- Try to combine files from different processors
In [1]:
from matplotlib import pylab
import matplotlib.pyplot as plt
import netCDF4 as nc
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from salishsea_tools import tidetools
from salishsea_tools import (nc_tools,viz_tools)
import matplotlib.cm as cm
from matplotlib import animation
from numpy import *
from pylab import *
%matplotlib inline
In [2]:
## for grid_T
plume_33 = nc.Dataset('/data/jieliu/MEOPAR/river-treatment/14nor_NW/1h_Plume_T_0033.nc')
plume_34 = nc.Dataset('/data/jieliu/MEOPAR/river-treatment/14nor_NW/1h_Plume_T_0034.nc')
plume_35 = nc.Dataset('/data/jieliu/MEOPAR/river-treatment/14nor_NW/1h_Plume_T_0035.nc')
plume_39 = nc.Dataset('/data/jieliu/MEOPAR/river-treatment/14nor_NW/1h_Plume_T_0039.nc')
plume_40 = nc.Dataset('/data/jieliu/MEOPAR/river-treatment/14nor_NW/1h_Plume_T_0040.nc')
plume_41 = nc.Dataset('/data/jieliu/MEOPAR/river-treatment/14nor_NW/1h_Plume_T_0041.nc')
plume_45 = nc.Dataset('/data/jieliu/MEOPAR/river-treatment/14nor_NW/1h_Plume_T_0045.nc')
plume_46 = nc.Dataset('/data/jieliu/MEOPAR/river-treatment/14nor_NW/1h_Plume_T_0046.nc')
plume_47 = nc.Dataset('/data/jieliu/MEOPAR/river-treatment/14nor_NW/1h_Plume_T_0047.nc')
plume_51 = nc.Dataset('/data/jieliu/MEOPAR/river-treatment/14nor_NW/1h_Plume_T_0051.nc')
plume_52 = nc.Dataset('/data/jieliu/MEOPAR/river-treatment/14nor_NW/1h_Plume_T_0052.nc')
plume_53 = nc.Dataset('/data/jieliu/MEOPAR/river-treatment/14nor_NW/1h_Plume_T_0053.nc')
In [3]:
grid = nc.Dataset('/ocean/jieliu/research/meopar/river-treatment/bathy_meter_SalishSea6.nc','r')
bathy, X, Y = tidetools.get_bathy_data(grid)
lats = grid.variables['nav_lat']
lons = grid.variables['nav_lon']
bath = grid.variables['Bathymetry']
In [4]:
fig, ax = plt.subplots(1, 1, figsize=(10, 8))
viz_tools.set_aspect(ax)
cmap = plt.get_cmap('winter_r')
cmap.set_bad('burlywood')
mesh = ax.pcolormesh(bathy, cmap=cmap)
fig.colorbar(mesh)
plt.axis((260, 398, 350, 520))
Out[4]:
(260, 398, 350, 520)
In [47]:
salinity[9,0,48,100]
Out[47]:
0.0
- ncdump -tsc 1h_plume_T_0033.nc could help find the domain range of each tail(processor)!
In [5]:
nc_tools.show_variables(plume_33)
[u'nav_lon', u'nav_lat', u'deptht', u'time_counter', u'time_counter_bnds', u'sossheig', u'votemper', u'vosaline', u'rain_rate', u'snow_rate']
In [203]:
depths = plume_33.variables['deptht']
time = plume_33.variables['time_counter']
time_counterbnds = plume_33.variables['time_counter_bnds']
In [182]:
size = time_counter_bnds.shape
size
Out[182]:
(336, 2)
In [165]:
time[0:10]
Out[165]:
array([ 88200., 91800., 95400., 99000., 102600., 106200.,
109800., 113400., 117000., 120600.])
In [6]:
lon33= plume_33.variables['nav_lon']
lon33.shape
Out[6]:
(36, 6)
Create grid_T file¶
In [7]:
## for longitude
lon33= plume_33.variables['nav_lon']
lon34= plume_34.variables['nav_lon']
lon35= plume_35.variables['nav_lon']
lon39= plume_39.variables['nav_lon']
lon40= plume_40.variables['nav_lon']
lon41= plume_41.variables['nav_lon']
lon45= plume_45.variables['nav_lon']
lon46= plume_46.variables['nav_lon']
lon47= plume_47.variables['nav_lon']
lon51= plume_51.variables['nav_lon']
lon52= plume_52.variables['nav_lon']
lon53= plume_53.variables['nav_lon']
In [8]:
## for latitude
lat33= plume_33.variables['nav_lat']
lat34= plume_34.variables['nav_lat']
lat35= plume_35.variables['nav_lat']
lat39= plume_39.variables['nav_lat']
lat40= plume_40.variables['nav_lat']
lat41= plume_41.variables['nav_lat']
lat45= plume_45.variables['nav_lat']
lat46= plume_46.variables['nav_lat']
lat47= plume_47.variables['nav_lat']
lat51= plume_51.variables['nav_lat']
lat52= plume_52.variables['nav_lat']
lat53= plume_53.variables['nav_lat']
In [9]:
## for ssh
ssh33 = plume_33.variables['sossheig']
ssh34 = plume_34.variables['sossheig']
ssh35 = plume_35.variables['sossheig']
ssh39 = plume_39.variables['sossheig']
ssh40 = plume_40.variables['sossheig']
ssh41 = plume_41.variables['sossheig']
ssh45 = plume_45.variables['sossheig']
ssh46 = plume_46.variables['sossheig']
ssh47 = plume_47.variables['sossheig']
ssh51 = plume_51.variables['sossheig']
ssh52 = plume_52.variables['sossheig']
ssh53 = plume_53.variables['sossheig']
In [10]:
## for temperature
temp33 = plume_33.variables['votemper']
temp34 = plume_34.variables['votemper']
temp35 = plume_35.variables['votemper']
temp39 = plume_39.variables['votemper']
temp40 = plume_40.variables['votemper']
temp41 = plume_41.variables['votemper']
temp45 = plume_45.variables['votemper']
temp46 = plume_46.variables['votemper']
temp47 = plume_47.variables['votemper']
temp51 = plume_51.variables['votemper']
temp52 = plume_52.variables['votemper']
temp53 = plume_53.variables['votemper']
In [11]:
saline33 = plume_33.variables['vosaline']
saline33.shape
Out[11]:
(336, 40, 36, 6)
In [12]:
saline34 = plume_34.variables['vosaline']
saline34.shape
Out[12]:
(336, 40, 36, 66)
In [13]:
saline35 = plume_35.variables['vosaline']
saline35.shape
Out[13]:
(336, 40, 36, 66)
In [14]:
saline39 = plume_39.variables['vosaline']
saline39.shape
Out[14]:
(336, 40, 64, 6)
In [15]:
saline40 = plume_40.variables['vosaline']
saline40.shape
Out[15]:
(336, 40, 64, 66)
In [16]:
saline41 = plume_41.variables['vosaline']
saline41.shape
Out[16]:
(336, 40, 64, 66)
In [17]:
saline45 = plume_45.variables['vosaline']
saline45.shape
Out[17]:
(336, 40, 64, 6)
In [18]:
saline46 = plume_46.variables['vosaline']
saline46.shape
Out[18]:
(336, 40, 64, 66)
In [19]:
saline47 = plume_47.variables['vosaline']
saline47.shape
Out[19]:
(336, 40, 64, 66)
In [20]:
saline51 = plume_51.variables['vosaline']
saline51.shape
Out[20]:
(336, 40, 6, 6)
In [21]:
saline52 = plume_52.variables['vosaline']
saline52.shape
Out[21]:
(336, 40, 6, 66)
In [22]:
saline53 = plume_53.variables['vosaline']
saline53.shape
Out[22]:
(336, 40, 6, 66)
In [23]:
## try to combine longitude and latitude in grid_T
lon = np.zeros([170, 138])
lat = np.zeros([170, 138])
## This is the edge for lon:
lon[0:36, 0:6] = lon33[:,:]
lon[36:100, 0:6] = lon39[:,:]
lon[100:164, 0:6] = lon45[:,:]
lon[164:170, 0:6] = lon51[:,:]
## This is the edge for lat:
lat[0:36, 0:6] = lat33[:,:]
lat[36:100, 0:6] = lat39[:,:]
lat[100:164, 0:6] = lat45[:,:]
lat[164:170, 0:6] = lat51[:,:]
In [24]:
## try to combine salinity & temperature & ssh in grid_T
salinity = np.zeros([336, 40, 170, 138])
temperature = np.zeros([336, 40, 170, 138])
ssh = np.zeros([336, 170, 138])
## This is the edge for salinity:
salinity[:,:, 0:36, 0:6] =saline33[:,:,:,:]
salinity[:,:, 36:100, 0:6] = saline39[:,:,:,:]
salinity[:,:, 100:164, 0:6 ] =saline45[:,:,:,:]
salinity[:,:, 164:170, 0:6] = saline51[:,:,:,:]
##for temperature:
temperature[:,:, 0:36, 0:6] =temp33[:,:,:,:]
temperature[:,:, 36:100, 0:6] = temp39[:,:,:,:]
temperature[:,:, 100:164, 0:6 ] =temp45[:,:,:,:]
temperature[:,:, 164:170, 0:6] = temp51[:,:,:,:]
## for ssh:
ssh[:, 0:36, 0:6] =ssh33[:,:,:]
ssh[:, 36:100, 0:6] = ssh39[:,:,:]
ssh[:, 100:164, 0:6 ] =ssh45[:,:,:]
ssh[:, 164:170, 0:6] = ssh51[:,:,:]
In [25]:
## salinity & lon & lat combination
for j in np.arange(36):
for i in np.arange(66):
salinity[:,:,j, 6+i] = saline34[:,:,j,i]
lon[j, 6+i] = lon34[j,i]
lat[j, 6+i] = lat34[j,i]
print (j)
0 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
In [26]:
for j in np.arange(36):
for i in np.arange(66):
salinity[:,:,j,72+i] = saline35[:,:,j,i]
lon[j, 72+i] = lon35[j,i]
lat[j, 72+i] = lat35[j,i]
print (j)
0 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
In [27]:
for j in np.arange(64):
for i in np.arange(66):
salinity[:,:, 36+j, 6+i] = saline40[:,:,j,i]
lon[36+j, 6+i] = lon40[j,i]
lat[36+j, 6+i] = lat40[j,i]
print (j)
for j in np.arange(64):
for i in np.arange(66):
salinity[:,:, 36+j, 72+i] = saline41[:,:,j,i]
lon[36+j, 72+i] = lon41[j,i]
lat[36+j, 72+i] = lat41[j,i]
print (j)
for j in np.arange(64):
for i in np.arange(66):
salinity[:,:,100 +j, 6+i] = saline46[:,:,j,i]
lon[100+j, 6+i] = lon46[j,i]
lat[100+j, 6+i] = lat46[j,i]
print (j)
for j in np.arange(64):
for i in np.arange(66):
salinity[:,:,100 +j, 72+i] = saline47[:,:,j,i]
lon[100+j, 72+i] = lon47[j,i]
lat[100+j, 72+i] = lat47[j,i]
print (j)
for j in np.arange(6):
for i in np.arange(66):
salinity[:,:,164 +j, 6+i] = saline52[:,:,j,i]
lon[164+j, 6+i] = lon52[j,i]
lat[164+j, 6+i] = lat52[j,i]
print (j)
for j in np.arange(6):
for i in np.arange(66):
salinity[:,:,164 +j, 72+i] = saline53[:,:,j,i]
lon[164+j, 72+i] = lon53[j,i]
lat[164+j, 72+i] = lat53[j,i]
print (j)
0 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 0 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 0 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 0 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 0 1 2 3 4 5 0 1 2 3 4 5
In [ ]:
## salinity & lon & lat combination
for j in np.arange(36):
for i in np.arange(66):
salinity[:,:,j, 6+i] = saline34[:,:,j,i]
lon[j, 6+i] = lon34[j,i]
lat[j, 6+i] = lat34[j,i]
print (j)
for j in np.arange(36):
for i in np.arange(66):
salinity[:,:,j,72+i] = saline35[:,:,j,i]
lon[j, 72+i] = lon35[j,i]
lat[j, 72+i] = lat35[j,i]
print (j)
for j in np.arange(64):
for i in np.arange(66):
salinity[:,:, 36+j, 6+i] = saline40[:,:,j,i]
lon[36+j, 6+i] = lon40[j,i]
lat[36+j, 6+i] = lat40[j,i]
print (j)
for j in np.arange(64):
for i in np.arange(66):
salinity[:,:, 36+j, 72+i] = saline41[:,:,j,i]
lon[36+j, 72+i] = lon41[j,i]
lat[36+j, 72+i] = lat41[j,i]
print (j)
for j in np.arange(64):
for i in np.arange(66):
salinity[:,:,100 +j, 6+i] = saline46[:,:,j,i]
lon[100+j, 6+i] = lon46[j,i]
lat[100+j, 6+i] = lat46[j,i]
print (j)
for j in np.arange(64):
for i in np.arange(66):
salinity[:,:,100 +j, 72+i] = saline47[:,:,j,i]
lon[100+j, 72+i] = lon47[j,i]
lat[100+j, 72+i] = lat47[j,i]
print (j)
for j in np.arange(6):
for i in np.arange(66):
salinity[:,:,164 +j, 6+i] = saline52[:,:,j,i]
lon[164+j, 6+i] = lon52[j,i]
lat[164+j, 6+i] = lat52[j,i]
print (j)
for j in np.arange(6):
for i in np.arange(66):
salinity[:,:,164 +j, 72+i] = saline53[:,:,j,i]
lon[164+j, 72+i] = lon53[j,i]
lat[164+j, 72+i] = lat53[j,i]
print (j)
In [44]:
plt.pcolormesh(salinity[9,0,:,:])
plt.colorbar()
Out[44]:
<matplotlib.colorbar.Colorbar instance at 0x7f5321c33638>
In [48]:
## temperature combination
for j in np.arange(36):
for i in np.arange(66):
temperature[:,:,j, 6+i] = temp34[:,:,j,i]
print (j)
for j in np.arange(36):
for i in np.arange(66):
temperature[:,:,j,72+i] = temp35[:,:,j,i]
print (j)
for j in np.arange(64):
for i in np.arange(66):
temperature[:,:, 36+j, 6+i] = temp40[:,:,j,i]
print (j)
for j in np.arange(64):
for i in np.arange(66):
temperature[:,:, 36+j, 72+i] = temp41[:,:,j,i]
print (j)
for j in np.arange(64):
for i in np.arange(66):
temperature[:,:,100 +j, 6+i] = temp46[:,:,j,i]
print (j)
for j in np.arange(64):
for i in np.arange(66):
temperature[:,:,100 +j, 72+i] = temp47[:,:,j,i]
print (j)
for j in np.arange(6):
for i in np.arange(66):
temperature[:,:,164 +j, 6+i] = temp52[:,:,j,i]
print (j)
for j in np.arange(6):
for i in np.arange(66):
temperature[:,:,164 +j, 72+i] = temp53[:,:,j,i]
print (j)
0 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 0 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 0 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 0 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 0 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 0 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 0 1 2 3 4 5 0 1 2 3 4 5
In [80]:
## ssh plot
plt.pcolormesh(ssh[334, :,:])
plt.colorbar()
Out[80]:
<matplotlib.colorbar.Colorbar instance at 0x7f51efe86128>
In [68]:
## ssh combination
for j in np.arange(36):
for i in np.arange(66):
ssh[:,j, 6+i] = ssh34[:,j,i]
print (j)
for j in np.arange(36):
for i in np.arange(66):
ssh[:,j,72+i] = ssh35[:,j,i]
print (j)
for j in np.arange(64):
for i in np.arange(66):
ssh[:, 36+j, 6+i] = ssh40[:,j,i]
print (j)
for j in np.arange(64):
for i in np.arange(66):
ssh[:, 36+j, 72+i] = ssh41[:,j,i]
print (j)
for j in np.arange(64):
for i in np.arange(66):
ssh[:,100 +j, 6+i] = ssh46[:,j,i]
print (j)
for j in np.arange(64):
for i in np.arange(66):
ssh[:,100 +j, 72+i] = ssh47[:,j,i]
print (j)
for j in np.arange(6):
for i in np.arange(66):
ssh[:,164 +j, 6+i] = ssh52[:,j,i]
print (j)
for j in np.arange(6):
for i in np.arange(66):
ssh[:,164 +j, 72+i] = ssh53[:,j,i]
print (j)
0 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 0 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 0 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 0 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 0 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 0 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 0 1 2 3 4 5 0 1 2 3 4 5
In [230]:
# build grid_T nc file
grid_T = nc.Dataset('1hplume06160629gridT.nc', 'w')
#nc_tools.init_dataset_attrs(
#grid_T,
#title='Salinity Temperature Initial Conditions based on Nowcast June 14, 2015 for extended Fraser river channel',
#notebook_name='Explore1h_plumeFileStructure',
#nc_filepath='/data/jieliu/MEOPAR/river-treatment/14nor_NW/1h_plume_06160629_grid_T.nc')
#comment='Combined grid_T file with extended Fraser bathymetry from June 16, to 29')
grid_T.createDimension('y', 170)
grid_T.createDimension('x', 138)
grid_T.createDimension('deptht',size = len(depths))
grid_T.createDimension('time_counter', size = len(time))
grid_T.createDimension('tbnds', size = 2)
nc_tools.show_dimensions(grid_T)
