In [1]:
import sqlalchemy
from sqlalchemy import (create_engine, Column, String, Integer, Float, MetaData,
Table, type_coerce, ForeignKey, case)
from sqlalchemy.orm import mapper, create_session, relationship, aliased, Session
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import case
import numpy as np
from sqlalchemy.ext.automap import automap_base
import matplotlib.pyplot as plt
import sqlalchemy.types as types
from sqlalchemy.sql import and_, or_, not_, func
from sqlalchemy.sql import select
import os
from os.path import isfile
import pandas as pd
import netCDF4 as nc
import datetime as dt
import glob
%matplotlib inline
In [2]:
t0=dt.datetime(2015,3,8)
datestr=t0.strftime('%Y%m%d')+'-'+(t0+dt.timedelta(days=9)).strftime('%Y%m%d')
PATH= '/data/eolson/MEOPAR/SS36runs/OrcinusRuns/spring2015_SMELTBKhTOrc/'
f0=nc.Dataset(glob.glob(PATH + 'SalishSea*1h*ptrc_T*' + datestr +'.nc')[0])
PATH1= '/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_KhT/'
f1=nc.Dataset(glob.glob(PATH1 + 'SalishSea*1h*ptrc_T*' + datestr +'.nc')[0])
mesh=nc.Dataset('/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702_noLPE.nc')
In [3]:
fig,ax=plt.subplots(1,3,figsize=(15,6))
tmask=mesh.variables['tmask'][0,:,:,:]
mask1=tmask[:-1,:,:]-tmask[1:,:,:]
var=np.sum(mask1*f0.variables['biogenic_silicon'][12,:-1,:,:],0)
var1=np.sum(mask1*f1.variables['biogenic_silicon'][12,:-1,:,:],0)
mesh0=ax[0].pcolormesh(np.ma.masked_where(mesh.variables['tmask'][0,0,:,:]==0,var),vmin=0,vmax=80)
fig.colorbar(mesh0,ax=ax[0])
mesh1=ax[1].pcolormesh(np.ma.masked_where(mesh.variables['tmask'][0,0,:,:]==0,var1),vmin=0,vmax=80)
fig.colorbar(mesh1,ax=ax[1])
mesh2=ax[2].pcolormesh(np.ma.masked_where(mesh.variables['tmask'][0,0,:,:]==0,var-var1),cmap=plt.get_cmap('seismic'),
vmin=-5,vmax=5)
fig.colorbar(mesh2,ax=ax[2])
Out[3]:
<matplotlib.colorbar.Colorbar at 0x7fb3afadc668>
In [14]:
fig,ax=plt.subplots(3,1,figsize=(15,9))
mesh0=ax[0].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,450,200:370]==0,
f0.variables['biogenic_silicon'][0,:,450,200:370]),vmin=-0,vmax=5)
ax[0].set_ylim(450,0)
fig.colorbar(mesh0,ax=ax[0])
mesh1=ax[1].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,450,200:370]==0,
f1.variables['biogenic_silicon'][0,:,450,200:370]),vmin=-0,vmax=5)
ax[1].set_ylim(450,0)
fig.colorbar(mesh1,ax=ax[1])
mesh2=ax[2].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,450,200:370]==0,
f0.variables['silicon'][0,:,450,200:370]-f1.variables['biogenic_silicon'][0,:,450,200:370]),cmap=plt.get_cmap('seismic'),
vmin=-5,vmax=5)
ax[2].set_ylim(450,0)
fig.colorbar(mesh2,ax=ax[2])
Out[14]:
<matplotlib.colorbar.Colorbar at 0x7fb3acc2b6d8>
In [15]:
fig,ax=plt.subplots(3,1,figsize=(15,9))
yind=200
mesh0=ax[0].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,yind,200:370]==0,
f0.variables['biogenic_silicon'][0,:,yind,200:370]),vmin=0,vmax=5)
ax[0].set_ylim(450,0)
fig.colorbar(mesh0,ax=ax[0])
mesh1=ax[1].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,yind,200:370]==0,
f1.variables['biogenic_silicon'][0,:,yind,200:370]),vmin=0,vmax=5)
ax[1].set_ylim(450,0)
fig.colorbar(mesh1,ax=ax[1])
mesh2=ax[2].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,yind,200:370]==0,
f0.variables['biogenic_silicon'][0,:,yind,200:370]-f1.variables['biogenic_silicon'][0,:,yind,200:370]),cmap=plt.get_cmap('seismic'),
vmin=-5,vmax=5)
ax[2].set_ylim(150,0)
fig.colorbar(mesh2,ax=ax[2])
Out[15]:
<matplotlib.colorbar.Colorbar at 0x7fb3aca11550>
In [ ]:
24*3600/2160
In [ ]:
.05/40*24*3600
In [ ]:
100/(24*3600)
In [16]:
t1=dt.datetime(2015,2,5)
datestr=t0.strftime('%Y%m%d')+'-'+(t0+dt.timedelta(days=9)).strftime('%Y%m%d')
PATH= '/data/eolson/MEOPAR/SS36runs/OrcinusRuns/spring2015_SMELTBKhTOrc/'
f01=nc.Dataset(glob.glob(PATH + 'SalishSea*1h*ptrc_T*' + datestr +'.nc')[0])
PATH1= '/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_KhT/'
f11=nc.Dataset(glob.glob(PATH1 + 'SalishSea*1h*ptrc_T*' + datestr +'.nc')[0])
In [18]:
fig,ax=plt.subplots(1,3,figsize=(15,6))
tmask=mesh.variables['tmask'][0,:,:,:]
mask1=tmask[0:-1,:,:]-tmask[1:,:,:]
var=np.sum(mask1*f01.variables['biogenic_silicon'][0,:-1,:,:],0)
var1=np.sum(mask1*f11.variables['biogenic_silicon'][0,:-1,:,:],0)
mesh0=ax[0].pcolormesh(np.ma.masked_where(mesh.variables['tmask'][0,0,:,:]==0,var),vmin=0,vmax=10)
fig.colorbar(mesh0,ax=ax[0])
mesh1=ax[1].pcolormesh(np.ma.masked_where(mesh.variables['tmask'][0,0,:,:]==0,var1),vmin=0,vmax=10)
fig.colorbar(mesh1,ax=ax[1])
mesh2=ax[2].pcolormesh(np.ma.masked_where(mesh.variables['tmask'][0,0,:,:]==0,var-var1),cmap=plt.get_cmap('seismic'),
vmin=-5,vmax=5)
fig.colorbar(mesh2,ax=ax[2])
Out[18]:
<matplotlib.colorbar.Colorbar at 0x7fb3ac5249e8>
In [20]:
f11T=nc.Dataset(glob.glob(PATH1 + 'SalishSea*1h*grid_T*' + datestr +'.nc')[0])
fig,ax=plt.subplots(1,3,figsize=(15,6))
tmask=mesh.variables['tmask'][0,:,:,:]
var=np.sum(f01.variables['biogenic_silicon'][-1,:,:,:]*f01.variables['e3t'][-1,:,:,:],0)
var1=np.sum(f11.variables['biogenic_silicon'][-1,:,:,:]*f11T.variables['e3t'][-1,:,:,:],0)
mesh0=ax[0].pcolormesh(np.ma.masked_where(mesh.variables['tmask'][0,0,:,:]==0,var),vmin=0,vmax=500)
fig.colorbar(mesh0,ax=ax[0])
mesh1=ax[1].pcolormesh(np.ma.masked_where(mesh.variables['tmask'][0,0,:,:]==0,var1),vmin=0,vmax=500)
fig.colorbar(mesh1,ax=ax[1])
mesh2=ax[2].pcolormesh(np.ma.masked_where(mesh.variables['tmask'][0,0,:,:]==0,var-var1),cmap=plt.get_cmap('seismic'),
vmin=-50,vmax=50)
fig.colorbar(mesh2,ax=ax[2])
/home/eolson/anaconda3/lib/python3.5/site-packages/numpy/ma/core.py:1006: RuntimeWarning: overflow encountered in multiply result = self.f(da, db, *args, **kwargs)
Out[20]:
<matplotlib.colorbar.Colorbar at 0x7fb3ac0b4208>
In [21]:
tf=nc.Dataset('/data/eolson/MEOPAR/SS36runs/OrcinusRuns/spring2015_SMELTBFastSink/SalishSea_1h_20150206_20150506_ptrc_T_20150206-20150215.nc')
In [22]:
fig,ax=plt.subplots(3,1,figsize=(15,9))
yind=200
mesh0=ax[0].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,yind,200:370]==0,
tf.variables['biogenic_silicon'][0,:,yind,200:370]))
ax[0].set_ylim(450,0)
fig.colorbar(mesh0,ax=ax[0])
mesh1=ax[1].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,450,200:370]==0,
tf.variables['biogenic_silicon'][0,:,450,200:370]))
ax[1].set_ylim(450,0)
fig.colorbar(mesh1,ax=ax[1])
Out[22]:
<matplotlib.colorbar.Colorbar at 0x7fb3a7eb5278>
In [25]:
fig,ax=plt.subplots(3,1,figsize=(15,9))
yind=200
mesh0=ax[0].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,yind,200:370]==0,
tf.variables['biogenic_silicon'][-1,:,yind,200:370]))
ax[0].set_ylim(450,0)
fig.colorbar(mesh0,ax=ax[0])
mesh1=ax[1].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,450,200:370]==0,
tf.variables['biogenic_silicon'][-1,:,450,200:370]),vmin=0,vmax=2)
ax[1].set_ylim(450,0)
fig.colorbar(mesh1,ax=ax[1])
Out[25]:
<matplotlib.colorbar.Colorbar at 0x7fb3a78c3080>
In [29]:
fig,ax=plt.subplots(3,1,figsize=(15,9))
yind=200
mesh0=ax[0].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,yind,200:370]==0,
tf.variables['silicon'][-1,:,yind,200:370]))
ax[0].set_ylim(450,0)
fig.colorbar(mesh0,ax=ax[0])
mesh1=ax[1].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,450,200:370]==0,
tf.variables['silicon'][-1,:,450,200:370]))
ax[1].set_ylim(450,0)
fig.colorbar(mesh1,ax=ax[1])
Out[29]:
<matplotlib.colorbar.Colorbar at 0x7fb3a7163cc0>
In [37]:
tf2=nc.Dataset('/data/eolson/MEOPAR/SS36runs/OrcinusRuns/spring2015_SMELTB0Orc/SalishSea_1h_20150206_20150506_ptrc_T_20150206-20150215.nc')
fig,ax=plt.subplots(3,1,figsize=(15,9))
yind=200
mesh0=ax[0].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,yind,200:370]==0,
tf2.variables['silicon'][-1,:,yind,200:370]))
ax[0].set_ylim(450,0)
fig.colorbar(mesh0,ax=ax[0])
mesh1=ax[1].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,450,200:370]==0,
tf2.variables['silicon'][-1,:,450,200:370]))
ax[1].set_ylim(450,0)
fig.colorbar(mesh1,ax=ax[1])
mesh2=ax[2].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,450,200:370]==0,
tf2.variables['silicon'][-1,:,450,200:370]-f11.variables['silicon'][-1,:,450,200:370]))
ax[2].set_ylim(450,0)
fig.colorbar(mesh2,ax=ax[2])
Out[37]:
<matplotlib.colorbar.Colorbar at 0x7fb3a603a710>
In [39]:
tf3=nc.Dataset('/data/eolson/MEOPAR/SS36runs/OrcinusRuns/spring2015_SMELTB0Orc/SalishSea_1h_20150206_20150506_ptrc_T_20150308-20150317.nc')
fig,ax=plt.subplots(3,1,figsize=(15,9))
yind=200
mesh0=ax[0].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,yind,200:370]==0,
tf2.variables['silicon'][-1,:,yind,200:370]))
ax[0].set_ylim(450,0)
fig.colorbar(mesh0,ax=ax[0])
mesh1=ax[1].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,450,200:370]==0,
tf2.variables['silicon'][-1,:,450,200:370]))
ax[1].set_ylim(450,0)
fig.colorbar(mesh1,ax=ax[1])
mesh2=ax[2].pcolormesh(np.arange(0,370-200),mesh.variables['gdept_1d'][0,:],np.ma.masked_where(tmask[:,450,200:370]==0,
tf3.variables['silicon'][-1,:,450,200:370]-f1.variables['silicon'][-1,:,450,200:370]),
vmin=-5,vmax=5,cmap=plt.get_cmap('seismic'))
ax[2].set_ylim(450,0)
fig.colorbar(mesh2,ax=ax[2])
Out[39]:
<matplotlib.colorbar.Colorbar at 0x7fb3a5b5bcf8>
In [ ]: