In [1]:

import numpy as np  # this module handles arrays, but here we need it for its NaN value
import pandas as pd # this module contains a lot of tools for handling tabular data
from matplotlib import pyplot as plt
from salishsea_tools import evaltools as et
import datetime as dt
import os
import gsw
import pickle
import netCDF4 as nc
import cmocean
from scipy import stats as spst
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
%matplotlib inline

load 2011 hplc file¶

In [2]:

floc='/ocean/eolson/MEOPAR/obs/NemcekHPLC/2011 composition changes with depth.xlsx'
df0=pd.read_excel(floc,sheet_name='HPLC plots',verbose=True,na_values=(-99,-99.9),engine='openpyxl')

Reading sheet HPLC plots

In [3]:

for var in ('Diatoms-1','Diatoms-2','Prasinophytes','Cryptophytes','Dinoflagellates-1','Haptophytes','Dictyo',
            'Raphido','Cyanobacteria'):
    df0.rename(columns={var:var+'x'},inplace=True)

for var in ('Diatoms-1','Diatoms-2','Prasinophytes','Cryptophytes','Dinoflagellates-1','Haptophytes','Dictyo',
            'Raphido','Cyanobacteria'):
    df0[var]=df0[var+'x']*df0['T chl a']

for var in ('Diatoms-1','Diatoms-2','Prasinophytes','Cryptophytes','Dinoflagellates-1','Haptophytes','Dictyo',
            'Raphido','Cyanobacteria'):
    df0.drop(columns=(var+'x'),inplace=True)

df0.dropna(subset=('Diatoms-1','Diatoms-2',),how='all',inplace=True)

In [4]:

df0.head(5)

Out[4]:

	Cruise	Month	Station	Distance	Sample#	T chl a	% diatoms	Diatoms-1	Diatoms-2	Prasinophytes	Cryptophytes	Dinoflagellates-1	Haptophytes	Dictyo	Raphido	Cyanobacteria
0	2011-08	April	102	0.000000	176.0	2.28816	0.675847	1.4197	0.126747	0.123511	0.181151	0.224466	0.0500825	0.0467157	0.10959	0.00619676
1	2011-08	April	75	14.146700	190.0	2.34223	0.70811	1.15614	0.502418	0.184418	0.153775	0.185794	0.058737	0.035052	0.0658965	0
2	2011-08	April	72	53.777709	203.0	1.12654	0.498815	0.554218	0.00771505	0.195122	0.139457	0.0956432	0.0131673	0.0200251	0.0893818	0.0118066
3	2011-08	April	ADCP	111.226061	213.0	0.740453	0.637956	0.445945	0.0264313	0.153361	0.0725701	0.000924017	0	0.000739395	0.0404824	0
4	2011-08	April	65	121.933291	223.0	0.719871	0.586513	0.398758	0.0234555	0.163365	0.0763358	0.0265331	0	0.000587094	0.030837	0

In [5]:

from sqlalchemy import create_engine, case
from sqlalchemy.orm import create_session 
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.sql import and_, or_, not_, func

In [9]:

basedir='/ocean/shared/SalishSeaCastData/DFO/BOT/'
dbname='DFO_OcProfDB.sqlite'
engine = create_engine('sqlite:///' + basedir + dbname, echo = False)
Base = automap_base()
# reflect the tables in salish.sqlite:
Base.prepare(engine, reflect=True)
# mapped classes have been created
# existing tables:
StationTBL=Base.classes.StationTBL
ObsTBL=Base.classes.ObsTBL
CalcsTBL=Base.classes.CalcsTBL
JDFLocsTBL=Base.classes.JDFLocsTBL
session = create_session(bind = engine, autocommit = False, autoflush = True)

In [ ]:

In [12]:

qry=session.query(StationTBL.StartYear.label('Year'),StationTBL.StartMonth.label('Month'),
                  StationTBL.StartDay.label('Day'),StationTBL.StartHour.label('Hour'),
             StationTBL.MISSION,StationTBL.Lat,StationTBL.Lon,
             ObsTBL.Sample_Number,ObsTBL.Depth,ObsTBL.Pressure).\
        select_from(StationTBL).join(ObsTBL,ObsTBL.StationTBLID==StationTBL.ID).filter(or_(StationTBL.MISSION=='2011-08',
                                                                StationTBL.MISSION=='2011-09',
                                                                StationTBL.MISSION=='2011-10'))
df1=pd.DataFrame(qry.all())

In [13]:

df1['Z']=np.where(df1['Depth']>=0,df1['Depth'],-1.0*gsw.z_from_p(p=df1['Pressure'],lat=df1['Lat']))
df1['dtUTC']=[dt.datetime(int(y),int(m),int(d))+dt.timedelta(hours=h) for y,m,d,h in zip(df1['Year'],df1['Month'],df1['Day'],df1['Hour'])]

In [14]:

df1

Out[14]:

	Year	Month	Day	Hour	MISSION	Lat	Lon	Sample_Number	Depth	Pressure	Z	dtUTC
0	2011.0	4.0	14.0	13.470000	2011-08	49.961667	-125.146500	11.0	None	2.8	2.77599	2011-04-14 13:28:12
1	2011.0	4.0	14.0	13.470000	2011-08	49.961667	-125.146500	10.0	None	3.8	3.76741	2011-04-14 13:28:12
2	2011.0	4.0	14.0	13.470000	2011-08	49.961667	-125.146500	9.0	None	9.0	8.92269	2011-04-14 13:28:12
3	2011.0	4.0	14.0	13.470000	2011-08	49.961667	-125.146500	8.0	None	22.9	22.7025	2011-04-14 13:28:12
4	2011.0	4.0	14.0	13.470000	2011-08	49.961667	-125.146500	7.0	None	30.7	30.4347	2011-04-14 13:28:12
...	...	...	...	...	...	...	...	...	...	...	...	...
789	2011.0	9.0	12.0	11.890278	2011-10	49.318667	-123.799167	169.0	None	174.8	173.239	2011-09-12 11:53:25
790	2011.0	9.0	12.0	11.890278	2011-10	49.318667	-123.799167	168.0	None	200.2	198.4	2011-09-12 11:53:25
791	2011.0	9.0	12.0	11.890278	2011-10	49.318667	-123.799167	167.0	None	250.9	248.614	2011-09-12 11:53:25
792	2011.0	9.0	12.0	11.890278	2011-10	49.318667	-123.799167	166.0	None	300.1	297.33	2011-09-12 11:53:25
793	2011.0	9.0	12.0	11.890278	2011-10	49.318667	-123.799167	165.0	None	346.4	343.164	2011-09-12 11:53:25

794 rows × 12 columns

In [17]:

df=pd.merge(df0, df1,  how='inner', 
                 left_on=['Cruise','Sample#'], right_on = ['MISSION','Sample_Number'])

In [18]:

print(len(df0),len(df1),len(df))

172 794 172

In [58]:

df.keys()

Out[58]:

Index(['Cruise', 'Month_x', 'Station', 'Distance', 'Sample#', 'depth',
       'T chl a', '% diatoms', 'Diatoms-1', 'Diatoms-2', 'Prasinophytes',
       'Cryptophytes', 'Dinoflagellates-1', 'Haptophytes', 'Dictyo', 'Raphido',
       'Cyanobacteria', 'Year', 'Month_y', 'Day', 'Hour', 'MISSION', 'Lat',
       'Lon', 'Sample_Number', 'Depth', 'Pressure', 'Z', 'dtUTC'],
      dtype='object')

In [60]:

np.unique(df1.Month),np.unique(df.Month_x),np.unique(df.Month_y)

Out[60]:

(array([4., 6., 9.]),
 array(['April', 'June', 'Sept'], dtype=object),
 array([4., 6., 9.]))

In [19]:

# Parameters
modSourceDir = "/results2/SalishSea/nowcast-green.201905/"
modver = "201905"
Chl_N = 1.8
fname = "compHPLCModelFirstLook-Regress-201905.ipynb"
startYMD=(2011,4,1)
endYMD=(2011,10,1)

In [20]:

start_date = dt.datetime(startYMD[0],startYMD[1],startYMD[2])
end_date = dt.datetime(endYMD[0],endYMD[1],endYMD[2]) #dt.datetime(2019,6,30)

In [21]:

datestr='_'+start_date.strftime('%Y%m%d')+'_'+end_date.strftime('%Y%m%d')

In [22]:

PATH= modSourceDir
flen=1
namfmt='nowcast'
filemap={'diatoms':'ptrc_T','ciliates':'ptrc_T','flagellates':'ptrc_T',}
fdict={'ptrc_T':1,'grid_T':1}
data=et.matchData(df,filemap,fdict,start_date,end_date,namfmt,PATH,flen)
print(np.unique(data.Month_y))
data.rename(columns={'Dictyo':'Dictyochophytes','Raphido':'Raphidophytes','Dinoflagellates-1':'Dinoflagellates'},
           inplace=True)

In [62]:

np.unique(data.Month_y)

Out[62]:

array([4., 6., 9.])

In [27]:

data['other']=0.0
for el in ('Cryptophytes', 'Cyanobacteria', 'Dictyochophytes', 'Dinoflagellates',
    'Haptophytes', 'Prasinophytes', 'Raphidophytes'):
    data['other']=data['other']+data[el]

In [36]:

fig,ax=plt.subplots(2,2,figsize=(12,8))
ax=ax.flatten()
m0=ax[0].scatter(data['Diatoms-1']+data['Diatoms-2'],Chl_N*data['mod_diatoms'],c=data['Z'],s=5)
ax[0].set_title('Diatoms (mg Chl/m$^3$)')
ax[0].set_xlabel('HPLC')
ax[0].set_ylabel('model')
ax[0].plot((0,18),(0,18),'r-',alpha=.3)
fig.colorbar(m0,ax=ax[0])

m1=ax[1].scatter(data['other'],Chl_N*(data['mod_flagellates']+data['mod_ciliates']),c=data['Z'],s=5)
ax[1].set_title('non-Diatoms (mg Chl/m$^3$)')
ax[1].set_xlabel('HPLC')
ax[1].set_ylabel('model')
ax[1].plot((0,12),(0,12),'r-',alpha=.3)
fig.colorbar(m1,ax=ax[1])

m2=ax[2].scatter(data['Diatoms-1']+data['Diatoms-2'],Chl_N*data['mod_diatoms'],c=data['Z'],s=5)
ax[2].set_title('Diatoms (mg Chl/m$^3$)')
ax[2].set_xlabel('HPLC')
ax[2].set_ylabel('model')
ax[2].plot((0,25),(0,25),'r-',alpha=.3)
ax[2].set_xlim((0,25))
ax[2].set_ylim((0,25))
fig.colorbar(m2,ax=ax[2])

m3=ax[3].scatter(data['other'],Chl_N*(data['mod_flagellates']+data['mod_ciliates']),c=data['Z'],s=5)
ax[3].set_title('non-Diatoms (mg Chl/m$^3$)')
ax[3].set_xlabel('HPLC')
ax[3].set_ylabel('model')
ax[3].plot((0,12),(0,12),'r-',alpha=.3)
ax[3].set_xlim((0,8))
ax[3].set_ylim((0,8))
fig.colorbar(m3,ax=ax[3])

Out[36]:

<matplotlib.colorbar.Colorbar at 0x7fc7a4c43f10>

In [39]:

def logt(x):
    return np.log10(x+.001)

In [44]:

logt(data['Diatoms-1'].astype(np.float)+data['Diatoms-2'].astype(np.float))

Out[44]:

0      0.786705
1      0.777465
2      0.619578
3      0.990800
4      0.945610
         ...   
167   -0.164509
168   -0.420451
169   -0.144978
170   -0.300427
171   -0.470969
Length: 172, dtype: float64

In [47]:

fig,ax=plt.subplots(1,2,figsize=(12,5))
ax[0].scatter(logt(data['Diatoms-1'].astype(np.float)+data['Diatoms-2'].astype(np.float)),
           logt(Chl_N*data['mod_diatoms'].astype(np.float)),c=data['Z'],s=5)
ax[0].set_title('log10[ Diatoms (mg Chl/m$^3$) + 0.001]',fontsize=18)
ax[0].set_xlabel('HPLC',fontsize=18)
ax[0].set_ylabel('model',fontsize=18)
ax[0].set_xlim(-3.1,2)
ax[0].set_ylim(-3.1,2)
ax[0].plot((-6,3),(-6,3),'r-',alpha=.3)

ax[1].scatter(logt(data['other'].astype(np.float)),
           logt(Chl_N*(data['mod_flagellates'].astype(np.float)+data['mod_ciliates'].astype(np.float))),c=data['Z'],s=5)
ax[1].set_title('log10[ non-Diatoms (mg Chl/m$^3$) + 0.001]',fontsize=18)
ax[1].set_xlabel('HPLC',fontsize=18)
ax[1].set_ylabel('model',fontsize=18)
ax[1].plot((-6,3),(-6,3),'r-',alpha=.3)
ax[1].set_xlim(-3.1,2)
ax[1].set_ylim(-3.1,2)

Out[47]:

(-3.1, 2.0)

In [51]:

plt.scatter(data['T chl a'],Chl_N*(data['mod_flagellates']+data['mod_ciliates']+data['mod_diatoms']),
           c=data['Z'],s=4)
plt.title('Total Chlorophyll (ug/L)')
plt.xlabel('HPLC')
plt.ylabel('model')
plt.plot((0,20),(0,20),'r-',alpha=.3)

Out[51]:

[<matplotlib.lines.Line2D at 0x7fc79d866f70>]

In [52]:

fig,ax=plt.subplots(1,1,figsize=(5,5))
ax.scatter(logt(data['T chl a'].astype(np.float)),
        logt(Chl_N*(data['mod_flagellates'].astype(np.float)+data['mod_ciliates'].astype(np.float)+\
                    data['mod_diatoms'].astype(np.float))),c=data['Z'],s=4)
ax.set_title('log10[ Total Chlorophyll (ug/L) + 0.001]')
ax.set_xlabel('HPLC')
ax.set_ylabel('model')
ax.plot((-6,5),(-6,5),'r-',alpha=.3)
ax.set_xlim(-1,2)
ax.set_ylim(-1,2);

In [53]:

def yd(idt):
    if type(idt)==dt.datetime:
        yd=(idt-dt.datetime(idt.year-1,12,31)).days
    else: # assume array or pandas
        yd=[(ii-dt.datetime(ii.year-1,12,31)).days for ii in idt]
    return yd

data['yd']=yd(data['dtUTC'])
data['Year']=[ii.year for ii in data['dtUTC']]

In [75]:

fig,ax=plt.subplots(1,1,figsize=(5,5))
m=ax.scatter(logt(data['T chl a'].astype(np.float)),
             logt(Chl_N*(data['mod_flagellates'].astype(np.float)+data['mod_ciliates'].astype(np.float)\
                         +data['mod_diatoms'].astype(np.float))),
          c=data['yd'],s=4,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax.set_title('log10[ Total Chlorophyll (ug/L) + 0.001] By Year Day')
ax.set_xlabel('HPLC')
ax.set_ylabel('model')
ax.plot((-6,5),(-6,5),'r-',alpha=.3)
ax.set_xlim(-1,2)
ax.set_ylim(-1,2);
fig.colorbar(m)

Out[75]:

<matplotlib.colorbar.Colorbar at 0x7fc79d4abfd0>

In [76]:

with nc.Dataset('/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702_noLPE.nc') as mesh:
    tmask=np.copy(mesh.variables['tmask'][0,:,:,:])
    navlat=np.copy(mesh.variables['nav_lat'][:,:])
    navlon=np.copy(mesh.variables['nav_lon'][:,:])

In [79]:

fig,ax=plt.subplots(2,5,figsize=(15,7))
fig.subplots_adjust(wspace=.4)
ax=ax.flatten()

chplc=('Diatoms-1', 'Diatoms-2','Cyanobacteria','Cryptophytes', 'Prasinophytes', 
       'Haptophytes',  'Dictyochophytes','Dinoflagellates','Raphidophytes','T chl a')

mvar1=Chl_N*data['mod_diatoms'].astype(np.float)
mvar2=Chl_N*data['mod_flagellates'].astype(np.float)

for ii in range(0,len(chplc)):
    ax[ii].plot(logt(data.loc[:,[chplc[ii]]].astype(np.float)),logt(mvar1),'.',color='blue',label='Diatoms')
    ax[ii].plot(logt(data.loc[:,[chplc[ii]]].astype(np.float)),logt(mvar2),'.',color='red',label='Flagellates')
    ax[ii].set_ylabel('Model Class')
    ax[ii].set_xlabel(chplc[ii])
    ax[ii].set_title('log10[Chl(mg/m3)+.001]')
    ax[ii].plot((-3,1.5),(-3,1.5),'k-',alpha=.2)
    ax[ii].set_xlim((-3,1.5))
    ax[ii].set_ylim((-3,1.5))
    ax[ii].set_aspect(1)
ax[0].legend()

Out[79]:

<matplotlib.legend.Legend at 0x7fc7a44ea040>

In [80]:

fig,ax=plt.subplots(1,3,figsize=(12,3))
fig.subplots_adjust(wspace=.5)
#ax[0].plot(logt(data['Diatoms-1']+data['Raphidophytes']+.5*data['Cryptophytes']),logt(data['mod_diatoms']),'r.')
ax[0].plot(logt(data['Diatoms-1'].astype(np.float)+data['Raphidophytes'].astype(np.float)),
           logt(data['mod_diatoms'].astype(np.float)),'k.')
ax[0].set_ylabel('Model diatoms')
ax[0].set_xlabel('Diatoms1+Raphido')
ax[0].set_title('log10[Chl(mg/m3)+.001]')
ax[0].plot((-3,1.5),(-3,1.5),'k-',alpha=.2)
ax[0].set_xlim((-3,1.5))
ax[0].set_ylim((-3,1.5))

ax[1].plot(logt(data['Cyanobacteria'].astype(np.float)+data['Cryptophytes'].astype(np.float)+\
                data['Haptophytes'].astype(np.float)),logt(data['mod_flagellates'].astype(np.float)),'k.')
ax[1].set_ylabel('Model flagellates')
ax[1].set_xlabel('Cyano+Crypto+Hapto')
ax[1].set_title('log10[Chl(mg/m3)+.001]')
ax[1].plot((-3,1.5),(-3,1.5),'k-',alpha=.2)
ax[1].set_xlim((-3,1.5))
ax[1].set_ylim((-3,1.5))


ax[2].plot(logt(data['Cyanobacteria'].astype(np.float)),logt(data['mod_ciliates'].astype(np.float)),'k.')
ax[2].set_ylabel('Model M. rubrum')
ax[2].set_xlabel('Cyano')
ax[2].set_title('log10[Chl(mg/m3)+.001]')
ax[2].plot((-3,1.5),(-3,1.5),'k-',alpha=.2)
ax[2].set_xlim((-3,1.5))
ax[2].set_ylim((-3,1.5))

Out[80]:

(-3.0, 1.5)

In [82]:

data['mod_diatoms_chl']=Chl_N*data['mod_diatoms']
data['mod_flagellates_chl']=Chl_N*data['mod_flagellates']
data['mod_ciliates_chl']=Chl_N*data['mod_ciliates']
data['mod_TChl']=data['mod_diatoms_chl']+data['mod_flagellates_chl']+data['mod_ciliates_chl']
data['CCPH']=data['Cryptophytes']+data['Prasinophytes']+data['Haptophytes']
data['DD']=data['Diatoms-1']+data['Diatoms-2']
data['TchlA (ug/L)']=data['T chl a']
dfVars=data.loc[:,['Diatoms-1', 'Diatoms-2','Cyanobacteria','Cryptophytes', 'Prasinophytes', 
       'Haptophytes',  'Dictyochophytes','Dinoflagellates','Raphidophytes','DD','CCPH','TchlA (ug/L)',
                   'mod_diatoms_chl','mod_flagellates_chl','mod_ciliates_chl','mod_TChl']]

New groups Model-Obs Comparison:¶

In [84]:

thresh=.8
msize=20
fig,ax=plt.subplots(1,2,figsize=(9,4))
m=ax[0].scatter(logt(data['DD'].astype(np.float)),logt(data['mod_diatoms_chl'].astype(np.float)),
          c=data['yd'],s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
m=ax[1].scatter(logt(data['CCPH'].astype(np.float)),logt(data['mod_flagellates_chl'].astype(np.float)),
          c=data['yd'],s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[0].set_xlim(-3,2)
ax[0].set_ylim(-3,2)
ax[1].set_xlim(-3,1.2)
ax[1].set_ylim(-3,1.2)
ax[0].set_xlabel('Diatoms 1 + Diatoms 2')
ax[0].set_ylabel('Model Diatoms')
ax[0].set_title('log10[Chl (mg/m$^3$)+.001]')
ax[1].set_xlabel('crypto+prasino+hapto')
ax[1].set_ylabel('Model flagellates')
ax[1].set_title('log10[Chl (mg/m$^3$)+.001]')
ax[0].plot((-3,2),(-3+thresh,2+thresh),'-',color='grey')
ax[0].plot((-3,2),(-3-thresh,2-thresh),'-',color='grey')
ax[1].plot((-3,2),(-3+thresh,2+thresh),'-',color='grey')
ax[1].plot((-3,2),(-3-thresh,2-thresh),'-',color='grey')
ax[0].plot((-3,2),(-3,2),'k-')
ax[1].plot((-3,1.2),(-3,1.2),'k-')
plt.tight_layout()
fig.colorbar(m,ax=ax[1])

Out[84]:

<matplotlib.colorbar.Colorbar at 0x7fc7a69585e0>

In [87]:

plt.hist(data['Z'],20)

Out[87]:

(array([38., 21.,  1.,  0.,  1., 19., 33.,  4.,  0.,  0.,  0.,  0., 20.,
        31.,  2.,  1.,  0.,  0.,  0.,  1.]),
 array([1.0907020648406247, 2.4934786846893675, 3.8962553045381103,
        5.2990319243868536, 6.701808544235596, 8.104585164084337,
        9.507361783933082, 10.910138403781826, 12.312915023630566,
        13.71569164347931, 15.118468263328051, 16.521244883176795,
        17.92402150302554, 19.32679812287428, 20.729574742723024,
        22.132351362571764, 23.53512798242051, 24.937904602269253,
        26.340681222117993, 27.743457841966737, 29.14623446181548],
       dtype=object),
 <BarContainer object of 20 artists>)

In [101]:

print('Diatoms/DD')
fig,ax=plt.subplots(3,4,figsize=(12,12),gridspec_kw={'width_ratios': [1,1,1,.1],'wspace':.5,'hspace':.35},)
ax=ax.flatten()
for iax in ax:
    iax.contour(navlon,navlat,tmask[0,:,:],levels=[0.5,],colors='gray')
    iax.set_xlim(-125.3,-122.5)
    iax.set_ylim(48,50.5)
xdata=data.loc[data.Z<5]
sc=0
ihi=logt(xdata['mod_diatoms_chl'].astype(np.float))>(logt(xdata['DD'].astype(np.float))+thresh)
ilo=logt(xdata['mod_diatoms_chl'].astype(np.float))<(logt(xdata['DD'].astype(np.float))-thresh)
idata=xdata.loc[(xdata.DD>=0)&ihi]
ax[0+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[0+sc].set_title('High\n log(mod)>log(obs)+'+str(thresh))
idata=xdata.loc[(xdata.DD>=0)&(~ihi)&(~ilo)]
ax[1+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[1+sc].set_title('Medium\n log(obs)-'+str(thresh)+'<log(mod)<log(obs)+'+str(thresh)+' Z=0')
idata=xdata.loc[(xdata.DD>=0)&ilo]
m=ax[2+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[2+sc].set_title('Low\n log(mod)<log(obs)-'+str(thresh));
fig.colorbar(m,cax=ax[3+sc]);


xdata=data.loc[(data.Z>8)&(data.Z<12)]
sc=4
ihi=logt(xdata['mod_diatoms_chl'].astype(np.float))>(logt(xdata['DD'].astype(np.float))+thresh)
ilo=logt(xdata['mod_diatoms_chl'].astype(np.float))<(logt(xdata['DD'].astype(np.float))-thresh)
idata=xdata.loc[(xdata.DD>=0)&ihi]
ax[0+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[0+sc].set_title('High\n log(mod)>log(obs)+'+str(thresh))
idata=xdata.loc[(xdata.DD>=0)&(~ihi)&(~ilo)]
ax[1+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[1+sc].set_title('Medium\n log(obs)-'+str(thresh)+'<log(mod)<log(obs)+'+str(thresh)+' Z=10')
idata=xdata.loc[(xdata.DD>=0)&ilo]
m=ax[2+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[2+sc].set_title('Low\n log(mod)<log(obs)-'+str(thresh));
fig.colorbar(m,cax=ax[3+sc]);

xdata=data.loc[(data.Z>18)&(data.Z<22)]
sc=8
ihi=logt(xdata['mod_diatoms_chl'].astype(np.float))>(logt(xdata['DD'].astype(np.float))+thresh)
ilo=logt(xdata['mod_diatoms_chl'].astype(np.float))<(logt(xdata['DD'].astype(np.float))-thresh)
idata=xdata.loc[(xdata.DD>=0)&ihi]
ax[0+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[0+sc].set_title('High\n log(mod)>log(obs)+'+str(thresh))
idata=xdata.loc[(xdata.DD>=0)&(~ihi)&(~ilo)]
ax[1+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[1+sc].set_title('Medium\n log(obs)-'+str(thresh)+'<log(mod)<log(obs)+'+str(thresh)+' Z=20')
idata=xdata.loc[(xdata.DD>=0)&ilo]
m=ax[2+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[2+sc].set_title('Low\n log(mod)<log(obs)-'+str(thresh));
fig.colorbar(m,cax=ax[3+sc]);

Diatoms/DD

In [102]:

print('Flagellates/CCPH')
fig,ax=plt.subplots(3,4,figsize=(12,12),gridspec_kw={'width_ratios': [1,1,1,.1],'wspace':.5,'hspace':.35},)
ax=ax.flatten()
for iax in ax:
    iax.contour(navlon,navlat,tmask[0,:,:],levels=[0.5,],colors='gray')
    iax.set_xlim(-125.3,-122.5)
    iax.set_ylim(48,50.5)
xdata=data.loc[data.Z<5]
sc=0
ihi=logt(xdata['mod_flagellates_chl'].astype(np.float))>(logt(xdata['CCPH'].astype(np.float))+thresh)
ilo=logt(xdata['mod_flagellates_chl'].astype(np.float))<(logt(xdata['CCPH'].astype(np.float))-thresh)
idata=xdata.loc[(xdata.DD>=0)&ihi]
ax[0+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[0+sc].set_title('High\n log(mod)>log(obs)+'+str(thresh))
idata=xdata.loc[(xdata.DD>=0)&(~ihi)&(~ilo)]
ax[1+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[1+sc].set_title('Medium\n log(obs)-'+str(thresh)+'<log(mod)<log(obs)+'+str(thresh)+' Z=0')
idata=xdata.loc[(xdata.DD>=0)&ilo]
m=ax[2+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[2+sc].set_title('Low\n log(mod)<log(obs)-'+str(thresh));
fig.colorbar(m,cax=ax[3+sc]);


xdata=data.loc[(data.Z>8)&(data.Z<12)]
sc=4
ihi=logt(xdata['mod_flagellates_chl'].astype(np.float))>(logt(xdata['CCPH'].astype(np.float))+thresh)
ilo=logt(xdata['mod_flagellates_chl'].astype(np.float))<(logt(xdata['CCPH'].astype(np.float))-thresh)
idata=xdata.loc[(xdata.DD>=0)&ihi]
ax[0+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[0+sc].set_title('High\n log(mod)>log(obs)+'+str(thresh))
idata=xdata.loc[(xdata.DD>=0)&(~ihi)&(~ilo)]
ax[1+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[1+sc].set_title('Medium\n log(obs)-'+str(thresh)+'<log(mod)<log(obs)+'+str(thresh)+' Z=10')
idata=xdata.loc[(xdata.DD>=0)&ilo]
m=ax[2+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[2+sc].set_title('Low\n log(mod)<log(obs)-'+str(thresh));
fig.colorbar(m,cax=ax[3+sc]);

xdata=data.loc[(data.Z>18)&(data.Z<22)]
sc=8
ihi=logt(xdata['mod_flagellates_chl'].astype(np.float))>(logt(xdata['CCPH'].astype(np.float))+thresh)
ilo=logt(xdata['mod_flagellates_chl'].astype(np.float))<(logt(xdata['CCPH'].astype(np.float))-thresh)
idata=xdata.loc[(xdata.DD>=0)&ihi]
ax[0+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[0+sc].set_title('High\n log(mod)>log(obs)+'+str(thresh))
idata=xdata.loc[(xdata.DD>=0)&(~ihi)&(~ilo)]
ax[1+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[1+sc].set_title('Medium\n log(obs)-'+str(thresh)+'<log(mod)<log(obs)+'+str(thresh)+' Z=20')
idata=xdata.loc[(xdata.DD>=0)&ilo]
m=ax[2+sc].scatter(idata.Lon,idata.Lat,c=idata.yd,s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[2+sc].set_title('Low\n log(mod)<log(obs)-'+str(thresh));
fig.colorbar(m,cax=ax[3+sc]);

Flagellates/CCPH

Diatom fraction¶

In [103]:

fig,ax=plt.subplots(1,2,figsize=(12,4))
diatFracMod=data['mod_diatoms']/(data['mod_diatoms']+data['mod_flagellates']+data['mod_ciliates'])
diatFracObs=data['DD']/data['TchlA (ug/L)']
m=ax[0].scatter(diatFracObs,diatFracMod,
          c=data['Z'],cmap=cmocean.cm.phase,vmin=0,vmax=40)
ax[0].set_xlabel('HPLC Diatom Fraction')
ax[0].set_ylabel('Model Diatom Fraction')
ax[0].set_xlim((0,1))
ax[0].set_ylim((0,1))
flFracMod=data['mod_flagellates']/(data['mod_diatoms']+data['mod_flagellates']+data['mod_ciliates'])
CCPHFracObs=data['CCPH']/data['TchlA (ug/L)']
m=ax[1].scatter(CCPHFracObs,flFracMod,
          c=data['Z'],cmap=cmocean.cm.phase,vmin=0,vmax=40)
ax[1].set_xlabel('HPLC Crypto Cyano Prasino Hapto Fraction')
ax[1].set_ylabel('Model Flagellate Fraction')
ax[1].set_xlim((0,1))
ax[1].set_ylim((0,1))
ax[0].set_aspect(1)
ax[1].set_aspect(1)

fig.colorbar(m)

Out[103]:

<matplotlib.colorbar.Colorbar at 0x7fc79b6d5850>

In [113]:

fig,ax=plt.subplots(1,3,figsize=(12,6),gridspec_kw={'width_ratios': [1,1,.1],'wspace':.5})
m=ax[0].scatter(data['mod_diatoms_chl']-data['DD'],data['Z'],
              c=data.yd,s=3,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[1].scatter(data['mod_flagellates_chl']-data['CCPH'],data['Z'],
              c=data.yd,s=3,cmap=cmocean.cm.phase,vmin=0,vmax=366)
for iax in ax:
    iax.set_ylabel('Depth (m)')
    iax.set_ylim(30,0)
    iax.set_xlabel('Model - Obs (mg/m3)')
ax[0].set_title('Diatoms')
ax[1].set_title('Flagellates')
ax[0].set_xlim(-10,10)
ax[1].set_xlim(-5,5)
fig.colorbar(m,cax=ax[2])

Out[113]:

<matplotlib.colorbar.Colorbar at 0x7fc79c63f850>

In [ ]: