%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= irene6251.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 irene6251.c-irene.mg1.tgcc.ccc.cea.fr using SEDNA_ALPHA_MONITOR file experiment, read from ../lib/SEDNA_ALPHA_MONITOR.yaml on year= * on month= 25 outputpath= ../results/SEDNA_ALPHA_MONITOR/25/ daskreport= ../results/dask/2585151irene6251.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_ALPHA_MONITOR_25Mooring/ CPU times: user 375 ms, sys: 234 ms, total: 609 ms Wall time: 12.2 s
Client
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Cluster
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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 | |
---|---|---|---|---|---|---|---|---|---|---|
Mooring_Arc_B | gridS.vosaline,gridT.votemper,param.depth,para... | data | Arc_B | Mooring | rainbow | {'vosaline': (28.0,34.4), 'votemper': (-2.0,2.0)} | None | x | ||
Mooring_Eur_B | gridS.vosaline,gridT.votemper,param.depth,para... | data | Eur_B | Mooring | rainbow | {'vosaline': (32.0,35.0), 'votemper': (-2.0,3.0)} | None | x |
Each computation consists of
%%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= True df.Inputs != nothing True ../lib/SEDNA_ALPHA_MONITOR.yaml using param_xios reading ../lib/SEDNA_ALPHA_MONITOR.yaml using param_xios reading <bound method DataSourceBase.describe of sources: param_xios: args: combine: by_coords 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': 'by_coords', 'concat_dim': 'y'}} 0 read gridS ['vosaline'] using load_data_xios reading gridS using load_data_xios reading <bound method DataSourceBase.describe of sources: data_xios: args: combine: by_coords concat_dim: time_counter,x,y urlpath: /ccc/scratch/cont003/gen7420/talandel/ONGOING-RUNS/SEDNA-ALPHA-XIOS.25/SEDNA-ALPHA_1d_gridS_*_0[0-5][0-9][0-9].nc xarray_kwargs: compat: override coords: minimal data_vars: minimal drop_variables: !!set deptht_bounds: null depthu_bounds: null nav_lat: null nav_lon: null time_centerd: null time_centered_bounds: null time_counter_bounds: null parallel: true preprocess: !!python/name:core.load.prep '' description: SEDNA NEMO outputs from different xios server driver: intake_xarray.netcdf.NetCDFSource metadata: catalog_dir: /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/../lib/ > took 247.0235767364502 seconds 0 merging gridS ['vosaline'] 1 read gridT ['votemper'] using load_data_xios reading gridT using load_data_xios reading <bound method DataSourceBase.describe of sources: data_xios: args: combine: by_coords concat_dim: time_counter,x,y urlpath: /ccc/scratch/cont003/gen7420/talandel/ONGOING-RUNS/SEDNA-ALPHA-XIOS.25/SEDNA-ALPHA_1d_gridT_*_0[0-5][0-9][0-9].nc xarray_kwargs: compat: override coords: minimal data_vars: minimal drop_variables: !!set deptht_bounds: null depthu_bounds: null nav_lat: null nav_lon: null time_centerd: null time_centered_bounds: null time_counter_bounds: null parallel: true preprocess: !!python/name:core.load.prep '' description: SEDNA NEMO outputs from different xios server driver: intake_xarray.netcdf.NetCDFSource metadata: catalog_dir: /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/../lib/ > took 132.9670958518982 seconds 1 merging gridT ['votemper'] took 0.6072893142700195 seconds param nav_lon will be included in data param mask2d will be included in data param depth will be included in data param nav_lat will be included in data param mask will be included in data 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 2min 49s, sys: 34.8 s, total: 3min 23s Wall time: 6min 37s
<xarray.Dataset> Dimensions: (t: 30, x: 6560, y: 6540, z: 150) Coordinates: * t (t) object 2004-09-01 12:00:00 ... 2004-09-30 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 * z (z) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150 nav_lon (y, x) float32 dask.array<chunksize=(130, 6560), meta=np.ndarray> mask2d (y, x) bool dask.array<chunksize=(130, 6560), meta=np.ndarray> depth (z, y, x) float32 dask.array<chunksize=(150, 130, 6560), meta=np.ndarray> nav_lat (y, x) float32 dask.array<chunksize=(130, 6560), meta=np.ndarray> mask (z, y, x) bool dask.array<chunksize=(150, 130, 6560), meta=np.ndarray> Data variables: vosaline (t, z, y, x) float32 dask.array<chunksize=(1, 150, 130, 6560), meta=np.ndarray> votemper (t, z, y, x) float32 dask.array<chunksize=(1, 150, 130, 6560), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 9, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 15, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 30, 12, 0, 0, 0)], dtype=object)
array([ 1, 2, 3, ..., 6538, 6539, 6540])
array([ 1, 2, 3, ..., 6558, 6559, 6560])
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150])
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%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
)
#calc= True #save= True #plot= False Zone='Arc_B' Value='Mooring_Arc_B' cmap='rainbow' clabel='None' clim= {'vosaline': (28.0, 34.4), 'votemper': (-2.0, 2.0)} outputpath='../results/SEDNA_ALPHA_MONITOR/25/' nc_outputpath='../nc_results/SEDNA_ALPHA_MONITOR/25/' filename='SEDNA_Mooring_Arc_B_Mooring_Arc_B' #2 Zooming Data dataa= zoom.Arc_B(data)
<xarray.Dataset> Dimensions: (t: 30, z: 102) Coordinates: * t (t) object 2004-09-01 12:00:00 ... 2004-09-30 12:00:00 y int64 4967 x int64 2471 * z (z) int64 1 2 3 4 5 6 7 8 9 10 ... 94 95 96 97 98 99 100 101 102 nav_lon float32 dask.array<chunksize=(), meta=np.ndarray> mask2d bool dask.array<chunksize=(), meta=np.ndarray> depth (z) float32 dask.array<chunksize=(102,), meta=np.ndarray> nav_lat float32 dask.array<chunksize=(), meta=np.ndarray> mask (z) bool dask.array<chunksize=(102,), meta=np.ndarray> Data variables: vosaline (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray> votemper (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 9, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 15, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 30, 12, 0, 0, 0)], dtype=object)
array(4967)
array(2471)
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102])
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#3 Start computing dtaa= data
<xarray.Dataset> Dimensions: (t: 30, z: 102) Coordinates: * t (t) object 2004-09-01 12:00:00 ... 2004-09-30 12:00:00 y int64 4967 x int64 2471 * z (z) int64 1 2 3 4 5 6 7 8 9 10 ... 94 95 96 97 98 99 100 101 102 nav_lon float32 dask.array<chunksize=(), meta=np.ndarray> mask2d bool dask.array<chunksize=(), meta=np.ndarray> depth (z) float32 dask.array<chunksize=(102,), meta=np.ndarray> nav_lat float32 dask.array<chunksize=(), meta=np.ndarray> mask (z) bool dask.array<chunksize=(102,), meta=np.ndarray> Data variables: vosaline (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray> votemper (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 9, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 15, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 30, 12, 0, 0, 0)], dtype=object)
array(4967)
array(2471)
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102])
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#4 Saving SEDNA_Mooring_Arc_B_Mooring_Arc_B dtaa=save.datas(data,plot=Plot,path=nc_outputpath,filename=filename) start saving data saving data in a csv file ../nc_results/SEDNA_ALPHA_MONITOR/25/SEDNA_Mooring_Arc_B_Mooring_Arc_B2004-09-01_2004-09-30.nc save computed data at ../nc_results/SEDNA_ALPHA_MONITOR/25/SEDNA_Mooring_Arc_B_Mooring_Arc_B2004-09-01_2004-09-30.nc completed Zone='Eur_B' Value='Mooring_Eur_B' cmap='rainbow' clabel='None' clim= {'vosaline': (32.0, 35.0), 'votemper': (-2.0, 3.0)} outputpath='../results/SEDNA_ALPHA_MONITOR/25/' nc_outputpath='../nc_results/SEDNA_ALPHA_MONITOR/25/' filename='SEDNA_Mooring_Eur_B_Mooring_Eur_B' #2 Zooming Data dataa= zoom.Eur_B(data)
<xarray.Dataset> Dimensions: (t: 30, z: 102) Coordinates: * t (t) object 2004-09-01 12:00:00 ... 2004-09-30 12:00:00 y int64 3621 x int64 2495 * z (z) int64 1 2 3 4 5 6 7 8 9 10 ... 94 95 96 97 98 99 100 101 102 nav_lon float32 dask.array<chunksize=(), meta=np.ndarray> mask2d bool dask.array<chunksize=(), meta=np.ndarray> depth (z) float32 dask.array<chunksize=(102,), meta=np.ndarray> nav_lat float32 dask.array<chunksize=(), meta=np.ndarray> mask (z) bool dask.array<chunksize=(102,), meta=np.ndarray> Data variables: vosaline (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray> votemper (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 9, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 15, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 30, 12, 0, 0, 0)], dtype=object)
array(3621)
array(2495)
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102])
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#3 Start computing dtaa= data
<xarray.Dataset> Dimensions: (t: 30, z: 102) Coordinates: * t (t) object 2004-09-01 12:00:00 ... 2004-09-30 12:00:00 y int64 3621 x int64 2495 * z (z) int64 1 2 3 4 5 6 7 8 9 10 ... 94 95 96 97 98 99 100 101 102 nav_lon float32 dask.array<chunksize=(), meta=np.ndarray> mask2d bool dask.array<chunksize=(), meta=np.ndarray> depth (z) float32 dask.array<chunksize=(102,), meta=np.ndarray> nav_lat float32 dask.array<chunksize=(), meta=np.ndarray> mask (z) bool dask.array<chunksize=(102,), meta=np.ndarray> Data variables: vosaline (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray> votemper (t, z) float32 dask.array<chunksize=(1, 102), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 9, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 15, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 30, 12, 0, 0, 0)], dtype=object)
array(3621)
array(2495)
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102])
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#4 Saving SEDNA_Mooring_Eur_B_Mooring_Eur_B dtaa=save.datas(data,plot=Plot,path=nc_outputpath,filename=filename) start saving data saving data in a csv file ../nc_results/SEDNA_ALPHA_MONITOR/25/SEDNA_Mooring_Eur_B_Mooring_Eur_B2004-09-01_2004-09-30.nc save computed data at ../nc_results/SEDNA_ALPHA_MONITOR/25/SEDNA_Mooring_Eur_B_Mooring_Eur_B2004-09-01_2004-09-30.nc completed CPU times: user 32min 26s, sys: 31.3 s, total: 32min 57s Wall time: 32min 52s