"""
This module provides functions to easily access ragged array datasets. If the datasets are
not accessed via cloud storage platforms or are not found on the local filesystem,
they will be downloaded from their upstream repositories and stored for later access
(~/.clouddrift for UNIX-based systems).
"""
import os
from collections.abc import Callable
import xarray as xr
from clouddrift import adapters
from clouddrift.adapters.hurdat2 import _BasinOption
from clouddrift.adapters.ibtracs import _Kind, _Version
[docs]
def gdp1h(decode_times: bool = True) -> xr.Dataset:
"""Returns the latest version of the NOAA Global Drifter Program (GDP) hourly
dataset as a ragged array Xarray dataset.
The data is accessed from zarr archive hosted on a public AWS S3 bucket accessible at
https://registry.opendata.aws/noaa-oar-hourly-gdp/. Original data source from NOAA NCEI
is https://doi.org/10.25921/x46c-3620).
Parameters
----------
decode_times : bool, optional
If True, decode the time coordinate into a datetime object. If False, the time
coordinate will be an int64 or float64 array of increments since the origin
time indicated in the units attribute. Default is True.
Returns
-------
xarray.Dataset
Hourly GDP dataset as a ragged array
Examples
--------
>>> from clouddrift.datasets import gdp1h
>>> ds = gdp1h()
>>> ds
<xarray.Dataset>
Dimensions: (traj: 19396, obs: 197214787)
Coordinates:
id (traj) int64 ...
time (obs) datetime64[ns] ...
Dimensions without coordinates: traj, obs
Data variables: (12/60)
BuoyTypeManufacturer (traj) |S20 ...
BuoyTypeSensorArray (traj) |S20 ...
CurrentProgram (traj) float32 ...
DeployingCountry (traj) |S20 ...
DeployingShip (traj) |S20 ...
DeploymentComments (traj) |S20 ...
... ...
start_lat (traj) float32 ...
start_lon (traj) float32 ...
typebuoy (traj) |S10 ...
typedeath (traj) int8 ...
ve (obs) float32 ...
vn (obs) float32 ...
Attributes: (12/16)
Conventions: CF-1.6
acknowledgement: Elipot, Shane; Sykulski, Adam; Lumpkin, Rick; Centurio...
contributor_name: NOAA Global Drifter Program
contributor_role: Data Acquisition Center
date_created: 2023-09-08T17:05:12.130123
doi: 10.25921/x46c-3620
... ...
processing_level: Level 2 QC by GDP drifter DAC
publisher_email: aoml.dftr@noaa.gov
publisher_name: GDP Drifter DAC
publisher_url: https://www.aoml.noaa.gov/phod/gdp
summary: Global Drifter Program hourly data
title: Global Drifter Program hourly drifting buoy collection
See Also
--------
:func:`gdp6h`
"""
url = "https://noaa-oar-hourly-gdp-pds.s3.amazonaws.com/latest/gdp-v2.01.1.zarr"
ds = xr.open_dataset(url, engine="zarr", decode_times=decode_times)
return ds
[docs]
def gdp6h(decode_times: bool = True) -> xr.Dataset:
"""Returns the NOAA Global Drifter Program (GDP) 6-hourly dataset as a ragged array
Xarray dataset.
The data is accessed from zarr archive hosted on a public AWS S3 bucket accessible at
s3://noaa-oar-hourly-gdp-pds/experimental/. Original data source from NOAA's Atlantic
Oceanographic and Meteorological Laboratory (AOML) accessible at
https://www.aoml.noaa.gov/phod/gdp/index.php.
This returns the December 2024 version of the dataset including data from 1979-02-15:00:00:00Z
to 2025-01-20:00:00:00Z.
Parameters
----------
decode_times : bool, optional
If True, decode the time coordinate into a datetime object. If False, the time
coordinate will be an int64 or float64 array of increments since the origin
time indicated in the units attribute. Default is True.
Returns
-------
xarray.Dataset
6-hourly GDP dataset as a ragged array
Examples
--------
>>> from clouddrift.datasets import gdp6h
>>> ds = gdp6h()
>>> ds
<xarray.Dataset> Size: 2GB
Dimensions: (traj: 28341, obs: 48044996)
Coordinates:
id (traj) int64 227kB ...
time (obs) datetime64[ns] 384MB ...
Dimensions without coordinates: traj, obs
Data variables: (12/48)
BuoyTypeManufacturer (traj) <U1 113kB ...
BuoyTypeSensorArray (traj) <U10 1MB ...
CurrentProgram (traj) float64 227kB ...
DeployingCountry (traj) <U14 2MB ...
DeployingShip (traj) <U20 2MB ...
DeploymentComments (traj) <U20 2MB ...
... ...
start_lon (traj) float32 113kB ...
temp (obs) float32 192MB ...
typebuoy (traj) |S10 283kB ...
typedeath (traj) int8 28kB ...
ve (obs) float32 192MB ...
vn (obs) float32 192MB ...
Attributes: (12/18)
Conventions: CF-1.6
acknowledgement: Lumpkin, Rick; Centurioni, Luca (2019). NOAA Global...
contributor_name: NOAA Global Drifter Program
contributor_role: Data Acquisition Center
date_created: 2025-07-03T13:33:40.973074
doi: 10.25921/7ntx-z961
... ...
publisher_name: GDP Drifter DAC
publisher_url: https://www.aoml.noaa.gov/phod/gdp
summary: Global Drifter Program six-hourly data
time_coverage_end: 2024-08-16:12:00:00Z
time_coverage_start: 1979-02-15:00:00:00Z
title: Global Drifter Program drifting buoy collection
See Also
--------
:func:`gdp1h`
"""
url = "https://noaa-oar-hourly-gdp-pds.s3.amazonaws.com/experimental/gdp6h_ragged_dec24.zarr"
ds = xr.open_dataset(url, decode_times=decode_times, engine="zarr")
return ds
[docs]
def gdp_source(
tmp_path: str = adapters.gdp_source._TMP_PATH,
max: int | None = None,
skip_download: bool = False,
use_fill_values: bool = True,
decode_times: bool = True,
) -> xr.Dataset:
"""Returns the NOAA Global Drifter Program (GDP) source (raw) dataset as a ragged array
Xarray dataset.
The function will first look for the ragged-array dataset on the local
filesystem. If it is not found, the dataset will be downloaded using the
corresponding adapter function and stored as zarr archive for later access.
The data is accessed from a public HTTPS server at NOAA's Atlantic
Oceanographic and Meteorological Laboratory (AOML) at
https://www.aoml.noaa.gov/ftp/pub/phod/pub/pazos/data/shane/sst/.
Parameters
----------
tmp_path: str, default adapter temp path (default)
Temporary path where intermediary files are stored.
max: int, optional
Maximum number of files to retrieve and parse to generate the aggregate file. Mainly used
for testing purposes.
skip_download: bool, False (default)
If True, skips downloading the data files and the code assumes the files have already been downloaded.
This is mainly used to skip downloading files if the remote doesn't provide the HTTP Last-Modified header.
use_fill_values: bool, True (default)
When True, missing metadata fields are replaced with fill values. When False and no metadata
is found for a given drifter its observations are ignored.
decode_times : bool, True (default)
If True, decode the time coordinate into a datetime object. If False, the time
coordinate will be an int64 or float64 array of increments since the origin
time indicated in the units attribute. Default is True.
Returns
-------
xarray.Dataset
source GDP dataset as a ragged array
Examples
--------
>>> from clouddrift.datasets import gdp_source
>>> ds = gdp_source()
>>> ds
<xarray.Dataset> Size: ...
Dimensions: (traj: ..., obs: ...)
Coordinates:
id (traj) int64 222kB ...
obs_index (obs) int32 1GB ...
Dimensions without coordinates: traj, obs
Data variables: (12/22)
buoys_type (traj) <U5 ...
death_code (traj) int64 ...
drogue (obs) float32 ...
drogue_off_date (traj) datetime64[ns] ...
end_date (traj) datetime64[ns] ...
end_lat (traj) float64 222kB ...
... ...
sst (obs) float32 ...
start_date (traj) datetime64[ns] ...
start_lat (traj) float64 ...
start_lon (traj) float64 ...
voltage (obs) float32 ...
wmo_number (traj) int64 ...
"""
file_selection_label = "all" if max is None else f"first-{max}"
return _dataset_filecache(
f"gdpsource_agg_{file_selection_label}.zarr",
decode_times,
lambda: adapters.gdp_source.to_raggedarray(
tmp_path, skip_download, max, use_fill_values=use_fill_values
),
)
[docs]
def glad(decode_times: bool = True) -> xr.Dataset:
"""Returns the Grand LAgrangian Deployment (GLAD) dataset as a ragged array
Xarray dataset.
The function will first look for the ragged-array dataset on the local
filesystem. If it is not found, the dataset will be downloaded using the
corresponding adapter function and stored for later access.
The upstream data is available at https://doi.org/10.7266/N7VD6WC8.
Parameters
----------
decode_times : bool, optional
If True, decode the time coordinate into a datetime object. If False, the time
coordinate will be an int64 or float64 array of increments since the origin
time indicated in the units attribute. Default is True.
Returns
-------
xarray.Dataset
GLAD dataset as a ragged array
Examples
--------
>>> from clouddrift.datasets import glad
>>> ds = glad()
>>> ds
<xarray.Dataset>
Dimensions: (obs: 1602883, traj: 297)
Coordinates:
time (obs) datetime64[ns] ...
id (traj) object ...
Data variables:
latitude (obs) float32 ...
longitude (obs) float32 ...
position_error (obs) float32 ...
u (obs) float32 ...
v (obs) float32 ...
velocity_error (obs) float32 ...
rowsize (traj) int64 ...
Attributes:
title: GLAD experiment CODE-style drifter trajectories (low-pass f...
institution: Consortium for Advanced Research on Transport of Hydrocarbo...
source: CODE-style drifters
history: Downloaded from https://data.gulfresearchinitiative.org/dat...
references: Özgökmen, Tamay. 2013. GLAD experiment CODE-style drifter t...
Reference
---------
Özgökmen, Tamay. 2013. GLAD experiment CODE-style drifter trajectories (low-pass filtered, 15 minute interval records), northern Gulf of Mexico near DeSoto Canyon, July-October 2012. Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N7VD6WC8
"""
return _dataset_filecache("glad.nc", decode_times, adapters.glad.to_xarray)
[docs]
def hurdat2(
basin: _BasinOption = "both",
decode_times: bool = True,
tmp_path: str = adapters.hurdat2._DEFAULT_FILE_PATH,
) -> xr.DataArray:
"""Returns the revised hurricane database (HURDAT2) as a ragged array xarray dataset.
The function will first look for the ragged array dataset on the local
filesystem. If it is not found, the dataset will be downloaded using the
corresponding adapter function and stored for later access.
The upstream data is available at https://www.aoml.noaa.gov/hrd/hurdat/hurdat2.html.
Parameters
----------
basin : "atlantic", "pacific", "both" (default)
Specify which ocean basin to download storm data for. Current available options are the
Atlantic Ocean "atlantic", Pacific Ocean "pacific" and, both "both" to download
storm data for both oceans.
decode_times : bool, optional
If True, decode the time coordinate into a datetime object. If False, the time
coordinate will be an int64 or float64 array of increments since the origin
time indicated in the units attribute. Default is True.
Returns
-------
xarray.Dataset
HURDAT2 dataset as a ragged array.
Standard usage of the dataset.
>>> from clouddrift.datasets import hurdat2
>>> ds = hurdat2()
>>> ds
<xarray.Dataset>
Dimensions: (traj: ..., obs: ...)
Coordinates:
atcf_identifier (traj) <U8 ...
time (obs) datetime64[ns] ...
Dimensions without coordinates: traj, obs
Data variables: (12/23)
basin (traj) <U2 ...
year (traj) int64 ...
rowsize (traj) int64 ...
record_identifier (obs) <U1 ...
system_status (obs) <U2 ...
... ...
max_med_wind_radius_nw (obs) float64 ...
max_high_wind_radius_ne (obs) float64 ...
max_high_wind_radius_se (obs) float64 ...
max_high_wind_radius_sw (obs) float64 ...
max_high_wind_radius_nw (obs) float64 ...
max_sustained_wind_speed_radius (obs) float64 ...
Attributes:
title: HURricane DATa 2nd generation (HURDAT2)
date_created: ...
publisher_name: NOAA AOML Hurricane Research Division
publisher_email: AOML.HRDWebmaster@noaa.gov
publisher_url: https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html
institution: NOAA Atlantic Oceanographic and Meteorological Laboratory
summary: The National Hurricane Center (NHC) conducts a post-sto...
...
To only retrieve records for the Atlantic Ocean basin.
>>> from clouddrift.datasets import hurdat2
>>> ds = hurdat2(basin="atlantic")
>>> ds
<xarray.Dataset>
...
Reference
---------
https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html.
"""
return _dataset_filecache(
f"hurdat2_{basin}.nc",
decode_times,
lambda: adapters.hurdat2.to_raggedarray(basin, tmp_path).to_xarray(),
)
[docs]
def ibtracs(
version: _Version = "v04r01",
kind: _Kind = "LAST_3_YEARS",
tmp_path: str = adapters.ibtracs._DEFAULT_FILE_PATH,
decode_times: bool = True,
force: bool = False,
) -> xr.Dataset:
"""Returns International Best Track Archive for Climate Stewardship (IBTrACS) as a ragged array xarray dataset.
The function will first look for the ragged array dataset on the local
filesystem. If it is not found, the dataset will be downloaded using the
corresponding adapter function and stored for later access.
The upstream data is available at https://www.ncei.noaa.gov/products/international-best-track-archive
Parameters
----------
version : str, optional
Specify the dataset version to retrieve. Default to the latest version. Default is "v04r01".
kind: str, optional
Specify the dataset kind to retrieve. Specifying the kind can speed up execution time of specific querries
and operations. Default is "LAST_3_YEARS".
tmp_path: str, default adapter temp path (default)
Temporary path where intermediary files are stored. Default is ${osSpecificLocation}/clouddrift/ibtracs/.
decode_times : bool, optional
If True, decode the time coordinate into a datetime object. If False, the time
coordinate will be an int64 or float64 array of increments since the origin
time indicated in the units attribute. Default is True.
force: bool, optional
If True, force the download process regardless if there is a cached version of the generated dataset. Default
is False.
Returns
-------
xarray.Dataset
IBTRACS dataset as a ragged array.
Examples
--------
>>> from clouddrift import datasets
>>> ds = datasets.ibtracs()
>>> ds
<xarray.Dataset> Size: 18MB
Dimensions: (storm: 380, obs: 22055, quadrant: 4)
Coordinates:
id (storm) int64 3kB ...
time (obs) datetime64[ns] 176kB ...
Dimensions without coordinates: storm, obs, quadrant
Data variables: (12/161)
numobs (storm) int64 3kB ...
sid (storm) |S13 5kB ...
season (storm) float32 2kB ...
number (storm) int16 760B ...
name (storm) |S128 49kB ...
source_usa (storm) |S128 49kB ...
... ...
usa_seahgt (obs) float32 88kB ...
usa_searad (obs, quadrant) float32 353kB ...
storm_speed (obs) float32 88kB ...
storm_dir (obs) float32 88kB ...
lat (obs) float32 88kB ...
lon (obs) float32 88kB ...
Attributes: (12/49)
title: IBTrACS - International Best Track Archive fo...
summary: The intent of the IBTrACS project is to overc...
source: The original data are tropical cyclone positi...
Conventions: ACDD-1.3
Conventions_note: Data are nearly CF-1.7 compliant. The sole is...
product_version: v04r01
... ...
history: Thu Oct 17 05:37:12 2024: ncks --no_abc --cnk...
license: These data may be redistributed and used with...
featureType: trajectory
cdm_data_type: Trajectory
comment: The tracks of TCs generally look like a traje...
NCO: netCDF Operators version 5.0.7 (Homepage = ht...
"""
return _dataset_filecache(
f"ibtracs_ra_{version}_{kind}.nc",
decode_times,
lambda: adapters.ibtracs.to_raggedarray(version, kind, tmp_path),
force,
)
[docs]
def mosaic(decode_times: bool = True) -> xr.Dataset:
"""Returns the MOSAiC sea-ice drift dataset as a ragged array Xarray dataset.
The function will first look for the ragged-array dataset on the local
filesystem. If it is not found, the dataset will be downloaded using the
corresponding adapter function and stored for later access.
The upstream data is available at https://arcticdata.io/catalog/view/doi:10.18739/A2KP7TS83.
Reference
---------
Angela Bliss, Jennifer Hutchings, Philip Anderson, Philipp Anhaus,
Hans Jakob Belter, Jørgen Berge, Vladimir Bessonov, Bin Cheng, Sylvia Cole,
Dave Costa, Finlo Cottier, Christopher J Cox, Pedro R De La Torre, Dmitry V Divine,
Gilbert Emzivat, Ying-Chih Fang, Steven Fons, Michael Gallagher, Maxime Geoffrey,
Mats A Granskog, ... Guangyu Zuo. (2022). Sea ice drift tracks from the Distributed
Network of autonomous buoys deployed during the Multidisciplinary drifting Observatory
for the Study of Arctic Climate (MOSAiC) expedition 2019 - 2021. Arctic Data Center.
doi:10.18739/A2KP7TS83.
Parameters
----------
decode_times : bool, optional
If True, decode the time coordinate into a datetime object. If False, the time
coordinate will be an int64 or float64 array of increments since the origin
time indicated in the units attribute. Default is True.
Returns
-------
xarray.Dataset
MOSAiC sea-ice drift dataset as a ragged array
Examples
--------
>>> from clouddrift.datasets import mosaic
>>> ds = mosaic()
>>> ds
<xarray.Dataset>
Dimensions: (obs: 1926226, traj: 216)
Coordinates:
time (obs) datetime64[ns] ...
id (traj) object ...
Dimensions without coordinates: obs, traj
Data variables: (12/19)
latitude (obs) float64 ...
longitude (obs) float64 ...
Deployment Leg (traj) int64 ...
DN Station ID (traj) object ...
IMEI (traj) object ...
Deployment Date (traj) datetime64[ns] ...
... ...
Buoy Type (traj) object ...
Manufacturer (traj) object ...
Model (traj) object ...
PI (traj) object ...
Data Authors (traj) object ...
rowsize (traj) int64 ...
"""
return _dataset_filecache("mosaic.nc", decode_times, adapters.mosaic.to_xarray)
[docs]
def spotters(decode_times: bool = True) -> xr.Dataset:
"""Returns the Sofar Ocean Spotter drifters ragged array dataset as an Xarray dataset.
The data is accessed from a zarr archive hosted on a public AWS S3 bucket accessible
at https://sofar-spotter-archive.s3.amazonaws.com/spotter_data_bulk_zarr.
Parameters
----------
decode_times : bool, optional
If True, decode the time coordinate into a datetime object. If False, the time
coordinate will be an int64 or float64 array of increments since the origin
time indicated in the units attribute. Default is True.
Returns
-------
xarray.Dataset
Sofar ocean floats dataset as a ragged array
Examples
--------
>>> from clouddrift.datasets import spotters
>>> ds = spotters()
>>> ds
<xarray.Dataset>
Dimensions: (index: 6390651, trajectory: 871)
Coordinates:
time (index) datetime64[ns] ...
* trajectory (trajectory) object 'SPOT-010001' ... 'SPOT-1975'
Dimensions without coordinates: index
Data variables:
latitude (index) float64 ...
longitude (index) float64 ...
meanDirection (index) float64 ...
meanDirectionalSpread (index) float64 ...
meanPeriod (index) float64 ...
peakDirection (index) float64 ...
peakDirectionalSpread (index) float64 ...
peakPeriod (index) float64 ...
rowsize (trajectory) int64 ...
significantWaveHeight (index) float64 ...
Attributes:
author: Isabel A. Houghton
creation_date: 2023-10-18 00:43:55.333537
email: isabel.houghton@sofarocean.com
institution: Sofar Ocean
references: https://content.sofarocean.com/hubfs/Spotter%20product%20...
source: Spotter wave buoy
title: Sofar Spotter Data Archive - Bulk Wave Parameters
"""
url = "https://sofar-spotter-archive.s3.amazonaws.com/spotter_data_bulk_zarr"
return xr.open_dataset(url, engine="zarr", decode_times=decode_times)
[docs]
def subsurface_floats(decode_times: bool = True) -> xr.Dataset:
"""Returns the subsurface floats dataset as a ragged array Xarray dataset.
The data is accessed from a public HTTPS server at NOAA's Atlantic
Oceanographic and Meteorological Laboratory (AOML) accessible at
https://www.aoml.noaa.gov/phod/gdp/index.php.
The upstream data is available at
https://www.aoml.noaa.gov/phod/float_traj/files/allFloats_12122017.mat.
This dataset of subsurface float observations was compiled by the WOCE Subsurface
Float Data Assembly Center (WFDAC) in Woods Hole maintained by Andree Ramsey and
Heather Furey and copied to NOAA/AOML in October 2014 (version 1) and in December
2017 (version 2). Subsequent updates will be included as additional appropriate
float data, quality controlled by the appropriate principal investigators, is
submitted for inclusion.
Note that these observations are collected by ALACE/RAFOS/Eurofloat-style
acoustically-tracked, neutrally-buoyant subsurface floats which collect data while
drifting beneath the ocean surface. These data are the result of the effort and
resources of many individuals and institutions. You are encouraged to acknowledge
the work of the data originators and Data Centers in publications arising from use
of these data.
The float data were originally divided by project at the WFDAC. Here they have been
compiled in a single Matlab data set. See here for more information on the variables
contained in these files.
Parameters
----------
decode_times : bool, optional
If True, decode the time coordinate into a datetime object. If False, the time
coordinate will be an int64 or float64 array of increments since the origin
time indicated in the units attribute. Default is True.
Returns
-------
xarray.Dataset
Subsurface floats dataset as a ragged array
Examples
--------
>>> from clouddrift.datasets import subsurface_floats
>>> ds = subsurface_floats()
>>> ds
<xarray.Dataset>
Dimensions: (traj: 2193, obs: 1402840)
Coordinates:
id (traj) uint16 ...
time (obs) datetime64[ns] ...
Dimensions without coordinates: traj, obs
Data variables: (12/13)
expList (traj) object ...
expName (traj) object ...
expOrg (traj) object ...
expPI (traj) object ...
indexExp (traj) uint8 ...
fltType (traj) object ...
... ...
lon (obs) float64 ...
lat (obs) float64 ...
pres (obs) float64 ...
temp (obs) float64 ...
ve (obs) float64 ...
vn (obs) float64 ...
Attributes:
title: Subsurface float trajectories dataset
history: December 2017 (version 2)
date_created: 2023-11-14T22:30:38.831656
publisher_name: WOCE Subsurface Float Data Assembly Center and NOAA AOML
publisher_url: https://www.aoml.noaa.gov/phod/float_traj/data.php
license: freely available
acknowledgement: Maintained by Andree Ramsey and Heather Furey from the ...
References
----------
WOCE Subsurface Float Data Assembly Center (WFDAC) https://www.aoml.noaa.gov/phod/float_traj/index.php
"""
return _dataset_filecache(
"subsurface_floats.nc", decode_times, adapters.subsurface_floats.to_xarray
)
[docs]
def yomaha(decode_times: bool = True) -> xr.Dataset:
"""Returns the YoMaHa dataset as a ragged array Xarray dataset.
The function will first look for the ragged-array dataset on the local
filesystem. If it is not found, the dataset will be downloaded using the
corresponding adapter function and stored for later access. The upstream
data is available at http://apdrc.soest.hawaii.edu/projects/yomaha/.
Parameters
----------
decode_times : bool, optional
If True, decode the time coordinate into a datetime object. If False, the time
coordinate will be an int64 or float64 array of increments since the origin
time indicated in the units attribute. Default is True.
Returns
-------
xarray.Dataset
YoMaHa'07 dataset as a ragged array
Examples
--------
>>> from clouddrift.datasets import yomaha
>>> ds = yomaha()
>>> ds
<xarray.Dataset>
Dimensions: (obs: 1926743, traj: 12196)
Coordinates:
time_d (obs) datetime64[ns] ...
time_s (obs) datetime64[ns] ...
time_lp (obs) datetime64[ns] ...
time_lc (obs) datetime64[ns] ...
id (traj) int64 ...
Dimensions without coordinates: obs, traj
Data variables: (12/27)
lon_d (obs) float64 ...
lat_d (obs) float64 ...
pres_d (obs) float32 ...
ve_d (obs) float32 ...
vn_d (obs) float32 ...
err_ve_d (obs) float32 ...
... ...
cycle (obs) int64 ...
time_inv (obs) int64 ...
rowsize (traj) int64 ...
wmo_id (traj) int64 ...
dac_id (traj) int64 ...
float_type (traj) int64 ...
Attributes:
title: YoMaHa'07: Velocity data assessed from trajectories of A...
history: Dataset updated on Tue Jun 28 03:14:34 HST 2022
date_created: 2023-12-08T00:52:08.478075
publisher_name: Asia-Pacific Data Research Center
publisher_url: http://apdrc.soest.hawaii.edu/index.php
license: Creative Commons Attribution 4.0 International License..
Reference
---------
Lebedev, K. V., Yoshinari, H., Maximenko, N. A., & Hacker, P. W. (2007). Velocity data
assessed from trajectories of Argo floats at parking level and at the sea
surface. IPRC Technical Note, 4(2), 1-16.
"""
return _dataset_filecache("yomaha.nc", decode_times, adapters.yomaha.to_xarray)
[docs]
def andro(decode_times: bool = True) -> xr.Dataset:
"""Returns the ANDRO as a ragged array Xarray dataset.
The function will first look for the ragged-array dataset on the local
filesystem. If it is not found, the dataset will be downloaded using the
corresponding adapter function and stored for later access. The upstream
data is available at https://www.seanoe.org/data/00360/47077/.
Parameters
----------
decode_times : bool, optional
If True, decode the time coordinate into a datetime object. If False, the time
coordinate will be an int64 or float64 array of increments since the origin
time indicated in the units attribute. Default is True.
Returns
-------
xarray.Dataset
ANDRO dataset as a ragged array
Examples
--------
>>> from clouddrift.datasets import andro
>>> ds = andro()
>>> ds
<xarray.Dataset> Size: 110MB
Dimensions: (obs: 510630, traj: 3344)
Coordinates:
time_d (obs) datetime64[ns] 4MB ...
time_s (obs) datetime64[ns] 4MB ...
time_lp (obs) datetime64[ns] 4MB ...
time_lc (obs) datetime64[ns] 4MB ...
id (traj) float32 13kB ...
Dimensions without coordinates: obs, traj
Data variables: (12/33)
lon_d (obs) float64 4MB ...
lat_d (obs) float64 4MB ...
pres_d (obs) float32 2MB ...
temp_d (obs) float32 2MB ...
sal_d (obs) float32 2MB ...
ve_d (obs) float32 2MB ...
... ...
lon_lc (obs) float64 4MB ...
lat_lc (obs) float64 4MB ...
surf_fix (obs) float32 2MB ...
cycle (obs) float32 2MB ...
profile_id (obs) float32 2MB ...
rowsize (traj) int64 27kB ...
Attributes:
title: ANDRO: An Argo-based deep displacement dataset (Quality ...
history: Dataset updated on 2024-03-25
date_created: 2024-07-02T12:11:50.643492
publisher_name: SEANOE (SEA scieNtific Open data Edition)
publisher_url: https://www.seanoe.org/data/00360/47077/
license: Creative Commons Attribution 4.0 International License (...
Reference
---------
Ollitrault Michel, Rannou Philippe, Brion Emilie, Cabanes Cecile, Piron Anne, Reverdin Gilles,
Kolodziejczyk Nicolas (2022). ANDRO: An Argo-based deep displacement dataset.
SEANOE. https://doi.org/10.17882/47077
"""
return _dataset_filecache("andro.nc", decode_times, adapters.andro.to_xarray)
def _dataset_filecache(
filename: str,
decode_times: bool,
get_ds: Callable[[], xr.Dataset],
force: bool = False,
):
_, ext = os.path.splitext(filename)
clouddrift_path = (
os.path.expanduser("~/.clouddrift")
if not os.getenv("CLOUDDRIFT_PATH")
else os.getenv("CLOUDDRIFT_PATH")
)
fp = f"{clouddrift_path}/data/{filename}"
os.makedirs(os.path.dirname(fp), exist_ok=True)
if not os.path.exists(fp) or force:
ds = get_ds()
if ext == ".nc":
ds.to_netcdf(fp)
elif ext == ".zarr":
ds.to_zarr(fp)
else:
ds.to_netcdf(fp)
engine = None
if ext == ".zarr":
engine = "zarr"
return xr.open_dataset(fp, decode_times=decode_times, engine=engine)
