def fresh_profile():
ssp = Profile()
d = np.arange(0.0, 100.0, 0.5)
vs = np.arange(500.0, 1500.0, 5.0)
t = np.arange(0.0, 100.0, 0.5)
s = np.arange(0.0, 100.0, 0.5)
ssp.init_proc(d.size)
ssp.proc.depth = d
ssp.proc.speed = vs
ssp.proc.temp = t
ssp.proc.sal = s
# ssp.proc.flag[40:50] = 1
# ssp.proc.flag[50:70] = 2
return ssp
def make_fake_ssp(bias=0.0):
ssp = Profile()
d = np.arange(0.0, 1000.0, 5.0)
vs = np.arange(1480.0 + bias, 1520.0 + bias, 0.2)
t = np.arange(0.0, 100.0, 0.5)
s = np.arange(0.0, 100.0, 0.5)
ssp.init_proc(d.size)
ssp.proc.depth = d
ssp.proc.speed = vs
ssp.proc.temp = t
ssp.proc.sal = s
ssp.meta.latitude = 43.13555
ssp.meta.longitude = -70.9395
ssp.meta.utc_time = datetime.utcnow()
return ssp
def make_fake_ssp(bias=0.0):
ssp = Profile()
d = np.arange(0.0, 100.0, 0.5)
vs = np.arange(1450.0 + bias, 1550.0 + bias, 0.5)
t = np.arange(0.0, 100.0, 0.5)
s = np.arange(0.0, 100.0, 0.5)
ssp.init_proc(d.size)
ssp.proc.depth = d
ssp.proc.speed = vs
ssp.proc.temp = t
ssp.proc.sal = s
ssp.proc.flag[40:50] = Dicts.flags['user']
ssp.proc.flag[50:70] = Dicts.flags['filtered']
ssp.meta.latitude = 43.13555
ssp.meta.longitude = -70.9395
ssp.meta.utc_time = datetime.utcnow()
return ssp
def fresh_profile():
ssp = Profile()
d = [random.gauss(val, .2) for val in np.arange(1.0, 51.0, 0.5)]
vs = [random.gauss(val, 5.0) for val in np.arange(1450.0, 1550.0, 1.0)]
t = [random.gauss(val, .2) for val in np.arange(15.0, 25.0, 0.1)]
s = [random.gauss(val, .1) for val in np.arange(10.0, 60.0, 0.5)]
ssp.init_proc(len(d))
ssp.proc.depth = np.array(d)
ssp.proc.speed = np.array(vs)
ssp.proc.temp = np.array(t)
ssp.proc.sal = np.array(s)
# ssp.proc.flag[40:50] = 1
# ssp.proc.flag[50:70] = 2
ssp.meta.latitude = 43.13555
ssp.meta.longitude = -70.9395
ssp.meta.utc_time = datetime.utcnow()
return ssp
def make_fake_ssp(ss_bias=0.0, d_bias=0.0, fixed=False):
ssp = Profile()
d = np.arange(10.0 + d_bias, 1010.0 + d_bias, 5.0)
if fixed:
vs = np.ones_like(d) * (1500 + ss_bias)
else:
vs = np.arange(1460.0 + ss_bias, 1540.0 + ss_bias, 0.4)
t = np.arange(0.0, 100.0, 0.5)
s = np.arange(0.0, 100.0, 0.5)
ssp.init_proc(d.size)
ssp.proc.depth = d
ssp.proc.speed = vs
ssp.proc.temp = t
ssp.proc.sal = s
ssp.meta.latitude = 43.13555
ssp.meta.longitude = -70.9395
ssp.meta.utc_time = datetime.utcnow()
return ssp
def make_fake_ssp(bias=0.0, nr_samples=100):
p = Profile()
d = np.linspace(0.0, 16.0, nr_samples)
vs_up = int(nr_samples / 4)
vs_down = nr_samples - 3 * vs_up
vs = np.concatenate([
np.linspace(1535.0 + bias, 1532.0 + bias, vs_up),
np.linspace(1532.0 + bias, 1528.0 + bias, vs_up),
np.linspace(1528.0 + bias, 1523.0 + bias, vs_up),
np.linspace(1523.0 + bias, 1500.0 + bias, vs_down)
])
t = np.linspace(10.0, 20.0, nr_samples)
s = np.linspace(34.0, 35.0, nr_samples)
p.init_proc(d.size)
p.proc.depth = d
p.proc.speed = vs
p.proc.temp = t
p.proc.sal = s
p.proc.flag[40:42] = Dicts.flags['user']
p.proc.flag[50:52] = Dicts.flags['filtered']
p.meta.latitude = 43.13555
p.meta.longitude = -70.9395
p.meta.utc_time = datetime.utcnow()
return p
def query(self, lat: float, lon: float, datestamp: Union[date, dt, None] = None, server_mode: bool = False):
"""Query WOA13 for passed location and timestamp"""
if datestamp is None:
datestamp = dt.utcnow()
if isinstance(datestamp, dt):
