OSNAP

import os
from pathlib import Path
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import xarray as xr
import iconspy as ispy
from iconspy.tests.conftest import get_ds_tgrid_lr

Grid setup

Load the ICON grid file and produce an ICONSPy dataset

ds_tgrid = get_ds_tgrid_lr()  # This can be replaced with your own model tgrid
ds_IsD = ispy.convert_tgrid_data(ds_tgrid)
ds_IsD

<xarray.Dataset> Size: 7MB
Dimensions:                 (cell: 15105, vertex: 8067, edge: 23207, nc_e: 2,
                             nv_c: 3, ne_c: 3, nv_v: 6, ne_v: 6, nv_e: 2,
                             nc_v: 6, nc_c: 3, cart: 3)
Coordinates:
  * cell                    (cell) int32 60kB 0 1 2 3 ... 15102 15103 15104
    clon                    (cell) float64 121kB 69.12 73.0 ... 74.57 76.01
    clat                    (cell) float64 121kB 72.74 74.52 ... -46.83 -44.85
  * vertex                  (vertex) int32 32kB 0 1 2 3 ... 8063 8064 8065 8066
    vlon                    (vertex) float64 65kB 73.0 69.39 ... 80.59 77.56
    vlat                    (vertex) float64 65kB 73.27 71.49 ... -45.35 -45.49
  * edge                    (edge) int32 93kB 0 1 2 3 ... 23204 23205 23206
    elon                    (edge) float64 186kB 71.11 76.98 ... 75.34 76.04
    elat                    (edge) float64 186kB 72.39 73.25 ... -46.52 -45.54
Dimensions without coordinates: nc_e, nv_c, ne_c, nv_v, ne_v, nv_e, nc_v, nc_c,
                                cart
Data variables: (12/40)
    grid_sphere_radius      float64 8B 6.371e+06
    grav                    float64 8B 9.807
    earth_angular_velocity  float64 8B 7.292e-05
    rho0                    float64 8B 1.025e+03
    rhoi                    float64 8B 917.0
    rhos                    float64 8B 300.0
    ...                      ...
    edge_cart_vec           (edge, cart) float64 557kB 0.09797 ... -0.7137
    dual_edge_cart_vec      (edge, cart) float64 557kB 0.09801 ... -0.7136
    edge_prim_norm          (edge, cart) float64 557kB 0.6463 -0.7466 ... 0.7001
    fc                      (cell) float64 121kB 0.0001393 ... -0.0001029
    fe                      (edge) float64 186kB 0.000139 ... -0.0001041
    fv                      (vertex) float64 65kB 0.0001397 ... -0.000104
Attributes:
    converted_tgrid:      True
    boundary_BallTree:    <iconspy.balltree.IspyBoundaryBallTree object at 0x...
    wet_BallTree:         <iconspy.balltree.IspyWetBallTree object at 0x7ffb5...
    uuidOfHGrid:          5bd948e8-ac1a-11ea-a6b1-d317264fdca9
    IsD_compatible_flag:  True

xarray.Dataset

• Dimensions:
  • cell: 15105
  • vertex: 8067
  • edge: 23207
  • nc_e: 2
  • nv_c: 3
  • ne_c: 3
  • nv_v: 6
  • ne_v: 6
  • nv_e: 2
  • nc_v: 6
  • nc_c: 3
  • cart: 3
• Coordinates: (9)
  • cell
    (cell)
    int32
    0 1 2 3 ... 15101 15102 15103 15104

    array([    0,     1,     2, ..., 15102, 15103, 15104],
          shape=(15105,), dtype=int32)

  • clon
    (cell)
    float64
    69.12 73.0 77.07 ... 74.57 76.01

    standard_name :

    longitude

    long_name :

    center longitude

    units :

    degrees

    bounds :

    clon_bnds

    array([69.11711949, 73.        , 77.06894695, ..., 77.71305988,
           74.57238902, 76.01041357], shape=(15105,))

  • clat
    (cell)
    float64
    72.74 74.52 73.74 ... -46.83 -44.85

    standard_name :

    latitude

    long_name :

    center latitude

    units :

    degrees

    bounds :

    clat_bnds

    array([ 72.73970646,  74.5199344 ,  73.73527011, ..., -46.75491703,
           -46.82864641, -44.85063277], shape=(15105,))

  • vertex
    (vertex)
    int32
    0 1 2 3 4 ... 8063 8064 8065 8066

    array([   0,    1,    2, ..., 8064, 8065, 8066], shape=(8067,), dtype=int32)

  • vlon
    (vertex)
    float64
    73.0 69.39 80.92 ... 80.59 77.56

    standard_name :

    longitude

    long_name :

    vertex longitude

    units :

    degrees

    bounds :

    vlon_bnds

    array([73.        , 69.39227498, 80.92183549, ..., 78.82819486,
           80.5890548 , 77.56070064], shape=(8067,))

  • vlat
    (vertex)
    float64
    73.27 71.49 73.15 ... -45.35 -45.49

    standard_name :

    latitude

    long_name :

    vertex latitude

    units :

    degrees

    bounds :

    vlat_bnds

    array([ 73.27072474,  71.48986466,  73.14778086, ..., -43.48866646,
           -45.35344314, -45.49203108], shape=(8067,))

  • edge
    (edge)
    int32
    0 1 2 3 ... 23203 23204 23205 23206

    array([    0,     1,     2, ..., 23204, 23205, 23206],
          shape=(23207,), dtype=int32)

  • elon
    (edge)
    float64
    71.11 76.98 69.02 ... 75.34 76.04

    standard_name :

    longitude

    long_name :

    edge midpoint longitude

    units :

    degrees

    bounds :

    elon_bnds

    array([71.10783316, 76.97502387, 69.02497613, ..., 76.88196882,
           75.33519465, 76.04201648], shape=(23207,))

  • elat
    (edge)
    float64
    72.39 73.25 73.25 ... -46.52 -45.54

    standard_name :

    latitude

    long_name :

    edge midpoint latitude

    units :

    degrees

    bounds :

    elat_bnds

    array([ 72.38846688,  73.24710912,  73.24710912, ..., -46.48432935,
           -46.51998579, -45.53685625], shape=(23207,))

• Data variables: (40)
  • grid_sphere_radius
    ()
    float64
    6.371e+06

    array(6371229.)

  • grav
    ()
    float64
    9.807

    array(9.80665)

  • earth_angular_velocity
    ()
    float64
    7.292e-05

    array(7.29212e-05)

  • rho0
    ()
    float64
    1.025e+03

    array(1025.022)

  • rhoi
    ()
    float64
    917.0

    array(917.)

  • rhos
    ()
    float64
    300.0

    array(300.)

  • sal_ref
    ()
    float64
    35.0

    array(35.)

  • sal_ice
    ()
    float64
    5.0

    array(5.)

  • cp
    ()
    float64
    4.193e+03

    array(4192.664112)

  • tref
    ()
    float64
    273.1

    array(273.15)

  • tmelt
    ()
    float64
    273.1

    array(273.15)

  • tfreeze
    ()
    float64
    -1.9

    array(-1.9)

  • alf
    ()
    float64
    3.337e+05

    array(333700.)

  • cell_area
    (cell)
    float64
    2.493e+10 2.476e+10 ... 2.573e+10

    standard_name :

    area

    long_name :

    area of grid cell

    units :

    m2

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    1

    institution :

    MPIMET

    array([2.49304853e+10, 2.47611892e+10, 2.45493312e+10, ...,
           2.57578286e+10, 2.57780327e+10, 2.57316587e+10], shape=(15105,))

  • cell_area_p
    (cell)
    float64
    2.493e+10 2.476e+10 ... 2.573e+10

    long_name :

    area of grid cell

    units :

    m2

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    1

    institution :

    MPIMET

    array([2.49304853e+10, 2.47611892e+10, 2.45493312e+10, ...,
           2.57578286e+10, 2.57780327e+10, 2.57316587e+10], shape=(15105,))

  • dual_area
    (vertex)
    float64
    4.948e+10 4.981e+10 ... 5.122e+10

    standard_name :

    area

    long_name :

    areas of dual hexagonal/pentagonal cells

    units :

    m2

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([4.94802278e+10, 4.98099313e+10, 4.94812149e+10, ...,
           5.10929589e+10, 5.11354654e+10, 5.12203693e+10], shape=(8067,))

  • edge_length
    (edge)
    float64
    2.322e+05 2.547e+05 ... 2.369e+05

    long_name :

    lengths of edges of triangular cells

    units :

    m

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([232209.5283607 , 254651.6178127 , 254651.6178127 , ...,
           244336.78538223, 247418.32545662, 236879.14370019], shape=(23207,))

  • dual_edge_length
    (edge)
    float64
    1.531e+05 1.109e+05 ... 1.516e+05

    long_name :

    lengths of dual edges (distances between triangular cell circumcenters)

    units :

    m

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([153072.08179307, 110866.17340271, 110866.17340271, ...,
           139227.64305611, 133746.68112488, 151552.7943174 ], shape=(23207,))

  • edge_cell_distance
    (edge, nc_e)
    float64
    7.61e+04 7.697e+04 ... 7.635e+04

    long_name :

    distances between edge midpoint and adjacent triangle midpoints

    units :

    m

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[76102.81287677, 76969.2689163 ],
           [56502.14344913, 54364.02995358],
           [56502.14344913, 54364.02995358],
           ...,
           [68982.50620366, 70245.13685246],
           [66179.39695196, 67567.28417292],
           [75205.30869442, 76347.48562299]], shape=(23207, 2))

  • vertex_of_cell
    (cell, nv_c)
    int32
    0 3 1 5 6 ... 8056 8056 8051 8066

    long_name :

    vertices of each cell

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[   0,    3,    1],
           [   5,    6,    0],
           [   0,    2,    5],
           ...,
           [8066, 7998, 8000],
           [8000, 7976, 8056],
           [8056, 8051, 8066]], shape=(15105, 3), dtype=int32)

  • edge_of_cell
    (cell, ne_c)
    int32
    2 3 0 5 ... 23205 23149 23197 23206

    long_name :

    edges of each cell

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[    2,     3,     0],
           [    5,     6,     7],
           [    1,     8,     5],
           ...,
           [23195, 22942, 23204],
           [22941, 23150, 23205],
           [23149, 23197, 23206]], shape=(15105, 3), dtype=int32)

