"""
This module defines functions used to adapt the Grand LAgrangian Deployment
(GLAD) dataset as a ragged-array Xarray Dataset.
The dataset and its description are hosted at https://doi.org/10.7266/N7VD6WC8.
Example
-------
>>> from clouddrift.adapters import glad
>>> ds = glad.to_xarray()
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
"""
from io import BytesIO
import numpy as np
import pandas as pd
import xarray as xr
from clouddrift.adapters.utils import download_with_progress
[docs]
def get_dataframe() -> pd.DataFrame:
"""Get the GLAD dataset as a pandas DataFrame."""
url = "https://data.gulfresearchinitiative.org/api/file/download/169841"
# GRIIDC server doesn't provide Content-Length header, so we'll hardcode
# the expected data length here.
file_size = 155330876
buf = BytesIO(b"")
download_with_progress([(url, buf, file_size)])
buf.seek(0)
column_names = [
"id",
"date",
"time",
"latitude",
"longitude",
"position_error",
"u",
"v",
"velocity_error",
]
df = pd.read_csv(buf, sep=r"\s+", skiprows=5, names=column_names)
df["obs"] = pd.to_datetime(df["date"] + " " + df["time"])
df.drop(["date", "time"], axis=1, inplace=True)
return df
[docs]
def to_xarray() -> xr.Dataset:
"""Return the GLAD data as a ragged-array Xarray Dataset."""
df = get_dataframe()
ds = df.to_xarray()
traj, rowsize = np.unique(ds.id, return_counts=True)
# Make the dataset compatible with clouddrift functions.
ds = (
ds.swap_dims({"index": "obs"})
.drop_vars(["id", "index"])
.assign_coords(traj=traj)
.assign({"rowsize": ("traj", rowsize)})
.rename_vars({"obs": "time", "traj": "id"})
)
# Cast double floats to singles
for var in ds.variables:
if ds[var].dtype == "float64":
ds[var] = ds[var].astype("float32")
# Set variable attributes
ds["longitude"].attrs = {
"long_name": "longitude",
"standard_name": "longitude",
"units": "degrees_east",
}
ds["latitude"].attrs = {
"long_name": "latitude",
"standard_name": "latitude",
"units": "degrees_north",
}
ds["position_error"].attrs = {
"long_name": "position_error",
"units": "m",
}
ds["u"].attrs = {
"long_name": "eastward_sea_water_velocity",
"standard_name": "eastward_sea_water_velocity",
"units": "m s-1",
}
ds["v"].attrs = {
"long_name": "northward_sea_water_velocity",
"standard_name": "northward_sea_water_velocity",
"units": "m s-1",
}
ds["velocity_error"].attrs = {
"long_name": "velocity_error",
"units": "m s-1",
}
# Set global attributes
ds.attrs = {
"title": "GLAD experiment CODE-style drifter trajectories (low-pass filtered, 15 minute interval records), northern Gulf of Mexico near DeSoto Canyon, July-October 2012",
"institution": "Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE)",
"source": "CODE-style drifters",
"history": "Downloaded from https://data.gulfresearchinitiative.org/data/R1.x134.073:0004 and post-processed into a ragged-array Xarray Dataset by CloudDrift",
"references": "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 ds
