for url in dap_urls:
if "hfrnet" in url:
print "Skipping HF Radar Obs Data"
continue
if "NECOFS" in url:
try:
print "Attemping to load {0}".format(url)
u = iris.load_cube(url, u_constraint)
v = iris.load_cube(url, v_constraint)
# take first 20 chars for model name
mod_name = u.attributes["title"][0:20]
r = u.shape
timevar = find_timevar(u)
lat = u.coord(axis="Y").points
lon = u.coord(axis="X").points
jd = timevar.units.num2date(timevar.points)
start = timevar.units.date2num(jd_start)
istart = timevar.nearest_neighbour_index(start)
stop = timevar.units.date2num(jd_stop)
istop = timevar.nearest_neighbour_index(stop)
# Only proceed if we have data in the range requested.
if istart != istop:
nsta = len(stations)
if len(r) == 4:
# HYCOM and ROMS
# Dimensions are time, elevation, lat, lon
d = u[0, 0:1, :, :].data
# <codecell>
min_var = 0.01
# <codecell>
for url in dap_urls:
try:
a = iris.load_cube(url, constraint)
# convert to units of meters
# a.convert_units('m') # this isn't working for unstructured data
# take first 20 chars for model name
mod_name = a.attributes['title'][0:20]
r = a.shape
timevar = find_timevar(a)
lat = a.coord(axis='Y').points
lon = a.coord(axis='X').points
jd = timevar.units.num2date(timevar.points)
start = timevar.units.date2num(jd_start)
istart = timevar.nearest_neighbour_index(start)
stop = timevar.units.date2num(jd_stop)
istop = timevar.nearest_neighbour_index(stop)
# Only proceed if we have data in the range requested.
if istart != istop:
nsta = len(obs_lon)
if len(r) == 3:
print('[Structured grid model]:', url)
d = a[0, :, :].data
# Find the closest non-land point from a structured grid model.
# Use only data within 0.04 degrees (about 4 km).
max_dist = 0.04
# Use only data where the standard deviation of the time series exceeds 0.01 m (1 cm).
# This eliminates flat line model time series that come from land points that should have had missing values.
min_var = 0.01
for url in dap_urls:
try:
print 'Attemping to load {0}'.format(url)
a = iris.load_cube(url, constraint)
# convert to units of meters
# a.convert_units('m') # this isn't working for unstructured data
# take first 20 chars for model name
mod_name = a.attributes['title'][0:20]
r = a.shape
timevar = find_timevar(a)
lat = a.coord(axis='Y').points
lon = a.coord(axis='X').points
jd = timevar.units.num2date(timevar.points)
start = timevar.units.date2num(jd_start)
istart = timevar.nearest_neighbour_index(start)
stop = timevar.units.date2num(jd_stop)
istop = timevar.nearest_neighbour_index(stop)
# Only proceed if we have data in the range requested.
if istart != istop:
nsta = len(station_list)
if len(r) == 3:
print('[Structured grid model]:', url)
d = a[0, :, :].data
# Find the closest non-land point from a structured grid model.
# Use only data where the standard deviation of the time series exceeds 0.01 m (1 cm).
# This eliminates flat line model time series that come from land points that should have had missing values.
min_var = 0.01
# print dap_urls
for url in dap_urls:
# model_df, model_lat, model_lon = get_model_data(url, model_df, max_dist, min_var)
try:
print 'Attemping to load {0}'.format(url)
u = iris.load_cube(url, u_constraint)
v = iris.load_cube(url, v_constraint)
# take first 20 chars for model name
mod_name = u.attributes['title'][0:30]
r = u.shape
timevar = find_timevar(u)
lat = u.coord(axis='Y').points
lon = u.coord(axis='X').points
# Convert longitude to [-180 180]
if max(lon) > 180:
lon[lon > 180] = lon[lon > 180] - 360
jd = timevar.units.num2date(timevar.points)
start = timevar.units.date2num(jd_start)
istart = timevar.nearest_neighbour_index(start)
stop = timevar.units.date2num(jd_stop)
istop = timevar.nearest_neighbour_index(stop)
# Only proceed if we have data in the range requested.
if istart != istop:
nsta = len(stations)
max_dist = 0.4
# Use only data where the standard deviation of the time series exceeds 0.01 m (1 cm).
# This eliminates flat line model time series that come from land points that should have had missing values.
min_var = 0.01
#Try the WaveWatchIII global model
url = 'http://oos.soest.hawaii.edu/thredds/dodsC/hioos/model/wav/ww3/WaveWatch_III_Global_Wave_Model_best.ncd'
try:
print 'Attemping to load {0}'.format(url)
cube = iris.load_cube(url, constraint)
# take first 20 chars for model name
mod_name = cube.attributes['title'][0:30]
r = cube.shape
timevar = find_timevar(cube)
lat = cube.coord(axis='Y').points
lon = cube.coord(axis='X').points
# Convert longitude to [-180 180]
if max(lon) > 180:
lon[lon > 180] = lon[lon > 180] - 360
jd = timevar.units.num2date(timevar.points)
start = timevar.units.date2num(jd_start)
istart = timevar.nearest_neighbour_index(start)
stop = timevar.units.date2num(jd_stop)
istop = timevar.nearest_neighbour_index(stop)
# Only proceed if we have data in the range requested.
if istart != istop:
# Wave Watch III uses a 4D grid (time, z, lat, lon)
d = cube[0, 0, :, :].data
max_dist = 0.4
# Use only data where the standard deviation of the time series exceeds 0.01 m (1 cm).
# This eliminates flat line model time series that come from land points that should have had missing values.
min_var = 0.01
#Try the WaveWatchIII global model
url = 'http://oos.soest.hawaii.edu/thredds/dodsC/hioos/model/wav/ww3/WaveWatch_III_Global_Wave_Model_best.ncd'
try:
print 'Attemping to load {0}'.format(url)
cube = iris.load_cube(url, constraint)
# take first 20 chars for model name
mod_name = cube.attributes['title'][0:30]
r = cube.shape
timevar = find_timevar(cube)
lat = cube.coord(axis='Y').points
lon = cube.coord(axis='X').points
# Convert longitude to [-180 180]
if max(lon) > 180:
lon[lon>180] = lon[lon>180]-360
jd = timevar.units.num2date(timevar.points)
start = timevar.units.date2num(jd_start)
istart = timevar.nearest_neighbour_index(start)
stop = timevar.units.date2num(jd_stop)
istop = timevar.nearest_neighbour_index(stop)
# Only proceed if we have data in the range requested.
if istart != istop:
# Wave Watch III uses a 4D grid (time, z, lat, lon)
d = cube[0, 0, :, :].data
