In [1]:
%matplotlib inline
import pandas as pd
import socket
host = socket.getfqdn()

from core import  load, zoom, calc, save,plots,monitor
In [2]:
#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)
Out[2]:
<module 'core.monitor' from '/ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/monitor.py'>

If you submit the job with job scheduler, above¶

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.¶

%env month=¶

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

#

%env control=FWC_SSH¶

name of control file to be used for computation/plots/save/ & how it is called from Monitor.sh¶

Monitor.sh calls M_MLD_2D

and AWTD.sh, Fluxnet.sh, Siconc.sh, IceClim.sh, FWC_SSH.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 M_Sectionx M_Sectiony

%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¶

%env lazy=False¶

For debugging this cell can help¶

%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¶

In [3]:
%%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= irene5865.c-irene.mg1.tgcc.ccc.cea.fr starting dask cluster on local= True workers 16
10000000000
False
tgcc local cluster starting
This code is running on  irene5865.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= 02  outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/6419115irene5865.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_02M_Fluxnet/
CPU times: user 4.14 s, sys: 772 ms, total: 4.91 s
Wall time: 1min 45s
Out[3]:

Client

Client-a16b2cdb-13da-11ed-9b6e-080038b93e1f

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

Cluster Info

LocalCluster

fb6b646a

Dashboard: http://127.0.0.1:8787/status Workers: 64
Total threads: 256 Total memory: 251.06 GiB
Status: running Using processes: True

Scheduler Info

Scheduler

Scheduler-495b74a3-c470-4117-8352-44fa1a4bb22d

Comm: tcp://127.0.0.1:36419 Workers: 64
Dashboard: http://127.0.0.1:8787/status Total threads: 256
Started: 1 minute ago Total memory: 251.06 GiB

Workers

Worker: 0
Comm: tcp://127.0.0.1:46819 Total threads: 4
Dashboard: http://127.0.0.1:43517/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:41510
Local directory: /tmp/dask-worker-space/worker-qkkgjth9

Worker: 1
Comm: tcp://127.0.0.1:46301 Total threads: 4
Dashboard: http://127.0.0.1:43395/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:35307
Local directory: /tmp/dask-worker-space/worker-4_jgit8c

Worker: 2
Comm: tcp://127.0.0.1:45623 Total threads: 4
Dashboard: http://127.0.0.1:41567/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:46048
Local directory: /tmp/dask-worker-space/worker-5lgkx599

Worker: 3
Comm: tcp://127.0.0.1:45269 Total threads: 4
Dashboard: http://127.0.0.1:46499/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:45766
Local directory: /tmp/dask-worker-space/worker-y_efh3bm

Worker: 4
Comm: tcp://127.0.0.1:43427 Total threads: 4
Dashboard: http://127.0.0.1:46660/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:43873
Local directory: /tmp/dask-worker-space/worker-z93_27k2

Worker: 5
Comm: tcp://127.0.0.1:45338 Total threads: 4
Dashboard: http://127.0.0.1:35076/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:35491
Local directory: /tmp/dask-worker-space/worker-5p3ynfgv

Worker: 6
Comm: tcp://127.0.0.1:40548 Total threads: 4
Dashboard: http://127.0.0.1:38733/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:39293
Local directory: /tmp/dask-worker-space/worker-anyo749j

Worker: 7
Comm: tcp://127.0.0.1:40939 Total threads: 4
Dashboard: http://127.0.0.1:37902/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:41236
Local directory: /tmp/dask-worker-space/worker-ex6xpyy4

Worker: 8
Comm: tcp://127.0.0.1:44938 Total threads: 4
Dashboard: http://127.0.0.1:38981/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:37412
Local directory: /tmp/dask-worker-space/worker-k2bg44dr

Worker: 9
Comm: tcp://127.0.0.1:43819 Total threads: 4
Dashboard: http://127.0.0.1:39224/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:36657
Local directory: /tmp/dask-worker-space/worker-biijdgid

Worker: 10
Comm: tcp://127.0.0.1:39822 Total threads: 4
Dashboard: http://127.0.0.1:38080/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:38636
Local directory: /tmp/dask-worker-space/worker-9p29z144

Worker: 11
Comm: tcp://127.0.0.1:33869 Total threads: 4
Dashboard: http://127.0.0.1:33191/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:33164
Local directory: /tmp/dask-worker-space/worker-am3ukjuf

Worker: 12
Comm: tcp://127.0.0.1:44111 Total threads: 4
Dashboard: http://127.0.0.1:39640/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:36865
Local directory: /tmp/dask-worker-space/worker-u7m6gzyi

Worker: 13
Comm: tcp://127.0.0.1:40095 Total threads: 4
Dashboard: http://127.0.0.1:39701/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:39541
Local directory: /tmp/dask-worker-space/worker-u_38u9us

Worker: 14
Comm: tcp://127.0.0.1:38635 Total threads: 4
Dashboard: http://127.0.0.1:44998/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:36361
Local directory: /tmp/dask-worker-space/worker-r_qkzi1i

Worker: 15
Comm: tcp://127.0.0.1:45527 Total threads: 4
Dashboard: http://127.0.0.1:41367/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:34742
Local directory: /tmp/dask-worker-space/worker-s_vl_zrw

Worker: 16
Comm: tcp://127.0.0.1:43617 Total threads: 4
Dashboard: http://127.0.0.1:44585/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:42595
Local directory: /tmp/dask-worker-space/worker-nldatlgr

Worker: 17
Comm: tcp://127.0.0.1:41392 Total threads: 4
Dashboard: http://127.0.0.1:38004/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:37923
Local directory: /tmp/dask-worker-space/worker-6x5ygv0o

Worker: 18
Comm: tcp://127.0.0.1:39406 Total threads: 4
Dashboard: http://127.0.0.1:32826/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:46632
Local directory: /tmp/dask-worker-space/worker-cj0mih6q

Worker: 19
Comm: tcp://127.0.0.1:40851 Total threads: 4
Dashboard: http://127.0.0.1:44843/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:44821
Local directory: /tmp/dask-worker-space/worker-2n6sygl6

Worker: 20
Comm: tcp://127.0.0.1:36539 Total threads: 4
Dashboard: http://127.0.0.1:35454/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:38966
Local directory: /tmp/dask-worker-space/worker-ppnjqbks

Worker: 21
Comm: tcp://127.0.0.1:35946 Total threads: 4
Dashboard: http://127.0.0.1:36362/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:36492
Local directory: /tmp/dask-worker-space/worker-pw_dxast

Worker: 22
Comm: tcp://127.0.0.1:42883 Total threads: 4
Dashboard: http://127.0.0.1:42239/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:40338
Local directory: /tmp/dask-worker-space/worker-3yj58jt5

Worker: 23
Comm: tcp://127.0.0.1:45526 Total threads: 4
Dashboard: http://127.0.0.1:46339/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:40257
Local directory: /tmp/dask-worker-space/worker-zty4v6ll

Worker: 24
Comm: tcp://127.0.0.1:39754 Total threads: 4
Dashboard: http://127.0.0.1:36204/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:43672
Local directory: /tmp/dask-worker-space/worker-z8sqa2be

Worker: 25
Comm: tcp://127.0.0.1:36667 Total threads: 4
Dashboard: http://127.0.0.1:35039/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:33580
Local directory: /tmp/dask-worker-space/worker-1trpug21

Worker: 26
Comm: tcp://127.0.0.1:45664 Total threads: 4
Dashboard: http://127.0.0.1:32877/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:33060
Local directory: /tmp/dask-worker-space/worker-yvdj3gfd

Worker: 27
Comm: tcp://127.0.0.1:38424 Total threads: 4
Dashboard: http://127.0.0.1:44428/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:34892
Local directory: /tmp/dask-worker-space/worker-4ymbh9kz

Worker: 28
Comm: tcp://127.0.0.1:34850 Total threads: 4
Dashboard: http://127.0.0.1:41127/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:42131
Local directory: /tmp/dask-worker-space/worker-p2qirrj4

Worker: 29
Comm: tcp://127.0.0.1:45864 Total threads: 4
Dashboard: http://127.0.0.1:34526/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:37352
Local directory: /tmp/dask-worker-space/worker-lpqltf9c

Worker: 30
Comm: tcp://127.0.0.1:33782 Total threads: 4
Dashboard: http://127.0.0.1:41945/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:34614
Local directory: /tmp/dask-worker-space/worker-v9i3i5i8

Worker: 31
Comm: tcp://127.0.0.1:38096 Total threads: 4
Dashboard: http://127.0.0.1:45323/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:38955
Local directory: /tmp/dask-worker-space/worker-c32e7m0s

Worker: 32
Comm: tcp://127.0.0.1:45095 Total threads: 4
Dashboard: http://127.0.0.1:44038/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:33082
Local directory: /tmp/dask-worker-space/worker-7k3jlcif

Worker: 33
Comm: tcp://127.0.0.1:34402 Total threads: 4
Dashboard: http://127.0.0.1:44732/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:34859
Local directory: /tmp/dask-worker-space/worker-c3ado21t

Worker: 34
Comm: tcp://127.0.0.1:43997 Total threads: 4
Dashboard: http://127.0.0.1:43159/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:36998
Local directory: /tmp/dask-worker-space/worker-0wvnn555

Worker: 35
Comm: tcp://127.0.0.1:40164 Total threads: 4
Dashboard: http://127.0.0.1:33349/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:46582
Local directory: /tmp/dask-worker-space/worker-7hflnqbo

Worker: 36
Comm: tcp://127.0.0.1:42474 Total threads: 4
Dashboard: http://127.0.0.1:33554/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:33641
Local directory: /tmp/dask-worker-space/worker-iv_gouzq

Worker: 37
Comm: tcp://127.0.0.1:36249 Total threads: 4
Dashboard: http://127.0.0.1:36205/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:43856
Local directory: /tmp/dask-worker-space/worker-93lnb63o

Worker: 38
Comm: tcp://127.0.0.1:33779 Total threads: 4
Dashboard: http://127.0.0.1:34691/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:43646
Local directory: /tmp/dask-worker-space/worker-26oi9_4l

Worker: 39
Comm: tcp://127.0.0.1:46506 Total threads: 4
Dashboard: http://127.0.0.1:37249/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:46460
Local directory: /tmp/dask-worker-space/worker-zx7nfey7

Worker: 40
Comm: tcp://127.0.0.1:38231 Total threads: 4
Dashboard: http://127.0.0.1:36396/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:42104
Local directory: /tmp/dask-worker-space/worker-x5gq2i0_

Worker: 41
Comm: tcp://127.0.0.1:35108 Total threads: 4
Dashboard: http://127.0.0.1:44752/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:42400
Local directory: /tmp/dask-worker-space/worker-uhf47vu_

Worker: 42
Comm: tcp://127.0.0.1:41262 Total threads: 4
Dashboard: http://127.0.0.1:37933/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:45763
Local directory: /tmp/dask-worker-space/worker-ondf2ta7

Worker: 43
Comm: tcp://127.0.0.1:38043 Total threads: 4
Dashboard: http://127.0.0.1:42564/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:38375
Local directory: /tmp/dask-worker-space/worker-sz_lblms

Worker: 44
Comm: tcp://127.0.0.1:40658 Total threads: 4
Dashboard: http://127.0.0.1:42888/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:36289
Local directory: /tmp/dask-worker-space/worker-s7kcbcu4

Worker: 45
Comm: tcp://127.0.0.1:41807 Total threads: 4
Dashboard: http://127.0.0.1:43764/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:43079
Local directory: /tmp/dask-worker-space/worker-ea3ixpnd

Worker: 46
Comm: tcp://127.0.0.1:36552 Total threads: 4
Dashboard: http://127.0.0.1:40943/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:40664
Local directory: /tmp/dask-worker-space/worker-csb5tkr5

Worker: 47
Comm: tcp://127.0.0.1:40866 Total threads: 4
Dashboard: http://127.0.0.1:36775/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:46833
Local directory: /tmp/dask-worker-space/worker-sh0_bk9t

Worker: 48
Comm: tcp://127.0.0.1:44504 Total threads: 4
Dashboard: http://127.0.0.1:35985/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:46749
Local directory: /tmp/dask-worker-space/worker-o3t7gm3g

Worker: 49
Comm: tcp://127.0.0.1:44751 Total threads: 4
Dashboard: http://127.0.0.1:40537/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:33107
Local directory: /tmp/dask-worker-space/worker-8bkl46_3

Worker: 50
Comm: tcp://127.0.0.1:42207 Total threads: 4
Dashboard: http://127.0.0.1:37196/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:32856
Local directory: /tmp/dask-worker-space/worker-fdbvqpqu

Worker: 51
Comm: tcp://127.0.0.1:42375 Total threads: 4
Dashboard: http://127.0.0.1:36044/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:37627
Local directory: /tmp/dask-worker-space/worker-pau_25h7

Worker: 52
Comm: tcp://127.0.0.1:40051 Total threads: 4
Dashboard: http://127.0.0.1:33785/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:33348
Local directory: /tmp/dask-worker-space/worker-7enll8_o

Worker: 53
Comm: tcp://127.0.0.1:40142 Total threads: 4
Dashboard: http://127.0.0.1:43750/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:41974
Local directory: /tmp/dask-worker-space/worker-c5dbb9m1

Worker: 54
Comm: tcp://127.0.0.1:34675 Total threads: 4
Dashboard: http://127.0.0.1:42968/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:43795
Local directory: /tmp/dask-worker-space/worker-hl5kfbb9

Worker: 55
Comm: tcp://127.0.0.1:33753 Total threads: 4
Dashboard: http://127.0.0.1:36468/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:37752
Local directory: /tmp/dask-worker-space/worker-vgwvzb2z

Worker: 56
Comm: tcp://127.0.0.1:46858 Total threads: 4
Dashboard: http://127.0.0.1:43463/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:40703
Local directory: /tmp/dask-worker-space/worker-t3j0mcgg

Worker: 57
Comm: tcp://127.0.0.1:42135 Total threads: 4
Dashboard: http://127.0.0.1:46310/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:44797
Local directory: /tmp/dask-worker-space/worker-n8wgk1n2

Worker: 58
Comm: tcp://127.0.0.1:41491 Total threads: 4
Dashboard: http://127.0.0.1:36866/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:35324
Local directory: /tmp/dask-worker-space/worker-9t_1xvvi

Worker: 59
Comm: tcp://127.0.0.1:39721 Total threads: 4
Dashboard: http://127.0.0.1:39859/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:43909
Local directory: /tmp/dask-worker-space/worker-2sf2rm48

Worker: 60
Comm: tcp://127.0.0.1:33471 Total threads: 4
Dashboard: http://127.0.0.1:37535/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:45442
Local directory: /tmp/dask-worker-space/worker-7bopl8eh

Worker: 61
Comm: tcp://127.0.0.1:44155 Total threads: 4
Dashboard: http://127.0.0.1:34007/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:42112
Local directory: /tmp/dask-worker-space/worker-97__mzjz

Worker: 62
Comm: tcp://127.0.0.1:43540 Total threads: 4
Dashboard: http://127.0.0.1:39259/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:38411
Local directory: /tmp/dask-worker-space/worker-reqyry_1

Worker: 63
Comm: tcp://127.0.0.1:35002 Total threads: 4
Dashboard: http://127.0.0.1:33871/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:45554
Local directory: /tmp/dask-worker-space/worker-951a5oq0

read plotting information from a csv file¶

In [4]:
df=load.controlfile(control)
#Take out 'later' tagged computations
#df=df[~df['Value'].str.contains('later')]
df
Out[4]:
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
In [5]:
%%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= False df.Inputs != nothing True lazy= False
CPU times: user 0 ns, sys: 380 µs, total: 380 µs
Wall time: 383 µs
Out[5]:
0
In [6]:
%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
            )

#calc= False
#save= False
#plot= True
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'
#3 no computing , loading starts
data=save.load_data(plot=Plot,path=nc_outputpath,filename=filename)
start saving data
load 1Dnc file from ../nc_results/SEDNA_DELTA_MONITOR/../*/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet*.nc
load computed data completed

<xarray.Dataset>
Dimensions:                (t: 90)
Coordinates:
    time_centered          (t) object dask.array<chunksize=(31,), meta=np.ndarray>
  * t                      (t) object 2012-01-01 12:00:00 ... 2012-01-31 12:0...
    y                      int64 ...
Data variables:
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    Volume flux Northward  (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux Net          (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux Northward    (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater Net         (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater Northward   (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Ice export             (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Volume flux South      (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux South        (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater South       (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
xarray.Dataset
    • t: 90
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      long_name :
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      time_origin :
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      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type object numpy.ndarray
      		90 1
    • t
      (t)
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      2012-01-01 12:00:00 ... 2012-01-...
      axis :
      T
      bounds :
      time_counter_bounds
      long_name :
      Time axis
      standard_name :
      time
      time_origin :
      1900-01-01 00:00:00
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       cftime.DatetimeNoLeap(2012, 1, 13, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 17, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 19, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 29, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 31, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 2, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 8, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 2, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 20, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 2, 22, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 2, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 13, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 21, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 28, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 31, 12, 0, 0, 0, has_year_zero=True)],
      dtype=object)
    • y
      ()
      int64
      ...
      array(2608)
    • Volume flux Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Volume flux Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Ice export
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Volume flux South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1 
#5 Plotting
filename= plots.Fluxnet_integrals(data,path=outputpath,filename=filename,save=savefig,cmap=cmap,clim=clim,clabel=clabel)

WARNING:param.CurvePlot02240: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02241: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02242: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02266: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02267: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02268: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.

../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet_20120101-20120228.html starts plotting

WARNING:param.CurvePlot02290: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02291: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02292: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02299: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.

plotting ../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet_20120101-20120228.html
../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet_20120101-20120228.html created

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'
#3 no computing , loading starts
data=save.load_data(plot=Plot,path=nc_outputpath,filename=filename)
start saving data
load 1Dnc file from ../nc_results/SEDNA_DELTA_MONITOR/../*/SEDNA_Fluxnet_integrals_Davis_Fluxnet*.nc
load computed data completed

<xarray.Dataset>
Dimensions:                (t: 90)
Coordinates:
    time_centered          (t) object dask.array<chunksize=(31,), meta=np.ndarray>
  * t                      (t) object 2012-01-01 12:00:00 ... 2012-01-31 12:0...
    y                      int64 ...
Data variables:
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    Volume flux Northward  (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux Net          (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux Northward    (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater Net         (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater Northward   (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Ice export             (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Volume flux South      (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux South        (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater South       (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
xarray.Dataset
    • t: 90
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      bounds :
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      long_name :
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      time_origin :
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      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type object numpy.ndarray
      		90 1
    • t
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      2012-01-01 12:00:00 ... 2012-01-...
      axis :
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      bounds :
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      long_name :
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      time_origin :
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       cftime.DatetimeNoLeap(2012, 1, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 17, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 21, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 29, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 31, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 21, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 22, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 21, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 22, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 29, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 31, 12, 0, 0, 0, has_year_zero=True)],
      dtype=object)
    • y
      ()
      int64
      ...
      array(1308)
    • Volume flux Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Volume flux Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Ice export
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Volume flux South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1 
#5 Plotting
filename= plots.Fluxnet_integrals(data,path=outputpath,filename=filename,save=savefig,cmap=cmap,clim=clim,clabel=clabel)

WARNING:param.CurvePlot03176: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03177: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03178: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03202: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03203: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03204: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.

../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_Davis_Fluxnet_20120101-20120228.html starts plotting

WARNING:param.CurvePlot03226: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03227: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03228: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03235: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.

plotting ../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_Davis_Fluxnet_20120101-20120228.html
../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_Davis_Fluxnet_20120101-20120228.html created

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'
#3 no computing , loading starts
data=save.load_data(plot=Plot,path=nc_outputpath,filename=filename)
start saving data
load 1Dnc file from ../nc_results/SEDNA_DELTA_MONITOR/../*/SEDNA_Fluxnet_integrals_Bering_Fluxnet*.nc
load computed data completed

<xarray.Dataset>
Dimensions:                (t: 90)
Coordinates:
    time_centered          (t) object dask.array<chunksize=(31,), meta=np.ndarray>
  * t                      (t) object 2012-01-01 12:00:00 ... 2012-01-31 12:0...
    y                      int64 ...
Data variables:
    Volume flux Net        (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Volume flux Northward  (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux Net          (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux Northward    (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater Net         (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater Northward   (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Ice export             (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Volume flux South      (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux South        (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater South       (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
xarray.Dataset
    • t: 90
    • time_centered
      (t)
      object
      dask.array<chunksize=(31,), 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 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type object numpy.ndarray
      		90 1
    • t
      (t)
      object
      2012-01-01 12:00:00 ... 2012-01-...
      axis :
      T
      bounds :
      time_counter_bounds
      long_name :
      Time axis
      standard_name :
      time
      time_origin :
      1900-01-01 00:00:00
      array([cftime.DatetimeNoLeap(2012, 1, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 21, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 22, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 29, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 31, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 21, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 22, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 21, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 22, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 29, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 31, 12, 0, 0, 0, has_year_zero=True)],
      dtype=object)
    • y
      ()
      int64
      ...
      array(6538)
    • Volume flux Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Volume flux Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Ice export
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Volume flux South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1 
#5 Plotting
filename= plots.Fluxnet_integrals(data,path=outputpath,filename=filename,save=savefig,cmap=cmap,clim=clim,clabel=clabel)

WARNING:param.CurvePlot04112: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04113: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04114: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04138: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04139: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04140: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04162: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.

../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_Bering_Fluxnet_20120101-20120228.html starts plotting

WARNING:param.CurvePlot04163: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04164: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04171: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.

plotting ../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_Bering_Fluxnet_20120101-20120228.html
../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_Bering_Fluxnet_20120101-20120228.html created

CPU times: user 14.7 s, sys: 6.45 s, total: 21.2 s
Wall time: 1min 12s