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]:

# Base Parameters
modSourceDir = "/results2/SalishSea/nowcast-green.201905/"
modver = "201905"
Chl_N = 1.8
start_date = dt.datetime(2015,1,1)
end_date = dt.datetime(2018,12,31)
clusterD='/data/tjarniko/MEOPAR/analysis_tereza/notebooks/CLUSTER_PAPER/CLEAN/KEY_PAPERFIGURES/pkls/'
cxf='Xcoords_for571_stations.pkl'
cyf='Ycoords_for571_stations.pkl'
cfile={2015:'BIO_clno_5_2015_reass.pkl',
       2016:'BIO_clno_5_2016_reass.pkl'} # only 2015 and 2016 overlap with hplc
cver='BIOMASS'

In [3]:

print('Cluster version is:', cver)

Cluster version is: BIO

In [4]:

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

First, assign each row of HPLC data frame a cluster ID¶

In [7]:

cx=pickle.load(open(clusterD+cxf, 'rb'))
cy=pickle.load(open(clusterD+cyf, 'rb'))

In [8]:

cf=dict()
for iyear in cfile.keys():
    cf[iyear]=pickle.load(open(clusterD+cfile[iyear],'rb'))

In [9]:

cx[:5],cy[:5]

Out[9]:

(array([12, 12, 12, 12, 12]), array([382, 392, 402, 412, 422]))

In [10]:

# 8-17=>12; 18-27=>22, etc
def round2(num):
    return int(np.trunc((num+2)/10)*10+2)

In [11]:

datestr='_'+start_date.strftime('%Y%m%d')+'_'+end_date.strftime('%Y%m%d')
data=pickle.load(open( 'matched_'+modver+datestr+'.pkl', 'rb' ) )

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

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']
data2=data.loc[(data.Year<2017)&(data.i>7)]
data=data2.copy(deep=True).reset_index()

In [12]:

data['Cluster']=np.zeros(len(data))

In [13]:

for ir, row in data.iterrows():
    ii=(cx==round2(row['i']))&(cy==round2(row['j']))
    if sum(ii)==1:
        cluster=cf[row['Year']][ii]
        data.at[ir,'Cluster']=int(cluster)

In [14]:

np.sum(data['Cluster']==0),len(data)

Out[14]:

(62, 2238)

In [16]:

data.keys()

Out[16]:

Index(['index', 'FIL:START TIME YYYY/MM/DD HH:MM:SS', 'LOC:STATION', 'Lat',
       'Lon', 'LOC:WATER DEPTH', 'Sample_Number', 'Temperature', 'Salinity',
       'Oxygen:Dissolved:CTD', 'pH:SBE:Nominal', 'Salinity:Bottle',
       'Flag:Salinity:Bottle', 'Flag:Chlorophyll:Extracted',
       'Flag:Nitrate_plus_Nitrite', 'Flag:Silicate', 'Flag:Phosphate',
       'Cruise', 'Oxygen:Dissolved', 'Flag:Oxygen:Dissolved', 'Cyanobacteria',
       'Prasinophytes', 'Cryptophytes', 'Diatoms-1', 'Diatoms-2',
       'Haptophytes', 'TchlA (ug/L)', 'Pressure [decibar]',
       'Transmissivity [*/metre]', 'PAR [uE/m^2/sec]',
       'PAR:Reference [uE/m^2/sec]', 'Oxygen:Dissolved:SBE [mL/L]',
       'Oxygen:Dissolved:SBE [umol/kg]', 'Chlorophyll:Extracted [mg/m^3]',
       'Phaeo-Pigment:Extracted [mg/m^3]', 'Nitrate_plus_Nitrite [umol/L]',
       'Silicate [umol/L]', 'Phosphate [umol/L]', 'Bottle_Number',
       'Oxygen:Dissolved [mL/L]', 'Oxygen:Dissolved [umol/kg]',
       'Depth [metres]', 'Fluorescence [mg/m^3]',
       'Oxygen:Dissolved:CTD [mL/L]', 'Oxygen:Dissolved:CTD [umol/kg]',
       'Depth:Nominal [metres]', 'Alkalinity:Total [umol/L]',
       'Flag:Alkalinity:Total', 'Carbon:Dissolved:Inorganic [umol/kg]',
       'Flag:Carbon:Dissolved:Inorganic', 'Dinoflagellates', 'Dictyochophytes',
       'Raphidophytes', 'Z', 'p', 'SA', 'CT', 'dtUTC', 'j', 'i', 'mod_nitrate',
       'mod_silicon', 'mod_ammonium', 'mod_diatoms', 'mod_ciliates',
       'mod_flagellates', 'mod_vosaline', 'mod_votemper', 'k', 'other', 'yd',
       'Year', 'mod_diatoms_chl', 'mod_flagellates_chl', 'mod_ciliates_chl',
       'mod_TChl', 'CCPH', 'DD', 'Cluster'],
      dtype='object')

In [19]:

pickle.dump(data,open('HPLCClusterBIO.pkl','wb'))

map HPLC data by cluster; 0 was not close to a cluster point¶

In [15]:

fig,ax=plt.subplots(1,1,figsize=(3,6))
cm0='Set1_r'
m=ax.scatter(data['i'],data['j'],c=data['Cluster'],cmap=cm0)
fig.colorbar(m)

Out[15]:

<matplotlib.colorbar.Colorbar at 0x7f89fc2b71f0>

In [16]:

data.keys()

Out[16]:

Index(['index', 'FIL:START TIME YYYY/MM/DD HH:MM:SS', 'LOC:STATION', 'Lat',
       'Lon', 'LOC:WATER DEPTH', 'Sample_Number', 'Temperature', 'Salinity',
       'Oxygen:Dissolved:CTD', 'pH:SBE:Nominal', 'Salinity:Bottle',
       'Flag:Salinity:Bottle', 'Flag:Chlorophyll:Extracted',
       'Flag:Nitrate_plus_Nitrite', 'Flag:Silicate', 'Flag:Phosphate',
       'Cruise', 'Oxygen:Dissolved', 'Flag:Oxygen:Dissolved', 'Cyanobacteria',
       'Prasinophytes', 'Cryptophytes', 'Diatoms-1', 'Diatoms-2',
       'Haptophytes', 'TchlA (ug/L)', 'Pressure [decibar]',
       'Transmissivity [*/metre]', 'PAR [uE/m^2/sec]',
       'PAR:Reference [uE/m^2/sec]', 'Oxygen:Dissolved:SBE [mL/L]',
       'Oxygen:Dissolved:SBE [umol/kg]', 'Chlorophyll:Extracted [mg/m^3]',
       'Phaeo-Pigment:Extracted [mg/m^3]', 'Nitrate_plus_Nitrite [umol/L]',
       'Silicate [umol/L]', 'Phosphate [umol/L]', 'Bottle_Number',
       'Oxygen:Dissolved [mL/L]', 'Oxygen:Dissolved [umol/kg]',
       'Depth [metres]', 'Fluorescence [mg/m^3]',
       'Oxygen:Dissolved:CTD [mL/L]', 'Oxygen:Dissolved:CTD [umol/kg]',
       'Depth:Nominal [metres]', 'Alkalinity:Total [umol/L]',
       'Flag:Alkalinity:Total', 'Carbon:Dissolved:Inorganic [umol/kg]',
       'Flag:Carbon:Dissolved:Inorganic', 'Dinoflagellates', 'Dictyochophytes',
       'Raphidophytes', 'Z', 'p', 'SA', 'CT', 'dtUTC', 'j', 'i', 'mod_nitrate',
       'mod_silicon', 'mod_ammonium', 'mod_diatoms', 'mod_ciliates',
       'mod_flagellates', 'mod_vosaline', 'mod_votemper', 'k', 'other', 'yd',
       'Year', 'mod_diatoms_chl', 'mod_flagellates_chl', 'mod_ciliates_chl',
       'mod_TChl', 'CCPH', 'DD', 'Cluster'],
      dtype='object')

In [17]:

## Depths!
plt.hist(data.loc[data.DD>=0,['Z']].values,np.arange(0,10,.5));
plt.xlabel('Depth (m)')
plt.ylabel('Numer of HPLC Samples')

Out[17]:

Text(0, 0.5, 'Numer of HPLC Samples')

In [18]:

np.arange(0,10,.5)

Out[18]:

array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5, 6. ,
       6.5, 7. , 7.5, 8. , 8.5, 9. , 9.5])

In [19]:

print('number of data points in each cluster:')
data.loc[:,['Cluster','Diatoms-1']].groupby('Cluster').count()

number of data points in each cluster:

Out[19]:

	Diatoms-1
Cluster
0.0	10
1.0	7
2.0	3
3.0	100
4.0	35
5.0	44

In [20]:

data.loc[:,['Pressure [decibar]','Diatoms-1']]

Out[20]:

	Pressure [decibar]	Diatoms-1
0	1.5	24.471
1	6.5	NaN
2	11.6	NaN
3	21.8	NaN
4	31.6	NaN
...	...	...
2233	175.1	NaN
2234	200.0	NaN
2235	249.9	NaN
2236	299.8	NaN
2237	346.3	NaN

2238 rows × 2 columns

plot Diatoms versus HPLC Diatoms 1 and 2 and flagellates versus HPLC CCPH=cyano+crypto+prasino+hapto for each cluster¶

In [21]:

fig,ax=plt.subplots(1,3,figsize=(15,5))
fig.subplots_adjust(wspace=.5)
m=ax[0].scatter(logt(data['DD']),logt(data['mod_diatoms_chl']),c=data['Cluster'],cmap=cm0)
ax[0].set_aspect(1)
ax[0].set_xlim(-3,1.5)
ax[0].set_ylim(-3,1.5)
ax[0].plot((-3,1.5),(-3,1.5),'k-',lw=1)
ax[0].set_xlabel('log10[Diatoms-1+Diatoms-2 (mg/m3)+.001]')
ax[0].set_ylabel('log10[Diatom Chl (mg/m3)+.001]')
ax[0].set_title('Model vs. HPLC')
ax[1].scatter(logt(data['CCPH']),logt(data['mod_flagellates_chl']),c=data['Cluster'],cmap=cm0)
ax[1].set_aspect(1)
ax[1].set_xlim(-3,1.5)
ax[1].set_ylim(-3,1.5)
ax[1].plot((-3,1.5),(-3,1.5),'k-',lw=1)
ax[1].set_xlabel('log10[Cyano+Crypto+Prasino+Hapto (mg/m3)+.001]')
ax[1].set_ylabel('log10[Flagellate Chl (mg/m3)+.001]')
ax[1].set_title('Model vs. HPLC')

ax[2].scatter(logt(data['TchlA (ug/L)']),logt(data['mod_TChl']),c=data['Cluster'],cmap=cm0)
ax[2].set_aspect(1)
ax[2].set_xlim(-1,1.5)
ax[2].set_ylim(-1,1.5)
ax[2].plot((-3,1.5),(-3,1.5),'k-',lw=1)
ax[2].set_xlabel('log10[Total HPLC Chl (mg/m3)+.001]')
ax[2].set_ylabel('log10[Model Chl (mg/m3)+.001]')
ax[2].set_title('Model vs. HPLC')
#fig.colorbar(m)
print('By Cluster:')

By Cluster:

In [22]:

print('Same plots, by Season:')
fig,ax=plt.subplots(1,3,figsize=(15,5))
fig.subplots_adjust(wspace=.5)
m=ax[0].scatter(logt(data['DD']),logt(data['mod_diatoms_chl']),c=data['yd'],cmap=cmocean.cm.phase)
ax[0].set_aspect(1)
ax[0].set_xlim(-3,1.5)
ax[0].set_ylim(-3,1.5)
ax[0].plot((-3,1.5),(-3,1.5),'k-',lw=1)
ax[0].set_xlabel('log10[Diatoms-1+Diatoms-2 (mg/m3)+.001]')
ax[0].set_ylabel('log10[Diatom Chl (mg/m3)+.001]')
ax[0].set_title('Model vs. HPLC')
ax[1].scatter(logt(data['CCPH']),logt(data['mod_flagellates_chl']),c=data['yd'],cmap=cmocean.cm.phase)
ax[1].set_aspect(1)
ax[1].set_xlim(-3,1.5)
ax[1].set_ylim(-3,1.5)
ax[1].plot((-3,1.5),(-3,1.5),'k-',lw=1)
ax[1].set_xlabel('log10[Cyano+Crypto+Prasino+Hapto (mg/m3)+.001]')
ax[1].set_ylabel('log10[Flagellate Chl (mg/m3)+.001]')
ax[1].set_title('Model vs. HPLC')

m=ax[2].scatter(logt(data['TchlA (ug/L)']),logt(data['mod_TChl']),c=data['yd'],cmap=cmocean.cm.phase)
ax[2].set_aspect(1)
ax[2].set_xlim(-1,1.5)
ax[2].set_ylim(-1,1.5)
ax[2].plot((-3,1.5),(-3,1.5),'k-',lw=1)
ax[2].set_xlabel('log10[Total HPLC Chl (mg/m3)+.001]')
ax[2].set_ylabel('log10[Model Chl (mg/m3)+.001]')
ax[2].set_title('Model vs. HPLC')
fig.colorbar(m,ax=ax[2])
#fig.colorbar(m)

Same plots, by Season:

Out[22]:

<matplotlib.colorbar.Colorbar at 0x7f89de78a430>

In [23]:

fig,ax=plt.subplots(5,3,figsize=(12,20))
fig.subplots_adjust(hspace=.5,wspace=.5)
for el in range(1,6):
    idata=data.loc[data.Cluster==el]
    ax[el-1,0].plot(logt(idata['DD']),logt(idata['mod_diatoms_chl']),'k.')
    ax[el-1,0].set_xlabel('log10[Diatoms-1+Diatoms-2 (mg/m3)+.001]')
    ax[el-1,0].set_ylabel('log10[Diatom Chl (mg/m3)+.001]')
    ax[el-1,0].set_title('Model vs. HPLC, Cluster='+str(el))
    
    ax[el-1,1].plot(logt(idata['CCPH']),logt(idata['mod_flagellates_chl']),'k.')
    ax[el-1,1].set_xlabel('log10[CCPH (mg/m3)+.001]')
    ax[el-1,1].set_ylabel('log10[Flagellate Chl (mg/m3)+.001]')
    ax[el-1,1].set_title('Model vs. HPLC, Cluster='+str(el))
    
    ax[el-1,2].plot(logt(idata['TchlA (ug/L)']),logt(idata['mod_TChl']),'k.')
    ax[el-1,2].set_xlabel('log10[HPLC Tot Chl (mg/m3)+.001]')
    ax[el-1,2].set_ylabel('log10[Model Tot Chl (mg/m3)+.001]')
    ax[el-1,2].set_title('Model vs. HPLC, Cluster='+str(el))
for iax in ax.flatten():
    iax.set_xlim(-3,2)
    iax.set_ylim(-3,2)

as chl fraction¶

In [24]:

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['Cluster'],cmap=cm0)
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['Cluster'],cmap=cm0)
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[24]:

<matplotlib.colorbar.Colorbar at 0x7f89de40d250>

Species makeup by cluster¶

In [25]:

for icluster in range(1,6):
    fig,ax=plt.subplots(1,1,figsize=(12,3.5))
    idata=data.loc[data.Cluster==icluster]
    hplclist=('Diatoms-1', 'Diatoms-2','Cyanobacteria','Cryptophytes', 'Prasinophytes', 
           'Haptophytes',  'Dictyochophytes','Dinoflagellates','Raphidophytes')
    x=np.array([logt(idata[el]) for el in hplclist]).T
    cols=('darkblue','cornflowerblue','cyan','olive','limegreen','magenta','orange','firebrick','plum')
    bins=np.linspace(-3,2,10)
    ax.hist(x, bins, density=False, histtype='bar', color=cols, label=hplclist);
    ax.legend(prop={'size': 12},bbox_to_anchor=(1,0.04));
    ax.set_xlim(-3,1)
    ax.set_xlabel('log10(Chl[ug/L]+.001)')
    ax.set_ylabel('Count')
    ax.set_title('Cluster='+str(icluster))

Seasonality by Cluster (color=cluster; see map at top of page)¶

In [26]:

## Mean and stdev for yd 150-280:
for icluster in (3,4,):
    print('Cluster: ',icluster)
    print('  Year days 150-280: ')
    print('    Diatoms-1+2 Mean: ',np.nanmean(data.loc[(data.yd>=150)&(data.yd<=280)&(data.Cluster==icluster),['DD']]))
    print('    Diatoms-1+2 St. Dev.: ',np.nanstd(data.loc[(data.yd>=150)&(data.yd<=280)&(data.Cluster==icluster),['DD']]))
    print('    Model Diatoms Mean: ',np.nanmean(data.loc[(data.yd>=150)&(data.yd<=280)&(data.Cluster==icluster),['mod_diatoms_chl']]))
    print('    Model Diatoms St. Dev.: ',np.nanstd(data.loc[(data.yd>=150)&(data.yd<=280)&(data.Cluster==icluster),['mod_diatoms_chl']]))

Cluster:  3
  Year days 150-280: 
    Diatoms-1+2 Mean:  1.3043235294117648
    Diatoms-1+2 St. Dev.:  1.6402355412021887
    Model Diatoms Mean:  0.14899766309857196
    Model Diatoms St. Dev.:  0.477232785882177
Cluster:  4
  Year days 150-280: 
    Diatoms-1+2 Mean:  0.5169545454545456
    Diatoms-1+2 St. Dev.:  0.4548825500033175
    Model Diatoms Mean:  0.1707624326672969
    Model Diatoms St. Dev.:  0.4864646347289268

In [27]:

for i in range(0,len(hplclist)):
    fig,ax=plt.subplots(1,1,figsize=(16,3))
    ax.scatter(data['yd'],data[hplclist[i]],c=data['Cluster'],label=hplclist[i],cmap=cm0)
    #ax.set_ylim(-3,1)
    ax.set_xlim((0,366))
    ax.set_title((hplclist[i]))
    ax.set_xlabel('Year Day')
    ax.set_ylabel('Chl [ug/l]')

In [28]:

print('Residuals: Model - Obs')
fig,ax=plt.subplots(2,1,figsize=(16,6))
ax[0].scatter(data['yd'],data['mod_diatoms_chl']-data['DD'],c=data['Cluster'],label=hplclist[i],cmap=cm0)
ax[0].set_xlim((0,366))
ax[0].set_title('Model diatoms - Diatoms1+2')
ax[0].set_xlabel('Year Day')
ax[0].set_ylabel('Difference, Chl [ug/l]')
ax[0].plot((0,366),(0,0),'k-',lw=.5)

ax[1].scatter(data['yd'],data['mod_flagellates_chl']-data['CCPH'],c=data['Cluster'],label=hplclist[i],cmap=cm0)
ax[1].set_xlim((0,366))
ax[1].set_title('Model flagellates - CCPH')
ax[1].set_xlabel('Year Day')
ax[1].set_ylabel('Difference, Chl [ug/l]')
ax[1].plot((0,366),(0,0),'k-',lw=.5)
plt.tight_layout()

Residuals: Model - Obs

Covariance Matrices¶

In [29]:

icluster=1
print('Cluster',icluster)
idata=data.loc[data.Cluster==icluster,['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']]
idata.cov()

Cluster 1

Out[29]:

	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	17.982473	-0.085762	-0.001709	-0.138147	0.048377	-0.116030	-0.014663	-0.039840	0.073285	17.896711	-0.207509	17.708618	2.932403	3.885786	0.539620	7.357808
Diatoms-2	-0.085762	0.036747	-0.000036	0.012955	0.000391	0.007329	0.002271	0.001081	-0.001560	-0.049015	0.020639	-0.026649	0.087746	0.104741	0.005999	0.198486
Cyanobacteria	-0.001709	-0.000036	0.000010	0.000073	0.000121	0.000079	0.000002	0.000074	0.000089	-0.001745	0.000283	-0.001298	-0.002535	-0.001329	-0.000120	-0.003984
Cryptophytes	-0.138147	0.012955	0.000073	0.007169	0.001601	0.004210	0.001037	0.001568	0.000099	-0.125192	0.013054	-0.109433	-0.033948	0.010174	-0.001866	-0.025641
Prasinophytes	0.048377	0.000391	0.000121	0.001601	0.002421	0.001093	0.000135	0.000971	0.001763	0.048769	0.005236	0.056884	-0.041055	0.010529	0.001065	-0.029461
Haptophytes	-0.116030	0.007329	0.000079	0.004210	0.001093	0.003378	0.000569	0.001470	0.000553	-0.108701	0.008760	-0.097340	-0.027344	-0.002444	-0.001832	-0.031620
Dictyochophytes	-0.014663	0.002271	0.000002	0.001037	0.000135	0.000569	0.000165	0.000141	-0.000064	-0.012393	0.001742	-0.010409	-0.000573	0.004870	0.000053	0.004350
Dinoflagellates	-0.039840	0.001081	0.000074	0.001568	0.000971	0.001470	0.000141	0.001058	0.000736	-0.038759	0.004083	-0.032727	-0.024777	-0.011909	-0.001370	-0.038056
Raphidophytes	0.073285	-0.001560	0.000089	0.000099	0.001763	0.000553	-0.000064	0.000736	0.001696	0.071725	0.002504	0.076615	-0.024743	0.012039	0.001875	-0.010829
DD	17.896711	-0.049015	-0.001745	-0.125192	0.048769	-0.108701	-0.012393	-0.038759	0.071725	17.847697	-0.186870	17.681969	3.020148	3.990527	0.545619	7.556294
CCPH	-0.207509	0.020639	0.000283	0.013054	0.005236	0.008760	0.001742	0.004083	0.002504	-0.186870	0.027332	-0.151187	-0.104882	0.016929	-0.002754	-0.090706
TchlA (ug/L)	17.708618	-0.026649	-0.001298	-0.109433	0.056884	-0.097340	-0.010409	-0.032727	0.076615	17.681969	-0.151187	17.564884	2.864528	4.012741	0.543449	7.420719
mod_diatoms_chl	2.932403	0.087746	-0.002535	-0.033948	-0.041055	-0.027344	-0.000573	-0.024777	-0.024743	3.020148	-0.104882	2.864528	0.613710	0.337643	0.055164	1.006517
mod_flagellates_chl	3.885786	0.104741	-0.001329	0.010174	0.010529	-0.002444	0.004870	-0.011909	0.012039	3.990527	0.016929	4.012741	0.337643	0.375787	0.048850	0.762280
mod_ciliates_chl	0.539620	0.005999	-0.000120	-0.001866	0.001065	-0.001832	0.000053	-0.001370	0.001875	0.545619	-0.002754	0.543449	0.055164	0.048850	0.008531	0.112545
mod_TChl	7.357808	0.198486	-0.003984	-0.025641	-0.029461	-0.031620	0.004350	-0.038056	-0.010829	7.556294	-0.090706	7.420719	1.006517	0.762280	0.112545	1.881342

In [30]:

icluster=2
print('Cluster',icluster)
idata=data.loc[data.Cluster==icluster,['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']]
idata.cov()

Cluster 2

Out[30]:

	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	5.830206	1.314561	-0.006969	0.073696	0.020161	-0.367340	-0.002069	-0.207422	-0.153106	7.144768	-0.280453	6.502392	4.267778	0.641032	0.097760	5.006570
Diatoms-2	1.314561	0.947550	-0.001613	-0.031364	-0.084918	-0.083827	-0.000883	-0.078686	-0.065305	2.262112	-0.201722	1.916076	2.063865	0.603229	0.043605	2.710698
Cyanobacteria	-0.006969	-0.001613	0.000008	-0.000085	-0.000018	0.000439	0.000003	0.000250	0.000185	-0.008582	0.000344	-0.007802	-0.005173	-0.000796	-0.000118	-0.006087
Cryptophytes	0.073696	-0.031364	-0.000085	0.004467	0.006847	-0.004570	0.000004	-0.000270	0.000333	0.042332	0.006659	0.049037	-0.027225	-0.025696	-0.000353	-0.053275
Prasinophytes	0.020161	-0.084918	-0.000018	0.006847	0.012361	-0.001133	0.000050	0.003668	0.003700	-0.064757	0.018057	-0.039335	-0.136593	-0.060804	-0.002624	-0.200022
Haptophytes	-0.367340	-0.083827	0.000439	-0.004570	-0.001133	0.023146	0.000131	0.013118	0.009694	-0.451168	0.017883	-0.410384	-0.270591	-0.041095	-0.006193	-0.317879
Dictyochophytes	-0.002069	-0.000883	0.000003	0.000004	0.000050	0.000131	0.000001	0.000094	0.000074	-0.002952	0.000188	-0.002595	-0.002218	-0.000521	-0.000048	-0.002787
Dinoflagellates	-0.207422	-0.078686	0.000250	-0.000270	0.003668	0.013118	0.000094	0.008944	0.006956	-0.286108	0.016766	-0.253392	-0.205832	-0.045290	-0.004535	-0.255657
Raphidophytes	-0.153106	-0.065305	0.000185	0.000333	0.003700	0.009694	0.000074	0.006956	0.005476	-0.218411	0.013912	-0.192030	-0.164154	-0.038519	-0.003587	-0.206259
DD	7.144768	2.262112	-0.008582	0.042332	-0.064757	-0.451168	-0.002952	-0.286108	-0.218411	9.406879	-0.482176	8.418468	6.331642	1.244261	0.141365	7.717268
CCPH	-0.280453	-0.201722	0.000344	0.006659	0.018057	0.017883	0.000188	0.016766	0.013912	-0.482176	0.042944	-0.408484	-0.439582	-0.128391	-0.009289	-0.577262
TchlA (ug/L)	6.502392	1.916076	-0.007802	0.049037	-0.039335	-0.410384	-0.002595	-0.253392	-0.192030	8.418468	-0.408484	7.563001	5.521040	1.031902	0.123931	6.676873
mod_diatoms_chl	4.267778	2.063865	-0.005173	-0.027225	-0.136593	-0.270591	-0.002218	-0.205832	-0.164154	6.331642	-0.439582	5.521040	2.186321	0.551402	0.141507	2.879230
mod_flagellates_chl	0.641032	0.603229	-0.000796	-0.025696	-0.060804	-0.041095	-0.000521	-0.045290	-0.038519	1.244261	-0.128391	1.031902	0.551402	0.162968	0.035958	0.750329
mod_ciliates_chl	0.097760	0.043605	-0.000118	-0.000353	-0.002624	-0.006193	-0.000048	-0.004535	-0.003587	0.141365	-0.009289	0.123931	0.141507	0.035958	0.016123	0.193588
mod_TChl	5.006570	2.710698	-0.006087	-0.053275	-0.200022	-0.317879	-0.002787	-0.255657	-0.206259	7.717268	-0.577262	6.676873	2.879230	0.750329	0.193588	3.823146

In [31]:

icluster=3
print('Cluster',icluster)
idata=data.loc[data.Cluster==icluster,['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']]
idata.cov()

Cluster 3

Out[31]:

	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	14.560905	0.331821	-0.074357	-0.303669	-0.402601	-0.423968	-0.129763	-0.054553	-0.105618	14.892726	-1.204595	13.398088	6.790147	-0.598518	0.115239	6.306869
Diatoms-2	0.331821	0.279671	0.000320	-0.003101	0.005285	-0.021247	0.112296	0.006027	-0.006412	0.611492	-0.018743	0.704635	0.164449	-0.010754	-0.011663	0.142033
Cyanobacteria	-0.074357	0.000320	0.005080	0.009279	0.020167	0.012739	0.000382	-0.000593	0.004159	-0.074038	0.047265	-0.022820	-0.060505	0.006372	-0.002540	-0.056673
Cryptophytes	-0.303669	-0.003101	0.009279	0.059971	0.071226	0.072185	-0.002133	0.010783	0.016802	-0.306770	0.212661	-0.068651	-0.269375	0.040940	-0.009456	-0.237891
Prasinophytes	-0.402601	0.005285	0.020167	0.071226	0.183340	0.065970	-0.004962	-0.000888	0.028529	-0.397315	0.340703	-0.033944	-0.402960	-0.017056	-0.024719	-0.444736
Haptophytes	-0.423968	-0.021247	0.012739	0.072185	0.065970	0.243609	-0.007217	0.031685	0.025328	-0.445215	0.394504	-0.000925	-0.223072	0.000083	-0.012931	-0.235920
Dictyochophytes	-0.129763	0.112296	0.000382	-0.002133	-0.004962	-0.007217	0.147774	0.010101	-0.002210	-0.017467	-0.013930	0.124225	-0.119626	0.066207	-0.008462	-0.061881
Dinoflagellates	-0.054553	0.006027	-0.000593	0.010783	-0.000888	0.031685	0.010101	0.011996	0.001120	-0.048526	0.040986	0.015668	-0.071370	0.022288	-0.001079	-0.050162
Raphidophytes	-0.105618	-0.006412	0.004159	0.016802	0.028529	0.025328	-0.002210	0.001120	0.012958	-0.112030	0.074819	-0.025326	-0.090100	-0.004134	-0.004899	-0.099133
DD	14.892726	0.611492	-0.074038	-0.306770	-0.397315	-0.445215	-0.017467	-0.048526	-0.112030	15.504218	-1.223338	14.102723	6.954597	-0.609272	0.103577	6.448902
CCPH	-1.204595	-0.018743	0.047265	0.212661	0.340703	0.394504	-0.013930	0.040986	0.074819	-1.223338	0.995133	-0.126341	-0.955913	0.030339	-0.049646	-0.975220
TchlA (ug/L)	13.398088	0.704635	-0.022820	-0.068651	-0.033944	-0.000925	0.124225	0.015668	-0.025326	14.102723	-0.126341	14.090768	5.717653	-0.494653	0.039499	5.262499
mod_diatoms_chl	6.790147	0.164449	-0.060505	-0.269375	-0.402960	-0.223072	-0.119626	-0.071370	-0.090100	6.954597	-0.955913	5.717653	1.509003	0.092472	0.081237	1.682712
mod_flagellates_chl	-0.598518	-0.010754	0.006372	0.040940	-0.017056	0.000083	0.066207	0.022288	-0.004134	-0.609272	0.030339	-0.494653	0.092472	0.579785	0.073608	0.745866
mod_ciliates_chl	0.115239	-0.011663	-0.002540	-0.009456	-0.024719	-0.012931	-0.008462	-0.001079	-0.004899	0.103577	-0.049646	0.039499	0.081237	0.073608	0.022772	0.177617
mod_TChl	6.306869	0.142033	-0.056673	-0.237891	-0.444736	-0.235920	-0.061881	-0.050162	-0.099133	6.448902	-0.975220	5.262499	1.682712	0.745866	0.177617	2.606194

In [32]:

icluster=4
print('Cluster',icluster)
idata=data.loc[data.Cluster==icluster,['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']]
idata.cov()

Cluster 4

Out[32]:

	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	0.668880	0.175677	-0.013244	-0.058728	-0.071523	-0.127238	-0.015159	-0.001292	-0.001346	0.844556	-0.270733	0.555942	0.423107	-0.234634	0.029243	0.217717
Diatoms-2	0.175677	0.178600	0.003750	0.007990	0.045230	-0.062868	0.007154	0.000657	-0.003882	0.354277	-0.005898	0.352330	0.088623	-0.088244	0.018460	0.018839
Cyanobacteria	-0.013244	0.003750	0.009544	0.021020	0.032777	0.017578	0.002652	0.002270	0.001457	-0.009495	0.080919	0.077804	-0.033450	0.002358	-0.003331	-0.034423
Cryptophytes	-0.058728	0.007990	0.021020	0.062095	0.090645	0.057250	0.011558	0.006822	0.003713	-0.050738	0.231010	0.202382	-0.110073	0.067806	-0.014055	-0.056321
Prasinophytes	-0.071523	0.045230	0.032777	0.090645	0.206562	0.037459	0.022546	0.003113	0.003496	-0.026292	0.367444	0.370431	-0.209902	0.012881	-0.026286	-0.223306
Haptophytes	-0.127238	-0.062868	0.017578	0.057250	0.037459	0.159663	0.003192	0.008735	0.004708	-0.190106	0.271950	0.098521	-0.044482	0.151625	-0.010354	0.096789
Dictyochophytes	-0.015159	0.007154	0.002652	0.011558	0.022546	0.003192	0.007441	0.000705	0.000052	-0.008004	0.039947	0.040165	-0.029969	0.035026	-0.003942	0.001115
Dinoflagellates	-0.001292	0.000657	0.002270	0.006822	0.003113	0.008735	0.000705	0.003055	0.000873	-0.000635	0.020941	0.024927	0.003345	0.017853	0.002822	0.024020
Raphidophytes	-0.001346	-0.003882	0.001457	0.003713	0.003496	0.004708	0.000052	0.000873	0.001226	-0.005228	0.013374	0.010286	-0.009865	-0.003203	-0.002084	-0.015152
DD	0.844556	0.354277	-0.009495	-0.050738	-0.026292	-0.190106	-0.008004	-0.000635	-0.005228	1.198833	-0.276631	0.908272	0.511731	-0.322877	0.047703	0.236556
CCPH	-0.270733	-0.005898	0.080919	0.231010	0.367444	0.271950	0.039947	0.020941	0.013374	-0.276631	0.951323	0.749138	-0.397906	0.234670	-0.054026	-0.217262
TchlA (ug/L)	0.555942	0.352330	0.077804	0.202382	0.370431	0.098521	0.040165	0.024927	0.010286	0.908272	0.749138	1.732910	0.077112	-0.038354	-0.009512	0.029246
mod_diatoms_chl	0.423107	0.088623	-0.033450	-0.110073	-0.209902	-0.044482	-0.029969	0.003345	-0.009865	0.511731	-0.397906	0.077112	0.465249	0.100417	0.069554	0.635221
mod_flagellates_chl	-0.234634	-0.088244	0.002358	0.067806	0.012881	0.151625	0.035026	0.017853	-0.003203	-0.322877	0.234670	-0.038354	0.100417	0.468660	0.065379	0.634457
mod_ciliates_chl	0.029243	0.018460	-0.003331	-0.014055	-0.026286	-0.010354	-0.003942	0.002822	-0.002084	0.047703	-0.054026	-0.009512	0.069554	0.065379	0.027684	0.162618
mod_TChl	0.217717	0.018839	-0.034423	-0.056321	-0.223306	0.096789	0.001115	0.024020	-0.015152	0.236556	-0.217262	0.029246	0.635221	0.634457	0.162618	1.432296

In [33]:

icluster=5
print('Cluster',icluster)
idata=data.loc[data.Cluster==icluster,['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']]
idata.cov()

Cluster 5

Out[33]:

	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	19.085968	0.278712	-0.005160	-0.169379	0.018722	-0.289899	-0.017112	0.082836	-0.095718	19.364680	-0.445716	18.888450	4.766033	0.964032	0.285393	6.015458
Diatoms-2	0.278712	0.184646	-0.000004	-0.009744	-0.000155	-0.009780	-0.000486	0.031448	-0.003436	0.463358	-0.019683	0.471144	-0.120053	-0.041605	0.001191	-0.160467
Cyanobacteria	-0.005160	-0.000004	0.000026	0.000856	0.000021	0.000097	0.000033	0.000133	0.000178	-0.005164	0.001000	-0.003821	0.006096	0.000667	0.000103	0.006866
Cryptophytes	-0.169379	-0.009744	0.000856	0.059843	0.007344	0.013938	0.001092	0.006859	0.009600	-0.179124	0.081980	-0.079626	0.445319	0.051696	0.008743	0.505758
Prasinophytes	0.018722	-0.000155	0.000021	0.007344	0.014842	0.007229	-0.000082	0.001771	0.005685	0.018567	0.029436	0.055389	0.047950	-0.008481	0.001845	0.041314
Haptophytes	-0.289899	-0.009780	0.000097	0.013938	0.007229	0.025317	0.000231	0.002152	0.008472	-0.299679	0.046580	-0.242236	-0.054482	-0.012418	-0.005339	-0.072239
Dictyochophytes	-0.017112	-0.000486	0.000033	0.001092	-0.000082	0.000231	0.000237	0.000217	0.000641	-0.017599	0.001274	-0.015228	0.013861	0.001236	0.000294	0.015391
Dinoflagellates	0.082836	0.031448	0.000133	0.006859	0.001771	0.002152	0.000217	0.015759	0.004042	0.114285	0.010915	0.145199	0.061037	0.003298	0.003492	0.067827
Raphidophytes	-0.095718	-0.003436	0.000178	0.009600	0.005685	0.008472	0.000641	0.004042	0.008336	-0.099154	0.023935	-0.062194	0.075149	0.005624	0.001576	0.082349
DD	19.364680	0.463358	-0.005164	-0.179124	0.018567	-0.299679	-0.017599	0.114285	-0.099154	19.828038	-0.465400	19.359594	4.645979	0.922428	0.286584	5.854991
CCPH	-0.445716	-0.019683	0.001000	0.081980	0.029436	0.046580	0.001274	0.010915	0.023935	-0.465400	0.158996	-0.270294	0.444883	0.031464	0.005350	0.481697
TchlA (ug/L)	18.888450	0.471144	-0.003821	-0.079626	0.055389	-0.242236	-0.015228	0.145199	-0.062194	19.359594	-0.270294	19.156478	5.240507	0.964003	0.297286	6.501795
mod_diatoms_chl	4.766033	-0.120053	0.006096	0.445319	0.047950	-0.054482	0.013861	0.061037	0.075149	4.645979	0.444883	5.240507	2.695001	0.446761	0.150819	3.292581
mod_flagellates_chl	0.964032	-0.041605	0.000667	0.051696	-0.008481	-0.012418	0.001236	0.003298	0.005624	0.922428	0.031464	0.964003	0.446761	0.114032	0.032678	0.593471
mod_ciliates_chl	0.285393	0.001191	0.000103	0.008743	0.001845	-0.005339	0.000294	0.003492	0.001576	0.286584	0.005350	0.297286	0.150819	0.032678	0.015922	0.199419
mod_TChl	6.015458	-0.160467	0.006866	0.505758	0.041314	-0.072239	0.015391	0.067827	0.082349	5.854991	0.481697	6.501795	3.292581	0.593471	0.199419	4.085470

Correlation matrices¶

In [34]:

icluster=1
print('Cluster',icluster)
idata=data.loc[data.Cluster==icluster,['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']]
idata.corr()

Cluster 1

Out[34]:

	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	-0.105501	-0.128087	-0.384745	0.231871	-0.470756	-0.269230	-0.288794	0.419654	0.998981	-0.295988	0.996408	0.481240	0.734380	0.909275	0.712966
Diatoms-2	-0.105501	1.000000	-0.059396	0.798136	0.041510	0.657758	0.922213	0.173292	-0.197633	-0.060523	0.651243	-0.033170	0.318550	0.437899	0.223603	0.425464
Cyanobacteria	-0.128087	-0.059396	1.000000	0.273762	0.782517	0.432655	0.037699	0.727435	0.687435	-0.131265	0.544229	-0.098402	-0.560466	-0.338490	-0.272749	-0.520161
Cryptophytes	-0.384745	0.798136	0.273762	1.000000	0.384389	0.855388	0.953205	0.569213	0.028358	-0.349979	0.932492	-0.308378	-0.279022	0.096293	-0.157463	-0.124432
Prasinophytes	0.231871	0.041510	0.782517	0.384389	1.000000	0.382096	0.214170	0.606875	0.870212	0.234629	0.643695	0.275866	-0.580713	0.171504	0.154658	-0.246054
Haptophytes	-0.470756	0.657758	0.432655	0.855388	0.382096	1.000000	0.761622	0.777240	0.230974	-0.442685	0.911610	-0.399593	-0.327395	-0.033694	-0.225277	-0.223540
Dictyochophytes	-0.269230	0.922213	0.037699	0.953205	0.214170	0.761622	1.000000	0.337586	-0.121049	-0.228399	0.820409	-0.193387	-0.031024	0.303904	0.029280	0.139183
Dinoflagellates	-0.288794	0.173292	0.727435	0.569213	0.606875	0.777240	0.337586	1.000000	0.549526	-0.282019	0.759233	-0.240038	-0.530046	-0.293379	-0.300862	-0.480687
Raphidophytes	0.419654	-0.197633	0.687435	0.028358	0.870212	0.230974	-0.121049	0.549526	1.000000	0.412268	0.367786	0.443908	-0.418130	0.234293	0.325341	-0.108049
DD	0.998981	-0.060523	-0.131265	-0.349979	0.234629	-0.442685	-0.228399	-0.282019	0.412268	1.000000	-0.267553	0.998658	0.497508	0.757017	0.922847	0.734959
CCPH	-0.295988	0.651243	0.544229	0.932492	0.643695	0.911610	0.820409	0.759233	0.367786	-0.267553	1.000000	-0.218199	-0.441493	0.082067	-0.119013	-0.225446
TchlA (ug/L)	0.996408	-0.033170	-0.098402	-0.308378	0.275866	-0.399593	-0.193387	-0.240038	0.443908	0.998658	-0.218199	1.000000	0.475657	0.767335	0.926548	0.727560
mod_diatoms_chl	0.481240	0.318550	-0.560466	-0.279022	-0.580713	-0.327395	-0.031024	-0.530046	-0.418130	0.497508	-0.441493	0.475657	1.000000	0.703081	0.762388	0.936711
mod_flagellates_chl	0.734380	0.437899	-0.338490	0.096293	0.171504	-0.033694	0.303904	-0.293379	0.234293	0.757017	0.082067	0.767335	0.703081	1.000000	0.862770	0.906588
mod_ciliates_chl	0.909275	0.223603	-0.272749	-0.157463	0.154658	-0.225277	0.029280	-0.300862	0.325341	0.922847	-0.119013	0.926548	0.762388	0.862770	1.000000	0.888370
mod_TChl	0.712966	0.425464	-0.520161	-0.124432	-0.246054	-0.223540	0.139183	-0.480687	-0.108049	0.734959	-0.225446	0.727560	0.936711	0.906588	0.888370	1.000000

In [35]:

icluster=2
print('Cluster',icluster)
idata=data.loc[data.Cluster==icluster,['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']]
idata.corr()

Cluster 2

Out[35]:

	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	0.559291	-0.999838	0.456658	0.075098	-0.999967	-0.856877	-0.908337	-0.856877	0.964769	-0.560487	0.979229	0.791426	0.423165	0.834615	0.726659
Diatoms-2	0.559291	1.000000	-0.574134	-0.482084	-0.784629	-0.566034	-0.906596	-0.854733	-0.906596	0.757687	-0.999999	0.715755	0.949359	0.987764	0.923418	0.975916
Cyanobacteria	-0.999838	-0.574134	1.000000	-0.440556	-0.057121	0.999951	0.866025	0.915724	0.866025	-0.969352	0.575316	-0.982723	-0.802310	-0.439420	-0.844403	-0.738918
Cryptophytes	0.456658	-0.482084	-0.440556	1.000000	0.921424	-0.449386	0.067329	-0.042716	0.067329	0.206506	0.480818	0.266790	-0.182397	-0.612821	-0.108912	-0.279348
Prasinophytes	0.075098	-0.784629	-0.057121	0.921424	1.000000	-0.066962	0.449715	0.348844	0.449715	-0.189903	0.783733	-0.128648	-0.550105	-0.871715	-0.486600	-0.630487
Haptophytes	-0.999967	-0.566034	0.999951	-0.449386	-0.066962	1.000000	0.861054	0.911718	0.861054	-0.966883	0.567224	-0.980850	-0.796385	-0.430542	-0.839080	-0.732239
Dictyochophytes	-0.856877	-0.906596	0.866025	0.067329	0.449715	0.861054	1.000000	0.993944	1.000000	-0.962321	0.907204	-0.943605	-0.993275	-0.829690	-0.999129	-0.976819
Dinoflagellates	-0.908337	-0.854733	0.915724	-0.042716	0.348844	0.911718	0.993944	1.000000	0.993944	-0.986373	0.855482	-0.974271	-0.974536	-0.763323	-0.988492	-0.947381
Raphidophytes	-0.856877	-0.906596	0.866025	0.067329	0.449715	0.861054	1.000000	0.993944	1.000000	-0.962321	0.907204	-0.943605	-0.993275	-0.829690	-0.999129	-0.976819
DD	0.964769	0.757687	-0.969352	0.206506	-0.189903	-0.966883	-0.962321	-0.986373	-0.962321	1.000000	-0.758629	0.998075	0.924366	0.646638	0.950134	0.881806
CCPH	-0.560487	-0.999999	0.575316	0.480818	0.783733	0.567224	0.907204	0.855482	0.907204	-0.758629	1.000000	-0.716763	-0.949811	-0.987538	-0.923971	-0.976230
TchlA (ug/L)	0.979229	0.715755	-0.982723	0.266790	-0.128648	-0.980850	-0.943605	-0.974271	-0.943605	0.998075	-0.716763	1.000000	0.898926	0.598086	0.928965	0.850860
mod_diatoms_chl	0.791426	0.949359	-0.802310	-0.182397	-0.550105	-0.796385	-0.993275	-0.974536	-0.993275	0.924366	-0.949811	0.898926	1.000000	0.923761	0.753701	0.995884
mod_flagellates_chl	0.423165	0.987764	-0.439420	-0.612821	-0.871715	-0.430542	-0.829690	-0.763323	-0.829690	0.646638	-0.987538	0.598086	0.923761	1.000000	0.701496	0.950582
mod_ciliates_chl	0.834615	0.923418	-0.844403	-0.108912	-0.486600	-0.839080	-0.999129	-0.988492	-0.999129	0.950134	-0.923971	0.928965	0.753701	0.701496	1.000000	0.779734
mod_TChl	0.726659	0.975916	-0.738918	-0.279348	-0.630487	-0.732239	-0.976819	-0.947381	-0.976819	0.881806	-0.976230	0.850860	0.995884	0.950582	0.779734	1.000000

In [36]:

icluster=3
print('Cluster',icluster)
idata=data.loc[data.Cluster==icluster,['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']]
idata.corr()

Cluster 3

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	1.000000	0.164432	-0.273405	-0.324963	-0.246406	-0.225108	-0.088462	-0.130526	-0.243153	0.991186	-0.316451	0.935365	0.651133	-0.155141	0.162084	0.618665
Diatoms-2	0.164432	1.000000	0.008477	-0.023942	0.023341	-0.081401	0.552383	0.104048	-0.106513	0.293658	-0.035528	0.354955	0.113787	-0.020113	-0.118360	0.100531
Cyanobacteria	-0.273405	0.008477	1.000000	0.531606	0.660840	0.362140	0.013960	-0.075996	0.512617	-0.263818	0.664779	-0.085296	-0.310637	0.088426	-0.191239	-0.297638
Cryptophytes	-0.324963	-0.023942	0.531606	1.000000	0.679258	0.597210	-0.022661	0.402021	0.602743	-0.318138	0.870511	-0.074681	-0.402504	0.165356	-0.207235	-0.363615
Prasinophytes	-0.246406	0.023341	0.660840	0.679258	1.000000	0.312156	-0.030147	-0.018943	0.585328	-0.235658	0.797640	-0.021119	-0.344365	-0.039400	-0.309840	-0.388785
Haptophytes	-0.225108	-0.081401	0.362140	0.597210	0.312156	1.000000	-0.038035	0.586110	0.450805	-0.229086	0.801242	-0.000499	-0.165380	0.000166	-0.140611	-0.178918
Dictyochophytes	-0.088462	0.552383	0.013960	-0.022661	-0.030147	-0.038035	1.000000	0.239917	-0.050509	-0.011540	-0.036324	0.086088	-0.113870	0.170351	-0.118140	-0.060255
Dinoflagellates	-0.130526	0.104048	-0.075996	0.402021	-0.018943	0.586110	0.239917	1.000000	0.089811	-0.112519	0.375123	0.038108	-0.238440	0.201272	-0.052864	-0.171428
Raphidophytes	-0.243153	-0.106513	0.512617	0.602743	0.585328	0.450805	-0.050509	0.089811	1.000000	-0.249945	0.658878	-0.059270	-0.289631	-0.035918	-0.230991	-0.325979
DD	0.991186	0.293658	-0.263818	-0.318138	-0.235658	-0.229086	-0.011540	-0.112519	-0.249945	1.000000	-0.311445	0.954137	0.646297	-0.153048	0.141180	0.613051
CCPH	-0.316451	-0.035528	0.664779	0.870511	0.797640	0.801242	-0.036324	0.375123	0.658878	-0.311445	1.000000	-0.033739	-0.350641	0.030082	-0.267100	-0.365930
TchlA (ug/L)	0.935365	0.354955	-0.085296	-0.074681	-0.021119	-0.000499	0.086088	0.038108	-0.059270	0.954137	-0.033739	1.000000	0.557359	-0.130339	0.056474	0.524760
mod_diatoms_chl	0.651133	0.113787	-0.310637	-0.402504	-0.344365	-0.165380	-0.113870	-0.238440	-0.289631	0.646297	-0.350641	0.557359	1.000000	0.098863	0.438238	0.848519
mod_flagellates_chl	-0.155141	-0.020113	0.088426	0.165356	-0.039400	0.000166	0.170351	0.201272	-0.035918	-0.153048	0.030082	-0.130339	0.098863	1.000000	0.640615	0.606770
mod_ciliates_chl	0.162084	-0.118360	-0.191239	-0.207235	-0.309840	-0.140611	-0.118140	-0.052864	-0.230991	0.141180	-0.267100	0.056474	0.438238	0.640615	1.000000	0.729094
mod_TChl	0.618665	0.100531	-0.297638	-0.363615	-0.388785	-0.178918	-0.060255	-0.171428	-0.325979	0.613051	-0.365930	0.524760	0.848519	0.606770	0.729094	1.000000

In [37]:

icluster=4
print('Cluster',icluster)
idata=data.loc[data.Cluster==icluster,['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']]
idata.corr()

Cluster 4

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	0.508275	-0.165768	-0.288165	-0.192417	-0.389352	-0.214873	-0.028576	-0.046990	0.943138	-0.339393	0.516378	0.363429	-0.302924	0.143840	0.149701
Diatoms-2	0.508275	1.000000	0.090820	0.075874	0.235486	-0.372293	0.196250	0.028114	-0.262349	0.765636	-0.014307	0.633316	0.147316	-0.220476	0.175719	0.025069
Cyanobacteria	-0.165768	0.090820	1.000000	0.863469	0.738230	0.450315	0.314656	0.420440	0.425904	-0.088767	0.849240	0.605007	-0.240536	0.025482	-0.137188	-0.198156
Cryptophytes	-0.288165	0.075874	0.863469	1.000000	0.800374	0.574969	0.537709	0.495324	0.425470	-0.185961	0.950471	0.616958	-0.310309	0.287317	-0.226897	-0.127101
Prasinophytes	-0.192417	0.235486	0.738230	0.800374	1.000000	0.206269	0.575104	0.123917	0.219690	-0.052835	0.828900	0.619147	-0.324440	0.029926	-0.232664	-0.276301
Haptophytes	-0.389352	-0.372293	0.450315	0.574969	0.206269	1.000000	0.092597	0.395508	0.336501	-0.434525	0.697788	0.187301	-0.078203	0.400670	-0.104246	0.136216
Dictyochophytes	-0.214873	0.196250	0.314656	0.537709	0.575104	0.092597	1.000000	0.147862	0.017152	-0.084752	0.474809	0.353718	-0.244071	0.428750	-0.183841	0.007267
Dinoflagellates	-0.028576	0.028114	0.420440	0.495324	0.123917	0.395508	0.147862	1.000000	0.451007	-0.010494	0.388430	0.342576	0.042517	0.341046	0.205372	0.244379
Raphidophytes	-0.046990	-0.262349	0.425904	0.425470	0.219690	0.336501	0.017152	0.451007	1.000000	-0.136360	0.391585	0.223131	-0.197903	-0.096597	-0.239360	-0.243328
DD	0.943138	0.765636	-0.088767	-0.185961	-0.052835	-0.434525	-0.084752	-0.010494	-0.136360	1.000000	-0.259034	0.630157	0.328326	-0.311370	0.175265	0.121496
CCPH	-0.339393	-0.014307	0.849240	0.950471	0.828900	0.697788	0.474809	0.388430	0.391585	-0.259034	1.000000	0.583459	-0.286589	0.254046	-0.222831	-0.125264
TchlA (ug/L)	0.516378	0.633316	0.605007	0.616958	0.619147	0.187301	0.353718	0.342576	0.223131	0.630157	0.583459	1.000000	0.041151	-0.030763	-0.029069	0.012494
mod_diatoms_chl	0.363429	0.147316	-0.240536	-0.310309	-0.324440	-0.078203	-0.244071	0.042517	-0.197903	0.328326	-0.286589	0.041151	1.000000	0.215049	0.612861	0.778154
mod_flagellates_chl	-0.302924	-0.220476	0.025482	0.287317	0.029926	0.400670	0.428750	0.341046	-0.096597	-0.311370	0.254046	-0.030763	0.215049	1.000000	0.573976	0.774385
mod_ciliates_chl	0.143840	0.175719	-0.137188	-0.226897	-0.232664	-0.104246	-0.183841	0.205372	-0.239360	0.175265	-0.222831	-0.029069	0.612861	0.573976	1.000000	0.816647
mod_TChl	0.149701	0.025069	-0.198156	-0.127101	-0.276301	0.136216	0.007267	0.244379	-0.243328	0.121496	-0.125264	0.012494	0.778154	0.774385	0.816647	1.000000

In [38]:

icluster=5
print('Cluster',icluster)
idata=data.loc[data.Cluster==icluster,['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']]
idata.corr()

Cluster 5

Out[38]:

	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	0.148467	-0.232265	-0.158488	0.035176	-0.417042	-0.254348	0.151045	-0.239964	0.995436	-0.255864	0.987828	0.389938	0.421184	0.640591	0.423702
Diatoms-2	0.148467	1.000000	-0.001709	-0.092699	-0.002969	-0.143037	-0.073506	0.583001	-0.087582	0.242163	-0.114877	0.250511	-0.099862	-0.184803	0.027175	-0.114912
Cyanobacteria	-0.232265	-0.001709	1.000000	0.687849	0.034613	0.119474	0.425095	0.207678	0.382328	-0.228043	0.492998	-0.171693	0.428516	0.250218	0.197940	0.415460
Cryptophytes	-0.158488	-0.092699	0.687849	1.000000	0.246412	0.358074	0.289762	0.223349	0.429798	-0.164439	0.840444	-0.074368	0.650669	0.403354	0.350450	0.636185
Prasinophytes	0.035176	-0.002969	0.034613	0.246412	1.000000	0.372909	-0.043510	0.115825	0.511088	0.034225	0.605949	0.103877	0.140683	-0.132876	0.148480	0.104351
Haptophytes	-0.417042	-0.143037	0.119474	0.358074	0.372909	1.000000	0.094206	0.107764	0.583172	-0.422967	0.734177	-0.347834	-0.122389	-0.148961	-0.329063	-0.139705
Dictyochophytes	-0.254348	-0.073506	0.425095	0.289762	-0.043510	0.094206	1.000000	0.112127	0.455527	-0.256637	0.207489	-0.225927	0.321705	0.153247	0.187287	0.307539
Dinoflagellates	0.151045	0.583001	0.207678	0.223349	0.115825	0.107764	0.112127	1.000000	0.352636	0.204451	0.218063	0.264270	0.173793	0.050141	0.272778	0.166262
Raphidophytes	-0.239964	-0.087582	0.382328	0.429798	0.511088	0.583172	0.455527	0.352636	1.000000	-0.243882	0.657418	-0.155633	0.294190	0.117561	0.169267	0.277533
DD	0.995436	0.242163	-0.228043	-0.164439	0.034225	-0.422967	-0.256637	0.204451	-0.243882	1.000000	-0.262116	0.993341	0.372935	0.395394	0.631112	0.404608
CCPH	-0.255864	-0.114877	0.492998	0.840444	0.605949	0.734177	0.207489	0.218063	0.657418	-0.262116	1.000000	-0.154877	0.398794	0.150610	0.131580	0.371732
TchlA (ug/L)	0.987828	0.250511	-0.171693	-0.074368	0.103877	-0.347834	-0.225927	0.264270	-0.155633	0.993341	-0.154877	1.000000	0.427968	0.420396	0.666056	0.457113
mod_diatoms_chl	0.389938	-0.099862	0.428516	0.650669	0.140683	-0.122389	0.321705	0.173793	0.294190	0.372935	0.398794	0.427968	1.000000	0.805902	0.728075	0.992284
mod_flagellates_chl	0.421184	-0.184803	0.250218	0.403354	-0.132876	-0.148961	0.153247	0.050141	0.117561	0.395394	0.150610	0.420396	0.805902	1.000000	0.766905	0.869491
mod_ciliates_chl	0.640591	0.027175	0.197940	0.350450	0.148480	-0.329063	0.187287	0.272778	0.169267	0.631112	0.131580	0.666056	0.728075	0.766905	1.000000	0.781889
mod_TChl	0.423702	-0.114912	0.415460	0.636185	0.104351	-0.139705	0.307539	0.166262	0.277533	0.404608	0.371732	0.457113	0.992284	0.869491	0.781889	1.000000

Stats by cluster and group¶

In [39]:

data.keys()

Out[39]:

Index(['index', 'FIL:START TIME YYYY/MM/DD HH:MM:SS', 'LOC:STATION', 'Lat',
       'Lon', 'LOC:WATER DEPTH', 'Sample_Number', 'Temperature', 'Salinity',
       'Oxygen:Dissolved:CTD', 'pH:SBE:Nominal', 'Salinity:Bottle',
       'Flag:Salinity:Bottle', 'Flag:Chlorophyll:Extracted',
       'Flag:Nitrate_plus_Nitrite', 'Flag:Silicate', 'Flag:Phosphate',
       'Cruise', 'Oxygen:Dissolved', 'Flag:Oxygen:Dissolved', 'Cyanobacteria',
       'Prasinophytes', 'Cryptophytes', 'Diatoms-1', 'Diatoms-2',
       'Haptophytes', 'TchlA (ug/L)', 'Pressure [decibar]',
       'Transmissivity [*/metre]', 'PAR [uE/m^2/sec]',
       'PAR:Reference [uE/m^2/sec]', 'Oxygen:Dissolved:SBE [mL/L]',
       'Oxygen:Dissolved:SBE [umol/kg]', 'Chlorophyll:Extracted [mg/m^3]',
       'Phaeo-Pigment:Extracted [mg/m^3]', 'Nitrate_plus_Nitrite [umol/L]',
       'Silicate [umol/L]', 'Phosphate [umol/L]', 'Bottle_Number',
       'Oxygen:Dissolved [mL/L]', 'Oxygen:Dissolved [umol/kg]',
       'Depth [metres]', 'Fluorescence [mg/m^3]',
       'Oxygen:Dissolved:CTD [mL/L]', 'Oxygen:Dissolved:CTD [umol/kg]',
       'Depth:Nominal [metres]', 'Alkalinity:Total [umol/L]',
       'Flag:Alkalinity:Total', 'Carbon:Dissolved:Inorganic [umol/kg]',
       'Flag:Carbon:Dissolved:Inorganic', 'Dinoflagellates', 'Dictyochophytes',
       'Raphidophytes', 'Z', 'p', 'SA', 'CT', 'dtUTC', 'j', 'i', 'mod_nitrate',
       'mod_silicon', 'mod_ammonium', 'mod_diatoms', 'mod_ciliates',
       'mod_flagellates', 'mod_vosaline', 'mod_votemper', 'k', 'other', 'yd',
       'Year', 'mod_diatoms_chl', 'mod_flagellates_chl', 'mod_ciliates_chl',
       'mod_TChl', 'CCPH', 'DD', 'Cluster'],
      dtype='object')

In [40]:

def printStats0(s,descript):
    print('  '+descript)
    print('     N=         ',s[0])
    print('     model mean=',s[1])
    print('     obs mean=  ',s[2])
    print('     bias=      ',s[3])
    print('     RMSE=      ',s[4])
    print('     WSS=       ',s[5])

def printStatsAll(icluster,data):
    idata=data.loc[data.Cluster==icluster]
    sD=et.stats(idata['DD'],idata['mod_diatoms_chl'])
    sF=et.stats(idata['CCPH'],idata['mod_flagellates_chl'])
    sT=et.stats(idata['TchlA (ug/L)'],idata['mod_TChl'])
    dataHPLCLocs=idata.loc[idata.DD>=0]
    sTem=et.stats(dataHPLCLocs['CT'],dataHPLCLocs['mod_votemper'])
    sSal=et.stats(dataHPLCLocs['SA'],dataHPLCLocs['mod_vosaline'])## units?
    print('Cluster=',icluster)
    printStats0(sD,'Diatoms-1+Diatoms-2 and Model Diatoms')
    printStats0(sF,'Crypto+Cyano+Prasino+Hapto and Model Flagellates')
    printStats0(sT,'Total Chlorophyll (ug/L)')
    printStats0(sTem,'Conservative Temperature')
    printStats0(sSal,'Reference Salinity')

In [41]:

for icluster in range(1,6):
    printStatsAll(icluster,data)
    data['Temperature']

Cluster= 1
  Diatoms-1+Diatoms-2 and Model Diatoms
     N=          7
     model mean= 1.541199406449284
     obs mean=   3.4787142857142856
     bias=       -1.9375148792650017
     RMSE=       3.9553006767020715
     WSS=        0.52738272203478
  Crypto+Cyano+Prasino+Hapto and Model Flagellates
     N=          7
     model mean= 0.9191453722970826
     obs mean=   0.22599999999999998
     bias=       0.6931453722970826
     RMSE=       1.345124594944328
     WSS=        0.10880015854805625
  Total Chlorophyll (ug/L)
     N=          7
     model mean= 2.7860084700797287
     obs mean=   3.7647142857142852
     bias=       -0.9787058156345565
     RMSE=       2.8928754068985754
     WSS=        0.7546943933533866
  Conservative Temperature
     N=          7
     model mean= 11.740195683070592
     obs mean=   10.779180372580294
     bias=       0.9610153104902981
     RMSE=       1.356938458645703
     WSS=        0.754007701433208
  Reference Salinity
     N=          7
     model mean= 29.215067999703543
     obs mean=   29.78387087042816
     bias=       -0.5688028707246175
     RMSE=       0.8865137592740546
     WSS=        0.7571490766079084
Cluster= 2
  Diatoms-1+Diatoms-2 and Model Diatoms
     N=          3
     model mean= 2.2314881980419163
     obs mean=   3.808
     bias=       -1.5765118019580835
     RMSE=       1.9077679400388772
     WSS=        0.8412853042284493
  Crypto+Cyano+Prasino+Hapto and Model Flagellates
     N=          3
     model mean= 0.5688631832599641
     obs mean=   0.5223333333333334
     bias=       0.04652984992663067
     RMSE=       0.6814562267097646
     WSS=        0.0
  Total Chlorophyll (ug/L)
     N=          3
     model mean= 3.1329140603542327
     obs mean=   4.500666666666667
     bias=       -1.367752606312434
     RMSE=       1.8542665197464965
     WSS=        0.8576228994796257
  Conservative Temperature
     N=          3
     model mean= 11.333172480265299
     obs mean=   10.94457519730328
     bias=       0.388597282962019
     RMSE=       0.4067459391098711
     WSS=        0.8818718129141834
  Reference Salinity
     N=          3
     model mean= 30.399763107299805
     obs mean=   30.679512751925078
     bias=       -0.27974964462527296
     RMSE=       0.3918259758517716
     WSS=        0.8977957008055653
Cluster= 3
  Diatoms-1+Diatoms-2 and Model Diatoms
     N=          100
     model mean= 1.389847192164045
     obs mean=   2.2496
     bias=       -0.8597528078359551
     RMSE=       3.1164097586397386
     WSS=        0.7579877104504467
  Crypto+Cyano+Prasino+Hapto and Model Flagellates
     N=          100
     model mean= 1.1381629794985055
     obs mean=   0.9924100000000001
     bias=       0.1457529794985054
     RMSE=       1.3993870346430897
     WSS=        0.3850441151733929
  Total Chlorophyll (ug/L)
     N=          100
     model mean= 2.9085302104311994
     obs mean=   3.4831600000000003
     bias=       -0.5746297895688008
     RMSE=       3.305471528124419
     WSS=        0.6977847612405823
  Conservative Temperature
     N=          100
     model mean= 14.081168527603149
     obs mean=   13.963695858058895
     bias=       0.11747266954425406
     RMSE=       0.9503826249641765
     WSS=        0.9737462151215563
  Reference Salinity
     N=          100
     model mean= 24.299228200912477
     obs mean=   24.851023900300216
     bias=       -0.5517956993877391
     RMSE=       2.4664068835962443
     WSS=        0.9150646120096915
Cluster= 4
  Diatoms-1+Diatoms-2 and Model Diatoms
     N=          35
     model mean= 0.787663998353694
     obs mean=   0.7617714285714287
     bias=       0.025892569782265396
     RMSE=       1.4626964260102135
     WSS=        0.5440617208372476
  Crypto+Cyano+Prasino+Hapto and Model Flagellates
     N=          35
     model mean= 0.918229034287589
     obs mean=   1.0680285714285715
     bias=       -0.1497995371409826
     RMSE=       1.1670328305108004
     WSS=        0.533164077383347
  Total Chlorophyll (ug/L)
     N=          35
     model mean= 2.0605138760645474
     obs mean=   1.9487714285714293
     bias=       0.11174244749311812
     RMSE=       2.170459993820359
     WSS=        0.3570883875321662
  Conservative Temperature
     N=          35
     model mean= 13.145041465759277
     obs mean=   13.389398740101768
     bias=       -0.24435727434249088
     RMSE=       1.0020433223934733
     WSS=        0.9647921668254179
  Reference Salinity
     N=          35
     model mean= 27.428720583234515
     obs mean=   27.686499213532674
     bias=       -0.2577786302981586
     RMSE=       1.0892288379916317
     WSS=        0.8656625313021775
Cluster= 5
  Diatoms-1+Diatoms-2 and Model Diatoms
     N=          44
     model mean= 2.5379747460892594
     obs mean=   3.8224318181818178
     bias=       -1.2844570720925583
     RMSE=       4.42671425724861
     WSS=        0.5909701075302164
  Crypto+Cyano+Prasino+Hapto and Model Flagellates
     N=          44
     model mean= 0.5451702012257142
     obs mean=   0.5252954545454545
     bias=       0.01987474668025979
     RMSE=       0.602105205135285
     WSS=        0.5037080951429683
  Total Chlorophyll (ug/L)
     N=          44
     model mean= 3.4059304682918916
     obs mean=   4.523068181818181
     bias=       -1.1171377135262897
     RMSE=       4.193086113517278
     WSS=        0.6571518638496183
  Conservative Temperature
     N=          44
     model mean= 11.244335412979126
     obs mean=   10.944506542413878
     bias=       0.2998288705652481
     RMSE=       0.7004402241421247
     WSS=        0.8415719672099807
  Reference Salinity
     N=          44
     model mean= 30.515184662558816
     obs mean=   30.850794390544184
     bias=       -0.3356097279853678
     RMSE=       0.6649381354853522
     WSS=        0.7993909209151008

In [ ]: