%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= irene6095.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  irene6095.c-irene.mg1.tgcc.ccc.cea.fr using  SEDNA_DELTA_MONITOR file experiment, read from  ../lib/SEDNA_DELTA_MONITOR.yaml  on year= 2015  on month= 09  outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/7449096irene6095.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_09M_Fluxnet/
CPU times: user 386 ms, sys: 116 ms, total: 503 ms
Wall time: 12.9 s

Client

Client-d171d8ed-6f42-11ed-8625-080038b93f55

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

Cluster Info

LocalCluster

fcfd047f

Dashboard: http://127.0.0.1:8787/status	Workers: 16
Total threads: 128	Total memory: 221.88 GiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-1b8c553a-32c9-4ea9-a8f4-97cdf2c0f1bc

Comm: tcp://127.0.0.1:44382	Workers: 16
Dashboard: http://127.0.0.1:8787/status	Total threads: 128
Started: Just now	Total memory: 221.88 GiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:44168	Total threads: 8
Dashboard: http://127.0.0.1:38394/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:46671
Local directory: /tmp/dask-worker-space/worker-gju5_6hb

Worker: 1

Comm: tcp://127.0.0.1:34284	Total threads: 8
Dashboard: http://127.0.0.1:41728/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:37336
Local directory: /tmp/dask-worker-space/worker-ws1qmn4y

Worker: 2

Comm: tcp://127.0.0.1:43556	Total threads: 8
Dashboard: http://127.0.0.1:41324/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:34639
Local directory: /tmp/dask-worker-space/worker-55u7suky

Worker: 3

Comm: tcp://127.0.0.1:36304	Total threads: 8
Dashboard: http://127.0.0.1:38106/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:38063
Local directory: /tmp/dask-worker-space/worker-ooe9n4ir

Worker: 4

Comm: tcp://127.0.0.1:36246	Total threads: 8
Dashboard: http://127.0.0.1:35036/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:37724
Local directory: /tmp/dask-worker-space/worker-jh37pn_9

Worker: 5

Comm: tcp://127.0.0.1:43869	Total threads: 8
Dashboard: http://127.0.0.1:35695/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:41645
Local directory: /tmp/dask-worker-space/worker-1okx75e2

Worker: 6

Comm: tcp://127.0.0.1:33949	Total threads: 8
Dashboard: http://127.0.0.1:37685/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:40897
Local directory: /tmp/dask-worker-space/worker-6vmfnmvr

Worker: 7

Comm: tcp://127.0.0.1:38309	Total threads: 8
Dashboard: http://127.0.0.1:35566/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:42464
Local directory: /tmp/dask-worker-space/worker-uae5cgly

Worker: 8

Comm: tcp://127.0.0.1:45626	Total threads: 8
Dashboard: http://127.0.0.1:43078/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:39446
Local directory: /tmp/dask-worker-space/worker-dcqcu4mn

Worker: 9

Comm: tcp://127.0.0.1:39269	Total threads: 8
Dashboard: http://127.0.0.1:34778/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:34699
Local directory: /tmp/dask-worker-space/worker-zu8qgkg9

Worker: 10

Comm: tcp://127.0.0.1:44816	Total threads: 8
Dashboard: http://127.0.0.1:38950/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:41379
Local directory: /tmp/dask-worker-space/worker-09m7z05p

Worker: 11

Comm: tcp://127.0.0.1:38739	Total threads: 8
Dashboard: http://127.0.0.1:44157/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:39064
Local directory: /tmp/dask-worker-space/worker-ruuwbpvr

Worker: 12

Comm: tcp://127.0.0.1:34981	Total threads: 8
Dashboard: http://127.0.0.1:44609/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:45290
Local directory: /tmp/dask-worker-space/worker-p76tdj5w

Worker: 13

Comm: tcp://127.0.0.1:40183	Total threads: 8
Dashboard: http://127.0.0.1:35058/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:37353
Local directory: /tmp/dask-worker-space/worker-kwycixgb

Worker: 14

Comm: tcp://127.0.0.1:46203	Total threads: 8
Dashboard: http://127.0.0.1:45895/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:45976
Local directory: /tmp/dask-worker-space/worker-iqrov6y6

Worker: 15

Comm: tcp://127.0.0.1:38120	Total threads: 8
Dashboard: http://127.0.0.1:46130/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:42192
Local directory: /tmp/dask-worker-space/worker-vbcci0rb

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
	Fluxnet	gridV.vomecrty,param.e3v_0,param.e1v,param.mas...	calc.Fluxnet(data)	FramS_All	Fluxnet_integrals	None	((-10,10),(-10,50) ,(-150,50),(-25,5) )	(Sv,TW, mSv,10^-2 Sv)	I-6
	Fluxnet	gridV.vomecrty,param.e3v_0,param.e1v,param.mas...	calc.Fluxnet(data)	Davis	Fluxnet_integrals	None	((-5.0,5.0),(-25,27) ,(-200,50),(-9,5) )	(Sv,TW, mSv,10^-2 Sv)	I-6
	Fluxnet	gridV.vomecrty,param.e3v_0,param.e1v,param.mas...	calc.Fluxnet(data)	Bering	Fluxnet_integrals	None	((-2,2),(-10,50) ,(-150,50),(-2,4) )	(Sv,TW, mSv,10^-2 Sv)	I-6

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/201509/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 70.00293159484863 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/201509/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 69.74167680740356 seconds
1 merging gridT ['votemper']
      took 0.7334818840026855 seconds
2 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/201509/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/talandel/TOOLS/monitor-sedna/notebook/../lib/
>
      took 73.90355849266052 seconds
2 merging gridV ['vomecrty']
      took 0.759779691696167 seconds
3 read icemod ['sivelv', 'sivolu']
lazy= False
using load_data_xios_kerchunk reading  icemod
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/201509/icemod_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 30.924241542816162 seconds
3 merging icemod ['sivelv', 'sivolu']
      took 0.7457094192504883 seconds
param e3v_0 will be included in data
param mask2d will be included in data
param e1v will be included in data
param mask will be included in data
param nav_lon will be included in data
param nav_lat will be included in data
CPU times: user 1min 30s, sys: 12 s, total: 1min 42s
Wall time: 4min 22s

<xarray.Dataset>
Dimensions:   (t: 30, z: 150, y: 6540, x: 6560)
Coordinates:
  * t         (t) object 2015-09-01 12:00:00 ... 2015-09-30 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
    nav_lat   (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray>
    nav_lon   (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray>
  * z         (z) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150
    e3v_0     (z, y, x) float64 dask.array<chunksize=(150, 13, 6560), meta=np.ndarray>
    mask2d    (y, x) bool dask.array<chunksize=(13, 6560), meta=np.ndarray>
    e1v       (y, x) float64 dask.array<chunksize=(13, 6560), meta=np.ndarray>
    mask      (z, y, x) bool dask.array<chunksize=(150, 13, 6560), meta=np.ndarray>
Data variables:
    vosaline  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 13, 6560), meta=np.ndarray>
    votemper  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 13, 6560), meta=np.ndarray>
    vomecrty  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 13, 6560), meta=np.ndarray>
    sivelv    (t, y, x) float32 dask.array<chunksize=(1, 13, 6560), meta=np.ndarray>
    sivolu    (t, y, x) float32 dask.array<chunksize=(1, 13, 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-Aug-17 11:28:47 GMT
    title:                   ocean T grid variables
    uuid:                    2eda39a7-ffb1-4669-a4c2-754df35b3832

xarray.Dataset

• Dimensions:
  • t: 30
  • z: 150
  • y: 6540
  • x: 6560
• Coordinates: (10)
  • t
    (t)
    object
    2015-09-01 12:00:00 ... 2015-09-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2015, 9, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 30, 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])

  • nav_lat
    (y, x)
    float32
    dask.array<chunksize=(13, 6560), meta=np.ndarray>

    Array Chunk Bytes 163.66 MiB 333.12 kiB Shape (6540, 6560) (13, 6560) Count 1632 Tasks 544 Chunks Type float32 numpy.ndarray

  • nav_lon
    (y, x)
    float32
    dask.array<chunksize=(13, 6560), meta=np.ndarray>

    Array Chunk Bytes 163.66 MiB 333.12 kiB Shape (6540, 6560) (13, 6560) Count 1632 Tasks 544 Chunks Type float32 numpy.ndarray

  • 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])

  • e3v_0
    (z, y, x)
    float64
    dask.array<chunksize=(150, 13, 6560), meta=np.ndarray>

    Array Chunk Bytes 47.95 GiB 97.60 MiB Shape (150, 6540, 6560) (150, 13, 6560) Count 1632 Tasks 544 Chunks Type float64 numpy.ndarray

  • mask2d
    (y, x)
    bool
    dask.array<chunksize=(13, 6560), meta=np.ndarray>

    Array Chunk Bytes 40.91 MiB 83.28 kiB Shape (6540, 6560) (13, 6560) Count 1632 Tasks 544 Chunks Type bool numpy.ndarray

  • e1v
    (y, x)
    float64
    dask.array<chunksize=(13, 6560), meta=np.ndarray>

    Array Chunk Bytes 327.32 MiB 666.25 kiB Shape (6540, 6560) (13, 6560) Count 1632 Tasks 544 Chunks Type float64 numpy.ndarray

  • mask
    (z, y, x)
    bool
    dask.array<chunksize=(150, 13, 6560), meta=np.ndarray>

    Array Chunk Bytes 5.99 GiB 12.20 MiB Shape (150, 6540, 6560) (150, 13, 6560) Count 1632 Tasks 544 Chunks Type bool numpy.ndarray

• Data variables: (5)
  • vosaline
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 13, 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 719.21 GiB 48.80 MiB Shape (30, 150, 6540, 6560) (1, 150, 13, 6560) Count 33184 Tasks 16320 Chunks Type float32 numpy.ndarray

  • votemper
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 13, 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 719.21 GiB 48.80 MiB Shape (30, 150, 6540, 6560) (1, 150, 13, 6560) Count 33184 Tasks 16320 Chunks Type float32 numpy.ndarray

  • vomecrty
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 13, 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 719.21 GiB 48.80 MiB Shape (30, 150, 6540, 6560) (1, 150, 13, 6560) Count 33184 Tasks 16320 Chunks Type float32 numpy.ndarray

  • sivelv
    (t, y, x)
    float32
    dask.array<chunksize=(1, 13, 6560), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    Y-component of sea ice velocity

    online_operation :

    average

    standard_name :

    sea_ice_y_velocity

    units :

    m/s

    Array Chunk Bytes 4.79 GiB 333.12 kiB Shape (30, 6540, 6560) (1, 13, 6560) Count 33184 Tasks 16320 Chunks Type float32 numpy.ndarray

  • sivolu
    (t, y, x)
    float32
    dask.array<chunksize=(1, 13, 6560), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    ice volume

    online_operation :

    average

    standard_name :

    sea_ice_thickness

    units :

    m

    Array Chunk Bytes 4.79 GiB 333.12 kiB Shape (30, 6540, 6560) (1, 13, 6560) Count 33184 Tasks 16320 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 Aug 18 09:38:58 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201509-201509_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2015/09/SEDNA-DELTA_1d_gridS_201509-201509_0000.nc Thu Aug 18 09:38:27 2022: ncrcat -n 30,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201509-201509_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.91/SEDNA-DELTA_1d_gridS

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Aug-17 11:28:47 GMT

  title :

  ocean T grid variables

  uuid :

  2eda39a7-ffb1-4669-a4c2-754df35b3832

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

#calc= True
#save= True
#plot= False
Value='Fluxnet'
Zone='FramS_All'
Plot='Fluxnet_integrals'
cmap='None'
clabel='(Sv,TW, mSv,10^-2 Sv)'
clim= ((-10, 10), (-10, 50), (-150, 50), (-25, 5))
outputpath='../results/SEDNA_DELTA_MONITOR/'
nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/'
filename='SEDNA_Fluxnet_integrals_FramS_All_Fluxnet'
data=monitor.optimize_dataset(data)
#2 Zooming Data
data= zoom.FramS_All(data)
data=monitor.optimize_dataset(data)

<xarray.Dataset>
Dimensions:   (t: 30, z: 150, y: 2, x: 601)
Coordinates:
  * t         (t) object 2015-09-01 12:00:00 ... 2015-09-30 12:00:00
  * y         (y) int64 2608 2609
  * x         (x) int64 3734 3735 3736 3737 3738 ... 4330 4331 4332 4333 4334
    nav_lat   (y, x) float32 dask.array<chunksize=(2, 601), meta=np.ndarray>
    nav_lon   (y, x) float32 dask.array<chunksize=(2, 601), meta=np.ndarray>
  * z         (z) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150
    e3v_0     (z, y, x) float64 dask.array<chunksize=(150, 2, 601), meta=np.ndarray>
    mask2d    (y, x) bool dask.array<chunksize=(2, 601), meta=np.ndarray>
    e1v       (y, x) float64 dask.array<chunksize=(2, 601), meta=np.ndarray>
    mask      (z, y, x) bool dask.array<chunksize=(150, 2, 601), meta=np.ndarray>
Data variables:
    vosaline  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 2, 601), meta=np.ndarray>
    votemper  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 2, 601), meta=np.ndarray>
    vomecrty  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 2, 601), meta=np.ndarray>
    sivelv    (t, y, x, z) float32 dask.array<chunksize=(1, 2, 601, 150), meta=np.ndarray>
    sivolu    (t, y, x, z) float32 dask.array<chunksize=(1, 2, 601, 150), 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-Aug-17 11:28:47 GMT
    title:                   ocean T grid variables
    uuid:                    2eda39a7-ffb1-4669-a4c2-754df35b3832

xarray.Dataset

• Dimensions:
  • t: 30
  • z: 150
  • y: 2
  • x: 601
• Coordinates: (10)
  • t
    (t)
    object
    2015-09-01 12:00:00 ... 2015-09-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2015, 9, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 30, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • y
    (y)
    int64
    2608 2609

    array([2608, 2609])

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

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

  • nav_lat
    (y, x)
    float32
    dask.array<chunksize=(2, 601), meta=np.ndarray>

    Array Chunk Bytes 4.70 kiB 4.70 kiB Shape (2, 601) (2, 601) Count 1633 Tasks 1 Chunks Type float32 numpy.ndarray

  • nav_lon
    (y, x)
    float32
    dask.array<chunksize=(2, 601), meta=np.ndarray>

    Array Chunk Bytes 4.70 kiB 4.70 kiB Shape (2, 601) (2, 601) Count 1633 Tasks 1 Chunks Type float32 numpy.ndarray

  • 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])

  • e3v_0
    (z, y, x)
    float64
    dask.array<chunksize=(150, 2, 601), meta=np.ndarray>

    Array Chunk Bytes 1.38 MiB 1.38 MiB Shape (150, 2, 601) (150, 2, 601) Count 1633 Tasks 1 Chunks Type float64 numpy.ndarray

  • mask2d
    (y, x)
    bool
    dask.array<chunksize=(2, 601), meta=np.ndarray>

    Array Chunk Bytes 1.17 kiB 1.17 kiB Shape (2, 601) (2, 601) Count 1633 Tasks 1 Chunks Type bool numpy.ndarray

  • e1v
    (y, x)
    float64
    dask.array<chunksize=(2, 601), meta=np.ndarray>

    Array Chunk Bytes 9.39 kiB 9.39 kiB Shape (2, 601) (2, 601) Count 1633 Tasks 1 Chunks Type float64 numpy.ndarray

  • mask
    (z, y, x)
    bool
    dask.array<chunksize=(150, 2, 601), meta=np.ndarray>

    Array Chunk Bytes 176.07 kiB 176.07 kiB Shape (150, 2, 601) (150, 2, 601) Count 1633 Tasks 1 Chunks Type bool numpy.ndarray

• Data variables: (5)
  • vosaline
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 2, 601), 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 20.63 MiB 704.30 kiB Shape (30, 150, 2, 601) (1, 150, 2, 601) Count 94 Tasks 30 Chunks Type float32 numpy.ndarray

  • votemper
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 2, 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 20.63 MiB 704.30 kiB Shape (30, 150, 2, 601) (1, 150, 2, 601) Count 94 Tasks 30 Chunks Type float32 numpy.ndarray

  • vomecrty
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 2, 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 20.63 MiB 704.30 kiB Shape (30, 150, 2, 601) (1, 150, 2, 601) Count 94 Tasks 30 Chunks Type float32 numpy.ndarray

  • sivelv
    (t, y, x, z)
    float32
    dask.array<chunksize=(1, 2, 601, 150), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    Y-component of sea ice velocity

    online_operation :

    average

    standard_name :

    sea_ice_y_velocity

    units :

    m/s

    Array Chunk Bytes 20.63 MiB 704.30 kiB Shape (30, 2, 601, 150) (1, 2, 601, 150) Count 94 Tasks 30 Chunks Type float32 numpy.ndarray

  • sivolu
    (t, y, x, z)
    float32
    dask.array<chunksize=(1, 2, 601, 150), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    ice volume

    online_operation :

    average

    standard_name :

    sea_ice_thickness

    units :

    m

    Array Chunk Bytes 20.63 MiB 704.30 kiB Shape (30, 2, 601, 150) (1, 2, 601, 150) Count 94 Tasks 30 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 Aug 18 09:38:58 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201509-201509_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2015/09/SEDNA-DELTA_1d_gridS_201509-201509_0000.nc Thu Aug 18 09:38:27 2022: ncrcat -n 30,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201509-201509_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.91/SEDNA-DELTA_1d_gridS

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Aug-17 11:28:47 GMT

  title :

  ocean T grid variables

  uuid :

  2eda39a7-ffb1-4669-a4c2-754df35b3832

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

<xarray.Dataset>
Dimensions:                (t: 30)
Coordinates:
  * t                      (t) object 2015-09-01 12:00:00 ... 2015-09-30 12:0...
    y                      int64 2608
Data variables:
    Volume flux Net        (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Volume flux Northward  (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Heat flux Net          (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Heat flux Northward    (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Freshwater Net         (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Freshwater Northward   (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Ice export             (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Volume flux South      (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Heat flux South        (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Freshwater South       (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • t: 30
• Coordinates: (2)
  • t
    (t)
    object
    2015-09-01 12:00:00 ... 2015-09-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2015, 9, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 30, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • y
    ()
    int64
    2608

    array(2608)

• Data variables: (10)
  • Volume flux Net
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 5299 Tasks 30 Chunks Type float64 numpy.ndarray

  • Volume flux Northward
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 5359 Tasks 30 Chunks Type float64 numpy.ndarray

  • Heat flux Net
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6411 Tasks 30 Chunks Type float64 numpy.ndarray

  • Heat flux Northward
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6471 Tasks 30 Chunks Type float64 numpy.ndarray

  • Freshwater Net
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6411 Tasks 30 Chunks Type float64 numpy.ndarray

  • Freshwater Northward
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6471 Tasks 30 Chunks Type float64 numpy.ndarray

  • Ice export
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 4689 Tasks 30 Chunks Type float64 numpy.ndarray

  • Volume flux South
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 5539 Tasks 30 Chunks Type float64 numpy.ndarray

  • Heat flux South
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6681 Tasks 30 Chunks Type float64 numpy.ndarray

  • Freshwater South
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6681 Tasks 30 Chunks Type float64 numpy.ndarray

• Attributes: (0)

#4 Saving  SEDNA_Fluxnet_integrals_FramS_All_Fluxnet
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_Fluxnet_integrals_FramS_All_Fluxnet2015-09-01_2015-09-30.nc
save computed data at ../nc_results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet2015-09-01_2015-09-30.nc completed
Value='Fluxnet'
Zone='Davis'
Plot='Fluxnet_integrals'
cmap='None'
clabel='(Sv,TW, mSv,10^-2 Sv)'
clim= ((-5.0, 5.0), (-25, 27), (-200, 50), (-9, 5))
outputpath='../results/SEDNA_DELTA_MONITOR/'
nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/'
filename='SEDNA_Fluxnet_integrals_Davis_Fluxnet'
data=monitor.optimize_dataset(data)
#2 Zooming Data
data= zoom.Davis(data)
data=monitor.optimize_dataset(data)

<xarray.Dataset>
Dimensions:   (t: 30, z: 150, y: 2, x: 421)
Coordinates:
  * t         (t) object 2015-09-01 12:00:00 ... 2015-09-30 12:00:00
  * y         (y) int64 1308 1309
  * x         (x) int64 1749 1750 1751 1752 1753 ... 2165 2166 2167 2168 2169
    nav_lat   (y, x) float32 dask.array<chunksize=(1, 421), meta=np.ndarray>
    nav_lon   (y, x) float32 dask.array<chunksize=(1, 421), meta=np.ndarray>
  * z         (z) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150
    e3v_0     (z, y, x) float64 dask.array<chunksize=(150, 1, 421), meta=np.ndarray>
    mask2d    (y, x) bool dask.array<chunksize=(1, 421), meta=np.ndarray>
    e1v       (y, x) float64 dask.array<chunksize=(1, 421), meta=np.ndarray>
    mask      (z, y, x) bool dask.array<chunksize=(150, 1, 421), meta=np.ndarray>
Data variables:
    vosaline  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 1, 421), meta=np.ndarray>
    votemper  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 1, 421), meta=np.ndarray>
    vomecrty  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 1, 421), meta=np.ndarray>
    sivelv    (t, y, x, z) float32 dask.array<chunksize=(1, 1, 421, 150), meta=np.ndarray>
    sivolu    (t, y, x, z) float32 dask.array<chunksize=(1, 1, 421, 150), 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-Aug-17 11:28:47 GMT
    title:                   ocean T grid variables
    uuid:                    2eda39a7-ffb1-4669-a4c2-754df35b3832

xarray.Dataset

• Dimensions:
  • t: 30
  • z: 150
  • y: 2
  • x: 421
• Coordinates: (10)
  • t
    (t)
    object
    2015-09-01 12:00:00 ... 2015-09-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2015, 9, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 30, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • y
    (y)
    int64
    1308 1309

    array([1308, 1309])

  • x
    (x)
    int64
    1749 1750 1751 ... 2167 2168 2169

    array([1749, 1750, 1751, ..., 2167, 2168, 2169])

  • nav_lat
    (y, x)
    float32
    dask.array<chunksize=(1, 421), meta=np.ndarray>

    Array Chunk Bytes 3.29 kiB 1.64 kiB Shape (2, 421) (1, 421) Count 1634 Tasks 2 Chunks Type float32 numpy.ndarray

  • nav_lon
    (y, x)
    float32
    dask.array<chunksize=(1, 421), meta=np.ndarray>

    Array Chunk Bytes 3.29 kiB 1.64 kiB Shape (2, 421) (1, 421) Count 1634 Tasks 2 Chunks Type float32 numpy.ndarray

  • 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])

  • e3v_0
    (z, y, x)
    float64
    dask.array<chunksize=(150, 1, 421), meta=np.ndarray>

    Array Chunk Bytes 0.96 MiB 493.36 kiB Shape (150, 2, 421) (150, 1, 421) Count 1634 Tasks 2 Chunks Type float64 numpy.ndarray

  • mask2d
    (y, x)
    bool
    dask.array<chunksize=(1, 421), meta=np.ndarray>

    Array Chunk Bytes 842 B 421 B Shape (2, 421) (1, 421) Count 1634 Tasks 2 Chunks Type bool numpy.ndarray

  • e1v
    (y, x)
    float64
    dask.array<chunksize=(1, 421), meta=np.ndarray>

    Array Chunk Bytes 6.58 kiB 3.29 kiB Shape (2, 421) (1, 421) Count 1634 Tasks 2 Chunks Type float64 numpy.ndarray

  • mask
    (z, y, x)
    bool
    dask.array<chunksize=(150, 1, 421), meta=np.ndarray>

    Array Chunk Bytes 123.34 kiB 61.67 kiB Shape (150, 2, 421) (150, 1, 421) Count 1634 Tasks 2 Chunks Type bool numpy.ndarray

• Data variables: (5)
  • vosaline
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 1, 421), 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 14.45 MiB 246.68 kiB Shape (30, 150, 2, 421) (1, 150, 1, 421) Count 188 Tasks 60 Chunks Type float32 numpy.ndarray

  • votemper
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 1, 421), 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 14.45 MiB 246.68 kiB Shape (30, 150, 2, 421) (1, 150, 1, 421) Count 188 Tasks 60 Chunks Type float32 numpy.ndarray

  • vomecrty
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 1, 421), 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 14.45 MiB 246.68 kiB Shape (30, 150, 2, 421) (1, 150, 1, 421) Count 188 Tasks 60 Chunks Type float32 numpy.ndarray

  • sivelv
    (t, y, x, z)
    float32
    dask.array<chunksize=(1, 1, 421, 150), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    Y-component of sea ice velocity

    online_operation :

    average

    standard_name :

    sea_ice_y_velocity

    units :

    m/s

    Array Chunk Bytes 14.45 MiB 246.68 kiB Shape (30, 2, 421, 150) (1, 1, 421, 150) Count 188 Tasks 60 Chunks Type float32 numpy.ndarray

  • sivolu
    (t, y, x, z)
    float32
    dask.array<chunksize=(1, 1, 421, 150), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    ice volume

    online_operation :

    average

    standard_name :

    sea_ice_thickness

    units :

    m

    Array Chunk Bytes 14.45 MiB 246.68 kiB Shape (30, 2, 421, 150) (1, 1, 421, 150) Count 188 Tasks 60 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 Aug 18 09:38:58 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201509-201509_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2015/09/SEDNA-DELTA_1d_gridS_201509-201509_0000.nc Thu Aug 18 09:38:27 2022: ncrcat -n 30,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201509-201509_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.91/SEDNA-DELTA_1d_gridS

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Aug-17 11:28:47 GMT

  title :

  ocean T grid variables

  uuid :

  2eda39a7-ffb1-4669-a4c2-754df35b3832

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

<xarray.Dataset>
Dimensions:                (t: 30)
Coordinates:
  * t                      (t) object 2015-09-01 12:00:00 ... 2015-09-30 12:0...
    y                      int64 1308
Data variables:
    Volume flux Net        (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Volume flux Northward  (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Heat flux Net          (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Heat flux Northward    (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Freshwater Net         (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Freshwater Northward   (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Ice export             (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Volume flux South      (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Heat flux South        (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Freshwater South       (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • t: 30
• Coordinates: (2)
  • t
    (t)
    object
    2015-09-01 12:00:00 ... 2015-09-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2015, 9, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 30, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • y
    ()
    int64
    1308

    array(1308)

• Data variables: (10)
  • Volume flux Net
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 5396 Tasks 30 Chunks Type float64 numpy.ndarray

  • Volume flux Northward
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 5456 Tasks 30 Chunks Type float64 numpy.ndarray

  • Heat flux Net
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6752 Tasks 30 Chunks Type float64 numpy.ndarray

  • Heat flux Northward
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6812 Tasks 30 Chunks Type float64 numpy.ndarray

  • Freshwater Net
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6752 Tasks 30 Chunks Type float64 numpy.ndarray

  • Freshwater Northward
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6812 Tasks 30 Chunks Type float64 numpy.ndarray

  • Ice export
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 5029 Tasks 30 Chunks Type float64 numpy.ndarray

  • Volume flux South
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 5636 Tasks 30 Chunks Type float64 numpy.ndarray

  • Heat flux South
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 7022 Tasks 30 Chunks Type float64 numpy.ndarray

  • Freshwater South
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 7022 Tasks 30 Chunks Type float64 numpy.ndarray

• Attributes: (0)

#4 Saving  SEDNA_Fluxnet_integrals_Davis_Fluxnet
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_Fluxnet_integrals_Davis_Fluxnet2015-09-01_2015-09-30.nc
save computed data at ../nc_results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_Davis_Fluxnet2015-09-01_2015-09-30.nc completed
Value='Fluxnet'
Zone='Bering'
Plot='Fluxnet_integrals'
cmap='None'
clabel='(Sv,TW, mSv,10^-2 Sv)'
clim= ((-2, 2), (-10, 50), (-150, 50), (-2, 4))
outputpath='../results/SEDNA_DELTA_MONITOR/'
nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/'
filename='SEDNA_Fluxnet_integrals_Bering_Fluxnet'
data=monitor.optimize_dataset(data)
#2 Zooming Data
data= zoom.Bering(data)
data=monitor.optimize_dataset(data)

<xarray.Dataset>
Dimensions:   (t: 30, z: 150, y: 2, x: 146)
Coordinates:
  * t         (t) object 2015-09-01 12:00:00 ... 2015-09-30 12:00:00
  * y         (y) int64 6538 6539
  * x         (x) int64 2421 2422 2423 2424 2425 ... 2562 2563 2564 2565 2566
    nav_lat   (y, x) float32 dask.array<chunksize=(2, 146), meta=np.ndarray>
    nav_lon   (y, x) float32 dask.array<chunksize=(2, 146), meta=np.ndarray>
  * z         (z) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150
    e3v_0     (z, y, x) float64 dask.array<chunksize=(150, 2, 146), meta=np.ndarray>
    mask2d    (y, x) bool dask.array<chunksize=(2, 146), meta=np.ndarray>
    e1v       (y, x) float64 dask.array<chunksize=(2, 146), meta=np.ndarray>
    mask      (z, y, x) bool dask.array<chunksize=(150, 2, 146), meta=np.ndarray>
Data variables:
    vosaline  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 2, 146), meta=np.ndarray>
    votemper  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 2, 146), meta=np.ndarray>
    vomecrty  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 2, 146), meta=np.ndarray>
    sivelv    (t, y, x, z) float32 dask.array<chunksize=(1, 2, 146, 150), meta=np.ndarray>
    sivolu    (t, y, x, z) float32 dask.array<chunksize=(1, 2, 146, 150), 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-Aug-17 11:28:47 GMT
    title:                   ocean T grid variables
    uuid:                    2eda39a7-ffb1-4669-a4c2-754df35b3832

xarray.Dataset

• Dimensions:
  • t: 30
  • z: 150
  • y: 2
  • x: 146
• Coordinates: (10)
  • t
    (t)
    object
    2015-09-01 12:00:00 ... 2015-09-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2015, 9, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 30, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • y
    (y)
    int64
    6538 6539

    array([6538, 6539])

  • x
    (x)
    int64
    2421 2422 2423 ... 2564 2565 2566

    array([2421, 2422, 2423, 2424, 2425, 2426, 2427, 2428, 2429, 2430, 2431, 2432,
           2433, 2434, 2435, 2436, 2437, 2438, 2439, 2440, 2441, 2442, 2443, 2444,
           2445, 2446, 2447, 2448, 2449, 2450, 2451, 2452, 2453, 2454, 2455, 2456,
           2457, 2458, 2459, 2460, 2461, 2462, 2463, 2464, 2465, 2466, 2467, 2468,
           2469, 2470, 2471, 2472, 2473, 2474, 2475, 2476, 2477, 2478, 2479, 2480,
           2481, 2482, 2483, 2484, 2485, 2486, 2487, 2488, 2489, 2490, 2491, 2492,
           2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502, 2503, 2504,
           2505, 2506, 2507, 2508, 2509, 2510, 2511, 2512, 2513, 2514, 2515, 2516,
           2517, 2518, 2519, 2520, 2521, 2522, 2523, 2524, 2525, 2526, 2527, 2528,
           2529, 2530, 2531, 2532, 2533, 2534, 2535, 2536, 2537, 2538, 2539, 2540,
           2541, 2542, 2543, 2544, 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552,
           2553, 2554, 2555, 2556, 2557, 2558, 2559, 2560, 2561, 2562, 2563, 2564,
           2565, 2566])

  • nav_lat
    (y, x)
    float32
    dask.array<chunksize=(2, 146), meta=np.ndarray>

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (2, 146) (2, 146) Count 1633 Tasks 1 Chunks Type float32 numpy.ndarray

  • nav_lon
    (y, x)
    float32
    dask.array<chunksize=(2, 146), meta=np.ndarray>

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (2, 146) (2, 146) Count 1633 Tasks 1 Chunks Type float32 numpy.ndarray

  • 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])

  • e3v_0
    (z, y, x)
    float64
    dask.array<chunksize=(150, 2, 146), meta=np.ndarray>

    Array Chunk Bytes 342.19 kiB 342.19 kiB Shape (150, 2, 146) (150, 2, 146) Count 1633 Tasks 1 Chunks Type float64 numpy.ndarray

  • mask2d
    (y, x)
    bool
    dask.array<chunksize=(2, 146), meta=np.ndarray>

    Array Chunk Bytes 292 B 292 B Shape (2, 146) (2, 146) Count 1633 Tasks 1 Chunks Type bool numpy.ndarray

  • e1v
    (y, x)
    float64
    dask.array<chunksize=(2, 146), meta=np.ndarray>

    Array Chunk Bytes 2.28 kiB 2.28 kiB Shape (2, 146) (2, 146) Count 1633 Tasks 1 Chunks Type float64 numpy.ndarray

  • mask
    (z, y, x)
    bool
    dask.array<chunksize=(150, 2, 146), meta=np.ndarray>

    Array Chunk Bytes 42.77 kiB 42.77 kiB Shape (150, 2, 146) (150, 2, 146) Count 1633 Tasks 1 Chunks Type bool numpy.ndarray

• Data variables: (5)
  • vosaline
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 2, 146), 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 5.01 MiB 171.09 kiB Shape (30, 150, 2, 146) (1, 150, 2, 146) Count 94 Tasks 30 Chunks Type float32 numpy.ndarray

  • votemper
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 2, 146), 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 5.01 MiB 171.09 kiB Shape (30, 150, 2, 146) (1, 150, 2, 146) Count 94 Tasks 30 Chunks Type float32 numpy.ndarray

  • vomecrty
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 2, 146), 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 5.01 MiB 171.09 kiB Shape (30, 150, 2, 146) (1, 150, 2, 146) Count 94 Tasks 30 Chunks Type float32 numpy.ndarray

  • sivelv
    (t, y, x, z)
    float32
    dask.array<chunksize=(1, 2, 146, 150), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    Y-component of sea ice velocity

    online_operation :

    average

    standard_name :

    sea_ice_y_velocity

    units :

    m/s

    Array Chunk Bytes 5.01 MiB 171.09 kiB Shape (30, 2, 146, 150) (1, 2, 146, 150) Count 94 Tasks 30 Chunks Type float32 numpy.ndarray

  • sivolu
    (t, y, x, z)
    float32
    dask.array<chunksize=(1, 2, 146, 150), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    ice volume

    online_operation :

    average

    standard_name :

    sea_ice_thickness

    units :

    m

    Array Chunk Bytes 5.01 MiB 171.09 kiB Shape (30, 2, 146, 150) (1, 2, 146, 150) Count 94 Tasks 30 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 Aug 18 09:38:58 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201509-201509_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2015/09/SEDNA-DELTA_1d_gridS_201509-201509_0000.nc Thu Aug 18 09:38:27 2022: ncrcat -n 30,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201509-201509_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.91/SEDNA-DELTA_1d_gridS

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Aug-17 11:28:47 GMT

  title :

  ocean T grid variables

  uuid :

  2eda39a7-ffb1-4669-a4c2-754df35b3832

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

<xarray.Dataset>
Dimensions:                (t: 30)
Coordinates:
  * t                      (t) object 2015-09-01 12:00:00 ... 2015-09-30 12:0...
    y                      int64 6538
Data variables:
    Volume flux Net        (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Volume flux Northward  (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Heat flux Net          (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Heat flux Northward    (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Freshwater Net         (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Freshwater Northward   (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Ice export             (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Volume flux South      (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Heat flux South        (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    Freshwater South       (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • t: 30
• Coordinates: (2)
  • t
    (t)
    object
    2015-09-01 12:00:00 ... 2015-09-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2015, 9, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 9, 30, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • y
    ()
    int64
    6538

    array(6538)

• Data variables: (10)
  • Volume flux Net
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 5299 Tasks 30 Chunks Type float64 numpy.ndarray

  • Volume flux Northward
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 5359 Tasks 30 Chunks Type float64 numpy.ndarray

  • Heat flux Net
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6411 Tasks 30 Chunks Type float64 numpy.ndarray

  • Heat flux Northward
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6471 Tasks 30 Chunks Type float64 numpy.ndarray

  • Freshwater Net
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6411 Tasks 30 Chunks Type float64 numpy.ndarray

  • Freshwater Northward
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6471 Tasks 30 Chunks Type float64 numpy.ndarray

  • Ice export
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 4689 Tasks 30 Chunks Type float64 numpy.ndarray

  • Volume flux South
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 5539 Tasks 30 Chunks Type float64 numpy.ndarray

  • Heat flux South
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6681 Tasks 30 Chunks Type float64 numpy.ndarray

  • Freshwater South
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 240 B 8 B Shape (30,) (1,) Count 6681 Tasks 30 Chunks Type float64 numpy.ndarray

• Attributes: (0)

#4 Saving  SEDNA_Fluxnet_integrals_Bering_Fluxnet
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_Fluxnet_integrals_Bering_Fluxnet2015-09-01_2015-09-30.nc
save computed data at ../nc_results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_Bering_Fluxnet2015-09-01_2015-09-30.nc completed
CPU times: user 24.6 s, sys: 1.09 s, total: 25.7 s
Wall time: 33.1 s