%matplotlib inline
import pandas as pd
import socket
host = socket.getfqdn()
from core import load, zoom, calc, save,plots,monitor
#reload funcs after updating ./core/*.py
import importlib
importlib.reload(load)
importlib.reload(zoom)
importlib.reload(calc)
importlib.reload(save)
importlib.reload(plots)
importlib.reload(monitor)
<module 'core.monitor' from '/ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/monitor.py'>
# 'month': = 'JOBID' almost month but not really,
# 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 control=FWC_SSH
# name of control file to be used for computation/plots/save/
#%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 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
%%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= irene4756.c-irene.mg1.tgcc.ccc.cea.fr starting dask cluster on local= True workers 16 10000000000 False not local in tgcc rome local cluster starting This code is running on irene4756.c-irene.mg1.tgcc.ccc.cea.fr using SEDNA_ALPHA_MONITOR file experiment, read from ../lib/SEDNA_ALPHA_MONITOR.yaml on year= * on month= 28 outputpath= ../results/SEDNA_ALPHA_MONITOR/28/ daskreport= ../results/dask/2673097irene4756.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_ALPHA_MONITOR_28Fluxnet/ CPU times: user 372 ms, sys: 219 ms, total: 590 ms Wall time: 11.2 s
Client
|
Cluster
|
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 |
Each computation consists of
%%time
import os
calcswitch=os.environ.get('calc', 'True')
loaddata=((df.Inputs != '').any())
print('calcswitch=',calcswitch,'df.Inputs != nothing',loaddata)
data = load.datas(catalog_url,df.Inputs,month,year,daskreport) if ((calcswitch=='True' )*loaddata) else 0
data
calcswitch= False df.Inputs != nothing True CPU times: user 712 µs, sys: 0 ns, total: 712 µs Wall time: 604 µs
0
%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
)
#calc= False #save= False #plot= True Zone='FramS_All' Value='Fluxnet' cmap='None' clabel='(Sv,TW, mSv,10^-2 Sv)' clim= ((-10, 10), (-10, 50), (-150, 50), (-25, 5)) outputpath='../results/SEDNA_ALPHA_MONITOR/28/' nc_outputpath='../nc_results/SEDNA_ALPHA_MONITOR/28/' filename='SEDNA_Fluxnet_integrals_FramS_All_Fluxnet' #3 no computing , loading starts dtaa=save.load_data(plot=Plot,path=nc_outputpath,filename=filename) start saving data load 1Dnc file from ../nc_results/SEDNA_ALPHA_MONITOR/28/../*/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet*.nc load computed data completed
<xarray.Dataset> Dimensions: (t: 167) Coordinates: * t (t) object 2004-06-01 12:00:00 ... 2004-11-15 12:0... y int64 ... Data variables: Volume flux Net (t) float64 dask.array<chunksize=(9,), meta=np.ndarray> Volume flux Northward (t) float64 dask.array<chunksize=(9,), meta=np.ndarray> Heat flux Net (t) float64 dask.array<chunksize=(9,), meta=np.ndarray> Heat flux Northward (t) float64 dask.array<chunksize=(9,), meta=np.ndarray> Freshwater Net (t) float64 dask.array<chunksize=(9,), meta=np.ndarray> Freshwater Northward (t) float64 dask.array<chunksize=(9,), meta=np.ndarray> Ice export (t) float64 dask.array<chunksize=(9,), meta=np.ndarray> Volume flux South (t) float64 dask.array<chunksize=(9,), meta=np.ndarray> Heat flux South (t) float64 dask.array<chunksize=(9,), meta=np.ndarray> Freshwater South (t) float64 dask.array<chunksize=(9,), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 6, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 15, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 30, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 15, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 30, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 31, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 15, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 30, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 31, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 15, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 30, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 30, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 10, 31, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 11, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 11, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 11, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 11, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 11, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 11, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 11, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 11, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 11, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 11, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 11, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 11, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 11, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 11, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 11, 15, 12, 0, 0, 0)], dtype=object)
array(2608)
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#5 Plotting filename= plots.Fluxnet_integrals(data,path=outputpath,filename=filename,save=savefig,cmap=cmap,clim=clim,clabel=clabel)
WARNING:param.CurvePlot04062: 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.CurvePlot04079: 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.CurvePlot04096: 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_ALPHA_MONITOR/28/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet_20040601-20041115.html starts plotting
WARNING:param.CurvePlot04219: 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.CurvePlot04236: 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.CurvePlot04253: 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.CurvePlot04375: 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.CurvePlot04392: 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.CurvePlot04409: 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.CurvePlot04439: 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_ALPHA_MONITOR/28/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet_20040601-20041115.html ../results/SEDNA_ALPHA_MONITOR/28/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet_20040601-20041115.html created
Zone='Davis' Value='Fluxnet' cmap='None' clabel='(Sv,TW, mSv,10^-2 Sv)' clim= ((-5.0, 5.0), (-25, 27), (-200, 50), (-9, 5)) outputpath='../results/SEDNA_ALPHA_MONITOR/28/' nc_outputpath='../nc_results/SEDNA_ALPHA_MONITOR/28/' filename='SEDNA_Fluxnet_integrals_Davis_Fluxnet' #3 no computing , loading starts dtaa=save.load_data(plot=Plot,path=nc_outputpath,filename=filename) start saving data load 1Dnc file from ../nc_results/SEDNA_ALPHA_MONITOR/28/../*/SEDNA_Fluxnet_integrals_Davis_Fluxnet*.nc
distributed.nanny - WARNING - Restarting worker
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <timed eval> in <module> /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/monitor.py in auto(df, val, savefig, daskreport, outputpath, file_exp) 68 print('dtaa=save.load_data(plot=Plot,path=nc_outputpath,filename=filename)' ) 69 with performance_report(filename=daskreport+"_calc_"+step.Value+".html"): ---> 70 data=save.load_data(plot=Plot,path=nc_outputpath,filename=filename) 71 #saveswitch=False 72 display(data) /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/save.py in load_data(plot, path, filename) 30 print('start saving data') 31 if 'int' in plot: ---> 32 data=load_integral(path,filename) 33 elif 'Mooring' in plot: 34 data=load_integral(path,filename) /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/save.py in load_integral(path, filename) 76 ,concat_dim=('t') 77 ,combine='by_coords' ---> 78 ,coords='minimal') 79 80 ~/monitor/lib/python3.7/site-packages/xarray/backends/api.py in open_mfdataset(paths, chunks, concat_dim, compat, preprocess, engine, lock, data_vars, coords, combine, autoclose, parallel, join, attrs_file, **kwargs) 980 coords=coords, 981 join=join, --> 982 combine_attrs="drop", 983 ) 984 else: ~/monitor/lib/python3.7/site-packages/xarray/core/combine.py in combine_by_coords(datasets, compat, data_vars, coords, fill_value, join, combine_attrs) 789 raise ValueError( 790 "Resulting object does not have monotonic" --> 791 " global indexes along dimension {}".format(dim) 792 ) 793 concatenated_grouped_by_data_vars.append(concatenated) ValueError: Resulting object does not have monotonic global indexes along dimension t