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

In [2]:

modSourceDir= '/results/SalishSea/nowcast-green.201812/'
modver='201812'
Chl_N=1.8 # Chl:N ratio
fname='compHPLCModelFirstLook-Regress-Base.ipynb'
startYMD=(2015,1,1)
endYMD=(2018,12,31)

In [3]:

# Parameters
modSourceDir = "/results/SalishSea/nowcast-green.201812/"
modver = "201812"
Chl_N = 1.8
fname = "compHPLCModelFirstLook-Regress-201812.ipynb"

In [4]:

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 [5]:

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

In [6]:

def subval(idf,colList):
    # first value in colList should be the column you are going to keep
    # follow with other columns that will be used to fill in when that column is NaN
    # in order of precedence
    if len(colList)==2:
        idf[colList[0]]=[r[colList[0]] if not pd.isna(r[colList[0]]) \
                         else  r[colList[1]] for i,r in idf.iterrows()]
    elif len(colList)==3:
        idf[colList[0]]=[r[colList[0]] if not pd.isna(r[colList[0]]) \
                         else  r[colList[1]] if not pd.isna(r[colList[1]]) \
                         else r[colList[2]] for i,r in idf.iterrows()]
    else:
        raise NotImplementedError('Add to code to handle this case')
    idf.drop(columns=list(colList[1:]),inplace=True)
    return idf

In [7]:

if os.path.isfile('matched_'+modver+datestr+'.pkl'):
    data=pickle.load(open( 'matched_'+modver+datestr+'.pkl', 'rb' ) )
else:
    # define paths to the source files and eventual output file
    flist=('/ocean/eolson/MEOPAR/obs/NemcekHPLC/bottlePhytoMerged2015.csv',
           '/ocean/eolson/MEOPAR/obs/NemcekHPLC/bottlePhytoMerged2016.csv',
           '/ocean/eolson/MEOPAR/obs/NemcekHPLC/bottlePhytoMerged2017.csv',
           '/ocean/eolson/MEOPAR/obs/NemcekHPLC/bottlePhytoMerged2018.csv')#,
           #'/ocean/eolson/MEOPAR/obs/NemcekHPLC/bottlePhytoMerged2019.csv')

    dfs=list()
    for fname in flist:
        idf=pd.read_csv(fname)
        print(fname,sorted(idf.keys()))
        dfs.append(idf)
    df=pd.concat(dfs,ignore_index=True,sort=False); # concatenate the list into a single table

    df.drop(labels=['ADM:MISSION','ADM:PROJECT','ADM:SCIENTIST','Zone','Zone.1','Temperature:Draw',
                    'Temperature:Draw [deg C (ITS90)]','Bottle:Firing_Sequence','Comments by sample_numbeR',
                    'File Name','LOC:EVENT_NUMBER','Number_of_bin_records'
                       ],axis=1,inplace=True)

    df=subval(df,('Dictyochophytes','Dictyo'))
    df=subval(df,('Chlorophyll:Extracted [mg/m^3]','Chlorophyll:Extracted'))
    df=subval(df,('Dinoflagellates','Dinoflagellates-1'))
    df=subval(df,('Fluorescence [mg/m^3]','Fluorescence:URU:Seapoint [mg/m^3]','Fluorescence:URU:Seapoint'))
    df=subval(df,('Lat','LOC:LATITUDE'))
    df=subval(df,('Lon','LOC:LONGITUDE'))
    df=subval(df,('Nitrate_plus_Nitrite [umol/L]','Nitrate_plus_Nitrite'))
    df=subval(df,('PAR [uE/m^2/sec]','PAR'))
    df=subval(df,('Phaeo-Pigment:Extracted [mg/m^3]','Phaeo-Pigment:Extracted'))
    df=subval(df,('Phosphate [umol/L]','Phosphate'))
    df=subval(df,('Pressure [decibar]','Pressure'))
    df=subval(df,('Raphidophytes','Raphido'))
    df=subval(df,('Salinity','Salinity [PSS-78]','Salinity:T1:C1 [PSS-78]'))
    df=subval(df,('Salinity:Bottle','Salinity:Bottle [PSS-78]'))
    df=subval(df,('Silicate [umol/L]','Silicate'))
    df=subval(df,('TchlA (ug/L)','TchlA'))
    df=subval(df,('Temperature','Temperature [deg C (ITS90)]','Temperature:Secondary [deg C (ITS90)]'))
    df=subval(df,('Transmissivity [*/metre]','Transmissivity'))

    df['Z']=np.where(pd.isna(df['Depth [metres]']),
                     -1*gsw.z_from_p(df['Pressure [decibar]'],df['Lat']),
                     df['Depth [metres]'])
    df['p']=np.where(pd.isna(df['Pressure [decibar]']),
                     gsw.p_from_z(-1*df['Depth [metres]'],df['Lat']),
                    df['Pressure [decibar]'])
    df['SA']=gsw.SA_from_SP(df['Salinity'],df['p'],df['Lon'],df['Lat'])
    df['CT']=gsw.CT_from_t(df['SA'],df['Temperature'],df['p'])

    df['dtUTC']=[dt.datetime.strptime(ii,'%Y-%m-%d %H:%M:%S') if isinstance(ii,str) else np.nan for ii in df['FIL:START TIME YYYY/MM/DD HH:MM:SS'] ]

    PATH= modSourceDir

    flen=1
    namfmt='nowcast'
    #varmap={'N':'nitrate','Si':'silicon','Ammonium':'ammonium'}
    filemap={'nitrate':'ptrc_T','silicon':'ptrc_T','ammonium':'ptrc_T','diatoms':'ptrc_T','ciliates':'ptrc_T','flagellates':'ptrc_T','vosaline':'grid_T','votemper':'grid_T'}
    #gridmap={'nitrate':'tmask','silicon':'tmask','ammonium':'tmask'}
    fdict={'ptrc_T':1,'grid_T':1}

    data=et.matchData(df,filemap,fdict,start_date,end_date,namfmt,PATH,flen)

    with open('matched_'+modver+datestr+'.pkl','wb') as f:
        pickle.dump(data,f)

In [8]:

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

In [9]:

fig,ax=plt.subplots(2,2,figsize=(12,8))
ax=ax.flatten()
ax[0].plot(data['Diatoms-1']+data['Diatoms-2'],Chl_N*data['mod_diatoms'],'k.')
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)

ax[1].plot(data['other'],Chl_N*(data['mod_flagellates']+data['mod_ciliates']),'k.')
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)

ax[2].plot(data['Diatoms-1']+data['Diatoms-2'],Chl_N*data['mod_diatoms'],'k.')
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))

ax[3].plot(data['other'],Chl_N*(data['mod_flagellates']+data['mod_ciliates']),'k.')
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))

Out[9]:

(0, 8)

In [10]:

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

In [11]:

fig,ax=plt.subplots(1,2,figsize=(12,5))
ax[0].plot(logt(data['Diatoms-1']+data['Diatoms-2']),logt(Chl_N*data['mod_diatoms']),'k.')
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].plot(logt(data['other']),logt(Chl_N*(data['mod_flagellates']+data['mod_ciliates'])),'k.')
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[11]:

(-3.1, 2)

In [12]:

plt.plot(data['TchlA (ug/L)'],Chl_N*(data['mod_flagellates']+data['mod_ciliates']+data['mod_diatoms']),'k.')
plt.title('Total Chlorophyll (ug/L)')
plt.xlabel('HPLC')
plt.ylabel('model')
plt.plot((0,20),(0,20),'r-',alpha=.3)

Out[12]:

[<matplotlib.lines.Line2D at 0x7fb4e3efcd30>]

In [13]:

fig,ax=plt.subplots(1,1,figsize=(5,5))
ax.plot(logt(data['TchlA (ug/L)']),logt(Chl_N*(data['mod_flagellates']+data['mod_ciliates']+data['mod_diatoms'])),'k.')
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 [14]:

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 [15]:

fig,ax=plt.subplots(1,1,figsize=(5,5))
m=ax.scatter(logt(data['TchlA (ug/L)']),logt(Chl_N*(data['mod_flagellates']+data['mod_ciliates']+data['mod_diatoms'])),
          c=data['yd'],cmap=cmocean.cm.phase)
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[15]:

<matplotlib.colorbar.Colorbar at 0x7fb4e0592340>

In [16]:

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'][:,:])

Multiple Linear Regression¶

Diatoms¶

In [17]:

ii=(~pd.isnull(data['mod_diatoms']))&(~pd.isnull(data['Diatoms-1']))&(~pd.isnull(data['Diatoms-2']))&(~pd.isnull(data['Cyanobacteria']))&\
        (~pd.isnull(data['Cryptophytes']))&(~pd.isnull(data['Prasinophytes']))&(~pd.isnull(data['Haptophytes']))&\
        (~pd.isnull(data['Dictyochophytes']))&(~pd.isnull(data['Dinoflagellates']))&(~pd.isnull(data['Raphidophytes']))
A=np.vstack([data.loc[ii]['Diatoms-1'],data.loc[ii]['Diatoms-2'],data.loc[ii]['Cyanobacteria'],data.loc[ii]['Cryptophytes'],
               data.loc[ii]['Prasinophytes'],data.loc[ii]['Haptophytes'],data.loc[ii]['Dictyochophytes'],data.loc[ii]['Dinoflagellates'],
             data.loc[ii]['Raphidophytes'],np.ones(np.shape(data.loc[ii]['Diatoms-1']))]).T
b=data.loc[ii]['mod_diatoms']
m=np.linalg.lstsq(A,b,rcond=None)[0]

In [18]:

Out[18]:

array([ 0.1461116 ,  0.02313048, -2.31397206,  1.47826858, -0.81415547,
       -0.04395027, -0.24189496, -0.1524798 ,  0.14179773,  0.55578246])

In [19]:

clist=('Diatoms-1','Diatoms-2','Cyanobacteria','Cryptophytes','Prasinophytes','Haptophytes',
        'Dictyochophytes','Dinoflagellates','Raphidophytes','ones')
for c, mm in zip(clist,m):
    print(c,mm)

Diatoms-1 0.1461115991821778
Diatoms-2 0.023130477809363337
Cyanobacteria -2.3139720559151065
Cryptophytes 1.4782685761125942
Prasinophytes -0.8141554674932009
Haptophytes -0.04395026758078864
Dictyochophytes -0.24189495781938905
Dinoflagellates -0.1524798022794198
Raphidophytes 0.1417977308016623
ones 0.5557824554075593

In [20]:

ii=(~pd.isnull(data['mod_diatoms']))&(~pd.isnull(data['Diatoms-1']))&(~pd.isnull(data['Diatoms-2']))&\
        (~pd.isnull(data['Cryptophytes']))&(~pd.isnull(data['Raphidophytes']))
A=np.vstack([data.loc[ii]['Diatoms-1'],data.loc[ii]['Diatoms-2'],data.loc[ii]['Cryptophytes'],
            data.loc[ii]['Raphidophytes'],np.ones(np.shape(data.loc[ii]['Diatoms-1']))]).T
b=data.loc[ii]['mod_diatoms']
m=np.linalg.lstsq(A,b,rcond=None)[0]

In [21]:

clist=('Diatoms-1','Diatoms-2','Cryptophytes','Raphidophytes','ones')
for c, mm in zip(clist,m):
    print(c,mm)

Diatoms-1 0.15610851304121548
Diatoms-2 -0.022104283973487515
Cryptophytes 0.3821125404573701
Raphidophytes 0.09267246105179777
ones 0.5044504026229967

In [22]:

ii=(~pd.isnull(data['mod_diatoms']))&(~pd.isnull(data['Diatoms-1']))&\
        (~pd.isnull(data['Cryptophytes']))&(~pd.isnull(data['Raphidophytes']))
A=np.vstack([data.loc[ii]['Diatoms-1'],data.loc[ii]['Cryptophytes'],
            data.loc[ii]['Raphidophytes'],np.ones(np.shape(data.loc[ii]['Diatoms-1']))]).T
b=data.loc[ii]['mod_diatoms']
m=np.linalg.lstsq(A,b,rcond=None)[0]

In [23]:

clist=('Diatoms-1','Cryptophytes','Raphidophytes','ones')
for c, mm in zip(clist,m):
    print(c,mm)

Diatoms-1 0.15594754152703202
Cryptophytes 0.3784930664990568
Raphidophytes 0.09178847050301298
ones 0.5004773386944285

flagellates¶

In [24]:

ii=(~pd.isnull(data['mod_flagellates']))&(~pd.isnull(data['Diatoms-1']))&(~pd.isnull(data['Diatoms-2']))&(~pd.isnull(data['Cyanobacteria']))&\
        (~pd.isnull(data['Cryptophytes']))&(~pd.isnull(data['Prasinophytes']))&(~pd.isnull(data['Haptophytes']))&\
        (~pd.isnull(data['Dictyochophytes']))&(~pd.isnull(data['Dinoflagellates']))&(~pd.isnull(data['Raphidophytes']))
A=np.vstack([data.loc[ii]['Diatoms-1'],data.loc[ii]['Diatoms-2'],data.loc[ii]['Cyanobacteria'],data.loc[ii]['Cryptophytes'],
               data.loc[ii]['Prasinophytes'],data.loc[ii]['Haptophytes'],data.loc[ii]['Dictyochophytes'],data.loc[ii]['Dinoflagellates'],
             data.loc[ii]['Raphidophytes'],np.ones(np.shape(data.loc[ii]['Diatoms-1']))]).T
b=data.loc[ii]['mod_flagellates']
m=np.linalg.lstsq(A,b,rcond=None)[0]

In [25]:

Out[25]:

array([ 0.010958  , -0.06728288,  1.5784138 ,  0.55221973,  0.18442328,
        0.25904224,  0.13220077,  0.09301702, -0.03950114,  0.67295675])

In [26]:

clist=('Diatoms-1','Diatoms-2','Cyanobacteria','Cryptophytes','Prasinophytes','Haptophytes',
        'Dictyochophytes','Dinoflagellates','Raphidophytes','ones')
for c, mm in zip(clist,m):
    print(c,mm)

Diatoms-1 0.01095799512224545
Diatoms-2 -0.06728287798013483
Cyanobacteria 1.5784138039580469
Cryptophytes 0.5522197277604812
Prasinophytes 0.18442328222794738
Haptophytes 0.2590422439445318
Dictyochophytes 0.1322007692040354
Dinoflagellates 0.09301702371658432
Raphidophytes -0.039501139563166754
ones 0.6729567467865272

In [27]:

ii=(~pd.isnull(data['mod_flagellates']))&(~pd.isnull(data['Diatoms-1']))&(~pd.isnull(data['Cyanobacteria']))&\
        (~pd.isnull(data['Cryptophytes']))&(~pd.isnull(data['Prasinophytes']))&(~pd.isnull(data['Haptophytes']))&\
        (~pd.isnull(data['Dictyochophytes']))&(~pd.isnull(data['Dinoflagellates']))&(~pd.isnull(data['Raphidophytes']))
A=np.vstack([data.loc[ii]['Diatoms-1'],data.loc[ii]['Cyanobacteria'],data.loc[ii]['Cryptophytes'],
               data.loc[ii]['Prasinophytes'],data.loc[ii]['Haptophytes'],data.loc[ii]['Dictyochophytes'],
             data.loc[ii]['Dinoflagellates'],np.ones(np.shape(data.loc[ii]['Diatoms-1']))]).T
b=data.loc[ii]['mod_flagellates']
m=np.linalg.lstsq(A,b,rcond=None)[0]

In [28]:

clist=('Diatoms-1','Cyanobacteria','Cryptophytes','Prasinophytes','Haptophytes',
        'Dictyochophytes','Dinoflagellates','ones')
for c, mm in zip(clist,m):
    print(c,mm)

Diatoms-1 0.005088551793469988
Cyanobacteria 0.8863588722238661
Cryptophytes 0.5998504352492076
Prasinophytes 0.2027439073948646
Haptophytes 0.2818416587510592
Dictyochophytes 0.06190338602397167
Dinoflagellates 0.01833498721034124
ones 0.6702256702362863

In [29]:

ii=(~pd.isnull(data['mod_flagellates']))&(~pd.isnull(data['Cyanobacteria']))&\
        (~pd.isnull(data['Cryptophytes']))&(~pd.isnull(data['Prasinophytes']))&(~pd.isnull(data['Haptophytes']))&\
        (~pd.isnull(data['Dictyochophytes']))&(~pd.isnull(data['Raphidophytes']))
A=np.vstack([data.loc[ii]['Cyanobacteria'],data.loc[ii]['Cryptophytes'],
               data.loc[ii]['Prasinophytes'],data.loc[ii]['Haptophytes'],data.loc[ii]['Dictyochophytes'],
             np.ones(np.shape(data.loc[ii]['Diatoms-1']))]).T
b=data.loc[ii]['mod_flagellates']
m=np.linalg.lstsq(A,b,rcond=None)[0]

In [30]:

clist=('Cyanobacteria','Cryptophytes','Prasinophytes','Haptophytes',
        'Dictyochophytes','ones')
for c, mm in zip(clist,m):
    print(c,mm)

Cyanobacteria 0.9248939785743201
Cryptophytes 0.592769155988474
Prasinophytes 0.18975008147644779
Haptophytes 0.2794204846195845
Dictyochophytes 0.058845777018170356
ones 0.688074260106763

M. rubrum¶

In [31]:

ii=(~pd.isnull(data['mod_ciliates']))&(~pd.isnull(data['Diatoms-1']))&(~pd.isnull(data['Diatoms-2']))&(~pd.isnull(data['Cyanobacteria']))&\
        (~pd.isnull(data['Cryptophytes']))&(~pd.isnull(data['Prasinophytes']))&(~pd.isnull(data['Haptophytes']))&\
        (~pd.isnull(data['Dictyochophytes']))&(~pd.isnull(data['Dinoflagellates']))&(~pd.isnull(data['Raphidophytes']))
A=np.vstack([data.loc[ii]['Diatoms-1'],data.loc[ii]['Diatoms-2'],data.loc[ii]['Cyanobacteria'],data.loc[ii]['Cryptophytes'],
               data.loc[ii]['Prasinophytes'],data.loc[ii]['Haptophytes'],data.loc[ii]['Dictyochophytes'],data.loc[ii]['Dinoflagellates'],
             data.loc[ii]['Raphidophytes'],np.ones(np.shape(data.loc[ii]['Diatoms-1']))]).T
b=data.loc[ii]['mod_ciliates']
m=np.linalg.lstsq(A,b,rcond=None)[0]

In [32]:

clist=('Diatoms-1','Diatoms-2','Cyanobacteria','Cryptophytes','Prasinophytes','Haptophytes',
        'Dictyochophytes','Dinoflagellates','Raphidophytes','ones')
for c, mm in zip(clist,m):
    print(c,mm)

Diatoms-1 0.014525503919068575
Diatoms-2 0.011395505677502693
Cyanobacteria 0.2710109702166438
Cryptophytes 0.005991003225883295
Prasinophytes 0.0018586582284126508
Haptophytes 0.040540304039717506
Dictyochophytes -0.04121046148561792
Dinoflagellates 0.03735938858964069
Raphidophytes -0.007362533398894991
ones 0.29632175969544594

Diatoms:

Cryptophytes 0.38
Diatoms-1 0.16
Raphidophytes 0.09

Flagellates:

Cyanobacteria 0.9248939785743201
Cryptophytes 0.592769155988474
Prasinophytes 0.18975008147644779
Haptophytes 0.2794204846195845
Dictyochophytes 0.058845777018170356

M. rubrum:

Cyanobacteria 0.2710109702166438

None:

Diatoms-2
Dinoflagellates

In [33]:

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','TchlA (ug/L)')

mvar1=Chl_N*data['mod_diatoms']
mvar2=Chl_N*data['mod_flagellates']

for ii in range(0,len(chplc)):
    ax[ii].plot(logt(data.loc[:,[chplc[ii]]].values),logt(mvar1),'.',color='blue',label='Diatoms')
    ax[ii].plot(logt(data.loc[:,[chplc[ii]]].values),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[33]:

<matplotlib.legend.Legend at 0x7fb4e3f4bd60>

In [34]:

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']+data['Raphidophytes']),logt(data['mod_diatoms']),'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']+data['Cryptophytes']+data['Haptophytes']),logt(data['mod_flagellates']),'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']),logt(data['mod_ciliates']),'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[34]:

(-3, 1.5)

In [35]:

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['Cyanobacteria']+data['Cryptophytes']+data['Prasinophytes']+data['Haptophytes']
data['DD']=data['Diatoms-1']+data['Diatoms-2']
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']]

Variance-Covariance Matrix¶

In [36]:

dfVars.cov()

Out[36]:

	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
Diatoms-1	17.088669	0.181409	-0.027553	-0.219378	-0.272211	-0.188631	-0.037350	0.094977	2.359256	17.270078	-0.707773	18.970444	5.043134	-0.321984	0.398324	5.119474
Diatoms-2	0.181409	0.474147	0.010128	0.013003	0.021991	0.022762	0.026880	0.016666	0.354072	0.655556	0.067885	1.122082	0.100717	-0.010134	0.015788	0.106372
Cyanobacteria	-0.027553	0.010128	0.009374	0.013606	0.022069	0.011891	0.001802	0.004556	0.121201	-0.017425	0.056939	0.166650	-0.014034	0.043923	0.003725	0.033614
Cryptophytes	-0.219378	0.013003	0.013606	0.069615	0.057233	0.044869	0.002417	0.015848	0.081468	-0.206374	0.185321	0.076676	-0.000847	0.139540	0.005187	0.143880
Prasinophytes	-0.272211	0.021991	0.022069	0.057233	0.120558	0.040799	0.007002	0.007663	0.187265	-0.250221	0.240658	0.190104	-0.147586	0.160600	0.005640	0.018654
Haptophytes	-0.188631	0.022762	0.011891	0.044869	0.040799	0.212689	0.001497	0.003169	-0.045714	-0.165869	0.310246	0.101071	-0.069378	0.189013	0.018178	0.137813
Dictyochophytes	-0.037350	0.026880	0.001802	0.002417	0.007002	0.001497	0.030964	0.002842	0.033695	-0.010470	0.012717	0.069551	-0.025783	0.012038	-0.001938	-0.015683
Dinoflagellates	0.094977	0.016666	0.004556	0.015848	0.007663	0.003169	0.002842	0.114819	0.263196	0.111643	0.031236	0.523692	0.071926	0.033823	0.009515	0.115265
Raphidophytes	2.359256	0.354072	0.121201	0.081468	0.187265	-0.045714	0.033695	0.263196	8.091115	2.713328	0.344220	11.441595	2.058529	-0.053503	0.034273	2.039298
DD	17.270078	0.655556	-0.017425	-0.206374	-0.250221	-0.165869	-0.010470	0.111643	2.713328	17.925634	-0.639888	20.092525	5.143851	-0.332118	0.414112	5.225845
CCPH	-0.707773	0.067885	0.056939	0.185321	0.240658	0.310246	0.012717	0.031236	0.344220	-0.639888	0.793165	0.534501	-0.231846	0.533076	0.032729	0.333960
TchlA (ug/L)	18.970444	1.122082	0.166650	0.076676	0.190104	0.101071	0.069551	0.523692	11.441595	20.092525	0.534501	32.691940	7.005347	0.184343	0.486486	7.676176
mod_diatoms_chl	5.043134	0.100717	-0.014034	-0.000847	-0.147586	-0.069378	-0.025783	0.071926	2.058529	5.143851	-0.231846	7.005347	2.121828	0.429424	0.239870	2.791121
mod_flagellates_chl	-0.321984	-0.010134	0.043923	0.139540	0.160600	0.189013	0.012038	0.033823	-0.053503	-0.332118	0.533076	0.184343	0.429424	0.899572	0.194144	1.523139
mod_ciliates_chl	0.398324	0.015788	0.003725	0.005187	0.005640	0.018178	-0.001938	0.009515	0.034273	0.414112	0.032729	0.486486	0.239870	0.194144	0.070876	0.504890
mod_TChl	5.119474	0.106372	0.033614	0.143880	0.018654	0.137813	-0.015683	0.115265	2.039298	5.225845	0.333960	7.676176	2.791121	1.523139	0.504890	4.819151

Correlation Coefficient Matrix¶

In [37]:

dfVars.corr()

Out[37]:

	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
Diatoms-1	1.000000	0.063731	-0.068843	-0.201135	-0.189651	-0.098943	-0.051347	0.067805	0.200640	0.986740	-0.192246	0.802607	0.469613	-0.067164	0.392248	0.430586
Diatoms-2	0.063731	1.000000	0.151924	0.071573	0.091978	0.071678	0.221845	0.071426	0.180772	0.224862	0.110696	0.285002	0.056291	-0.012687	0.093315	0.053697
Cyanobacteria	-0.068843	0.151924	1.000000	0.532622	0.656506	0.266305	0.105757	0.138890	0.440098	-0.042509	0.660354	0.301046	-0.055781	0.391063	0.156554	0.120670
Cryptophytes	-0.201135	0.071573	0.532622	1.000000	0.624736	0.368740	0.052058	0.177262	0.108551	-0.184743	0.788667	0.050826	-0.001235	0.455929	0.080002	0.189554
Prasinophytes	-0.189651	0.091978	0.656506	0.624736	1.000000	0.254785	0.114602	0.065129	0.189607	-0.170211	0.778254	0.095758	-0.163579	0.398737	0.066111	0.018675
Haptophytes	-0.098943	0.071678	0.266305	0.368740	0.254785	1.000000	0.018442	0.020279	-0.034848	-0.084948	0.755358	0.038330	-0.057908	0.353399	0.160450	0.103896
Dictyochophytes	-0.051347	0.221845	0.105757	0.052058	0.114602	0.018442	1.000000	0.047658	0.067319	-0.014054	0.081149	0.069128	-0.056385	0.058973	-0.044820	-0.030978
Dinoflagellates	0.067805	0.071426	0.138890	0.177262	0.065129	0.020279	0.047658	1.000000	0.273066	0.077819	0.103507	0.270301	0.081679	0.086040	0.114267	0.118227
Raphidophytes	0.200640	0.180772	0.440098	0.108551	0.189607	-0.034848	0.067319	0.273066	1.000000	0.225300	0.135878	0.703497	0.278473	-0.016213	0.049029	0.249173
DD	0.986740	0.224862	-0.042509	-0.184743	-0.170211	-0.084948	-0.014054	0.077819	0.225300	1.000000	-0.169701	0.829998	0.467636	-0.067635	0.398127	0.429112
CCPH	-0.192246	0.110696	0.660354	0.788667	0.778254	0.755358	0.081149	0.103507	0.135878	-0.169701	1.000000	0.104966	-0.100180	0.515976	0.149555	0.130337
TchlA (ug/L)	0.802607	0.285002	0.301046	0.050826	0.095758	0.038330	0.069128	0.270301	0.703497	0.829998	0.104966	1.000000	0.471547	0.027796	0.346298	0.466697
mod_diatoms_chl	0.469613	0.056291	-0.055781	-0.001235	-0.163579	-0.057908	-0.056385	0.081679	0.278473	0.467636	-0.100180	0.471547	1.000000	0.310823	0.618544	0.872848
mod_flagellates_chl	-0.067164	-0.012687	0.391063	0.455929	0.398737	0.353399	0.058973	0.086040	-0.016213	-0.067635	0.515976	0.027796	0.310823	1.000000	0.768872	0.731537
mod_ciliates_chl	0.392248	0.093315	0.156554	0.080002	0.066111	0.160450	-0.044820	0.114267	0.049029	0.398127	0.149555	0.346298	0.618544	0.768872	1.000000	0.863895
mod_TChl	0.430586	0.053697	0.120670	0.189554	0.018675	0.103896	-0.030978	0.118227	0.249173	0.429112	0.130337	0.466697	0.872848	0.731537	0.863895	1.000000

Cov matrix with log transformed values:¶

In [38]:

dflog=pd.DataFrame()
for el in ['Diatoms-1', 'Diatoms-2','Cyanobacteria','Cryptophytes', 'Prasinophytes', 
       'Haptophytes',  'Dictyochophytes','Dinoflagellates','Raphidophytes','CCPH','TchlA (ug/L)',
                   'mod_diatoms_chl','mod_flagellates_chl','mod_ciliates_chl','mod_TChl']:
    dflog[el]=logt(data[el])
dflog.cov()

Out[38]:

	Diatoms-1	Diatoms-2	Cyanobacteria	Cryptophytes	Prasinophytes	Haptophytes	Dictyochophytes	Dinoflagellates	Raphidophytes	CCPH	TchlA (ug/L)	mod_diatoms_chl	mod_flagellates_chl	mod_ciliates_chl	mod_TChl
Diatoms-1	1.143896	0.056499	-0.355166	-0.201235	-0.319583	-0.345067	-0.113694	0.172803	-0.108098	-0.214717	0.260771	0.346662	-0.041995	0.044671	0.077346
Diatoms-2	0.056499	1.252068	0.038452	0.037690	0.058012	0.086532	0.333742	0.219432	0.072434	0.023329	0.082449	0.079207	0.006960	0.021096	0.024322
Cyanobacteria	-0.355166	0.038452	0.787662	0.279866	0.441064	0.361159	0.268237	0.106178	0.400838	0.302004	0.039907	-0.140277	0.141455	0.040641	0.040815
Cryptophytes	-0.201235	0.037690	0.279866	0.457056	0.417703	0.201440	0.190611	0.220589	0.313164	0.292897	0.010702	-0.090242	0.060265	-0.009554	0.000028
Prasinophytes	-0.319583	0.058012	0.441064	0.417703	0.812701	0.242934	0.281955	0.168061	0.461546	0.366617	-0.011221	-0.246706	0.096363	-0.009408	-0.024992
Haptophytes	-0.345067	0.086532	0.361159	0.201440	0.242934	1.179832	0.177061	0.093571	0.190644	0.386125	0.054316	-0.032627	0.093118	0.033194	0.041063
Dictyochophytes	-0.113694	0.333742	0.268237	0.190611	0.281955	0.177061	0.603438	0.207367	0.260694	0.166714	0.034498	-0.072012	0.045673	-0.005506	-0.001612
Dinoflagellates	0.172803	0.219432	0.106178	0.220589	0.168061	0.093571	0.207367	0.979169	0.384626	0.146428	0.156830	0.140414	0.017455	0.008123	0.030022
Raphidophytes	-0.108098	0.072434	0.400838	0.313164	0.461546	0.190644	0.260694	0.384626	1.112382	0.274009	0.088644	-0.031674	0.070968	0.013565	0.033753
CCPH	-0.214717	0.023329	0.302004	0.292897	0.366617	0.386125	0.166714	0.146428	0.274009	0.328797	0.030574	-0.064229	0.065192	0.008474	0.015918
TchlA (ug/L)	0.260771	0.082449	0.039907	0.010702	-0.011221	0.054316	0.034498	0.156830	0.088644	0.030574	0.202963	0.190279	0.035447	0.048160	0.079926
mod_diatoms_chl	0.346662	0.079207	-0.140277	-0.090242	-0.246706	-0.032627	-0.072012	0.140414	-0.031674	-0.064229	0.190279	0.882337	0.540017	0.581006	0.659758
mod_flagellates_chl	-0.041995	0.006960	0.141455	0.060265	0.096363	0.093118	0.045673	0.017455	0.070968	0.065192	0.035447	0.540017	0.584947	0.559495	0.614361
mod_ciliates_chl	0.044671	0.021096	0.040641	-0.009554	-0.009408	0.033194	-0.005506	0.008123	0.013565	0.008474	0.048160	0.581006	0.559495	0.571740	0.608740
mod_TChl	0.077346	0.024322	0.040815	0.000028	-0.024992	0.041063	-0.001612	0.030022	0.033753	0.015918	0.079926	0.659758	0.614361	0.608740	0.675632

Corr Coeff matrix with log transformed values:¶

In [39]:

dflog.corr()

Out[39]:

	Diatoms-1	Diatoms-2	Cyanobacteria	Cryptophytes	Prasinophytes	Haptophytes	Dictyochophytes	Dinoflagellates	Raphidophytes	CCPH	TchlA (ug/L)	mod_diatoms_chl	mod_flagellates_chl	mod_ciliates_chl	mod_TChl
Diatoms-1	1.000000	0.047210	-0.374169	-0.278308	-0.331455	-0.297030	-0.136844	0.163278	-0.095829	-0.350114	0.541200	0.385234	-0.108036	0.138508	0.178481
Diatoms-2	0.047210	1.000000	0.038720	0.049823	0.057510	0.071196	0.383955	0.198179	0.061376	0.036360	0.163554	0.084199	0.017127	0.062572	0.053687
Cyanobacteria	-0.374169	0.038720	1.000000	0.466440	0.551272	0.374643	0.389074	0.120903	0.428225	0.593442	0.099810	-0.187759	0.438313	0.151781	0.113442
Cryptophytes	-0.278308	0.049823	0.466440	1.000000	0.685359	0.274316	0.362950	0.329739	0.439197	0.755556	0.035138	-0.158559	0.245130	-0.046837	0.000101
Prasinophytes	-0.331455	0.057510	0.551272	0.685359	1.000000	0.248092	0.402622	0.188397	0.485426	0.709223	-0.027628	-0.324968	0.293846	-0.034576	-0.068360
Haptophytes	-0.297030	0.071196	0.374643	0.274316	0.248092	1.000000	0.209844	0.087056	0.166413	0.619947	0.110997	-0.035701	0.235880	0.101346	0.093303
Dictyochophytes	-0.136844	0.383955	0.389074	0.362950	0.402622	0.209844	1.000000	0.269770	0.318191	0.374275	0.098576	-0.110214	0.161826	-0.023514	-0.005124
Dinoflagellates	0.163278	0.198179	0.120903	0.329739	0.188397	0.087056	0.269770	1.000000	0.368539	0.258067	0.351797	0.168612	0.048522	0.027215	0.074861
Raphidophytes	-0.095829	0.061376	0.428225	0.439197	0.485426	0.166413	0.318191	0.368539	1.000000	0.453079	0.186557	-0.035728	0.185320	0.042693	0.079059
CCPH	-0.350114	0.036360	0.593442	0.755556	0.709223	0.619947	0.374275	0.258067	0.453079	1.000000	0.118352	-0.133059	0.312649	0.048982	0.068474
TchlA (ug/L)	0.541200	0.163554	0.099810	0.035138	-0.027628	0.110997	0.098576	0.351797	0.186557	0.118352	1.000000	0.502026	0.216506	0.354536	0.437884
mod_diatoms_chl	0.385234	0.084199	-0.187759	-0.158559	-0.324968	-0.035701	-0.110214	0.168612	-0.035728	-0.133059	0.502026	1.000000	0.751678	0.818019	0.854500
mod_flagellates_chl	-0.108036	0.017127	0.438313	0.245130	0.293846	0.235880	0.161826	0.048522	0.185320	0.312649	0.216506	0.751678	1.000000	0.967472	0.977260
mod_ciliates_chl	0.138508	0.062572	0.151781	-0.046837	-0.034576	0.101346	-0.023514	0.027215	0.042693	0.048982	0.354536	0.818019	0.967472	1.000000	0.979439
mod_TChl	0.178481	0.053687	0.113442	0.000101	-0.068360	0.093303	-0.005124	0.074861	0.079059	0.068474	0.437884	0.854500	0.977260	0.979439	1.000000

New groups Model-Obs Comparison:¶

In [40]:

thresh=.8
msize=20
fig,ax=plt.subplots(1,2,figsize=(9,4))
m=ax[0].scatter(logt(data['DD']),logt(data['mod_diatoms_chl']),
          c=data['yd'],s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
m=ax[1].scatter(logt(data['CCPH']),logt(data['mod_flagellates_chl']),
          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('cyano+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[40]:

<matplotlib.colorbar.Colorbar at 0x7fb4e407ad00>

Nina says Do not include cyanos- they are not flagellates and their abundance is low¶

In [41]:

thresh=.8
msize=20
fig,ax=plt.subplots(1,2,figsize=(9,4))
m=ax[0].scatter(logt(data['DD']),logt(data['mod_diatoms_chl']),
          c=data['yd'],s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
m=ax[1].scatter(logt(data['CCPH']-data['Cyanobacteria']),logt(data['mod_flagellates_chl']),
          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[41]:

<matplotlib.colorbar.Colorbar at 0x7fb4e04d20d0>

In [42]:

thresh=.8
msize=20
fig,ax=plt.subplots(1,2,figsize=(9,4))
m=ax[0].scatter(data['DD'],data['mod_diatoms_chl'],
          c=data['yd'],s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
m=ax[1].scatter(data['CCPH']-data['Cyanobacteria'],data['mod_flagellates_chl'],
          c=data['yd'],s=msize,cmap=cmocean.cm.phase,vmin=0,vmax=366)
ax[0].set_xlim(0,30)
ax[0].set_ylim(0,30)
ax[1].set_xlim(0,10)
ax[1].set_ylim(0,10)
ax[0].set_xlabel('Diatoms 1 + Diatoms 2')
ax[0].set_ylabel('Model Diatoms')
ax[0].set_title('Chl (mg/m$^3$)+.001')
ax[1].set_xlabel('crypto+prasino+hapto')
ax[1].set_ylabel('Model flagellates')
ax[1].set_title('Chl (mg/m$^3$)+.001')
ax[0].plot((0,30),(0,30),'k-')
ax[1].plot((0,10),(0,10),'k-')
plt.tight_layout()
fig.colorbar(m,ax=ax[1])

Out[42]:

<matplotlib.colorbar.Colorbar at 0x7fb4e42682b0>

In [43]:

A2=np.vstack([data.loc[ii]['DD'],np.ones(len(data.loc[ii]['DD']))]).T
Y2=data.loc[ii]['mod_diatoms_chl']
m2=np.linalg.lstsq(A2,Y2,rcond=None)[0]

In [54]:

ii=(data['DD']>=0)&(data['mod_diatoms_chl']>=0)
slope, intercept, r_value, p_value, std_err = spst.linregress(data.loc[ii]['DD'],
                                                              data.loc[ii]['mod_diatoms_chl'])

In [55]:

slope, intercept, r_value*r_value

Out[55]:

(0.286558433589653, 1.0619623445361692, 0.21868341139647007)

In [60]:

ii=(data['CCPH']>=0)&(data['mod_flagellates_chl']>=0)
slope, intercept, r_value, p_value, std_err = spst.linregress(data.loc[ii]['CCPH']-data.loc[ii]['Cyanobacteria'],
                                                              data.loc[ii]['mod_flagellates_chl'])

In [61]:

slope, intercept, r_value*r_value

Out[61]:

(0.7090390612453443, 1.2608581414674351, 0.2581949489908788)

In [62]:

ii=(data['DD']>=0)&(data['mod_diatoms_chl']>=0)
slope, intercept, r_value, p_value, std_err = spst.linregress(logt(data.loc[ii]['DD']),
                                                              logt(data.loc[ii]['mod_diatoms_chl']))

In [63]:

slope, intercept, r_value*r_value

Out[63]:

(0.412612207492385, -0.24060926513650613, 0.17789955744163427)

In [43]:

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

Diatoms/DD

In [44]:

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

Flagellates/CCPH

Diatom fraction¶

In [45]:

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['yd'],cmap=cmocean.cm.phase)
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['yd'],cmap=cmocean.cm.phase)
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[45]:

<matplotlib.colorbar.Colorbar at 0x7fadd2a8f8b0>

In [46]:

#Reminder:
np.random.seed(42)
df = pd.DataFrame(np.random.randn(1000, 4),columns=['a', 'b', 'c','d'])
df['a+b+c']=.2*df['a']+.3*df['b']+.45*df['c']

In [47]:

df.cov()

Out[47]:

	a	b	c	d	a+b+c
a	0.929140	0.007946	-0.037053	-0.022301	0.171538
b	0.007946	1.023910	-0.032939	0.004689	0.293940
c	-0.037053	-0.032939	1.012188	0.034186	0.438192
d	-0.022301	0.004689	0.034186	1.013977	0.012330
a+b+c	0.171538	0.293940	0.438192	0.012330	0.319676

In [48]:

df.corr()

Out[48]:

	a	b	c	d	a+b+c
a	1.000000	0.008146	-0.038208	-0.022976	0.314749
b	0.008146	1.000000	-0.032355	0.004602	0.513774
c	-0.038208	-0.032355	1.000000	0.033745	0.770334
d	-0.022976	0.004602	0.033745	1.000000	0.021657
a+b+c	0.314749	0.513774	0.770334	0.021657	1.000000

In [49]:

A=np.vstack([df['a'],df['b'],df['c'],df['d'],np.ones(np.shape(df['a']))]).T
b=df['a+b+c']
m=np.linalg.lstsq(A,b,rcond=None)[0]
print(m)

[ 2.00000000e-01  3.00000000e-01  4.50000000e-01  1.59594560e-16
 -1.00613962e-16]