In [1]:
import numpy as np
import netCDF4 as nc
import matplotlib.pyplot as plt
%matplotlib inline
In [2]:
f=nc.Dataset('/data/eolson/results/MEOPAR/SS36runs/GrahamRuns/oxyTestShort3/SalishSea_1h_20150401_20150401_ptrc_T_20150401-20150410.nc')
In [3]:
f.variables.keys()
Out[3]:
dict_keys(['nav_lat', 'nav_lon', 'bounds_nav_lon', 'bounds_nav_lat', 'area', 'deptht', 'deptht_bounds', 'time_centered', 'time_centered_bounds', 'time_counter', 'time_counter_bounds', 'NO3', 'O2', 'NH4', 'DIAT', 'Si', 'PHY', 'bSi'])
In [4]:
f.variables['O2']
Out[4]:
<class 'netCDF4._netCDF4.Variable'>
float32 O2(time_counter, deptht, y, x)
standard_name: dissolved_oxygen_expressed_as_mole_equivalent
long_name: Dissolved Oxygen Concentration
units: mmol m-3
online_operation: average
interval_operation: 40 s
interval_write: 1 h
cell_methods: time: mean (interval: 40 s)
cell_measures: area: area
_FillValue: 1e+20
missing_value: 1e+20
coordinates: time_centered nav_lat nav_lon
unlimited dimensions: time_counter
current shape = (3, 40, 898, 398)
filling on
In [5]:
plt.pcolormesh(f.variables['O2'][-1,0,:,:])
plt.colorbar()
Out[5]:
<matplotlib.colorbar.Colorbar at 0x7fbed3a6e490>
In [6]:
plt.pcolormesh(f.variables['O2'][-1,:,:,250])
plt.colorbar()
Out[6]:
<matplotlib.colorbar.Colorbar at 0x7fbec763a9d0>
In [7]:
plt.pcolormesh(f.variables['NH4'][-1,0,:,:])
plt.colorbar()
Out[7]:
<matplotlib.colorbar.Colorbar at 0x7fbec75dcdf0>
In [8]:
f2=nc.Dataset('/data/eolson/results/MEOPAR/SS36runs/GrahamRuns/oxyTestShortOff/SalishSea_1h_20150401_20150401_ptrc_T_20150401-20150410.nc')
In [10]:
f2.variables.keys()
Out[10]:
dict_keys(['nav_lat', 'nav_lon', 'bounds_nav_lon', 'bounds_nav_lat', 'area', 'deptht', 'deptht_bounds', 'time_centered', 'time_centered_bounds', 'time_counter', 'time_counter_bounds', 'NO3', 'NH4', 'DIAT', 'Si', 'PHY', 'bSi'])
In [12]:
np.max(np.abs(f2.variables['NO3'][:]-f.variables['NO3'][:]))
Out[12]:
0.0
In [13]:
np.max(np.abs(f2.variables['DIAT'][:]-f.variables['DIAT'][:]))
Out[13]:
0.0
In [14]:
np.max(np.abs(f2.variables['bSi'][:]-f.variables['bSi'][:]))
Out[14]:
0.0
In [15]:
np.max(np.abs(f2.variables['PHY'][:]-f.variables['PHY'][:]))
Out[15]:
0.0
In [16]:
np.max(np.abs(f2.variables['NH4'][:]-f.variables['NH4'][:]))
Out[16]:
0.0
In [18]:
np.max(np.abs(f2.variables['NH4'][:]))
Out[18]:
4.651838
In [13]:
92*24*3600/40
Out[13]:
198720.0
In [14]:
0.00024414062/198720
Out[14]:
1.228565921900161e-09
In [ ]: