Utility functions¶
[1]:
import ocean_data_gateway as odg
import xarray as xr
import matplotlib.pyplot as plt
Read in¶
Model output¶
Let’s say you have some model output:
[2]:
url = 'https://gcoos5.geos.tamu.edu/thredds/dodsC/NcML/nowcast_agg.nc'
# url = 'http://barataria.geos.tamu.edu/thredds/dodsC/NcML/forecast_his_archive_agg.nc'
dsm = xr.open_dataset(url, chunks={'ocean_time': 1})
# add more cf-xarray info
dsm = dsm.cf.guess_coord_axis()
dsm
[2]:
<xarray.Dataset>
Dimensions: (eta_psi: 190, eta_rho: 191, eta_u: 191, eta_v: 190, ocean_time: 872, s_rho: 30, s_w: 31, tracer: 6, xi_psi: 670, xi_rho: 671, xi_u: 670, xi_v: 671)
Coordinates:
* s_rho (s_rho) float64 -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
* s_w (s_w) float64 -1.0 -0.9667 -0.9333 ... -0.06667 -0.03333 0.0
lon_rho (eta_rho, xi_rho) float64 dask.array<chunksize=(191, 671), meta=np.ndarray>
lat_rho (eta_rho, xi_rho) float64 dask.array<chunksize=(191, 671), meta=np.ndarray>
lon_u (eta_u, xi_u) float64 dask.array<chunksize=(191, 670), meta=np.ndarray>
lat_u (eta_u, xi_u) float64 dask.array<chunksize=(191, 670), meta=np.ndarray>
lon_v (eta_v, xi_v) float64 dask.array<chunksize=(190, 671), meta=np.ndarray>
lat_v (eta_v, xi_v) float64 dask.array<chunksize=(190, 671), meta=np.ndarray>
lon_psi (eta_psi, xi_psi) float64 dask.array<chunksize=(190, 670), meta=np.ndarray>
lat_psi (eta_psi, xi_psi) float64 dask.array<chunksize=(190, 670), meta=np.ndarray>
* ocean_time (ocean_time) datetime64[ns] 2021-07-19 ... 2021-08-23
Dimensions without coordinates: eta_psi, eta_rho, eta_u, eta_v, tracer, xi_psi, xi_rho, xi_u, xi_v
Data variables: (12/101)
ntimes int32 ...
ndtfast int32 ...
dt float64 ...
dtfast float64 ...
dstart datetime64[ns] ...
shuffle int32 ...
... ...
Uwind (ocean_time, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 191, 671), meta=np.ndarray>
Vwind (ocean_time, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 191, 671), meta=np.ndarray>
shflux (ocean_time, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 191, 671), meta=np.ndarray>
ssflux (ocean_time, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 191, 671), meta=np.ndarray>
sustr (ocean_time, eta_u, xi_u) float32 dask.array<chunksize=(1, 191, 670), meta=np.ndarray>
svstr (ocean_time, eta_v, xi_v) float32 dask.array<chunksize=(1, 190, 671), meta=np.ndarray>
Attributes: (12/36)
file: /scratch/user/d.kobashi/projects/ROFS/projects...
format: netCDF-4/HDF5 file
Conventions: CF-1.4, SGRID-0.3
type: ROMS/TOMS history file
title: TXLA Regional Ocean Forecast Sysetm (ROFS) wit...
var_info: varinfo.dat
... ...
tiling: 010x012
history: ROMS/TOMS, Version 3.7, Thursday - August 19, ...
ana_file: /scratch/user/d.kobashi/source_code/COAWST/Fun...
CPP_options: TXLA2, ANA_BPFLUX, ANA_BSFLUX, ANA_BTFLUX, ANA...
EXTRA_DIMENSION.N: 30
EXTRA_DIMENSION.boundary: 4xarray.Dataset
- eta_psi: 190
- eta_rho: 191
- eta_u: 191
- eta_v: 190
- ocean_time: 872
- s_rho: 30
- s_w: 31
- tracer: 6
- xi_psi: 670
- xi_rho: 671
- xi_u: 670
- xi_v: 671
- s_rho(s_rho)float64-0.9833 -0.95 ... -0.05 -0.01667
- long_name :
- S-coordinate at RHO-points
- valid_min :
- -1.0
- valid_max :
- 0.0
- positive :
- up
- standard_name :
- ocean_s_coordinate_g2
- formula_terms :
- s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc
- field :
- s_rho, scalar
array([-0.983333, -0.95 , -0.916667, -0.883333, -0.85 , -0.816667, -0.783333, -0.75 , -0.716667, -0.683333, -0.65 , -0.616667, -0.583333, -0.55 , -0.516667, -0.483333, -0.45 , -0.416667, -0.383333, -0.35 , -0.316667, -0.283333, -0.25 , -0.216667, -0.183333, -0.15 , -0.116667, -0.083333, -0.05 , -0.016667]) - s_w(s_w)float64-1.0 -0.9667 ... -0.03333 0.0
- long_name :
- S-coordinate at W-points
- valid_min :
- -1.0
- valid_max :
- 0.0
- positive :
- up
- standard_name :
- ocean_s_coordinate_g2
- formula_terms :
- s: s_w C: Cs_w eta: zeta depth: h depth_c: hc
- field :
- s_w, scalar
array([-1. , -0.966667, -0.933333, -0.9 , -0.866667, -0.833333, -0.8 , -0.766667, -0.733333, -0.7 , -0.666667, -0.633333, -0.6 , -0.566667, -0.533333, -0.5 , -0.466667, -0.433333, -0.4 , -0.366667, -0.333333, -0.3 , -0.266667, -0.233333, -0.2 , -0.166667, -0.133333, -0.1 , -0.066667, -0.033333, 0. ]) - lon_rho(eta_rho, xi_rho)float64dask.array<chunksize=(191, 671), meta=np.ndarray>
- units :
- degree_east
- standard_name :
- longitude
- long_name :
- longitude of RHO-points
- field :
- lon_rho, scalar
- _ChunkSizes :
- [191 671]
Array Chunk Bytes 0.98 MiB 0.98 MiB Shape (191, 671) (191, 671) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - lat_rho(eta_rho, xi_rho)float64dask.array<chunksize=(191, 671), meta=np.ndarray>
- units :
- degree_north
- standard_name :
- latitude
- long_name :
- latitude of RHO-points
- field :
- lat_rho, scalar
- _ChunkSizes :
- [191 671]
Array Chunk Bytes 0.98 MiB 0.98 MiB Shape (191, 671) (191, 671) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - lon_u(eta_u, xi_u)float64dask.array<chunksize=(191, 670), meta=np.ndarray>
- units :
- degree_east
- standard_name :
- longitude
- long_name :
- longitude of U-points
- field :
- lon_u, scalar
- _ChunkSizes :
- [191 670]
Array Chunk Bytes 0.98 MiB 0.98 MiB Shape (191, 670) (191, 670) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - lat_u(eta_u, xi_u)float64dask.array<chunksize=(191, 670), meta=np.ndarray>
- units :
- degree_north
- standard_name :
- latitude
- long_name :
- latitude of U-points
- field :
- lat_u, scalar
- _ChunkSizes :
- [191 670]
Array Chunk Bytes 0.98 MiB 0.98 MiB Shape (191, 670) (191, 670) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - lon_v(eta_v, xi_v)float64dask.array<chunksize=(190, 671), meta=np.ndarray>
- units :
- degree_east
- standard_name :
- longitude
- long_name :
- longitude of V-points
- field :
- lon_v, scalar
- _ChunkSizes :
- [190 671]
Array Chunk Bytes 0.97 MiB 0.97 MiB Shape (190, 671) (190, 671) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - lat_v(eta_v, xi_v)float64dask.array<chunksize=(190, 671), meta=np.ndarray>
- units :
- degree_north
- standard_name :
- latitude
- long_name :
- latitude of V-points
- field :
- lat_v, scalar
- _ChunkSizes :
- [190 671]
Array Chunk Bytes 0.97 MiB 0.97 MiB Shape (190, 671) (190, 671) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - lon_psi(eta_psi, xi_psi)float64dask.array<chunksize=(190, 670), meta=np.ndarray>
- units :
- degree_east
- standard_name :
- longitude
- long_name :
- longitude of PSI-points
- field :
- lon_psi, scalar
- _ChunkSizes :
- [190 670]
Array Chunk Bytes 0.97 MiB 0.97 MiB Shape (190, 670) (190, 670) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - lat_psi(eta_psi, xi_psi)float64dask.array<chunksize=(190, 670), meta=np.ndarray>
- units :
- degree_north
- standard_name :
- latitude
- long_name :
- latitude of PSI-points
- field :
- lat_psi, scalar
- _ChunkSizes :
- [190 670]
Array Chunk Bytes 0.97 MiB 0.97 MiB Shape (190, 670) (190, 670) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - ocean_time(ocean_time)datetime64[ns]2021-07-19 ... 2021-08-23
- axis :
- T
- standard_name :
- time
- long_name :
- time since initialization
- field :
- time, scalar, series
- _ChunkSizes :
- 512
array(['2021-07-19T00:00:00.000000000', '2021-07-19T01:00:00.000000000', '2021-07-19T02:00:00.000000000', ..., '2021-08-22T22:00:00.000000000', '2021-08-22T23:00:00.000000000', '2021-08-23T00:00:00.000000000'], dtype='datetime64[ns]')
- ntimes()int32...
- long_name :
- number of long time-steps
array(2160, dtype=int32)
- ndtfast()int32...
- long_name :
- number of short time-steps
array(40, dtype=int32)
- dt()float64...
- long_name :
- size of long time-steps
- units :
- second
array(40.)
- dtfast()float64...
- long_name :
- size of short time-steps
- units :
- second
array(1.)
- dstart()datetime64[ns]...
- long_name :
- time stamp assigned to model initilization
array('2021-08-18T00:00:00.000000000', dtype='datetime64[ns]') - shuffle()int32...
- long_name :
- NetCDF-4/HDF5 file format shuffle filer flag
array(1, dtype=int32)
- deflate()int32...
- long_name :
- NetCDF-4/HDF5 file format deflate filer flag
array(1, dtype=int32)
- deflate_level()int32...
- long_name :
- NetCDF-4/HDF5 file format deflate level parameter
array(1, dtype=int32)
- nHIS()int32...
- long_name :
- number of time-steps between history records
array(90, dtype=int32)
- ndefHIS()int32...
- long_name :
- number of time-steps between the creation of history files
array(0, dtype=int32)
- nRST()int32...
- long_name :
- number of time-steps between restart records
- cycle :
- only latest two records are maintained
array(2160, dtype=int32)
- ntsAVG()int32...
- long_name :
- starting time-step for accumulation of time-averaged fields
array(1, dtype=int32)
- nAVG()int32...
- long_name :
- number of time-steps between time-averaged records
array(2160, dtype=int32)
- ndefAVG()int32...
- long_name :
- number of time-steps between the creation of average files
array(0, dtype=int32)
- nSTA()int32...
- long_name :
- number of time-steps between stations records
array(90, dtype=int32)
- Falpha()float64...
- long_name :
- Power-law shape barotropic filter parameter
array(2.)
- Fbeta()float64...
- long_name :
- Power-law shape barotropic filter parameter
array(4.)
- Fgamma()float64...
- long_name :
- Power-law shape barotropic filter parameter
array(0.284)
- nl_tnu2(tracer)float64dask.array<chunksize=(6,), meta=np.ndarray>
- long_name :
- nonlinear model Laplacian mixing coefficient for tracers
- units :
- meter2 second-1
Array Chunk Bytes 48 B 48 B Shape (6,) (6,) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - nl_visc2()float64...
- long_name :
- nonlinear model Laplacian mixing coefficient for momentum
- units :
- meter2 second-1
array(5.)
- LuvSponge()int32...
- long_name :
- horizontal viscosity sponge activation switch
- flag_values :
- [0 1]
- flag_meanings :
- .FALSE. .TRUE.
array(1, dtype=int32)
- LtracerSponge(tracer)int32dask.array<chunksize=(6,), meta=np.ndarray>
- long_name :
- horizontal diffusivity sponge activation switch
- flag_values :
- [0 1]
- flag_meanings :
- .FALSE. .TRUE.
Array Chunk Bytes 24 B 24 B Shape (6,) (6,) Count 3 Tasks 1 Chunks Type int32 numpy.ndarray - Akt_bak(tracer)float64dask.array<chunksize=(6,), meta=np.ndarray>
- long_name :
- background vertical mixing coefficient for tracers
- units :
- meter2 second-1
Array Chunk Bytes 48 B 48 B Shape (6,) (6,) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - Akv_bak()float64...
- long_name :
- background vertical mixing coefficient for momentum
- units :
- meter2 second-1
array(1.e-05)
- Akk_bak()float64...
- long_name :
- background vertical mixing coefficient for turbulent energy
- units :
- meter2 second-1
array(5.e-06)
- Akp_bak()float64...
- long_name :
- background vertical mixing coefficient for length scale
- units :
- meter2 second-1
array(5.e-06)
- rdrg()float64...
- long_name :
- linear drag coefficient
- units :
- meter second-1
array(0.0003)
- rdrg2()float64...
- long_name :
- quadratic drag coefficient
array(0.003)
- Zob()float64...
- long_name :
- bottom roughness
- units :
- meter
array(0.005)
- Zos()float64...
- long_name :
- surface roughness
- units :
- meter
array(0.02)
- gls_p()float64...
- long_name :
- stability exponent
array(-1.)
- gls_m()float64...
- long_name :
- turbulent kinetic energy exponent
array(0.5)
- gls_n()float64...
- long_name :
- turbulent length scale exponent
array(-1.)
- gls_cmu0()float64...
- long_name :
- stability coefficient
array(0.5477)
- gls_c1()float64...
- long_name :
- shear production coefficient
array(0.555)
- gls_c2()float64...
- long_name :
- dissipation coefficient
array(0.833)
- gls_c3m()float64...
- long_name :
- buoyancy production coefficient (minus)
array(-0.6)
- gls_c3p()float64...
- long_name :
- buoyancy production coefficient (plus)
array(1.)
- gls_sigk()float64...
- long_name :
- constant Schmidt number for TKE
array(2.)
- gls_sigp()float64...
- long_name :
- constant Schmidt number for PSI
array(2.)
- gls_Kmin()float64...
- long_name :
- minimum value of specific turbulent kinetic energy
array(1.e-09)
- gls_Pmin()float64...
- long_name :
- minimum Value of dissipation
array(1.e-12)
- Charnok_alpha()float64...
- long_name :
- Charnok factor for surface roughness
array(1400.)
- Zos_hsig_alpha()float64...
- long_name :
- wave amplitude factor for surface roughness
array(0.5)
- sz_alpha()float64...
- long_name :
- surface flux from wave dissipation
array(0.25)
- CrgBan_cw()float64...
- long_name :
- surface flux due to Craig and Banner wave breaking
array(100.)
- Znudg()float64...
- long_name :
- free-surface nudging/relaxation inverse time scale
- units :
- day-1
array(0.)
- M2nudg()float64...
- long_name :
- 2D momentum nudging/relaxation inverse time scale
- units :
- day-1
array(1.)
- M3nudg()float64...
- long_name :
- 3D momentum nudging/relaxation inverse time scale
- units :
- day-1
array(1.)
- Tnudg(tracer)float64dask.array<chunksize=(6,), meta=np.ndarray>
- long_name :
- Tracers nudging/relaxation inverse time scale
- units :
- day-1
Array Chunk Bytes 48 B 48 B Shape (6,) (6,) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - Tnudg_SSS()float64...
- long_name :
- SSS nudging/relaxation inverse time scale
- units :
- day-1
array(0.)
- rho0()float64...
- long_name :
- mean density used in Boussinesq approximation
- units :
- kilogram meter-3
array(1025.)
- gamma2()float64...
- long_name :
- slipperiness parameter
array(1.)
- LuvSrc()int32...
- long_name :
- momentum point sources and sink activation switch
- flag_values :
- [0 1]
- flag_meanings :
- .FALSE. .TRUE.
array(1, dtype=int32)
- LwSrc()int32...
- long_name :
- mass point sources and sink activation switch
- flag_values :
- [0 1]
- flag_meanings :
- .FALSE. .TRUE.
array(0, dtype=int32)
- LtracerSrc(tracer)int32dask.array<chunksize=(6,), meta=np.ndarray>
- long_name :
- tracer point sources and sink activation switch
- flag_values :
- [0 1]
- flag_meanings :
- .FALSE. .TRUE.
Array Chunk Bytes 24 B 24 B Shape (6,) (6,) Count 3 Tasks 1 Chunks Type int32 numpy.ndarray - LsshCLM()int32...
- long_name :
- sea surface height climatology processing switch
- flag_values :
- [0 1]
- flag_meanings :
- .FALSE. .TRUE.
array(0, dtype=int32)
- Lm2CLM()int32...
- long_name :
- 2D momentum climatology processing switch
- flag_values :
- [0 1]
- flag_meanings :
- .FALSE. .TRUE.
array(0, dtype=int32)
- Lm3CLM()int32...
- long_name :
- 3D momentum climatology processing switch
- flag_values :
- [0 1]
- flag_meanings :
- .FALSE. .TRUE.
array(1, dtype=int32)
- LtracerCLM(tracer)int32dask.array<chunksize=(6,), meta=np.ndarray>
- long_name :
- tracer climatology processing switch
- flag_values :
- [0 1]
- flag_meanings :
- .FALSE. .TRUE.
Array Chunk Bytes 24 B 24 B Shape (6,) (6,) Count 3 Tasks 1 Chunks Type int32 numpy.ndarray - LnudgeM2CLM()int32...
- long_name :
- 2D momentum climatology nudging activation switch
- flag_values :
- [0 1]
- flag_meanings :
- .FALSE. .TRUE.
array(0, dtype=int32)
- LnudgeM3CLM()int32...
- long_name :
- 3D momentum climatology nudging activation switch
- flag_values :
- [0 1]
- flag_meanings :
- .FALSE. .TRUE.
array(1, dtype=int32)
- LnudgeTCLM(tracer)int32dask.array<chunksize=(6,), meta=np.ndarray>
- long_name :
- tracer climatology nudging activation switch
- flag_values :
- [0 1]
- flag_meanings :
- .FALSE. .TRUE.
Array Chunk Bytes 24 B 24 B Shape (6,) (6,) Count 3 Tasks 1 Chunks Type int32 numpy.ndarray - spherical()int32...
- long_name :
- grid type logical switch
- flag_values :
- [0 1]
- flag_meanings :
- Cartesian spherical
array(1, dtype=int32)
- xl()float64...
- long_name :
- domain length in the XI-direction
- units :
- meter
array(9.96921e+36)
- el()float64...
- long_name :
- domain length in the ETA-direction
- units :
- meter
array(9.96921e+36)
- Vtransform()int32...
- long_name :
- vertical terrain-following transformation equation
array(2, dtype=int32)
- Vstretching()int32...
- long_name :
- vertical terrain-following stretching function
array(4, dtype=int32)
- theta_s()float64...
- long_name :
- S-coordinate surface control parameter
array(5.)
- theta_b()float64...
- long_name :
- S-coordinate bottom control parameter
array(0.4)
- Tcline()float64...
- long_name :
- S-coordinate surface/bottom layer width
- units :
- meter
array(20.)
- hc()float64...
- long_name :
- S-coordinate parameter, critical depth
- units :
- meter
array(20.)
- grid()int32...
- cf_role :
- grid_topology
- topology_dimension :
- 2
- node_dimensions :
- xi_psi eta_psi
- face_dimensions :
- xi_rho: xi_psi (padding: both) eta_rho: eta_psi (padding: both)
- edge1_dimensions :
- xi_u: xi_psi eta_u: eta_psi (padding: both)
- edge2_dimensions :
- xi_v: xi_psi (padding: both) eta_v: eta_psi
- node_coordinates :
- lon_psi lat_psi
- face_coordinates :
- lon_rho lat_rho
- edge1_coordinates :
- lon_u lat_u
- edge2_coordinates :
- lon_v lat_v
- vertical_dimensions :
- s_rho: s_w (padding: none)
array(1, dtype=int32)
- Cs_r(s_rho)float64dask.array<chunksize=(30,), meta=np.ndarray>
- long_name :
- S-coordinate stretching curves at RHO-points
- valid_min :
- -1.0
- valid_max :
- 0.0
- field :
- Cs_r, scalar
Array Chunk Bytes 240 B 240 B Shape (30,) (30,) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - Cs_w(s_w)float64dask.array<chunksize=(31,), meta=np.ndarray>
- long_name :
- S-coordinate stretching curves at W-points
- valid_min :
- -1.0
- valid_max :
- 0.0
- field :
- Cs_w, scalar
Array Chunk Bytes 248 B 248 B Shape (31,) (31,) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - h(eta_rho, xi_rho)float64dask.array<chunksize=(191, 671), meta=np.ndarray>
- long_name :
- bathymetry at RHO-points
- units :
- meter
- grid :
- grid
- location :
- face
- field :
- bath, scalar
- _ChunkSizes :
- [191 671]
Array Chunk Bytes 0.98 MiB 0.98 MiB Shape (191, 671) (191, 671) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - f(eta_rho, xi_rho)float64dask.array<chunksize=(191, 671), meta=np.ndarray>
- long_name :
- Coriolis parameter at RHO-points
- units :
- second-1
- grid :
- grid
- location :
- face
- field :
- coriolis, scalar
- _ChunkSizes :
- [191 671]
Array Chunk Bytes 0.98 MiB 0.98 MiB Shape (191, 671) (191, 671) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - pm(eta_rho, xi_rho)float64dask.array<chunksize=(191, 671), meta=np.ndarray>
- long_name :
- curvilinear coordinate metric in XI
- units :
- meter-1
- grid :
- grid
- location :
- face
- field :
- pm, scalar
- _ChunkSizes :
- [191 671]
Array Chunk Bytes 0.98 MiB 0.98 MiB Shape (191, 671) (191, 671) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - pn(eta_rho, xi_rho)float64dask.array<chunksize=(191, 671), meta=np.ndarray>
- long_name :
- curvilinear coordinate metric in ETA
- units :
- meter-1
- grid :
- grid
- location :
- face
- field :
- pn, scalar
- _ChunkSizes :
- [191 671]
Array Chunk Bytes 0.98 MiB 0.98 MiB Shape (191, 671) (191, 671) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - angle(eta_rho, xi_rho)float64dask.array<chunksize=(191, 671), meta=np.ndarray>
- long_name :
- angle between XI-axis and EAST
- units :
- radians
- grid :
- grid
- location :
- face
- field :
- angle, scalar
- _ChunkSizes :
- [191 671]
Array Chunk Bytes 0.98 MiB 0.98 MiB Shape (191, 671) (191, 671) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - mask_rho(eta_rho, xi_rho)float64dask.array<chunksize=(191, 671), meta=np.ndarray>
- long_name :
- mask on RHO-points
- flag_values :
- [0. 1.]
- flag_meanings :
- land water
- grid :
- grid
- location :
- face
- _ChunkSizes :
- [191 671]
Array Chunk Bytes 0.98 MiB 0.98 MiB Shape (191, 671) (191, 671) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - mask_u(eta_u, xi_u)float64dask.array<chunksize=(191, 670), meta=np.ndarray>
- long_name :
- mask on U-points
- flag_values :
- [0. 1.]
- flag_meanings :
- land water
- grid :
- grid
- location :
- edge1
- _ChunkSizes :
- [191 670]
Array Chunk Bytes 0.98 MiB 0.98 MiB Shape (191, 670) (191, 670) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - mask_v(eta_v, xi_v)float64dask.array<chunksize=(190, 671), meta=np.ndarray>
- long_name :
- mask on V-points
- flag_values :
- [0. 1.]
- flag_meanings :
- land water
- grid :
- grid
- location :
- edge2
- _ChunkSizes :
- [190 671]
Array Chunk Bytes 0.97 MiB 0.97 MiB Shape (190, 671) (190, 671) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - mask_psi(eta_psi, xi_psi)float64dask.array<chunksize=(190, 670), meta=np.ndarray>
- long_name :
- mask on psi-points
- flag_values :
- [0. 1.]
- flag_meanings :
- land water
- grid :
- grid
- location :
- node
- _ChunkSizes :
- [190 670]
Array Chunk Bytes 0.97 MiB 0.97 MiB Shape (190, 670) (190, 670) Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - zeta(ocean_time, eta_rho, xi_rho)float32dask.array<chunksize=(1, 191, 671), meta=np.ndarray>
- long_name :
- free-surface
- units :
- meter
- time :
- ocean_time
- grid :
- grid
- location :
- face
- field :
- free-surface, scalar, series
- _ChunkSizes :
- [ 1 191 671]
Array Chunk Bytes 426.32 MiB 500.63 kiB Shape (872, 191, 671) (1, 191, 671) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - u(ocean_time, s_rho, eta_u, xi_u)float32dask.array<chunksize=(1, 30, 191, 670), meta=np.ndarray>
- long_name :
- u-momentum component
- units :
- meter second-1
- time :
- ocean_time
- grid :
- grid
- location :
- edge1
- field :
- u-velocity, scalar, series
- _ChunkSizes :
- [ 1 30 191 670]
Array Chunk Bytes 12.47 GiB 14.65 MiB Shape (872, 30, 191, 670) (1, 30, 191, 670) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - v(ocean_time, s_rho, eta_v, xi_v)float32dask.array<chunksize=(1, 30, 190, 671), meta=np.ndarray>
- long_name :
- v-momentum component
- units :
- meter second-1
- time :
- ocean_time
- grid :
- grid
- location :
- edge2
- field :
- v-velocity, scalar, series
- _ChunkSizes :
- [ 1 30 190 671]
Array Chunk Bytes 12.42 GiB 14.59 MiB Shape (872, 30, 190, 671) (1, 30, 190, 671) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - w(ocean_time, s_w, eta_rho, xi_rho)float32dask.array<chunksize=(1, 31, 191, 671), meta=np.ndarray>
- long_name :
- vertical momentum component
- units :
- meter second-1
- time :
- ocean_time
- standard_name :
- upward_sea_water_velocity
- grid :
- grid
- location :
- face
- field :
- w-velocity, scalar, series
- _ChunkSizes :
- [ 1 31 191 671]
Array Chunk Bytes 12.91 GiB 15.16 MiB Shape (872, 31, 191, 671) (1, 31, 191, 671) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - temp(ocean_time, s_rho, eta_rho, xi_rho)float32dask.array<chunksize=(1, 30, 191, 671), meta=np.ndarray>
- long_name :
- potential temperature
- units :
- Celsius
- time :
- ocean_time
- grid :
- grid
- location :
- face
- field :
- temperature, scalar, series
- _ChunkSizes :
- [ 1 30 191 671]
Array Chunk Bytes 12.49 GiB 14.67 MiB Shape (872, 30, 191, 671) (1, 30, 191, 671) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - salt(ocean_time, s_rho, eta_rho, xi_rho)float32dask.array<chunksize=(1, 30, 191, 671), meta=np.ndarray>
- long_name :
- salinity
- time :
- ocean_time
- grid :
- grid
- location :
- face
- field :
- salinity, scalar, series
- _ChunkSizes :
- [ 1 30 191 671]
Array Chunk Bytes 12.49 GiB 14.67 MiB Shape (872, 30, 191, 671) (1, 30, 191, 671) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - dye_01(ocean_time, s_rho, eta_rho, xi_rho)float32dask.array<chunksize=(1, 30, 191, 671), meta=np.ndarray>
- long_name :
- dye concentration, type 01
- units :
- kilogram meter-3
- time :
- ocean_time
- grid :
- grid
- location :
- face
- field :
- dye_01, scalar, series
- _ChunkSizes :
- [ 1 30 191 671]
Array Chunk Bytes 12.49 GiB 14.67 MiB Shape (872, 30, 191, 671) (1, 30, 191, 671) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - dye_02(ocean_time, s_rho, eta_rho, xi_rho)float32dask.array<chunksize=(1, 30, 191, 671), meta=np.ndarray>
- long_name :
- dye concentration, type 02
- units :
- kilogram meter-3
- time :
- ocean_time
- grid :
- grid
- location :
- face
- field :
- dye_02, scalar, series
- _ChunkSizes :
- [ 1 30 191 671]
Array Chunk Bytes 12.49 GiB 14.67 MiB Shape (872, 30, 191, 671) (1, 30, 191, 671) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - dye_03(ocean_time, s_rho, eta_rho, xi_rho)float32dask.array<chunksize=(1, 30, 191, 671), meta=np.ndarray>
- long_name :
- dye concentration, type 03
- units :
- kilogram meter-3
- time :
- ocean_time
- grid :
- grid
- location :
- face
- field :
- dye_03, scalar, series
- _ChunkSizes :
- [ 1 30 191 671]
Array Chunk Bytes 12.49 GiB 14.67 MiB Shape (872, 30, 191, 671) (1, 30, 191, 671) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - dye_04(ocean_time, s_rho, eta_rho, xi_rho)float32dask.array<chunksize=(1, 30, 191, 671), meta=np.ndarray>
- long_name :
- dye concentration, type 04
- units :
- kilogram meter-3
- time :
- ocean_time
- grid :
- grid
- location :
- face
- field :
- dye_04, scalar, series
- _ChunkSizes :
- [ 1 30 191 671]
Array Chunk Bytes 12.49 GiB 14.67 MiB Shape (872, 30, 191, 671) (1, 30, 191, 671) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - Pair(ocean_time, eta_rho, xi_rho)float32dask.array<chunksize=(1, 191, 671), meta=np.ndarray>
- long_name :
- surface air pressure
- units :
- millibar
- time :
- ocean_time
- grid :
- grid
- location :
- face
- field :
- Pair, scalar, series
- _ChunkSizes :
- [ 1 191 671]
Array Chunk Bytes 426.32 MiB 500.63 kiB Shape (872, 191, 671) (1, 191, 671) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - Uwind(ocean_time, eta_rho, xi_rho)float32dask.array<chunksize=(1, 191, 671), meta=np.ndarray>
- long_name :
- surface u-wind component
- units :
- meter second-1
- time :
- ocean_time
- grid :
- grid
- location :
- face
- field :
- u-wind, scalar, series
- _ChunkSizes :
- [ 1 191 671]
Array Chunk Bytes 426.32 MiB 500.63 kiB Shape (872, 191, 671) (1, 191, 671) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - Vwind(ocean_time, eta_rho, xi_rho)float32dask.array<chunksize=(1, 191, 671), meta=np.ndarray>
- long_name :
- surface v-wind component
- units :
- meter second-1
- time :
- ocean_time
- grid :
- grid
- location :
- face
- field :
- v-wind, scalar, series
- _ChunkSizes :
- [ 1 191 671]
Array Chunk Bytes 426.32 MiB 500.63 kiB Shape (872, 191, 671) (1, 191, 671) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - shflux(ocean_time, eta_rho, xi_rho)float32dask.array<chunksize=(1, 191, 671), meta=np.ndarray>
- long_name :
- surface net heat flux
- units :
- watt meter-2
- negative_value :
- upward flux, cooling
- positive_value :
- downward flux, heating
- time :
- ocean_time
- grid :
- grid
- location :
- face
- field :
- surface heat flux, scalar, series
- _ChunkSizes :
- [ 1 191 671]
Array Chunk Bytes 426.32 MiB 500.63 kiB Shape (872, 191, 671) (1, 191, 671) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - ssflux(ocean_time, eta_rho, xi_rho)float32dask.array<chunksize=(1, 191, 671), meta=np.ndarray>
- long_name :
- surface net salt flux, (E-P)*SALT
- units :
- meter second-1
- negative_value :
- upward flux, freshening (net precipitation)
- positive_value :
- downward flux, salting (net evaporation)
- time :
- ocean_time
- grid :
- grid
- location :
- face
- field :
- surface net salt flux, scalar, series
- _ChunkSizes :
- [ 1 191 671]
Array Chunk Bytes 426.32 MiB 500.63 kiB Shape (872, 191, 671) (1, 191, 671) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - sustr(ocean_time, eta_u, xi_u)float32dask.array<chunksize=(1, 191, 670), meta=np.ndarray>
- long_name :
- surface u-momentum stress
- units :
- newton meter-2
- time :
- ocean_time
- grid :
- grid
- location :
- edge1
- field :
- surface u-momentum stress, scalar, series
- _ChunkSizes :
- [ 1 191 670]
Array Chunk Bytes 425.68 MiB 499.88 kiB Shape (872, 191, 670) (1, 191, 670) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray - svstr(ocean_time, eta_v, xi_v)float32dask.array<chunksize=(1, 190, 671), meta=np.ndarray>
- long_name :
- surface v-momentum stress
- units :
- newton meter-2
- time :
- ocean_time
- grid :
- grid
- location :
- edge2
- field :
- surface v-momentum stress, scalar, series
- _ChunkSizes :
- [ 1 190 671]
Array Chunk Bytes 424.08 MiB 498.01 kiB Shape (872, 190, 671) (1, 190, 671) Count 873 Tasks 872 Chunks Type float32 numpy.ndarray
- file :
- /scratch/user/d.kobashi/projects/ROFS/projects/txla2/outputs/TXLA2.ocn.his.2021_08_18_a.nc
- format :
- netCDF-4/HDF5 file
- Conventions :
- CF-1.4, SGRID-0.3
- type :
- ROMS/TOMS history file
- title :
- TXLA Regional Ocean Forecast Sysetm (ROFS) with dyes and oxygen
- var_info :
- varinfo.dat
- rst_file :
- /scratch/user/d.kobashi/projects/ROFS/projects/txla2/outputs/TXLA2.ocn.rst.2021_08_18_a.nc
- his_file :
- /scratch/user/d.kobashi/projects/ROFS/projects/txla2/outputs/TXLA2.ocn.his.2021_08_18_a.nc
- avg_file :
- /scratch/user/d.kobashi/projects/ROFS/projects/txla2/outputs/TXLA2.ocn.avg.2021_08_18_a.nc
- sta_file :
- /scratch/user/d.kobashi/projects/ROFS/projects/txla2/outputs/TXLA2.ocn.stn.2021_08_18_a.nc
- grd_file :
- /scratch/user/d.kobashi/projects/ROFS/projects/txla2/inputs/grids/txla2_grd_v4_test_lcut_hglo_wtype.nc
- ini_file :
- /scratch/user/d.kobashi/projects/ROFS/projects/txla2/inputs/ini/txla2.ini.2021_08_18_a.nc
- frc_file_01 :
- /scratch/user/d.kobashi/projects/ROFS/projects/txla2/inputs/frc//txla2_frc_2021_08_18_a.nc
- frc_file_02 :
- /scratch/user/d.kobashi/projects/ROFS/projects/txla2/inputs/flx/txla_flx_clm_1999_2040.nc
- bry_file_01 :
- /scratch/user/d.kobashi/projects/ROFS/projects/txla2/inputs/bryclm/txla2_bry_2021_08_18_a.nc
- clm_file_01 :
- /scratch/user/d.kobashi/projects/ROFS/projects/txla2/inputs/bryclm/txla2_clm_2021_08_18_a.nc
- nud_file :
- /scratch/user/d.kobashi/projects/ROFS/projects/txla2/inputs/clm_nudge/txla2_child_nudge_out_0.3d_6pt_ewns_linear.nc
- script_file :
- spos_file :
- /scratch/user/d.kobashi/projects/ROFS/projects/txla2/inputs/stations.in
- NLM_LBC :
- EDGE: WEST SOUTH EAST NORTH zeta: Che Che Che Clo ubar: Shc Shc Shc Clo vbar: Shc Shc Shc Clo u: Rad Rad Rad Clo v: Rad Rad Rad Clo temp: Rad Rad Rad Clo salt: Rad Rad Rad Clo dye_01: Gra Gra Gra Clo dye_02: Rad Rad Rad Clo dye_03: Rad Rad Rad Clo dye_04: Rad Rad Rad Clo tke: Gra Gra Gra Clo
- svn_url :
- https:://myroms.org/svn/src
- svn_rev :
- Unversioned directory
- code_dir :
- /scratch/user/d.kobashi/source_code/COAWST/COAWST.v3p5
- header_dir :
- /home/d.kobashi/ROFS/projects/txla2/compile
- header_file :
- txla2.h
- os :
- Linux
- cpu :
- x86_64
- compiler_system :
- ifort
- compiler_command :
- /sw/eb/sw/impi/2019.9.304-iccifort-2020.4.304/intel64/bin/mpiifort
- compiler_flags :
- -fp-model fast -heap-arrays -ip -O3
- tiling :
- 010x012
- history :
- ROMS/TOMS, Version 3.7, Thursday - August 19, 2021 - 2:46:34 AM
- ana_file :
- /scratch/user/d.kobashi/source_code/COAWST/Functionals/ana_btflux.h, /scratch/user/d.kobashi/source_code/COAWST/Functionals/ana_sponge.h, /scratch/user/d.kobashi/source_code/COAWST/Functionals/ana_stflux.h
- CPP_options :
- TXLA2, ANA_BPFLUX, ANA_BSFLUX, ANA_BTFLUX, ANA_SPFLUX, ANA_SPONGE, ASSUMED_SHAPE, AVERAGES, !BOUNDARY_A BULK_FLUXES, !COLLECT_ALL..., CURVGRID, DEFLATE, DIFF_GRID, DJ_GRADPS, DOUBLE_PRECISION, EMINUSP, GLS_MIXING, HDF5, KANTHA_CLAYSON, LONGWAVE, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, NONLINEAR, NONLIN_EOS, NO_LBC_ATT, N2S2_HORAVG, POWER_LAW, PROFILE, K_GSCHEME, RADIATION_2D, REDUCE_ALLGATHER, RI_SPLINES, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SPLINES_VDIFF, SPLINES_VVISC, SPHERICAL, STATIONS, T_PASSIVE, TS_MPDATA, TS_DIF2, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_LOGDRAG, UV_VIS2, VAR_RHO_2D, VISC_GRID, WTYPE_GRID
- EXTRA_DIMENSION.N :
- 30
- EXTRA_DIMENSION.boundary :
- 4
Data¶
[3]:
dataset_id = 'noaa_nos_co_ops_8770822'
kwargs = {
'approach': 'stations',
'readers': [odg.erddap],
'stations': dataset_id,
'erddap': {
'known_server': 'ioos',
# 'stations': [dataset_id]
},
}
data = odg.Gateway(**kwargs)
print(data.dataset_ids)
dsd = data['noaa_nos_co_ops_8770822']
dsd
['noaa_nos_co_ops_8770822']
[3]:
<xarray.Dataset>
Dimensions: (time: 507855, timeseries: 1)
Coordinates:
latitude (timeseries) float64 ...
longitude (timeseries) float64 ...
* time (time) datetime64[ns] ...
Dimensions without coordinates: timeseries
Data variables: (12/13)
station (timeseries) object ...
rowSize (timeseries) int32 ...
z (time, timeseries) float64 ...
air_pressure (time, timeseries) float64 ...
sea_water_electrical_conductivity (time, timeseries) float64 ...
sea_water_practical_salinity (time, timeseries) float64 ...
... ...
sea_water_temperature (time, timeseries) float64 ...
sea_surface_height_amplitude_due_to_geocentric_ocean_tide_geoid_mllw (time, timeseries) float64 ...
sea_surface_height_above_sea_level_geoid_mllw (time, timeseries) float64 ...
wind_speed_of_gust (time, timeseries) float64 ...
wind_speed (time, timeseries) float64 ...
wind_from_direction (time, timeseries) float64 ...
Attributes: (12/53)
cdm_data_type: TimeSeries
cdm_timeseries_variables: station,longitude,latitude
contributor_email: None,feedback@axiomdatascience.com
contributor_name: Gulf of Mexico Coastal Ocean Observing Sys...
contributor_role: funder,processor
contributor_role_vocabulary: NERC
... ...
standard_name_vocabulary: CF Standard Name Table v72
summary: Timeseries data from 'Texas Point, Sabine ...
time_coverage_end: 2021-08-19T16:14:00Z
time_coverage_start: 2015-09-03T15:42:00Z
title: Texas Point, Sabine Pass
Westernmost_Easting: -93.8369xarray.Dataset
- time: 507855
- timeseries: 1
- latitude(timeseries)float64...
- _CoordinateAxisType :
- Lat
- actual_range :
- [29.6781 29.6781]
- axis :
- Y
- ioos_category :
- Location
- long_name :
- Latitude
- standard_name :
- latitude
- units :
- degrees_north
array([29.6781])
- longitude(timeseries)float64...
- _CoordinateAxisType :
- Lon
- actual_range :
- [-93.8369 -93.8369]
- axis :
- X
- ioos_category :
- Location
- long_name :
- Longitude
- standard_name :
- longitude
- units :
- degrees_east
array([-93.8369])
- time(time)datetime64[ns]2015-09-03T15:42:00 ... 2021-08-...
- _CoordinateAxisType :
- Time
- actual_range :
- [1.44129492e+09 1.62938964e+09]
- axis :
- T
- ioos_category :
- Time
- long_name :
- Time
- standard_name :
- time
- time_origin :
- 01-JAN-1970 00:00:00
array(['2015-09-03T15:42:00.000000000', '2015-09-03T15:48:00.000000000', '2015-09-03T15:54:00.000000000', ..., '2021-08-19T15:12:00.000000000', '2021-08-19T15:18:00.000000000', '2021-08-19T16:14:00.000000000'], dtype='datetime64[ns]')
- station(timeseries)object...
- cf_role :
- timeseries_id
- ioos_category :
- Identifier
- ioos_code :
- urn:ioos:station:com.axiomdatascience:57559
- long_name :
- Texas Point, Sabine Pass
- short_name :
- urn:ioos:station:NOAA.NOS.CO-OPS:8770822
- type :
- fixed
array(['Texas Point, Sabine Pass'], dtype=object)
- rowSize(timeseries)int32...
- ioos_category :
- Identifier
- long_name :
- Number of Observations for this TimeSeries
- sample_dimension :
- obs
array([507855], dtype=int32)
- z(time, timeseries)float640.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
- _CoordinateAxisType :
- Height
- _CoordinateZisPositive :
- up
- actual_range :
- [0. 0.]
- axis :
- Z
- ioos_category :
- Location
- long_name :
- Altitude
- positive :
- up
- standard_name :
- altitude
- units :
- m
array([[0.], [0.], [0.], ..., [0.], [0.], [0.]]) - air_pressure(time, timeseries)float641.016e+03 1.016e+03 ... nan
- actual_range :
- [ 980.2 1040.3]
- id :
- 440415
- ioos_category :
- Other
- long_name :
- Barometric Pressure
- platform :
- station
- standard_name :
- air_pressure
- standard_name_url :
- http://mmisw.org/ont/cf/parameter/air_pressure
- units :
- millibars
array([[1015.6], [1015.6], [1015.6], ..., [1016.2], [1016.3], [ nan]]) - sea_water_electrical_conductivity(time, timeseries)float64nan nan nan nan ... nan nan nan nan
- actual_range :
- [ 5.36 14.59]
- id :
- 634068
- ioos_category :
- Other
- long_name :
- Conductivity
- platform :
- station
- standard_name :
- sea_water_electrical_conductivity
- standard_name_url :
- http://mmisw.org/ont/cf/parameter/sea_water_electrical_conductivity
- units :
- mS.cm-1
array([[nan], [nan], [nan], ..., [nan], [nan], [nan]]) - sea_water_practical_salinity(time, timeseries)float64nan nan nan nan ... nan nan nan nan
- actual_range :
- [2.63 7.87]
- id :
- 634069
- ioos_category :
- Other
- long_name :
- Salinity
- platform :
- station
- standard_name :
- sea_water_practical_salinity
- standard_name_url :
- http://mmisw.org/ont/cf/parameter/sea_water_practical_salinity
- units :
- 1e-3
array([[nan], [nan], [nan], ..., [nan], [nan], [nan]]) - air_temperature(time, timeseries)float6426.3 26.4 26.4 ... 30.3 30.2 nan
- actual_range :
- [-9.2 33.2]
- id :
- 440416
- ioos_category :
- Other
- long_name :
- Air Temperature
- platform :
- station
- standard_name :
- air_temperature
- standard_name_url :
- http://mmisw.org/ont/cf/parameter/air_temperature
- units :
- degree_Celsius
array([[26.3], [26.4], [26.4], ..., [30.3], [30.2], [ nan]]) - sea_water_temperature(time, timeseries)float64nan nan nan nan ... 30.9 30.9 nan
- actual_range :
- [ 4.3 35.6]
- id :
- 474286
- ioos_category :
- Other
- long_name :
- Water Temperature
- platform :
- station
- standard_name :
- sea_water_temperature
- standard_name_url :
- http://mmisw.org/ont/cf/parameter/sea_water_temperature
- units :
- degree_Celsius
array([[ nan], [ nan], [ nan], ..., [30.9], [30.9], [ nan]]) - sea_surface_height_amplitude_due_to_geocentric_ocean_tide_geoid_mllw(time, timeseries)float64nan nan nan nan ... nan nan 56.0
- actual_range :
- [-35. 77.]
- id :
- 606044
- ioos_category :
- Other
- long_name :
- Water Level Predictions (Tides)
- platform :
- station
- standard_name :
- sea_surface_height_amplitude_due_to_geocentric_ocean_tide
- standard_name_url :
- http://mmisw.org/ont/cf/parameter/sea_surface_height_amplitude_due_to_geocentric_ocean_tide
- units :
- cm
- vertical_datum :
- MLLW
array([[nan], [nan], [nan], ..., [nan], [nan], [56.]]) - sea_surface_height_above_sea_level_geoid_mllw(time, timeseries)float64nan nan nan nan ... 0.79 0.793 nan
- actual_range :
- [-0.669 3.769]
- id :
- 546976
- ioos_category :
- Other
- long_name :
- Water Level
- platform :
- station
- standard_name :
- sea_surface_height_above_sea_level
- standard_name_url :
- http://mmisw.org/ont/cf/parameter/sea_surface_height_above_sea_level
- units :
- m
- vertical_datum :
- MLLW
array([[ nan], [ nan], [ nan], ..., [0.79 ], [0.793], [ nan]]) - wind_speed_of_gust(time, timeseries)float64nan nan nan nan ... 14.99 16.33 nan
- actual_range :
- [ 0. 100.66]
- id :
- 440418
- ioos_category :
- Other
- long_name :
- Wind Gust
- platform :
- station
- standard_name :
- wind_speed_of_gust
- standard_name_url :
- http://mmisw.org/ont/cf/parameter/wind_speed_of_gust
- units :
- mile.hour-1
array([[ nan], [ nan], [ nan], ..., [14.987], [16.33 ], [ nan]]) - wind_speed(time, timeseries)float64nan nan nan nan ... 5.1 5.7 6.2 nan
- actual_range :
- [ 0. 35.9]
- id :
- 440419
- ioos_category :
- Other
- long_name :
- Wind Speed
- platform :
- station
- standard_name :
- wind_speed
- standard_name_url :
- http://mmisw.org/ont/cf/parameter/wind_speed
- units :
- m.s-1
array([[nan], [nan], [nan], ..., [5.7], [6.2], [nan]]) - wind_from_direction(time, timeseries)float64nan nan nan nan ... 171.0 168.0 nan
- actual_range :
- [ 0. 360.]
- id :
- 440417
- ioos_category :
- Other
- long_name :
- Wind From Direction
- platform :
- station
- standard_name :
- wind_from_direction
- standard_name_url :
- http://mmisw.org/ont/cf/parameter/wind_from_direction
- units :
- degrees
array([[ nan], [ nan], [ nan], ..., [171.], [168.], [ nan]])
- cdm_data_type :
- TimeSeries
- cdm_timeseries_variables :
- station,longitude,latitude
- contributor_email :
- None,feedback@axiomdatascience.com
- contributor_name :
- Gulf of Mexico Coastal Ocean Observing System (GCOOS),Axiom Data Science
- contributor_role :
- funder,processor
- contributor_role_vocabulary :
- NERC
- contributor_url :
- http://gcoos.org/,https://www.axiomdatascience.com
- Conventions :
- IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.0
- creator_country :
- USA
- creator_email :
- None
- creator_institution :
- NOAA Center for Operational Oceanographic Products and Services (CO-OPS)
- creator_name :
- NOAA Center for Operational Oceanographic Products and Services (CO-OPS)
- creator_sector :
- gov_federal
- creator_type :
- institution
- creator_url :
- https://tidesandcurrents.noaa.gov/
- defaultDataQuery :
- sea_surface_height_above_sea_level_geoid_mllw,air_temperature,sea_water_electrical_conductivity,sea_surface_height_amplitude_due_to_geocentric_ocean_tide_geoid_mllw,wind_speed_of_gust,sea_water_temperature,z,wind_speed,time,wind_from_direction,air_pressure,sea_water_practical_salinity&time>=max(time)-3days
- Easternmost_Easting :
- -93.8369
- featureType :
- TimeSeries
- geospatial_lat_max :
- 29.6781
- geospatial_lat_min :
- 29.6781
- geospatial_lat_units :
- degrees_north
- geospatial_lon_max :
- -93.8369
- geospatial_lon_min :
- -93.8369
- geospatial_lon_units :
- degrees_east
- geospatial_vertical_positive :
- up
- geospatial_vertical_units :
- m
- history :
- Downloaded from NOAA Center for Operational Oceanographic Products and Services (CO-OPS) at 2021-08-19T19:44:51Z https://sensors.axds.co/api/ 2021-08-19T19:44:51Z http://erddap.sensors.ioos.us/erddap/tabledap/noaa_nos_co_ops_8770822.ncCF
- id :
- 57559
- infoUrl :
- https://sensors.ioos.us/#metadata/57559/station
- institution :
- NOAA Center for Operational Oceanographic Products and Services (CO-OPS)
- license :
- The data may be used and redistributed for free but is not intended for legal use, since it may contain inaccuracies. Neither the data Contributor, ERD, NOAA, nor the United States Government, nor any of their employees or contractors, makes any warranty, express or implied, including warranties of merchantability and fitness for a particular purpose, or assumes any legal liability for the accuracy, completeness, or usefulness, of this information.
- naming_authority :
- com.axiomdatascience
- Northernmost_Northing :
- 29.6781
- platform :
- fixed
- platform_name :
- Texas Point, Sabine Pass
- platform_vocabulary :
- http://mmisw.org/ont/ioos/platform
- processing_level :
- Level 2
- publisher_country :
- USA
- publisher_email :
- None
- publisher_institution :
- NOAA Center for Operational Oceanographic Products and Services (CO-OPS)
- publisher_name :
- NOAA Center for Operational Oceanographic Products and Services (CO-OPS)
- publisher_sector :
- gov_federal
- publisher_type :
- institution
- publisher_url :
- https://tidesandcurrents.noaa.gov/
- references :
- https://tidesandcurrents.noaa.gov/stationhome.html?id=8770822,,
- sourceUrl :
- https://sensors.axds.co/api/
- Southernmost_Northing :
- 29.6781
- standard_name_vocabulary :
- CF Standard Name Table v72
- summary :
- Timeseries data from 'Texas Point, Sabine Pass' (urn:ioos:station:NOAA.NOS.CO-OPS:8770822)
- time_coverage_end :
- 2021-08-19T16:14:00Z
- time_coverage_start :
- 2015-09-03T15:42:00Z
- title :
- Texas Point, Sabine Pass
- Westernmost_Easting :
- -93.8369
[4]:
dsd.time
[4]:
<xarray.DataArray 'time' (time: 507855)>
array(['2015-09-03T15:42:00.000000000', '2015-09-03T15:48:00.000000000',
'2015-09-03T15:54:00.000000000', ..., '2021-08-19T15:12:00.000000000',
'2021-08-19T15:18:00.000000000', '2021-08-19T16:14:00.000000000'],
dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 2015-09-03T15:42:00 ... 2021-08-19T16:14:00
Attributes:
_CoordinateAxisType: Time
actual_range: [1.44129492e+09 1.62938964e+09]
axis: T
ioos_category: Time
long_name: Time
standard_name: time
time_origin: 01-JAN-1970 00:00:00xarray.DataArray
'time'
- time: 507855
- 2015-09-03T15:42:00 2015-09-03T15:48:00 ... 2021-08-19T16:14:00
array(['2015-09-03T15:42:00.000000000', '2015-09-03T15:48:00.000000000', '2015-09-03T15:54:00.000000000', ..., '2021-08-19T15:12:00.000000000', '2021-08-19T15:18:00.000000000', '2021-08-19T16:14:00.000000000'], dtype='datetime64[ns]') - time(time)datetime64[ns]2015-09-03T15:42:00 ... 2021-08-...
- _CoordinateAxisType :
- Time
- actual_range :
- [1.44129492e+09 1.62938964e+09]
- axis :
- T
- ioos_category :
- Time
- long_name :
- Time
- standard_name :
- time
- time_origin :
- 01-JAN-1970 00:00:00
array(['2015-09-03T15:42:00.000000000', '2015-09-03T15:48:00.000000000', '2015-09-03T15:54:00.000000000', ..., '2021-08-19T15:12:00.000000000', '2021-08-19T15:18:00.000000000', '2021-08-19T16:14:00.000000000'], dtype='datetime64[ns]')
- _CoordinateAxisType :
- Time
- actual_range :
- [1.44129492e+09 1.62938964e+09]
- axis :
- T
- ioos_category :
- Time
- long_name :
- Time
- standard_name :
- time
- time_origin :
- 01-JAN-1970 00:00:00
resample_like¶
Resample one Dataset to have the same times as another Dataset.
[5]:
dsd_resampled = odg.utils.resample_like(dsd, dsm)
dsd_resampled.time
[5]:
<xarray.DataArray 'time' (time: 52250)>
array(['2015-09-03T15:00:00.000000000', '2015-09-03T16:00:00.000000000',
'2015-09-03T17:00:00.000000000', ..., '2021-08-19T14:00:00.000000000',
'2021-08-19T15:00:00.000000000', '2021-08-19T16:00:00.000000000'],
dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 2015-09-03T15:00:00 ... 2021-08-19T16:00:00
Attributes:
_CoordinateAxisType: Time
actual_range: [1.44129492e+09 1.62938964e+09]
axis: T
ioos_category: Time
long_name: Time
standard_name: time
time_origin: 01-JAN-1970 00:00:00xarray.DataArray
'time'
- time: 52250
- 2015-09-03T15:00:00 2015-09-03T16:00:00 ... 2021-08-19T16:00:00
array(['2015-09-03T15:00:00.000000000', '2015-09-03T16:00:00.000000000', '2015-09-03T17:00:00.000000000', ..., '2021-08-19T14:00:00.000000000', '2021-08-19T15:00:00.000000000', '2021-08-19T16:00:00.000000000'], dtype='datetime64[ns]') - time(time)datetime64[ns]2015-09-03T15:00:00 ... 2021-08-...
- _CoordinateAxisType :
- Time
- actual_range :
- [1.44129492e+09 1.62938964e+09]
- axis :
- T
- ioos_category :
- Time
- long_name :
- Time
- standard_name :
- time
- time_origin :
- 01-JAN-1970 00:00:00
array(['2015-09-03T15:00:00.000000000', '2015-09-03T16:00:00.000000000', '2015-09-03T17:00:00.000000000', ..., '2021-08-19T14:00:00.000000000', '2021-08-19T15:00:00.000000000', '2021-08-19T16:00:00.000000000'], dtype='datetime64[ns]')
- _CoordinateAxisType :
- Time
- actual_range :
- [1.44129492e+09 1.62938964e+09]
- axis :
- T
- ioos_category :
- Time
- long_name :
- Time
- standard_name :
- time
- time_origin :
- 01-JAN-1970 00:00:00
The resampling looks correct!
[6]:
fig, ax = plt.subplots(1,1, figsize=(15,5))
dsd.cf['ssh'].sel(time='2019-1').plot(ax=ax)
dsd_resampled.cf['ssh'].sel(time='2019-1').plot(ax=ax)
[6]:
[<matplotlib.lines.Line2D at 0x158eff760>]
