PyGeoAPI

Here we are going it explore setting up and using PyGeoAPI and the OGC EDR API to request timeseries data.

We’re going to use a Wave Watch 3 forecast for the East Coast, and request data for Buoy E01 (Lat: 43.72 Lon: -69.35).

We’ll start with a quick look a the dataset (and grabbing some info from it that we need to setup the PyGeoAPI config).

import xarray as xr
import requests

This dataset is avaliable from https://gmri-research-data.nyc3.digitaloceanspaces.com/IOOS-code-sprint/ww3_72_east_coast_2022041112.nc

ds = xr.open_dataset("../datasets/ww3_72_east_coast_2022041112.nc")
ds

<xarray.Dataset>
Dimensions:                  (longitude: 381, latitude: 351, time: 73,
                              forecast_reference_time: 1)
Coordinates:
  * longitude                (longitude) float32 -93.0 -92.9 ... -55.1 -55.0
  * latitude                 (latitude) float32 20.0 20.1 20.2 ... 54.9 55.0
  * time                     (time) datetime64[ns] 2022-04-11T12:00:00 ... 20...
  * forecast_reference_time  (forecast_reference_time) datetime64[ns] 2022-04...
Data variables: (12/21)
    hs                       (forecast_reference_time, time, latitude, longitude) float32 ...
    t02                      (forecast_reference_time, time, latitude, longitude) float32 ...
    t0m1                     (forecast_reference_time, time, latitude, longitude) float32 ...
    t01                      (forecast_reference_time, time, latitude, longitude) float32 ...
    fp                       (forecast_reference_time, time, latitude, longitude) float32 ...
    dir                      (forecast_reference_time, time, latitude, longitude) float32 ...
    ...                       ...
    pdir2                    (forecast_reference_time, time, latitude, longitude) float32 ...
    pws0                     (forecast_reference_time, time, latitude, longitude) float32 ...
    pws1                     (forecast_reference_time, time, latitude, longitude) float32 ...
    pws2                     (forecast_reference_time, time, latitude, longitude) float32 ...
    tws                      (forecast_reference_time, time, latitude, longitude) float32 ...
    pnr                      (forecast_reference_time, time, latitude, longitude) float32 ...
Attributes: (12/16)
    WAVEWATCH_III_version_number:     5.16
    WAVEWATCH_III_switches:           F90 SHRD NOGRB NOPA LRB4 NC4 TRKNC PR3 ...
    SIN4 namelist parameter BETAMAX:  1.65
    product_name:                     ww3.20220411.nc
    area:                             NW Atlantic  6 arc min grd2
    latitude_resolution:              0.1000000
    ...                               ...
    easternmost_longitude:            -55.00000
    minimum_altitude:                 -12000 m
    maximum_altitude:                 9000 m
    altitude_resolution:              n/a
    start_date:                       2022-04-11 12:00:00
    stop_date:                        2022-04-11 23:00:00

xarray.Dataset

• Dimensions:
  • longitude: 381
  • latitude: 351
  • time: 73
  • forecast_reference_time: 1
• Coordinates: (4)
  • longitude
    (longitude)
    float32
    -93.0 -92.9 -92.8 ... -55.1 -55.0

    units :

    degree_east

    long_name :

    longitude

    standard_name :

    longitude

    valid_min :

    -180.0

    valid_max :

    360.0

    axis :

    X

    array([-93. , -92.9, -92.8, ..., -55.2, -55.1, -55. ], dtype=float32)

  • latitude
    (latitude)
    float32
    20.0 20.1 20.2 ... 54.8 54.9 55.0

    units :

    degree_north

    long_name :

    latitude

    standard_name :

    latitude

    valid_min :

    -90.0

    valid_max :

    180.0

    axis :

    Y

    array([20. , 20.1, 20.2, ..., 54.8, 54.9, 55. ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2022-04-11T12:00:00 ... 2022-04-...

    long_name :

    julian day (UT)

    standard_name :

    time

    conventions :

    relative julian days with decimal part (as parts of the day )

    axis :

    T

    array(['2022-04-11T12:00:00.000000000', '2022-04-11T12:59:59.999986560',
           '2022-04-11T14:00:00.000013440', '2022-04-11T15:00:00.000000000',
           '2022-04-11T15:59:59.999986560', '2022-04-11T17:00:00.000013440',
           '2022-04-11T18:00:00.000000000', '2022-04-11T18:59:59.999986560',
           '2022-04-11T20:00:00.000013440', '2022-04-11T21:00:00.000000000',
           '2022-04-11T21:59:59.999986560', '2022-04-11T23:00:00.000013440',
           '2022-04-12T00:00:00.000000000', '2022-04-12T00:59:59.999986560',
           '2022-04-12T02:00:00.000013440', '2022-04-12T03:00:00.000000000',
           '2022-04-12T03:59:59.999986560', '2022-04-12T05:00:00.000013440',
           '2022-04-12T06:00:00.000000000', '2022-04-12T06:59:59.999986560',
           '2022-04-12T08:00:00.000013440', '2022-04-12T09:00:00.000000000',
           '2022-04-12T09:59:59.999986560', '2022-04-12T11:00:00.000013440',
           '2022-04-12T12:00:00.000000000', '2022-04-12T12:59:59.999986560',
           '2022-04-12T14:00:00.000013440', '2022-04-12T15:00:00.000000000',
           '2022-04-12T15:59:59.999986560', '2022-04-12T17:00:00.000013440',
           '2022-04-12T18:00:00.000000000', '2022-04-12T18:59:59.999986560',
           '2022-04-12T20:00:00.000013440', '2022-04-12T21:00:00.000000000',
           '2022-04-12T21:59:59.999986560', '2022-04-12T23:00:00.000013440',
           '2022-04-13T00:00:00.000000000', '2022-04-13T00:59:59.999986560',
           '2022-04-13T02:00:00.000013440', '2022-04-13T03:00:00.000000000',
           '2022-04-13T03:59:59.999986560', '2022-04-13T05:00:00.000013440',
           '2022-04-13T06:00:00.000000000', '2022-04-13T06:59:59.999986560',
           '2022-04-13T08:00:00.000013440', '2022-04-13T09:00:00.000000000',
           '2022-04-13T09:59:59.999986560', '2022-04-13T11:00:00.000013440',
           '2022-04-13T12:00:00.000000000', '2022-04-13T12:59:59.999986560',
           '2022-04-13T14:00:00.000013440', '2022-04-13T15:00:00.000000000',
           '2022-04-13T15:59:59.999986560', '2022-04-13T17:00:00.000013440',
           '2022-04-13T18:00:00.000000000', '2022-04-13T18:59:59.999986560',
           '2022-04-13T20:00:00.000013440', '2022-04-13T21:00:00.000000000',
           '2022-04-13T21:59:59.999986560', '2022-04-13T23:00:00.000013440',
           '2022-04-14T00:00:00.000000000', '2022-04-14T00:59:59.999986560',
           '2022-04-14T02:00:00.000013440', '2022-04-14T03:00:00.000000000',
           '2022-04-14T03:59:59.999986560', '2022-04-14T05:00:00.000013440',
           '2022-04-14T06:00:00.000000000', '2022-04-14T06:59:59.999986560',
           '2022-04-14T08:00:00.000013440', '2022-04-14T09:00:00.000000000',
           '2022-04-14T09:59:59.999986560', '2022-04-14T11:00:00.000013440',
           '2022-04-14T12:00:00.000000000'], dtype='datetime64[ns]')

  • forecast_reference_time
    (forecast_reference_time)
    datetime64[ns]
    2022-04-11T12:00:00

    long_name :

    Forecast Reference Time

    standard_name :

    forecast_reference_time

    conventions :

    relative julian days with decimal part (as parts of the day )

    array(['2022-04-11T12:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (21)
  • hs
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    significant height of wind and swell waves

    standard_name :

    sea_surface_wave_significant_height

    globwave_name :

    significant_wave_height

    units :

    m

    valid_min :

    0

    valid_max :

    32000

    [9762363 values with dtype=float32]

  • t02
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    mean period T02

    standard_name :

    sea_surface_wind_wave_mean_period_from_variance_spectral_density_second_frequency_moment

    globwave_name :

    mean_period_t02

    units :

    s

    valid_min :

    0

    valid_max :

    5000

    [9762363 values with dtype=float32]

  • t0m1
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    mean period T0m1

    standard_name :

    sea_surface_wind_wave_mean_period_from_variance_spectral_density_inverse_frequency_moment

    globwave_name :

    mean_period_t0m1

    units :

    s

    valid_min :

    0

    valid_max :

    5000

    [9762363 values with dtype=float32]

  • t01
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    mean period T01

    standard_name :

    sea_surface_wind_wave_mean_period_from_variance_spectral_density_first_frequency_moment

    globwave_name :

    mean_period_t01

    units :

    s

    valid_min :

    0

    valid_max :

    5000

    [9762363 values with dtype=float32]

  • fp
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    wave peak frequency

    standard_name :

    sea_surface_wave_peak_frequency

    globwave_name :

    dominant_wave_frequency

    units :

    s-1

    valid_min :

    0

    valid_max :

    10000

    [9762363 values with dtype=float32]

  • dir
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    wave mean direction

    standard_name :

    sea_surface_wave_from_direction

    globwave_name :

    wave_from_direction

    units :

    degree

    valid_min :

    0

    valid_max :

    3600

    [9762363 values with dtype=float32]

  • dp
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    peak direction

    standard_name :

    sea_surface_wave_peak_direction

    globwave_name :

    dominant_wave_direction

    units :

    degree

    valid_min :

    0

    valid_max :

    360

    [9762363 values with dtype=float32]

  • phs0
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    wave significant height partition 0

    standard_name :

    sea_surface_wave_significant_height_partition_0

    globwave_name :

    significant_wave_height_partition_0

    units :

    m

    valid_min :

    0

    valid_max :

    32000

    [9762363 values with dtype=float32]

  • phs1
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    wave significant height partition 1

    standard_name :

    sea_surface_wave_significant_height_partition_1

    globwave_name :

    significant_wave_height_partition_1

    units :

    m

    valid_min :

    0

    valid_max :

    32000

    [9762363 values with dtype=float32]

  • phs2
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    wave significant height partition 2

    standard_name :

    sea_surface_wave_significant_height_partition_2

    globwave_name :

    significant_wave_height_partition_2

    units :

    m

    valid_min :

    0

    valid_max :

    32000

    [9762363 values with dtype=float32]

  • ptp0
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    peak period partition 0

    standard_name :

    sea_surface_wave_peak_period_partition_0

    globwave_name :

    dominant_wave_period_partition_0

    units :

    s

    valid_min :

    0

    valid_max :

    10000

    [9762363 values with dtype=float32]

  • ptp1
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    peak period partition 1

    standard_name :

    sea_surface_wave_peak_period_partition_1

    globwave_name :

    dominant_wave_period_partition_1

    units :

    s

    valid_min :

    0

    valid_max :

    10000

    [9762363 values with dtype=float32]

  • ptp2
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    peak period partition 2

    standard_name :

    sea_surface_wave_peak_period_partition_2

    globwave_name :

    dominant_wave_period_partition_2

    units :

    s

    valid_min :

    0

    valid_max :

    10000

    [9762363 values with dtype=float32]

  • pdir0
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    wave mean direction partition 0

    standard_name :

    sea_surface_wave_from_direction_partition_0

    globwave_name :

    wave_from_direction_partition_0

    units :

    degree

    valid_min :

    0

    valid_max :

    360

    [9762363 values with dtype=float32]

  • pdir1
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    wave mean direction partition 1

    standard_name :

    sea_surface_wave_from_direction_partition_1

    globwave_name :

    wave_from_direction_partition_1

    units :

    degree

    valid_min :

    0

    valid_max :

    360

    [9762363 values with dtype=float32]

  • pdir2
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    wave mean direction partition 2

    standard_name :

    sea_surface_wave_from_direction_partition_2

    globwave_name :

    wave_from_direction_partition_2

    units :

    degree

    valid_min :

    0

    valid_max :

    360

    [9762363 values with dtype=float32]

  • pws0
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    wind sea fraction in partition 0

    standard_name :

    wind_sea_fraction_in_partition_0

    globwave_name :

    wind_sea_fraction_in_partition_0

    units :

    1

    valid_min :

    0

    valid_max :

    1000

    [9762363 values with dtype=float32]

  • pws1
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    wind sea fraction in partition 1

    standard_name :

    wind_sea_fraction_in_partition_1

    globwave_name :

    wind_sea_fraction_in_partition_1

    units :

    1

    valid_min :

    0

    valid_max :

    1000

    [9762363 values with dtype=float32]

  • pws2
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    wind sea fraction in partition 2

    standard_name :

    wind_sea_fraction_in_partition_2

    globwave_name :

    wind_sea_fraction_in_partition_2

    units :

    1

    valid_min :

    0

    valid_max :

    1000

    [9762363 values with dtype=float32]

  • tws
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    wind sea fraction

    standard_name :

    wind_sea_fraction

    globwave_name :

    wind_sea_fraction

    units :

    1

    valid_min :

    0

    valid_max :

    1000

    [9762363 values with dtype=float32]

  • pnr
    (forecast_reference_time, time, latitude, longitude)
    float32
    ...

    long_name :

    number of wave partitions

    standard_name :

    number_of_wave_partitions

    globwave_name :

    number_of_wave_partitions

    units :

    1

    valid_min :

    0

    valid_max :

    100

    [9762363 values with dtype=float32]

• Attributes: (16)

  WAVEWATCH_III_version_number :

  5.16

  WAVEWATCH_III_switches :

  F90 SHRD NOGRB NOPA LRB4 NC4 TRKNC PR3 UQ FLX0 LN1 ST4 STAB0 NL1 BT1 DB1 MLIM IC0 IS0 REF0 WNT2 WNX2 RWND CRT1 CRX1 TR0 BS0 XX0 O0 O1 O2 O3 O4 O5 O6 O7

  SIN4 namelist parameter BETAMAX :

  1.65

  product_name :

  ww3.20220411.nc

  area :

  NW Atlantic 6 arc min grd2

  latitude_resolution :

  0.1000000

  longitude_resolution :

  0.1000000

  southernmost_latitude :

  20.00000

  northernmost_latitude :

  55.00000

  westernmost_longitude :

  -93.00000

  easternmost_longitude :

  -55.00000

  minimum_altitude :

  -12000 m

  maximum_altitude :

  9000 m

  altitude_resolution :

  n/a

  start_date :

  2022-04-11 12:00:00

  stop_date :

  2022-04-11 23:00:00

PyGeoAPI needs the bounds of the dataset. I’ve already got most of the config needed for the dataset, it just needs updated temporal extents.

ds['time'].min(), ds['time'].max()

(<xarray.DataArray 'time' ()>
 array('2022-04-11T12:00:00.000000000', dtype='datetime64[ns]'),
 <xarray.DataArray 'time' ()>
 array('2022-04-14T12:00:00.000000000', dtype='datetime64[ns]'))

PyGeoAPI configuration

PyGeoAPI uses a YAML config file. Individual datasets are setup as resources, and different providers within a resource are responsible for API types.

There is a lot of general configuration it does not like you to skip.

!cat ./config.yaml

server:
  bind:
    host: 0.0.0.0
    port: 5002
  cors: true
  language: en-US
  manager:
    connection: /tmp/pygeoapi-process-manager.db
    name: TinyDB
    output_dir: /tmp/
  map:
    attribution:
      <a href="https://wikimediafoundation.org/wiki/Maps_Terms_of_Use">Wikimedia
      maps</a> | Map data &copy; <a href="https://openstreetmap.org/copyright">OpenStreetMap
      contributors</a>
    url: https://maps.wikimedia.org/osm-intl/{z}/{x}/{y}.png
  url: http://localhost:5002
logging:
  level: DEBUG
metadata:
  contact:
    address: 195 New Hampshire Ave, Suite 240
    city: Portsmouth
    country: United States
    email: tom@neracoos.org
    name: Shyka, Tom
    phone: +01-603-319-1785
    position: Product & Engagement Manager
    postalcode: 03801
    role: pointOfContact
    stateorprovince: New Hampshire
    url: http://neracoos.org
  identification:
    description: OGC APIs for NERACOOS services
    keywords:
      - geospatial
      - data
      - api
      - oceanographic
    keywords_type: theme
    terms_of_service: https://creativecommons.org/licenses/by/4.0/
    title: data.neracoos.org
    url: http://neracoos.org
  license:
    name: CC-BY 4.0 license
    url: https://creativecommons.org/licenses/by/4.0/
  provider:
    name: NERACOOS
    url: https://neracoos.org
resources:
  bio_ww3_east_coast_latest:
    description:
      Bedford Institute of Oceanography Wave Watch 3 72 hour forecast for
      the East Coast
    extents:
      spatial:
        bbox:
          - -93.0
          - 20.0
          - -55.0
          - 55.0
        crs: http://www.opengis.net/def/crs/OGC/1.3/CRS84
      temporal:
        begin: 2022-04-11 12:00:00
        end: 2022-04-14 12:00:00
    keywords:
      - forecast
      - wave
    links: []
    providers:
      - data: ../datasets/ww3_72_east_coast_2022041112.nc
        format:
          mimetype: application/x-netcdf
          name: NetCDF
        name: xarray-edr
        time_field: time
        type: edr
        x_field: longitude
        y_field: latitude
    title:
      Bedford Institute of Oceanography Wave Watch 3 72 hour forecast for the East
      Coast
    type: collection

Running PyGeoAPI

From this directory run PYGEOAPI_CONFIG=./config.yaml pygeoapi serve.

Then you should be able to access PyGeoAPI at http://localhost:5002/

From there you can explore into collections, then check out the collection that we defined.