datestamp = datestamp.date()
if not isinstance(datestamp, date):
raise RuntimeError("invalid date passed: %s" % type(datestamp))
logger.debug("query: %s @ (%.6f, %.6f)" % (datestamp, lon, lat))
# check the inputs
if (lat is None) or (lon is None) or (datestamp is None):
logger.error("invalid query: %s @ (%s, %s)" % (datestamp.strftime("%Y%m%d"), lon, lat))
return None
if not self.has_data_loaded:
if not self.load_grids():
logger.error("No data")
return None
self.calc_indices(month=datestamp.month)
# Find the nearest grid node
lat_base_idx, lon_base_idx = self.grid_coords(lat=lat, lon=lon)
lat_offsets = range(lat_base_idx - self.search_radius, lat_base_idx + self.search_radius + 1)
lon_offsets = range(lon_base_idx - self.search_radius, lon_base_idx + self.search_radius + 1)
# Search nodes surrounding the requested position to find the closest non-land
t = np.zeros(self.num_levels)
s = np.zeros(self.num_levels)
t_min = np.zeros(self.num_levels)
s_min = np.zeros(self.num_levels)
t_max = np.zeros(self.num_levels)
s_max = np.zeros(self.num_levels)
dist_arr = np.zeros(self.num_levels)
dist_t_sd = np.zeros(self.num_levels)
dist_s_sd = np.zeros(self.num_levels)
dist_arr[:] = 99999999
dist_t_sd[:] = 99999999
dist_s_sd[:] = 99999999
min_dist = 999999999
num_visited = 0
# These keep track of the closest node found, this will also
# be used to populate lat/lon of the cast to be delivered
lat_idx = -1
lon_idx = -1
for this_lat_index in lat_offsets:
for this_lon_index in lon_offsets:
if this_lon_index >= self.lon.size:
# logger.debug("[%s] >= [%s]" % (this_lon_index, self.t[self.month_idx].variables['lon'].size))
this_lon_index -= self.lat.size
# Check to see if we're at sea or on land
if self.landsea[this_lat_index][this_lon_index] == 1:
# logger.debug("at land: %s, %s" % (this_lat_index, this_lon_index))
# from matplotlib import pyplot
# pyplot.imshow(self.landsea, origin='lower')
# pyplot.scatter([this_lon_index], [this_lat_index], c='r', s=40)
# pyplot.show()
continue
# calculate the distance to the grid node
dist = self.g.distance(lon, lat, self.lon[this_lon_index], self.lat[this_lat_index])
# logger.debug("[%s %s] [%s %s]" % (self.lon.shape, self.lat.shape, this_lon_index, this_lat_index))
# logger.debug("dist: %s" % dist)
# Keep track of the closest valid grid node to report the pseudo-cast position
if dist < min_dist:
min_dist = dist
lat_idx = this_lat_index
lon_idx = this_lon_index
# Extract monthly temperature and salinity profile for this location
t_profile = self.t[self.month_idx].variables['t_an'][0, :, this_lat_index, this_lon_index]
s_profile = self.s[self.month_idx].variables['s_an'][0, :, this_lat_index, this_lon_index]
# Extract seasonal temperature and salinity profile for this location
t_profile2 = self.t[self.season_idx].variables['t_an'][0, :, this_lat_index, this_lon_index]
s_profile2 = self.s[self.season_idx].variables['s_an'][0, :, this_lat_index, this_lon_index]
# Now do the same for the standard deviation profiles
t_sd_profile = self.t[self.month_idx].variables['t_sd'][0, :, this_lat_index, this_lon_index]
s_sd_profile = self.s[self.month_idx].variables['s_sd'][0, :, this_lat_index, this_lon_index]
t_sd_profile2 = self.t[self.season_idx].variables['t_sd'][0, :, this_lat_index, this_lon_index]
