pp_smtnatl_dissipation

%matplotlib inline
%load_ext autoreload
%autoreload 2

import pyicon as pyic
import xarray as xr
import glob
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import seawater as sw
import maps_icon_smt_temp as smt
import datetime
from matplotlib.patches import Rectangle

from icon_smt_levels import dzw, dzt, depthc, depthi

import dask
import dask.array as da
from dask.distributed import Client, LocalCluster
import multiprocessing
from dask.diagnostics import ProgressBar

-----calc
sys glob os
numpy
netcdf
Done modules calc.
-----calc_xr
sys glob os
numpy
netcdf
xarray
Done modules calc.
-----tb
sys
json
numpy
scipy
netcdf datetime
matplotlib
mybreak
pnadas
xarray
done xarray
-----IconData
-----plotting
-----accessor
-----view
-----calc
-----calc_xr
-----tb
-----IconData
-----plotting
-----accessor
-----view
-----quickplots
-----quickplots

run = 'ngSMT_tke'
savefig = False
path_fig = '../pics/'
nnf=0

gname = 'smt'
lev = 'L128'

# path_data = f'/work/mh0287/users/leonidas/icon/ngSMT/results/????-??/'
path_data = '/work/mh0287/from_Mistral/mh0287/users/leonidas/icon/ngSMT/results/????-??/'
#fpath_tgrid = '/mnt/lustre01/work/mh0287/users/leonidas/icon/submeso/grid/cell_grid-OceanOnly_SubmesoNA_2500m_srtm30-icon.nc'
fpath_tgrid = '/home/m/m300602/work/icon/grids/smt/smt_tgrid.nc'
fpath_Tri = '/work/mh0033/m300602/tmp/Tri.pkl'

path_grid     = f'/work/mh0033/m300602/icon/grids/{gname}/'
path_ckdtree  = f'{path_grid}ckdtree/'
# fpath_ckdtree = f'{path_grid}ckdtree/rectgrids/{gname}_res0.30_180W-180E_90S-90N.npz'
fpath_ckdtree = '/work/mh0033/m300602/icon/grids/smt/ckdtree/rectgrids/smt_res0.02_180W-180E_90S-90N.npz'

# time0 = np.datetime64('2010-02-23T19:00:00')
time0 = np.datetime64('2010-03-09T01:00:00')

lon_reg_1 = [-88, 0]
lat_reg_1 = [ 5, 68]
lon_reg_2 = [-75, -55]
# lat_reg_2 = [30, 40]
lat_reg_2 = [33, 43]
# lon_reg_3 = [-72, -68]
# lat_reg_3 = [34, 36]
# lon_reg_3 = [-69.5, -65.5]
# lat_reg_3 = [36, 38]
lon_reg_3 = [-68.5, -66.5]
lat_reg_3 = [36.5, 37.5]

lon_reg_4 = [-66., -64.]
lat_reg_4 = [41, 42]

depth0 = 50.
iz = (depthc<depth0).sum()
iz, depthc[iz]

(16, 51.5)

Load the grid

ds_tg = xr.open_dataset(fpath_tgrid)

Crop grid

clon = ds_tg.clon.compute().data * 180./np.pi
clat = ds_tg.clat.compute().data * 180./np.pi

lon_reg = [-75, -55]
lat_reg = [33, 43]

%%time
ireg_c = np.where(
      (clon>lon_reg[0]) & (clon<=lon_reg[1]) & (clat>lat_reg[0]) & (clat<=lat_reg[1])
  )[0]

CPU times: user 135 ms, sys: 84.6 ms, total: 220 ms
Wall time: 219 ms

Do it step by step

%%time
vertex_of_cell = ds_tg.vertex_of_cell.compute().data[:,ireg_c].transpose()-1
edge_of_cell = ds_tg.edge_of_cell.compute().data[:,ireg_c].transpose()-1
ireg_e, inde = np.unique(edge_of_cell, return_index=True)
ireg_v, indv = np.unique(vertex_of_cell, return_index=True)

