%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= irene8000.c-irene.mg1.tgcc.ccc.cea.fr starting dask cluster on local= True workers 16 10000000000 rome local cluster starting This code is running on irene8000.c-irene.mg1.tgcc.ccc.cea.fr using SEDNA_DELTA_MONITOR file experiment, read from ../lib/SEDNA_DELTA_MONITOR.yaml on year= 2015 on month= 12 outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/7402483irene8000.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_12M_Mean_temp_velo/ CPU times: user 591 ms, sys: 214 ms, total: 805 ms Wall time: 15.7 s
Client-bcd27ed8-6dac-11ed-a8af-080038bfd9c6
Connection method: Cluster object | Cluster type: distributed.LocalCluster |
Dashboard: http://127.0.0.1:8787/status |
c1a4e05c
Dashboard: http://127.0.0.1:8787/status | Workers: 16 |
Total threads: 128 | Total memory: 2.86 TiB |
Status: running | Using processes: True |
Scheduler-12164b7c-7b32-4531-830a-517d89827729
Comm: tcp://127.0.0.1:37045 | Workers: 16 |
Dashboard: http://127.0.0.1:8787/status | Total threads: 128 |
Started: Just now | Total memory: 2.86 TiB |
Comm: tcp://127.0.0.1:37608 | Total threads: 8 |
Dashboard: http://127.0.0.1:35868/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:38702 | |
Local directory: /tmp/dask-worker-space/worker-rx5jpku_ |
Comm: tcp://127.0.0.1:35485 | Total threads: 8 |
Dashboard: http://127.0.0.1:32787/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:45776 | |
Local directory: /tmp/dask-worker-space/worker-4yni72j2 |
Comm: tcp://127.0.0.1:43876 | Total threads: 8 |
Dashboard: http://127.0.0.1:37144/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:45071 | |
Local directory: /tmp/dask-worker-space/worker-kjp91vux |
Comm: tcp://127.0.0.1:40624 | Total threads: 8 |
Dashboard: http://127.0.0.1:40771/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:42091 | |
Local directory: /tmp/dask-worker-space/worker-lkom5b4i |
Comm: tcp://127.0.0.1:44323 | Total threads: 8 |
Dashboard: http://127.0.0.1:43231/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:45878 | |
Local directory: /tmp/dask-worker-space/worker-vresacpv |
Comm: tcp://127.0.0.1:36554 | Total threads: 8 |
Dashboard: http://127.0.0.1:43463/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:42492 | |
Local directory: /tmp/dask-worker-space/worker-hoax_ji3 |
Comm: tcp://127.0.0.1:45415 | Total threads: 8 |
Dashboard: http://127.0.0.1:46183/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:37720 | |
Local directory: /tmp/dask-worker-space/worker-6bt9p9_u |
Comm: tcp://127.0.0.1:44617 | Total threads: 8 |
Dashboard: http://127.0.0.1:35433/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:43030 | |
Local directory: /tmp/dask-worker-space/worker-e1jvz8xy |
Comm: tcp://127.0.0.1:46071 | Total threads: 8 |
Dashboard: http://127.0.0.1:36373/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:32794 | |
Local directory: /tmp/dask-worker-space/worker-rqlhhf_z |
Comm: tcp://127.0.0.1:34761 | Total threads: 8 |
Dashboard: http://127.0.0.1:46218/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:44264 | |
Local directory: /tmp/dask-worker-space/worker-yi_stjem |
Comm: tcp://127.0.0.1:36886 | Total threads: 8 |
Dashboard: http://127.0.0.1:40267/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:36535 | |
Local directory: /tmp/dask-worker-space/worker-b970yrpq |
Comm: tcp://127.0.0.1:39954 | Total threads: 8 |
Dashboard: http://127.0.0.1:44013/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:43410 | |
Local directory: /tmp/dask-worker-space/worker-3p1w_5ve |
Comm: tcp://127.0.0.1:34997 | Total threads: 8 |
Dashboard: http://127.0.0.1:44092/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:36371 | |
Local directory: /tmp/dask-worker-space/worker-hh87bvf7 |
Comm: tcp://127.0.0.1:33157 | Total threads: 8 |
Dashboard: http://127.0.0.1:34888/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:39145 | |
Local directory: /tmp/dask-worker-space/worker-ausl5n85 |
Comm: tcp://127.0.0.1:42029 | Total threads: 8 |
Dashboard: http://127.0.0.1:34370/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:42484 | |
Local directory: /tmp/dask-worker-space/worker-weamljf5 |
Comm: tcp://127.0.0.1:34252 | Total threads: 8 |
Dashboard: http://127.0.0.1:42028/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:40701 | |
Local directory: /tmp/dask-worker-space/worker-um_r8_ch |
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 343 µs, sys: 70 µs, total: 413 µs Wall time: 409 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: 365) 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 2.3 s, sys: 924 ms, total: 3.22 s Wall time: 11.7 s