%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=Ints_monitor
# 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 calc=True
%%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 not local in tgcc c-irene.tgcc local FORCED tgcc local cluster starting This code is running on irene8000.c-irene.tgcc.ccc.cea.fr using SEDNA_ALPHA_MONITOR file experiment, read from ../lib/SEDNA_ALPHA_MONITOR.yaml on year= * on month= 23 outputpath= ../results/xlarge_SEDNA_ALPHA_MONITOR/23/ daskreport= ../results/dask/7361403irene8000.c-irene.tgcc.ccc.cea.fr_SEDNA_ALPHA_MONITOR_23Flux_moni/ CPU times: user 683 ms, sys: 736 ms, total: 1.42 s Wall time: 14.4 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_volu | gridV.vomecrty,param.e3v_0,param.e1v,param.mask | calc.Fluxnet_volu(data) | FramS_All | integrals | None | (-10,10) | Sv | I-6 |
Each computation consists of
%%time
#todo add 'year' here.
import os
calcswitch=os.environ.get('calc', 'True')
print('calcswitch=',calcswitch)
#if calcswitch=='True':
data = load.datas(catalog_url,df.Inputs,month,year,daskreport) if calcswitch=='True' else 0
data
#print('#1 Data: created:')
#print('# if we raed too much file, we can do sel to take out some dates here')
#data
calcswitch= 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 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 264.68121314048767 seconds 0 merging gridV ['vomecrty'] param e3v_0 will be included in data param nav_lat will be included in data param mask will be included in data param mask2d will be included in data param e1v 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 1min 58s, sys: 35 s, total: 2min 33s Wall time: 4min 44s
<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 * 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, 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> mask2d (y, x) bool dask.array<chunksize=(130, 6560), meta=np.ndarray> e1v (y, x) float64 dask.array<chunksize=(130, 6560), meta=np.ndarray> nav_lon (y, x) float32 dask.array<chunksize=(130, 6560), meta=np.ndarray> Data variables: vomecrty (t, z, y, x) float32 dask.array<chunksize=(1, 150, 130, 6560), meta=np.ndarray> Attributes: name: /ccc/scratch/cont003/gen7420/talandel/ONGOING-RU... description: ocean V grid variables title: ocean V grid variables Conventions: CF-1.6 timeStamp: 2021-Aug-04 15:32:53 GMT uuid: 9238b3cc-22b3-417b-9c14-850ecf8a01dd ibegin: 0 ni: 6560 jbegin: 0 nj: 13 DOMAIN_number_total: 544 DOMAIN_number: 0 DOMAIN_dimensions_ids: [2 3] DOMAIN_size_global: [6560 6540] DOMAIN_size_local: [6560 13] DOMAIN_position_first: [1 1] DOMAIN_position_last: [6560 13] DOMAIN_halo_size_start: [0 0] DOMAIN_halo_size_end: [0 0] DOMAIN_type: box start_date: 20030101 output_frequency: 1d CONFIG: SEDNA CASE: ALPHA
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])
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= True Zone='FramS_All' Value='Fluxnet_volu' cmap='None' clabel='Sv' clim= (-10, 10) outputpath='../results/xlarge_SEDNA_ALPHA_MONITOR/23/' nc_outputpath='../nc_results/xlarge_SEDNA_ALPHA_MONITOR/23/' filename='SEDNA_integrals_FramS_All_Fluxnet_volu' #2 Zooming Data dataa= zoom.FramS_All(data)
<xarray.Dataset> Dimensions: (t: 31, x: 554, z: 137) Coordinates: * t (t) object 2004-07-01 12:00:00 ... 2004-07-31 12:00:00 y int64 2609 * x (x) int64 3749 3750 3751 3752 3753 ... 4298 4299 4300 4301 4302 * z (z) int64 1 2 3 4 5 6 7 8 9 ... 130 131 132 133 134 135 136 137 e3v_0 (z, x) float64 dask.array<chunksize=(137, 554), meta=np.ndarray> nav_lat (x) float32 dask.array<chunksize=(554,), meta=np.ndarray> mask (z, x) bool dask.array<chunksize=(137, 554), meta=np.ndarray> mask2d (x) bool dask.array<chunksize=(554,), meta=np.ndarray> e1v (x) float64 dask.array<chunksize=(554,), meta=np.ndarray> nav_lon (x) float32 dask.array<chunksize=(554,), meta=np.ndarray> Data variables: vomecrty (t, z, x) float32 dask.array<chunksize=(1, 137, 554), meta=np.ndarray> Attributes: name: /ccc/scratch/cont003/gen7420/talandel/ONGOING-RU... description: ocean V grid variables title: ocean V grid variables Conventions: CF-1.6 timeStamp: 2021-Aug-04 15:32:53 GMT uuid: 9238b3cc-22b3-417b-9c14-850ecf8a01dd ibegin: 0 ni: 6560 jbegin: 0 nj: 13 DOMAIN_number_total: 544 DOMAIN_number: 0 DOMAIN_dimensions_ids: [2 3] DOMAIN_size_global: [6560 6540] DOMAIN_size_local: [6560 13] DOMAIN_position_first: [1 1] DOMAIN_position_last: [6560 13] DOMAIN_halo_size_start: [0 0] DOMAIN_halo_size_end: [0 0] DOMAIN_type: box start_date: 20030101 output_frequency: 1d CONFIG: SEDNA CASE: ALPHA
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(2609)
array([3749, 3750, 3751, ..., 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, 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_volu(data)
<xarray.Dataset> Dimensions: (t: 31) Coordinates: * t (t) object 2004-07-01 12:00:00 ... 2004-07-31 12... y int64 2609 Data variables: fluxnet_volu_trans2D (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> fluxnet_IN_volu_trans2D (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(2609)
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#4 Saving SEDNA_integrals_FramS_All_Fluxnet_volu dtaa=save.datas(data,plot=Plot,path=nc_outputpath,filename=filename) start saving data saving data in a csv file ../nc_results/xlarge_SEDNA_ALPHA_MONITOR/23/SEDNA_integrals_FramS_All_Fluxnet_volu2004-07-01_2004-07-31.nc save computed data at ../nc_results/xlarge_SEDNA_ALPHA_MONITOR/23/SEDNA_integrals_FramS_All_Fluxnet_volu2004-07-01_2004-07-31.nc completed #5 Plotting filename= plots.integrals(data,path=outputpath,filename=filename,save=savefig,cmap=cmap,clim=clim,clabel=clabel) title SEDNA_integrals_FramS_All_Fluxnet_volu
WARNING:param.CurvePlot03013: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend. WARNING:param.CurvePlot03030: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
../results/xlarge_SEDNA_ALPHA_MONITOR/23/SEDNA_integrals_FramS_All_Fluxnet_volu_20040701-20040731.html starts plotting ../results/xlarge_SEDNA_ALPHA_MONITOR/23/SEDNA_integrals_FramS_All_Fluxnet_volu_20040701-20040731.html created
CPU times: user 7min 37s, sys: 7.63 s, total: 7min 45s Wall time: 7min 51s