import intake
import hvplot.pandas
import hvplot.xarray
import cook_inlet_catalogs as cic
import holoviews as hv

HF Radar (UAF)

HF Radar from UAF.

Files are:

• Upper Cook Inlet (System A): 2002-2003 and 2009

• Lower Cook Inlet (System B): 2006-2007

Data variables available include tidally filtered and weekly averaged along with tidal constituents calculated from hourly data.

Some of the data is written up in reports:

• https://espis.boem.gov/final%20reports/5009.pdf

• https://www.govinfo.gov/app/details/GOVPUB-I-47b721482d69e308aec1cca9b3e51955

cat = intake.open_catalog(cic.utils.cat_path("hfradar"))

Plot all datasets in catalog

dd, ddlabels = cic.utils.combine_datasets_for_map(cat)
dd.hvplot(**cat.metadata["map"]) * ddlabels.hvplot(**cat.metadata["maplabels"])

List available datasets in the catalog

dataset_ids = list(cat)
dataset_ids

['lower-ci_system-B_2006-2007',
 'lower-ci_system-B_2006-2007_subtidal_weekly_mean',
 'lower-ci_system-B_2006-2007_tidecons',
 'lower-ci_system-B_2006_subtidal_daily_mean',
 'lower-ci_system-B_2006_subtidal_weekly_mean',
 'lower-ci_system-B_2006_tidecons',
 'upper-ci_system-A_2002-2003',
 'upper-ci_system-A_2002-2003_subtidal_weekly_mean',
 'upper-ci_system-A_2002-2003_tidecons',
 'upper-ci_system-A_2003_subtidal_daily_mean',
 'upper-ci_system-A_2003_subtidal_weekly_mean',
 'upper-ci_system-A_2003_tidecons',
 'upper-ci_system-A_2009',
 'upper-ci_system-A_2009_subtidal_weekly_mean',
 'upper-ci_system-A_2009_tidecons']

Select one dataset to investigate

try:
    dataset_id = dataset_ids[2]
except:
    dataset_id = dataset_ids[0]
print(dataset_id)

dd = cat[dataset_id].read()
dd

lower-ci_system-B_2006-2007_tidecons

<xarray.Dataset> Size: 248kB
Dimensions:      (x: 26, y: 35, ntidecons: 8, tideconvals: 4)
Coordinates:
  * ntidecons    (ntidecons) <U2 64B 'M2' 'S2' 'K2' 'K1' 'N2' 'P1' 'O1' 'Q1'
  * tideconvals  (tideconvals) <U11 176B 'major' 'minor' 'inclination' 'phase'
    lat          (x, y) float64 7kB dask.array<chunksize=(26, 35), meta=np.ndarray>
    z            float64 8B ...
  * x            (x) int64 208B 0 1 2 3 4 5 6 7 8 ... 17 18 19 20 21 22 23 24 25
  * y            (y) int64 280B 0 1 2 3 4 5 6 7 8 ... 26 27 28 29 30 31 32 33 34
    lon          (x, y) float64 7kB dask.array<chunksize=(26, 35), meta=np.ndarray>
Data variables:
    tidecons     (x, y, ntidecons, tideconvals) float64 233kB dask.array<chunksize=(26, 35, 8, 4), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • x: 26
  • y: 35
  • ntidecons: 8
  • tideconvals: 4
• Coordinates: (7)
  • ntidecons
    (ntidecons)
    <U2
    'M2' 'S2' 'K2' ... 'P1' 'O1' 'Q1'

    array(['M2', 'S2', 'K2', 'K1', 'N2', 'P1', 'O1', 'Q1'], dtype='<U2')

  • tideconvals
    (tideconvals)
    <U11
    'major' 'minor' ... 'phase'

    array(['major', 'minor', 'inclination', 'phase'], dtype='<U11')

  • lat
    (x, y)
    float64
    dask.array<chunksize=(26, 35), meta=np.ndarray>

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    Array Chunk Bytes 7.11 kiB 7.11 kiB Shape (26, 35) (26, 35) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • z
    ()
    float64
    ...

    units :

    m

    standard_name :

    depth

    long_name :

    depth

    positive :

    down

    axis :

    Z

    [1 values with dtype=float64]

  • x
    (x)
    int64
    0 1 2 3 4 5 6 ... 20 21 22 23 24 25

    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])

  • y
    (y)
    int64
    0 1 2 3 4 5 6 ... 29 30 31 32 33 34

    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])

  • lon
    (x, y)
    float64
    dask.array<chunksize=(26, 35), meta=np.ndarray>

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    Array Chunk Bytes 7.11 kiB 7.11 kiB Shape (26, 35) (26, 35) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• Data variables: (1)
  • tidecons
    (x, y, ntidecons, tideconvals)
    float64
    dask.array<chunksize=(26, 35, 8, 4), meta=np.ndarray>

    Array Chunk Bytes 227.50 kiB 227.50 kiB Shape (26, 35, 8, 4) (26, 35, 8, 4) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • ntidecons
    PandasIndex

    PandasIndex(Index(['M2', 'S2', 'K2', 'K1', 'N2', 'P1', 'O1', 'Q1'], dtype='object', name='ntidecons'))

  • tideconvals
    PandasIndex

    PandasIndex(Index(['major', 'minor', 'inclination', 'phase'], dtype='object', name='tideconvals'))

  • x
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int64', name='x'))

  • y
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int64', name='y'))

• Attributes: (0)

Plot one dataset

keys = list(cat[dataset_id].metadata["plots"].keys())
print(keys)

plots = []
for key in keys:
    plot_kwargs = cat[dataset_id].metadata["plots"][key]
    if "clim" in plot_kwargs and isinstance(plot_kwargs["clim"], list):
        plot_kwargs["clim"] = tuple(plot_kwargs["clim"])
    if "dynamic" in plot_kwargs:
        plot_kwargs["dynamic"] = False
    plots.append(cat[dataset_id].ToHvPlot(**plot_kwargs).read())
hv.Layout(plots).cols(1)

['tidecons']