CPU times: user 1.26 s, sys: 553 ms, total: 1.81 s
Wall time: 1.92 s

%%time
ds_tg_cut = xr.Dataset(coords=dict(
  clon=ds_tg['clon'].data[ireg_c],
  clat=ds_tg['clat'].data[ireg_c],
  elon=ds_tg['elon'].data[ireg_e],
  elat=ds_tg['elat'].data[ireg_e],
  vlon=ds_tg['vlon'].data[ireg_v],
  vlat=ds_tg['vlat'].data[ireg_v],
))
ds_tg_cut['ireg_e'] = xr.DataArray(ireg_e, dims=['edge'])
ds_tg_cut['ireg_v'] = xr.DataArray(ireg_v, dims=['vertex'])

CPU times: user 85.3 ms, sys: 40.9 ms, total: 126 ms
Wall time: 125 ms

%%time
reindex_c = np.zeros_like(ds_tg.clon, dtype='int32')-1
reindex_c[ireg_c] = np.arange(ireg_c.size, dtype='int32')
reindex_e = np.zeros_like(ds_tg.elon, dtype='int32')-1
reindex_e[ireg_e] = np.arange(ireg_e.size, dtype='int32')
reindex_v = np.zeros_like(ds_tg.vlon, dtype='int32')-1
reindex_v[ireg_v] = np.arange(ireg_v.size, dtype='int32')

CPU times: user 131 ms, sys: 142 ms, total: 273 ms
Wall time: 273 ms

%%time
var = 'vertex_of_cell'
da = ds_tg[var].data[:,ireg_c]-1
data = reindex_v[da.flatten()].reshape(da.shape)
ds_tg_cut[var] = xr.DataArray(data+1, dims=ds_tg[var].dims)

var = 'vertices_of_vertex'
da = ds_tg[var].data[:,ireg_v]-1
data = reindex_v[da.flatten()].reshape(da.shape)
ds_tg_cut[var] = xr.DataArray(data+1, dims=ds_tg[var].dims)

var = 'edge_of_cell'
da = ds_tg[var].data[:,ireg_c]-1
data = reindex_e[da.flatten()].reshape(da.shape)
ds_tg_cut[var] = xr.DataArray(data+1, dims=ds_tg[var].dims)

var = 'edges_of_vertex'
da = ds_tg[var].data[:,ireg_v]-1
data = reindex_e[da.flatten()].reshape(da.shape)
ds_tg_cut[var] = xr.DataArray(data+1, dims=ds_tg[var].dims)

var = 'adjacent_cell_of_edge'
da = ds_tg[var].data[:,ireg_e]-1
data = reindex_c[da.flatten()].reshape(da.shape)
ds_tg_cut[var] = xr.DataArray(data+1, dims=ds_tg[var].dims)

var = 'cells_of_vertex'
da = ds_tg[var].data[:,ireg_v]-1
data = reindex_c[da.flatten()].reshape(da.shape)
ds_tg_cut[var] = xr.DataArray(data+1, dims=ds_tg[var].dims)

CPU times: user 847 ms, sys: 1.53 s, total: 2.37 s
Wall time: 2.73 s

%%time
cvars = ['dual_area', 'edge_orientation',
      'cartesian_x_vertices', 'cartesian_y_vertices', 'cartesian_z_vertices']
# cvars = ['dual_area']
for var in cvars:
  ds_tg_cut[var] = ds_tg[var].compute().isel(vertex=ireg_v)

CPU times: user 139 ms, sys: 1.14 s, total: 1.28 s
Wall time: 1.69 s

Use pyicon to do it all at once

%%time
ds_tg_cut = pyic.xr_crop_tgrid(ds_tg, ireg_c)

sys glob os
numpy
netcdf
xarray
Done modules calc.
find edges
cut coordinates
reindex
cut vertex variables
cut edge variables
cut cell variables
CPU times: user 3.65 s, sys: 13.4 s, total: 17.1 s
Wall time: 19.8 s

ds_tg_cut

<xarray.Dataset>
Dimensions:                         (clon: 5615241, clat: 5615241,
                                     elon: 8428132, elat: 8428132,
                                     vlon: 2812772, vlat: 2812772,
                                     edge: 8428132, vertex: 2812772, nv: 3,
                                     cell: 5615241, ne: 6, nc: 2)
Coordinates:
  * clon                            (clon) float64 -0.9602 -0.9602 ... -1.309
  * clat                            (clat) float64 0.6375 0.6376 ... 0.6028
  * elon                            (elon) float64 -0.9602 -0.9602 ... -1.309
  * elat                            (elat) float64 0.6376 0.6375 ... 0.6028
  * vlon                            (vlon) float64 -0.9601 -0.9603 ... -1.309
  * vlat                            (vlat) float64 0.6376 0.6375 ... 0.6029
Dimensions without coordinates: edge, vertex, nv, cell, ne, nc
Data variables: (12/34)
    ireg_e                          (edge) int32 50208 50209 ... 46040008
    ireg_v                          (vertex) int32 16930 16931 ... 15384155
    vertex_of_cell                  (nv, cell) int32 1 1 2 ... 2812762 2812772
    vertices_of_vertex              (ne, vertex) int32 2 1 2 ... 2812751 2812762
    edge_of_cell                    (nv, cell) int32 1 4 6 ... 8428131 8428132
    edges_of_vertex                 (ne, vertex) int32 1 1 2 ... 8428129 8428132
    ...                              ...
    cell_area_p                     (cell) float64 3.17e+05 ... 4.242e+05
    cell_sea_land_mask              (cell) int32 -2 -2 -2 -2 -2 ... -2 -2 -2 -2
    orientation_of_normal           (nv, cell) int32 1 1 1 1 1 ... -1 1 -1 -1 -1
    cell_circumcenter_cartesian_x   (cell) float64 0.4607 0.4607 ... 0.2133
    cell_circumcenter_cartesian_y   (cell) float64 -0.6584 -0.6584 ... -0.7956
    cell_circumcenter_cartesian_z   (cell) float64 0.5952 0.5953 ... 0.567

xarray.Dataset

• Dimensions:
  • clon: 5615241
  • clat: 5615241
  • elon: 8428132
  • elat: 8428132
  • vlon: 2812772
  • vlat: 2812772
  • edge: 8428132
  • vertex: 2812772
  • nv: 3
  • cell: 5615241
  • ne: 6
  • nc: 2
• Coordinates: (6)
  • clon
    (clon)
    float64
    -0.9602 -0.9602 ... -1.309 -1.309

    array([-0.960196, -0.960239, -0.960247, ..., -1.308972, -1.30897 , -1.308814])

  • clat
    (clat)
    float64
    0.6375 0.6376 ... 0.6027 0.6028

    array([0.637522, 0.63759 , 0.637457, ..., 0.602883, 0.602722, 0.602821])

  • elon
    (elon)
    float64
    -0.9602 -0.9602 ... -1.309 -1.309

    array([-0.960217, -0.960221, -0.960146, ..., -1.308942, -1.308941, -1.308863])

  • elat
    (elat)
    float64
    0.6376 0.6375 ... 0.6028 0.6028

    array([0.637556, 0.63749 , 0.63752 , ..., 0.602852, 0.602771, 0.60282 ])

  • vlon
    (vlon)
    float64
    -0.9601 -0.9603 ... -1.309 -1.309

    array([-0.960142, -0.960292, -0.96015 , ..., -1.308709, -1.308866, -1.308864])

  • vlat
    (vlat)
    float64
    0.6376 0.6375 ... 0.6031 0.6029

    array([0.637586, 0.637526, 0.637454, ..., 0.603   , 0.603063, 0.602901])

• Data variables: (34)
  • ireg_e
    (edge)
    int32
    50208 50209 ... 46040007 46040008

    array([   50208,    50209,    50210, ..., 46040006, 46040007, 46040008],
          dtype=int32)

  • ireg_v
    (vertex)
    int32
    16930 16931 ... 15384154 15384155

    array([   16930,    16931,    16932, ..., 15384153, 15384154, 15384155],
          dtype=int32)

  • vertex_of_cell
    (nv, cell)
    int32
    1 1 2 3 ... 2812750 2812762 2812772

    array([[      1,       1,       2, ..., 2812772, 2812750, 2812762],
           [      2,       4,       5, ..., 2812749, 2812745, 2812757],
           [      3,       2,       3, ..., 2812750, 2812762, 2812772]],
          dtype=int32)

  • vertices_of_vertex
    (ne, vertex)
    int32
    2 1 2 1 ... 2401628 2812751 2812762

    array([[      2,       1,       2, ..., 2812771, 2812770, 2812771],
           [      3,       3,       1, ..., 2812772, 2812772, 2812770],
           [      4,       4,       5, ..., 2401626, 2401626, 2812749],
           [      6,       5,       6, ..., 2812757, 2812749, 2812757],
           [      7,      10,      13, ..., 2812761, 2401627, 2812750],
           [      8,      11,      14, ..., 2401628, 2812751, 2812762]],
          dtype=int32)

  • edge_of_cell
    (nv, cell)
    int32
    1 4 6 9 ... 8428130 8428131 8428132

    array([[      1,       4,       6, ..., 8428121, 8428045, 8428075],
           [      2,       5,       7, ..., 8428046, 8428076, 8428123],
           [      3,       1,       2, ..., 8428130, 8428131, 8428132]],
          dtype=int32)

  • edges_of_vertex
    (ne, vertex)
    int32
    1 1 2 4 ... 8428126 8428129 8428132

    array([[      1,       1,       2, ..., 8428115, 8428115, 8428116],
           [      3,       2,       3, ..., 8428117, 8428116, 8428117],
           [      4,       5,       7, ..., 8428118, 8428119, 8428121],
           [      8,       6,       9, ..., 8428122, 8428120, 8428123],
           [     10,      17,      24, ..., 8428124, 8428127, 8428130],
           [     12,      19,      26, ..., 8428126, 8428129, 8428132]],
          dtype=int32)

  • adjacent_cell_of_edge
    (nc, edge)
    int32
    1 1 1 2 ... 5615239 5615240 5615241

    array([[      1,       1,       1, ..., 5615238, 5615238, 5615238],
           [      2,       3,       4, ..., 5615239, 5615240, 5615241]],
          dtype=int32)

  • cells_of_vertex
    (ne, vertex)
    int32
    1 1 1 2 ... 5615233 5615237 5615241

    array([[      1,       1,       1, ..., 5615226, 5615226, 5615226],
           [      2,       2,       3, ..., 5615227, 5615227, 5615228],
           [      4,       3,       4, ..., 5615229, 5615228, 5615229],
           [      5,       9,      13, ..., 5615230, 5615234, 5615238],
           [      6,      10,      14, ..., 5615231, 5615235, 5615239],
           [      8,      12,      16, ..., 5615233, 5615237, 5615241]],
          dtype=int32)

  • dual_area
    (vertex)
    float64
    6.339e+05 6.338e+05 ... 8.484e+05

    long_name :

    areas of dual hexagonal/pentagonal cells

    units :

    m2

    standard_name :

    area

    array([633932.3430453 , 633831.80624164, 633943.28283269, ...,
           848057.33232246, 848235.39388174, 848429.32023549])

  • edge_orientation
    (ne, vertex)
    int32
    -1 1 1 1 1 -1 1 1 ... 1 1 1 1 1 1 1

    long_name :

    edge orientation

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

  • cartesian_x_vertices
    (vertex)
    float64
    0.4607 0.4607 ... 0.2133 0.2133

    long_name :

    vertex cartesian coordinate x on unit sp

    units :

    meters

    array([0.4607495 , 0.46067161, 0.46078969, ..., 0.21340256, 0.21326783,
           0.21329331])

  • cartesian_y_vertices
    (vertex)
    float64
    -0.6583 -0.6584 ... -0.7955 -0.7956

    long_name :

    vertex cartesian coordinate y on unit sp

    units :

    meters

    array([-0.65831457, -0.65841331, -0.65838297, ..., -0.79551144,
           -0.79551094, -0.79559899])

  • cartesian_z_vertices
    (vertex)
    float64
    0.5953 0.5952 ... 0.5672 0.567

    long_name :

    vertex cartesian coordinate z on unit sp

    units :

    meters

    array([0.59525778, 0.59520886, 0.59515101, ..., 0.56711629, 0.56716768,
           0.56703458])

  • edge_length
    (edge)
    float64
    859.7 859.7 ... 919.1 1.029e+03

    long_name :

    lengths of edges of triangular cells

    units :

    m

    array([ 859.71691937,  859.72062025,  847.47415675, ..., 1034.11806762,
            919.0864452 , 1028.68817334])

  • dual_edge_length
    (edge)
    float64
    486.9 486.9 508.0 ... 694.8 518.6

    long_name :

    lengths of dual edges (distances between triangular cell circumcenters)

    units :

    m

    array([486.90677036, 486.90886378, 507.99790356, ..., 507.84312645,
           694.7993798 , 518.61173809])

  • edge_sea_land_mask
    (edge)
    int32
    -2 -2 -2 -2 -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,-

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

  • edge_cell_distance
    (nc, edge)
    float64
    243.5 243.5 254.0 ... 347.5 259.2

    long_name :

    distances between edge midpoint and adjacent triangle midpoints

    units :

    m

    array([[243.50124658, 243.49797067, 254.00399076, ..., 253.92243289,
            347.34228612, 259.37771706],
           [243.40552378, 243.4108931 , 253.99391279, ..., 253.92069356,
            347.45709368, 259.23402103]])

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

    long_name :

    edge system orientation

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

  • edge_middle_cartesian_x
    (edge)
    float64
    0.4607 0.4607 ... 0.2133 0.2133

    long_name :

    prime edge center cartesian coordinate x on unit sphere

    units :

    meters

    array([0.46071056, 0.46073065, 0.4607696 , ..., 0.21323849, 0.21325107,
           0.21330589])

  • edge_middle_cartesian_y
    (edge)
    float64
    -0.6584 -0.6584 ... -0.7957 -0.7956

    long_name :

    prime edge center cartesian coordinate y on unit sphere

    units :

    meters

    array([-0.65836394, -0.65839814, -0.65834877, ..., -0.79564277,
           -0.79568678, -0.79564301])

  • edge_middle_cartesian_z
    (edge)
    float64
    0.5952 0.5952 ... 0.5669 0.567

    long_name :

    prime edge center cartesian coordinate z on unit sphere

    units :

    meters

    array([0.59523332, 0.59517993, 0.59520439, ..., 0.56699377, 0.56692727,
           0.56696808])

  • edge_dual_middle_cartesian_x
    (edge)
    float64
    0.4607 0.4607 ... 0.2133 0.2133

    long_name :

    dual edge center cartesian coordinate x on unit sphere

    units :

    meters

    array([0.46071056, 0.46073065, 0.4607696 , ..., 0.21323849, 0.21325107,
           0.21330588])

  • edge_dual_middle_cartesian_y
    (edge)
    float64
    -0.6584 -0.6584 ... -0.7957 -0.7956

    long_name :

    dual edge center cartesian coordinate y on unit sphere

    units :

    meters

    array([-0.65836394, -0.65839814, -0.65834877, ..., -0.79564277,
           -0.79568678, -0.79564301])

  • edge_dual_middle_cartesian_z
    (edge)
    float64
    0.5952 0.5952 ... 0.5669 0.567

    long_name :

    dual edge center cartesian coordinate z on unit sphere

    units :

    meters

    array([0.59523331, 0.59517994, 0.59520439, ..., 0.56699377, 0.56692727,
           0.56696808])

  • edge_primal_normal_cartesian_x
    (edge)
    float64
    -0.6742 -0.1485 ... -0.2853 0.9645

    long_name :

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

    units :

    meters

    array([-0.67421486, -0.14846776,  0.8345138 , ..., -0.70589274,
           -0.28528094,  0.96447679])

  • edge_primal_normal_cartesian_y
    (edge)
    float64
    0.1766 -0.7183 ... -0.6057 0.2641

    long_name :

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

    units :

    meters

    array([ 0.17655377, -0.7183191 ,  0.54967221, ...,  0.27575693,
           -0.60569914,  0.26405537])

  • edge_primal_normal_cartesian_z
    (edge)
    float64
    0.7171 -0.6797 ... -0.7428 0.007699

    long_name :

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

    units :

    meters

    array([ 0.71712139, -0.67968742, -0.03804165, ...,  0.65243663,
           -0.74279427,  0.00769924])

  • cell_area
    (cell)
    float64
    3.17e+05 3.169e+05 ... 4.242e+05

    long_name :

    area of grid cell

    units :

    m2

    standard_name :

    area

    grid_type :

    unstructured

    number_of_grid_in_reference :

    1

    array([316972.1217915 , 316909.91552288, 316915.38018338, ...,
           424296.75478667, 424392.86118585, 424207.70201987])

  • cell_area_p
    (cell)
    float64
    3.17e+05 3.169e+05 ... 4.242e+05

    long_name :

    area of grid cell

    units :

    m2

    grid_type :

    unstructured

    number_of_grid_in_reference :

    1

    array([316972.1217915 , 316909.91552288, 316915.38018338, ...,
           424296.75478667, 424392.86118585, 424207.70201987])

  • cell_sea_land_mask
    (cell)
    int32
    -2 -2 -2 -2 -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,-

    grid_type :

    unstructured

    number_of_grid_in_reference :

    1

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

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

    long_name :

    orientations of normals to triangular cell edges

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

  • cell_circumcenter_cartesian_x
    (cell)
    float64
    0.4607 0.4607 ... 0.2132 0.2133

    long_name :

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

    units :

    meters

    array([0.46073633, 0.4606848 , 0.46072498, ..., 0.21321036, 0.21323552,
           0.21334513])

  • cell_circumcenter_cartesian_y
    (cell)
    float64
    -0.6584 -0.6584 ... -0.7957 -0.7956

    long_name :

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

    units :

    meters

    array([-0.65837069, -0.6583572 , -0.65842558, ..., -0.79563177,
           -0.79571981, -0.79563226])

  • cell_circumcenter_cartesian_z
    (cell)
    float64
    0.5952 0.5953 ... 0.5669 0.567

    long_name :

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

    units :

    meters

    array([0.59520591, 0.59526071, 0.59515397, ..., 0.56701977, 0.56688676,
           0.56696839])

• Attributes: (0)

Calculate coefficients

%%time
with ProgressBar():
    ds_IcD = pyic.convert_tgrid_data(ds_tg_cut).compute()

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File ~/miniconda3/envs/pyicon_py39_cartopy19/lib/python3.9/site-packages/xarray/core/dataset.py:1317, in Dataset._construct_dataarray(self, name)
   1316 try:
-> 1317     variable = self._variables[name]
   1318 except KeyError:

KeyError: 'edge_vertices'

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
File <timed exec>:2, in <module>

File ~/pyicon/pyicon/pyicon_calc_xr.py:58, in convert_tgrid_data(ds_tg)
     56 ds_IcD['vertices_of_vertex'] = ds_tg['vertices_of_vertex'].transpose()-1
     57 ds_IcD['edges_of_vertex'] = ds_tg['edges_of_vertex'].transpose()-1
---> 58 ds_IcD['edge_vertices'] = ds_tg['edge_vertices'].transpose()-1
     59 ds_IcD['adjacent_cell_of_edge'] = ds_tg['adjacent_cell_of_edge'].transpose()-1
     60 ds_IcD['cells_of_vertex'] = ds_tg['cells_of_vertex'].transpose()-1

File ~/miniconda3/envs/pyicon_py39_cartopy19/lib/python3.9/site-packages/xarray/core/dataset.py:1410, in Dataset.__getitem__(self, key)
   1408     return self.isel(**key)
   1409 if utils.hashable(key):
-> 1410     return self._construct_dataarray(key)
   1411 if utils.iterable_of_hashable(key):
   1412     return self._copy_listed(key)

File ~/miniconda3/envs/pyicon_py39_cartopy19/lib/python3.9/site-packages/xarray/core/dataset.py:1319, in Dataset._construct_dataarray(self, name)
   1317     variable = self._variables[name]
   1318 except KeyError:
-> 1319     _, name, variable = _get_virtual_variable(self._variables, name, self.dims)
   1321 needed_dims = set(variable.dims)
   1323 coords: dict[Hashable, Variable] = {}

File ~/miniconda3/envs/pyicon_py39_cartopy19/lib/python3.9/site-packages/xarray/core/dataset.py:175, in _get_virtual_variable(variables, key, dim_sizes)
    173 split_key = key.split(".", 1)
    174 if len(split_key) != 2:
--> 175     raise KeyError(key)
    177 ref_name, var_name = split_key
    178 ref_var = variables[ref_name]

KeyError: 'edge_vertices'

%%time
edge2cell_coeff_cc = pyic.xr_calc_edge2cell_coeff_cc(ds_IcD)
div_coeff = pyic.xr_calc_div_coeff(ds_IcD)
grad_coeff = pyic.xr_calc_grad_coeff(ds_IcD)
rot_coeff = (  ds_IcD.dual_edge_length[ds_IcD.edges_of_vertex]*ds_IcD.edge_orientation ) / ds_IcD.dual_area

CPU times: user 15 s, sys: 19 s, total: 34 s
Wall time: 34.8 s

Load the data

mfdset_kwargs = dict(combine='nested', concat_dim='time',
                     data_vars='minimal', coords='minimal', compat='override', join='override',
                     parallel=True,
                    )

%%time
search_str = f'{run}_vn_sp_001-016_*.nc'
flist = np.array(glob.glob(path_data+search_str))
flist.sort()

ds = xr.open_mfdataset(flist, **mfdset_kwargs, chunks=dict(time=1))

CPU times: user 1.75 s, sys: 1.06 s, total: 2.82 s
Wall time: 2.07 s

ds

<xarray.Dataset>
Dimensions:   (time: 984, ncells: 89813639)
Coordinates:
  * time      (time) float64 2.01e+07 2.01e+07 2.01e+07 ... 2.01e+07 2.01e+07
Dimensions without coordinates: ncells
Data variables: (12/16)
    vn001_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn002_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn003_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn004_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn005_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn006_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    ...        ...
    vn011_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn012_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn013_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn014_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn015_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn016_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
Attributes:
    CDI:                  Climate Data Interface version 1.8.2 (http://mpimet...
    Conventions:          CF-1.6
    number_of_grid_used:  42
    uuidOfHGrid:          0d39853e-c26b-11e9-8454-0b16a6d45f73
    institution:          Max Planck Institute for Meteorology/Deutscher Wett...
    title:                ICON simulation
    source:               git@gitlab.dkrz.de:icon/icon-oes.git@e7e7a45736e586...
    history:              /work/mh0287/users/leonidas/icon/icon-oes_ngSMT/int...
    references:           see MPIM/DWD publications
    comment:              Leonidas Linardakis (m300056) on m20180 (Linux 2.6....

xarray.Dataset

• Dimensions:
  • time: 984
  • ncells: 89813639
• Coordinates: (1)
  • time
    (time)
    float64
    2.01e+07 2.01e+07 ... 2.01e+07

    standard_name :

    time

    calendar :

    proleptic_gregorian

    axis :

    T

    units :

    day as %Y%m%d.%f

    array([20100109.041667, 20100109.125   , 20100109.208333, ..., 20100331.791667,
           20100331.875   , 20100331.958333])

• Data variables: (16)
  • vn001_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 001

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn002_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 002

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn003_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 003

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn004_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 004

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn005_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 005

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn006_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 006

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn007_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 007

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn008_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 008

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn009_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 009

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn010_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 010

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn011_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 011

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn012_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 012

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn013_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 013

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn014_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 014

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn015_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 015

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn016_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 016

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

• Attributes: (10)

  CDI :

  Climate Data Interface version 1.8.2 (http://mpimet.mpg.de/cdi)

  Conventions :

  CF-1.6

  number_of_grid_used :

  42

  uuidOfHGrid :

  0d39853e-c26b-11e9-8454-0b16a6d45f73

  institution :

  Max Planck Institute for Meteorology/Deutscher Wetterdienst

  title :

  ICON simulation

  source :

  git@gitlab.dkrz.de:icon/icon-oes.git@e7e7a45736e58624ebc7010cad392a127f2c5a3d

  history :

  /work/mh0287/users/leonidas/icon/icon-oes_ngSMT/intel/bin/icon at 20200501 011150

  references :

  see MPIM/DWD publications

  comment :

  Leonidas Linardakis (m300056) on m20180 (Linux 2.6.32-754.14.2.el6.x86_64 x86_64)

depthc_xr = xr.DataArray(depthc[:15], dims=['depth'])
depthc_xr

<xarray.DataArray (depth: 15)>
array([ 2.5,  6.5,  9.5, 12.5, 15.5, 18.5, 21.5, 24.5, 27.5, 30.5, 33.5,
       36.5, 39.5, 42.5, 45.5])
Dimensions without coordinates: depth

xarray.DataArray

• depth: 15

• 2.5 6.5 9.5 12.5 15.5 18.5 21.5 ... 27.5 30.5 33.5 36.5 39.5 42.5 45.5

  array([ 2.5,  6.5,  9.5, 12.5, 15.5, 18.5, 21.5, 24.5, 27.5, 30.5, 33.5,
         36.5, 39.5, 42.5, 45.5])

• Coordinates: (0)
• Attributes: (0)

ds

<xarray.Dataset>
Dimensions:   (time: 984, ncells: 89813639)
Coordinates:
  * time      (time) float64 2.01e+07 2.01e+07 2.01e+07 ... 2.01e+07 2.01e+07
Dimensions without coordinates: ncells
Data variables: (12/16)
    vn001_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn002_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn003_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn004_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn005_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn006_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    ...        ...
    vn011_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn012_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn013_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn014_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn015_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
    vn016_sp  (time, ncells) float32 dask.array<chunksize=(1, 89813639), meta=np.ndarray>
Attributes:
    CDI:                  Climate Data Interface version 1.8.2 (http://mpimet...
    Conventions:          CF-1.6
    number_of_grid_used:  42
    uuidOfHGrid:          0d39853e-c26b-11e9-8454-0b16a6d45f73
    institution:          Max Planck Institute for Meteorology/Deutscher Wett...
    title:                ICON simulation
    source:               git@gitlab.dkrz.de:icon/icon-oes.git@e7e7a45736e586...
    history:              /work/mh0287/users/leonidas/icon/icon-oes_ngSMT/int...
    references:           see MPIM/DWD publications
    comment:              Leonidas Linardakis (m300056) on m20180 (Linux 2.6....

xarray.Dataset

• Dimensions:
  • time: 984
  • ncells: 89813639
• Coordinates: (1)
  • time
    (time)
    float64
    2.01e+07 2.01e+07 ... 2.01e+07

    standard_name :

    time

    calendar :

    proleptic_gregorian

    axis :

    T

    units :

    day as %Y%m%d.%f

    array([20100109.041667, 20100109.125   , 20100109.208333, ..., 20100331.791667,
           20100331.875   , 20100331.958333])

• Data variables: (16)
  • vn001_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 001

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn002_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 002

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn003_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 003

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn004_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 004

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn005_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 005

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn006_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 006

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn007_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 007

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn008_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 008

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn009_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 009

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn010_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 010

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn011_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 011

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn012_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 012

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn013_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 013

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn014_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 014

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn015_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 015

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

  • vn016_sp
    (time, ncells)
    float32
    dask.array<chunksize=(1, 89813639), meta=np.ndarray>

    standard_name :

    vn at level 016

    code :

    255

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    3

    Array Chunk Bytes 329.23 GiB 342.61 MiB Shape (984, 89813639) (1, 89813639) Count 165 Graph Layers 984 Chunks Type float32 numpy.ndarray

• Attributes: (10)

  CDI :

  Climate Data Interface version 1.8.2 (http://mpimet.mpg.de/cdi)

  Conventions :

  CF-1.6

  number_of_grid_used :

  42

  uuidOfHGrid :

  0d39853e-c26b-11e9-8454-0b16a6d45f73

  institution :

  Max Planck Institute for Meteorology/Deutscher Wetterdienst

  title :

  ICON simulation

  source :

  git@gitlab.dkrz.de:icon/icon-oes.git@e7e7a45736e58624ebc7010cad392a127f2c5a3d

  history :

  /work/mh0287/users/leonidas/icon/icon-oes_ngSMT/intel/bin/icon at 20200501 011150

  references :

  see MPIM/DWD publications

  comment :

  Leonidas Linardakis (m300056) on m20180 (Linux 2.6.32-754.14.2.el6.x86_64 x86_64)

%%time
ve = xr.concat([
    ds['vn001_sp'], ds['vn002_sp'], ds['vn003_sp'], ds['vn004_sp'],
    ds['vn005_sp'], ds['vn006_sp'], ds['vn007_sp'], ds['vn008_sp'],
    ds['vn009_sp'], ds['vn010_sp'], ds['vn011_sp'], ds['vn012_sp'],
    ds['vn013_sp'], ds['vn014_sp'], ds['vn015_sp'],# ds['vn016_sp'],
    ], dim=depthc_xr[:15])

CPU times: user 119 ms, sys: 168 ms, total: 286 ms
Wall time: 286 ms

ve.dims

('depth', 'time', 'ncells')

ds_IcD.edges_of_vertex.dims

('vertex', 'ne')

ve0 = ve.isel(depth=0, time=0)

%%time
curl_v = (ve0.isel(ncells=ds_IcD.edges_of_vertex))# * rot_coeff)

KeyboardInterrupt