%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= irene5423.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 irene5423.c-irene.mg1.tgcc.ccc.cea.fr using SEDNA_ALPHA_MONITOR file experiment, read from ../lib/SEDNA_ALPHA_MONITOR.yaml on year= * on month= 23 outputpath= ../results/SEDNA_ALPHA_MONITOR/23/ daskreport= ../results/dask/2530971irene5423.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_ALPHA_MONITOR_23Fluxnet/ CPU times: user 339 ms, sys: 265 ms, total: 604 ms Wall time: 11 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 | |
---|---|---|---|---|---|---|---|---|---|---|
Fluxnet | gridV.vomecrty,param.e3v_0,param.e1v,param.mas... | calc.Fluxnet(data) | FramS_All | Fluxnet_integrals | None | ((-10,10),(-10,50) ,(-150,50),(-25,5) ) | (Sv,TW, mSv,10^-2 Sv) | I-6 |
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.23/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 358.6762058734894 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.23/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 169.41277694702148 seconds 1 merging gridT ['votemper'] took 0.5362842082977295 seconds 2 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.23/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 174.69799304008484 seconds 2 merging gridV ['vomecrty'] took 0.5957114696502686 seconds 3 read icemod ['sivolu', 'sivelv'] using load_data_xios reading icemod 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.23/SEDNA-ALPHA_1d_icemod_*_0[0-5][0-9][0-9].nc xarray_kwargs: compat: override coords: minimal data_vars: minimal drop_variables: !!set botpres: null deptht_bounds: null depthu_bounds: null iicestru: null iicestrv: null intstrx: null intstry: null mldkz5: null rhop_sig0: null siages: null sidive: null sisali: null sishea: null sistre: null sitemp: null snthic: null snvolu: null sometauy: null sozotaux: null time_centered_bounds: null time_counter_bounds: null utau_atmoce: null utau_iceoce: null uwspd10: null vtau_atmoce: null vtau_iceoce: null vwspd10: 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 351.4900460243225 seconds 3 merging icemod ['sivolu', 'sivelv'] took 0.5276660919189453 seconds param e3v_0 will be included in data param mask2d will be included in data param mask will be included in data param e1v will be included in data param nav_lon will be included in data param nav_lat will be included in data CPU times: user 7min 56s, sys: 1min 21s, total: 9min 18s Wall time: 17min 53s
<xarray.Dataset> Dimensions: (t: 31, x: 6560, y: 6540, z: 150) Coordinates: * t (t) object 2004-07-01 12:00:00 ... 2004-07-31 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 nav_lat (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray> nav_lon (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray> * z (z) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150 e3v_0 (z, y, x) float64 dask.array<chunksize=(150, 13, 6560), meta=np.ndarray> mask2d (y, x) bool dask.array<chunksize=(13, 6560), meta=np.ndarray> mask (z, y, x) bool dask.array<chunksize=(150, 13, 6560), meta=np.ndarray> e1v (y, x) float64 dask.array<chunksize=(13, 6560), meta=np.ndarray> Data variables: vosaline (t, z, y, x) float32 dask.array<chunksize=(1, 150, 13, 6560), meta=np.ndarray> votemper (t, z, y, x) float32 dask.array<chunksize=(1, 150, 13, 6560), meta=np.ndarray> vomecrty (t, z, y, x) float32 dask.array<chunksize=(1, 150, 13, 6560), meta=np.ndarray> sivolu (t, y, x) float32 dask.array<chunksize=(1, 13, 6560), meta=np.ndarray> sivelv (t, y, x) float32 dask.array<chunksize=(1, 13, 6560), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 7, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 15, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 30, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 31, 12, 0, 0, 0)], dtype=object)
array([ 1, 2, 3, ..., 6538, 6539, 6540])
array([ 1, 2, 3, ..., 6558, 6559, 6560])
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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_All' Value='Fluxnet' cmap='None' clabel='(Sv,TW, mSv,10^-2 Sv)' clim= ((-10, 10), (-10, 50), (-150, 50), (-25, 5)) outputpath='../results/SEDNA_ALPHA_MONITOR/23/' nc_outputpath='../nc_results/SEDNA_ALPHA_MONITOR/23/' filename='SEDNA_Fluxnet_integrals_FramS_All_Fluxnet' #2 Zooming Data dataa= zoom.FramS_All(data)
<xarray.Dataset> Dimensions: (t: 31, x: 556, y: 2, z: 137) Coordinates: * t (t) object 2004-07-01 12:00:00 ... 2004-07-31 12:00:00 * y (y) int64 2608 2609 * x (x) int64 3748 3749 3750 3751 3752 ... 4299 4300 4301 4302 4303 nav_lat (y, x) float32 dask.array<chunksize=(2, 556), meta=np.ndarray> nav_lon (y, x) float32 dask.array<chunksize=(2, 556), meta=np.ndarray> * z (z) int64 1 2 3 4 5 6 7 8 9 ... 130 131 132 133 134 135 136 137 e3v_0 (z, y, x) float64 dask.array<chunksize=(137, 2, 556), meta=np.ndarray> mask2d (y, x) bool dask.array<chunksize=(2, 556), meta=np.ndarray> mask (z, y, x) bool dask.array<chunksize=(137, 2, 556), meta=np.ndarray> e1v (y, x) float64 dask.array<chunksize=(2, 556), meta=np.ndarray> Data variables: vosaline (t, z, y, x) float32 dask.array<chunksize=(1, 137, 2, 556), meta=np.ndarray> votemper (t, z, y, x) float32 dask.array<chunksize=(1, 137, 2, 556), meta=np.ndarray> vomecrty (t, z, y, x) float32 dask.array<chunksize=(1, 137, 2, 556), meta=np.ndarray> sivolu (t, y, x, z) float32 dask.array<chunksize=(1, 2, 556, 137), meta=np.ndarray> sivelv (t, y, x, z) float32 dask.array<chunksize=(1, 2, 556, 137), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 7, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 15, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 30, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 31, 12, 0, 0, 0)], dtype=object)
array([2608, 2609])
array([3748, 3749, 3750, ..., 4301, 4302, 4303])
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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])
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#3 Start computing dtaa= calc.Fluxnet(data)
<xarray.Dataset> Dimensions: (t: 31) Coordinates: * t (t) object 2004-07-01 12:00:00 ... 2004-07-31 12:0... y int64 2608 Data variables: Volume flux Net (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Volume flux Northward (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Heat flux Net (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Heat flux Northward (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Freshwater Net (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Freshwater Northward (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Ice export (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Volume flux South (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Heat flux South (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Freshwater South (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 7, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 10, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 11, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 12, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 13, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 14, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 15, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 30, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 31, 12, 0, 0, 0)], dtype=object)
array(2608)
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#4 Saving SEDNA_Fluxnet_integrals_FramS_All_Fluxnet 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/23/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet2004-07-01_2004-07-31.nc save computed data at ../nc_results/SEDNA_ALPHA_MONITOR/23/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet2004-07-01_2004-07-31.nc completed CPU times: user 1h 4min 11s, sys: 52.9 s, total: 1h 5min 4s Wall time: 1h 4min 25s