%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= irene4352.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 irene4352.c-irene.mg1.tgcc.ccc.cea.fr using SEDNA_ALPHA_MONITOR file experiment, read from ../lib/SEDNA_ALPHA_MONITOR.yaml on year= * on month= 24 outputpath= ../results/SEDNA_ALPHA_MONITOR/24/ daskreport= ../results/dask/2551828irene4352.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_ALPHA_MONITOR_24Mean_temp_velo/ CPU times: user 338 ms, sys: 236 ms, total: 574 ms Wall time: 8.93 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 | |
---|---|---|---|---|---|---|---|---|---|---|
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')
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 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.24/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 712.5763630867004 seconds 0 merging gridT ['votemper'] 1 read gridV ['vomecrty'] using load_data_xios reading gridV 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.24/SEDNA-ALPHA_1d_gridV_*_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 387.4011263847351 seconds 1 merging gridV ['vomecrty'] took 1.703331470489502 seconds param mask will be included in data param depth will be included in data param mask2d will be included in data param nav_lat will be included in data param nav_lon 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 4min 13s, sys: 54.9 s, total: 5min 8s Wall time: 18min 37s
<xarray.Dataset> Dimensions: (t: 31, x: 6560, y: 6540, z: 150) Coordinates: * t (t) object 2004-08-01 12:00:00 ... 2004-08-31 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 * z (z) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150 mask (z, y, x) bool dask.array<chunksize=(150, 130, 6560), meta=np.ndarray> depth (z, y, x) float32 dask.array<chunksize=(150, 130, 6560), meta=np.ndarray> mask2d (y, x) bool dask.array<chunksize=(130, 6560), meta=np.ndarray> 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> Data variables: votemper (t, z, y, x) float32 dask.array<chunksize=(1, 150, 130, 6560), meta=np.ndarray> vomecrty (t, z, y, x) float32 dask.array<chunksize=(1, 150, 130, 6560), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 8, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 15, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 30, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 31, 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='FramS_Small' Value='Mean Temp & Velocity' cmap='None' clabel='(°C,cm.s^-1)' clim= ((0, 4), (0, 10)) outputpath='../results/SEDNA_ALPHA_MONITOR/24/' nc_outputpath='../nc_results/SEDNA_ALPHA_MONITOR/24/' filename='SEDNA_Mean_temp_velo_integrals_FramS_Small_Mean_Temp_&_Velocity' #2 Zooming Data dataa= zoom.FramS_Small(data)
<xarray.Dataset> Dimensions: (t: 31, x: 146, z: 97) Coordinates: * t (t) object 2004-08-01 12:00:00 ... 2004-08-31 12:00:00 y int64 2609 * x (x) int64 4157 4158 4159 4160 4161 ... 4298 4299 4300 4301 4302 * z (z) int64 1 2 3 4 5 6 7 8 9 10 ... 88 89 90 91 92 93 94 95 96 97 mask (z, x) bool dask.array<chunksize=(97, 146), meta=np.ndarray> depth (z, x) float32 dask.array<chunksize=(97, 146), meta=np.ndarray> mask2d (x) bool dask.array<chunksize=(146,), meta=np.ndarray> nav_lat (x) float32 dask.array<chunksize=(146,), meta=np.ndarray> nav_lon (x) float32 dask.array<chunksize=(146,), meta=np.ndarray> Data variables: votemper (t, z, x) float32 dask.array<chunksize=(1, 97, 146), meta=np.ndarray> vomecrty (t, z, x) float32 dask.array<chunksize=(1, 97, 146), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 8, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 15, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 30, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 31, 12, 0, 0, 0)], dtype=object)
array(2609)
array([4157, 4158, 4159, 4160, 4161, 4162, 4163, 4164, 4165, 4166, 4167, 4168, 4169, 4170, 4171, 4172, 4173, 4174, 4175, 4176, 4177, 4178, 4179, 4180, 4181, 4182, 4183, 4184, 4185, 4186, 4187, 4188, 4189, 4190, 4191, 4192, 4193, 4194, 4195, 4196, 4197, 4198, 4199, 4200, 4201, 4202, 4203, 4204, 4205, 4206, 4207, 4208, 4209, 4210, 4211, 4212, 4213, 4214, 4215, 4216, 4217, 4218, 4219, 4220, 4221, 4222, 4223, 4224, 4225, 4226, 4227, 4228, 4229, 4230, 4231, 4232, 4233, 4234, 4235, 4236, 4237, 4238, 4239, 4240, 4241, 4242, 4243, 4244, 4245, 4246, 4247, 4248, 4249, 4250, 4251, 4252, 4253, 4254, 4255, 4256, 4257, 4258, 4259, 4260, 4261, 4262, 4263, 4264, 4265, 4266, 4267, 4268, 4269, 4270, 4271, 4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281, 4282, 4283, 4284, 4285, 4286, 4287, 4288, 4289, 4290, 4291, 4292, 4293, 4294, 4295, 4296, 4297, 4298, 4299, 4300, 4301, 4302])
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])
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#3 Start computing dtaa= calc.Mean_temp_velo(data)
<xarray.Dataset> Dimensions: (t: 31) Coordinates: * t (t) object 2004-08-01 12:00:00 ... 2004-08-31 12:00:00 y int64 2609 Data variables: Mean Tempreture (t) float32 dask.array<chunksize=(1,), meta=np.ndarray> Mean Velocity (t) float32 dask.array<chunksize=(1,), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 8, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 15, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 30, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 8, 31, 12, 0, 0, 0)], dtype=object)
array(2609)
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#4 Saving SEDNA_Mean_temp_velo_integrals_FramS_Small_Mean_Temp_&_Velocity 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/24/SEDNA_Mean_temp_velo_integrals_FramS_Small_Mean_Temp_&_Velocity2004-08-01_2004-08-31.nc save computed data at ../nc_results/SEDNA_ALPHA_MONITOR/24/SEDNA_Mean_temp_velo_integrals_FramS_Small_Mean_Temp_&_Velocity2004-08-01_2004-08-31.nc completed CPU times: user 15min 55s, sys: 12.9 s, total: 16min 8s Wall time: 16min 12s