In [1]:

import numpy as np
from matplotlib import pyplot as plt
import netCDF4 as nc
import re
import shutil
%matplotlib inline

In [2]:

src='/ocean/eolson/MEOPAR/northernNO3PaperCalcs/bioModel/runFiles/01may15/SalishSea_01153440_restart_trc.nc'
dst='/ocean/eolson/MEOPAR/northernNO3PaperCalcs/bioModel/runFiles/01may15/SalishSea_01153440_restart_trc_SinkTest.nc'

In [3]:

shutil.copy(src, dst)

Out[3]:

'/ocean/eolson/MEOPAR/northernNO3PaperCalcs/bioModel/runFiles/01may15/SalishSea_01153440_restart_trc_SinkTest.nc'

In [4]:

f=nc.Dataset(dst,'r+')

In [5]:

print(f.variables.keys())

odict_keys(['nav_lon', 'nav_lat', 'nav_lev', 'time_counter', 'kt', 'ndastp', 'adatrj', 'rnf_pis_NO3_b', 'rnf_pis_NH4_b', 'rnf_pis_Si_b', 'rnf_pis_DIAT_b', 'rnf_pis_PHY_b', 'rnf_pis_MYRI_b', 'rnf_pis_MICZ_b', 'rnf_pis_DON_b', 'rnf_pis_PON_b', 'rnf_pis_bSi_b', 'rnf_pis_TRA_b', 'rnf_pis_DIC_b', 'rnf_pis_TA_b', 'rnf_pis_O2_b', 'sbc_NO3_b', 'sbc_NH4_b', 'sbc_Si_b', 'sbc_DIAT_b', 'sbc_PHY_b', 'sbc_MYRI_b', 'sbc_MICZ_b', 'sbc_DON_b', 'sbc_PON_b', 'sbc_bSi_b', 'sbc_TRA_b', 'sbc_DIC_b', 'sbc_TA_b', 'sbc_O2_b', 'sbc_MYTRC1_b', 'rdttrc1', 'TRNNO3', 'TRNNH4', 'TRNSi', 'TRNDIAT', 'TRNPHY', 'TRNMYRI', 'TRNMICZ', 'TRNDON', 'TRNPON', 'TRNbSi', 'TRNTRA', 'TRNDIC', 'TRNTA', 'TRNO2', 'TRNMYTRC1', 'TRBNO3', 'TRBNH4', 'TRBSi', 'TRBDIAT', 'TRBPHY', 'TRBMYRI', 'TRBMICZ', 'TRBDON', 'TRBPON', 'TRBbSi', 'TRBTRA', 'TRBDIC', 'TRBTA', 'TRBO2', 'TRBMYTRC1'])

In [6]:

f.variables['TRNDIAT']

Out[6]:

<class 'netCDF4._netCDF4.Variable'>
float64 TRNDIAT(t, z, y, x)
unlimited dimensions: t
current shape = (1, 40, 898, 398)
filling on, default _FillValue of 9.969209968386869e+36 used

In [7]:

for var in ('TRNNO3', 'TRNNH4', 'TRNSi', 'TRNDIAT', 'TRNPHY', 'TRNMYRI', 'TRNMICZ', 
            'TRNDON', 'TRNPON', 'TRNbSi', 'TRNTRA', 'TRNDIC', 'TRNTA', 'TRNO2', 
            'TRNMYTRC1', 'TRBNO3', 'TRBNH4', 'TRBSi', 'TRBDIAT', 'TRBPHY', 'TRBMYRI', 
            'TRBMICZ', 'TRBDON', 'TRBPON', 'TRBbSi', 'TRBTRA', 'TRBDIC', 'TRBTA', 'TRBO2', 
            'TRBMYTRC1'):
    f.variables[var][:]=0.0

In [8]:

f.variables['TRBNO3'][:,5,:,:]=100.0
f.variables['TRNNO3'][:,5,:,:]=100.0

In [9]:

f.variables['TRBDIAT'][:,5,:,:]=100.0
f.variables['TRNDIAT'][:,5,:,:]=100.0

In [10]:

f.variables['TRBbSi'][:,5,:,:]=100.0
f.variables['TRNbSi'][:,5,:,:]=100.0

In [11]:

f.variables['TRBPON'][:,33,:,:]=100.0
f.variables['TRNPON'][:,33,:,:]=100.0

In [ ]:

In [12]:

np.max(f.variables['TRNPHY']), np.min(f.variables['TRNPHY'])

Out[12]:

(0.0, 0.0)

In [13]:

np.max(f.variables['TRNMYRI']), np.min(f.variables['TRNMYRI'])

Out[13]:

(0.0, 0.0)

In [14]:

np.max(f.variables['sbc_MYRI_b']), np.min(f.variables['sbc_MYRI_b'])

Out[14]:

(0.0, 0.0)

In [15]:

f.close()

In [16]:

f=nc.Dataset(dst)

In [17]:

np.max(f.variables['TRNPHY']), np.min(f.variables['TRNPHY'])

Out[17]:

(0.0, 0.0)

In [18]:

np.max(f.variables['TRNMYRI']), np.min(f.variables['TRNMYRI'])

Out[18]:

(0.0, 0.0)

In [19]:

np.max(f.variables['TRBMYRI']), np.min(f.variables['TRBMYRI'])

Out[19]:

(0.0, 0.0)

In [20]:

f.close()

In [ ]: