%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/odakatin/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= irene5277.c-irene.mg1.tgcc.ccc.cea.fr starting dask cluster on local= True workers 16
10000000000
rome local cluster starting
This code is running on  irene5277.c-irene.mg1.tgcc.ccc.cea.fr using  SEDNA_DELTA_MONITOR file experiment, read from  ../lib/SEDNA_DELTA_MONITOR.yaml  on year= 2012  on month= 03  outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/6419614irene5277.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_03M_Mean_temp_velo/
CPU times: user 598 ms, sys: 158 ms, total: 756 ms
Wall time: 24.2 s

Client

Client-9f3b2a93-13e4-11ed-8cf9-080038b93a47

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

Cluster Info

LocalCluster

5434b976

Dashboard: http://127.0.0.1:8787/status	Workers: 16
Total threads: 128	Total memory: 251.06 GiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-c91a3dc5-8981-4ff7-b554-9e04a650f28b

Comm: tcp://127.0.0.1:44660	Workers: 16
Dashboard: http://127.0.0.1:8787/status	Total threads: 128
Started: Just now	Total memory: 251.06 GiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:39936	Total threads: 8
Dashboard: http://127.0.0.1:33879/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:45363
Local directory: /tmp/dask-worker-space/worker-evolgsl3

Worker: 1

Comm: tcp://127.0.0.1:36389	Total threads: 8
Dashboard: http://127.0.0.1:35909/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:34012
Local directory: /tmp/dask-worker-space/worker-84rxvtm1

Worker: 2

Comm: tcp://127.0.0.1:37601	Total threads: 8
Dashboard: http://127.0.0.1:46744/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:46865
Local directory: /tmp/dask-worker-space/worker-gwwy_pb4

Worker: 3

Comm: tcp://127.0.0.1:45329	Total threads: 8
Dashboard: http://127.0.0.1:39392/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:35740
Local directory: /tmp/dask-worker-space/worker-gnxi7hoo

Worker: 4

Comm: tcp://127.0.0.1:46741	Total threads: 8
Dashboard: http://127.0.0.1:41697/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:41392
Local directory: /tmp/dask-worker-space/worker-62vmi8nm

Worker: 5

Comm: tcp://127.0.0.1:39973	Total threads: 8
Dashboard: http://127.0.0.1:43836/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:41670
Local directory: /tmp/dask-worker-space/worker-hqkr2987

Worker: 6

Comm: tcp://127.0.0.1:41895	Total threads: 8
Dashboard: http://127.0.0.1:35442/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:34432
Local directory: /tmp/dask-worker-space/worker-xjwsfp9r

Worker: 7

Comm: tcp://127.0.0.1:39886	Total threads: 8
Dashboard: http://127.0.0.1:45602/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:35779
Local directory: /tmp/dask-worker-space/worker-2neptvky

Worker: 8

Comm: tcp://127.0.0.1:37511	Total threads: 8
Dashboard: http://127.0.0.1:42923/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:37848
Local directory: /tmp/dask-worker-space/worker-pivg0qtg

Worker: 9

Comm: tcp://127.0.0.1:40919	Total threads: 8
Dashboard: http://127.0.0.1:33375/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:44498
Local directory: /tmp/dask-worker-space/worker-1g5159g2

Worker: 10

Comm: tcp://127.0.0.1:43374	Total threads: 8
Dashboard: http://127.0.0.1:46236/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:40471
Local directory: /tmp/dask-worker-space/worker-qmaq5uce

Worker: 11

Comm: tcp://127.0.0.1:35887	Total threads: 8
Dashboard: http://127.0.0.1:41260/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:34529
Local directory: /tmp/dask-worker-space/worker-z0oiqflg

Worker: 12

Comm: tcp://127.0.0.1:36597	Total threads: 8
Dashboard: http://127.0.0.1:40559/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:40824
Local directory: /tmp/dask-worker-space/worker-u33kqjxr

Worker: 13

Comm: tcp://127.0.0.1:44705	Total threads: 8
Dashboard: http://127.0.0.1:33408/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:41245
Local directory: /tmp/dask-worker-space/worker-t117vnq8

Worker: 14

Comm: tcp://127.0.0.1:36639	Total threads: 8
Dashboard: http://127.0.0.1:32889/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:45861
Local directory: /tmp/dask-worker-space/worker-7etnb9gh

Worker: 15

Comm: tcp://127.0.0.1:43522	Total threads: 8
Dashboard: http://127.0.0.1:38951/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:43976
Local directory: /tmp/dask-worker-space/worker-ukz8kc4b

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
	Mean Temp & Velocity	gridV.vomecrty,gridT.votemper,param.mask,param...	calc.Mean_temp_velo(data)	FramS_Small	Mean_temp_velo_integrals	None	((0,4),(0,10))	(°C,cm.s^-1)	I-5

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/odakatin/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 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/201203/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/odakatin/monitor-sedna/notebook/../lib/
>
      took 20.636799812316895 seconds
0 merging gridT ['votemper']
1 read gridV ['vomecrty']
lazy= False
using load_data_xios_kerchunk reading  gridV
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/201203/gridV_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/odakatin/monitor-sedna/notebook/../lib/
>
      took 43.440168142318726 seconds
1 merging gridV ['vomecrty']
      took 0.7720010280609131 seconds
param depth will be included in data
param nav_lon will be included in data
param nav_lat will be included in data
param mask2d will be included in data
param mask 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 38.7 s, sys: 5.46 s, total: 44.1 s
Wall time: 1min 31s

<xarray.Dataset>
Dimensions:        (t: 31, z: 150, y: 6540, x: 6560)
Coordinates:
    time_centered  (t) object dask.array<chunksize=(1,), meta=np.ndarray>
  * t              (t) object 2012-03-01 12:00:00 ... 2012-03-31 12:00:00
  * y              (y) int64 1 2 3 4 5 6 7 ... 6535 6536 6537 6538 6539 6540
  * x              (x) int64 1 2 3 4 5 6 7 ... 6555 6556 6557 6558 6559 6560
  * z              (z) int64 1 2 3 4 5 6 7 8 ... 143 144 145 146 147 148 149 150
    depth          (z, y, x) float32 dask.array<chunksize=(150, 65, 6560), meta=np.ndarray>
    nav_lon        (y, x) float32 dask.array<chunksize=(65, 6560), meta=np.ndarray>
    nav_lat        (y, x) float32 dask.array<chunksize=(65, 6560), meta=np.ndarray>
    mask2d         (y, x) bool dask.array<chunksize=(65, 6560), meta=np.ndarray>
    mask           (z, y, x) bool dask.array<chunksize=(150, 65, 6560), meta=np.ndarray>
Data variables:
    votemper       (t, z, y, x) float32 dask.array<chunksize=(1, 150, 65, 6560), meta=np.ndarray>
    vomecrty       (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-Jan-19 12:40:31 GMT
    title:                   ocean T grid variables
    uuid:                    233fa8eb-c3dc-48ee-bca1-177776645a29

xarray.Dataset

• Dimensions:
  • t: 31
  • z: 150
  • y: 6540
  • x: 6560
• Coordinates: (10)
  • time_centered
    (t)
    object
    dask.array<chunksize=(1,), meta=np.ndarray>

    bounds :

    time_centered_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    Array Chunk Bytes 248 B 8 B Shape (31,) (1,) Count 189 Tasks 31 Chunks Type object numpy.ndarray

  • t
    (t)
    object
    2012-03-01 12:00:00 ... 2012-03-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2012, 3, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 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])

  • 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

  • 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

  • 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

  • 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

  • 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

• Data variables: (2)
  • 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

  • vomecrty
    (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 :

    ocean current along j-axis

    online_operation :

    average

    standard_name :

    sea_water_y_velocity

    units :

    m/s

    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 :

  Thu Jan 20 11:43:58 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridT_201203-201203_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2012/03/SEDNA-DELTA_1d_gridT_201203-201203_0000.nc Thu Jan 20 11:43:30 2022: ncrcat -n 31,2,1 SEDNA-DELTA_1d_gridT_0000_01.nc SEDNA-DELTA_1d_gridT_201203-201203_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.48/SEDNA-DELTA_1d_gridT

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Jan-19 12:40:31 GMT

  title :

  ocean T grid variables

  uuid :

  233fa8eb-c3dc-48ee-bca1-177776645a29

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

#calc= True
#save= True
#plot= False
Value='Mean Temp & Velocity'
Zone='FramS_Small'
Plot='Mean_temp_velo_integrals'
cmap='None'
clabel='(°C,cm.s^-1)'
clim= ((0, 4), (0, 10))
outputpath='../results/SEDNA_DELTA_MONITOR/'
nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/'
filename='SEDNA_Mean_temp_velo_integrals_FramS_Small_Mean_Temp_&_Velocity'
data=monitor.optimize_dataset(data)
#2 Zooming Data
data= zoom.FramS_Small(data)
data=monitor.optimize_dataset(data)

<xarray.Dataset>
Dimensions:        (t: 31, z: 150, x: 601)
Coordinates:
    time_centered  (t) object dask.array<chunksize=(1,), meta=np.ndarray>
  * t              (t) object 2012-03-01 12:00:00 ... 2012-03-31 12:00:00
    y              int64 2609
  * x              (x) int64 3734 3735 3736 3737 3738 ... 4331 4332 4333 4334
  * z              (z) int64 1 2 3 4 5 6 7 8 ... 143 144 145 146 147 148 149 150
    depth          (z, x) float32 dask.array<chunksize=(150, 601), meta=np.ndarray>
    nav_lon        (x) float32 dask.array<chunksize=(601,), meta=np.ndarray>
    nav_lat        (x) float32 dask.array<chunksize=(601,), meta=np.ndarray>
    mask2d         (x) bool dask.array<chunksize=(601,), meta=np.ndarray>
    mask           (z, x) bool dask.array<chunksize=(150, 601), meta=np.ndarray>
Data variables:
    votemper       (t, z, x) float32 dask.array<chunksize=(1, 150, 601), meta=np.ndarray>
    vomecrty       (t, z, x) float32 dask.array<chunksize=(1, 150, 601), 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-Jan-19 12:40:31 GMT
    title:                   ocean T grid variables
    uuid:                    233fa8eb-c3dc-48ee-bca1-177776645a29

xarray.Dataset

• Dimensions:
  • t: 31
  • z: 150
  • x: 601
• Coordinates: (10)
  • time_centered
    (t)
    object
    dask.array<chunksize=(1,), meta=np.ndarray>

    bounds :

    time_centered_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    Array Chunk Bytes 248 B 8 B Shape (31,) (1,) Count 189 Tasks 31 Chunks Type object numpy.ndarray

  • t
    (t)
    object
    2012-03-01 12:00:00 ... 2012-03-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2012, 3, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 31, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • y
    ()
    int64
    2609

    array(2609)

  • x
    (x)
    int64
    3734 3735 3736 ... 4332 4333 4334

    array([3734, 3735, 3736, ..., 4332, 4333, 4334])

  • 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])

  • depth
    (z, x)
    float32
    dask.array<chunksize=(150, 601), meta=np.ndarray>

    Array Chunk Bytes 352.15 kiB 352.15 kiB Shape (150, 601) (150, 601) Count 1742 Tasks 1 Chunks Type float32 numpy.ndarray

  • nav_lon
    (x)
    float32
    dask.array<chunksize=(601,), meta=np.ndarray>

    Array Chunk Bytes 2.35 kiB 2.35 kiB Shape (601,) (601,) Count 1742 Tasks 1 Chunks Type float32 numpy.ndarray

  • nav_lat
    (x)
    float32
    dask.array<chunksize=(601,), meta=np.ndarray>

    Array Chunk Bytes 2.35 kiB 2.35 kiB Shape (601,) (601,) Count 1742 Tasks 1 Chunks Type float32 numpy.ndarray

  • mask2d
    (x)
    bool
    dask.array<chunksize=(601,), meta=np.ndarray>

    Array Chunk Bytes 601 B 601 B Shape (601,) (601,) Count 1742 Tasks 1 Chunks Type bool numpy.ndarray

  • mask
    (z, x)
    bool
    dask.array<chunksize=(150, 601), meta=np.ndarray>

    Array Chunk Bytes 88.04 kiB 88.04 kiB Shape (150, 601) (150, 601) Count 1742 Tasks 1 Chunks Type bool numpy.ndarray

• Data variables: (2)
  • votemper
    (t, z, x)
    float32
    dask.array<chunksize=(1, 150, 601), 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 10.66 MiB 352.15 kiB Shape (31, 150, 601) (1, 150, 601) Count 459 Tasks 31 Chunks Type float32 numpy.ndarray

  • vomecrty
    (t, z, x)
    float32
    dask.array<chunksize=(1, 150, 601), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    ocean current along j-axis

    online_operation :

    average

    standard_name :

    sea_water_y_velocity

    units :

    m/s

    Array Chunk Bytes 10.66 MiB 352.15 kiB Shape (31, 150, 601) (1, 150, 601) Count 459 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 :

  Thu Jan 20 11:43:58 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridT_201203-201203_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2012/03/SEDNA-DELTA_1d_gridT_201203-201203_0000.nc Thu Jan 20 11:43:30 2022: ncrcat -n 31,2,1 SEDNA-DELTA_1d_gridT_0000_01.nc SEDNA-DELTA_1d_gridT_201203-201203_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.48/SEDNA-DELTA_1d_gridT

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Jan-19 12:40:31 GMT

  title :

  ocean T grid variables

  uuid :

  233fa8eb-c3dc-48ee-bca1-177776645a29

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

<xarray.Dataset>
Dimensions:          (t: 31)
Coordinates:
    time_centered    (t) object dask.array<chunksize=(1,), meta=np.ndarray>
  * t                (t) object 2012-03-01 12:00:00 ... 2012-03-31 12:00:00
    y                int64 2609
Data variables:
    Mean Tempreture  (t) float32 dask.array<chunksize=(1,), meta=np.ndarray>
    Mean Velocity    (t) float32 dask.array<chunksize=(1,), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • t: 31
• Coordinates: (3)
  • time_centered
    (t)
    object
    dask.array<chunksize=(1,), meta=np.ndarray>

    bounds :

    time_centered_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    Array Chunk Bytes 248 B 8 B Shape (31,) (1,) Count 189 Tasks 31 Chunks Type object numpy.ndarray

  • t
    (t)
    object
    2012-03-01 12:00:00 ... 2012-03-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2012, 3, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 31, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • y
    ()
    int64
    2609

    array(2609)

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

    Array Chunk Bytes 124 B 4 B Shape (31,) (1,) Count 2295 Tasks 31 Chunks Type float32 numpy.ndarray

  • Mean Velocity
    (t)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 124 B 4 B Shape (31,) (1,) Count 2326 Tasks 31 Chunks Type float32 numpy.ndarray

• Attributes: (0)

#4 Saving  SEDNA_Mean_temp_velo_integrals_FramS_Small_Mean_Temp_&_Velocity
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_Mean_temp_velo_integrals_FramS_Small_Mean_Temp_&_Velocity2012-03-01_2012-03-31.nc
save computed data at ../nc_results/SEDNA_DELTA_MONITOR/SEDNA_Mean_temp_velo_integrals_FramS_Small_Mean_Temp_&_Velocity2012-03-01_2012-03-31.nc completed
CPU times: user 5.37 s, sys: 429 ms, total: 5.8 s
Wall time: 10.3 s