%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= irene5338.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  irene5338.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/6253274irene5338.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_02M_Ice_quantities/
CPU times: user 374 ms, sys: 224 ms, total: 598 ms
Wall time: 11.6 s

Client Scheduler: tcp://127.0.0.1:37008 Dashboard: http://127.0.0.1:8787/status

Cluster Workers: 16 Cores: 256 Memory: 269.57 GB

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
	Ice_quantities	param.e1te2t,icemod.sivelo,icemod.sivolu,icemo...	calc.Ice_quant(data)	ALL	Ice_intquant	None	(0,20)	cm s^(-1)	I-2

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') 
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 508 µs, sys: 0 ns, total: 508 µs
Wall time: 454 µs

0

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

#calc= False
#save= False
#plot= True
Zone='ALL'
Value='Ice_quantities'
cmap='None'
clabel='cm s^(-1)'
clim= (0, 20)
outputpath='../results/SEDNA_DELTA_MONITOR/'
nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/'
filename='SEDNA_Ice_intquant_ALL_Ice_quantities'
#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_DELTA_MONITOR/../*/SEDNA_Ice_intquant_ALL_Ice_quantities*.nc
load computed data completed

<xarray.Dataset>
Dimensions:     (t: 59)
Coordinates:
  * t           (t) object 2012-01-01 12:00:00 ... 2012-02-28 12:00:00
Data variables:
    Ice volume  (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Ice area    (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Ice extent  (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Ice drift   (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>

#5 Plotting
filename= plots.Ice_intquant(data,path=outputpath,filename=filename,save=savefig,cmap=cmap,clim=clim,clabel=clabel)

WARNING:param.CurvePlot03598: 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.CurvePlot03629: 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.CurvePlot03659: 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.CurvePlot03689: 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_Ice_intquant_ALL_Ice_quantities_20120101-20120228.html starts plotting
plotting ../results/SEDNA_DELTA_MONITOR/SEDNA_Ice_intquant_ALL_Ice_quantities_20120101-20120228.html
../results/SEDNA_DELTA_MONITOR/SEDNA_Ice_intquant_ALL_Ice_quantities_20120101-20120228.html created

CPU times: user 1.25 s, sys: 274 ms, total: 1.53 s
Wall time: 5.14 s