  • vertices_of_vertex
    (vertex, nv_v)
    int32
    1 2 3 5 6 ... 7998 8051 8000 8056

    long_name :

    vertices around each vertex

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[   1,    2,    3,    5,    6,   -1],
           [   0,    3,    4,    8,    9,   -1],
           [   0,    5,   11,   -1,   -1,   -1],
           ...,
           [8065, 8066, 7719, 8051, 8055, 7721],
           [8064, 8066, 7719, 7998, 7720, 8001],
           [8065, 8064, 7998, 8051, 8000, 8056]], shape=(8067, 6), dtype=int32)

  • edges_of_vertex
    (vertex, ne_v)
    int32
    0 1 2 5 ... 23195 23197 23204 23206

    long_name :

    edges around each vertex

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[    0,     1,     2,     5,     7,    -1],
           [    0,     3,     4,    12,    14,    -1],
           [    1,     8,    -1,    -1,    -1,    -1],
           ...,
           [23189, 23191, 23192, 23196, 23198, 23200],
           [23189, 23190, 23193, 23194, 23201, 23203],
           [23190, 23191, 23195, 23197, 23204, 23206]],
          shape=(8067, 6), dtype=int32)

  • edge_vertices
    (edge, nv_e)
    int32
    0 1 2 0 0 ... 8000 8056 8056 8066

    long_name :

    vertices at the end of of each edge

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[   0,    1],
           [   2,    0],
           [   0,    3],
           ...,
           [8066, 8000],
           [8000, 8056],
           [8056, 8066]], shape=(23207, 2), dtype=int32)

  • adjacent_cell_of_edge
    (edge, nc_e)
    int32
    -1 0 -1 2 ... 15103 15101 15104

    long_name :

    cells adjacent to each edge

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[   -1,     0],
           [   -1,     2],
           [    0,     4],
           ...,
           [15101, 15102],
           [15101, 15103],
           [15101, 15104]], shape=(23207, 2), dtype=int32)

  • cells_of_vertex
    (vertex, nc_v)
    int32
    -1 -1 0 1 ... 15101 15102 15104

    long_name :

    cells around each vertex

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[   -1,    -1,     0,     1,     2,     4],
           [   -1,     0,    -1,     5,     6,     8],
           [   -1,    -1,     2,    -1,    -1,    -1],
           ...,
           [15089, 15090, 15092, 15093, 15094, 15096],
           [15089, 15090, 15091, 15097, 15098, 15100],
           [15089, 15091, 15092, 15101, 15102, 15104]],
          shape=(8067, 6), dtype=int32)

  • adjacent_cell_of_cell
    (cell, nc_c)
    int32
    4 6 -1 2 ... 15054 15092 15101

    long_name :

    cell neighbor index

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[    4,     6,    -1],
           [    2,     3,     4],
           [   -1,    -1,     1],
           ...,
           [15091, 14904, 15101],
           [14903, 15055, 15101],
           [15054, 15092, 15101]], shape=(15105, 3), dtype=int32)

  • orientation_of_normal
    (cell, ne_c)
    int32
    1 1 -1 1 1 1 ... -1 -1 -1 -1 -1 -1

    long_name :

    orientations of normals to triangular cell edges

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[ 1,  1, -1],
           [ 1,  1,  1],
           [-1,  1, -1],
           ...,
           [-1, -1, -1],
           [-1, -1, -1],
           [-1, -1, -1]], shape=(15105, 3), dtype=int32)

  • edge_orientation
    (vertex, ne_v)
    int32
    -1 1 -1 -1 1 0 1 ... -1 1 -1 1 1 -1

    long_name :

    edge orientation

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[-1,  1, -1, -1,  1,  0],
           [ 1,  1, -1, -1,  1,  0],
           [-1, -1,  0,  0,  0,  0],
           ...,
           [ 1, -1,  1, -1,  1, -1],
           [-1,  1, -1,  1,  1, -1],
           [-1,  1, -1,  1,  1, -1]], shape=(8067, 6), dtype=int32)

  • tangent_orientation
    (edge)
    int32
    -1 -1 -1 -1 -1 -1 ... 1 1 1 1 1 1

    long_name :

    edge system orientation

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([-1, -1, -1, ...,  1,  1,  1], shape=(23207,), dtype=int32)

  • cell_sea_land_mask
    (cell)
    int32
    1 -1 1 -2 -1 -1 ... -2 -2 -2 -2 -2

    long_name :

    sea (-2 inner, -1 boundary) land (2 inner, 1 boundary) mask for the cell

    units :

    2,1,-1,-

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    1

    institution :

    MPIMET

    array([ 1, -1,  1, ..., -2, -2, -2], shape=(15105,), dtype=int32)

  • edge_sea_land_mask
    (edge)
    int32
    2 2 0 0 2 0 ... -2 -2 -2 -2 -2 -2

    long_name :

    sea (-2 inner, -1 boundary) land (2 inner, 1 boundary) mask for the cell

    units :

    2,1,-1,-

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([ 2,  2,  0, ..., -2, -2, -2], shape=(23207,), dtype=int32)

  • cell_cart_vec
    (cell, cart)
    float64
    0.1058 0.2772 ... 0.6879 -0.7053

    long_name :

    cartesian position of the prime cell circumcenter on the unit sphere, coordinate x

    units :

    meters

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[ 0.10576603,  0.27722236,  0.95496665],
           [ 0.07803491,  0.2552407 ,  0.96372337],
           [ 0.06267491,  0.27297311,  0.95997788],
           ...,
           [ 0.1457989 ,  0.66942718, -0.72842977],
           [ 0.18200671,  0.65952962, -0.72931079],
           [ 0.17138496,  0.68792015, -0.70526099]], shape=(15105, 3))

  • vert_cart_vec
    (vertex, cart)
    float64
    0.08416 0.2753 ... 0.6846 -0.7132

    long_name :

    vertex cartesian coordinate x on unit sp

    units :

    meters

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[ 0.08415916,  0.27527221,  0.95767554],
           [ 0.11174008,  0.29715801,  0.94826751],
           [ 0.04574159,  0.28627284,  0.95705568],
           ...,
           [ 0.14056883,  0.71176254, -0.68821108],
           [ 0.11490672,  0.69327329, -0.71145526],
           [ 0.15100085,  0.68455212, -0.71315296]], shape=(8067, 3))

  • edge_cart_vec
    (edge, cart)
    float64
    0.09797 0.2863 ... 0.6798 -0.7137

    long_name :

    prime edge center cartesian coordinate x on unit sphere

    units :

    meters

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[ 0.09796589,  0.28626264,  0.95312978],
           [ 0.06496335,  0.2808286 ,  0.95755682],
           [ 0.10318031,  0.2691445 ,  0.95755682],
           ...,
           [ 0.15627247,  0.67058487, -0.72518608],
           [ 0.17420235,  0.66568553, -0.72561444],
           [ 0.16895582,  0.67976803, -0.71370117]], shape=(23207, 3))

  • dual_edge_cart_vec
    (edge, cart)
    float64
    0.09801 0.2862 ... 0.6798 -0.7136

    long_name :

    dual edge center cartesian coordinate x on unit sphere

    units :

    meters

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[ 0.09800983,  0.28621187,  0.95314051],
           [ 0.0650083 ,  0.28098287,  0.95750851],
           [ 0.1032293 ,  0.26929754,  0.95750851],
           ...,
           [ 0.15617842,  0.67057483, -0.72521562],
           [ 0.17428261,  0.66562269, -0.72565282],
           [ 0.16897408,  0.67982937, -0.71363842]], shape=(23207, 3))

  • edge_prim_norm
    (edge, cart)
    float64
    0.6463 -0.7466 ... 0.6844 0.7001

    long_name :

    unit normal to the prime edge 3D vector, coordinate x

    units :

    meters

    CDI_grid_type :

    unstructured

    institution :

    MPIMET

    array([[ 0.64627398, -0.74660958,  0.15781023],
           [-0.26792051, -0.91944234,  0.28782701],
           [-0.29202946, -0.91207149,  0.28782701],
           ...,
           [-0.94910995, -0.10130766, -0.29820642],
           [ 0.73684649, -0.57694992, -0.35240041],
           [ 0.20372986,  0.68438731,  0.70007725]], shape=(23207, 3))

  • fc
    (cell)
    float64
    0.0001393 0.0001406 ... -0.0001029

    standard_name :

    latitude

    long_name :

    center latitude

    units :

    degrees

    bounds :

    clat_bnds

    array([ 0.00013927,  0.00014055,  0.00014001, ..., -0.00010624,
           -0.00010636, -0.00010286], shape=(15105,))

  • fe
    (edge)
    float64
    0.000139 0.0001397 ... -0.0001041

    standard_name :

    latitude

    long_name :

    edge midpoint latitude

    units :

    degrees

    bounds :

    elat_bnds

    array([ 0.00013901,  0.00013965,  0.00013965, ..., -0.00010576,
           -0.00010583, -0.00010409], shape=(23207,))

  • fv
    (vertex)
    float64
    0.0001397 0.0001383 ... -0.000104

    standard_name :

    latitude

    long_name :

    vertex latitude

    units :

    degrees

    bounds :

    vlat_bnds

    array([ 0.00013967,  0.0001383 ,  0.00013958, ..., -0.00010037,
           -0.00010376, -0.00010401], shape=(8067,))

• Attributes: (5)

  converted_tgrid :

  True

  boundary_BallTree :

  <iconspy.balltree.IspyBoundaryBallTree object at 0x7ffb5ee73cb0>

  wet_BallTree :

  <iconspy.balltree.IspyWetBallTree object at 0x7ffb5edcc1a0>

  uuidOfHGrid :

  5bd948e8-ac1a-11ea-a6b1-d317264fdca9

  IsD_compatible_flag :

  True

Get the coordinates of the OSNAP-Array

# Open the mooring location files
import numpy as np
ds_osnap_west = xr.open_dataset(
    "https://swift.dkrz.de/v1/dkrz_7fa6baba-db43-4d12-a295-8e3ebb1a01ed/iconspy_test_data/OSNAP_mooring_positions_west.nc?temp_url_sig=0a2fb0675ab8e26fc5059903e5fb70b1a042c598&temp_url_expires=2036-07-11T11:41:31Z",
    engine="h5netcdf"
)

ds_osnap_east = xr.open_dataset(
    "https://swift.dkrz.de/v1/dkrz_7fa6baba-db43-4d12-a295-8e3ebb1a01ed/iconspy_test_data/OSNAP_mooring_positions_east.nc?temp_url_sig=e700534e9ce072aab794f0f6b6fd2a6b49f98f34&temp_url_expires=2036-07-11T11:42:22Z",
    engine="h5netcdf"
)

# Visualise the mooring positions
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})

ax.plot(
    ds_osnap_west["lon_sect"],
    ds_osnap_west["lat_sect"],
    transform=ccrs.PlateCarree(),
)

ax.plot(
    ds_osnap_east["lon_sect"],
    ds_osnap_east["lat_sect"],
    transform=ccrs.PlateCarree(),
)

ax.coastlines()

<cartopy.mpl.feature_artist.FeatureArtist at 0x7ffb2450f4d0>

OSNAP-West

Find stations

We begin by finding the stations which make up OSNAP-West and discarding any stations which duplicate vertices

# Create a list of the target stations at OSNAP West
osnap_west_target_stations = []
for mooring in ds_osnap_west["mooring_name"].values:
    # Makr the boundary stations as such
    if mooring.startswith("OSNAP_West"):
        boundary = True
    else:
        boundary = None

    osnap_west_target_stations += [
        ispy.TargetStation(
            name=mooring,
            lon=float(ds_osnap_west["lon_sect"].sel(mooring_name=mooring).values),
            lat=float(ds_osnap_west["lat_sect"].sel(mooring_name=mooring).values),
            boundary=boundary,
        )
    ]

# Convert the target stations to model stations, but some of these may be duplicates.
_osnap_west_model_stations = [target_station.to_model_station(ds_IsD) for target_station in osnap_west_target_stations]
_osnap_west_model_stations

# Remove repeat stations
osnap_west_model_stations = []
osnap_west_model_stations_vertices = []
for model_station in _osnap_west_model_stations:
    if model_station.vertex not in osnap_west_model_stations_vertices:
        osnap_west_model_stations.append(model_station)
        osnap_west_model_stations_vertices.append(model_station.vertex)
        print(f"Added {model_station.name} at vertex {model_station.vertex}")
    else:
        print(f"Skipped {model_station.name} at vertex {model_station.vertex} because it is a duplicate")

# Visualise the model stations on a map
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})

for i, model_station in enumerate(osnap_west_model_stations):
    ax.text(
        model_station.model_lon,
        model_station.model_lat,
        f"{i}",
        transform=ccrs.PlateCarree(),
        color="red",
    )

ax.coastlines()
ax.set_extent([-60, -40, 50, 70])

Added OSNAP_West-West at vertex 900
Added C1/1872 at vertex 903
Skipped C1/1881 at vertex 903 because it is a duplicate
Skipped C2/1873 at vertex 903 because it is a duplicate
Skipped C2/1882 at vertex 903 because it is a duplicate
Skipped C3/1874 at vertex 903 because it is a duplicate
Skipped K7 at vertex 903 because it is a duplicate
Added K8 at vertex 890
Skipped DSOW1 at vertex 890 because it is a duplicate
Skipped K9 at vertex 890 because it is a duplicate
Skipped DSOW2 at vertex 890 because it is a duplicate
Added K10 at vertex 892
Skipped DSOW5 at vertex 892 because it is a duplicate
Added DSOW3 at vertex 906
Skipped LS8 at vertex 906 because it is a duplicate
Skipped DSOW4 at vertex 906 because it is a duplicate
Skipped LS7 at vertex 906 because it is a duplicate
Skipped LS6 at vertex 906 because it is a duplicate
Skipped LS5 at vertex 906 because it is a duplicate
Skipped LS4 at vertex 906 because it is a duplicate
Skipped LS3 at vertex 906 because it is a duplicate
Skipped LS2 at vertex 906 because it is a duplicate
Skipped LS1 at vertex 906 because it is a duplicate
Added OSNAP_West-East at vertex 908

Construct section

# First we generate a list of sections connecting the model stations
osnap_west_sections = []
for i in range(len(osnap_west_model_stations) - 1):
    model_station_a = osnap_west_model_stations[i]
    model_station_b = osnap_west_model_stations[i + 1]

    name = str(model_station_a.name) + " to " + str(model_station_b.name)
    print(name)
    osnap_west_sections.append(
        ispy.Section(
            name=name,
            model_station_a=model_station_a,
            model_station_b=model_station_b,
            ds_IsD=ds_IsD,
            section_type="great circle",
        )
    )

osnap_west_sections

OSNAP_West-West to C1/1872
C1/1872 to K8
K8 to K10
K10 to DSOW3
DSOW3 to OSNAP_West-East

[Section(OSNAP_West-West to C1/1872, OSNAP_West-West, C1/1872, great circle),
 Section(C1/1872 to K8, C1/1872, K8, great circle),
 Section(K8 to K10, K8, K10, great circle),
 Section(K10 to DSOW3, K10, DSOW3, great circle),
 Section(DSOW3 to OSNAP_West-East, DSOW3, OSNAP_West-East, great circle)]

# Now we combine these sections and visualise
osnap_west_combined = ispy.CombinedSection("OSNAP West", osnap_west_sections, ds_IsD)
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})
osnap_west_combined.plot(ax=ax)
ax.set_extent([-60, -40, 50, 70])

OSNAP-East

# Create a list of the target stations at OSNAP East
osnap_east_target_stations = []
for mooring in ds_osnap_east["mooring_name"].values:
    # Makr the boundary stations as such
    if mooring.startswith("OSNAP_East"):
        boundary = True
    else:
        boundary = None

    osnap_east_target_stations += [
        ispy.TargetStation(
            name=mooring,
            lon=float(ds_osnap_east["lon_sect"].sel(mooring_name=mooring).values),
            lat=float(ds_osnap_east["lat_sect"].sel(mooring_name=mooring).values),
            boundary=boundary,
        )
    ]

# Convert the target stations to model stations, but some of these may be duplicates.
_osnap_east_model_stations = [target_station.to_model_station(ds_IsD) for target_station in osnap_east_target_stations]
_osnap_east_model_stations

# Remove repeat stations
osnap_east_model_stations = []
osnap_east_model_stations_vertices = []
for model_station in _osnap_east_model_stations:
    # LS2 is one edge away from LS1 which causes things to break.
    # Need to fix ispy to cope with this
    if model_station.name.name == "LS2":
        print(f"Skipped {model_station.name} at vertex {model_station.vertex} because it is a near duplicate")
    elif model_station.vertex not in osnap_east_model_stations_vertices:
        osnap_east_model_stations.append(model_station)
        osnap_east_model_stations_vertices.append(model_station.vertex)
        print(f"Added {model_station.name} at vertex {model_station.vertex}")
    else:
        print(f"Skipped {model_station.name} at vertex {model_station.vertex} because it is a duplicate")

# Visualise the model stations on a map
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})

for i, model_station in enumerate(osnap_east_model_stations):
    ax.text(
        model_station.model_lon,
        model_station.model_lat,
        f"{i}",
        transform=ccrs.PlateCarree(),
        color="red",
    )

ax.coastlines()
ax.set_extent([-60, 0, 50, 70])

Added OSNAP_East-West at vertex 908
Skipped CF1 at vertex 908 because it is a duplicate
Skipped CF2 at vertex 908 because it is a duplicate
Skipped CF3 at vertex 908 because it is a duplicate
Skipped CF4 at vertex 908 because it is a duplicate
Skipped CF5 at vertex 908 because it is a duplicate
Skipped CF6 at vertex 908 because it is a duplicate
Skipped CF7 at vertex 908 because it is a duplicate
Skipped NOC-M1 at vertex 908 because it is a duplicate
Added NOC-M2 at vertex 911
Skipped NOC-M3 at vertex 911 because it is a duplicate
Skipped FLMA at vertex 911 because it is a duplicate
Skipped FLMB at vertex 911 because it is a duplicate
Skipped NOC-M4 at vertex 911 because it is a duplicate
Skipped NOC-M5 at vertex 911 because it is a duplicate
Added IC0 at vertex 912
Skipped IC1 at vertex 912 because it is a duplicate
Added RREX Moor.1 IRW at vertex 1297
Skipped IC2 at vertex 1297 because it is a duplicate
Skipped RREX Moor.2 IRM at vertex 1297 because it is a duplicate
Skipped IC3 at vertex 1297 because it is a duplicate
Skipped RREX Moor.3 IRE at vertex 1297 because it is a duplicate
Skipped IC4 at vertex 1297 because it is a duplicate
Skipped UM-M1 at vertex 1297 because it is a duplicate
Skipped UM-D1 at vertex 1297 because it is a duplicate
Skipped UM-D2 at vertex 1297 because it is a duplicate
Added UM-D3 at vertex 1292
Skipped UM-M2 at vertex 1292 because it is a duplicate
Skipped UM-D4 at vertex 1292 because it is a duplicate
Added UM-M3 at vertex 1291
Added UM-M4 at vertex 1275
Added SAMS Glider East Waypoint at vertex 1271
Added RTWB1 at vertex 1274
Skipped RTWB2 at vertex 1274 because it is a duplicate
Added RTEB1 at vertex 1284
Skipped RTADCP2 at vertex 1284 because it is a duplicate
Added OSNAP_East-East at vertex 1235

osnap_east_sections = []
for i in range(len(osnap_east_model_stations) - 1):
    model_station_a = osnap_east_model_stations[i]
    model_station_b = osnap_east_model_stations[i + 1]

    name = str(model_station_a.name) + " to " + str(model_station_b.name)
    print(name)
    osnap_east_sections.append(
        ispy.Section(
            name=name,
            model_station_a=model_station_a,
            model_station_b=model_station_b,
            ds_IsD=ds_IsD,
            section_type="great circle",
        )
    )

osnap_east_sections

OSNAP_East-West to NOC-M2
NOC-M2 to IC0
IC0 to RREX Moor.1 IRW
RREX Moor.1 IRW to UM-D3
UM-D3 to UM-M3
UM-M3 to UM-M4
UM-M4 to SAMS Glider East Waypoint
SAMS Glider East Waypoint to RTWB1
RTWB1 to RTEB1
RTEB1 to OSNAP_East-East

[Section(OSNAP_East-West to NOC-M2, OSNAP_East-West, NOC-M2, great circle),
 Section(NOC-M2 to IC0, NOC-M2, IC0, great circle),
 Section(IC0 to RREX Moor.1 IRW, IC0, RREX Moor.1 IRW, great circle),
 Section(RREX Moor.1 IRW to UM-D3, RREX Moor.1 IRW, UM-D3, great circle),
 Section(UM-D3 to UM-M3, UM-D3, UM-M3, great circle),
 Section(UM-M3 to UM-M4, UM-M3, UM-M4, great circle),
 Section(UM-M4 to SAMS Glider East Waypoint, UM-M4, SAMS Glider East Waypoint, great circle),
 Section(SAMS Glider East Waypoint to RTWB1, SAMS Glider East Waypoint, RTWB1, great circle),
 Section(RTWB1 to RTEB1, RTWB1, RTEB1, great circle),
 Section(RTEB1 to OSNAP_East-East, RTEB1, OSNAP_East-East, great circle)]

osnap_east_combined = ispy.CombinedSection("OSNAP East", osnap_east_sections, ds_IsD)
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})
osnap_east_combined.plot(ax=ax)
ax.set_extent([-60, 0, 50, 70])

Output in xarray formats

ds_osnap_east = osnap_east_combined.to_ispy_section()
ds_osnap_east

<xarray.Dataset> Size: 668B
Dimensions:           (step_in_path: 10, step_in_path_v: 11)
Coordinates:
  * step_in_path      (step_in_path) int64 80B 0 1 2 3 4 5 6 7 8 9
    elon              (step_in_path) float64 80B -40.95 -36.93 ... -10.47 -7.136
    elat              (step_in_path) float64 80B 59.69 58.78 ... 57.51 56.79
  * step_in_path_v    (step_in_path_v) int64 88B 0 1 2 3 4 5 6 7 8 9 10
    vlon              (step_in_path_v) float64 88B -43.04 -38.92 ... -5.111
    vlat              (step_in_path_v) float64 88B 60.1 59.25 ... 56.67 56.89
Data variables:
    edge_path         (step_in_path) int32 40B 2434 2433 3427 ... 3360 3386 3397
    vertex_path       (step_in_path_v) int32 44B 908 911 912 ... 1274 1284 1235
    path_orientation  (step_in_path) float64 80B 1.0 1.0 1.0 ... 1.0 1.0 -1.0
Attributes:
    date:          2026-07-14 14:12:18
    ispy version:  0.2.0
    uuidOfHGrid:   5bd948e8-ac1a-11ea-a6b1-d317264fdca9
    section name:  OSNAP East
    Created by:    m301014

xarray.Dataset

• Dimensions:
  • step_in_path: 10
  • step_in_path_v: 11
• Coordinates: (6)
  • step_in_path
    (step_in_path)
    int64
    0 1 2 3 4 5 6 7 8 9

    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

  • elon
    (step_in_path)
    float64
    -40.95 -36.93 ... -10.47 -7.136

    standard_name :

    longitude

    long_name :

    edge midpoint longitude

    units :

    degrees

    bounds :

    elon_bnds

    array([-40.95447021, -36.9316414 , -33.0683586 , -29.22064335,
           -25.41832256, -21.69056246, -18.05678418, -13.94246955,
           -10.46853984,  -7.13604969])

  • elat
    (step_in_path)
    float64
    59.69 58.78 58.78 ... 57.51 56.79

    standard_name :

    latitude

    long_name :

    edge midpoint latitude

    units :

    degrees

    bounds :

    elat_bnds

    array([59.68775127, 58.77945393, 58.77945393, 58.66491455, 58.44268066,
           58.10671482, 57.67468909, 58.14471837, 57.5074947 , 56.79293464])

  • step_in_path_v
    (step_in_path_v)
    int64
    0 1 2 3 4 5 6 7 8 9 10

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])

  • vlon
    (step_in_path_v)
    float64
    -43.04 -38.92 ... -9.149 -5.111

    standard_name :

    longitude

    long_name :

    vertex longitude

    units :

    degrees

    bounds :

    vlon_bnds

    array([-43.04356323, -38.91767748, -35.        , -31.08232252,
           -27.42139129, -23.37272257, -20.07321372, -16.01091984,
           -11.8498772 ,  -9.14893813,  -5.11127358])

  • vlat
    (step_in_path_v)
    float64
    60.1 59.25 58.28 ... 56.67 56.89

    standard_name :

    latitude

    long_name :

    vertex latitude

    units :

    degrees

    bounds :

    vlat_bnds

    array([60.09626751, 59.24686493, 58.2823536 , 59.24686493, 58.05698849,
           58.79646934, 57.3956551 , 57.92117782, 58.3343808 , 56.66619149,
           56.88707145])

• Data variables: (3)
  • edge_path
    (step_in_path)
    int32
    2434 2433 3427 ... 3360 3386 3397

    array([2434, 2433, 3427, 3423, 3409, 3416, 3362, 3360, 3386, 3397], dtype=int32)

  • vertex_path
    (step_in_path_v)
    int32
    908 911 912 1297 ... 1274 1284 1235

    array([ 908,  911,  912, 1297, 1292, 1291, 1275, 1271, 1274, 1284, 1235],
          dtype=int32)

  • path_orientation
    (step_in_path)
    float64
    1.0 1.0 1.0 1.0 ... 1.0 1.0 -1.0

    sign convention :

    positive to the left when traversing the path

    array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1., -1.])

• Attributes: (5)

  date :

  2026-07-14 14:12:18

  ispy version :

  0.2.0

  uuidOfHGrid :

  5bd948e8-ac1a-11ea-a6b1-d317264fdca9

  section name :

  OSNAP East

  Created by :

  m301014

ds_osnap_west = osnap_east_combined.to_ispy_section()
ds_osnap_west

<xarray.Dataset> Size: 668B
Dimensions:           (step_in_path: 10, step_in_path_v: 11)
Coordinates:
  * step_in_path      (step_in_path) int64 80B 0 1 2 3 4 5 6 7 8 9
    elon              (step_in_path) float64 80B -40.95 -36.93 ... -10.47 -7.136
    elat              (step_in_path) float64 80B 59.69 58.78 ... 57.51 56.79
  * step_in_path_v    (step_in_path_v) int64 88B 0 1 2 3 4 5 6 7 8 9 10
    vlon              (step_in_path_v) float64 88B -43.04 -38.92 ... -5.111
    vlat              (step_in_path_v) float64 88B 60.1 59.25 ... 56.67 56.89
Data variables:
    edge_path         (step_in_path) int32 40B 2434 2433 3427 ... 3360 3386 3397
    vertex_path       (step_in_path_v) int32 44B 908 911 912 ... 1274 1284 1235
    path_orientation  (step_in_path) float64 80B 1.0 1.0 1.0 ... 1.0 1.0 -1.0
Attributes:
    date:          2026-07-14 14:12:56
    ispy version:  0.2.0
    uuidOfHGrid:   5bd948e8-ac1a-11ea-a6b1-d317264fdca9
    section name:  OSNAP East
    Created by:    m301014

xarray.Dataset

• Dimensions:
  • step_in_path: 10
  • step_in_path_v: 11
• Coordinates: (6)
  • step_in_path
    (step_in_path)
    int64
    0 1 2 3 4 5 6 7 8 9

    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

  • elon
    (step_in_path)
    float64
    -40.95 -36.93 ... -10.47 -7.136

    standard_name :

    longitude

    long_name :

    edge midpoint longitude

    units :

    degrees

    bounds :

    elon_bnds

    array([-40.95447021, -36.9316414 , -33.0683586 , -29.22064335,
           -25.41832256, -21.69056246, -18.05678418, -13.94246955,
           -10.46853984,  -7.13604969])

  • elat
    (step_in_path)
    float64
    59.69 58.78 58.78 ... 57.51 56.79

    standard_name :

    latitude

    long_name :

    edge midpoint latitude

    units :

    degrees

    bounds :

    elat_bnds

    array([59.68775127, 58.77945393, 58.77945393, 58.66491455, 58.44268066,
           58.10671482, 57.67468909, 58.14471837, 57.5074947 , 56.79293464])

  • step_in_path_v
    (step_in_path_v)
    int64
    0 1 2 3 4 5 6 7 8 9 10

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])

  • vlon
    (step_in_path_v)
    float64
    -43.04 -38.92 ... -9.149 -5.111

    standard_name :

    longitude

    long_name :

    vertex longitude

    units :

    degrees

    bounds :

    vlon_bnds

    array([-43.04356323, -38.91767748, -35.        , -31.08232252,
           -27.42139129, -23.37272257, -20.07321372, -16.01091984,
           -11.8498772 ,  -9.14893813,  -5.11127358])

  • vlat
    (step_in_path_v)
    float64
    60.1 59.25 58.28 ... 56.67 56.89

    standard_name :

    latitude

    long_name :

    vertex latitude

    units :

    degrees

    bounds :

    vlat_bnds

    array([60.09626751, 59.24686493, 58.2823536 , 59.24686493, 58.05698849,
           58.79646934, 57.3956551 , 57.92117782, 58.3343808 , 56.66619149,
           56.88707145])

• Data variables: (3)
  • edge_path
    (step_in_path)
    int32
    2434 2433 3427 ... 3360 3386 3397

    array([2434, 2433, 3427, 3423, 3409, 3416, 3362, 3360, 3386, 3397], dtype=int32)

  • vertex_path
    (step_in_path_v)
    int32
    908 911 912 1297 ... 1274 1284 1235

    array([ 908,  911,  912, 1297, 1292, 1291, 1275, 1271, 1274, 1284, 1235],
          dtype=int32)

  • path_orientation
    (step_in_path)
    float64
    1.0 1.0 1.0 1.0 ... 1.0 1.0 -1.0

    sign convention :

    positive to the left when traversing the path

    array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1., -1.])

• Attributes: (5)

  date :

  2026-07-14 14:12:56

  ispy version :

  0.2.0

  uuidOfHGrid :

  5bd948e8-ac1a-11ea-a6b1-d317264fdca9

  section name :

  OSNAP East

  Created by :

  m301014