s_sd_profile2 = self.s[self.season_idx].variables['s_sd'][0, :, this_lat_index, this_lon_index]
# Overwrite the top of the seasonal profiles with the monthly profiles
t_profile2[0:t_profile.size] = t_profile
s_profile2[0:s_profile.size] = s_profile
t_sd_profile2[0:t_sd_profile.size] = t_sd_profile
s_sd_profile2[0:s_sd_profile.size] = s_sd_profile
# For each element in the profile, only keep those whose distance is closer than values
# found from previous iterations (maintain the closest value at each depth level)
# logger.debug("profile sz: %d\n%s" % (t_profile2.size, t_profile2))
for i in range(t_profile2.size):
if dist >= dist_arr[i]:
continue
if (t_profile2[i] < 50.0) and (s_profile2[i] < 500.0) and (s_profile2[i] >= 0):
t[i] = t_profile2[i]
s[i] = s_profile2[i]
dist_arr[i] = dist
# Now do the same thing for the temperature standard deviations
for i in range(t_sd_profile2.size):
if dist >= dist_t_sd[i]:
continue
if (t_sd_profile2[i] < 50.0) and (t_sd_profile2[i] > -2):
t_min[i] = t_profile2[i] - t_sd_profile2[i]
if t_min[i] < -2.0: # can't have overly cold water
t_min[i] = -2.0
t_max[i] = t_profile2[i] + t_sd_profile2[i]
dist_t_sd[i] = dist
# Now do the same thing for the salinity standard deviations
for i in range(s_sd_profile2.size):
if dist >= dist_s_sd[i]:
continue
if (s_sd_profile2[i] < 500.0) and (s_sd_profile2[i] >= 0):
s_min[i] = s_profile2[i] - s_sd_profile2[i]
if s_min[i] < 0: # Can't have a negative salinity
s_min[i] = 0
s_max[i] = s_profile2[i] + s_sd_profile2[i]
dist_s_sd[i] = dist
num_visited += 1
if (lat_idx == -1) and (lon_idx == -1):
logger.info("possible request on land")
return None
valid = dist_arr != 99999999
num_values = t[valid].size
logger.debug("valid: %s" % num_values)
if lon > 180.0: # Go back to negative longitude
lon -= 360.0
# populate output profiles
ssp = Profile()
ssp.meta.sensor_type = Dicts.sensor_types['Synthetic']
ssp.meta.probe_type = Dicts.probe_types['WOA13']
ssp.meta.latitude = lat
ssp.meta.longitude = lon
ssp.meta.utc_time = dt(year=datestamp.year, month=datestamp.month, day=datestamp.day)
ssp.init_data(num_values)
ssp.data.depth = self.t[self.season_idx].variables['depth'][0:num_values]
ssp.data.temp = t[valid]
ssp.data.sal = s[valid]
ssp.calc_data_speed()
ssp.clone_data_to_proc()
ssp.init_sis()
# - min/max
# Isolate realistic values
for i in range(t_min.size):
if dist_t_sd[i] == 99999999 or dist_s_sd[i] == 99999999:
num_values = i
break
# -- min
ssp_min = Profile()
ssp_min.meta.sensor_type = Dicts.sensor_types['Synthetic']
ssp_min.meta.probe_type = Dicts.probe_types['WOA13']
ssp_min.meta.latitude = lat
ssp_min.meta.longitude = lon
ssp_min.meta.utc_time = dt(year=datestamp.year, month=datestamp.month, day=datestamp.day)
if num_values > 0:
ssp_min.init_data(num_values)
ssp_min.data.depth = self.t[self.season_idx].variables['depth'][0:num_values]
ssp_min.data.temp = t_min[valid][0:num_values]
ssp_min.data.sal = s_min[valid][0:num_values]
ssp_min.calc_data_speed()
ssp_min.clone_data_to_proc()
ssp_min.init_sis()
else:
ssp_min = None
# -- max
ssp_max = Profile()
ssp_max.meta.sensor_type = Dicts.sensor_types['Synthetic']
ssp_max.meta.probe_type = Dicts.probe_types['WOA13']
ssp_max.meta.latitude = lat
ssp_max.meta.longitude = lon
ssp_max.meta.utc_time = dt(year=datestamp.year, month=datestamp.month, day=datestamp.day)
ssp.meta.original_path = "WOA13_%s" % datestamp.strftime("%Y%m%d")
if num_values > 0:
ssp_max.init_data(num_values)
ssp_max.data.depth = self.t[self.season_idx].variables['depth'][0:num_values].astype(np.float64)
ssp_max.data.temp = t_max[valid][0:num_values]
ssp_max.data.sal = s_max[valid][0:num_values]
ssp_max.calc_data_speed()
ssp_max.clone_data_to_proc()
ssp_max.init_sis()
else:
ssp_max = None
profiles = ProfileList()
profiles.append_profile(ssp)
if ssp_min:
profiles.append_profile(ssp_min)
if ssp_max:
profiles.append_profile(ssp_max)
profiles.current_index = 0
# logger.debug("retrieved: %s" % profiles)
return profiles
def query(self,
lat: Optional[float],
lon: Optional[float],
datestamp: Union[date, dt, None] = None,
server_mode: bool = False):
"""Query RTOFS for passed location and timestamp"""
if datestamp is None:
datestamp = dt.utcnow()
if isinstance(datestamp, dt):
datestamp = datestamp.date()
if not isinstance(datestamp, date):
raise RuntimeError("invalid date passed: %s" % type(datestamp))
logger.debug("query: %s @ (%.6f, %.6f)" % (datestamp, lon, lat))
# check the inputs
if (lat is None) or (lon is None) or (datestamp is None):
logger.error("invalid query: %s @ (%s, %s)" %
(datestamp.strftime("%Y%m%d"), lon, lat))
return None
try:
lat_idx, lon_idx = self.grid_coords(lat,
lon,
datestamp=datestamp,
server_mode=server_mode)
if lat_idx is None:
logger.info("location outside of GoMOFS coverage")
return None
except TypeError as e:
logger.critical("while converting location to grid coords, %s" % e)
return None
logger.debug("idx > lat: %s, lon: %s" % (lat_idx, lon_idx))
lat_s_idx = lat_idx - self._search_half_window
if lat_s_idx < 0:
lat_s_idx = 0
lat_n_idx = lat_idx + self._search_half_window
if lat_n_idx >= self._lat.shape[0]:
lat_n_idx = self._lat.shape[0] - 1
lon_w_idx = lon_idx - self._search_half_window
if lon_w_idx < 0:
lon_w_idx = 0
lon_e_idx = lon_idx + self._search_half_window
if lon_e_idx >= self._lon.shape[1]:
lon_e_idx = self._lon.shape[1] - 1
# logger.info("indices -> %s %s %s %s" % (lat_s_idx, lat_n_idx, lon_w_idx, lon_e_idx))
lat_search_window = lat_n_idx - lat_s_idx + 1
lon_search_window = lon_e_idx - lon_w_idx + 1
logger.info("updated search window: (%s, %s)" %
(lat_search_window, lon_search_window))
# Need +1 on the north and east indices since it is the "stop" value in these slices
t = self._file.variables['temp'][self._day_idx, :,
lat_s_idx:lat_n_idx + 1,
lon_w_idx:lon_e_idx + 1]
s = self._file.variables['salt'][self._day_idx, :,
lat_s_idx:lat_n_idx + 1,
lon_w_idx:lon_e_idx + 1]
# Set 'unfilled' elements to NANs (BUT when the entire array has valid data, it returns numpy.ndarray)
if isinstance(t, np.ma.core.MaskedArray):
t_mask = t.mask
t._sharedmask = False
t[t_mask] = np.nan
if isinstance(s, np.ma.core.MaskedArray):
s_mask = s.mask
s._sharedmask = False
s[s_mask] = np.nan
# Calculate distances from requested position to each of the grid node locations
distances = np.zeros(
(self._d.size, lon_search_window, lat_search_window))
longitudes = self._lon[lat_s_idx:lat_n_idx + 1,
lon_w_idx:lon_e_idx + 1]
latitudes = self._lat[lat_s_idx:lat_n_idx + 1, lon_w_idx:lon_e_idx + 1]
for i in range(lat_search_window):
for j in range(lon_search_window):
dist = self.g.distance(longitudes[i, j], latitudes[i, j], lon,
lat)
distances[:, i, j] = dist
# logger.info("node (%s %s), pos: %3.2f, %3.2f, dist: %3.1f"
# % (i, j, latitudes[i, j], longitudes[i, j], distances[0, i, j]))
# Get mask of "no data" elements and replace these with NaNs in distance array
t_mask = np.isnan(t)
distances[t_mask] = np.nan
s_mask = np.isnan(s)
distances[s_mask] = np.nan
# logger.info("distance array:\n%s" % distances[0])
# Spin through all the depth levels
temp_pot = np.zeros(self._d.size)
temp_in_situ = np.zeros(self._d.size)
d = np.zeros(self._d.size)
sal = np.zeros(self._d.size)
num_values = 0
for i in range(self._d.size):
t_level = t[i]
s_level = s[i]
d_level = distances[i]
try:
ind = np.nanargmin(d_level)
except ValueError:
# logger.info("%s: all-NaN slices" % i)
continue
if np.isnan(ind):
logger.info("%s: bottom of valid data" % i)
break
ind2 = np.unravel_index(ind, t_level.shape)
t_closest = t_level[ind2]
s_closest = s_level[ind2]
# d_closest = d_level[ind2]
temp_pot[i] = t_closest
sal[i] = s_closest
d[i] = self._d[i]
# Calculate in-situ temperature
p = Oc.d2p(d[i], lat)
temp_in_situ[i] = Oc.in_situ_temp(s=sal[i],
t=t_closest,
p=p,
pr=self._ref_p)
# logger.info("%02d: %6.1f %6.1f > T/S/Dist: %3.1f %3.1f %3.1f [pot.temp. %3.1f]"
# % (i, d[i], p, temp_in_situ[i], s_closest, d_closest, t_closest))
num_values += 1
if num_values == 0:
logger.info("no data from lookup!")
return None
# ind = np.nanargmin(distances[0])
# ind2 = np.unravel_index(ind, distances[0].shape)
# switching to the query location
# lat_out = latitudes[ind2]
# lon_out = longitudes[ind2]
# while lon_out > 180.0:
# lon_out -= 360.0
# Make a new SV object to return our query in
ssp = Profile()
ssp.meta.sensor_type = Dicts.sensor_types['Synthetic']
ssp.meta.probe_type = Dicts.probe_types['GoMOFS']
ssp.meta.latitude = lat
if lon > 180.0: # Go back to negative longitude
lon -= 360.0
ssp.meta.longitude = lon
ssp.meta.utc_time = dt(year=datestamp.year,
month=datestamp.month,
day=datestamp.day)
ssp.meta.original_path = "GoMOFS_%s" % datestamp.strftime("%Y%m%d")
ssp.init_data(num_values)
ssp.data.depth = d[0:num_values]
ssp.data.temp = temp_in_situ[0:num_values]
ssp.data.sal = sal[0:num_values]
ssp.calc_data_speed()
ssp.clone_data_to_proc()
ssp.init_sis()
profiles = ProfileList()
profiles.append_profile(ssp)
return profiles
def query(self, nc_path: str, lat: float,
lon: float) -> Optional[ProfileList]:
if not os.path.exists(nc_path):
raise RuntimeError('Unable to locate %s' % nc_path)
logger.debug('nc path: %s' % nc_path)
if (lat is None) or (lon is None):
logger.error("invalid location query: (%s, %s)" % (lon, lat))
return None
logger.debug('query location: %s, %s' % (lat, lon))
progress = CliProgress()
try:
self._file = Dataset(nc_path)
progress.update(20)
except (RuntimeError, IOError) as e:
logger.warning("unable to access data: %s" % e)
self.clear_data()
progress.end()
return None
try:
self.name = self._file.title
time = self._file.variables['time']
self._timestamp = num2date(time[0], units=time.units)
logger.debug("Retrieved time: %s" % self._timestamp.isoformat())
# Now get latitudes, longitudes and depths for x,y,z referencing
self._lats = self._file.variables['lat'][:]
self._lons = self._file.variables['lon'][:]
# logger.debug('lat:(%s)\n%s' % (self._lats.shape, self._lats))
# logger.debug('lon:(%s)\n%s' % (self._lons.shape, self._lons))
self._zeta = self._file.variables['zeta'][0, :]
self._siglay = self._file.variables['siglay'][:]
self._h = self._file.variables['h'][:]
# logger.debug('zeta:(%s)\n%s' % (self._zeta.shape, self._zeta))
# logger.debug('siglay:(%s)\n%s' % (self._siglay.shape, self._siglay[:, 0]))
# logger.debug('h:(%s)\n%s' % (self._h.shape, self._h))
self._temp = self._file.variables['temp'][:]
self._sal = self._file.variables['salinity'][:]
# logger.debug('temp:(%s)\n%s' % (self._temp.shape, self._temp[:, 0]))
# logger.debug('sal:(%s)\n%s' % (self._sal.shape, self._sal[:, 0]))
except Exception as e:
logger.error(
"troubles in variable lookup for lat/long grid and/or depth: %s"
% e)
self.clear_data()
progress.end()
return None
min_dist = 100000.0
min_idx = None
for idx, _ in enumerate(self._lats):
nc_lat = self._lats[idx]
nc_lon = self._lons[idx]
if nc_lon > 180.0:
nc_lon = nc_lon - 360.0
nc_dist = self.g.distance(nc_lon, nc_lat, lon, lat)
# logger.debug('loc: %.6f, %.6f -> %.6f' % (nc_lat, nc_lon, nc_dist))
if nc_dist < min_dist:
min_dist = nc_dist
min_idx = idx
if min_dist >= 10000.0:
logger.error("location too far from model nodes: %.f" % min_dist)
self.clear_data()
progress.end()
return None
self._loc_idx = min_idx
self._lon = self._lons[self._loc_idx]
if self._lon > 180.0:
self._lon = self._lon - 360.0
self._lat = self._lats[self._loc_idx]
logger.debug('closest node: %d [%s, %s] -> %s' %
(self._loc_idx, self._lat, self._lon, min_dist))
zeta = self._zeta[self._loc_idx]
h = self._h[self._loc_idx]
siglay = -self._siglay[:, self._loc_idx]
# logger.debug('zeta: %s, h: %s, siglay: %s' % (zeta, h, siglay))
self._d = siglay * (h + zeta)
# logger.debug('d:(%s)\n%s' % (self._h.shape, self._d))
# Make a new SV object to return our query in
ssp = Profile()
ssp.meta.sensor_type = Dicts.sensor_types['Synthetic']
ssp.meta.probe_type = Dicts.probe_types[self.name]
ssp.meta.latitude = self._lat
ssp.meta.longitude = self._lon
ssp.meta.utc_time = dt(year=self._timestamp.year,
month=self._timestamp.month,
day=self._timestamp.day,
hour=self._timestamp.hour,
minute=self._timestamp.minute,
second=self._timestamp.second)
ssp.meta.original_path = "%s_%s" % (
self.name, self._timestamp.strftime("%Y%m%d_%H%M%S"))
ssp.init_data(self._d.shape[0])
ssp.data.depth = self._d[:]
ssp.data.temp = self._temp[0, :, self._loc_idx]
ssp.data.sal = self._sal[0, :, self._loc_idx]
ssp.calc_data_speed()
ssp.clone_data_to_proc()
ssp.init_sis()
profiles = ProfileList()
profiles.append_profile(ssp)
progress.end()
return profiles
def append(self):
self._list.append(Profile())
self.current_index = len(self._list) - 1
def query(self, lat: Optional[float], lon: Optional[float], dtstamp: Optional[dt] = None,
server_mode: bool = False):
"""Query RTOFS for passed location and timestamp"""
if dtstamp is None:
dtstamp = dt.utcnow()
if not isinstance(dtstamp, dt):
raise RuntimeError("invalid datetime passed: %s" % type(dtstamp))
logger.debug("query: %s @ (%.6f, %.6f)" % (dtstamp, lon, lat))
# check the inputs
if (lat is None) or (lon is None):
logger.error("invalid query: %s @ (%s, %s)" % (dtstamp.strftime("%Y/%m/%d %H:%M:%S"), lon, lat))
return None
try:
lat_idx, lon_idx = self.grid_coords(lat, lon, dtstamp=dtstamp, server_mode=server_mode)
except TypeError as e:
logger.critical("while converting location to grid coords, %s" % e)
return None
# logger.debug("idx > lat: %s, lon: %s" % (lat_idx, lon_idx))
lat_s_idx = lat_idx - self._search_half_window
lat_n_idx = lat_idx + self._search_half_window
lon_w_idx = lon_idx - self._search_half_window
lon_e_idx = lon_idx + self._search_half_window
# logger.info("indices -> %s %s %s %s" % (lat_s_idx, lat_n_idx, lon_w_idx, lon_e_idx))
if lon < self._lon_0: # Make all longitudes safe
lon += 360.0
longitudes = np.zeros((self._search_window, self._search_window))
if (lon_e_idx < self._lon.size) and (lon_w_idx >= 0):
# logger.info("safe case")
# Need +1 on the north and east indices since it is the "stop" value in these slices
t = self._file_temp.variables['temperature'][self._day_idx, :, lat_s_idx:lat_n_idx + 1,
lon_w_idx:lon_e_idx + 1]
s = self._file_sal.variables['salinity'][self._day_idx, :, lat_s_idx:lat_n_idx + 1, lon_w_idx:lon_e_idx + 1]
# Set 'unfilled' elements to NANs (BUT when the entire array has valid data, it returns numpy.ndarray)
if isinstance(t, np.ma.core.MaskedArray):
t_mask = t.mask
t._sharedmask = False
t[t_mask] = np.nan
if isinstance(s, np.ma.core.MaskedArray):
s_mask = s.mask
s._sharedmask = False
s[s_mask] = np.nan
lons = self._lon[lon_w_idx:lon_e_idx + 1]
for i in range(self._search_window):
longitudes[i, :] = lons
else:
logger.info("split case")
# --- Do the left portion of the array first, this will run into the wrap longitude
lon_e_idx = self._lon.size - 1
# lon_west_index can be negative if lon_index is on the westernmost end of the array
if lon_w_idx < 0:
lon_w_idx = lon_w_idx + self._lon.size
# logger.info("using lon west/east indices -> %s %s" % (lon_w_idx, lon_e_idx))
# Need +1 on the north and east indices since it is the "stop" value in these slices
t_left = self._file_temp.variables['temperature'][self._day_idx, :, lat_s_idx:lat_n_idx + 1,
lon_w_idx:lon_e_idx + 1]
s_left = self._file_sal.variables['salinity'][self._day_idx, :, lat_s_idx:lat_n_idx + 1,
lon_w_idx:lon_e_idx + 1]
# Set 'unfilled' elements to NANs (BUT when the entire array has valid data, it returns numpy.ndarray)
if isinstance(t_left, np.ma.core.MaskedArray):
t_mask = t_left.mask
t_left[t_mask] = np.nan
if isinstance(s_left, np.ma.core.MaskedArray):
s_mask = s_left.mask
s_left[s_mask] = np.nan
lons_left = self._lon[lon_w_idx:lon_e_idx + 1]
for i in range(self._search_window):
longitudes[i, 0:lons_left.size] = lons_left
# logger.info("longitudes are now: %s" % longitudes)
# --- Do the right portion of the array first, this will run into the wrap
# longitude so limit it accordingly
lon_w_idx = 0
lon_e_idx = self._search_window - lons_left.size - 1
# Need +1 on the north and east indices since it is the "stop" value in these slices
t_right = self._file_temp.variables['temperature'][self._day_idx, :, lat_s_idx:lat_n_idx + 1,
lon_w_idx:lon_e_idx + 1]
s_right = self._file_sal.variables['salinity'][self._day_idx, :, lat_s_idx:lat_n_idx + 1,
lon_w_idx:lon_e_idx + 1]
# Set 'unfilled' elements to NANs (BUT when the entire array has valid data, it returns numpy.ndarray)
if isinstance(t_right, np.ma.core.MaskedArray):
t_mask = t_right.mask
t_right[t_mask] = np.nan
if isinstance(s_right, np.ma.core.MaskedArray):
s_mask = s_right.mask
s_right[s_mask] = np.nan
lons_right = self._lon[lon_w_idx:lon_e_idx + 1]
for i in range(self._search_window):
longitudes[i, lons_left.size:self._search_window] = lons_right
# merge data
t = np.zeros((self._file_temp.variables['lev'].size, self._search_window, self._search_window))
t[:, :, 0:lons_left.size] = t_left
t[:, :, lons_left.size:self._search_window] = t_right
s = np.zeros((self._file_temp.variables['lev'].size, self._search_window, self._search_window))
s[:, :, 0:lons_left.size] = s_left
s[:, :, lons_left.size:self._search_window] = s_right
# Calculate distances from requested position to each of the grid node locations
distances = np.zeros((self._d.size, self._search_window, self._search_window))
latitudes = np.zeros((self._search_window, self._search_window))
lats = self._lat[lat_s_idx:lat_n_idx + 1]
for i in range(self._search_window):
latitudes[:, i] = lats
for i in range(self._search_window):
for j in range(self._search_window):
dist = self.g.distance(longitudes[i, j], latitudes[i, j], lon, lat)
distances[:, i, j] = dist
# logger.info("node %s, pos: %3.1f, %3.1f, dist: %3.1f"
# % (i, latitudes[i, j], longitudes[i, j], distances[0, i, j]))
# logger.info("distance array:\n%s" % distances[0])
# Get mask of "no data" elements and replace these with NaNs in distance array
t_mask = np.isnan(t)
distances[t_mask] = np.nan
s_mask = np.isnan(s)
distances[s_mask] = np.nan
# Spin through all the depth levels
temp_pot = np.zeros(self._d.size)
temp_in_situ = np.zeros(self._d.size)
d = np.zeros(self._d.size)
sal = np.zeros(self._d.size)
num_values = 0
for i in range(self._d.size):
t_level = t[i]
s_level = s[i]
d_level = distances[i]
try:
ind = np.nanargmin(d_level)
except ValueError:
# logger.info("%s: all-NaN slices" % i)
continue
if np.isnan(ind):
logger.info("%s: bottom of valid data" % i)
break
ind2 = np.unravel_index(ind, t_level.shape)
t_closest = t_level[ind2]
s_closest = s_level[ind2]
# d_closest = d_level[ind2]
temp_pot[i] = t_closest
sal[i] = s_closest
d[i] = self._d[i]
# Calculate in-situ temperature
p = Oc.d2p(d[i], lat)
temp_in_situ[i] = Oc.in_situ_temp(s=sal[i], t=t_closest, p=p, pr=self._ref_p)
# logger.info("%02d: %6.1f %6.1f > T/S/Dist: %3.1f %3.1f %3.1f [pot.temp. %3.1f]"
# % (i, d[i], p, temp_in_situ[i], s_closest, d_closest, t_closest))
num_values += 1
if num_values == 0:
logger.info("no data from lookup!")
return None
# ind = np.nanargmin(distances[0])
# ind2 = np.unravel_index(ind, distances[0].shape)
# switching to the query location
# lat_out = latitudes[ind2]
# lon_out = longitudes[ind2]
# while lon_out > 180.0:
# lon_out -= 360.0
# Make a new SV object to return our query in
ssp = Profile()
ssp.meta.sensor_type = Dicts.sensor_types['Synthetic']
ssp.meta.probe_type = Dicts.probe_types['RTOFS']
ssp.meta.latitude = lat
if lon > 180.0: # Go back to negative longitude
lon -= 360.0
ssp.meta.longitude = lon
ssp.meta.utc_time = dt(year=dtstamp.year, month=dtstamp.month, day=dtstamp.day,
hour=dtstamp.hour, minute=dtstamp.minute, second=dtstamp.second)
ssp.meta.original_path = "RTOFS_%s" % dtstamp.strftime("%Y%m%d_%H%M%S")
ssp.init_data(num_values)
ssp.data.depth = d[0:num_values]
ssp.data.temp = temp_in_situ[0:num_values]
ssp.data.sal = sal[0:num_values]
ssp.calc_data_speed()
ssp.clone_data_to_proc()
ssp.init_sis()
profiles = ProfileList()
profiles.append_profile(ssp)
return profiles
