%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
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'
proceed saving? True or False , Default is setted as True
proceed plotting? True or False , Default is setted as True
proceed computation? or just load computed result? True or False , Default is setted as True
save output file used for plotting
using kerchunked file -> False, not using kerhcunk -> True
name of control file to be used for computation/plots/save/ We have number of M_xxx.csv
Monitor.sh calls M_MLD_2D
and AWTD.sh, Fluxnet.sh, Siconc.sh, IceClim.sh, FWC_SSH.sh, Integrals.sh , Sections.sh
M_AWTMD
M_Fluxnet
M_Ice_quantities
M_IceClim M_IceConce M_IceThick
M_FWC_2D M_FWC_integrals M_FWC_SSH M_SSH_anomaly
M_Mean_temp_velo M_Mooring
M_Sectionx M_Sectiony
%%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= irene8002.c-irene.tgcc.ccc.cea.fr starting dask cluster on local= True workers 16 10000000000 False tgcc local cluster starting This code is running on irene8002.c-irene.tgcc.ccc.cea.fr using SEDNA_DELTA_MONITOR file experiment, read from ../lib/SEDNA_DELTA_MONITOR.yaml on year= 2015 on month= 12 outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/6610463irene8002.c-irene.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_12M_Mean_temp_velo/ CPU times: user 618 ms, sys: 187 ms, total: 805 ms Wall time: 17.7 s
Client-4ca2169b-2a67-11ed-9e25-080038bfdcae
Connection method: Cluster object | Cluster type: distributed.LocalCluster |
Dashboard: http://127.0.0.1:8787/status |
99323fff
Dashboard: http://127.0.0.1:8787/status | Workers: 12 |
Total threads: 48 | Total memory: 2.86 TiB |
Status: running | Using processes: True |
Scheduler-6d79f4d0-b396-40ea-bb7c-1b6a7e853309
Comm: tcp://127.0.0.1:38712 | Workers: 12 |
Dashboard: http://127.0.0.1:8787/status | Total threads: 48 |
Started: Just now | Total memory: 2.86 TiB |
Comm: tcp://127.0.0.1:39763 | Total threads: 4 |
Dashboard: http://127.0.0.1:44921/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:36384 | |
Local directory: /tmp/dask-worker-space/worker-qepizr46 |
Comm: tcp://127.0.0.1:39157 | Total threads: 4 |
Dashboard: http://127.0.0.1:45005/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:41999 | |
Local directory: /tmp/dask-worker-space/worker-sekalsfl |
Comm: tcp://127.0.0.1:39366 | Total threads: 4 |
Dashboard: http://127.0.0.1:43590/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:45920 | |
Local directory: /tmp/dask-worker-space/worker-q46zstk5 |
Comm: tcp://127.0.0.1:32885 | Total threads: 4 |
Dashboard: http://127.0.0.1:42340/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:46438 | |
Local directory: /tmp/dask-worker-space/worker-g4wkwdyc |
Comm: tcp://127.0.0.1:33870 | Total threads: 4 |
Dashboard: http://127.0.0.1:38379/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:41838 | |
Local directory: /tmp/dask-worker-space/worker-zvyclv0h |
Comm: tcp://127.0.0.1:40577 | Total threads: 4 |
Dashboard: http://127.0.0.1:33530/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:46185 | |
Local directory: /tmp/dask-worker-space/worker-fm3lt2rw |
Comm: tcp://127.0.0.1:40206 | Total threads: 4 |
Dashboard: http://127.0.0.1:34649/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:36249 | |
Local directory: /tmp/dask-worker-space/worker-1ziqohef |
Comm: tcp://127.0.0.1:41607 | Total threads: 4 |
Dashboard: http://127.0.0.1:34528/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:32933 | |
Local directory: /tmp/dask-worker-space/worker-duse0s5g |
Comm: tcp://127.0.0.1:42720 | Total threads: 4 |
Dashboard: http://127.0.0.1:45233/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:39239 | |
Local directory: /tmp/dask-worker-space/worker-6upywgv9 |
Comm: tcp://127.0.0.1:36020 | Total threads: 4 |
Dashboard: http://127.0.0.1:40345/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:42028 | |
Local directory: /tmp/dask-worker-space/worker-yigowm74 |
Comm: tcp://127.0.0.1:36380 | Total threads: 4 |
Dashboard: http://127.0.0.1:43540/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:46002 | |
Local directory: /tmp/dask-worker-space/worker-y4rg05ri |
Comm: tcp://127.0.0.1:37222 | Total threads: 4 |
Dashboard: http://127.0.0.1:33671/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:36976 | |
Local directory: /tmp/dask-worker-space/worker-ips5992a |
df=load.controlfile(control)
#Take out 'later' tagged computations
#df=df[~df['Value'].str.contains('later')]
df
Value | Inputs | Equation | Zone | Plot | Colourmap | MinMax | Unit | Oldname | Unnamed: 10 | |
---|---|---|---|---|---|---|---|---|---|---|
Mean Temp & Velocity | gridV.vomecrty,gridT.votemper,param.mask,param... | calc.Mean_temp_velo(data) | FramS_Small | Mean_temp_velo_integrals | None | ((0,4),(0,10)) | (°C,cm.s^-1) | I-5 |
Each computation consists of
%%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 404 µs, sys: 0 ns, total: 404 µs Wall time: 400 µs
0
%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
)
#calc= False #save= False #plot= True Value='Mean Temp & Velocity' Zone='FramS_Small' Plot='Mean_temp_velo_integrals' cmap='None' clabel='(°C,cm.s^-1)' clim= ((0, 4), (0, 10)) outputpath='../results/SEDNA_DELTA_MONITOR/' nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/' filename='SEDNA_Mean_temp_velo_integrals_FramS_Small_Mean_Temp_&_Velocity' #3 no computing , loading starts data=save.load_data(plot=Plot,path=nc_outputpath,filename=filename) start loading data load 1Dnc file from ../nc_results/SEDNA_DELTA_MONITOR/../*/SEDNA_Mean_temp_velo_integrals_FramS_Small_Mean_Temp_&_Velocity*.nc load computed data completed
<xarray.Dataset> Dimensions: (t: 305) Coordinates: * t (t) object 2015-01-01 12:00:00 ... 2015-12-31 12:00:00 y int64 ... Data variables: Mean Tempreture (t) float32 dask.array<chunksize=(31,), meta=np.ndarray> Mean Velocity (t) float32 dask.array<chunksize=(31,), meta=np.ndarray>
#5 Plotting filename= plots.Mean_temp_velo_integrals(data,path=outputpath,filename=filename,save=savefig,cmap=cmap,clim=clim,clabel=clabel)
WARNING:param.CurvePlot01755: 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.CurvePlot01764: 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_Mean_temp_velo_integrals_FramS_Small_Mean_Temp_&_Velocity_20150101-20151231.html starts plotting plotting ../results/SEDNA_DELTA_MONITOR/SEDNA_Mean_temp_velo_integrals_FramS_Small_Mean_Temp_&_Velocity_20150101-20151231.html ../results/SEDNA_DELTA_MONITOR/SEDNA_Mean_temp_velo_integrals_FramS_Small_Mean_Temp_&_Velocity_20150101-20151231.html created
CPU times: user 2.44 s, sys: 838 ms, total: 3.28 s Wall time: 16.3 s