Some first SMT-WAVE diagnostics

# if this line is not separated from the line below this cell has to be executed twice
# %matplotlib notebook
%matplotlib inline
%load_ext autoreload
%autoreload 2

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

import pyicon as pyic
import numpy as np
from netCDF4 import Dataset
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import cartopy.crs as ccrs
import xarray as xr
import glob
import pandas as pd
import seawater as sw
import matplotlib.transforms as transforms
import matplotlib
import os
import cmocean

-----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

from dask.distributed import Client, LocalCluster
cluster = LocalCluster()
client = Client(cluster)

client

Client

Client-e067845b-3d19-11ed-a69c-080038c0762f

Connection method: Cluster object	Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:8787/status

Cluster Info

LocalCluster

8f6e1566

Dashboard: http://127.0.0.1:8787/status	Workers: 16
Total threads: 256	Total memory: 195.31 GiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-e2499b77-8b1a-490f-b891-472bcd613c81

Comm: tcp://127.0.0.1:44855	Workers: 16
Dashboard: http://127.0.0.1:8787/status	Total threads: 256
Started: Just now	Total memory: 195.31 GiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:42749	Total threads: 16
Dashboard: http://127.0.0.1:40173/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:41261
Local directory: /tmp/dask-worker-space/worker-gnxph412

Worker: 1

Comm: tcp://127.0.0.1:39887	Total threads: 16
Dashboard: http://127.0.0.1:41321/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:40943
Local directory: /tmp/dask-worker-space/worker-0g0fwjyv

Worker: 2

Comm: tcp://127.0.0.1:46409	Total threads: 16
Dashboard: http://127.0.0.1:41203/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:33149
Local directory: /tmp/dask-worker-space/worker-esj5u6jv

Worker: 3

Comm: tcp://127.0.0.1:46791	Total threads: 16
Dashboard: http://127.0.0.1:43793/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:45601
Local directory: /tmp/dask-worker-space/worker-svzbu4_e

Worker: 4

Comm: tcp://127.0.0.1:43849	Total threads: 16
Dashboard: http://127.0.0.1:35163/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:38605
Local directory: /tmp/dask-worker-space/worker-7t9i5ggw

Worker: 5

Comm: tcp://127.0.0.1:34779	Total threads: 16
Dashboard: http://127.0.0.1:38361/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:36009
Local directory: /tmp/dask-worker-space/worker-hel0zlla

Worker: 6

Comm: tcp://127.0.0.1:37277	Total threads: 16
Dashboard: http://127.0.0.1:41497/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:42869
Local directory: /tmp/dask-worker-space/worker-7u2q8092

Worker: 7

Comm: tcp://127.0.0.1:33999	Total threads: 16
Dashboard: http://127.0.0.1:36295/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:41239
Local directory: /tmp/dask-worker-space/worker-cim9vb22

Worker: 8

Comm: tcp://127.0.0.1:46239	Total threads: 16
Dashboard: http://127.0.0.1:44493/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:43415
Local directory: /tmp/dask-worker-space/worker-3itrbua1

Worker: 9

Comm: tcp://127.0.0.1:33253	Total threads: 16
Dashboard: http://127.0.0.1:42139/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:40129
Local directory: /tmp/dask-worker-space/worker-jltwfdvg

Worker: 10

Comm: tcp://127.0.0.1:37065	Total threads: 16
Dashboard: http://127.0.0.1:38683/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:45839
Local directory: /tmp/dask-worker-space/worker-rctvc_dl

Worker: 11

Comm: tcp://127.0.0.1:46201	Total threads: 16
Dashboard: http://127.0.0.1:37633/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:33231
Local directory: /tmp/dask-worker-space/worker-_qfbyx1r

Worker: 12

Comm: tcp://127.0.0.1:37219	Total threads: 16
Dashboard: http://127.0.0.1:33869/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:40845
Local directory: /tmp/dask-worker-space/worker-coyqd3h2

Worker: 13

Comm: tcp://127.0.0.1:46003	Total threads: 16
Dashboard: http://127.0.0.1:39529/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:45673
Local directory: /tmp/dask-worker-space/worker-3c8y1qx0

Worker: 14

Comm: tcp://127.0.0.1:33303	Total threads: 16
Dashboard: http://127.0.0.1:35683/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:38345
Local directory: /tmp/dask-worker-space/worker-i98dbtx_

Worker: 15

Comm: tcp://127.0.0.1:35423	Total threads: 16
Dashboard: http://127.0.0.1:43205/status	Memory: 12.21 GiB
Nanny: tcp://127.0.0.1:45575
Local directory: /tmp/dask-worker-space/worker-jd5fk7xm

def savefig(prfx):
    fpath = f'../pics/smtwv_diagnose_smtwv0004_{prfx}.pdf'
    print(f'Saving figure {fpath}')
    plt.savefig(fpath)
    return

lon_s2 = [-12, -7]
lat_s2 = [-37.5, -34.5]
lon_s1 = [1, 10]
lat_s1 = [-35, -30]
from matplotlib.patches import Rectangle
def add_sonett(ax, color='k'):
    rectprop = dict(facecolor='none', edgecolor=color, lw=2)
    ax.add_patch(Rectangle((-12,-37.5), 5, 4, **rectprop))
    ax.text(-9.5, -33.5, 'SONETT II', ha='center', va='bottom', fontsize=8, color=color)

    rectprop = dict(facecolor='none', edgecolor=color, lw=2)
    ax.add_patch(Rectangle((1,-35), 9, 5, **rectprop))
    ax.text(5.5, -30., 'SONETT I', ha='center', va='bottom', fontsize=8, color=color)

    ax.text(-2.5, -35, 'Walvis Ridge', va='center', ha='center', rotation=45, fontsize=8, color=color)
    return

Load data

gname = 'smtwv_oce_2022'
path_grid = f'/work/mh0033/m300602/icon/grids/{gname}/'
fpath_tgrid = f'{path_grid}{gname}_tgrid.nc'

fpath_ckdtree = f'{path_grid}ckdtree/rectgrids/{gname}_res0.02_180W-180E_90S-90N.nc'
# fpath_ckdtree = f'{path_grid}ckdtree/rectgrids/{gname}_res0.30_180W-180E_90S-90N.npz'

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

run = 'smtwv0004'
path_data = f'/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/{run}/'

#!ls -d /work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/*

flist = glob.glob(f'{path_data}/{run}_oce_3d_to_PT1H_*.nc')
flist.sort()
flist = flist[:-1]
flist

['/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190701T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190702T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190703T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190704T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190705T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190706T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190707T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190708T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190709T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190710T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190711T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190712T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190713T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190714T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190715T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190716T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190717T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190718T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190719T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190720T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190721T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190722T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190723T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190724T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190725T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190726T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190727T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190728T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190729T001500Z.nc',
 '/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_3d_to_PT1H_20190730T001500Z.nc']

ds_3d = xr.open_mfdataset(flist, **mfdset_kwargs, chunks=dict(time=1, depth=1, depth_2=1))

flist = glob.glob(f'{path_data}/{run}_oce_2d_PT1H_*.nc')
flist.sort()
flist = flist[:-1]
flist[-1]

'/work/bm1102/m300602/proj_smtwv/icon-oes-zstar4/experiments/smtwv0004/smtwv0004_oce_2d_PT1H_20190730T001500Z.nc'

ds_2d = xr.open_mfdataset(flist, **mfdset_kwargs, chunks=dict(time=1, depth=1, depth_2=1))

%%time
ds_tg = xr.open_mfdataset(fpath_tgrid, **mfdset_kwargs)
# tgrid = tgrid.rename(dict(ncells='cell'))
ds_tg['dtype'] = 'float32'
# ds_tg['clon'] *= 180./np.pi
# ds_tg['clat'] *= 180./np.pi
# ds_tg['elon'] *= 180./np.pi
# ds_tg['elat'] *= 180./np.pi
# ds_tg['vlon'] *= 180./np.pi
# ds_tg['vlat'] *= 180./np.pi
ds_tg

