Reading and using a contiguous ragged dataset

import matplotlib.pyplot as plt
from pathlib import Path
import xarray as xr
import trajan
import coloredlogs

# the test data: a small extract from the Sofar dataset https://sofar-spotter-archive.s3.amazonaws.com/index.html
path_to_test_data = Path.cwd().parent / "tests" / "test_data" / "xr_spotter_bulk_test_data.nc"

xr_data = xr.load_dataset(path_to_test_data)

# this is a ragged contiguous dataset; the data variables are 1D arrays
xr_data

<xarray.Dataset> Size: 5kB
Dimensions:                (index: 65, trajectory: 2)
Coordinates:
    time                   (index) datetime64[ns] 520B 2022-03-15T04:07:25 .....
  * trajectory             (trajectory) <U11 88B 'SPOT-010102' 'SPOT-010103'
Dimensions without coordinates: index
Data variables:
    significantWaveHeight  (index) float64 520B 1.736 1.817 ... 1.263 1.136
    peakPeriod             (index) float64 520B 7.877 8.533 ... 7.314 7.314
    meanPeriod             (index) float64 520B 6.804 6.652 ... 6.629 6.497
    peakDirection          (index) float64 520B 147.9 155.5 ... 105.2 102.2
    peakDirectionalSpread  (index) float64 520B 30.37 25.48 26.04 ... 37.2 38.09
    meanDirection          (index) float64 520B 146.7 144.5 ... 127.3 120.8
    meanDirectionalSpread  (index) float64 520B 40.34 40.47 ... 50.39 51.12
    latitude               (index) float64 520B -16.41 -16.43 ... -12.64 -12.65
    longitude              (index) float64 520B 65.03 65.03 ... 70.15 70.14
    rowsize                (trajectory) int64 16B 20 45
Attributes:
    title:          Sofar Spotter Data Archive - Bulk Wave Parameters
    institution:    Sofar Ocean
    source:         Spotter wave buoy
    creation_date:  2023-10-18 00:43:55.333537
    author:         Isabel A. Houghton
    email:          isabel.houghton@sofarocean.com
    references:     https://content.sofarocean.com/hubfs/Spotter%20product%20...
    modified:       modified to only keep a couple of buoys, to be used as a ...

xarray.Dataset

• Dimensions:
  • index: 65
  • trajectory: 2
• Coordinates: (2)
  • time
    (index)
    datetime64[ns]
    2022-03-15T04:07:25 ... 2022-03-...

    standard_name :

    time

    long_name :

    Time [UTC]

    comment :

    UTC time

    array(['2022-03-15T04:07:25.000000000', '2022-03-15T05:07:25.000000000',
           '2022-03-15T06:07:25.000000000', '2022-03-15T07:07:25.000000000',
           '2022-03-15T08:07:25.000000000', '2022-03-15T09:07:25.000000000',
           '2022-03-15T10:07:25.000000000', '2022-03-15T11:07:25.000000000',
           '2022-03-15T12:07:25.000000000', '2022-03-15T13:07:25.000000000',
           '2022-03-15T14:07:25.000000000', '2022-03-15T15:07:25.000000000',
           '2022-03-15T16:07:25.000000000', '2022-03-15T17:07:25.000000000',
           '2022-03-15T18:07:25.000000000', '2022-03-15T19:07:25.000000000',
           '2022-03-15T20:07:25.000000000', '2022-03-15T21:07:25.000000000',
           '2022-03-15T22:07:25.000000000', '2022-03-15T23:07:25.000000000',
           '2022-03-14T03:04:25.000000000', '2022-03-14T04:04:25.000000000',
           '2022-03-14T05:04:25.000000000', '2022-03-14T06:04:25.000000000',
           '2022-03-14T07:04:25.000000000', '2022-03-14T08:04:25.000000000',
           '2022-03-14T09:04:25.000000000', '2022-03-14T10:04:25.000000000',
           '2022-03-14T11:04:25.000000000', '2022-03-14T12:04:25.000000000',
           '2022-03-14T13:04:25.000000000', '2022-03-14T14:04:25.000000000',
           '2022-03-14T15:04:25.000000000', '2022-03-14T16:04:25.000000000',
           '2022-03-14T17:04:25.000000000', '2022-03-14T18:04:25.000000000',
           '2022-03-14T19:04:25.000000000', '2022-03-14T20:04:25.000000000',
           '2022-03-14T21:04:25.000000000', '2022-03-14T22:04:25.000000000',
           '2022-03-14T23:04:25.000000000', '2022-03-15T00:04:25.000000000',
           '2022-03-15T01:04:25.000000000', '2022-03-15T02:04:25.000000000',
           '2022-03-15T03:04:25.000000000', '2022-03-15T04:04:25.000000000',
           '2022-03-15T05:04:25.000000000', '2022-03-15T06:04:25.000000000',
           '2022-03-15T07:04:25.000000000', '2022-03-15T08:04:25.000000000',
           '2022-03-15T09:04:25.000000000', '2022-03-15T10:04:25.000000000',
           '2022-03-15T11:04:25.000000000', '2022-03-15T12:04:25.000000000',
           '2022-03-15T13:04:25.000000000', '2022-03-15T14:04:25.000000000',
           '2022-03-15T15:04:25.000000000', '2022-03-15T16:04:25.000000000',
           '2022-03-15T17:04:25.000000000', '2022-03-15T18:04:25.000000000',
           '2022-03-15T19:04:25.000000000', '2022-03-15T20:04:25.000000000',
           '2022-03-15T21:04:25.000000000', '2022-03-15T22:04:25.000000000',
           '2022-03-15T23:04:25.000000000'], dtype='datetime64[ns]')

  • trajectory
    (trajectory)
    <U11
    'SPOT-010102' 'SPOT-010103'

    cf_role :

    trajectory_id

    long_name :

    Trajectory Name

    array(['SPOT-010102', 'SPOT-010103'], dtype='<U11')

• Data variables: (10)
  • significantWaveHeight
    (index)
    float64
    1.736 1.817 1.851 ... 1.263 1.136

    units :

    m

    standard_name :

    significant_wave_height

    long_name :

    Total Significant Wave Height

    array([1.736, 1.817, 1.851, 1.765, 1.848, 1.845, 1.972, 2.051, 2.145,
           2.182, 2.005, 1.935, 2.092, 2.001, 1.932, 1.835, 1.854, 1.952,
           1.866, 1.688, 1.116, 1.15 , 1.122, 1.15 , 1.12 , 1.122, 1.153,
           1.149, 1.148, 1.158, 1.162, 1.179, 1.169, 1.151, 1.069, 1.13 ,
           1.123, 1.178, 1.204, 1.203, 1.146, 1.127, 1.143, 1.177, 1.191,
           1.254, 1.229, 1.267, 1.284, 1.273, 1.311, 1.409, 1.314, 1.37 ,
           1.35 , 1.331, 1.273, 1.289, 1.255, 1.247, 1.27 , 1.207, 1.209,
           1.263, 1.136])

  • peakPeriod
    (index)
    float64
    7.877 8.533 8.533 ... 7.314 7.314

    units :

    s

    standard_name :

    peak_period

    long_name :

    Peak Period

    comment :

    Tp

    array([ 7.877,  8.533,  8.533,  7.877,  7.314,  7.877,  7.877, 10.24 ,
           10.24 , 10.24 ,  9.309,  9.309,  9.309,  9.309,  9.309,  9.309,
            8.533,  9.309,  8.533,  8.533, 14.628, 12.799,  7.314, 14.628,
           12.799, 12.799, 12.799, 12.799, 12.799, 12.799, 12.799, 12.799,
           12.799, 12.799, 12.799, 12.799, 12.799, 12.799, 12.799, 12.799,
            7.877, 12.799,  7.877, 12.799,  7.314,  7.877,  7.877,  7.877,
            7.877,  7.877,  7.314,  7.314,  7.314,  7.314,  7.314,  7.877,
            7.877,  7.877,  7.877,  7.314,  7.877,  7.314,  7.314,  7.314,
            7.314])

  • meanPeriod
    (index)
    float64
    6.804 6.652 6.809 ... 6.629 6.497

    units :

    s

    standard_name :

    mean_period

    long_name :

    peak period

    comment :

    Tm01

    array([6.804, 6.652, 6.809, 6.704, 6.817, 7.03 , 7.363, 7.607, 7.665,
           7.903, 7.789, 7.821, 7.624, 7.599, 7.58 , 7.3  , 7.354, 7.193,
           6.799, 6.454, 7.788, 7.82 , 7.158, 7.059, 7.232, 7.424, 7.63 ,
           7.272, 6.632, 6.237, 5.998, 6.081, 5.654, 5.854, 5.548, 5.903,
           5.754, 5.839, 5.869, 5.994, 5.957, 5.86 , 5.738, 5.696, 5.774,
           5.59 , 5.934, 5.827, 5.749, 5.645, 5.81 , 5.887, 5.854, 6.113,
           6.297, 6.207, 6.218, 6.327, 6.334, 6.323, 6.395, 6.233, 6.574,
           6.629, 6.497])

  • peakDirection
    (index)
    float64
    147.9 155.5 156.1 ... 105.2 102.2

    units :

    degrees

    standard_name :

    peak_direction

    long_name :

    Peak Direction

    comment :

    Dp, measured clockwise in degrees from North and points in the direction the waves come from.

    array([147.919, 155.52 , 156.145, 146.328, 126.87 , 134.956, 132.269,
           182.646, 179.352, 176.312, 164.351, 165.345, 160.801, 160.54 ,
           159.837, 156.94 , 150.513, 153.408, 147.949, 146.843, 210.668,
           204.762, 120.742, 208.666, 207.699, 207.046, 207.166, 207.917,
           207.744, 210.785, 208.199, 208.129, 202.83 , 207.486, 210.902,
           211.262, 208.136, 208.076, 210.719, 206.681, 122.655, 204.061,
           121.753, 205.013, 114.945, 114.011, 108.62 , 103.2  , 103.871,
           105.568, 112.036, 110.473, 103.053, 106.699, 105.194, 106.116,
            99.375, 105.319, 105.905, 104.22 ,  98.434, 116.466, 112.495,
           105.224, 102.17 ])

  • peakDirectionalSpread
    (index)
    float64
    30.37 25.48 26.04 ... 37.2 38.09

    units :

    degrees

    standard_name :

    peak_directional_spreading

    long_name :

    Peak Directional Spreading

    array([30.372, 25.476, 26.038, 28.505, 29.216, 25.002, 27.471, 21.193,
           18.081, 19.46 , 24.366, 25.591, 25.154, 20.963, 20.969, 25.505,
           23.277, 20.365, 23.035, 25.291, 24.266, 26.974, 29.841, 27.794,
           27.688, 27.393, 23.437, 29.145, 26.576, 30.171, 30.481, 27.928,
           32.518, 32.111, 37.973, 32.591, 33.491, 28.237, 29.988, 30.092,
           36.66 , 35.283, 40.178, 32.491, 31.318, 34.912, 29.951, 28.356,
           34.416, 34.771, 32.255, 29.308, 30.647, 30.612, 28.042, 30.778,
           31.787, 31.662, 39.419, 35.154, 33.107, 39.786, 36.725, 37.204,
           38.093])

  • meanDirection
    (index)
    float64
    146.7 144.5 144.0 ... 127.3 120.8

    units :

    degrees

    standard_name :

    mean_direction

    long_name :

    Mean Direction

    comment :

    Dm, measured clockwise in degrees from North and points in the direction the waves come from.

    array([146.718, 144.456, 143.972, 144.118, 139.933, 142.53 , 145.054,
           151.808, 152.217, 148.722, 145.461, 147.824, 142.181, 143.361,
           141.567, 143.352, 142.299, 140.563, 137.494, 133.083, 159.958,
           163.125, 156.292, 160.437, 160.777, 156.402, 161.072, 157.84 ,
           155.832, 152.543, 150.642, 151.469, 136.864, 141.906, 132.371,
           137.512, 134.479, 135.224, 129.88 , 128.635, 126.714, 125.317,
           123.422, 123.533, 117.644, 116.402, 115.866, 113.343, 115.941,
           111.505, 115.668, 114.129, 112.743, 112.283, 112.283, 114.504,
           114.371, 119.73 , 118.786, 114.571, 116.679, 119.103, 126.43 ,
           127.283, 120.801])

  • meanDirectionalSpread
    (index)
    float64
    40.34 40.47 38.86 ... 50.39 51.12

    units :

    degrees

    standard_name :

    mean_directional_spreading

    long_name :

    Mean Directional Spreading

    array([40.336, 40.471, 38.862, 40.356, 40.057, 40.61 , 41.533, 40.799,
           40.109, 39.521, 40.436, 41.606, 38.908, 38.739, 40.639, 41.4  ,
           39.203, 40.8  , 42.242, 42.477, 51.683, 50.613, 51.431, 49.294,
           51.063, 50.583, 51.558, 51.888, 51.998, 55.103, 54.15 , 55.069,
           54.545, 56.265, 56.719, 56.527, 55.664, 54.159, 54.165, 53.091,
           51.54 , 52.754, 52.297, 53.963, 52.288, 54.309, 52.263, 49.746,
           50.044, 47.488, 47.556, 44.634, 46.61 , 45.617, 44.975, 45.35 ,
           48.759, 47.554, 47.888, 48.848, 47.958, 49.524, 49.624, 50.386,
           51.12 ])

  • latitude
    (index)
    float64
    -16.41 -16.43 ... -12.64 -12.65

    standard_name :

    latitude

    long_name :

    Latitude

    units :

    degrees_north

    array([-16.41417, -16.42592, -16.43607, -16.44648, -16.45737, -16.46613,
           -16.47533, -16.48528, -16.49507, -16.49905, -16.50022, -16.50192,
           -16.50268, -16.50493, -16.50728, -16.51035, -16.51498, -16.5217 ,
           -16.52765, -16.53502, -12.26875, -12.277  , -12.28927, -12.29187,
           -12.29287, -12.29653, -12.3021 , -12.30555, -12.31507, -12.3263 ,
           -12.33953, -12.35352, -12.36388, -12.37135, -12.37677, -12.38008,
           -12.38533, -12.38983, -12.39568, -12.40178, -12.40957, -12.41497,
           -12.41952, -12.4286 , -12.43642, -12.44145, -12.44872, -12.45775,
           -12.46702, -12.48093, -12.49607, -12.51042, -12.52175, -12.53167,
           -12.54167, -12.55257, -12.56385, -12.57605, -12.58838, -12.59995,
           -12.61272, -12.62548, -12.6334 , -12.64405, -12.64983])

  • longitude
    (index)
    float64
    65.03 65.03 65.03 ... 70.15 70.14

    standard_name :

    longitude

    long_name :

    Longitude

    units :

    degrees_east

    array([65.02732, 65.02607, 65.02658, 65.02682, 65.0277 , 65.02987,
           65.03018, 65.02932, 65.03262, 65.03535, 65.0357 , 65.0345 ,
           65.03393, 65.03235, 65.02633, 65.01658, 65.005  , 64.99142,
           64.97752, 64.96347, 70.86967, 70.84977, 70.8337 , 70.81832,
           70.80372, 70.7884 , 70.77198, 70.75307, 70.72943, 70.70698,
           70.68638, 70.66672, 70.65038, 70.63328, 70.61583, 70.59903,
           70.58043, 70.56597, 70.55075, 70.5363 , 70.5181 , 70.4998 ,
           70.48538, 70.47188, 70.45518, 70.43695, 70.41753, 70.3975 ,
           70.3763 , 70.3547 , 70.33423, 70.31645, 70.29987, 70.28273,
           70.26532, 70.24922, 70.23332, 70.21892, 70.2066 , 70.19465,
           70.18573, 70.17352, 70.161  , 70.15377, 70.14275])

  • rowsize
    (trajectory)
    int64
    20 45

    long_name :

    Number of observations for each trajectory

    comments :

    Use to select trajectory along index dimension

    sample_dimension :

    trajectory

    array([20, 45])

• Indexes: (1)
  • trajectory
    PandasIndex

    PandasIndex(Index(['SPOT-010102', 'SPOT-010103'], dtype='object', name='trajectory'))

• Attributes: (8)

  title :

  Sofar Spotter Data Archive - Bulk Wave Parameters

  institution :

  Sofar Ocean

  source :

  Spotter wave buoy

  creation_date :

  2023-10-18 00:43:55.333537

  author :

  Isabel A. Houghton

  email :

  isabel.houghton@sofarocean.com

  references :

  https://content.sofarocean.com/hubfs/Spotter%20product%20documentation%20page/Sofar%20-%20Technical_Reference_Manual.pdf

  modified :

  modified to only keep a couple of buoys, to be used as a test data file for the trajan package

# trajan is able to plot etc
xr_data.traj.plot()
plt.show()

2024-12-16 16:26:14 fv-az1766-447 trajan.accessor[2121] DEBUG Detecting trajectory dimension
2024-12-16 16:26:14 fv-az1766-447 trajan.accessor[2121] DEBUG 1D storage dataset; detected: obs_dim = 'index', timecoord = 'time', trajectory_dim = 'trajectory', rowsize
2024-12-16 16:26:14 fv-az1766-447 trajan.accessor[2121] DEBUG Detecting trajectory dimension
2024-12-16 16:26:14 fv-az1766-447 trajan.accessor[2121] DEBUG Detecting time-variable for "obs"..
2024-12-16 16:26:14 fv-az1766-447 trajan.accessor[2121] DEBUG Detected obs-dim: obs, detected time-variable: time.
2024-12-16 16:26:14 fv-az1766-447 trajan.accessor[2121] DEBUG Detected un-structured (2D) trajectory dataset
2024-12-16 16:26:14 fv-az1766-447 trajan.traj[2121] DEBUG Setting up new plot object.
2024-12-16 16:26:14 fv-az1766-447 trajan.plot[2121] DEBUG Plotting lines
2024-12-16 16:26:14 fv-az1766-447 trajan.traj[2121] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
2024-12-16 16:26:14 fv-az1766-447 trajan.plot[2121] DEBUG Creating new figure and axes..
2024-12-16 16:26:14 fv-az1766-447 trajan.traj[2121] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
2024-12-16 16:26:14 fv-az1766-447 trajan.traj[2121] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
2024-12-16 16:26:14 fv-az1766-447 trajan.traj[2121] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
2024-12-16 16:26:14 fv-az1766-447 trajan.traj[2121] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
/home/runner/micromamba/envs/trajan/lib/python3.11/site-packages/shapely/creation.py:119: RuntimeWarning: invalid value encountered in linestrings
  return lib.linestrings(coords, out=out, **kwargs)

# it is possible to get a 2d-array dataset version using trajan:

# compare:
print(f"{xr_data = }")

# with:
xr_data_as_2darray = xr_data.traj.to_2d()
print(f"{xr_data_as_2darray = }")

# naturally, the 2darray version can be dumped to disk if you want:
xr_data_as_2darray.to_netcdf("./xr_spotter_bulk_test_data_as_2darray.nc")

xr_data = <xarray.Dataset> Size: 5kB
Dimensions:                (index: 65, trajectory: 2)
Coordinates:
    time                   (index) datetime64[ns] 520B 2022-03-15T04:07:25 .....
  * trajectory             (trajectory) <U11 88B 'SPOT-010102' 'SPOT-010103'
Dimensions without coordinates: index
Data variables:
    significantWaveHeight  (index) float64 520B 1.736 1.817 ... 1.263 1.136
    peakPeriod             (index) float64 520B 7.877 8.533 ... 7.314 7.314
    meanPeriod             (index) float64 520B 6.804 6.652 ... 6.629 6.497
    peakDirection          (index) float64 520B 147.9 155.5 ... 105.2 102.2
    peakDirectionalSpread  (index) float64 520B 30.37 25.48 26.04 ... 37.2 38.09
    meanDirection          (index) float64 520B 146.7 144.5 ... 127.3 120.8
    meanDirectionalSpread  (index) float64 520B 40.34 40.47 ... 50.39 51.12
    latitude               (index) float64 520B -16.41 -16.43 ... -12.64 -12.65
    longitude              (index) float64 520B 65.03 65.03 ... 70.15 70.14
    rowsize                (trajectory) int64 16B 20 45
Attributes:
    title:          Sofar Spotter Data Archive - Bulk Wave Parameters
    institution:    Sofar Ocean
    source:         Spotter wave buoy
    creation_date:  2023-10-18 00:43:55.333537
    author:         Isabel A. Houghton
    email:          isabel.houghton@sofarocean.com
    references:     https://content.sofarocean.com/hubfs/Spotter%20product%20...
    modified:       modified to only keep a couple of buoys, to be used as a ...
xr_data_as_2darray = <xarray.Dataset> Size: 7kB
Dimensions:                (trajectory: 2, obs: 45)
Coordinates:
  * trajectory             (trajectory) <U11 88B 'SPOT-010102' 'SPOT-010103'
Dimensions without coordinates: obs
Data variables:
    time                   (trajectory, obs) datetime64[ns] 720B 2022-03-15T0...
    significantWaveHeight  (trajectory, obs) float64 720B 1.736 1.817 ... 1.136
    peakPeriod             (trajectory, obs) float64 720B 7.877 8.533 ... 7.314
    meanPeriod             (trajectory, obs) float64 720B 6.804 6.652 ... 6.497
    peakDirection          (trajectory, obs) float64 720B 147.9 155.5 ... 102.2
    peakDirectionalSpread  (trajectory, obs) float64 720B 30.37 25.48 ... 38.09
    meanDirection          (trajectory, obs) float64 720B 146.7 144.5 ... 120.8
    meanDirectionalSpread  (trajectory, obs) float64 720B 40.34 40.47 ... 51.12
    lat                    (trajectory, obs) float64 720B -16.41 ... -12.65
    lon                    (trajectory, obs) float64 720B 65.03 65.03 ... 70.14
Attributes:
    title:            Sofar Spotter Data Archive - Bulk Wave Parameters
    institution:      Sofar Ocean
    source:           Spotter wave buoy
    creation_date:    2023-10-18 00:43:55.333537
    author:           Isabel A. Houghton
    email:            isabel.houghton@sofarocean.com
    references:       https://content.sofarocean.com/hubfs/Spotter%20product%...
    modified:         modified to only keep a couple of buoys, to be used as ...
    trajan_modified:  this was initially a contiguous ragged Dataset, which w...