%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.tgcc.ccc.cea.fr starting dask cluster on local= True workers 16 10000000000 False tgcc local cluster starting This code is running on irene8000.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/6610517irene8000.c-irene.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_12M_IceClim/ CPU times: user 510 ms, sys: 172 ms, total: 682 ms Wall time: 15.9 s
Client-1bfc6169-2ab6-11ed-be18-080038bfd9c6
Connection method: Cluster object | Cluster type: distributed.LocalCluster |
Dashboard: http://127.0.0.1:8787/status |
7b0104c3
Dashboard: http://127.0.0.1:8787/status | Workers: 12 |
Total threads: 48 | Total memory: 2.86 TiB |
Status: running | Using processes: True |
Scheduler-a93e4d06-2dcc-43b3-936c-868d86726b18
Comm: tcp://127.0.0.1:38833 | Workers: 12 |
Dashboard: http://127.0.0.1:8787/status | Total threads: 48 |
Started: Just now | Total memory: 2.86 TiB |
Comm: tcp://127.0.0.1:39923 | Total threads: 4 |
Dashboard: http://127.0.0.1:43831/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:39024 | |
Local directory: /tmp/dask-worker-space/worker-z9vlc7gs |
Comm: tcp://127.0.0.1:43802 | Total threads: 4 |
Dashboard: http://127.0.0.1:37445/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:34898 | |
Local directory: /tmp/dask-worker-space/worker-_qm3a1qx |
Comm: tcp://127.0.0.1:34945 | Total threads: 4 |
Dashboard: http://127.0.0.1:43070/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:34757 | |
Local directory: /tmp/dask-worker-space/worker-85975ffn |
Comm: tcp://127.0.0.1:32903 | Total threads: 4 |
Dashboard: http://127.0.0.1:37084/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:42055 | |
Local directory: /tmp/dask-worker-space/worker-qa4uv4pc |
Comm: tcp://127.0.0.1:44228 | Total threads: 4 |
Dashboard: http://127.0.0.1:44859/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:36466 | |
Local directory: /tmp/dask-worker-space/worker-41_vzkgs |
Comm: tcp://127.0.0.1:37991 | Total threads: 4 |
Dashboard: http://127.0.0.1:42641/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:40469 | |
Local directory: /tmp/dask-worker-space/worker-8l6145c7 |
Comm: tcp://127.0.0.1:33586 | Total threads: 4 |
Dashboard: http://127.0.0.1:35983/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:43380 | |
Local directory: /tmp/dask-worker-space/worker-_h48iwjt |
Comm: tcp://127.0.0.1:41529 | Total threads: 4 |
Dashboard: http://127.0.0.1:38348/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:41266 | |
Local directory: /tmp/dask-worker-space/worker-cfqj07k2 |
Comm: tcp://127.0.0.1:39586 | Total threads: 4 |
Dashboard: http://127.0.0.1:38116/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:45798 | |
Local directory: /tmp/dask-worker-space/worker-ptpp5v2t |
Comm: tcp://127.0.0.1:37551 | Total threads: 4 |
Dashboard: http://127.0.0.1:35343/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:40834 | |
Local directory: /tmp/dask-worker-space/worker-ygekczun |
Comm: tcp://127.0.0.1:38383 | Total threads: 4 |
Dashboard: http://127.0.0.1:38700/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:35248 | |
Local directory: /tmp/dask-worker-space/worker-0q5sk3a4 |
Comm: tcp://127.0.0.1:41978 | Total threads: 4 |
Dashboard: http://127.0.0.1:39587/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:37632 | |
Local directory: /tmp/dask-worker-space/worker-gkalnu3g |
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 | |
---|---|---|---|---|---|---|---|---|---|---|
IceClim | calc.IceClim_load(data,nc_outputpath) | ALL | IceClim | Spectral | (0,5) | m | M-4 |
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= True df.Inputs != nothing False lazy= True CPU times: user 318 µs, sys: 67 µs, total: 385 µs Wall time: 392 µs
0
%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
)
#calc= True #save= False #plot= True Value='IceClim' Zone='ALL' Plot='IceClim' cmap='Spectral' clabel='m' clim= (0, 5) outputpath='../results/SEDNA_DELTA_MONITOR/' nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/' filename='SEDNA_IceClim_ALL_IceClim' #3 Start computing data= calc.IceClim_load(data,nc_outputpath) monitor.optimize_dataset(data) start loading data filename= ../nc_results/SEDNA_DELTA_MONITOR/SEDNA_maps_ALL_IceConce/t_*/y_*/x_*.nc dim ('x', 'y', 't') load computed data completed start loading data filename= ../nc_results/SEDNA_DELTA_MONITOR/SEDNA_maps_ALL_IceThickness/t_*/y_*/x_*.nc dim ('x', 'y', 't') load computed data completed add optimise here once otimise can recognise
<xarray.Dataset> Dimensions: (t: 365, y: 6540, x: 6560) Coordinates: nav_lat (y, x) float32 dask.array<chunksize=(130, 6560), meta=np.ndarray> nav_lon (y, x) float32 dask.array<chunksize=(130, 6560), meta=np.ndarray> * t (t) object 2015-01-01 12:00:00 ... 2015-12-31 12:00:00 * y (y) int64 1 2 3 4 5 6 7 8 ... 6534 6535 6536 6537 6538 6539 6540 * x (x) int64 1 2 3 4 5 6 7 8 ... 6554 6555 6556 6557 6558 6559 6560 mask2d (y, x) bool dask.array<chunksize=(130, 6560), meta=np.ndarray> Data variables: siconc (t, y, x) float32 dask.array<chunksize=(31, 130, 6560), meta=np.ndarray> sivolu (t, y, x) float32 dask.array<chunksize=(31, 130, 6560), meta=np.ndarray>
#5 Plotting filename= plots.IceClim(data,path=outputpath,filename=filename,save=savefig,cmap=cmap,clim=clim,clabel=clabel) ../results/SEDNA_DELTA_MONITOR/SEDNA_IceClim_ALL_IceClim_20150101-20151231.html starts plotting
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/geoviews/operation/projection.py:99: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry. if proj_geom.geom_type == 'GeometryCollection' and len(proj_geom) == 0: /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/geoviews/operation/projection.py:99: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry. if proj_geom.geom_type == 'GeometryCollection' and len(proj_geom) == 0:
../results/SEDNA_DELTA_MONITOR/SEDNA_IceClim_ALL_IceClim_20150101-20151231.html created
CPU times: user 5h 12min, sys: 1h 46min 22s, total: 6h 58min 23s Wall time: 6h 47min 52s