import xarray as xr
import xroms
import matplotlib.pyplot as plt
import cartopy
import numpy as np
# import hvplot.xarray
# import geoviews as gv
import cmocean.cm as cmo
import xcmocean

How to plot

This notebook demonstrates how to plot ROMS model output from a planview ($x$-$y$) and an $x$-$z$ cross-section. Static and interactive approaches are shown. The cartopy package is used for managing projections for mapview plots, which also gives many options for input (some shown below).

Note you need version 0.11 of Datashader for rasterizing to work in the interactive plots. (https://github.com/holoviz/hvplot/issues/434)

Load in data

Load in example dataset. More information at in input/output page

ds = xroms.datasets.fetch_ROMS_example_full_grid()
ds, xgrid = xroms.roms_dataset(ds, include_cell_volume=True, include_Z0=True)
ds.xroms.set_grid(xgrid)
ds

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

<xarray.Dataset> Size: 957MB
Dimensions:     (eta_rho: 191, xi_rho: 300, s_rho: 30, s_w: 31, ocean_time: 2,
                 xi_u: 299, eta_v: 190)
Coordinates: (12/29)
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * s_w         (s_w) float64 248B -1.0 -0.9667 -0.9333 ... -0.03333 0.0
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
    lon_u       (eta_rho, xi_u) float64 457kB dask.array<chunksize=(191, 299), meta=np.ndarray>
    ...          ...
    z_rho_v0    (s_rho, eta_v, xi_rho) float64 14MB dask.array<chunksize=(30, 190, 300), meta=np.ndarray>
    z_rho_psi0  (s_rho, eta_v, xi_u) float64 14MB dask.array<chunksize=(30, 190, 299), meta=np.ndarray>
    z_w0        (s_w, eta_rho, xi_rho) float64 14MB dask.array<chunksize=(31, 191, 300), meta=np.ndarray>
    z_w_u0      (s_w, eta_rho, xi_u) float64 14MB dask.array<chunksize=(31, 191, 299), meta=np.ndarray>
    z_w_v0      (s_w, eta_v, xi_rho) float64 14MB dask.array<chunksize=(31, 190, 300), meta=np.ndarray>
    z_w_psi0    (s_w, eta_v, xi_u) float64 14MB dask.array<chunksize=(31, 190, 299), meta=np.ndarray>
Data variables: (12/53)
    angle       (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    hc          float64 8B ...
    Cs_r        (s_rho) float64 240B dask.array<chunksize=(30,), meta=np.ndarray>
    zeta        (ocean_time, eta_rho, xi_rho) float32 458kB dask.array<chunksize=(2, 191, 300), meta=np.ndarray>
    h           (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    Cs_w        (s_w) float64 248B dask.array<chunksize=(31,), meta=np.ndarray>
    ...          ...
    dV_w_u      (ocean_time, s_w, eta_rho, xi_u) float64 28MB dask.array<chunksize=(2, 31, 191, 299), meta=np.ndarray>
    dV_v        (ocean_time, s_rho, eta_v, xi_rho) float64 27MB dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>
    dV_w_v      (ocean_time, s_w, eta_v, xi_rho) float64 28MB dask.array<chunksize=(2, 31, 190, 300), meta=np.ndarray>
    dV_psi      (ocean_time, s_rho, eta_v, xi_u) float64 27MB dask.array<chunksize=(2, 30, 190, 299), meta=np.ndarray>
    dV_w_psi    (ocean_time, s_w, eta_v, xi_u) float64 28MB dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>
    rho0        int64 8B 1025
Attributes: (12/29)
    file:              ocean_his_0150.nc
    format:            netCDF-3 classic file
    Conventions:       CF-1.4
    type:              ROMS/TOMS history file
    title:             Texas and Louisiana Shelf case (Nesting)
    rst_file:          ocean_rst.nc
    ...                ...
    compiler_command:  /g/software/openmpi/1.4.3/intel/bin/mpif90
    compiler_flags:    -heap-arrays -fp-model precise -assume 2underscores -c...
    tiling:            016x032
    history:           ROMS/TOMS, Version 3.4, Sunday - December 4, 2011 -  7...
    ana_file:          /scratch/zhangxq/projects/txla_nesting6/Functionals/an...
    CPP_options:       TXLA, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, BULK_FLUX...

xarray.Dataset

• Dimensions:
  • eta_rho: 191
  • xi_rho: 300
  • s_rho: 30
  • s_w: 31
  • ocean_time: 2
  • xi_u: 299
  • eta_v: 190
• Coordinates: (29)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • 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

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    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

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_w C: Cs_w eta: zeta depth: h depth_c: hc

    field :

    s_w, scalar

    axis :

    Z

    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.      ])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lon_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • xi_u
    (xi_u)
    int64
    0 1 2 3 4 5 ... 294 295 296 297 298

    axis :

    X

    array([  0,   1,   2, ..., 296, 297, 298])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • eta_v
    (eta_v)
    int64
    0 1 2 3 4 5 ... 185 186 187 188 189

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189])

  • z_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

    long_name :

    depth of W-points

    time :

    ocean_time

    field :

    z_w, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

  • z_w_u
    (ocean_time, s_w, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 31, 191, 299), meta=np.ndarray>

    long_name :

    depth of U-points on vertical W grid

    time :

    ocean_time

    field :

    z_w_u, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 27.01 MiB 27.01 MiB Shape (2, 31, 191, 299) (2, 31, 191, 299) Dask graph 1 chunks in 32 graph layers Data type float64 numpy.ndarray

  • z_w_v
    (ocean_time, s_w, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 190, 300), meta=np.ndarray>

    long_name :

    depth of V-points on vertical W grid

    time :

    ocean_time

    field :

    z_w_v, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.96 MiB 26.96 MiB Shape (2, 31, 190, 300) (2, 31, 190, 300) Dask graph 1 chunks in 34 graph layers Data type float64 numpy.ndarray

  • z_w_psi
    (ocean_time, s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>

    long_name :

    depth of PSI-points on vertical W grid

    time :

    ocean_time

    field :

    z_w_psi, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.87 MiB 26.87 MiB Shape (2, 31, 190, 299) (2, 31, 190, 299) Dask graph 1 chunks in 37 graph layers Data type float64 numpy.ndarray

  • z_rho
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

  • z_rho_u
    (ocean_time, s_rho, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 30, 191, 299), meta=np.ndarray>

    long_name :

    depth of U-points on vertical RHO grid

    time :

    ocean_time

    field :

    z_rho_u, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.14 MiB 26.14 MiB Shape (2, 30, 191, 299) (2, 30, 191, 299) Dask graph 1 chunks in 32 graph layers Data type float64 numpy.ndarray

  • z_rho_v
    (ocean_time, s_rho, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>

    long_name :

    depth of V-points on vertical RHO grid

    time :

    ocean_time

    field :

    z_rho_v, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.09 MiB 26.09 MiB Shape (2, 30, 190, 300) (2, 30, 190, 300) Dask graph 1 chunks in 34 graph layers Data type float64 numpy.ndarray

  • z_rho_psi
    (ocean_time, s_rho, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 30, 190, 299), meta=np.ndarray>

    long_name :

    depth of PSI-points on vertical RHO grid

    time :

    ocean_time

    field :

    z_rho_psi, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.01 MiB 26.01 MiB Shape (2, 30, 190, 299) (2, 30, 190, 299) Dask graph 1 chunks in 37 graph layers Data type float64 numpy.ndarray

  • z_rho0
    (s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(30, 191, 300), meta=np.ndarray>

    long_name :

    depth of RHO-points

    field :

    z_rho0, scalar

    units :

    m

    Array Chunk Bytes 13.11 MiB 13.11 MiB Shape (30, 191, 300) (30, 191, 300) Dask graph 1 chunks in 15 graph layers Data type float64 numpy.ndarray

  • z_rho_u0
    (s_rho, eta_rho, xi_u)
    float64
    dask.array<chunksize=(30, 191, 299), meta=np.ndarray>

    long_name :

    depth of U-points on vertical RHO grid

    field :

    z_rho_u0, scalar

    units :

    m

    Array Chunk Bytes 13.07 MiB 13.07 MiB Shape (30, 191, 299) (30, 191, 299) Dask graph 1 chunks in 18 graph layers Data type float64 numpy.ndarray

  • z_rho_v0
    (s_rho, eta_v, xi_rho)
    float64
    dask.array<chunksize=(30, 190, 300), meta=np.ndarray>

    long_name :

    depth of V-points on vertical RHO grid

    field :

    z_rho_v0, scalar

    units :

    m

    Array Chunk Bytes 13.05 MiB 13.05 MiB Shape (30, 190, 300) (30, 190, 300) Dask graph 1 chunks in 20 graph layers Data type float64 numpy.ndarray

  • z_rho_psi0
    (s_rho, eta_v, xi_u)
    float64
    dask.array<chunksize=(30, 190, 299), meta=np.ndarray>

    long_name :

    depth of PSI-points on vertical RHO grid

    field :

    z_rho_psi0, scalar

    units :

    m

    Array Chunk Bytes 13.00 MiB 13.00 MiB Shape (30, 190, 299) (30, 190, 299) Dask graph 1 chunks in 23 graph layers Data type float64 numpy.ndarray

  • z_w0
    (s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(31, 191, 300), meta=np.ndarray>

    long_name :

    depth of W-points

    field :

    z_w0, scalar

    units :

    m

    Array Chunk Bytes 13.55 MiB 13.55 MiB Shape (31, 191, 300) (31, 191, 300) Dask graph 1 chunks in 15 graph layers Data type float64 numpy.ndarray

  • z_w_u0
    (s_w, eta_rho, xi_u)
    float64
    dask.array<chunksize=(31, 191, 299), meta=np.ndarray>

    long_name :

    depth of U-points on vertical W grid

    field :

    z_w_u0, scalar

    units :

    m

    Array Chunk Bytes 13.51 MiB 13.51 MiB Shape (31, 191, 299) (31, 191, 299) Dask graph 1 chunks in 18 graph layers Data type float64 numpy.ndarray

  • z_w_v0
    (s_w, eta_v, xi_rho)
    float64
    dask.array<chunksize=(31, 190, 300), meta=np.ndarray>

    long_name :

    depth of V-points on vertical W grid

    field :

    z_w_v0, scalar

    units :

    m

    Array Chunk Bytes 13.48 MiB 13.48 MiB Shape (31, 190, 300) (31, 190, 300) Dask graph 1 chunks in 20 graph layers Data type float64 numpy.ndarray

  • z_w_psi0
    (s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(31, 190, 299), meta=np.ndarray>

    long_name :

    depth of PSI-points on vertical W grid

    field :

    z_w_psi0, scalar

    units :

    m

    Array Chunk Bytes 13.44 MiB 13.44 MiB Shape (31, 190, 299) (31, 190, 299) Dask graph 1 chunks in 23 graph layers Data type float64 numpy.ndarray

• Data variables: (53)
  • angle
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    angle between XI-axis and EAST

    units :

    radians

    field :

    angle, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • hc
    ()
    float64
    ...

    long_name :

    S-coordinate parameter, critical depth

    units :

    meter

    [1 values with dtype=float64]

  • Cs_r
    (s_rho)
    float64
    dask.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,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • zeta
    (ocean_time, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(2, 191, 300), meta=np.ndarray>

    long_name :

    free-surface

    units :

    meter

    time :

    ocean_time

    field :

    free-surface, scalar, series

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (2, 191, 300) (2, 191, 300) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • h
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    bathymetry at RHO-points

    units :

    meter

    field :

    bath, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • Cs_w
    (s_w)
    float64
    dask.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,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mask_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    mask on RHO-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mask_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    long_name :

    mask on U-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mask_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    long_name :

    mask on V-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • u
    (ocean_time, s_rho, eta_rho, xi_u)
    float32
    dask.array<chunksize=(2, 30, 191, 299), meta=np.ndarray>

    long_name :

    u-momentum component

    units :

    meter second-1

    time :

    ocean_time

    field :

    u-velocity, scalar, series

    Array Chunk Bytes 13.07 MiB 13.07 MiB Shape (2, 30, 191, 299) (2, 30, 191, 299) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v
    (ocean_time, s_rho, eta_v, xi_rho)
    float32
    dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>

    long_name :

    v-momentum component

    units :

    meter second-1

    time :

    ocean_time

    field :

    v-velocity, scalar, series

    Array Chunk Bytes 13.05 MiB 13.05 MiB Shape (2, 30, 190, 300) (2, 30, 190, 300) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • temp
    (ocean_time, s_rho, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    potential temperature

    units :

    Celsius

    time :

    ocean_time

    field :

    temperature, scalar, series

    Array Chunk Bytes 13.11 MiB 13.11 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • salt
    (ocean_time, s_rho, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    salinity

    time :

    ocean_time

    field :

    salinity, scalar, series

    Array Chunk Bytes 13.11 MiB 13.11 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Vtransform
    ()
    int32
    ...

    long_name :

    vertical terrain-following transformation equation

    [1 values with dtype=int32]

  • pm
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    curvilinear coordinate metric in XI

    units :

    meter-1

    field :

    pm, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • pn
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    curvilinear coordinate metric in ETA

    units :

    meter-1

    field :

    pn, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • f
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    Coriolis parameter at RHO-points

    units :

    second-1

    field :

    coriolis, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • spherical
    ()
    int64
    1

    array(1)

  • 3d
    ()
    bool
    True

    array(True)

  • dx
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI

    units :

    meter

    field :

    dx, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

  • dx_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI on U grid

    units :

    meter

    field :

    dx_u, scalar

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 6 graph layers Data type float64 numpy.ndarray

  • dx_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI on V grid

    units :

    meter

    field :

    dx_v, scalar

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 8 graph layers Data type float64 numpy.ndarray

  • dx_psi
    (eta_v, xi_u)
    float64
    dask.array<chunksize=(190, 299), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI on PSI grid

    units :

    meter

    field :

    dx_psi, scalar

    Array Chunk Bytes 443.83 kiB 443.83 kiB Shape (190, 299) (190, 299) Dask graph 1 chunks in 11 graph layers Data type float64 numpy.ndarray

  • dy
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA

    units :

    meter

    field :

    dy, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

  • dy_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA on U grid

    units :

    meter

    field :

    dy_u, scalar

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 6 graph layers Data type float64 numpy.ndarray

  • dy_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA on V grid

    units :

    meter

    field :

    dy_v, scalar

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 8 graph layers Data type float64 numpy.ndarray

  • dy_psi
    (eta_v, xi_u)
    float64
    dask.array<chunksize=(190, 299), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA on PSI grid

    units :

    meter

    field :

    dy_psi, scalar

    Array Chunk Bytes 443.83 kiB 443.83 kiB Shape (190, 299) (190, 299) Dask graph 1 chunks in 11 graph layers Data type float64 numpy.ndarray

  • dz
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid

    time :

    ocean_time

    field :

    dz, scalar, series

    units :

    m

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 34 graph layers Data type float64 numpy.ndarray

  • dz_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid

    time :

    ocean_time

    field :

    dz_w, scalar, series

    units :

    m

    Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 38 graph layers Data type float64 numpy.ndarray

  • dz_u
    (ocean_time, s_rho, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 30, 191, 299), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid on U grid

    time :

    ocean_time

    field :

    dz_u, scalar, series

    units :

    m

    Array Chunk Bytes 26.14 MiB 26.14 MiB Shape (2, 30, 191, 299) (2, 30, 191, 299) Dask graph 1 chunks in 37 graph layers Data type float64 numpy.ndarray

  • dz_w_u
    (ocean_time, s_w, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 31, 191, 299), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid on U grid

    time :

    ocean_time

    field :

    dz_w_u, scalar, series

    units :

    m

    Array Chunk Bytes 27.01 MiB 27.01 MiB Shape (2, 31, 191, 299) (2, 31, 191, 299) Dask graph 1 chunks in 41 graph layers Data type float64 numpy.ndarray

  • dz_v
    (ocean_time, s_rho, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid on V grid

    time :

    ocean_time

    field :

    dz_v, scalar, series

    units :

    m

    Array Chunk Bytes 26.09 MiB 26.09 MiB Shape (2, 30, 190, 300) (2, 30, 190, 300) Dask graph 1 chunks in 39 graph layers Data type float64 numpy.ndarray

  • dz_w_v
    (ocean_time, s_w, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 190, 300), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid on V grid

    time :

    ocean_time

    field :

    dz_w_v, scalar, series

    units :

    m

    Array Chunk Bytes 26.96 MiB 26.96 MiB Shape (2, 31, 190, 300) (2, 31, 190, 300) Dask graph 1 chunks in 43 graph layers Data type float64 numpy.ndarray

  • dz_psi
    (ocean_time, s_rho, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 30, 190, 299), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid on PSI grid

    time :

    ocean_time

    field :

    dz_psi, scalar, series

    units :

    m

    Array Chunk Bytes 26.01 MiB 26.01 MiB Shape (2, 30, 190, 299) (2, 30, 190, 299) Dask graph 1 chunks in 42 graph layers Data type float64 numpy.ndarray

  • dz_w_psi
    (ocean_time, s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid on PSI grid

    time :

    ocean_time

    field :

    dz_w_psi, scalar, series

    units :

    m

    Array Chunk Bytes 26.87 MiB 26.87 MiB Shape (2, 31, 190, 299) (2, 31, 190, 299) Dask graph 1 chunks in 46 graph layers Data type float64 numpy.ndarray

  • dz0
    (s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(30, 191, 300), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid

    field :

    dz0, scalar

    units :

    m

    Array Chunk Bytes 13.11 MiB 13.11 MiB Shape (30, 191, 300) (30, 191, 300) Dask graph 1 chunks in 20 graph layers Data type float64 numpy.ndarray

  • dz_w0
    (s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(31, 191, 300), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid

    field :

    dz_w0, scalar

    units :

    m

    Array Chunk Bytes 13.55 MiB 13.55 MiB Shape (31, 191, 300) (31, 191, 300) Dask graph 1 chunks in 24 graph layers Data type float64 numpy.ndarray

  • dz_u0
    (s_rho, eta_rho, xi_u)
    float64
    dask.array<chunksize=(30, 191, 299), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid on U grid

    field :

    dz_u0, scalar

    units :

    m

    Array Chunk Bytes 13.07 MiB 13.07 MiB Shape (30, 191, 299) (30, 191, 299) Dask graph 1 chunks in 23 graph layers Data type float64 numpy.ndarray

  • dz_w_u0
    (s_w, eta_rho, xi_u)
    float64
    dask.array<chunksize=(31, 191, 299), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid on U grid

    field :

    dz_w_u0, scalar

    units :

    m

    Array Chunk Bytes 13.51 MiB 13.51 MiB Shape (31, 191, 299) (31, 191, 299) Dask graph 1 chunks in 27 graph layers Data type float64 numpy.ndarray

  • dz_v0
    (s_rho, eta_v, xi_rho)
    float64
    dask.array<chunksize=(30, 190, 300), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid on V grid

    field :

    dz_v0, scalar

    units :

    m

    Array Chunk Bytes 13.05 MiB 13.05 MiB Shape (30, 190, 300) (30, 190, 300) Dask graph 1 chunks in 25 graph layers Data type float64 numpy.ndarray

  • dz_w_v0
    (s_w, eta_v, xi_rho)
    float64
    dask.array<chunksize=(31, 190, 300), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid on V grid

    field :

    dz_w_v0, scalar

    units :

    m

    Array Chunk Bytes 13.48 MiB 13.48 MiB Shape (31, 190, 300) (31, 190, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

  • dz_psi0
    (s_rho, eta_v, xi_u)
    float64
    dask.array<chunksize=(30, 190, 299), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid on PSI grid

    field :

    dz_psi0, scalar

    units :

    m

    Array Chunk Bytes 13.00 MiB 13.00 MiB Shape (30, 190, 299) (30, 190, 299) Dask graph 1 chunks in 28 graph layers Data type float64 numpy.ndarray

  • dz_w_psi0
    (s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(31, 190, 299), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid on PSI grid

    field :

    dz_w_psi0, scalar

    units :

    m

    Array Chunk Bytes 13.44 MiB 13.44 MiB Shape (31, 190, 299) (31, 190, 299) Dask graph 1 chunks in 32 graph layers Data type float64 numpy.ndarray

  • dA
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    area metric in XI and ETA on RHO grid

    units :

    meter2

    field :

    dA, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 7 graph layers Data type float64 numpy.ndarray

  • dV
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on RHO/RHO grids

    units :

    meter3

    field :

    dV, scalar

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 44 graph layers Data type float64 numpy.ndarray

  • dV_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on RHO/W grids

    units :

    meter3

    field :

    dV_w, scalar

    Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 48 graph layers Data type float64 numpy.ndarray

  • dV_u
    (ocean_time, s_rho, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 30, 191, 299), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on U/RHO grids

    units :

    meter3

    field :

    dV_u, scalar

    Array Chunk Bytes 26.14 MiB 26.14 MiB Shape (2, 30, 191, 299) (2, 30, 191, 299) Dask graph 1 chunks in 53 graph layers Data type float64 numpy.ndarray

  • dV_w_u
    (ocean_time, s_w, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 31, 191, 299), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on U/W grids

    units :

    meter3

    field :

    dV_w_u, scalar

    Array Chunk Bytes 27.01 MiB 27.01 MiB Shape (2, 31, 191, 299) (2, 31, 191, 299) Dask graph 1 chunks in 57 graph layers Data type float64 numpy.ndarray

  • dV_v
    (ocean_time, s_rho, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on V/RHO grids

    units :

    meter3

    field :

    dV_v, scalar

    Array Chunk Bytes 26.09 MiB 26.09 MiB Shape (2, 30, 190, 300) (2, 30, 190, 300) Dask graph 1 chunks in 59 graph layers Data type float64 numpy.ndarray

  • dV_w_v
    (ocean_time, s_w, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 190, 300), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on V/W grids

    units :

    meter3

    field :

    dV_w_v, scalar

    Array Chunk Bytes 26.96 MiB 26.96 MiB Shape (2, 31, 190, 300) (2, 31, 190, 300) Dask graph 1 chunks in 63 graph layers Data type float64 numpy.ndarray

  • dV_psi
    (ocean_time, s_rho, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 30, 190, 299), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on PSI/RHO grids

    units :

    meter3

    field :

    dV_psi, scalar

    Array Chunk Bytes 26.01 MiB 26.01 MiB Shape (2, 30, 190, 299) (2, 30, 190, 299) Dask graph 1 chunks in 68 graph layers Data type float64 numpy.ndarray

  • dV_w_psi
    (ocean_time, s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on PSI/W grids

    units :

    meter3

    field :

    dV_w_psi, scalar

    Array Chunk Bytes 26.87 MiB 26.87 MiB Shape (2, 31, 190, 299) (2, 31, 190, 299) Dask graph 1 chunks in 72 graph layers Data type float64 numpy.ndarray

  • rho0
    ()
    int64
    1025

    array(1025)

• Indexes: (7)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • s_w
    PandasIndex

    PandasIndex(Index([                -1.0,  -0.9666666666666667,  -0.9333333333333333,
                           -0.9,  -0.8666666666666667,  -0.8333333333333334,
                           -0.8,  -0.7666666666666666,  -0.7333333333333333,
                           -0.7,  -0.6666666666666666,  -0.6333333333333333,
                           -0.6,  -0.5666666666666667,  -0.5333333333333333,
                           -0.5,  -0.4666666666666667, -0.43333333333333335,
                           -0.4, -0.36666666666666664,  -0.3333333333333333,
                           -0.3, -0.26666666666666666, -0.23333333333333334,
                           -0.2, -0.16666666666666666, -0.13333333333333333,
                           -0.1, -0.06666666666666667, -0.03333333333333333,
                            0.0],
          dtype='float64', name='s_w'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • xi_u
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           289, 290, 291, 292, 293, 294, 295, 296, 297, 298],
          dtype='int64', name='xi_u', length=299))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

  • eta_v
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           180, 181, 182, 183, 184, 185, 186, 187, 188, 189],
          dtype='int64', name='eta_v', length=190))

• Attributes: (29)

  file :

  ocean_his_0150.nc

  format :

  netCDF-3 classic file

  Conventions :

  CF-1.4

  type :

  ROMS/TOMS history file

  title :

  Texas and Louisiana Shelf case (Nesting)

  rst_file :

  ocean_rst.nc

  his_base :

  ocean_his

  grd_file :

  txla_grd_v4_new.nc

  ini_file :

  ocean_rst.nc

  frc_file_01 :

  txla_blk_narr_2009.nc

  frc_file_02 :

  TXLA_river_4dyes_2011.nc

  frc_file_03 :

  txla_flx_frc_9911.nc

  bry_file :

  txla_bry_2009_new.nc

  clm_file :

  txla_clm_2009_new.nc

  script_file :

  External/ocean_txla.in

  svn_url :

  https://www.myroms.org/svn/src/trunk

  svn_rev :

  exported

  code_dir :

  /scratch/zhangxq/ROMS

  header_dir :

  /scratch/zhangxq/projects/txla_nesting6

  header_file :

  txla.h

  os :

  Linux

  cpu :

  x86_64

  compiler_system :

  ifort

  compiler_command :

  /g/software/openmpi/1.4.3/intel/bin/mpif90

  compiler_flags :

  -heap-arrays -fp-model precise -assume 2underscores -convert big_endian -ip -O3 -free

  tiling :

  016x032

  history :

  ROMS/TOMS, Version 3.4, Sunday - December 4, 2011 - 7:17:45 AM

  ana_file :

  /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_btflux.h, /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_hmixcoef.h, /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_nudgcoef.h, /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_stflux.h

  CPP_options :

  TXLA, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, BULK_FLUXES, CURVGRID, DIFF_GRID, DJ_GRADPS, DOUBLE_PRECISION, EAST_FSCHAPMAN, EAST_M2FLATHER, EAST_M3NUDGING, EAST_M3RADIATION, EAST_TNUDGING, EAST_TRADIATION, EMINUSP, LONGWAVE, M3CLIMATOLOGY, M3CLM_NUDGING, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, MY25_MIXING, NONLINEAR, NONLIN_EOS, NORTHERN_WALL, N2S2_HORAVG, POWER_LAW, PROFILE, QCORRECTION, K_GSCHEME, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SOUTH_FSCHAPMAN, SOUTH_M2FLATHER, SOUTH_M3NUDGING, SOUTH_M3RADIATION, SOUTH_TNUDGING, SOUTH_TRADIATION, SPLINES, TCLIMATOLOGY, TCLM_NUDGING, THREE_GHOST, T_PASSIVE, TS_MPDATA, TS_DIF2, TS_PSOURCE, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_LOGDRAG, UV_PSOURCE, UV_VIS2, VAR_RHO_2D, VISC_GRID, WEST_FSCHAPMAN, WEST_M2FLATHER, WEST_M3NUDGING, WEST_M3RADIATION, WEST_TNUDGING, WEST_TRADIATION

Setup for plotting

Use cartopy when plotting with a projection and/or wanting to add context like coastline.

proj = cartopy.crs.LambertConformal(central_longitude=-98, central_latitude=30)
pc = cartopy.crs.PlateCarree()

Using cf-xarray with plots

As described in the select data page, xroms is built to use the cf-xarray accessor to make working with dimensions easier.

See description of a DataArray with

ds.salt.cf.describe()

which returns

ds.salt.cf.describe()

Coordinates:
             CF Axes: * X: ['xi_rho']
                      * Y: ['eta_rho']
                      * Z: ['s_rho']
                      * T: ['ocean_time']

      CF Coordinates:   longitude: ['lon_rho']
                        latitude: ['lat_rho']
                        vertical: ['z_rho']
                      * time: ['ocean_time']

       Cell Measures:   area, volume: n/a

      Standard Names:   depth: ['z_rho']
                        latitude: ['lat_rho']
                        longitude: ['lon_rho']
                      * ocean_s_coordinate_g1: ['s_rho']
                      * time: ['ocean_time']

              Bounds:   n/a

       Grid Mappings:   n/a

/tmp/ipykernel_2909/990108105.py:1: DeprecationWarning: 'obj.cf.describe()' will be removed in a future version. Use instead 'repr(obj.cf)' or 'obj.cf' in a Jupyter environment.
  ds.salt.cf.describe()

From this you know that you can use the cf-xarray accessor to call many typical xarray functions from your Dataset or DataArray by substituting the generic Axes names (X, Y, Z, or T) in place of the specific dimension name on the right side of the Axes description. For example:

ds.salt.cf.sel(X=20, T='2010-1-1')

instead of

ds.salt.sel(xi_rho=20, ocean_time='2010-1-1')

A similar syntax works for isel commands.

You also know from this description that calls that take in coordinates for xarray can be used with their generic coordinate name listed above (longitude, latitude, vertical, time). For example:

ds.salt.cf.plot(x='longitude', y='latitude')

instead of

ds.salt.plot(x='lon_rho', y='lat_rho')

We use these shorter generalized Axes and Coordinates below.

xcmocean for choosing colormaps

The xcmocean accessor can be used when plotting with xarray to automatically choose a “good” colormap for the plot based on the DataArray name and attributes and colormaps from the cmocean colormaps package.

To use the colormap accessor, add .cmo before the plot call from xarray (but skip the plot if specifying the type of plot subsequently, like pcolormesh). These are the call options:

• cmo.plot()

• cmo.pcolormesh() to specify pcolormesh

• cmo.contourf() to specify contourf

• cmo.contour() to specify contour

ds.v.cf.isel(T=0, Z=-1).cmo.pcolormesh()

<matplotlib.collections.QuadMesh at 0x7f50c96698d0>

xcmocean can also be used in conjunction with cf-xarray but with slightly different syntax. You can access the same plot options while using cf-xarray in the plot call with the following:

• cmo.cfplot()

• cmo.cfpcolormesh()

• cmo.cfcontourf()

• cmo.cfcontour()

ds.temp.cf.isel(T=0, Z=-1).cmo.cfpcolormesh(x='longitude', y='latitude')

<matplotlib.collections.QuadMesh at 0x7f50be2a6e60>

Static: xarray

Can plot directly in xarray since it has wrapped many Matplotlib plotting routines. This is great for quick plots and continues to improve, but if you need more control then try the next section of plotting directly in Matplotlib.

map view

Overview

Plotted with dimension indices instead of coordinates:

ds.v.cf.isel(T=0, Z=-1).plot(cmap=cmo.delta)

<matplotlib.collections.QuadMesh at 0x7f50bdfbec50>

Plotted with coordinates lon/lat:

ds.v.cf.isel(T=0, Z=-1).cf.plot(x='longitude', y='latitude', cmap=cmo.delta)

<matplotlib.collections.QuadMesh at 0x7f50be0a19f0>

magnified

ds.salt.cf.isel(T=0, Z=-1, X=slice(100,300), Y=slice(75,100)).plot()

<matplotlib.collections.QuadMesh at 0x7f50bd7934c0>

ds.salt.cf.isel(T=0, Z=-1, X=slice(100,300), Y=slice(75,100)).cf.plot(x='longitude', y='latitude')

<matplotlib.collections.QuadMesh at 0x7f50bd6577f0>

cross-section

dss = ds.u.cf.isel(X=200, T=0)
dss.where(~dss.isnull().compute(), drop=True).cf.plot(x='latitude', y='vertical', cmap=cmo.delta, figsize=(10,6))

<matplotlib.collections.QuadMesh at 0x7f50bd538cd0>

Static: Matplotlib

map view

Overview

Here is a basic plan-view map, using cartopy for projection handling. You can add many different types of natural data for context. Shown here are coastline, land, rivers, and state borders. You can control the resolution of the data by changing the input to with_scale (options are ‘10m’, ‘50m’, or ‘110m’, corresponding to 1:10,000,000, 1:50,000,000, and 1:110,000,000 scale). More cartopy feature information available here: https://scitools.org.uk/cartopy/docs/v0.16/matplotlib/feature_interface.html.

fig = plt.figure(figsize=(12,8))
ax = plt.axes(projection=proj)

# Add natural features
ax.add_feature(cartopy.feature.LAND.with_scale('110m'), facecolor='0.8')
ax.add_feature(cartopy.feature.COASTLINE.with_scale('10m'), edgecolor='0.2')
ax.add_feature(cartopy.feature.RIVERS.with_scale('110m'), edgecolor='b')
ax.add_feature(cartopy.feature.STATES.with_scale('110m'), edgecolor='k')

gl = ax.gridlines(draw_labels=True, x_inline=False, y_inline=False, xlocs=np.arange(-104,-80,2))

# manipulate `gridliner` object to change locations of labels
gl.top_labels = False
gl.right_labels = False

ds.salt.cf.isel(T=0, Z=-1).cf.plot(ax=ax, x='longitude', y='latitude', transform=pc)

<cartopy.mpl.geocollection.GeoQuadMesh at 0x7f50bd45f220>

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_land.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_coastline.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_rivers_lake_centerlines.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_admin_1_states_provinces_lakes.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)

Magnified

Use set_extent to narrow the view to magnify a subregion.

fig = plt.figure(figsize=(12,8))
ax = plt.axes(projection=proj)

ax.set_extent([-94, -90, 27.5, 30], crs=pc)
ax.add_feature(cartopy.feature.LAND.with_scale('110m'), facecolor='0.8')
gl = ax.gridlines(draw_labels=True, x_inline=False, y_inline=False, xlocs=np.arange(-104,-80,2))

# manipulate `gridliner` object to change locations of labels
gl.top_labels = False
gl.right_labels = False

ds.salt.cf.isel(T=0, Z=-1).cf.plot(ax=ax, x='longitude', y='latitude', transform=pc)

<cartopy.mpl.geocollection.GeoQuadMesh at 0x7f50bd755f60>

cross-section

This is the same as the example above for plotting directly from xarray since projections on plan-view maps make most of the difference.

dss = ds.u.cf.isel(X=200, T=0)
dss.where(~dss.isnull().compute(), drop=True).cf.plot(x='latitude', y='vertical', cmap=cmo.delta, figsize=(10,6))

<matplotlib.collections.QuadMesh at 0x7f50bd1f9240>

Interactive

In these interactive plots, you can zoom, pan, and save plots using the menu on the right-hand side. There is a mouse hover option to display specific values; this can also be turned off. The plot automatically makes a widget to the right of the plot to easily vary over that variable. Currently the plots below are set to vary over time.

These plots aren’t working interactively in the docs, but are left here as examples for your own use.

map view

The tiles allow for different basemap options. The rasterize option is really important here by allowing a lower resolution presentation when zoomed out and increasing resolution with magnification, potentially saving a lot of time.

Vary over time (for surface)

tiles = gv.tile_sources.ESRI  # optional, for a basemap
ds.salt.cf.isel(s_rho=-1).hvplot.quadmesh(x='lon_rho', y='lat_rho', width=650, height=500,
                        cmap="cmo.haline", rasterize=True, crs=pc) * tiles

Vary over sigma level and time

tiles = gv.tile_sources.ESRI  # optional, for a basemap
ds.salt.hvplot.quadmesh(x='lon_rho', y='lat_rho', width=650, height=500,
                        cmap=cmo.haline, rasterize=True, crs=pc) * tiles

Vary over depth and time

Since the vertical dimension is in sigma coordinates instead of fixed depths, it is not immediate to be able to use a widget to vary depth in these plots. However, it is still possible to do using xroms code. First set up the calculation for several depths you want to examine using xroms.isoslice, then send that xarray object to hvplot for plotting. It is slow because it has to calculate everything, but it is nevertheless interactive. Having these files locally would speed it up.

In the following example, we use the accessor version of the isoslice interpolation to find slices of salinity at fixed depths. To save some time, we use the time-constant depths (z_rho0) associated with salinity instead of the time-varying version (z_rho).

zsalt = ds.salt.xroms.isoslice([-10, -20, -30], iso_array=ds.salt.z_rho0, axis='Z')

tiles = gv.tile_sources.ESRI  # optional, for a basemap
zsalt.hvplot.quadmesh(x='lon_rho', y='lat_rho', width=650, height=500,
                        cmap=cmo.haline, rasterize=True, crs=pc) * tiles

cross-section

In this case, the plots are similar whether rasterize=True is used or not.

ds.temp.isel(xi_rho=300).hvplot.quadmesh(x='lat_rho', y='z_rho0', width=750, height=400,
                        cmap=cmo.thermal)