CPU times: user 215 ms, sys: 352 ms, total: 567 ms
Wall time: 610 ms

<xarray.Dataset>
Dimensions:                         (cell: 66877252, nv: 3, vertex: 33484696,
                                     ne: 6, edge: 100363924, no: 4, nc: 2,
                                     two_grf: 2, cell_grf: 14, max_chdom: 1,
                                     edge_grf: 24, vert_grf: 13)
Coordinates:
    clon                            (cell) float64 dask.array<chunksize=(66877252,), meta=np.ndarray>
    clat                            (cell) float64 dask.array<chunksize=(66877252,), meta=np.ndarray>
    vlon                            (vertex) float64 dask.array<chunksize=(33484696,), meta=np.ndarray>
    vlat                            (vertex) float64 dask.array<chunksize=(33484696,), meta=np.ndarray>
    elon                            (edge) float64 dask.array<chunksize=(100363924,), meta=np.ndarray>
    elat                            (edge) float64 dask.array<chunksize=(100363924,), meta=np.ndarray>
Dimensions without coordinates: cell, nv, vertex, ne, edge, no, nc, two_grf,
                                cell_grf, max_chdom, edge_grf, vert_grf
Data variables: (12/85)
    clon_vertices                   (cell, nv) float64 dask.array<chunksize=(66877252, 3), meta=np.ndarray>
    clat_vertices                   (cell, nv) float64 dask.array<chunksize=(66877252, 3), meta=np.ndarray>
    vlon_vertices                   (vertex, ne) float64 dask.array<chunksize=(33484696, 6), meta=np.ndarray>
    vlat_vertices                   (vertex, ne) float64 dask.array<chunksize=(33484696, 6), meta=np.ndarray>
    elon_vertices                   (edge, no) float64 dask.array<chunksize=(100363924, 4), meta=np.ndarray>
    elat_vertices                   (edge, no) float64 dask.array<chunksize=(100363924, 4), meta=np.ndarray>
    ...                              ...
    edge_dual_normal_cartesian_y    (edge) float64 dask.array<chunksize=(100363924,), meta=np.ndarray>
    edge_dual_normal_cartesian_z    (edge) float64 dask.array<chunksize=(100363924,), meta=np.ndarray>
    cell_circumcenter_cartesian_x   (cell) float64 dask.array<chunksize=(66877252,), meta=np.ndarray>
    cell_circumcenter_cartesian_y   (cell) float64 dask.array<chunksize=(66877252,), meta=np.ndarray>
    cell_circumcenter_cartesian_z   (cell) float64 dask.array<chunksize=(66877252,), meta=np.ndarray>
    dtype                           <U7 'float32'
Attributes: (12/34)
    title:                    ICON grid description
    institution:              Max Planck Institute for Meteorology/Deutscher ...
    source:                   git@gitlab.dkrz.de:mpim-sw/grid-generator.git
    revision:                 ac0519bd41b0a9f5ae428ac946646dd39c6268b2
    history:                  /work/mh0033/leonidas/grid-generator/intel/bin/...
    date:                     20220816 at 083853
    ...                       ...
    mean_cell_area:           6080879.452321341
    mean_dual_cell_area:      12161758.324725308
    domain_length:            40031612.44147649
    domain_height:            40031612.44147649
    sphere_radius:            6371229.0
    domain_cartesian_center:  [0. 0. 0.]

xarray.Dataset

• Dimensions:
  • cell: 66877252
  • nv: 3
  • vertex: 33484696
  • ne: 6
  • edge: 100363924
  • no: 4
  • nc: 2
  • two_grf: 2
  • cell_grf: 14
  • max_chdom: 1
  • edge_grf: 24
  • vert_grf: 13
• Coordinates: (6)
  • clon
    (cell)
    float64
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

    center longitude

    units :

    radian

    standard_name :

    grid_longitude

    bounds :

    clon_vertices

    Array Chunk Bytes 510.23 MiB 510.23 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • clat
    (cell)
    float64
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

    center latitude

    units :

    radian

    standard_name :

    grid_latitude

    bounds :

    clat_vertices

    Array Chunk Bytes 510.23 MiB 510.23 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • vlon
    (vertex)
    float64
    dask.array<chunksize=(33484696,), meta=np.ndarray>

    long_name :

    vertex longitude

    units :

    radian

    standard_name :

    grid_longitude

    bounds :

    vlon_vertices

    Array Chunk Bytes 255.47 MiB 255.47 MiB Shape (33484696,) (33484696,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • vlat
    (vertex)
    float64
    dask.array<chunksize=(33484696,), meta=np.ndarray>

    long_name :

    vertex latitude

    units :

    radian

    standard_name :

    grid_latitude

    bounds :

    vlat_vertices

    Array Chunk Bytes 255.47 MiB 255.47 MiB Shape (33484696,) (33484696,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • elon
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    edge midpoint longitude

    units :

    radian

    standard_name :

    grid_longitude

    bounds :

    elon_vertices

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • elat
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    edge midpoint latitude

    units :

    radian

    standard_name :

    grid_latitude

    bounds :

    elat_vertices

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

• Data variables: (85)
  • clon_vertices
    (cell, nv)
    float64
    dask.array<chunksize=(66877252, 3), meta=np.ndarray>

    units :

    radian

    Array Chunk Bytes 1.49 GiB 1.49 GiB Shape (66877252, 3) (66877252, 3) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • clat_vertices
    (cell, nv)
    float64
    dask.array<chunksize=(66877252, 3), meta=np.ndarray>

    units :

    radian

    Array Chunk Bytes 1.49 GiB 1.49 GiB Shape (66877252, 3) (66877252, 3) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • vlon_vertices
    (vertex, ne)
    float64
    dask.array<chunksize=(33484696, 6), meta=np.ndarray>

    units :

    radian

    Array Chunk Bytes 1.50 GiB 1.50 GiB Shape (33484696, 6) (33484696, 6) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • vlat_vertices
    (vertex, ne)
    float64
    dask.array<chunksize=(33484696, 6), meta=np.ndarray>

    units :

    radian

    Array Chunk Bytes 1.50 GiB 1.50 GiB Shape (33484696, 6) (33484696, 6) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • elon_vertices
    (edge, no)
    float64
    dask.array<chunksize=(100363924, 4), meta=np.ndarray>

    units :

    radian

    Array Chunk Bytes 2.99 GiB 2.99 GiB Shape (100363924, 4) (100363924, 4) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • elat_vertices
    (edge, no)
    float64
    dask.array<chunksize=(100363924, 4), meta=np.ndarray>

    units :

    radian

    Array Chunk Bytes 2.99 GiB 2.99 GiB Shape (100363924, 4) (100363924, 4) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • cell_area
    (cell)
    float64
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

    area of grid cell

    units :

    m2

    standard_name :

    area

    grid_type :

    unstructured

    number_of_grid_in_reference :

    1

    Array Chunk Bytes 510.23 MiB 510.23 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • dual_area
    (vertex)
    float64
    dask.array<chunksize=(33484696,), meta=np.ndarray>

    long_name :

    areas of dual hexagonal/pentagonal cells

    units :

    m2

    standard_name :

    area

    Array Chunk Bytes 255.47 MiB 255.47 MiB Shape (33484696,) (33484696,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • phys_cell_id
    (cell)
    int32
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

    physical domain ID of cell

    grid_type :

    unstructured

    number_of_grid_in_reference :

    1

    Array Chunk Bytes 255.12 MiB 255.12 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • phys_edge_id
    (edge)
    int32
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    physical domain ID of edge

    Array Chunk Bytes 382.86 MiB 382.86 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • lon_cell_centre
    (cell)
    float64
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

    longitude of cell centre

    units :

    radian

    grid_type :

    unstructured

    number_of_grid_in_reference :

    1

    Array Chunk Bytes 510.23 MiB 510.23 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • lat_cell_centre
    (cell)
    float64
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

    latitude of cell centre

    units :

    radian

    grid_type :

    unstructured

    number_of_grid_in_reference :

    1

    Array Chunk Bytes 510.23 MiB 510.23 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • longitude_vertices
    (vertex)
    float64
    dask.array<chunksize=(33484696,), meta=np.ndarray>

    long_name :

    longitude of vertices

    units :

    radian

    Array Chunk Bytes 255.47 MiB 255.47 MiB Shape (33484696,) (33484696,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • latitude_vertices
    (vertex)
    float64
    dask.array<chunksize=(33484696,), meta=np.ndarray>

    long_name :

    latitude of vertices

    units :

    radian

    Array Chunk Bytes 255.47 MiB 255.47 MiB Shape (33484696,) (33484696,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • lon_edge_centre
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    longitudes of edge midpoints

    units :

    radian

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • lat_edge_centre
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    latitudes of edge midpoints

    units :

    radian

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_of_cell
    (nv, cell)
    int32
    dask.array<chunksize=(3, 66877252), meta=np.ndarray>

    long_name :

    edges of each cellvertices

    Array Chunk Bytes 765.35 MiB 765.35 MiB Shape (3, 66877252) (3, 66877252) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • vertex_of_cell
    (nv, cell)
    int32
    dask.array<chunksize=(3, 66877252), meta=np.ndarray>

    long_name :

    vertices of each cellcells ad

    Array Chunk Bytes 765.35 MiB 765.35 MiB Shape (3, 66877252) (3, 66877252) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • adjacent_cell_of_edge
    (nc, edge)
    int32
    dask.array<chunksize=(2, 100363924), meta=np.ndarray>

    long_name :

    cells adjacent to each edge

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (2, 100363924) (2, 100363924) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • edge_vertices
    (nc, edge)
    int32
    dask.array<chunksize=(2, 100363924), meta=np.ndarray>

    long_name :

    vertices at the end of of each edge

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (2, 100363924) (2, 100363924) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • cells_of_vertex
    (ne, vertex)
    int32
    dask.array<chunksize=(6, 33484696), meta=np.ndarray>

    long_name :

    cells around each vertex

    Array Chunk Bytes 766.40 MiB 766.40 MiB Shape (6, 33484696) (6, 33484696) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • edges_of_vertex
    (ne, vertex)
    int32
    dask.array<chunksize=(6, 33484696), meta=np.ndarray>

    long_name :

    edges around each vertex

    Array Chunk Bytes 766.40 MiB 766.40 MiB Shape (6, 33484696) (6, 33484696) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • vertices_of_vertex
    (ne, vertex)
    int32
    dask.array<chunksize=(6, 33484696), meta=np.ndarray>

    long_name :

    vertices around each vertex

    Array Chunk Bytes 766.40 MiB 766.40 MiB Shape (6, 33484696) (6, 33484696) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • cell_area_p
    (cell)
    float64
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

    area of grid cell

    units :

    m2

    grid_type :

    unstructured

    number_of_grid_in_reference :

    1

    Array Chunk Bytes 510.23 MiB 510.23 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • cell_elevation
    (cell)
    float64
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

    elevation at the cell centers

    units :

    m

    grid_type :

    unstructured

    number_of_grid_in_reference :

    1

    Array Chunk Bytes 510.23 MiB 510.23 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • cell_sea_land_mask
    (cell)
    int32
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    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 Chunk Bytes 255.12 MiB 255.12 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • dual_area_p
    (vertex)
    float64
    dask.array<chunksize=(33484696,), meta=np.ndarray>

    long_name :

    areas of dual hexagonal/pentagonal cells

    units :

    m2

    Array Chunk Bytes 255.47 MiB 255.47 MiB Shape (33484696,) (33484696,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_length
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    lengths of edges of triangular cells

    units :

    m

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_cell_distance
    (nc, edge)
    float64
    dask.array<chunksize=(2, 100363924), meta=np.ndarray>

    long_name :

    distances between edge midpoint and adjacent triangle midpoints

    units :

    m

    Array Chunk Bytes 1.50 GiB 1.50 GiB Shape (2, 100363924) (2, 100363924) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • dual_edge_length
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    lengths of dual edges (distances between triangular cell circumcenters)

    units :

    m

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edgequad_area
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    area around the edge formed by the two adjacent triangles

    units :

    m2

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_elevation
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    elevation at the edge centers

    units :

    m

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_sea_land_mask
    (edge)
    int32
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

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

    units :

    2,1,-1,-

    Array Chunk Bytes 382.86 MiB 382.86 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • edge_vert_distance
    (nc, edge)
    float64
    dask.array<chunksize=(2, 100363924), meta=np.ndarray>

    long_name :

    distances between edge midpoint and vertices of that edge

    units :

    m

    Array Chunk Bytes 1.50 GiB 1.50 GiB Shape (2, 100363924) (2, 100363924) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • zonal_normal_primal_edge
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    zonal component of normal to primal edge

    units :

    radian

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • meridional_normal_primal_edge
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    meridional component of normal to primal edge

    units :

    radian

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • zonal_normal_dual_edge
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    zonal component of normal to dual edge

    units :

    radian

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • meridional_normal_dual_edge
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    meridional component of normal to dual edge

    units :

    radian

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • orientation_of_normal
    (nv, cell)
    int32
    dask.array<chunksize=(3, 66877252), meta=np.ndarray>

    long_name :

    orientations of normals to triangular cell edges

    Array Chunk Bytes 765.35 MiB 765.35 MiB Shape (3, 66877252) (3, 66877252) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • cell_index
    (cell)
    int32
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

    cell index

    Array Chunk Bytes 255.12 MiB 255.12 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • parent_cell_index
    (cell)
    int32
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

    parent cell index

    Array Chunk Bytes 255.12 MiB 255.12 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • parent_cell_type
    (cell)
    int32
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

    parent cell type

    Array Chunk Bytes 255.12 MiB 255.12 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • neighbor_cell_index
    (nv, cell)
    int32
    dask.array<chunksize=(3, 66877252), meta=np.ndarray>

    long_name :

    cell neighbor index

    Array Chunk Bytes 765.35 MiB 765.35 MiB Shape (3, 66877252) (3, 66877252) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • child_cell_index
    (no, cell)
    int32
    dask.array<chunksize=(4, 66877252), meta=np.ndarray>

    long_name :

    child cell index

    Array Chunk Bytes 1.00 GiB 1.00 GiB Shape (4, 66877252) (4, 66877252) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • child_cell_id
    (cell)
    int32
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

    domain ID of child cell

    Array Chunk Bytes 255.12 MiB 255.12 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • edge_index
    (edge)
    int32
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    edge index

    Array Chunk Bytes 382.86 MiB 382.86 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • edge_parent_type
    (edge)
    int32
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    edge paren

    Array Chunk Bytes 382.86 MiB 382.86 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • vertex_index
    (vertex)
    int32
    dask.array<chunksize=(33484696,), meta=np.ndarray>

    long_name :

    vertices index

    Array Chunk Bytes 127.73 MiB 127.73 MiB Shape (33484696,) (33484696,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • edge_orientation
    (ne, vertex)
    int32
    dask.array<chunksize=(6, 33484696), meta=np.ndarray>

    long_name :

    edge orientation

    Array Chunk Bytes 766.40 MiB 766.40 MiB Shape (6, 33484696) (6, 33484696) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • edge_system_orientation
    (edge)
    int32
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    edge system orientation

    Array Chunk Bytes 382.86 MiB 382.86 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • refin_c_ctrl
    (cell)
    int32
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

    refinement control flag for cells

    Array Chunk Bytes 255.12 MiB 255.12 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • index_c_list
    (two_grf, cell_grf)
    int32
    dask.array<chunksize=(2, 14), meta=np.ndarray>

    long_name :

    list of start and end indices for each refinement control level for cells

    Array Chunk Bytes 112 B 112 B Shape (2, 14) (2, 14) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • start_idx_c
    (max_chdom, cell_grf)
    int32
    dask.array<chunksize=(1, 14), meta=np.ndarray>

    long_name :

    list of start indices for each refinement control level for cells

    Array Chunk Bytes 56 B 56 B Shape (1, 14) (1, 14) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • end_idx_c
    (max_chdom, cell_grf)
    int32
    dask.array<chunksize=(1, 14), meta=np.ndarray>

    long_name :

    list of end indices for each refinement control level for cells

    Array Chunk Bytes 56 B 56 B Shape (1, 14) (1, 14) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • refin_e_ctrl
    (edge)
    int32
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    refinement control flag for edges

    Array Chunk Bytes 382.86 MiB 382.86 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • index_e_list
    (two_grf, edge_grf)
    int32
    dask.array<chunksize=(2, 24), meta=np.ndarray>

    long_name :

    list of start and end indices for each refinement control level for edges

    Array Chunk Bytes 192 B 192 B Shape (2, 24) (2, 24) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • start_idx_e
    (max_chdom, edge_grf)
    int32
    dask.array<chunksize=(1, 24), meta=np.ndarray>

    long_name :

    list of start indices for each refinement control level for edges

    Array Chunk Bytes 96 B 96 B Shape (1, 24) (1, 24) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • end_idx_e
    (max_chdom, edge_grf)
    int32
    dask.array<chunksize=(1, 24), meta=np.ndarray>

    long_name :

    list of end indices for each refinement control level for edges

    Array Chunk Bytes 96 B 96 B Shape (1, 24) (1, 24) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • refin_v_ctrl
    (vertex)
    int32
    dask.array<chunksize=(33484696,), meta=np.ndarray>

    long_name :

    refinement control flag for vertices

    Array Chunk Bytes 127.73 MiB 127.73 MiB Shape (33484696,) (33484696,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • index_v_list
    (two_grf, vert_grf)
    int32
    dask.array<chunksize=(2, 13), meta=np.ndarray>

    long_name :

    list of start and end indices for each refinement control level for vertices

    Array Chunk Bytes 104 B 104 B Shape (2, 13) (2, 13) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • start_idx_v
    (max_chdom, vert_grf)
    int32
    dask.array<chunksize=(1, 13), meta=np.ndarray>

    long_name :

    list of start indices for each refinement control level for vertices

    Array Chunk Bytes 52 B 52 B Shape (1, 13) (1, 13) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • end_idx_v
    (max_chdom, vert_grf)
    int32
    dask.array<chunksize=(1, 13), meta=np.ndarray>

    long_name :

    list of end indices for each refinement control level for vertices

    Array Chunk Bytes 52 B 52 B Shape (1, 13) (1, 13) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • parent_edge_index
    (edge)
    int32
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    parent edge index

    Array Chunk Bytes 382.86 MiB 382.86 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • child_edge_index
    (no, edge)
    int32
    dask.array<chunksize=(4, 100363924), meta=np.ndarray>

    long_name :

    child edge index

    Array Chunk Bytes 1.50 GiB 1.50 GiB Shape (4, 100363924) (4, 100363924) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • child_edge_id
    (edge)
    int32
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    domain ID of child edge

    Array Chunk Bytes 382.86 MiB 382.86 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • parent_vertex_index
    (vertex)
    int32
    dask.array<chunksize=(33484696,), meta=np.ndarray>

    long_name :

    parent vertex index

    Array Chunk Bytes 127.73 MiB 127.73 MiB Shape (33484696,) (33484696,) Count 2 Graph Layers 1 Chunks Type int32 numpy.ndarray

  • cartesian_x_vertices
    (vertex)
    float64
    dask.array<chunksize=(33484696,), meta=np.ndarray>

    long_name :

    vertex cartesian coordinate x on unit sp

    units :

    meters

    Array Chunk Bytes 255.47 MiB 255.47 MiB Shape (33484696,) (33484696,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • cartesian_y_vertices
    (vertex)
    float64
    dask.array<chunksize=(33484696,), meta=np.ndarray>

    long_name :

    vertex cartesian coordinate y on unit sp

    units :

    meters

    Array Chunk Bytes 255.47 MiB 255.47 MiB Shape (33484696,) (33484696,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • cartesian_z_vertices
    (vertex)
    float64
    dask.array<chunksize=(33484696,), meta=np.ndarray>

    long_name :

    vertex cartesian coordinate z on unit sp

    units :

    meters

    Array Chunk Bytes 255.47 MiB 255.47 MiB Shape (33484696,) (33484696,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_middle_cartesian_x
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    prime edge center cartesian coordinate x on unit sphere

    units :

    meters

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_middle_cartesian_y
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    prime edge center cartesian coordinate y on unit sphere

    units :

    meters

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_middle_cartesian_z
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    prime edge center cartesian coordinate z on unit sphere

    units :

    meters

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_dual_middle_cartesian_x
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    dual edge center cartesian coordinate x on unit sphere

    units :

    meters

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_dual_middle_cartesian_y
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    dual edge center cartesian coordinate y on unit sphere

    units :

    meters

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_dual_middle_cartesian_z
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

    dual edge center cartesian coordinate z on unit sphere

    units :

    meters

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_primal_normal_cartesian_x
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

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

    units :

    meters

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_primal_normal_cartesian_y
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

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

    units :

    meters

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_primal_normal_cartesian_z
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

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

    units :

    meters

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_dual_normal_cartesian_x
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

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

    units :

    meters

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_dual_normal_cartesian_y
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

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

    units :

    meters

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • edge_dual_normal_cartesian_z
    (edge)
    float64
    dask.array<chunksize=(100363924,), meta=np.ndarray>

    long_name :

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

    units :

    meters

    Array Chunk Bytes 765.72 MiB 765.72 MiB Shape (100363924,) (100363924,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • cell_circumcenter_cartesian_x
    (cell)
    float64
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

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

    units :

    meters

    Array Chunk Bytes 510.23 MiB 510.23 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • cell_circumcenter_cartesian_y
    (cell)
    float64
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

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

    units :

    meters

    Array Chunk Bytes 510.23 MiB 510.23 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • cell_circumcenter_cartesian_z
    (cell)
    float64
    dask.array<chunksize=(66877252,), meta=np.ndarray>

    long_name :

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

    units :

    meters

    Array Chunk Bytes 510.23 MiB 510.23 MiB Shape (66877252,) (66877252,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • dtype
    ()
    <U7
    'float32'

    array('float32', dtype='<U7')

• Attributes: (34)

  title :

  ICON grid description

  institution :

  Max Planck Institute for Meteorology/Deutscher Wetterdienst

  source :

  git@gitlab.dkrz.de:mpim-sw/grid-generator.git

  revision :

  ac0519bd41b0a9f5ae428ac946646dd39c6268b2

  history :

  /work/mh0033/leonidas/grid-generator/intel/bin/grid_command

  date :

  20220816 at 083853

  user_name :

  Leonidas Linardakis (m300056)

  os_name :

  Linux 4.9.0-19-amd64 x86_64

  uuidOfHGrid :

  187f057e-1d2e-11ed-965f-8922a1e6a5b1

  grid_mapping_name :

  lat_long_on_sphere

  crs_id :

  urn:ogc:def:cs:EPSG:6.0:6422

  crs_name :

  Spherical 2D Coordinate System

  ellipsoid_name :

  Sphere

  semi_major_axis :

  6371229.0

  inverse_flattening :

  0.0

  grid_level :

  10

  grid_root :

  2

  grid_ID :

  1

  parent_grid_ID :

  0

  no_of_subgrids :

  1

  start_subgrid_id :

  1

  max_childdom :

  1

  boundary_depth_index :

  0

  rotation_vector :

  [0. 0. 0.]

  grid_geometry :

  1

  grid_cell_type :

  3

  mean_edge_length :

  2629.586037173805

  mean_dual_edge_length :

  1518.203462543614

  mean_cell_area :

  6080879.452321341

  mean_dual_cell_area :

  12161758.324725308

  domain_length :

  40031612.44147649

  domain_height :

  40031612.44147649

  sphere_radius :

  6371229.0

  domain_cartesian_center :

  [0. 0. 0.]

Prepare plotting on original grid

lon_reg = [3.5, 9.5]
lat_reg = [-31.5, -28.5]

%%time
clon = ds_tg.clon.compute().data * 180./np.pi
clat = ds_tg.clat.compute().data * 180./np.pi
vlon = ds_tg.vlon.compute().data * 180./np.pi
vlat = ds_tg.vlat.compute().data * 180./np.pi

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

ireg_v = np.where(
  (vlon>lon_reg[0]) & (vlon<=lon_reg[1]) & (vlat>lat_reg[0]) & (vlat<=lat_reg[1])
)[0]

CPU times: user 1.14 s, sys: 3.46 s, total: 4.6 s
Wall time: 7.65 s

%%time
# clon = ds_tg.clon.compute().data * 180./np.pi
# clat = ds_tg.clat.compute().data * 180./np.pi
# ireg_c = np.where(
#   (clon>lon_reg[0]) & (clon<=lon_reg[1]) & (clat>lat_reg[0]) & (clat<=lat_reg[1])
# )[0]
ds_tg_cut = pyic.xr_crop_tgrid(ds_tg, ireg_c)
ireg_v = ds_tg_cut['ireg_v'].data

CPU times: user 13.5 s, sys: 37.1 s, total: 50.6 s
Wall time: 51.1 s

%%time
clon_bnds, clat_bnds, vlon_bnds, vlat_bnds, cells_of_vertex = pyic.patch_plot_derive_bnds(ds_tg_cut)
patches_c, patches_v = pyic.patch_plot_patches_from_bnds(
  clon_bnds.compute(), clat_bnds.compute(),
  vlon_bnds.compute(), vlat_bnds.compute(),
  cells_of_vertex#.compute()
)

%%time
ireg_c_triang, Tri = pyic.triangulation(ds_tg, lon_reg=lon_reg, lat_reg=lat_reg)

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/worker.py:3871: UserWarning: Large object of size 4.63 MiB detected in task graph:
  ("('truediv-66d4d8bf90621c329810dbacfd95fcf3', 0)" ... , 10426289]),))
Consider scattering large objects ahead of time
with client.scatter to reduce scheduler burden and
keep data on workers

    future = client.submit(func, big_data)    # bad

    big_future = client.scatter(big_data)     # good
    future = client.submit(func, big_future)  # good
  warnings.warn(

CPU times: user 6.9 s, sys: 4.42 s, total: 11.3 s
Wall time: 25.4 s

First plots

its = -1
iz = 0

list(ds_2d)

['clon_bnds',
 'clat_bnds',
 'vlon_bnds',
 'vlat_bnds',
 'zos',
 'stretch_c',
 'vort',
 'to',
 'so',
 'u',
 'v',
 'mlotst',
 'hi',
 'hs',
 'conc',
 'ice_u',
 'ice_v']

%%time
toi = pyic.interp_to_rectgrid_xr(ds_2d.to[its,iz,:], fpath_ckdtree, lon_reg=lon_reg, lat_reg=lat_reg)
soi = pyic.interp_to_rectgrid_xr(ds_2d.so[its,iz,:], fpath_ckdtree, lon_reg=lon_reg, lat_reg=lat_reg)
kini = pyic.interp_to_rectgrid_xr(0.5*(ds_2d.u[its,iz,:]**2+ds_2d.v[its,iz,:]**2), fpath_ckdtree, lon_reg=lon_reg, lat_reg=lat_reg)
vorti = pyic.interp_to_rectgrid_xr(ds_2d.vort[its,iz,:], fpath_ckdtree, lon_reg=lon_reg, lat_reg=lat_reg, coordinates='vlat vlon')

CPU times: user 871 ms, sys: 1.49 s, total: 2.36 s
Wall time: 4.17 s

lon = toi.lon
lat = toi.lat

projection = ccrs.PlateCarree()
hca, hcb = pyic.arrange_axes(2,2, plot_cb=True, asp=0.5, fig_size_fac=2,
                             sharex=True, sharey=True, xlabel="", ylabel="",
                             projection=projection,
                            )
ii=-1


ii+=1; ax=hca[ii]; cax=hcb[ii]
hm = pyic.shade(lon, lat, toi, ax=ax, cax=cax, clim=[15.5, 18.5], projection=projection)
ax.set_title(f'temperature')

ii+=1; ax=hca[ii]; cax=hcb[ii]
hm = pyic.shade(lon, lat, soi, ax=ax, cax=cax, clim=[35.1, 35.8], projection=projection,
               cmap=cmocean.cm.haline)
ax.set_title(f'salinity')

ii+=1; ax=hca[ii]; cax=hcb[ii]
hm = pyic.shade(lon, lat, kini.data, ax=ax, cax=cax, clim=[-4,0], logplot=True, projection=projection)
ax.set_title('log$_{10}$(kin. energy) [m$^2$/s$^2$]')

ii+=1; ax=hca[ii]; cax=hcb[ii]
hm = pyic.shade(lon, lat, vorti, ax=ax, cax=cax, clim=1e-4, projection=projection)
ax.set_title(f'temperature')

for ax in hca:
    pyic.plot_settings(ax, xlim=lon_reg, ylim=lat_reg)
#     add_sonett(ax)

# savefig('mld')

Overview plot

%%time
# lon_reg = [-14, 22]
# lat_reg = [-45, -13]
lon_reg = [-30, 30]
lat_reg = [-60, -10]

kini = pyic.interp_to_rectgrid_xr(0.5*(ds_2d.u[its,iz,:]**2+ds_2d.v[its,iz,:]**2), fpath_ckdtree, lon_reg=lon_reg, lat_reg=lat_reg)

CPU times: user 3.14 s, sys: 1.18 s, total: 4.32 s
Wall time: 4.93 s

asp = (lat_reg[1]-lat_reg[0])/(lon_reg[1]-lon_reg[0])
asp

0.8333333333333334

%%time
res = np.sqrt(ds_tg.cell_area).compute()
resi = pyic.interp_to_rectgrid_xr(res, fpath_ckdtree, lon_reg=lon_reg, lat_reg=lat_reg)

CPU times: user 757 ms, sys: 1.58 s, total: 2.33 s
Wall time: 2.8 s

projection = ccrs.PlateCarree()
hca, hcb = pyic.arrange_axes(1,1, plot_cb=True, asp=asp, fig_size_fac=3,
                             sharex=True, sharey=True, xlabel="", ylabel="",
                             projection=projection, axlab_kw=None,
                            )
ii=-1

ii+=1; ax=hca[ii]; cax=hcb[ii]
hm = pyic.shade(kini.lon, kini.lat, kini.data, ax=ax, cax=cax, clim=[-4,0], logplot=True, cmap='RdBu_r')
Cl = ax.contour(resi.lon, resi.lat, resi, np.arange(500,1000,100), colors='0.3')
ax.clabel(Cl, fmt='%.fm', fontsize=8, zorder=2)

pos_cax = cax.get_position()
cax.set_position([0.72,0.58,0.03,0.32])
cax.set_ylabel('log$_{10}$(kin. energy) [m$^2$/s$^2$]')

ax.set_title('kin. energy from spinup')

for ax in hca:
    pyic.plot_settings(ax, xlim=lon_reg, ylim=lat_reg)
    add_sonett(ax)

savefig('kin_ov')

Saving figure ../pics/smtwv_diagnose_smtwv0004_kin_ov.pdf

Plot on original grid

%%time
to_rg = ds_2d.to.isel(depth=0, time=-1, ncells_2=ireg_c).compute()
so_rg = ds_2d.so.isel(depth=0, time=-1, ncells_2=ireg_c).compute()
kin_rg = ds_2d.kin.isel(depth=0, time=-1, ncells_2=ireg_c).compute()
vort_rg = ds_2d.vort.isel(depth=0, time=-1, ncells=ireg_v).compute()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
File <timed exec>:1, in <module>

File ~/miniconda3/envs/pyicon_py39_cartopy19/lib/python3.9/site-packages/xarray/core/dataarray.py:1291, in DataArray.isel(self, indexers, drop, missing_dims, **indexers_kwargs)
   1286     return self._from_temp_dataset(ds)
   1288 # Much faster algorithm for when all indexers are ints, slices, one-dimensional
   1289 # lists, or zero or one-dimensional np.ndarray's
-> 1291 variable = self._variable.isel(indexers, missing_dims=missing_dims)
   1292 indexes, index_variables = isel_indexes(self.xindexes, indexers)
   1294 coords = {}

File ~/miniconda3/envs/pyicon_py39_cartopy19/lib/python3.9/site-packages/xarray/core/variable.py:1223, in Variable.isel(self, indexers, missing_dims, **indexers_kwargs)
   1199 """Return a new array indexed along the specified dimension(s).
   1200
   1201 Parameters
   (...)
   1219     indexer, in which case the data will be a copy.
   1220 """
   1221 indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "isel")
-> 1223 indexers = drop_dims_from_indexers(indexers, self.dims, missing_dims)
   1225 key = tuple(indexers.get(dim, slice(None)) for dim in self.dims)
   1226 return self[key]

File ~/miniconda3/envs/pyicon_py39_cartopy19/lib/python3.9/site-packages/xarray/core/utils.py:850, in drop_dims_from_indexers(indexers, dims, missing_dims)
    848     invalid = indexers.keys() - set(dims)
    849     if invalid:
--> 850         raise ValueError(
    851             f"Dimensions {invalid} do not exist. Expected one or more of {dims}"
    852         )
    854     return indexers
    856 elif missing_dims == "warn":
    857
    858     # don't modify input

ValueError: Dimensions {'ncells_2'} do not exist. Expected one or more of ('time', 'depth', 'ncells')

%%time
clon_rg = clon[ireg_c]
clat_rg = clat[ireg_c]
vlon_rg = vlon[ireg_v]
vlat_rg = vlat[ireg_v]

CPU times: user 8 ms, sys: 3 ms, total: 11 ms
Wall time: 6.9 ms

# projection = ccrs.PlateCarree()
# hca, hcb = pyic.arrange_axes(2,2, plot_cb=True, asp=0.5, fig_size_fac=2,
#                              sharex=True, sharey=True, xlabel="", ylabel="",
#                              projection=projection,
#                             )
# ii=-1

# ns = 10


# ii+=1; ax=hca[ii]; cax=hcb[ii]
# ax.scatter(clon_rg[::ns], clat_rg[::ns], c=to_rg[::ns], s=2, vmin=15.5, vmax=18.5, cmap='RdYlBu_r')
# ax.set_title(f'temperature [$^o$C]')

# # ii+=1; ax=hca[ii]; cax=hcb[ii]
# # hm = pyic.shade(Tri, so_rg, ax=ax, cax=cax, clim=[35.1, 35.8], projection=projection)
# # ax.set_title(f'salinity [kg/m$^3$]')

# # ii+=1; ax=hca[ii]; cax=hcb[ii]
# # hm = pyic.shade(Tri, kin_rg.data, ax=ax, cax=cax, clim=[-4,0], logplot=True, projection=projection)
# # ax.set_title('log$_{10}$(kin. energy) [m$^2$/s$^2$]')

# # ii+=1; ax=hca[ii]; cax=hcb[ii]
# # hm3 = pyic.patch_plot_shade(patches_v, vort_rg, ax=ax, cax=cax, clim=1e-4)
# # ax.set_title(f'vorticity')

# for ax in hca:
#     pyic.plot_settings(ax, xlim=lon_reg, ylim=lat_reg)
# #     add_sonett(ax)

# savefig('overview')

projection = ccrs.PlateCarree()
hca, hcb = pyic.arrange_axes(2,2, plot_cb=True, asp=0.5, fig_size_fac=2,
                             sharex=True, sharey=True, xlabel="", ylabel="",
                             projection=projection,
                            )
ii=-1


ii+=1; ax=hca[ii]; cax=hcb[ii]
hm = pyic.shade(Tri, to_rg, ax=ax, cax=cax, clim=[15.5, 18.5], projection=projection)
ax.set_title(f'temperature [$^o$C]')

ii+=1; ax=hca[ii]; cax=hcb[ii]
hm = pyic.shade(Tri, so_rg, ax=ax, cax=cax, clim=[35.1, 35.8], projection=projection)
ax.set_title(f'salinity [kg/m$^3$]')

ii+=1; ax=hca[ii]; cax=hcb[ii]
hm = pyic.shade(Tri, kin_rg.data, ax=ax, cax=cax, clim=[-4,0], logplot=True, projection=projection)
ax.set_title('log$_{10}$(kin. energy) [m$^2$/s$^2$]')

ii+=1; ax=hca[ii]; cax=hcb[ii]
hm3 = pyic.patch_plot_shade(patches_v, vort_rg, ax=ax, cax=cax, clim=1e-4)
ax.set_title(f'vorticity')

for ax in hca:
    pyic.plot_settings(ax, xlim=lon_reg, ylim=lat_reg)
#     add_sonett(ax)

savefig('overview')

distributed.utils_perf - WARNING - full garbage collections took 12% CPU time recently (threshold: 10%)

Saving figure ../pics/smtwv_diagnose_smtwv0002_overview.pdf

Vertical velocity

%%time
wvel_rg = ds_3d.w.isel(time=-1, ncells=ireg_c).sel(depth_2=[50, 100, 1000, 2000], method='nearest').compute()

CPU times: user 232 ms, sys: 2.81 s, total: 3.04 s
Wall time: 3.28 s

wvel_rg

<xarray.DataArray 'w' (depth_2: 4, ncells: 265943)>
array([[ 0.00047756,  0.00036307,  0.00062074, ..., -0.00162562,
        -0.00107059, -0.00150702],
       [ 0.00053502,  0.00038434,  0.00084729, ..., -0.00240545,
        -0.00203855, -0.00197635],
       [ 0.00070504,  0.0005771 ,  0.00093624, ...,  0.00079223,
         0.00099528,  0.00043519],
       [ 0.00132668,  0.00137925,  0.00134183, ...,  0.00304148,
         0.00283049,  0.00320326]], dtype=float32)
Coordinates:
    time     datetime64[ns] 2019-07-25T22:15:00
    clon     (ncells) float32 0.1346 0.1346 0.1347 ... 0.102 0.1019 0.1021
    clat     (ncells) float32 -0.5521 -0.5522 -0.5521 ... -0.5237 -0.5236
  * depth_2  (depth_2) float64 49.4 101.3 990.3 2.025e+03
Dimensions without coordinates: ncells
Attributes:
    standard_name:                w
    long_name:                    vertical velocity at cells
    units:                        m/s
    code:                         255
    CDI_grid_type:                unstructured
    number_of_grid_in_reference:  1

projection = ccrs.PlateCarree()
hca, hcb = pyic.arrange_axes(2,2, plot_cb=True, asp=0.5, fig_size_fac=2,
                             sharex=True, sharey=True, xlabel="", ylabel="",
                             projection=projection,
                            )
ii=-1


for kk in range(wvel_rg.depth_2.size):
    ii+=1; ax=hca[ii]; cax=hcb[ii]
    hm = pyic.shade(Tri, wvel_rg[kk,:], ax=ax, cax=cax, clim=2.5e-3, projection=projection)
    ax.set_title(f'depth = {wvel_rg.depth_2[kk].data:.1f}m', loc='right')
    ax.set_title('vert. vel. [m/s]')

for ax in hca:
    pyic.plot_settings(ax, xlim=lon_reg, ylim=lat_reg)

Plotting a section

lon_reg, lat_reg

([3.5, 9.5], [-31.5, -28.5])

%%time
p1 = [3.5, -31.5]
p2 = [9.5, -28.5]
# p1 = [-13.5, -39]
# p2 = [15, -28]
lon_sec, lat_sec, dist_sec = pyic.derive_section_points(p1, p2, 101)
dist, inds = pyic.calc_ckdtree(clon, clat, lon_sec, lat_sec, use_npconcatenate=True)
# if False:
#     dist, inds = pyic.calc_ckdtree(ds_tg.clon.compute().data, ds_tg.clat.compute().data, lon_sec, lat_sec, use_npconcatenate=True)
#     np.savez('tmp_smtwave_section_points.npz', dist=dist, inds=inds)
# else:
#     ddnpz = np.load('tmp_smtwave_section_points.npz')
#     dist = ddnpz['dist']
#     inds = ddnpz['inds']

CPU times: user 47.1 s, sys: 4.46 s, total: 51.6 s
Wall time: 44.1 s

projection = ccrs.PlateCarree()
hca, hcb = pyic.arrange_axes(1,1, plot_cb=True, asp=0.5, fig_size_fac=2,
                             sharex=True, sharey=True, xlabel="", ylabel="",
                             projection=projection,
                            )
ii=-1


ii+=1; ax=hca[ii]; cax=hcb[ii]
hm = pyic.shade(lon, lat, toi, ax=ax, cax=cax, clim=[10, 25], projection=projection)
ax.plot(lon_sec, lat_sec)
ax.set_title(f'section')

for ax in hca:
    pyic.plot_settings(ax, xlim=[-20,30], ylim=[-45,-20])
    add_sonett(ax)

# savefig('mld')

ds_sec = ds_3d[['to', 'tke', 'w']]

%%time
ds_sec = ds_sec.isel(ncells=inds, time=-1)#.compute()

CPU times: user 434 ms, sys: 1.1 s, total: 1.53 s
Wall time: 1.33 s

ds_3d.depth_2[96]

<xarray.DataArray 'depth_2' ()>
array(2025.1)
Coordinates:
    depth_2  float64 2.025e+03
Attributes:
    standard_name:  depth
    long_name:      depth_below_sea
    units:          m
    positive:       down
    axis:           Z

%%time
ds_sec = ds_sec.compute()

ERROR! Session/line number was not unique in database. History logging moved to new session 1112

distributed.nanny - WARNING - Worker process still alive after 3 seconds, killing
distributed.nanny - WARNING - Worker process still alive after 3 seconds, killing
distributed.nanny - WARNING - Worker process still alive after 3 seconds, killing
distributed.nanny - WARNING - Worker process still alive after 3 seconds, killing
distributed.nanny - WARNING - Worker process still alive after 3 seconds, killing
distributed.nanny - WARNING - Worker process still alive after 3 seconds, killing
distributed.nanny - WARNING - Worker process still alive after 3 seconds, killing
distributed.comm.tcp - WARNING - Closing dangling stream in <TCP  local=tcp://127.0.0.1:60430 remote=tcp://127.0.0.1:38653>
ERROR:root:Internal Python error in the inspect module.
Below is the traceback from this internal error.

Traceback (most recent call last):
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/client.py", line 2707, in get
    results = self.gather(packed, asynchronous=asynchronous, direct=direct)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/client.py", line 2021, in gather
    return self.sync(
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/client.py", line 862, in sync
    return sync(
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/utils.py", line 335, in sync
    e.wait(10)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/threading.py", line 574, in wait
    signaled = self._cond.wait(timeout)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/threading.py", line 316, in wait
    gotit = waiter.acquire(True, timeout)
KeyboardInterrupt

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/selector_events.py", line 140, in _write_to_self
    csock.send(b'\0')
BlockingIOError: [Errno 11] Resource temporarily unavailable

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/magics/execution.py", line 1321, in time
    exec(code, glob, local_ns)
  File "<timed exec>", line 1, in <module>
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/xarray/core/dataset.py", line 1016, in compute
    return new.load(**kwargs)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/xarray/core/dataset.py", line 850, in load
    evaluated_data = da.compute(*lazy_data.values(), **kwargs)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/dask/base.py", line 567, in compute
    results = schedule(dsk, keys, **kwargs)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/client.py", line 2710, in get
    f.release()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/client.py", line 362, in release
    self.client.loop.add_callback(self.client._dec_ref, stringify(self.key))
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/platform/asyncio.py", line 227, in add_callback
    call_soon(self._run_callback, functools.partial(callback, *args, **kwargs))
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 797, in call_soon_threadsafe
    self._write_to_self()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/selector_events.py", line 140, in _write_to_self
    csock.send(b'\0')
KeyboardInterrupt

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 2061, in showtraceback
    stb = value._render_traceback_()
AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/ultratb.py", line 1101, in get_records
    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/ultratb.py", line 248, in wrapped
    return f(*args, **kwargs)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/ultratb.py", line 281, in _fixed_getinnerframes
    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/inspect.py", line 1541, in getinnerframes
    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/inspect.py", line 1503, in getframeinfo
    lines, lnum = findsource(frame)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/ultratb.py", line 182, in findsource
    lines = linecache.getlines(file, globals_dict)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/linecache.py", line 46, in getlines
    return updatecache(filename, module_globals)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/linecache.py", line 137, in updatecache
    lines = fp.readlines()
KeyboardInterrupt

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/client.py in get(self, dsk, keys, workers, allow_other_workers, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs)
   2706             try:
-> 2707                 results = self.gather(packed, asynchronous=asynchronous, direct=direct)
   2708             finally:

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/client.py in gather(self, futures, errors, direct, asynchronous)
   2020                 local_worker = None
-> 2021             return self.sync(
   2022                 self._gather,

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/client.py in sync(self, func, asynchronous, callback_timeout, *args, **kwargs)
    861         else:
--> 862             return sync(
    863                 self.loop, func, *args, callback_timeout=callback_timeout, **kwargs

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/utils.py in sync(loop, func, callback_timeout, *args, **kwargs)
    334         while not e.is_set():
--> 335             e.wait(10)
    336     if error[0]:

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/threading.py in wait(self, timeout)
    573             if not signaled:
--> 574                 signaled = self._cond.wait(timeout)
    575             return signaled

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/threading.py in wait(self, timeout)
    315                 if timeout > 0:
--> 316                     gotit = waiter.acquire(True, timeout)
    317                 else:

KeyboardInterrupt:

During handling of the above exception, another exception occurred:

BlockingIOError                           Traceback (most recent call last)
/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/selector_events.py in _write_to_self(self)
    139         try:
--> 140             csock.send(b'\0')
    141         except OSError:

BlockingIOError: [Errno 11] Resource temporarily unavailable

During handling of the above exception, another exception occurred:

KeyboardInterrupt                         Traceback (most recent call last)
/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/magics/execution.py in time(self, line, cell, local_ns)
   1320             try:
-> 1321                 exec(code, glob, local_ns)
   1322                 out=None

<timed exec> in <module>

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/xarray/core/dataset.py in compute(self, **kwargs)
   1015         new = self.copy(deep=False)
-> 1016         return new.load(**kwargs)
   1017

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/xarray/core/dataset.py in load(self, **kwargs)
    849             # evaluate all the dask arrays simultaneously
--> 850             evaluated_data = da.compute(*lazy_data.values(), **kwargs)
    851

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/dask/base.py in compute(*args, **kwargs)
    566
--> 567     results = schedule(dsk, keys, **kwargs)
    568     return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/client.py in get(self, dsk, keys, workers, allow_other_workers, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs)
   2709                 for f in futures.values():
-> 2710                     f.release()
   2711                 if getattr(thread_state, "key", False) and should_rejoin:

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/client.py in release(self, _in_destructor)
    361             try:
--> 362                 self.client.loop.add_callback(self.client._dec_ref, stringify(self.key))
    363             except TypeError:

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/platform/asyncio.py in add_callback(self, callback, *args, **kwargs)
    226         try:
--> 227             call_soon(self._run_callback, functools.partial(callback, *args, **kwargs))
    228         except RuntimeError:

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py in call_soon_threadsafe(self, callback, context, *args)
    796             del handle._source_traceback[-1]
--> 797         self._write_to_self()
    798         return handle

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/selector_events.py in _write_to_self(self)
    139         try:
--> 140             csock.send(b'\0')
    141         except OSError:

KeyboardInterrupt:

During handling of the above exception, another exception occurred:

AttributeError                            Traceback (most recent call last)
/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/interactiveshell.py in showtraceback(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)
   2060                         # in the engines. This should return a list of strings.
-> 2061                         stb = value._render_traceback_()
   2062                     except Exception:

AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_21444/2422761924.py in <module>
----> 1 get_ipython().run_cell_magic('time', '', 'ds_sec = ds_sec.compute()\n')

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/interactiveshell.py in run_cell_magic(self, magic_name, line, cell)
   2401             with self.builtin_trap:
   2402                 args = (magic_arg_s, cell)
-> 2403                 result = fn(*args, **kwargs)
   2404             return result
   2405

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/decorator.py in fun(*args, **kw)
    230             if not kwsyntax:
    231                 args, kw = fix(args, kw, sig)
--> 232             return caller(func, *(extras + args), **kw)
    233     fun.__name__ = func.__name__
    234     fun.__doc__ = func.__doc__

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/magic.py in <lambda>(f, *a, **k)
    185     # but it's overkill for just that one bit of state.
    186     def magic_deco(arg):
--> 187         call = lambda f, *a, **k: f(*a, **k)
    188
    189         if callable(arg):

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/magics/execution.py in time(self, line, cell, local_ns)
   1326                     out = eval(code_2, glob, local_ns)
   1327             except:
-> 1328                 self.shell.showtraceback()
   1329                 return
   1330             end = clock2()

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/interactiveshell.py in showtraceback(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)
   2061                         stb = value._render_traceback_()
   2062                     except Exception:
-> 2063                         stb = self.InteractiveTB.structured_traceback(etype,
   2064                                             value, tb, tb_offset=tb_offset)
   2065

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/ultratb.py in structured_traceback(self, etype, value, tb, tb_offset, number_of_lines_of_context)
   1365         else:
   1366             self.tb = tb
-> 1367         return FormattedTB.structured_traceback(
   1368             self, etype, value, tb, tb_offset, number_of_lines_of_context)
   1369

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/ultratb.py in structured_traceback(self, etype, value, tb, tb_offset, number_of_lines_of_context)
   1265         if mode in self.verbose_modes:
   1266             # Verbose modes need a full traceback
-> 1267             return VerboseTB.structured_traceback(
   1268                 self, etype, value, tb, tb_offset, number_of_lines_of_context
   1269             )

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/ultratb.py in structured_traceback(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)
   1122         """Return a nice text document describing the traceback."""
   1123
-> 1124         formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,
   1125                                                                tb_offset)
   1126

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/ultratb.py in format_exception_as_a_whole(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)
   1080
   1081
-> 1082         last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)
   1083
   1084         frames = self.format_records(records, last_unique, recursion_repeat)

/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/ultratb.py in find_recursion(etype, value, records)
    380     # first frame (from in to out) that looks different.
    381     if not is_recursion_error(etype, value, records):
--> 382         return len(records), 0
    383
    384     # Select filename, lineno, func_name to track frames with

TypeError: object of type 'NoneType' has no len()

# to_sec = to_sec.where(to_sec!=0)

hca, hcb = pyic.arrange_axes(1,3, plot_cb=True, asp=0.5, fig_size_fac=2,
                             sharex=True, sharey=True, xlabel="longitude", ylabel="depth [m]",
                            )
ii=-1


ii+=1; ax=hca[ii]; cax=hcb[ii]
da = ds_sec['to']
pyic.shade(lon_sec, ds_sec.depth, da, ax=ax, cax=cax, clim=[4,18.5])
ax.set_title(f'{da.long_name} [{da.units}]')

ii+=1; ax=hca[ii]; cax=hcb[ii]
da = ds_sec['tke']
pyic.shade(lon_sec, ds_sec.depth_2, da.data, ax=ax, cax=cax, clim=[-6,-2], logplot=True)
ax.set_title(f'{da.long_name} [{da.units}]')

ii+=1; ax=hca[ii]; cax=hcb[ii]
da = ds_sec['w']
pyic.shade(lon_sec, ds_sec.depth_2, da, ax=ax, cax=cax, clim=5e-3)
ax.set_title(f'{da.long_name} [{da.units}]')

for ax in hca:
    ax.set_ylim(1000,0)

distributed.utils_perf - WARNING - full garbage collections took 12% CPU time recently (threshold: 10%)
Process Dask Worker process (from Nanny):
Traceback (most recent call last):
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/multiprocessing/process.py", line 315, in _bootstrap
    self.run()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/process.py", line 191, in _run
    target(*args, **kwargs)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/nanny.py", line 835, in _run
    loop.run_sync(do_stop)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/ioloop.py", line 524, in run_sync
    self.start()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/platform/asyncio.py", line 199, in start
    self.asyncio_loop.run_forever()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 596, in run_forever
    self._run_once()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 1890, in _run_once
    handle._run()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/events.py", line 80, in _run
    self._context.run(self._callback, *self._args)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/ioloop.py", line 688, in <lambda>
    lambda f: self._run_callback(functools.partial(callback, future))
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/ioloop.py", line 741, in _run_callback
    ret = callback()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/ioloop.py", line 765, in _discard_future_result
    future.result()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/nanny.py", line 828, in _run
    loop.run_sync(run)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/ioloop.py", line 524, in run_sync
    self.start()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/platform/asyncio.py", line 199, in start
    self.asyncio_loop.run_forever()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 596, in run_forever
    self._run_once()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 1890, in _run_once
    handle._run()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/events.py", line 80, in _run
    self._context.run(self._callback, *self._args)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/worker.py", line 3042, in execute
    self.transition(ts, "memory", value=value)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/worker.py", line 1702, in transition
    state = func(ts, **kwargs)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/worker.py", line 1988, in transition_executing_done
    self.put_key_in_memory(ts, value, transition=False)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/worker.py", line 2241, in put_key_in_memory
    self.data[ts.key] = value
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/spill.py", line 69, in __setitem__
    super().__setitem__(key, value)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/zict/buffer.py", line 87, in __setitem__
    self.fast[key] = value
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/zict/lru.py", line 70, in __setitem__
    self.evict()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/zict/lru.py", line 89, in evict
    cb(k, v)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/zict/buffer.py", line 60, in fast_to_slow
    self.slow[key] = value
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/zict/func.py", line 41, in __setitem__
    self.d[key] = self.dump(value)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/zict/file.py", line 82, in __setitem__
    f.write(v)
KeyboardInterrupt
Process Dask Worker process (from Nanny):
Process Dask Worker process (from Nanny):
Process Dask Worker process (from Nanny):
Traceback (most recent call last):
Traceback (most recent call last):
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/nanny.py", line 828, in _run
    loop.run_sync(run)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/nanny.py", line 828, in _run
    loop.run_sync(run)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/ioloop.py", line 524, in run_sync
    self.start()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/ioloop.py", line 524, in run_sync
    self.start()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/platform/asyncio.py", line 199, in start
    self.asyncio_loop.run_forever()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/platform/asyncio.py", line 199, in start
    self.asyncio_loop.run_forever()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 596, in run_forever
    self._run_once()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 596, in run_forever
    self._run_once()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 1854, in _run_once
    event_list = self._selector.select(timeout)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 1854, in _run_once
    event_list = self._selector.select(timeout)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/selectors.py", line 469, in select
    fd_event_list = self._selector.poll(timeout, max_ev)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/selectors.py", line 469, in select
    fd_event_list = self._selecERROR:root:Internal Python error in the inspect module.
Below is the traceback from this internal error.

tor.poll(timeout, max_ev)
KeyboardInterrupt
KeyboardInterrupt

During handling of the above exception, another exception occurred:


During handling of the above exception, another exception occurred:

Traceback (most recent call last):
Traceback (most recent call last):
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/multiprocessing/process.py", line 315, in _bootstrap
    self.run()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/multiprocessing/process.py", line 315, in _bootstrap
    self.run()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/process.py", line 191, in _run
    target(*args, **kwargs)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/process.py", line 191, in _run
    target(*args, **kwargs)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/nanny.py", line 835, in _run
    loop.run_sync(do_stop)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/nanny.py", line 835, in _run
    loop.run_sync(do_stop)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/ioloop.py", line 524, in run_sync
    self.start()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/ioloop.py", line 524, in run_sync
    self.start()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/platform/asyncio.py", line 199, in start
    self.asyncio_loop.run_forever()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/platform/asyncio.py", line 199, in start
    self.asyncio_loop.run_forever()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 596, in run_forever
    self._run_once()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 596, in run_forever
    self._run_once()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 1890, in _run_once
    handle._run()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 1890, in _run_once
    handle._run()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/events.py", line 80, in _run
    self._context.run(self._callback, *self._args)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/events.py", line 80, in _run
    self._context.run(self._callback, *self._args)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/nanny.py", line 751, in do_stop
    await worker.close(
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/nanny.py", line 751, in do_stop
    await worker.close(
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/worker.py", line 1311, in close
    executor.shutdown(wait=executor_wait, timeout=timeout)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/threadpoolexecutor.py", line 105, in shutdown
    t.join(timeout=timeout2)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/worker.py", line 1311, in close
    executor.shutdown(wait=executor_wait, timeout=timeout)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/threading.py", line 1057, in join
    self._wait_for_tstate_lock(timeout=max(timeout, 0))
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/threadpoolexecutor.py", line 105, in shutdown
    t.join(timeout=timeout2)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/threading.py", line 1069, in _wait_for_tstate_lock
    elif lock.acquire(block, timeout):
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/threading.py", line 1057, in join
    self._wait_for_tstate_lock(timeout=max(timeout, 0))
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/threading.py", line 1069, in _wait_for_tstate_lock
    elif lock.acquire(block, timeout):
KeyboardInterrupt
KeyboardInterrupt
Traceback (most recent call last):
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/nanny.py", line 828, in _run
    loop.run_sync(run)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/ioloop.py", line 524, in run_sync
    self.start()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/platform/asyncio.py", line 199, in start
    self.asyncio_loop.run_forever()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 596, in run_forever
    self._run_once()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 1854, in _run_once
    event_list = self._selector.select(timeout)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/selectors.py", line 469, in select
    fd_event_list = self._selector.poll(timeout, max_ev)
KeyboardInterrupt

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/multiprocessing/process.py", line 315, in _bootstrap
    self.run()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/process.py", line 191, in _run
    target(*args, **kwargs)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/nanny.py", line 835, in _run
    loop.run_sync(do_stop)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/ioloop.py", line 524, in run_sync
    self.start()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/platform/asyncio.py", line 199, in start
    self.asyncio_loop.run_forever()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 596, in run_forever
    self._run_once()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 1890, in _run_once
    handle._run()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/events.py", line 80, in _run
    self._context.run(self._callback, *self._args)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/nanny.py", line 751, in do_stop
    await worker.close(
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/worker.py", line 1311, in close
    executor.shutdown(wait=executor_wait, timeout=timeout)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/threadpoolexecutor.py", line 105, in shutdown
    t.join(timeout=timeout2)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/threading.py", line 1057, in join
    self._wait_for_tstate_lock(timeout=max(timeout, 0))
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/threading.py", line 1069, in _wait_for_tstate_lock
    elif lock.acquire(block, timeout):
KeyboardInterrupt
Process Dask Worker process (from Nanny):
Traceback (most recent call last):
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/nanny.py", line 828, in _run
    loop.run_sync(run)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/ioloop.py", line 524, in run_sync
    self.start()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/platform/asyncio.py", line 199, in start
    self.asyncio_loop.run_forever()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 596, in run_forever
    self._run_once()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 1854, in _run_once
    event_list = self._selector.select(timeout)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/selectors.py", line 469, in select
    fd_event_list = self._selector.poll(timeout, max_ev)
KeyboardInterrupt

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/multiprocessing/process.py", line 315, in _bootstrap
    self.run()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/process.py", line 191, in _run
    target(*args, **kwargs)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/nanny.py", line 835, in _run
    loop.run_sync(do_stop)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/ioloop.py", line 524, in run_sync
    self.start()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/tornado/platform/asyncio.py", line 199, in start
    self.asyncio_loop.run_forever()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 596, in run_forever
    self._run_once()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/base_events.py", line 1890, in _run_once
    handle._run()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/asyncio/events.py", line 80, in _run
    self._context.run(self._callback, *self._args)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/nanny.py", line 751, in do_stop
    await worker.close(
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/worker.py", line 1291, in close
    self._workdir.release()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/diskutils.py", line 90, in release
    self._finalizer()
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/weakref.py", line 580, in __call__
    return info.func(*info.args, **(info.kwargs or {}))
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/diskutils.py", line 95, in _finalize
    workspace._purge_directory(dir_path)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/diskutils.py", line 186, in _purge_directory
    shutil.rmtree(dir_path, onerror=self._on_remove_error)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/shutil.py", line 718, in rmtree
    _rmtree_safe_fd(fd, path, onerror)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/shutil.py", line 655, in _rmtree_safe_fd
    _rmtree_safe_fd(dirfd, fullname, onerror)
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/shutil.py", line 673, in _rmtree_safe_fd
    os.unlink(entry.name, dir_fd=topfd)
KeyboardInterrupt
Error in atexit._run_exitfuncs:
Traceback (most recent call last):
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/distributed/deploy/local.py", line 266, in close_clusters
KeyboardInterrupt
ERROR:root:Internal Python error in the inspect module.
Below is the traceback from this internal error.

ERROR:root:Internal Python error in the inspect module.
Below is the traceback from this internal error.

Traceback (most recent call last):
  File "/work/mh0033/m300602/miniconda3/envs/pyicon_py39/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3441, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "/tmp/ipykernel_21444/426675411.py", line 9, in <module>
    pyic.shade(lon_sec, ds_sec.depth, da, ax=ax, cax=cax, clim=[4,18.5])

KeyboardInterrupt