DFO Si Plots¶

In [1]:

import numpy as np
import matplotlib.pyplot as plt
import os
import pandas as pd
import netCDF4 as nc
import datetime as dt
from salishsea_tools import evaltools as et, viz_tools
import gsw
import matplotlib.gridspec as gridspec
import matplotlib as mpl
import matplotlib.dates as mdates
import cmocean as cmo
import scipy.interpolate as sinterp
import pickle
import cmocean

mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)
mpl.rc('legend', fontsize=12)
mpl.rc('axes', titlesize=12)
mpl.rc('axes', labelsize=12)
mpl.rc('figure', titlesize=12)
mpl.rc('font', size=12)
mpl.rc('text', usetex=True)
mpl.rc('text.latex', preamble = ','.join(r'''
 \usepackage{txfonts}
 \usepackage{lmodern}
 '''.split()))
mpl.rc('font', family='sans-serif', weight='normal', style='normal')
%matplotlib inline

In [2]:

df=et.loadDFO()
df.head()

Out[2]:

	Year	Month	Day	Hour	Lat	Lon	Pressure	Depth	Ammonium	Ammonium_units	Chlorophyll_Extracted	Chlorophyll_Extracted_units	N	Si	Silicate_units	AbsSal	ConsT	Z	dtUTC
0	1982.0	7.0	16.0	1.35	49.25	-123.943	NaN	12.0	NaN	None	5.28	mg/m^3	6.6	13.6	umol/L	NaN	NaN	12.0	1982-07-16 01:21:00
1	1982.0	7.0	16.0	1.35	49.25	-123.943	NaN	21.5	NaN	None	0.61	mg/m^3	21.2	45.0	umol/L	NaN	NaN	21.5	1982-07-16 01:21:00
2	1982.0	7.0	16.0	1.35	49.25	-123.943	NaN	30.5	NaN	None	NaN	mg/m^3	23.5	47.4	umol/L	NaN	NaN	30.5	1982-07-16 01:21:00
3	1982.0	7.0	16.0	1.35	49.25	-123.943	NaN	52.3	NaN	None	NaN	mg/m^3	28.0	50.2	umol/L	NaN	NaN	52.3	1982-07-16 01:21:00
4	1982.0	7.0	16.0	1.35	49.25	-123.943	NaN	75.4	NaN	None	NaN	mg/m^3	26.5	49.1	umol/L	NaN	NaN	75.4	1982-07-16 01:21:00

In [3]:

with nc.Dataset('/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702_noLPE.nc') as mesh:
    navlon=mesh.variables['nav_lon'][:,:]
    navlat=mesh.variables['nav_lat'][:,:]
    tmask=mesh.variables['tmask'][:,:,:,:]
    gdept=mesh.variables['gdept_1d'][0,:]
    e3t0=mesh.variables['e3t_0'][0,:,:,:]
bathy=np.sum(e3t0,0)
with nc.Dataset('/results/SalishSea/hindcast.201812/01jan16/SalishSea_1h_20160101_20160101_ptrc_T.nc') as ftemp:
        bounds=np.copy(ftemp.variables['deptht_bounds'][:,:])

In [4]:

#d1=et._gridHoriz(df.loc[df.Z>150].copy(deep=True),tmask,navlon,navlat,wrapSearch=False,resetIndex=True);
d1=et._gridHoriz(df.copy(deep=True),tmask,navlon,navlat,wrapSearch=False,resetIndex=True);
d1['k']=np.nan
for ind, row in d1.iterrows():
    ik=et._getZInd_bin(row['Z'],None,boundsFlag=True)
    if (not np.isnan(ik)) and (tmask[0,ik,row['j'],row['i']]==1):
        d1.loc[ind,['k']]=ik

(Lat,Lon)= 47.05 -122.31666666666666  not matched to domain
(Lat,Lon)= 47.1 -122.7  not matched to domain
(Lat,Lon)= 47.1 -122.68333333333334  not matched to domain
(Lat,Lon)= 47.166666666666664 -122.51666666666667  not matched to domain
(Lat,Lon)= 49.29933333333334 -122.983  not matched to domain
(Lat,Lon)= 49.30222166666667 -122.94861166666666  not matched to domain
(Lat,Lon)= 49.3025 -122.939445  not matched to domain
(Lat,Lon)= 49.3025 -122.93277833333333  not matched to domain
(Lat,Lon)= 49.304833333333335 -122.94116666666666  not matched to domain
(Lat,Lon)= 49.306945 -122.941945  not matched to domain
(Lat,Lon)= 49.30722166666666 -122.93527833333333  not matched to domain
(Lat,Lon)= 49.312778333333334 -122.94111166666667  not matched to domain
(Lat,Lon)= 49.312778333333334 -122.9325  not matched to domain
(Lat,Lon)= 50.47666666666667 -126.17833333333333  not matched to domain
(Lat,Lon)= 50.4882 -126.3484  not matched to domain
(Lat,Lon)= 50.49666666666667 -126.31166666666667  not matched to domain
(Lat,Lon)= 50.60466666666667 -126.35616666666667  not matched to domain
(Lat,Lon)= 50.60516666666667 -126.3565  not matched to domain
(Lat,Lon)= 50.605333333333334 -126.3565  not matched to domain
(Lat,Lon)= 50.6318 -126.4979  not matched to domain
(Lat,Lon)= 50.63466666666667 -126.422  not matched to domain
(Lat,Lon)= 50.63483333333333 -126.42216666666667  not matched to domain
(Lat,Lon)= 50.63483333333333 -126.42166666666667  not matched to domain
(Lat,Lon)= 50.63483333333333 -126.4215  not matched to domain
(Lat,Lon)= 50.63483333333333 -126.42133333333334  not matched to domain
(Lat,Lon)= 50.635 -126.425  not matched to domain
(Lat,Lon)= 50.635 -126.4225  not matched to domain
(Lat,Lon)= 50.63516666666666 -126.42233333333333  not matched to domain
(Lat,Lon)= 50.6355 -126.4245  not matched to domain
(Lat,Lon)= 50.63783333333333 -126.35433333333333  not matched to domain
(Lat,Lon)= 50.655 -126.183  not matched to domain
(Lat,Lon)= 50.655166666666666 -126.18466666666667  not matched to domain
(Lat,Lon)= 50.65533333333333 -126.183  not matched to domain
(Lat,Lon)= 50.65533333333333 -126.18283333333333  not matched to domain
(Lat,Lon)= 50.66016666666667 -126.10983333333333  not matched to domain
(Lat,Lon)= 50.66983333333334 -126.067  not matched to domain
(Lat,Lon)= 50.67066666666667 -126.48383333333334  not matched to domain
(Lat,Lon)= 50.671166666666664 -126.48416666666667  not matched to domain
(Lat,Lon)= 50.671166666666664 -126.48383333333334  not matched to domain
(Lat,Lon)= 50.671166666666664 -126.48316666666666  not matched to domain
(Lat,Lon)= 50.67133333333334 -126.48333333333333  not matched to domain
(Lat,Lon)= 50.6715 -126.4835  not matched to domain
(Lat,Lon)= 50.6715 -126.48316666666666  not matched to domain
(Lat,Lon)= 50.67283333333334 -126.48016666666666  not matched to domain
(Lat,Lon)= 50.6795 -125.93683333333334  not matched to domain
(Lat,Lon)= 50.68443333333333 -126.23803333333333  not matched to domain
(Lat,Lon)= 50.69166666666667 -125.8445  not matched to domain
(Lat,Lon)= 50.69316666666667 -125.7835  not matched to domain
(Lat,Lon)= 50.693333333333335 -125.785  not matched to domain
(Lat,Lon)= 50.693666666666665 -125.78316666666667  not matched to domain
(Lat,Lon)= 50.72 -126.5735  not matched to domain
(Lat,Lon)= 50.72 -126.57333333333334  not matched to domain
(Lat,Lon)= 50.72 -126.57316666666667  not matched to domain
(Lat,Lon)= 50.720166666666664 -126.5735  not matched to domain
(Lat,Lon)= 50.720166666666664 -126.57316666666667  not matched to domain
(Lat,Lon)= 50.720166666666664 -126.57266666666666  not matched to domain
(Lat,Lon)= 50.7265 -126.18183333333333  not matched to domain
(Lat,Lon)= 50.7265 -126.1815  not matched to domain
(Lat,Lon)= 50.7265 -126.18116666666667  not matched to domain
(Lat,Lon)= 50.72666666666667 -126.182  not matched to domain
(Lat,Lon)= 50.727 -126.18116666666667  not matched to domain
(Lat,Lon)= 50.72716666666667 -126.18066666666667  not matched to domain
(Lat,Lon)= 50.73611666666667 -126.15408333333333  not matched to domain
(Lat,Lon)= 50.783166666666666 -126.61166666666666  not matched to domain
(Lat,Lon)= 50.7835 -126.61166666666666  not matched to domain
(Lat,Lon)= 50.78366666666667 -126.61133333333333  not matched to domain
(Lat,Lon)= 50.784 -126.61366666666666  not matched to domain
(Lat,Lon)= 50.784 -126.61183333333334  not matched to domain
(Lat,Lon)= 50.7845 -126.611  not matched to domain
(Lat,Lon)= 50.784666666666666 -126.61933333333333  not matched to domain
(Lat,Lon)= 50.78516666666667 -126.618  not matched to domain
(Lat,Lon)= 50.7926 -126.48951666666666  not matched to domain
(Lat,Lon)= 50.797016666666664 -126.49456666666667  not matched to domain
(Lat,Lon)= 50.801 -125.62166666666667  not matched to domain
(Lat,Lon)= 50.801050000000004 -126.49818333333333  not matched to domain
(Lat,Lon)= 50.802533333333336 -126.49893333333333  not matched to domain
(Lat,Lon)= 50.8046 -126.5291  not matched to domain
(Lat,Lon)= 50.80765 -126.4941  not matched to domain
(Lat,Lon)= 50.81283333333333 -126.52383333333333  not matched to domain
(Lat,Lon)= 50.813 -126.52483333333333  not matched to domain
(Lat,Lon)= 50.81333333333333 -126.52533333333334  not matched to domain
(Lat,Lon)= 50.814 -126.52566666666667  not matched to domain
(Lat,Lon)= 50.815666666666665 -126.42466666666667  not matched to domain
(Lat,Lon)= 50.81763333333333 -126.2207  not matched to domain
(Lat,Lon)= 50.820166666666665 -126.39266666666667  not matched to domain
(Lat,Lon)= 50.839 -126.33066666666667  not matched to domain
(Lat,Lon)= 50.839 -126.32833333333333  not matched to domain
(Lat,Lon)= 50.839166666666664 -126.329  not matched to domain
(Lat,Lon)= 50.839333333333336 -126.328  not matched to domain
(Lat,Lon)= 50.84 -126.33  not matched to domain
(Lat,Lon)= 50.84016666666667 -126.32883333333334  not matched to domain
(Lat,Lon)= 50.840333333333334 -126.33166666666666  not matched to domain
(Lat,Lon)= 50.8405 -126.3295  not matched to domain
(Lat,Lon)= 50.8553 -126.51111666666667  not matched to domain
(Lat,Lon)= 50.861333333333334 -126.6195  not matched to domain
(Lat,Lon)= 50.8615 -126.6195  not matched to domain
(Lat,Lon)= 50.8615 -126.61866666666667  not matched to domain
(Lat,Lon)= 50.861666666666665 -126.62  not matched to domain
(Lat,Lon)= 50.86183333333334 -126.6215  not matched to domain
(Lat,Lon)= 50.862 -126.619  not matched to domain
(Lat,Lon)= 50.8735 -126.552  not matched to domain
(Lat,Lon)= 50.873916666666666 -126.62033333333333  not matched to domain
(Lat,Lon)= 50.8762 -126.6183  not matched to domain
(Lat,Lon)= 50.884966666666664 -126.42748333333333  not matched to domain
(Lat,Lon)= 50.89233333333333 -126.57666666666667  not matched to domain
(Lat,Lon)= 50.9086 -126.5451  not matched to domain
(Lat,Lon)= 50.91466666666667 -126.24516666666666  not matched to domain
(Lat,Lon)= 50.915 -126.2455  not matched to domain
(Lat,Lon)= 50.91566666666667 -126.245  not matched to domain
(Lat,Lon)= 50.91898333333334 -126.27181666666667  not matched to domain
(Lat,Lon)= 50.935833333333335 -126.482  not matched to domain
(Lat,Lon)= 50.93633333333333 -126.4815  not matched to domain
(Lat,Lon)= 50.941066666666664 -126.48468333333334  not matched to domain
(Lat,Lon)= 50.99816666666667 -126.72866666666667  not matched to domain
(Lat,Lon)= 50.998333333333335 -126.72766666666666  not matched to domain
(Lat,Lon)= 50.998333333333335 -126.7275  not matched to domain
(Lat,Lon)= 50.9985 -126.7275  not matched to domain
(Lat,Lon)= 51.008833333333335 -126.70366666666666  not matched to domain
(Lat,Lon)= 51.009 -126.7035  not matched to domain
(Lat,Lon)= 51.009 -126.70283333333333  not matched to domain
(Lat,Lon)= 51.009166666666665 -126.70416666666667  not matched to domain
(Lat,Lon)= 51.018 -126.523  not matched to domain
(Lat,Lon)= 51.037166666666664 -126.71633333333334  not matched to domain
(Lat,Lon)= 51.0375 -126.71716666666667  not matched to domain
(Lat,Lon)= 51.0375 -126.717  not matched to domain
(Lat,Lon)= 51.03783333333333 -126.71633333333334  not matched to domain
(Lat,Lon)= 51.038333333333334 -126.7165  not matched to domain
(Lat,Lon)= 51.041333333333334 -125.568  not matched to domain
(Lat,Lon)= 51.062333333333335 -126.7455  not matched to domain
(Lat,Lon)= 51.0625 -126.742  not matched to domain
(Lat,Lon)= 51.06333333333333 -126.74333333333334  not matched to domain
(Lat,Lon)= 51.0635 -126.74266666666666  not matched to domain
(Lat,Lon)= 51.099666666666664 -126.74783333333333  not matched to domain
(Lat,Lon)= 51.099833333333336 -126.74983333333333  not matched to domain
(Lat,Lon)= 51.1 -126.75  not matched to domain
(Lat,Lon)= 51.10016666666667 -126.75  not matched to domain
(Lat,Lon)= 51.10066666666667 -126.74916666666667  not matched to domain
(Lat,Lon)= 51.13633333333333 -126.6915  not matched to domain
(Lat,Lon)= 51.1665 -126.94683333333333  not matched to domain
(Lat,Lon)= 51.1665 -126.94666666666667  not matched to domain
(Lat,Lon)= 51.166666666666664 -126.9465  not matched to domain
(Lat,Lon)= 51.1805 -126.92416666666666  not matched to domain
(Lat,Lon)= 51.18066666666667 -126.92433333333334  not matched to domain
(Lat,Lon)= 51.183166666666665 -126.65833333333333  not matched to domain
(Lat,Lon)= 51.183166666666665 -126.658  not matched to domain
(Lat,Lon)= 51.1835 -126.65833333333333  not matched to domain
(Lat,Lon)= 51.18366666666667 -126.6585  not matched to domain
(Lat,Lon)= 51.3595 -127.11933333333333  not matched to domain
(Lat,Lon)= 51.67433333333334 -127.28216666666667  not matched to domain
(Lat,Lon)= 51.67466666666667 -127.284  not matched to domain
(Lat,Lon)= 51.675 -127.28416666666666  not matched to domain
(Lat,Lon)= 51.675 -127.28366666666666  not matched to domain
(Lat,Lon)= 51.67516666666667 -127.28466666666667  not matched to domain
(Lat,Lon)= 51.675333333333334 -127.285  not matched to domain
(Lat,Lon)= 51.675333333333334 -127.2835  not matched to domain
(Lat,Lon)= 51.6755 -127.2835  not matched to domain
(Lat,Lon)= 51.675666666666665 -127.293  not matched to domain
(Lat,Lon)= 51.6775 -127.3335  not matched to domain
(Lat,Lon)= 51.678 -127.33333333333333  not matched to domain
(Lat,Lon)= 51.678333333333335 -127.332  not matched to domain
(Lat,Lon)= 51.678333333333335 -127.33166666666666  not matched to domain
(Lat,Lon)= 51.6785 -127.332  not matched to domain
(Lat,Lon)= 51.678666666666665 -127.33266666666667  not matched to domain
(Lat,Lon)= 51.678666666666665 -127.332  not matched to domain
(Lat,Lon)= 51.678666666666665 -127.3315  not matched to domain
(Lat,Lon)= 51.67883333333333 -127.3325  not matched to domain
(Lat,Lon)= 51.679 -127.33233333333334  not matched to domain

In [5]:

# drop Saanich Inlet
tmask2=np.copy(tmask[0,0,:,:])
tmask2[328:371,180:217]=tmask[0,0,328:371,180:217]+2
plt.pcolormesh(tmask2)
plt.xlim(150,250)
plt.ylim(310,400)
d1.drop(d1.loc[(d1.i>=180)&(d1.i<217)&(d1.j>=328)&(d1.j<371)].index.values,inplace=True)
# also drop east side Vancouver Island points:
d1.drop(d1.loc[(d1.i<10)&(d1.j>=460)&(d1.j<480)].index.values,inplace=True)

In [6]:

d1['YD']=[(dt.datetime(int(yy),int(mm),int(dd))-dt.datetime(int(yy),1,1)).total_seconds()/(24*3600) for yy,mm,dd in zip(d1['Year'].values,d1['Month'].values,d1['Day'].values)]

In [7]:

np.unique(d1.loc[(d1.N==0)&(d1.Z>20),['Year']])

Out[7]:

array([2006.])

In [8]:

d1.loc[(d1.N==0)&(d1.Z>20)]

Out[8]:

	Year	Month	Day	Hour	Lat	Lon	Pressure	Depth	Ammonium	Ammonium_units	...	Si	Silicate_units	AbsSal	ConsT	Z	dtUTC	j	i	k	YD
11627	2006.0	6.0	16.0	6.132222	49.443333	-124.337167	125.2	NaN	NaN	None	...	17.4	umol/L	30.487816	8.774221	124.095477	2006-06-16 06:07:56	567	183	27.0	166.0

1 rows × 23 columns

In [9]:

d1.loc[(d1.Si==0)&(d1.Z>20)]

Out[9]:

	Year	Month	Day	Hour	Lat	Lon	Pressure	Depth	Ammonium	Ammonium_units	...	Si	Silicate_units	AbsSal	ConsT	Z	dtUTC	j	i	k	YD
21422	1932.0	4.0	1.0	22.0	48.866667	-122.866667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1932-04-01 22:00:00	351	327	21.0	91.0
21423	1932.0	4.0	1.0	22.0	48.866667	-122.866667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1932-04-01 22:00:00	351	327	23.0	91.0
21424	1932.0	4.0	1.0	22.0	48.866667	-122.866667	NaN	100.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	100.0	1932-04-01 22:00:00	351	327	NaN	91.0
21425	1932.0	4.0	1.0	22.0	48.866667	-122.866667	NaN	180.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	180.0	1932-04-01 22:00:00	351	327	NaN	91.0
21433	1932.0	4.0	1.0	18.0	48.866667	-122.866667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1932-04-01 18:00:00	351	327	21.0	91.0
21434	1932.0	4.0	1.0	18.0	48.866667	-122.866667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1932-04-01 18:00:00	351	327	23.0	91.0
21435	1932.0	4.0	1.0	18.0	48.866667	-122.866667	NaN	100.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	100.0	1932-04-01 18:00:00	351	327	NaN	91.0
21436	1932.0	4.0	1.0	18.0	48.866667	-122.866667	NaN	180.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	180.0	1932-04-01 18:00:00	351	327	NaN	91.0
21444	1932.0	4.0	2.0	5.0	48.866667	-122.866667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1932-04-02 05:00:00	351	327	21.0	92.0
21445	1932.0	4.0	2.0	5.0	48.866667	-122.866667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1932-04-02 05:00:00	351	327	23.0	92.0
21446	1932.0	4.0	2.0	5.0	48.866667	-122.866667	NaN	100.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	100.0	1932-04-02 05:00:00	351	327	NaN	92.0
21447	1932.0	4.0	2.0	5.0	48.866667	-122.866667	NaN	180.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	180.0	1932-04-02 05:00:00	351	327	NaN	92.0
21455	1932.0	4.0	17.0	22.0	48.566667	-123.016667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1932-04-17 22:00:00	303	267	21.0	107.0
21456	1932.0	4.0	17.0	22.0	48.566667	-123.016667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1932-04-17 22:00:00	303	267	23.0	107.0
21457	1932.0	4.0	17.0	22.0	48.566667	-123.016667	NaN	100.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	100.0	1932-04-17 22:00:00	303	267	26.0	107.0
21465	1932.0	4.0	18.0	11.0	48.566667	-123.016667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1932-04-18 11:00:00	303	267	21.0	108.0
21466	1932.0	4.0	18.0	11.0	48.566667	-123.016667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1932-04-18 11:00:00	303	267	23.0	108.0
21467	1932.0	4.0	18.0	11.0	48.566667	-123.016667	NaN	100.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	100.0	1932-04-18 11:00:00	303	267	26.0	108.0
21474	1932.0	5.0	5.0	7.0	49.966667	-125.166667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1932-05-05 07:00:00	728	126	21.0	125.0
21475	1932.0	5.0	5.0	7.0	49.966667	-125.166667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1932-05-05 07:00:00	728	126	23.0	125.0
21476	1932.0	5.0	5.0	7.0	49.966667	-125.166667	NaN	100.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	100.0	1932-05-05 07:00:00	728	126	NaN	125.0
21477	1932.0	5.0	5.0	7.0	49.966667	-125.166667	NaN	180.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	180.0	1932-05-05 07:00:00	728	126	NaN	125.0
21485	1932.0	5.0	5.0	20.0	49.966667	-125.166667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1932-05-05 20:00:00	728	126	21.0	125.0
21486	1932.0	5.0	5.0	20.0	49.966667	-125.166667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1932-05-05 20:00:00	728	126	23.0	125.0
21487	1932.0	5.0	5.0	20.0	49.966667	-125.166667	NaN	100.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	100.0	1932-05-05 20:00:00	728	126	NaN	125.0
21488	1932.0	5.0	5.0	20.0	49.966667	-125.166667	NaN	180.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	180.0	1932-05-05 20:00:00	728	126	NaN	125.0
21496	1932.0	6.0	24.0	23.0	48.866667	-122.866667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1932-06-24 23:00:00	351	327	21.0	175.0
21497	1932.0	6.0	24.0	23.0	48.866667	-122.866667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1932-06-24 23:00:00	351	327	23.0	175.0
21498	1932.0	6.0	24.0	23.0	48.866667	-122.866667	NaN	100.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	100.0	1932-06-24 23:00:00	351	327	NaN	175.0
21499	1932.0	6.0	24.0	23.0	48.866667	-122.866667	NaN	200.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	200.0	1932-06-24 23:00:00	351	327	NaN	175.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
21569	1932.0	8.0	14.0	18.0	49.966667	-125.166667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1932-08-14 18:00:00	728	126	21.0	226.0
21570	1932.0	8.0	14.0	18.0	49.966667	-125.166667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1932-08-14 18:00:00	728	126	23.0	226.0
21571	1932.0	8.0	14.0	18.0	49.966667	-125.166667	NaN	100.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	100.0	1932-08-14 18:00:00	728	126	NaN	226.0
21572	1932.0	8.0	14.0	18.0	49.966667	-125.166667	NaN	150.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	150.0	1932-08-14 18:00:00	728	126	NaN	226.0
21580	1932.0	8.0	14.0	10.0	49.966667	-125.166667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1932-08-14 10:00:00	728	126	21.0	226.0
21581	1932.0	8.0	14.0	10.0	49.966667	-125.166667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1932-08-14 10:00:00	728	126	23.0	226.0
21582	1932.0	8.0	14.0	10.0	49.966667	-125.166667	NaN	100.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	100.0	1932-08-14 10:00:00	728	126	NaN	226.0
21583	1932.0	8.0	14.0	10.0	49.966667	-125.166667	NaN	150.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	150.0	1932-08-14 10:00:00	728	126	NaN	226.0
21591	1932.0	8.0	14.0	6.0	49.966667	-125.166667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1932-08-14 06:00:00	728	126	21.0	226.0
21592	1932.0	8.0	14.0	6.0	49.966667	-125.166667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1932-08-14 06:00:00	728	126	23.0	226.0
21593	1932.0	8.0	14.0	6.0	49.966667	-125.166667	NaN	100.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	100.0	1932-08-14 06:00:00	728	126	NaN	226.0
21594	1932.0	8.0	14.0	6.0	49.966667	-125.166667	NaN	150.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	150.0	1932-08-14 06:00:00	728	126	NaN	226.0
21602	1932.0	8.0	14.0	1.0	49.966667	-125.166667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1932-08-14 01:00:00	728	126	21.0	226.0
21603	1932.0	8.0	14.0	1.0	49.966667	-125.166667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1932-08-14 01:00:00	728	126	23.0	226.0
21604	1932.0	8.0	14.0	1.0	49.966667	-125.166667	NaN	100.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	100.0	1932-08-14 01:00:00	728	126	NaN	226.0
21605	1932.0	8.0	14.0	1.0	49.966667	-125.166667	NaN	150.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	150.0	1932-08-14 01:00:00	728	126	NaN	226.0
21613	1932.0	8.0	18.0	19.0	48.566667	-123.016667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1932-08-18 19:00:00	303	267	21.0	230.0
21614	1932.0	8.0	18.0	19.0	48.566667	-123.016667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1932-08-18 19:00:00	303	267	23.0	230.0
21622	1932.0	8.0	18.0	8.0	48.566667	-123.016667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1932-08-18 08:00:00	303	267	21.0	230.0
21623	1932.0	8.0	18.0	8.0	48.566667	-123.016667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1932-08-18 08:00:00	303	267	23.0	230.0
21631	1932.0	8.0	19.0	2.0	48.566667	-123.016667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1932-08-19 02:00:00	303	267	21.0	231.0
21632	1932.0	8.0	19.0	2.0	48.566667	-123.016667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1932-08-19 02:00:00	303	267	23.0	231.0
22348	1953.0	7.0	21.0	23.0	47.516667	-122.466667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1953-07-21 23:00:00	62	219	21.0	201.0
22349	1953.0	7.0	21.0	23.0	47.516667	-122.466667	NaN	50.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	50.0	1953-07-21 23:00:00	62	219	NaN	201.0
22350	1953.0	7.0	21.0	23.0	47.516667	-122.466667	NaN	75.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	75.0	1953-07-21 23:00:00	62	219	NaN	201.0
22351	1953.0	7.0	21.0	23.0	47.516667	-122.466667	NaN	100.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	100.0	1953-07-21 23:00:00	62	219	NaN	201.0
22360	1953.0	7.0	22.0	15.0	48.416667	-122.583333	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1953-07-22 15:00:00	244	312	NaN	202.0
22542	1954.0	7.0	11.0	17.5	47.216667	-122.816667	NaN	30.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	30.0	1954-07-11 17:30:00	28	132	21.0	191.0
22543	1954.0	7.0	11.0	17.5	47.216667	-122.816667	NaN	35.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	35.0	1954-07-11 17:30:00	28	132	22.0	191.0
22549	1954.0	7.0	11.0	10.6	47.366667	-122.666667	NaN	25.0	NaN	None	...	0.0	mmol/m**3	NaN	NaN	25.0	1954-07-11 10:36:00	47	172	NaN	191.0

80 rows × 23 columns

In [10]:

def binmean(ddf,latEdges,lonEdges):
    latbins=np.digitize(ddf.Lat,latEdges)
    lonbins=np.digitize(ddf.Lon,lonEdges)
    midLats=.5*(latEdges[:-1]+latEdges[1:])
    midLons=.5*(lonEdges[:-1]+lonEdges[1:])
    xxlon,xxlat=np.meshgrid(midLons,midLats)
    sums=np.zeros(np.shape(xxlon))
    counts=np.zeros(np.shape(xxlon))
    means=np.nan*np.ones(np.shape(xxlon))
    for jj in range(0,len(midLats)):
        for ii in range(0,len(midLons)):
            if len(ddf.loc[(latbins==jj+1)&(lonbins==ii+1),['Si']])>=1:
                means[jj,ii]=np.sum(ddf.loc[(latbins==jj+1)&(lonbins==ii+1),['Si']])/len(ddf.loc[(latbins==jj+1)&(lonbins==ii+1),['Si']])
    return xxlon, xxlat, means

In [11]:

fig,ax=plt.subplots(12,4,figsize=(16,50))
#kk=30 #36,34,32,30
simin=40
simax=80
for mon in range(1,13):
    iax=ax[mon-1,0]
    kk=30
    iax.contourf(navlon,navlat,tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','k','k'))
    viz_tools.set_aspect(iax,coords='map')
    idf=d1.loc[(d1.k==kk)&(d1.Si>=0)&(d1.Month==mon)]
    lo,la,mm=binmean(idf,latEdges=np.linspace(47,51,31),lonEdges=np.linspace(-126,-122,41))
    #iax.pcolormesh(lo,la,np.ma.masked_where(np.isnan(mm),mm))
    mesh=iax.scatter(lo,la,c=mm,s=6,vmin=simin,vmax=simax)
    plt.colorbar(mesh,ax=iax)
    iax.set_title('Depth='+str(gdept[kk]))
    iax.set_ylabel('Month='+str(mon))
    iax.set_xlim(-126,-122)
    iax.set_ylim(47.1,50.5)
    
    iax=ax[mon-1,1]
    kk=32
    iax.contourf(navlon,navlat,tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','k','k'))
    viz_tools.set_aspect(iax,coords='map')
    idf=d1.loc[(d1.k==kk)&(d1.Si>=0)&(d1.Month==mon)]
    lo,la,mm=binmean(idf,latEdges=np.linspace(47,51,31),lonEdges=np.linspace(-126,-122,41))
    #iax.pcolormesh(lo,la,np.ma.masked_where(np.isnan(mm),mm))
    mesh=iax.scatter(lo,la,c=mm,s=6,vmin=simin,vmax=simax)
    plt.colorbar(mesh,ax=iax)
    iax.set_title('Depth='+str(gdept[kk]))
    iax.set_xlim(-126,-122)
    iax.set_ylim(47.1,50.5)
    
    iax=ax[mon-1,2]
    kk=34
    iax.contourf(navlon,navlat,tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','k','k'))
    viz_tools.set_aspect(iax,coords='map')
    idf=d1.loc[(d1.k==kk)&(d1.Si>=0)&(d1.Month==mon)]
    lo,la,mm=binmean(idf,latEdges=np.linspace(47,51,31),lonEdges=np.linspace(-126,-122,41))
    #iax.pcolormesh(lo,la,np.ma.masked_where(np.isnan(mm),mm))
    mesh=iax.scatter(lo,la,c=mm,s=6,vmin=simin,vmax=simax)
    plt.colorbar(mesh,ax=iax)
    iax.set_title('Depth='+str(gdept[kk]))
    iax.set_xlim(-126,-122)
    iax.set_ylim(47.1,50.5)
    
    iax=ax[mon-1,3]
    kk=36
    iax.contourf(navlon,navlat,tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','k','k'))
    viz_tools.set_aspect(iax,coords='map')
    idf=d1.loc[(d1.k==kk)&(d1.Si>=0)&(d1.Month==mon)]
    lo,la,mm=binmean(idf,latEdges=np.linspace(47,51,31),lonEdges=np.linspace(-126,-122,41))
    #iax.pcolormesh(lo,la,np.ma.masked_where(np.isnan(mm),mm))
    mesh=iax.scatter(lo,la,c=mm,s=6,vmin=simin,vmax=simax)
    plt.colorbar(mesh,ax=iax)
    iax.set_title('Depth='+str(gdept[kk]))
    iax.set_xlim(-126,-122)
    iax.set_ylim(47.1,50.5)

In [12]:

fig,ax=plt.subplots(1,2,figsize=(10,4))
#kk=30 #36,34,32,30
simin=40
simax=80
for mon in range(1,2):

    iax=ax[0];#ax[mon-1,1]
    kk=32
    iax.contourf(navlon,navlat,tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','k','k'))
    viz_tools.set_aspect(iax,coords='map')
    d1bath=np.array([bathy[j,i] for i,j in zip(d1['i'],d1['j'])])
    df0=d1.loc[(d1.Lon<-123.3)&(d1.Lon>-123.6)&(d1.Lat<49.2)&(d1.Lat>48.85)&(d1.Si>=0)&(d1bath>400)]
    lo,la,mm=binmean(df0,latEdges=np.linspace(47,51,31),lonEdges=np.linspace(-126,-122,41))
    #iax.pcolormesh(lo,la,np.ma.masked_where(np.isnan(mm),mm))
    mesh=iax.scatter(lo,la,c=mm,s=6,vmin=simin,vmax=simax)
    plt.colorbar(mesh,ax=iax)
    #iax.set_title('Depth='+str(gdept[kk]))
    
    

In [13]:

fig,ax=plt.subplots(1,3,figsize=(16,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
     'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
    ik=37
    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
    stds=groupedm.groupby(['k']).agg({'Si':np.std})
    ns=groupedm.groupby(['k'])['Si'].count()
    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
               label=str(int(valm)))
    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
         bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs CT, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')

Out[13]:

<matplotlib.text.Text at 0x7fd7337b4978>

In [14]:

fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
     'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
    ik=36
    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
    stds=groupedm.groupby(['k']).agg({'Si':np.std})
    ns=groupedm.groupby(['k'])['Si'].count()
    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
               label=str(int(valm)))
    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
         bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')

Out[14]:

<matplotlib.text.Text at 0x7fd7334499e8>

In [15]:

fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
     'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
    ik=34
    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
    stds=groupedm.groupby(['k']).agg({'Si':np.std})
    ns=groupedm.groupby(['k'])['Si'].count()
    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
               label=str(int(valm)))
    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
         bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')

Out[15]:

<matplotlib.text.Text at 0x7fd73304d908>

In [16]:

fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
     'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
    ik=32
    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
    stds=groupedm.groupby(['k']).agg({'Si':np.std})
    ns=groupedm.groupby(['k'])['Si'].count()
    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
               label=str(int(valm)))
    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
         bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')

Out[16]:

<matplotlib.text.Text at 0x7fd7316bad68>

In [17]:

fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
     'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
    ik=30
    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
    stds=groupedm.groupby(['k']).agg({'Si':np.std})
    ns=groupedm.groupby(['k'])['Si'].count()
    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
               label=str(int(valm)))
    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
         bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')

Out[17]:

<matplotlib.text.Text at 0x7fd7542cae80>

In [18]:

fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
     'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
    ik=29
    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
    stds=groupedm.groupby(['k']).agg({'Si':np.std})
    ns=groupedm.groupby(['k'])['Si'].count()
    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
               label=str(int(valm)))
    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
         bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')

Out[18]:

<matplotlib.text.Text at 0x7fd755e64940>

In [19]:

fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
     'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
    ik=28
    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
    stds=groupedm.groupby(['k']).agg({'Si':np.std})
    ns=groupedm.groupby(['k'])['Si'].count()
    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
               label=str(int(valm)))
    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
         bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')

Out[19]:

<matplotlib.text.Text at 0x7fd756325e48>

In [20]:

fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
     'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
    ik=34
    ax[1].plot(groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['AbsSal']],
               groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['Si']].values/groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['N']].values,
               'o',color=col[int(valm)],alpha=1)
    ax[2].plot(groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['ConsT']],
               groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['Si']].values/groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['N']].values,
               'o',color=col[int(valm)],alpha=1)
    ax[3].plot(groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['AbsSal']],
               groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['ConsT']].values,
               'o',color=col[int(valm)],alpha=1)
    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
    stds=groupedm.groupby(['k']).agg({'Si':np.std})
    ns=groupedm.groupby(['k'])['Si'].count()
    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
               label=str(int(valm)))
    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
         bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')

Out[20]:

<matplotlib.text.Text at 0x7fd756825ba8>

In [21]:

df0.loc[(df0.k==34)].groupby(['Year'])['Si'].count()

Out[21]:

Year
1978.0     1
1996.0     7
1997.0     4
1998.0     6
1999.0     2
2000.0     9
2001.0    11
2002.0    16
2003.0    14
2004.0    11
2005.0    13
2006.0    12
2007.0    11
2008.0    12
2009.0    12
2010.0    20
2011.0    15
2012.0    12
2013.0     9
2014.0    14
2015.0     9
2016.0    12
2017.0    12
2018.0     9
Name: Si, dtype: int64

In [22]:

df0.loc[(df0.k==35)].groupby(['Year'])['Si'].count()

Out[22]:

Year
1996.0    3
1997.0    3
1998.0    3
1999.0    1
2000.0    3
2001.0    3
2002.0    3
2003.0    3
2004.0    2
2005.0    2
2006.0    3
2008.0    3
2010.0    4
2011.0    3
2013.0    2
2014.0    3
2015.0    1
2016.0    2
2017.0    4
2018.0    2
Name: Si, dtype: int64

In [23]:

df0.loc[(df0.k==36)].groupby(['Year'])['Si'].count()

Out[23]:

Year
1996.0     3
1997.0     2
1998.0     3
1999.0     1
2000.0     3
2001.0     5
2002.0     7
2003.0     9
2004.0     6
2005.0     6
2006.0     5
2007.0     5
2008.0     5
2009.0     8
2010.0    11
2011.0     3
2012.0     5
2013.0     4
2014.0     5
2015.0     3
2016.0     6
2017.0     4
2018.0     2
Name: Si, dtype: int64

In [24]:

fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
     'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
df1=df0.loc[df0.Year==2010]
for valm, groupedm in df1.groupby(['Month']):
    ik=34
    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
    stds=groupedm.groupby(['k']).agg({'Si':np.std})
    ns=groupedm.groupby(['k'])['Si'].count()
    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
               label=str(int(valm)))
    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[ii] for ii in range(1,13) if ii in ps.keys()],
         bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('2010 Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')

Out[24]:

<matplotlib.text.Text at 0x7fd7380d6978>

In [25]:

fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
     'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
df1=df0.loc[df0.Year==2010]
for valm, groupedm in df1.groupby(['Month']):
    ik=35
    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
    stds=groupedm.groupby(['k']).agg({'Si':np.std})
    ns=groupedm.groupby(['k'])['Si'].count()
    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
               label=str(int(valm)))
    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[ii] for ii in range(1,13) if ii in ps.keys()],
         bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('2010 Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')

Out[25]:

<matplotlib.text.Text at 0x7fd75656fa58>

In [26]:

fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
     'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
df1=df0.loc[df0.Year==2002]
for valm, groupedm in df1.groupby(['Month']):
    ik=34
    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
    stds=groupedm.groupby(['k']).agg({'Si':np.std})
    ns=groupedm.groupby(['k'])['Si'].count()
    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
               label=str(int(valm)))
    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[ii] for ii in range(1,13) if ii in ps.keys()],
         bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title(' 2002 Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')

Out[26]:

<matplotlib.text.Text at 0x7fd756726198>

In [27]:

fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
     'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
df1=df0.loc[df0.Year==2003]
for valm, groupedm in df1.groupby(['Month']):
    ik=36
    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
    stds=groupedm.groupby(['k']).agg({'Si':np.std})
    ns=groupedm.groupby(['k'])['Si'].count()
    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
               label=str(int(valm)))
    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[ii] for ii in range(1,13) if ii in ps.keys()],
         bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title(' 2003 Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')

Out[27]:

<matplotlib.text.Text at 0x7fd7560ea6d8>

In [28]:

df1.keys()

Out[28]:

Index(['Year', 'Month', 'Day', 'Hour', 'Lat', 'Lon', 'Pressure', 'Depth',
       'Ammonium', 'Ammonium_units', 'Chlorophyll_Extracted',
       'Chlorophyll_Extracted_units', 'N', 'Si', 'Silicate_units', 'AbsSal',
       'ConsT', 'Z', 'dtUTC', 'j', 'i', 'k', 'YD'],
      dtype='object')

In [29]:

fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
     'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
df1=df0.loc[df0.Year==2003]
for valm, groupedm in df1.groupby(['Month']):
    ik=36
    cmin=0
    cmax=365
    if np.sum(groupedm.k==ik)>0:
        ax[1].scatter(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],c=groupedm.loc[groupedm.k==ik,['YD']],
                      vmin=cmin,vmax=cmax,cmap=plt.get_cmap('hsv'))
        ax[2].scatter(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],c=groupedm.loc[groupedm.k==ik,['YD']],
                      vmin=cmin,vmax=cmax,cmap=plt.get_cmap('hsv'))
        m=ax[3].scatter(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],c=groupedm.loc[groupedm.k==ik,['YD']],
                        vmin=cmin,vmax=cmax,cmap=plt.get_cmap('hsv'))
    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
    stds=groupedm.groupby(['k']).agg({'Si':np.std})
    ns=groupedm.groupby(['k'])['Si'].count()
    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
               label=str(int(valm)))
    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[ii] for ii in range(1,13) if ii in ps.keys()],
         bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title(' 2003 Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
fig.colorbar(m,ax=ax[3])

Out[29]:

<matplotlib.colorbar.Colorbar at 0x7fd73870f0f0>

In [30]:

df1.loc[df1.k==ik,['Year','Month','Day','Lat','Lon','Pressure','Si','AbsSal','ConsT']]

Out[30]:

	Year	Month	Day	Lat	Lon	Pressure	Si	AbsSal	ConsT
40566	2003.0	4.0	26.0	49.163000	-123.550167	371.7	70.7	31.364321	9.209034
40599	2003.0	4.0	27.0	49.162833	-123.549500	375.0	71.6	31.352728	9.208075
40699	2003.0	6.0	19.0	49.164000	-123.548167	352.7	73.4	31.260225	9.148055
40700	2003.0	6.0	19.0	49.164000	-123.548167	369.9	75.0	31.274537	9.164992
40732	2003.0	6.0	20.0	49.163333	-123.549500	352.3	73.2	31.275391	9.166978
40733	2003.0	6.0	20.0	49.163333	-123.549500	372.2	72.9	31.293075	9.181456
40807	2003.0	9.0	5.0	49.162333	-123.547500	372.3	56.9	31.491569	9.452973
40914	2003.0	12.0	4.0	49.163667	-123.547833	353.2	65.9	31.417017	9.665293
40915	2003.0	12.0	4.0	49.163667	-123.547833	373.8	65.3	31.423961	9.656753

In [31]:

plt.scatter(df1.loc[df1.k==ik,['Lon']],df1.loc[df1.k==ik,['Lat']],c=df1.loc[df1.k==ik,['Si']])
plt.xlim(-123.58,-123.53)
plt.ylim(49.16,49.17)

Out[31]:

(49.16, 49.17)

In [32]:

plt.scatter(xx,xx,c=xx)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-32-8fe53c5eb46c> in <module>()
----> 1 plt.scatter(xx,xx,c=xx)

NameError: name 'xx' is not defined

In [ ]: