%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
#%env lazy=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= irene4213.c-irene.mg1.tgcc.ccc.cea.fr starting dask cluster on local= True workers 16 10000000000 False rome local cluster starting This code is running on irene4213.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= 01 outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/6413745irene4213.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_01M_IceThick/ CPU times: user 521 ms, sys: 135 ms, total: 656 ms Wall time: 20.7 s
Client-01c0aa89-1344-11ed-be89-080038b93865
| Connection method: Cluster object | Cluster type: distributed.LocalCluster |
| Dashboard: http://127.0.0.1:8787/status |
02269398
| Dashboard: http://127.0.0.1:8787/status | Workers: 16 |
| Total threads: 128 | Total memory: 251.06 GiB |
| Status: running | Using processes: True |
Scheduler-6cb5a6bd-4759-4543-806d-233de651ef64
| Comm: tcp://127.0.0.1:41782 | Workers: 16 |
| Dashboard: http://127.0.0.1:8787/status | Total threads: 128 |
| Started: Just now | Total memory: 251.06 GiB |
| Comm: tcp://127.0.0.1:38862 | Total threads: 8 |
| Dashboard: http://127.0.0.1:38344/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:42476 | |
| Local directory: /tmp/dask-worker-space/worker-51ivek71 | |
| Comm: tcp://127.0.0.1:39403 | Total threads: 8 |
| Dashboard: http://127.0.0.1:32880/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:45832 | |
| Local directory: /tmp/dask-worker-space/worker-1wfxhimz | |
| Comm: tcp://127.0.0.1:41358 | Total threads: 8 |
| Dashboard: http://127.0.0.1:46578/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:40402 | |
| Local directory: /tmp/dask-worker-space/worker-yn7u3i2b | |
| Comm: tcp://127.0.0.1:36730 | Total threads: 8 |
| Dashboard: http://127.0.0.1:34742/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:40878 | |
| Local directory: /tmp/dask-worker-space/worker-gtz3fn0v | |
| Comm: tcp://127.0.0.1:36554 | Total threads: 8 |
| Dashboard: http://127.0.0.1:44482/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:39712 | |
| Local directory: /tmp/dask-worker-space/worker-26hsiuo5 | |
| Comm: tcp://127.0.0.1:45077 | Total threads: 8 |
| Dashboard: http://127.0.0.1:39947/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:34945 | |
| Local directory: /tmp/dask-worker-space/worker-bqnkl196 | |
| Comm: tcp://127.0.0.1:33341 | Total threads: 8 |
| Dashboard: http://127.0.0.1:44172/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:42641 | |
| Local directory: /tmp/dask-worker-space/worker-ksw0rdw7 | |
| Comm: tcp://127.0.0.1:39419 | Total threads: 8 |
| Dashboard: http://127.0.0.1:40318/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:33558 | |
| Local directory: /tmp/dask-worker-space/worker-79qp4u34 | |
| Comm: tcp://127.0.0.1:41994 | Total threads: 8 |
| Dashboard: http://127.0.0.1:44442/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:35807 | |
| Local directory: /tmp/dask-worker-space/worker-w4tzp4h5 | |
| Comm: tcp://127.0.0.1:43692 | Total threads: 8 |
| Dashboard: http://127.0.0.1:45982/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:38162 | |
| Local directory: /tmp/dask-worker-space/worker-jpp3i8fs | |
| Comm: tcp://127.0.0.1:43449 | Total threads: 8 |
| Dashboard: http://127.0.0.1:45600/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:39152 | |
| Local directory: /tmp/dask-worker-space/worker-z6az583s | |
| Comm: tcp://127.0.0.1:38576 | Total threads: 8 |
| Dashboard: http://127.0.0.1:37397/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:34890 | |
| Local directory: /tmp/dask-worker-space/worker-e3jbn24l | |
| Comm: tcp://127.0.0.1:45743 | Total threads: 8 |
| Dashboard: http://127.0.0.1:40227/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:40027 | |
| Local directory: /tmp/dask-worker-space/worker-1gcg_842 | |
| Comm: tcp://127.0.0.1:46109 | Total threads: 8 |
| Dashboard: http://127.0.0.1:42550/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:38748 | |
| Local directory: /tmp/dask-worker-space/worker-zgura3zh | |
| Comm: tcp://127.0.0.1:37917 | Total threads: 8 |
| Dashboard: http://127.0.0.1:36973/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:36395 | |
| Local directory: /tmp/dask-worker-space/worker-qr3w7mla | |
| Comm: tcp://127.0.0.1:43765 | Total threads: 8 |
| Dashboard: http://127.0.0.1:40710/status | Memory: 15.69 GiB |
| Nanny: tcp://127.0.0.1:35014 | |
| Local directory: /tmp/dask-worker-space/worker-2fhc5df9 | |
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 | |
|---|---|---|---|---|---|---|---|---|---|---|
| IceThickness | icemod.sivolu | (data.sivolu.where(data.sivolu >0)).to_dataset... | ALL | maps | 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 True lazy= False
../lib/SEDNA_DELTA_MONITOR.yaml
using param_xios reading ../lib/SEDNA_DELTA_MONITOR.yaml
using param_xios reading <bound method DataSourceBase.describe of sources:
param_xios:
args:
combine: nested
concat_dim: y
urlpath: /ccc/work/cont003/gen7420/odakatin/CONFIGS/SEDNA/SEDNA-I/SEDNA_Domain_cfg_Tgt_20210423_tsh10m_L1/param_f32/x_*.nc
xarray_kwargs:
compat: override
coords: minimal
data_vars: minimal
parallel: true
description: SEDNA NEMO parameters from MPI output nav_lon lat fails
driver: intake_xarray.netcdf.NetCDFSource
metadata:
catalog_dir: /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/../lib/
>
{'name': 'param_xios', 'container': 'xarray', 'plugin': ['netcdf'], 'driver': ['netcdf'], 'description': 'SEDNA NEMO parameters from MPI output nav_lon lat fails', 'direct_access': 'forbid', 'user_parameters': [{'name': 'path', 'description': 'file coordinate', 'type': 'str', 'default': '/ccc/work/cont003/gen7420/odakatin/CONFIGS/SEDNA/MESH/SEDNA_mesh_mask_Tgt_20210423_tsh10m_L1/param'}], 'metadata': {}, 'args': {'urlpath': '/ccc/work/cont003/gen7420/odakatin/CONFIGS/SEDNA/SEDNA-I/SEDNA_Domain_cfg_Tgt_20210423_tsh10m_L1/param_f32/x_*.nc', 'combine': 'nested', 'concat_dim': 'y'}}
0 read icemod ['sivolu']
using load_data_xios_kerchunk reading icemod
using load_data_xios_kerchunk reading <bound method DataSourceBase.describe of sources:
data_xios_kerchunk:
args:
consolidated: false
storage_options:
fo: file:////ccc/cont003/home/ra5563/ra5563/catalogue/DELTA/201201/icemod_0[0-5][0-9][0-9].json
target_protocol: file
urlpath: reference://
description: CREG025 NEMO outputs from different xios server in kerchunk format
driver: intake_xarray.xzarr.ZarrSource
metadata:
catalog_dir: /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/../lib/
>
took 31.93233346939087 seconds
0 merging icemod ['sivolu']
param nav_lon will be included in data
param mask2d will be included in data
param nav_lat will be included in data
ychunk= 10 calldatas_y_rechunk
sum_num (13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12)
start rechunking with (130, 122, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 48)
end of y_rechunk
CPU times: user 34.3 s, sys: 2.52 s, total: 36.9 s
Wall time: 56.4 s
<xarray.Dataset>
Dimensions: (t: 31, 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>
time_centered (t) object dask.array<chunksize=(1,), meta=np.ndarray>
* t (t) object 2012-01-01 12:00:00 ... 2012-01-31 12:00:00
* y (y) int64 1 2 3 4 5 6 7 ... 6535 6536 6537 6538 6539 6540
* x (x) int64 1 2 3 4 5 6 7 ... 6555 6556 6557 6558 6559 6560
mask2d (y, x) bool dask.array<chunksize=(130, 6560), meta=np.ndarray>
Data variables:
sivolu (t, y, x) float32 dask.array<chunksize=(1, 130, 6560), meta=np.ndarray>
Attributes: (12/26)
CASE: DELTA
CONFIG: SEDNA
Conventions: CF-1.6
DOMAIN_dimensions_ids: [2, 3]
DOMAIN_halo_size_end: [0, 0]
DOMAIN_halo_size_start: [0, 0]
... ...
nj: 13
output_frequency: 1d
start_date: 20090101
timeStamp: 2022-Jan-17 19:00:05 GMT
title: ice variables
uuid: 65f78891-6a37-4a91-8ad4-7c8b5dc0d456%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
)
#calc= True
#save= True
#plot= False
monitor.optimize_dataset(data)
Value='IceThickness'
Zone='ALL'
Plot='maps'
cmap='Spectral'
clabel='m'
clim= (0, 5)
outputpath='../results/SEDNA_DELTA_MONITOR/'
nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/'
filename='SEDNA_maps_ALL_IceThickness'
#3 Start computing
dtaa= (data.sivolu.where(data.sivolu >0)).to_dataset(name='sivolu').chunk({ 't': -1 }).unify_chunks().persist()
monitor.optimize_dataset(dtaa)
<xarray.Dataset>
Dimensions: (y: 6540, x: 6560, t: 31)
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>
time_centered (t) object dask.array<chunksize=(31,), meta=np.ndarray>
* t (t) object 2012-01-01 12:00:00 ... 2012-01-31 12:00:00
* y (y) int64 1 2 3 4 5 6 7 ... 6535 6536 6537 6538 6539 6540
* x (x) int64 1 2 3 4 5 6 7 ... 6555 6556 6557 6558 6559 6560
mask2d (y, x) bool dask.array<chunksize=(130, 6560), meta=np.ndarray>
Data variables:
sivolu (t, y, x) float32 dask.array<chunksize=(31, 130, 6560), meta=np.ndarray>#4 Saving SEDNA_maps_ALL_IceThickness dtaa=save.datas(data,plot=Plot,path=nc_outputpath,filename=filename) start saving data saving data in a file
--------------------------------------------------------------------------- UnboundLocalError Traceback (most recent call last) File <timed eval>:1, in <module> File /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/monitor.py:84, in auto(df, val, savefig, daskreport, outputpath, file_exp) 82 print('dtaa=save.datas(data,plot=Plot,path=nc_outputpath,filename=filename)' ) 83 with performance_report(filename=daskreport+"_save_"+step.Value+".html"): ---> 84 save.datas(data,plot=Plot,path=nc_outputpath,filename=filename) 85 # 5. Plot 86 if plotswitch=='True': File /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/save.py:24, in datas(data, plot, path, filename) 22 twoD(data,path,filename,nested=False) 23 else : ---> 24 twoD(data,path,filename) 25 return None File /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/save.py:57, in twoD(data, path, filename, nested) 55 print('saving data in a file') 56 filesave=path+filename ---> 57 return to_mfnetcdf_map(data,prefix=filesave, nested=nested) File /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/save.py:219, in to_mfnetcdf_map(ds, prefix, nested) 217 slices.append(slice(start, stop)) 218 start = stop --> 219 chunk_slices[dim] = slices 220 for i in chunk_slices['t']: 221 print(i) UnboundLocalError: local variable 'slices' referenced before assignment