%matplotlib inline
import pandas as pd
import socket
host = socket.getfqdn()

from core import  load, zoom, calc, save,plots,monitor

#reload funcs after updating ./core/*.py
import importlib
importlib.reload(load)
importlib.reload(zoom)
importlib.reload(calc)
importlib.reload(save)
importlib.reload(plots)
importlib.reload(monitor)

<module 'core.monitor' from '/ccc/work/cont003/gen7420/talandel/TOOLS/monitor-sedna/notebook/core/monitor.py'>

If you submit the job with job scheduler; below are list of enviroment variable one can pass

• %env local='2"

local : if True run dask local cluster, if not true, put number of workers setted in the 'local' if no 'local ' given, local will be setted automatically to 'True'

• %env ychunk='2', #%env tchunk='2'

  controls chunk. 'False' sets no modification from original netcdf file's chunk.
  ychunk=10 will group the original netcdf file to 10 by 10 tchunk=1 will chunk the time coordinate one by one

• %env file_exp=

  'file_exp': Which 'experiment' name is it? this corresopnds to intake catalog name without path and .yaml

• #%env year=

  for Validation, this correspoinds to path/year/month 's year for monitoring, this corresponids to 'date' having means do all files in the monitoring directory setting it as 0[0-9] &1[0-9]& [2-3][0-9], the job can be separated in three lots. For DELTA experiment, year corresponds to really 'year'

• %env month=

  for monitoring this corresponds to file path path-XIOS.{month}/

For DELTA experiment, year corresponds to really 'month'

• #%env save=

proceed saving? True or False , Default is setted as True

• #%env plot=

proceed plotting? True or False , Default is setted as True

• #%env calc=

proceed computation? or just load computed result? True or False , Default is setted as True

• #%env save=False

save output file used for plotting

• #%env lazy=False

using kerchunked file -> False, not using kerhcunk -> True

• #%env control=FWC_SSH

name of control file to be used for computation/plots/save/ We have number of M_xxx.csv

Monitor.sh automate all of above enviroment settings.

Monitor.sh calls M_MLD_2D

and AWTD.sh, Fluxnet.sh, Siconc.sh, IceClim.sh, FWC_SSH.sh, Integrals.sh , Sections.sh

• AWTD.sh

M_AWTMD

• Fluxnet.sh

M_Fluxnet

• Siconc.sh

M_Ice_quantities

• IceClim.sh

M_IceClim M_IceConce M_IceThick

• FWC_SSH.sh

M_FWC_2D M_FWC_integrals M_FWC_SSH M_SSH_anomaly

• Integrals.sh

M_Mean_temp_velo M_Mooring

• Sections.sh

M_Sectionx M_Sectiony

# For debugging this cell can help turn this cell to 'code' and modify variables manually. %env file_exp=SEDNA_DELTA_MONITOR %env year=2012 %env month=01 #0[1-2] %env ychunk=10 %env ychunk=False %env save=False %env plot=True %env calc=True # %env lazy=False #False %env control=M_Fluxnet #M_Sectiony ok with ychunk=False local=True lazy=False

%%time
# 'savefig': Do we save output in html? or not. keep it true. 
savefig=True
client,cluster,control,catalog_url,month,year,daskreport,outputpath = load.set_control(host)
!mkdir -p $outputpath
!mkdir -p $daskreport
client

local True
using host= irene8000.c-irene.tgcc.ccc.cea.fr starting dask cluster on local= True workers 16
10000000000
False
tgcc local cluster starting
This code is running on  irene8000.c-irene.tgcc.ccc.cea.fr using  SEDNA_DELTA_MONITOR file experiment, read from  ../lib/SEDNA_DELTA_MONITOR.yaml  on year= 2013  on month= 01  outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/6570220irene8000.c-irene.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_01M_Mooring/
CPU times: user 2.01 s, sys: 542 ms, total: 2.55 s
Wall time: 1min 43s

Client

Client-5a3770c7-2455-11ed-9f77-080038bfd9c6

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

Cluster Info

LocalCluster

c0d176c2

Dashboard: http://127.0.0.1:8787/status	Workers: 12
Total threads: 48	Total memory: 2.86 TiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-db762566-d518-49c6-a5a5-59884a8b077d

Comm: tcp://127.0.0.1:40962	Workers: 12
Dashboard: http://127.0.0.1:8787/status	Total threads: 48
Started: 1 minute ago	Total memory: 2.86 TiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:44697	Total threads: 4
Dashboard: http://127.0.0.1:44859/status	Memory: 244.27 GiB
Nanny: tcp://127.0.0.1:33282
Local directory: /tmp/dask-worker-space/worker-_ohn4bfe

Worker: 1

Comm: tcp://127.0.0.1:41493	Total threads: 4
Dashboard: http://127.0.0.1:44059/status	Memory: 244.27 GiB
Nanny: tcp://127.0.0.1:43181
Local directory: /tmp/dask-worker-space/worker-achqpsrp

Worker: 2

Comm: tcp://127.0.0.1:32935	Total threads: 4
Dashboard: http://127.0.0.1:34964/status	Memory: 244.27 GiB
Nanny: tcp://127.0.0.1:39056
Local directory: /tmp/dask-worker-space/worker-riwjdqku

Worker: 3

Comm: tcp://127.0.0.1:36706	Total threads: 4
Dashboard: http://127.0.0.1:34192/status	Memory: 244.27 GiB
Nanny: tcp://127.0.0.1:38319
Local directory: /tmp/dask-worker-space/worker-c1epnhjq

Worker: 4

Comm: tcp://127.0.0.1:35331	Total threads: 4
Dashboard: http://127.0.0.1:45072/status	Memory: 244.27 GiB
Nanny: tcp://127.0.0.1:35693
Local directory: /tmp/dask-worker-space/worker-kz9ouux9

Worker: 5

Comm: tcp://127.0.0.1:43390	Total threads: 4
Dashboard: http://127.0.0.1:43060/status	Memory: 244.27 GiB
Nanny: tcp://127.0.0.1:35536
Local directory: /tmp/dask-worker-space/worker-dq_zdb5e

Worker: 6

Comm: tcp://127.0.0.1:42950	Total threads: 4
Dashboard: http://127.0.0.1:34396/status	Memory: 244.27 GiB
Nanny: tcp://127.0.0.1:33808
Local directory: /tmp/dask-worker-space/worker-9k7v1btw

Worker: 7

Comm: tcp://127.0.0.1:43975	Total threads: 4
Dashboard: http://127.0.0.1:44204/status	Memory: 244.27 GiB
Nanny: tcp://127.0.0.1:40848
Local directory: /tmp/dask-worker-space/worker-lwcdijpy

Worker: 8

Comm: tcp://127.0.0.1:40567	Total threads: 4
Dashboard: http://127.0.0.1:40057/status	Memory: 244.27 GiB
Nanny: tcp://127.0.0.1:35727
Local directory: /tmp/dask-worker-space/worker-x9fjgasx

Worker: 9

Comm: tcp://127.0.0.1:41153	Total threads: 4
Dashboard: http://127.0.0.1:36221/status	Memory: 244.27 GiB
Nanny: tcp://127.0.0.1:41329
Local directory: /tmp/dask-worker-space/worker-qrfbl4x9

Worker: 10

Comm: tcp://127.0.0.1:41573	Total threads: 4
Dashboard: http://127.0.0.1:36966/status	Memory: 244.27 GiB
Nanny: tcp://127.0.0.1:37678
Local directory: /tmp/dask-worker-space/worker-ozlm9s57

Worker: 11

Comm: tcp://127.0.0.1:41293	Total threads: 4
Dashboard: http://127.0.0.1:46503/status	Memory: 244.27 GiB
Nanny: tcp://127.0.0.1:42037
Local directory: /tmp/dask-worker-space/worker-y90zy1yo

read plotting information from a csv file

df=load.controlfile(control)
#Take out 'later' tagged computations
#df=df[~df['Value'].str.contains('later')]
df

	Value	Inputs	Equation	Zone	Plot	Colourmap	MinMax	Unit	Oldname	Unnamed: 10
	Mooring_Arc_B	gridS.vosaline,gridT.votemper,param.depth,para...	data	Arc_B	Mooring	rainbow	{'vosaline': (28.0,34.4), 'votemper': (-2.0,2.0)}	None	x
	Mooring_Eur_B	gridS.vosaline,gridT.votemper,param.depth,para...	data	Eur_B	Mooring	rainbow	{'vosaline': (32.0,35.0), 'votemper': (-2.0,3.0)}	None	x

Computation starts here

Each computation consists of

1. Load NEMO data set
2. Zoom data set
3. Compute (or load computed data set)
4. Save
5. Plot
6. Close

%%time
import os
calcswitch=os.environ.get('calc', 'True') 
lazy=os.environ.get('lazy','False' )
loaddata=((df.Inputs != '').any()) 
print('calcswitch=',calcswitch,'df.Inputs != nothing',loaddata, 'lazy=',lazy)
data = load.datas(catalog_url,df.Inputs,month,year,daskreport,lazy=lazy) if ((calcswitch=='True' )*loaddata) else 0 
data

calcswitch= True df.Inputs != nothing True lazy= False
../lib/SEDNA_DELTA_MONITOR.yaml
using param_xios reading  ../lib/SEDNA_DELTA_MONITOR.yaml
using param_xios reading  <bound method DataSourceBase.describe of sources:
  param_xios:
    args:
      combine: nested
      concat_dim: y
      urlpath: /ccc/work/cont003/gen7420/odakatin/CONFIGS/SEDNA/SEDNA-I/SEDNA_Domain_cfg_Tgt_20210423_tsh10m_L1/param_f32/x_*.nc
      xarray_kwargs:
        compat: override
        coords: minimal
        data_vars: minimal
        parallel: true
    description: SEDNA NEMO parameters from MPI output  nav_lon lat fails
    driver: intake_xarray.netcdf.NetCDFSource
    metadata:
      catalog_dir: /ccc/work/cont003/gen7420/talandel/TOOLS/monitor-sedna/notebook/../lib/
>
{'name': 'param_xios', 'container': 'xarray', 'plugin': ['netcdf'], 'driver': ['netcdf'], 'description': 'SEDNA NEMO parameters from MPI output  nav_lon lat fails', 'direct_access': 'forbid', 'user_parameters': [{'name': 'path', 'description': 'file coordinate', 'type': 'str', 'default': '/ccc/work/cont003/gen7420/odakatin/CONFIGS/SEDNA/MESH/SEDNA_mesh_mask_Tgt_20210423_tsh10m_L1/param'}], 'metadata': {}, 'args': {'urlpath': '/ccc/work/cont003/gen7420/odakatin/CONFIGS/SEDNA/SEDNA-I/SEDNA_Domain_cfg_Tgt_20210423_tsh10m_L1/param_f32/x_*.nc', 'combine': 'nested', 'concat_dim': 'y'}}
0 read gridS ['vosaline']
lazy= False
using load_data_xios_kerchunk reading  gridS
using load_data_xios_kerchunk reading  <bound method DataSourceBase.describe of sources:
  data_xios_kerchunk:
    args:
      consolidated: false
      storage_options:
        fo: file:////ccc/cont003/home/ra5563/ra5563/catalogue/DELTA/201301/gridS_0[0-5][0-9][0-9].json
        target_protocol: file
      urlpath: reference://
    description: CREG025 NEMO outputs from different xios server in kerchunk format
    driver: intake_xarray.xzarr.ZarrSource
    metadata:
      catalog_dir: /ccc/work/cont003/gen7420/talandel/TOOLS/monitor-sedna/notebook/../lib/
>
      took 61.737545013427734 seconds
0 merging gridS ['vosaline']
1 read gridT ['votemper']
lazy= False
using load_data_xios_kerchunk reading  gridT
using load_data_xios_kerchunk reading  <bound method DataSourceBase.describe of sources:
  data_xios_kerchunk:
    args:
      consolidated: false
      storage_options:
        fo: file:////ccc/cont003/home/ra5563/ra5563/catalogue/DELTA/201301/gridT_0[0-5][0-9][0-9].json
        target_protocol: file
      urlpath: reference://
    description: CREG025 NEMO outputs from different xios server in kerchunk format
    driver: intake_xarray.xzarr.ZarrSource
    metadata:
      catalog_dir: /ccc/work/cont003/gen7420/talandel/TOOLS/monitor-sedna/notebook/../lib/
>
      took 57.032193183898926 seconds
1 merging gridT ['votemper']
      took 1.529552936553955 seconds
param nav_lat will be included in data
param depth will be included in data
param mask will be included in data
param mask2d will be included in data
param nav_lon will be included in data
ychunk= 5 calldatas_y_rechunk
sum_num (13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12)
start rechunking with (65, 65, 62, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 48)
end of y_rechunk
before rechunking t item (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
start rechunking t with 1
end of t_rechunk
CPU times: user 44.9 s, sys: 9.14 s, total: 54 s
Wall time: 3min 30s

<xarray.Dataset>
Dimensions:   (t: 31, z: 150, y: 6540, x: 6560)
Coordinates:
  * t         (t) object 2013-01-01 12:00:00 ... 2013-01-31 12:00:00
  * y         (y) int64 1 2 3 4 5 6 7 8 ... 6534 6535 6536 6537 6538 6539 6540
  * x         (x) int64 1 2 3 4 5 6 7 8 ... 6554 6555 6556 6557 6558 6559 6560
  * z         (z) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150
    nav_lat   (y, x) float32 dask.array<chunksize=(65, 6560), meta=np.ndarray>
    depth     (z, y, x) float32 dask.array<chunksize=(150, 65, 6560), meta=np.ndarray>
    mask      (z, y, x) bool dask.array<chunksize=(150, 65, 6560), meta=np.ndarray>
    mask2d    (y, x) bool dask.array<chunksize=(65, 6560), meta=np.ndarray>
    nav_lon   (y, x) float32 dask.array<chunksize=(65, 6560), meta=np.ndarray>
Data variables:
    vosaline  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 65, 6560), meta=np.ndarray>
    votemper  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 65, 6560), meta=np.ndarray>
Attributes: (12/26)
    CASE:                    DELTA
    CONFIG:                  SEDNA
    Conventions:             CF-1.6
    DOMAIN_dimensions_ids:   [2, 3]
    DOMAIN_halo_size_end:    [0, 0]
    DOMAIN_halo_size_start:  [0, 0]
    ...                      ...
    nj:                      13
    output_frequency:        1d
    start_date:              20090101
    timeStamp:               2022-Feb-01 10:20:00 GMT
    title:                   ocean T grid variables
    uuid:                    a7eddcc9-d16c-44f0-a2ea-c635e489b218

xarray.Dataset

• Dimensions:
  • t: 31
  • z: 150
  • y: 6540
  • x: 6560
• Coordinates: (9)
  • t
    (t)
    object
    2013-01-01 12:00:00 ... 2013-01-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2013, 1, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 31, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • y
    (y)
    int64
    1 2 3 4 5 ... 6537 6538 6539 6540

    array([   1,    2,    3, ..., 6538, 6539, 6540])

  • x
    (x)
    int64
    1 2 3 4 5 ... 6557 6558 6559 6560

    array([   1,    2,    3, ..., 6558, 6559, 6560])

  • z
    (z)
    int64
    1 2 3 4 5 6 ... 146 147 148 149 150

    array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
            15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
            29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,
            43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,
            57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,
            71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,
            85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
            99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
           113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,
           127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140,
           141, 142, 143, 144, 145, 146, 147, 148, 149, 150])

  • nav_lat
    (y, x)
    float32
    dask.array<chunksize=(65, 6560), meta=np.ndarray>

    Array Chunk Bytes 163.66 MiB 1.63 MiB Shape (6540, 6560) (65, 6560) Count 1741 Tasks 109 Chunks Type float32 numpy.ndarray

  • depth
    (z, y, x)
    float32
    dask.array<chunksize=(150, 65, 6560), meta=np.ndarray>

    Array Chunk Bytes 23.97 GiB 243.99 MiB Shape (150, 6540, 6560) (150, 65, 6560) Count 1741 Tasks 109 Chunks Type float32 numpy.ndarray

  • mask
    (z, y, x)
    bool
    dask.array<chunksize=(150, 65, 6560), meta=np.ndarray>

    Array Chunk Bytes 5.99 GiB 61.00 MiB Shape (150, 6540, 6560) (150, 65, 6560) Count 1741 Tasks 109 Chunks Type bool numpy.ndarray

  • mask2d
    (y, x)
    bool
    dask.array<chunksize=(65, 6560), meta=np.ndarray>

    Array Chunk Bytes 40.91 MiB 416.41 kiB Shape (6540, 6560) (65, 6560) Count 1741 Tasks 109 Chunks Type bool numpy.ndarray

  • nav_lon
    (y, x)
    float32
    dask.array<chunksize=(65, 6560), meta=np.ndarray>

    Array Chunk Bytes 163.66 MiB 1.63 MiB Shape (6540, 6560) (65, 6560) Count 1741 Tasks 109 Chunks Type float32 numpy.ndarray

• Data variables: (2)
  • vosaline
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 65, 6560), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    salinity

    online_operation :

    average

    standard_name :

    sea_water_practical_salinity

    units :

    1e-3

    Array Chunk Bytes 743.18 GiB 243.99 MiB Shape (31, 150, 6540, 6560) (1, 150, 65, 6560) Count 37651 Tasks 3379 Chunks Type float32 numpy.ndarray

  • votemper
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 65, 6560), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    temperature

    online_operation :

    average

    standard_name :

    sea_water_potential_temperature

    units :

    degC

    Array Chunk Bytes 743.18 GiB 243.99 MiB Shape (31, 150, 6540, 6560) (1, 150, 65, 6560) Count 37651 Tasks 3379 Chunks Type float32 numpy.ndarray

• Attributes: (26)

  CASE :

  DELTA

  CONFIG :

  SEDNA

  Conventions :

  CF-1.6

  DOMAIN_dimensions_ids :

  [2, 3]

  DOMAIN_halo_size_end :

  [0, 0]

  DOMAIN_halo_size_start :

  [0, 0]

  DOMAIN_number :

  0

  DOMAIN_number_total :

  544

  DOMAIN_position_first :

  [1, 1]

  DOMAIN_position_last :

  [6560, 13]

  DOMAIN_size_global :

  [6560, 6540]

  DOMAIN_size_local :

  [6560, 13]

  DOMAIN_type :

  box

  NCO :

  netCDF Operators version 4.9.1 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

  description :

  ocean T grid variables

  history :

  Wed Feb 2 11:31:06 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201301-201301_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2013/01/SEDNA-DELTA_1d_gridS_201301-201301_0000.nc Wed Feb 2 11:29:41 2022: ncrcat -n 31,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201301-201301_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.58/SEDNA-DELTA_1d_gridS

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Feb-01 10:20:00 GMT

  title :

  ocean T grid variables

  uuid :

  a7eddcc9-d16c-44f0-a2ea-c635e489b218

%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
            )

#calc= True
#save= True
#plot= False
Value='Mooring_Arc_B'
Zone='Arc_B'
Plot='Mooring'
cmap='rainbow'
clabel='None'
clim= {'vosaline': (28.0, 34.4), 'votemper': (-2.0, 2.0)}
outputpath='../results/SEDNA_DELTA_MONITOR/'
nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/'
filename='SEDNA_Mooring_Arc_B_Mooring_Arc_B'
data=monitor.optimize_dataset(data)
#2 Zooming Data
data= zoom.Arc_B(data)
data=monitor.optimize_dataset(data)

<xarray.Dataset>
Dimensions:   (t: 31, z: 102)
Coordinates:
  * t         (t) object 2013-01-01 12:00:00 ... 2013-01-31 12:00:00
    y         int64 4967
    x         int64 2471
  * z         (z) int64 1 2 3 4 5 6 7 8 9 10 ... 94 95 96 97 98 99 100 101 102
    nav_lat   float32 dask.array<chunksize=(), meta=np.ndarray>
    depth     (z) float32 dask.array<chunksize=(102,), meta=np.ndarray>
    mask      (z) bool dask.array<chunksize=(102,), meta=np.ndarray>
    mask2d    bool dask.array<chunksize=(), meta=np.ndarray>
    nav_lon   float32 dask.array<chunksize=(), meta=np.ndarray>
Data variables:
    vosaline  (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray>
    votemper  (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray>
Attributes: (12/26)
    CASE:                    DELTA
    CONFIG:                  SEDNA
    Conventions:             CF-1.6
    DOMAIN_dimensions_ids:   [2, 3]
    DOMAIN_halo_size_end:    [0, 0]
    DOMAIN_halo_size_start:  [0, 0]
    ...                      ...
    nj:                      13
    output_frequency:        1d
    start_date:              20090101
    timeStamp:               2022-Feb-01 10:20:00 GMT
    title:                   ocean T grid variables
    uuid:                    a7eddcc9-d16c-44f0-a2ea-c635e489b218

xarray.Dataset

• Dimensions:
  • t: 31
  • z: 102
• Coordinates: (9)
  • t
    (t)
    object
    2013-01-01 12:00:00 ... 2013-01-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2013, 1, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 31, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • y
    ()
    int64
    4967

    array(4967)

  • x
    ()
    int64
    2471

    array(2471)

  • z
    (z)
    int64
    1 2 3 4 5 6 ... 98 99 100 101 102

    array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
            15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
            29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,
            43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,
            57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,
            71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,
            85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
            99, 100, 101, 102])

  • nav_lat
    ()
    float32
    dask.array<chunksize=(), meta=np.ndarray>

    Array Chunk Bytes 4 B 4 B Shape () () Count 1743 Tasks 1 Chunks Type float32 numpy.ndarray

  • depth
    (z)
    float32
    dask.array<chunksize=(102,), meta=np.ndarray>

    Array Chunk Bytes 408 B 408 B Shape (102,) (102,) Count 1744 Tasks 1 Chunks Type float32 numpy.ndarray

  • mask
    (z)
    bool
    dask.array<chunksize=(102,), meta=np.ndarray>

    Array Chunk Bytes 102 B 102 B Shape (102,) (102,) Count 1744 Tasks 1 Chunks Type bool numpy.ndarray

  • mask2d
    ()
    bool
    dask.array<chunksize=(), meta=np.ndarray>

    Array Chunk Bytes 1 B 1 B Shape () () Count 1743 Tasks 1 Chunks Type bool numpy.ndarray

  • nav_lon
    ()
    float32
    dask.array<chunksize=(), meta=np.ndarray>

    Array Chunk Bytes 4 B 4 B Shape () () Count 1743 Tasks 1 Chunks Type float32 numpy.ndarray

• Data variables: (2)
  • vosaline
    (t, z)
    float32
    dask.array<chunksize=(1, 102), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    salinity

    online_operation :

    average

    standard_name :

    sea_water_practical_salinity

    units :

    1e-3

    Array Chunk Bytes 12.35 kiB 408 B Shape (31, 102) (1, 102) Count 425 Tasks 31 Chunks Type float32 numpy.ndarray

  • votemper
    (t, z)
    float32
    dask.array<chunksize=(1, 102), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    temperature

    online_operation :

    average

    standard_name :

    sea_water_potential_temperature

    units :

    degC

    Array Chunk Bytes 12.35 kiB 408 B Shape (31, 102) (1, 102) Count 425 Tasks 31 Chunks Type float32 numpy.ndarray

• Attributes: (26)

  CASE :

  DELTA

  CONFIG :

  SEDNA

  Conventions :

  CF-1.6

  DOMAIN_dimensions_ids :

  [2, 3]

  DOMAIN_halo_size_end :

  [0, 0]

  DOMAIN_halo_size_start :

  [0, 0]

  DOMAIN_number :

  0

  DOMAIN_number_total :

  544

  DOMAIN_position_first :

  [1, 1]

  DOMAIN_position_last :

  [6560, 13]

  DOMAIN_size_global :

  [6560, 6540]

  DOMAIN_size_local :

  [6560, 13]

  DOMAIN_type :

  box

  NCO :

  netCDF Operators version 4.9.1 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

  description :

  ocean T grid variables

  history :

  Wed Feb 2 11:31:06 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201301-201301_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2013/01/SEDNA-DELTA_1d_gridS_201301-201301_0000.nc Wed Feb 2 11:29:41 2022: ncrcat -n 31,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201301-201301_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.58/SEDNA-DELTA_1d_gridS

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Feb-01 10:20:00 GMT

  title :

  ocean T grid variables

  uuid :

  a7eddcc9-d16c-44f0-a2ea-c635e489b218

#3 Start computing 
data= data
monitor.optimize_dataset(data)
add optimise here once otimise can recognise

<xarray.Dataset>
Dimensions:   (t: 31, z: 102)
Coordinates:
  * t         (t) object 2013-01-01 12:00:00 ... 2013-01-31 12:00:00
    y         int64 4967
    x         int64 2471
  * z         (z) int64 1 2 3 4 5 6 7 8 9 10 ... 94 95 96 97 98 99 100 101 102
    nav_lat   float32 dask.array<chunksize=(), meta=np.ndarray>
    depth     (z) float32 dask.array<chunksize=(102,), meta=np.ndarray>
    mask      (z) bool dask.array<chunksize=(102,), meta=np.ndarray>
    mask2d    bool dask.array<chunksize=(), meta=np.ndarray>
    nav_lon   float32 dask.array<chunksize=(), meta=np.ndarray>
Data variables:
    vosaline  (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray>
    votemper  (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray>
Attributes: (12/26)
    CASE:                    DELTA
    CONFIG:                  SEDNA
    Conventions:             CF-1.6
    DOMAIN_dimensions_ids:   [2, 3]
    DOMAIN_halo_size_end:    [0, 0]
    DOMAIN_halo_size_start:  [0, 0]
    ...                      ...
    nj:                      13
    output_frequency:        1d
    start_date:              20090101
    timeStamp:               2022-Feb-01 10:20:00 GMT
    title:                   ocean T grid variables
    uuid:                    a7eddcc9-d16c-44f0-a2ea-c635e489b218

xarray.Dataset

• Dimensions:
  • t: 31
  • z: 102
• Coordinates: (9)
  • t
    (t)
    object
    2013-01-01 12:00:00 ... 2013-01-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2013, 1, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 31, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • y
    ()
    int64
    4967

    array(4967)

  • x
    ()
    int64
    2471

    array(2471)

  • z
    (z)
    int64
    1 2 3 4 5 6 ... 98 99 100 101 102

    array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
            15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
            29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,
            43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,
            57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,
            71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,
            85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
            99, 100, 101, 102])

  • nav_lat
    ()
    float32
    dask.array<chunksize=(), meta=np.ndarray>

    Array Chunk Bytes 4 B 4 B Shape () () Count 1743 Tasks 1 Chunks Type float32 numpy.ndarray

  • depth
    (z)
    float32
    dask.array<chunksize=(102,), meta=np.ndarray>

    Array Chunk Bytes 408 B 408 B Shape (102,) (102,) Count 1744 Tasks 1 Chunks Type float32 numpy.ndarray

  • mask
    (z)
    bool
    dask.array<chunksize=(102,), meta=np.ndarray>

    Array Chunk Bytes 102 B 102 B Shape (102,) (102,) Count 1744 Tasks 1 Chunks Type bool numpy.ndarray

  • mask2d
    ()
    bool
    dask.array<chunksize=(), meta=np.ndarray>

    Array Chunk Bytes 1 B 1 B Shape () () Count 1743 Tasks 1 Chunks Type bool numpy.ndarray

  • nav_lon
    ()
    float32
    dask.array<chunksize=(), meta=np.ndarray>

    Array Chunk Bytes 4 B 4 B Shape () () Count 1743 Tasks 1 Chunks Type float32 numpy.ndarray

• Data variables: (2)
  • vosaline
    (t, z)
    float32
    dask.array<chunksize=(1, 102), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    salinity

    online_operation :

    average

    standard_name :

    sea_water_practical_salinity

    units :

    1e-3

    Array Chunk Bytes 12.35 kiB 408 B Shape (31, 102) (1, 102) Count 425 Tasks 31 Chunks Type float32 numpy.ndarray

  • votemper
    (t, z)
    float32
    dask.array<chunksize=(1, 102), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    temperature

    online_operation :

    average

    standard_name :

    sea_water_potential_temperature

    units :

    degC

    Array Chunk Bytes 12.35 kiB 408 B Shape (31, 102) (1, 102) Count 425 Tasks 31 Chunks Type float32 numpy.ndarray

• Attributes: (26)

  CASE :

  DELTA

  CONFIG :

  SEDNA

  Conventions :

  CF-1.6

  DOMAIN_dimensions_ids :

  [2, 3]

  DOMAIN_halo_size_end :

  [0, 0]

  DOMAIN_halo_size_start :

  [0, 0]

  DOMAIN_number :

  0

  DOMAIN_number_total :

  544

  DOMAIN_position_first :

  [1, 1]

  DOMAIN_position_last :

  [6560, 13]

  DOMAIN_size_global :

  [6560, 6540]

  DOMAIN_size_local :

  [6560, 13]

  DOMAIN_type :

  box

  NCO :

  netCDF Operators version 4.9.1 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

  description :

  ocean T grid variables

  history :

  Wed Feb 2 11:31:06 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201301-201301_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2013/01/SEDNA-DELTA_1d_gridS_201301-201301_0000.nc Wed Feb 2 11:29:41 2022: ncrcat -n 31,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201301-201301_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.58/SEDNA-DELTA_1d_gridS

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Feb-01 10:20:00 GMT

  title :

  ocean T grid variables

  uuid :

  a7eddcc9-d16c-44f0-a2ea-c635e489b218

#4 Saving  SEDNA_Mooring_Arc_B_Mooring_Arc_B
data=save.datas(data,plot=Plot,path=nc_outputpath,filename=filename)
start saving data
saving data in a  csv file ../nc_results/SEDNA_DELTA_MONITOR/SEDNA_Mooring_Arc_B_Mooring_Arc_B2013-01-01_2013-01-31.nc
save computed data at ../nc_results/SEDNA_DELTA_MONITOR/SEDNA_Mooring_Arc_B_Mooring_Arc_B2013-01-01_2013-01-31.nc completed
Value='Mooring_Eur_B'
Zone='Eur_B'
Plot='Mooring'
cmap='rainbow'
clabel='None'
clim= {'vosaline': (32.0, 35.0), 'votemper': (-2.0, 3.0)}
outputpath='../results/SEDNA_DELTA_MONITOR/'
nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/'
filename='SEDNA_Mooring_Eur_B_Mooring_Eur_B'
data=monitor.optimize_dataset(data)
#2 Zooming Data
data= zoom.Eur_B(data)
data=monitor.optimize_dataset(data)

<xarray.Dataset>
Dimensions:   (t: 31, z: 102)
Coordinates:
  * t         (t) object 2013-01-01 12:00:00 ... 2013-01-31 12:00:00
    y         int64 3621
    x         int64 2495
  * z         (z) int64 1 2 3 4 5 6 7 8 9 10 ... 94 95 96 97 98 99 100 101 102
    nav_lat   float32 dask.array<chunksize=(), meta=np.ndarray>
    depth     (z) float32 dask.array<chunksize=(102,), meta=np.ndarray>
    mask      (z) bool dask.array<chunksize=(102,), meta=np.ndarray>
    mask2d    bool dask.array<chunksize=(), meta=np.ndarray>
    nav_lon   float32 dask.array<chunksize=(), meta=np.ndarray>
Data variables:
    vosaline  (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray>
    votemper  (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray>
Attributes: (12/26)
    CASE:                    DELTA
    CONFIG:                  SEDNA
    Conventions:             CF-1.6
    DOMAIN_dimensions_ids:   [2, 3]
    DOMAIN_halo_size_end:    [0, 0]
    DOMAIN_halo_size_start:  [0, 0]
    ...                      ...
    nj:                      13
    output_frequency:        1d
    start_date:              20090101
    timeStamp:               2022-Feb-01 10:20:00 GMT
    title:                   ocean T grid variables
    uuid:                    a7eddcc9-d16c-44f0-a2ea-c635e489b218

xarray.Dataset

• Dimensions:
  • t: 31
  • z: 102
• Coordinates: (9)
  • t
    (t)
    object
    2013-01-01 12:00:00 ... 2013-01-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2013, 1, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 31, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • y
    ()
    int64
    3621

    array(3621)

  • x
    ()
    int64
    2495

    array(2495)

  • z
    (z)
    int64
    1 2 3 4 5 6 ... 98 99 100 101 102

    array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
            15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
            29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,
            43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,
            57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,
            71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,
            85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
            99, 100, 101, 102])

  • nav_lat
    ()
    float32
    dask.array<chunksize=(), meta=np.ndarray>

    Array Chunk Bytes 4 B 4 B Shape () () Count 1743 Tasks 1 Chunks Type float32 numpy.ndarray

  • depth
    (z)
    float32
    dask.array<chunksize=(102,), meta=np.ndarray>

    Array Chunk Bytes 408 B 408 B Shape (102,) (102,) Count 1744 Tasks 1 Chunks Type float32 numpy.ndarray

  • mask
    (z)
    bool
    dask.array<chunksize=(102,), meta=np.ndarray>

    Array Chunk Bytes 102 B 102 B Shape (102,) (102,) Count 1744 Tasks 1 Chunks Type bool numpy.ndarray

  • mask2d
    ()
    bool
    dask.array<chunksize=(), meta=np.ndarray>

    Array Chunk Bytes 1 B 1 B Shape () () Count 1743 Tasks 1 Chunks Type bool numpy.ndarray

  • nav_lon
    ()
    float32
    dask.array<chunksize=(), meta=np.ndarray>

    Array Chunk Bytes 4 B 4 B Shape () () Count 1743 Tasks 1 Chunks Type float32 numpy.ndarray

• Data variables: (2)
  • vosaline
    (t, z)
    float32
    dask.array<chunksize=(1, 102), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    salinity

    online_operation :

    average

    standard_name :

    sea_water_practical_salinity

    units :

    1e-3

    Array Chunk Bytes 12.35 kiB 408 B Shape (31, 102) (1, 102) Count 425 Tasks 31 Chunks Type float32 numpy.ndarray

  • votemper
    (t, z)
    float32
    dask.array<chunksize=(1, 102), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    temperature

    online_operation :

    average

    standard_name :

    sea_water_potential_temperature

    units :

    degC

    Array Chunk Bytes 12.35 kiB 408 B Shape (31, 102) (1, 102) Count 425 Tasks 31 Chunks Type float32 numpy.ndarray

• Attributes: (26)

  CASE :

  DELTA

  CONFIG :

  SEDNA

  Conventions :

  CF-1.6

  DOMAIN_dimensions_ids :

  [2, 3]

  DOMAIN_halo_size_end :

  [0, 0]

  DOMAIN_halo_size_start :

  [0, 0]

  DOMAIN_number :

  0

  DOMAIN_number_total :

  544

  DOMAIN_position_first :

  [1, 1]

  DOMAIN_position_last :

  [6560, 13]

  DOMAIN_size_global :

  [6560, 6540]

  DOMAIN_size_local :

  [6560, 13]

  DOMAIN_type :

  box

  NCO :

  netCDF Operators version 4.9.1 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

  description :

  ocean T grid variables

  history :

  Wed Feb 2 11:31:06 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201301-201301_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2013/01/SEDNA-DELTA_1d_gridS_201301-201301_0000.nc Wed Feb 2 11:29:41 2022: ncrcat -n 31,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201301-201301_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.58/SEDNA-DELTA_1d_gridS

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Feb-01 10:20:00 GMT

  title :

  ocean T grid variables

  uuid :

  a7eddcc9-d16c-44f0-a2ea-c635e489b218

#3 Start computing 
data= data
monitor.optimize_dataset(data)
add optimise here once otimise can recognise

<xarray.Dataset>
Dimensions:   (t: 31, z: 102)
Coordinates:
  * t         (t) object 2013-01-01 12:00:00 ... 2013-01-31 12:00:00
    y         int64 3621
    x         int64 2495
  * z         (z) int64 1 2 3 4 5 6 7 8 9 10 ... 94 95 96 97 98 99 100 101 102
    nav_lat   float32 dask.array<chunksize=(), meta=np.ndarray>
    depth     (z) float32 dask.array<chunksize=(102,), meta=np.ndarray>
    mask      (z) bool dask.array<chunksize=(102,), meta=np.ndarray>
    mask2d    bool dask.array<chunksize=(), meta=np.ndarray>
    nav_lon   float32 dask.array<chunksize=(), meta=np.ndarray>
Data variables:
    vosaline  (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray>
    votemper  (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray>
Attributes: (12/26)
    CASE:                    DELTA
    CONFIG:                  SEDNA
    Conventions:             CF-1.6
    DOMAIN_dimensions_ids:   [2, 3]
    DOMAIN_halo_size_end:    [0, 0]
    DOMAIN_halo_size_start:  [0, 0]
    ...                      ...
    nj:                      13
    output_frequency:        1d
    start_date:              20090101
    timeStamp:               2022-Feb-01 10:20:00 GMT
    title:                   ocean T grid variables
    uuid:                    a7eddcc9-d16c-44f0-a2ea-c635e489b218

xarray.Dataset

• Dimensions:
  • t: 31
  • z: 102
• Coordinates: (9)
  • t
    (t)
    object
    2013-01-01 12:00:00 ... 2013-01-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2013, 1, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2013, 1, 31, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • y
    ()
    int64
    3621

    array(3621)

  • x
    ()
    int64
    2495

    array(2495)

  • z
    (z)
    int64
    1 2 3 4 5 6 ... 98 99 100 101 102

    array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
            15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
            29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,
            43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,
            57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,
            71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,
            85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
            99, 100, 101, 102])

  • nav_lat
    ()
    float32
    dask.array<chunksize=(), meta=np.ndarray>

    Array Chunk Bytes 4 B 4 B Shape () () Count 1743 Tasks 1 Chunks Type float32 numpy.ndarray

  • depth
    (z)
    float32
    dask.array<chunksize=(102,), meta=np.ndarray>

    Array Chunk Bytes 408 B 408 B Shape (102,) (102,) Count 1744 Tasks 1 Chunks Type float32 numpy.ndarray

  • mask
    (z)
    bool
    dask.array<chunksize=(102,), meta=np.ndarray>

    Array Chunk Bytes 102 B 102 B Shape (102,) (102,) Count 1744 Tasks 1 Chunks Type bool numpy.ndarray

  • mask2d
    ()
    bool
    dask.array<chunksize=(), meta=np.ndarray>

    Array Chunk Bytes 1 B 1 B Shape () () Count 1743 Tasks 1 Chunks Type bool numpy.ndarray

  • nav_lon
    ()
    float32
    dask.array<chunksize=(), meta=np.ndarray>

    Array Chunk Bytes 4 B 4 B Shape () () Count 1743 Tasks 1 Chunks Type float32 numpy.ndarray

• Data variables: (2)
  • vosaline
    (t, z)
    float32
    dask.array<chunksize=(1, 102), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    salinity

    online_operation :

    average

    standard_name :

    sea_water_practical_salinity

    units :

    1e-3

    Array Chunk Bytes 12.35 kiB 408 B Shape (31, 102) (1, 102) Count 425 Tasks 31 Chunks Type float32 numpy.ndarray

  • votemper
    (t, z)
    float32
    dask.array<chunksize=(1, 102), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    temperature

    online_operation :

    average

    standard_name :

    sea_water_potential_temperature

    units :

    degC

    Array Chunk Bytes 12.35 kiB 408 B Shape (31, 102) (1, 102) Count 425 Tasks 31 Chunks Type float32 numpy.ndarray

• Attributes: (26)

  CASE :

  DELTA

  CONFIG :

  SEDNA

  Conventions :

  CF-1.6

  DOMAIN_dimensions_ids :

  [2, 3]

  DOMAIN_halo_size_end :

  [0, 0]

  DOMAIN_halo_size_start :

  [0, 0]

  DOMAIN_number :

  0

  DOMAIN_number_total :

  544

  DOMAIN_position_first :

  [1, 1]

  DOMAIN_position_last :

  [6560, 13]

  DOMAIN_size_global :

  [6560, 6540]

  DOMAIN_size_local :

  [6560, 13]

  DOMAIN_type :

  box

  NCO :

  netCDF Operators version 4.9.1 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

  description :

  ocean T grid variables

  history :

  Wed Feb 2 11:31:06 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201301-201301_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2013/01/SEDNA-DELTA_1d_gridS_201301-201301_0000.nc Wed Feb 2 11:29:41 2022: ncrcat -n 31,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201301-201301_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.58/SEDNA-DELTA_1d_gridS

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Feb-01 10:20:00 GMT

  title :

  ocean T grid variables

  uuid :

  a7eddcc9-d16c-44f0-a2ea-c635e489b218

#4 Saving  SEDNA_Mooring_Eur_B_Mooring_Eur_B
data=save.datas(data,plot=Plot,path=nc_outputpath,filename=filename)
start saving data
saving data in a  csv file ../nc_results/SEDNA_DELTA_MONITOR/SEDNA_Mooring_Eur_B_Mooring_Eur_B2013-01-01_2013-01-31.nc
save computed data at ../nc_results/SEDNA_DELTA_MONITOR/SEDNA_Mooring_Eur_B_Mooring_Eur_B2013-01-01_2013-01-31.nc completed
CPU times: user 11.9 s, sys: 1.31 s, total: 13.2 s
Wall time: 50.2 s