# show variables
nav_lat = grid_T.createVariable('nav_lat', 'float32', ('y','x'))
nav_lat.long_name = 'Latitude'
nav_lat.units = 'degrees_north'
nav_lat[:] = lat
nav_lon = grid_T.createVariable('nav_lon', 'float32', ('y','x'))
nav_lon.long_name = 'Longitude'
nav_lon.units = 'degrees_east'
nav_lon[:] = lon
deptht = grid_T.createVariable('deptht', 'float32', ('deptht'), zlib=True)
#depths = foo.createVariable('Bathymetry', float, ('y', 'x'), zlib=True, least_significant_digit=1, fill_value=0)
deptht.long_name = 'Depth'
deptht.coordinates = 'deptht'
deptht.units = 'm'
deptht.positive = 'down'
deptht.valid_range = np.array((4., 428.))##minimum depth 4m
deptht[:] = depths
time_counter = grid_T.createVariable('time_counter', 'float64', ('time_counter'))
time_counter.units = 'seconds since 2015-06-15 0:00:00'
time_counter.long_name = 'Time axis'
time_counter.time_origin = ' 2015-JUN-15 00:00:00'
#time.coordinates = 'time_counter'
time_counter[:] = time
time_counter_bnds = grid_T.createVariable('time_counter_bnds', 'float64',
('time_counter', 'tbnds'))
#time_counter.units = 'seconds since 2015-06-16 0:00:00'
#time_counter.long_name = 'Time axis'
#time.coordinates = 'time_counter'
time_counter_bnds[:] = time_counterbnds
vosaline = grid_T.createVariable('vosaline', 'float32',
('time_counter','deptht','y','x'))
vosaline.units = 'none'
vosaline.long_name = 'Practical Salinity'
vosaline.coordinates = 'nav_lon nav_lat deptht time_counter'
vosaline.grid = 'SalishSea6, extended and deepen Fraser'
vosaline[:] = salinity
votemper = grid_T.createVariable('votemper', 'float32',
('time_counter','deptht','y','x'))
votemper.units = 'degC'
votemper.long_name = 'Temperature'
votemper.coordinates = 'nav_lon nav_lat deptht time_counter'
votemper[:] = temperature
sossheig = grid_T.createVariable('sossheig', 'float32',
('time_counter','y','x'))
sossheig.units = 'm'
sossheig.long_name = 'sea surface height'
sossheig.coordinates = 'time_counter nav_lon nav_lat '
sossheig[:] = ssh
grid_T.history = """[2015-06-16] Created"""
<type 'netCDF4.Dimension'>: name = 'y', size = 170 <type 'netCDF4.Dimension'>: name = 'x', size = 138 <type 'netCDF4.Dimension'>: name = 'deptht', size = 40 <type 'netCDF4.Dimension'>: name = 'time_counter', size = 336 <type 'netCDF4.Dimension'>: name = 'tbnds', size = 2
In [231]:
grid_T.close()
In [232]:
a= nc.Dataset('1hplume06160629gridT.nc')
nc_tools.show_variables(a)
t = a.variables['time_counter']
t.shape
[u'nav_lat', u'nav_lon', u'deptht', u'time_counter', u'time_counter_bnds', u'vosaline', u'votemper', u'sossheig']
Out[232]:
(336,)
In [233]:
t_bnd = a.variables['time_counter_bnds'][:]
t_bnd[0:5,0]
Out[233]:
array([ 86400., 90000., 93600., 97200., 100800.])
In [234]:
dep = a.variables['deptht']
dep[5]
Out[234]:
5.5001507
In [235]:
lonn = a.variables['nav_lon']
lonn[20,40]
Out[235]:
-123.0729
In [236]:
salin = a.variables['vosaline']
salin[0:10,4,0,0]
Out[236]:
array([ 28.97470665, 29.35420418, 28.88977814, 28.94039536,
29.10746956, 29.316679 , 29.31981087, 29.06804848,
28.9330864 , 29.00049782], dtype=float32)
In [237]:
height = a.variables['sossheig']
height[0:20, 40, 30]
Out[237]:
array([ 1.11585462, 1.32632232, 1.28466237, 1.0586797 , 0.74332529,
0.4306263 , 0.26111588, 0.27983394, 0.46531975, 0.75417536,
1.05211711, 1.24915302, 1.22117734, 0.92198849, 0.38408458,
-0.27982149, -0.99126869, -1.6157608 , -2.00654578, -2.0516367 ], dtype=float32)
In [238]:
tem = a.variables['votemper']
tem[0:5, 4, 0, 0]
Out[238]:
array([ 11.71061897, 11.31514263, 11.69520473, 11.62864399, 11.4464798 ], dtype=float32)
In [ ]: