.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "gallery/example_model_along_trajectory.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_gallery_example_model_along_trajectory.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_gallery_example_model_along_trajectory.py:


Read variables from a model along the trajectories of drifters.
=============================================================================

.. GENERATED FROM PYTHON SOURCE LINES 5-13

.. code-block:: Python


    import xarray as xr
    import numpy as np
    import cf_xarray as _
    import pyproj
    import trajan as ta
    import matplotlib.pyplot as plt








.. GENERATED FROM PYTHON SOURCE LINES 14-15

Open drifter dataset from a CSV file

.. GENERATED FROM PYTHON SOURCE LINES 15-22

.. code-block:: Python

    ds = ta.read_csv('bug05_pos.csv.xz',
                     lon='Longitude',
                     lat='Latitude',
                     time='Time',
                     name='Device')
    print(ds)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    2024-10-31 20:48:49 fv-az1024-670 trajan.accessor[2149] DEBUG Detecting time-dimension for "obs"..
    2024-10-31 20:48:49 fv-az1024-670 trajan.accessor[2149] DEBUG Detected obs-dim: obs, detected time-dim: time.
    2024-10-31 20:48:49 fv-az1024-670 trajan.accessor[2149] DEBUG Detected un-structured (2D) trajectory dataset
    2024-10-31 20:48:49 fv-az1024-670 trajan.traj2d[2149] DEBUG Condensing 844 observations.
    2024-10-31 20:48:49 fv-az1024-670 trajan.traj2d[2149] DEBUG Condensed observations from: 844 to 844
    2024-10-31 20:48:49 fv-az1024-670 trajan.accessor[2149] DEBUG Detecting time-dimension for "obs"..
    2024-10-31 20:48:49 fv-az1024-670 trajan.accessor[2149] DEBUG Detected obs-dim: obs, detected time-dim: time.
    2024-10-31 20:48:49 fv-az1024-670 trajan.accessor[2149] DEBUG Detected un-structured (2D) trajectory dataset
    2024-10-31 20:48:49 fv-az1024-670 trajan.traj[2149] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
    2024-10-31 20:48:49 fv-az1024-670 trajan.traj[2149] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
    2024-10-31 20:48:49 fv-az1024-670 trajan.traj[2149] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
    2024-10-31 20:48:49 fv-az1024-670 trajan.traj[2149] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..
    <xarray.Dataset> Size: 27kB
    Dimensions:     (trajectory: 1, obs: 844)
    Coordinates:
      * trajectory  (trajectory) <U18 72B 'dev864475040536665'
      * obs         (obs) int64 7kB 0 1 2 3 4 5 6 7 ... 837 838 839 840 841 842 843
    Data variables:
        time        (trajectory, obs) datetime64[ns] 7kB 2022-05-10T12:56:16.5000...
        lon         (trajectory, obs) float64 7kB 5.332 5.332 5.332 ... 5.25 5.25
        lat         (trajectory, obs) float64 7kB 60.38 60.38 60.38 ... 60.45 60.45
    Attributes:
        Conventions:          CF-1.10
        featureType:          trajectory
        geospatial_lat_min:   60.38380600000001
        geospatial_lat_max:   60.45141000000001
        geospatial_lon_min:   5.244493666666667
        geospatial_lon_max:   5.33242
        time_coverage_start:  2020-01-01T00:00:07.250000
        time_coverage_end:    2022-05-20T11:03:46.120000




.. GENERATED FROM PYTHON SOURCE LINES 23-24

Open the Norkyst800 model

.. GENERATED FROM PYTHON SOURCE LINES 24-27

.. code-block:: Python

    nk = xr.open_dataset(
        'https://thredds.met.no/thredds/dodsC/sea/norkyst800m/1h/aggregate_be')








.. GENERATED FROM PYTHON SOURCE LINES 28-29

Use cf-xarray to get the CRS

.. GENERATED FROM PYTHON SOURCE LINES 29-33

.. code-block:: Python

    gm = nk.cf['grid_mapping']
    nk_crs = pyproj.CRS.from_cf(gm.attrs)
    print(nk_crs)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    2024-10-31 20:48:59 fv-az1024-670 pyproj[2149] DEBUG PROJ_ERROR: proj_create: several objects matching this name: Krovak (Greenwich), Equal Earth Greenwich, Laborde Grid (Greenwich), Modified Krovak (Greenwich), Krovak East North (Greenwich), Modified Krovak East North (Greenwich), ...
    {"$schema": "https://proj.org/schemas/v0.2/projjson.schema.json", "type": "ProjectedCRS", "name": "undefined", "base_crs": {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "GeographicCRS", "name": "undefined", "datum": {"type": "GeodeticReferenceFrame", "name": "undefined", "ellipsoid": {"name": "undefined", "semi_major_axis": 6378137, "semi_minor_axis": 6356752.3142}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Longitude", "abbreviation": "lon", "direction": "east", "unit": "degree"}, {"name": "Latitude", "abbreviation": "lat", "direction": "north", "unit": "degree"}]}}, "conversion": {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "Conversion", "name": "unknown", "method": {"name": "Polar Stereographic (variant B)", "id": {"authority": "EPSG", "code": 9829}}, "parameters": [{"name": "Latitude of standard parallel", "value": 60, "unit": "degree", "id": {"authority": "EPSG", "code": 8832}}, {"name": "Longitude of origin", "value": 70, "unit": "degree", "id": {"authority": "EPSG", "code": 8833}}, {"name": "False easting", "value": 3192800, "unit": "metre", "id": {"authority": "EPSG", "code": 8806}}, {"name": "False northing", "value": 1784000, "unit": "metre", "id": {"authority": "EPSG", "code": 8807}}]}, "coordinate_system": {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "CoordinateSystem", "subtype": "Cartesian", "axis": [{"name": "Easting", "abbreviation": "E", "direction": "east", "unit": "metre"}, {"name": "Northing", "abbreviation": "N", "direction": "north", "unit": "metre"}]}}




.. GENERATED FROM PYTHON SOURCE LINES 34-35

Grid the drifter dataset to the timesteps of the model.

.. GENERATED FROM PYTHON SOURCE LINES 35-41

.. code-block:: Python

    times = nk.sel(time=slice('2022-05-10', '2022-05-20')).time.values
    ds = ds.traj.gridtime(times)
    ds = ds.dropna('time', how='all')

    print(ds)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    2024-10-31 20:48:59 fv-az1024-670 trajan.accessor[2149] DEBUG Detecting time-dimension for "obs"..
    2024-10-31 20:48:59 fv-az1024-670 trajan.accessor[2149] DEBUG Detected obs-dim: obs, detected time-dim: time.
    2024-10-31 20:48:59 fv-az1024-670 trajan.accessor[2149] DEBUG Detected un-structured (2D) trajectory dataset
    <xarray.Dataset> Size: 6kB
    Dimensions:     (trajectory: 1, time: 239)
    Coordinates:
      * time        (time) datetime64[ns] 2kB 2022-05-10T13:00:00 ... 2022-05-20T...
      * trajectory  (trajectory) <U18 72B 'dev864475040536665'
    Data variables:
        lon         (trajectory, time) float64 2kB 5.332 5.332 5.332 ... 5.249 5.25
        lat         (trajectory, time) float64 2kB 60.38 60.38 60.38 ... 60.44 60.45
    Attributes:
        Conventions:          CF-1.10
        featureType:          trajectory
        geospatial_lat_min:   60.38380600000001
        geospatial_lat_max:   60.45141000000001
        geospatial_lon_min:   5.244493666666667
        geospatial_lon_max:   5.33242
        time_coverage_start:  2020-01-01T00:00:07.250000
        time_coverage_end:    2022-05-20T11:03:46.120000




.. GENERATED FROM PYTHON SOURCE LINES 42-43

Transform the drifter dataset to the CRS of the model

.. GENERATED FROM PYTHON SOURCE LINES 43-45

.. code-block:: Python

    tx, ty = ds.traj.transform(nk_crs, ds.traj.tx, ds.traj.ty)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    2024-10-31 20:48:59 fv-az1024-670 trajan.accessor[2149] DEBUG Detected obs-dim: time, detected time-dim: time.
    2024-10-31 20:48:59 fv-az1024-670 trajan.accessor[2149] DEBUG Detected structured (1D) trajectory dataset
    2024-10-31 20:48:59 fv-az1024-670 trajan.traj[2149] DEBUG No grid-mapping specified, checking if coordinates are lon/lat..




.. GENERATED FROM PYTHON SOURCE LINES 46-47

By making sure the coordinates has defined dimensions xarray can select along the dimensions, and does not return slices along all coordinates. See `xarray slicing <https://docs.xarray.dev/en/stable/user-guide/indexing.html#vectorized-indexing>`_ for more details.

.. GENERATED FROM PYTHON SOURCE LINES 47-50

.. code-block:: Python

    tx = xr.DataArray(tx, dims=['trajectory', 'time'])
    ty = xr.DataArray(ty, dims=['trajectory', 'time'])








.. GENERATED FROM PYTHON SOURCE LINES 51-52

Extract the values of a variable for the trajectory

.. GENERATED FROM PYTHON SOURCE LINES 52-58

.. code-block:: Python

    temp = nk.isel(depth=0).sel(time=ds.time,
                                X=tx,
                                Y=ty,
                                method='nearest').temperature
    print(temp)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    <xarray.DataArray 'temperature' (time: 239, trajectory: 1)> Size: 956B
    [239 values with dtype=float32]
    Coordinates:
        X        (trajectory, time) float64 2kB 3.416e+05 3.416e+05 ... 3.464e+05
        Y        (trajectory, time) float64 2kB 4.344e+05 4.344e+05 ... 4.416e+05
        depth    float64 8B 0.0
      * time     (time) datetime64[ns] 2kB 2022-05-10T13:00:00 ... 2022-05-20T11:...
        lat      (trajectory, time) float64 2kB 60.38 60.38 60.38 ... 60.44 60.45
        lon      (trajectory, time) float64 2kB 5.33 5.33 5.33 ... 5.243 5.243 5.249
    Dimensions without coordinates: trajectory
    Attributes:
        units:          Celsius
        time:           ocean_time
        grid:           grid
        location:       face
        field:          temperature, scalar, series
        grid_mapping:   projection_stere
        long_name:      Sea water potential temperature
        standard_name:  sea_water_potential_temperature
        _ChunkSizes:    [   1    1   17 2602]




.. GENERATED FROM PYTHON SOURCE LINES 59-60

Notice that the `lat` and `lon` variables from Norkyst match the original `lat` and `lon` from the dataset.

.. GENERATED FROM PYTHON SOURCE LINES 60-64

.. code-block:: Python


    plt.figure()
    temp.plot()
    plt.show()



.. image-sg:: /gallery/images/sphx_glr_example_model_along_trajectory_001.png
   :alt: depth = 0.0 [m]
   :srcset: /gallery/images/sphx_glr_example_model_along_trajectory_001.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (1 minutes 22.442 seconds)


.. _sphx_glr_download_gallery_example_model_along_trajectory.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: example_model_along_trajectory.ipynb <example_model_along_trajectory.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: example_model_along_trajectory.py <example_model_along_trajectory.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: example_model_along_trajectory.zip <example_model_along_trajectory.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_