GM.get("c_pitch")
bp = GM.get("m_battpos")
bpc = GM.get("c_battpos")
dbd = dbdreader.MultiDBD(
pattern="/home/lucas/gliderdata/toulon_201504/hd/comet-2015-098-03-000.?bd"
)
md = dbd.get("m_depth")
mb = dbd.get("m_de_oil_vol")
mp = dbd.get("m_pitch")
cbp = dbd.get("c_battpos")
mbp = dbd.get("m_battpos")
import pylab as pl
import publication
publication.setup()
tm -= 0
t0 = md[0][0]
f, ax = pl.subplots(3, 1, sharex=True, figsize=(10, 7.3))
def read_gliderdata(self, lat, lon):
path = os.path.join(self.gliders_directory, self.glider_name,
'from-glider', '%s*.[st]bd' % (self.glider_name))
dbd = dbdreader.MultiDBD(pattern=path)
if self.glider_name == 'sim':
print("Warning: assuming simulator. I am making up CTD data!")
t, P = dbd.get("m_depth")
P /= 10
C = np.ones_like(P) * 4
T = np.ones_like(P) * 15
else:
tmp = dbd.get_sync("sci_water_cond",
"sci_water_temp sci_water_pressure".split())
t_last = tmp[0][-1]
age = t_last - tmp[0]
t, C, T, P = tmp.compress(np.logical_and(tmp[1] > 0,
age < self.AGE * 3600),
axis=1)
try:
_, u, v = dbd.get_sync("m_water_vx", ["m_water_vy"])
except dbdreader.DbdError:
try:
_, u, v = dbd.get_sync("m_final_water_vx",
["m_final_water_vy"])
except dbdreader.DbdError:
u = np.array([0])
v = np.array([0])
u, v = np.compress(np.logical_and(
np.abs(u) < 1.5,
np.abs(v) < 1.5), [u, v],
axis=1)
rho = fast_gsw.rho(C * 10, T, P * 10, lon, lat)
SA = fast_gsw.SA(C * 10, T, P * 10, lon, lat)
# compute the age of each measurement, and the resulting weight.
dt = t.max() - t
weights = np.exp(-dt / (self.AGE * 3600))
# make binned averages
max_depth = P.max() * 10
dz = 5
zi = np.arange(dz / 2, max_depth + dz / 2, dz)
bins = np.arange(0, max_depth + dz, dz)
bins[0] = -10
idx = np.digitize(P * 10, bins) - 1
rho_avg = np.zeros_like(zi, float)
SA_avg = np.zeros_like(zi, float)
T_avg = np.zeros_like(zi, float)
weights_sum = np.zeros_like(zi, float)
for _idx, _w, _rho, _SA, _T in zip(idx, weights, rho, SA, T):
try:
rho_avg[_idx] += _rho * _w
SA_avg[_idx] += _SA * _w
T_avg[_idx] += _T * _w
weights_sum[_idx] += _w
except IndexError:
continue
# if data are sparse, it can be that ther are gaps
j = np.unique(idx)
zj = zi[j]
rho_avg = rho_avg[j] / weights_sum[j]
SA_avg = SA_avg[j] / weights_sum[j]
T_avg = T_avg[j] / weights_sum[j]
self.rho_fun = interp1d(zj,
rho_avg,
bounds_error=False,
fill_value=(rho_avg[0], rho_avg[-1]))
self.SA_fun = interp1d(zj,
SA_avg,
bounds_error=False,
fill_value=(SA_avg[0], SA_avg[-1]))
self.T_fun = interp1d(zj,
T_avg,
bounds_error=False,
fill_value=(T_avg[0], T_avg[-1]))
if self.u_fun is None: # not intialised yet, use last water current estimate available.
self.u_fun = lambda x: u[-1]
self.v_fun = lambda x: v[-1]
def test_get_ctd_sync(self):
print("get_ctd_sync")
pattern="../data/amadeus-2014-*.[de]bd"
dbd=dbdreader.MultiDBD(pattern = pattern, cacheDir='../data/cac')
dbd.close()
tctd, C, T, P, depth = dbd.get_CTD_sync("m_depth")
def test_non_standard_cache_dir(self):
print("non_standard_cache_dir")
dbd=dbdreader.MultiDBD(pattern = self.pattern, cacheDir='../data/cac')
dbd.close()
depth = dbd.get("m_depth")
self.assertEqual(len(depth), 2)
def test_get_sync(self):
print("get_sync")
dbd=dbdreader.MultiDBD(pattern=self.pattern)
dbd.close()
t, d, lat, lon = dbd.get_sync("m_depth",'m_lat','m_lon')
# needs to be read, and not every possible variable to find start and
# end times of each file.
#
# There are basically two ways of narrowing down the number of files
# processed. You can use the set_time_limits() method of the MultiDBD
# class or you can use DBDPatternSelect. The latter selects files
# according to a pattern, or as a list of files. The select() method
# returns a list of files that match from and until dates, which can
# be used to create a MultiDBD instance using the filenames=[]
# keyword.
# Below this is put to the test.
# open some files, using a pattern
dbd = dbdreader.MultiDBD(pattern="../data/amadeus*.[st]bd")
# print what parameters are available:
print("we the following science parameters:")
for i, p in enumerate(dbd.parameterNames['sci']):
print("%2d: %s" % (i, p))
print("\n and engineering paramters:")
for i, p in enumerate(dbd.parameterNames['eng']):
print("%2d: %s" % (i, p))
# get the measured depth
tm, depth = dbd.get("m_depth")
max_depth = depth.max()
print("\nmax depth found is %f m" % (max_depth))