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]:

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

In [3]:

modSourceDir= '/results2/SalishSea/nowcast-green.201812/'
modver='201812'
Chl_N=1.8 # Chl:N ratio
start_date = dt.datetime(2015,1,1)
end_date = dt.datetime(2018,12,31) #dt.datetime(2019,6,30)

In [4]:

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

In [5]:

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 [6]:

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 [7]:

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

In [8]:

groups=pd.read_csv('/data/eolson/results/MEOPAR/clusterGroups/cluster_consolidated.csv')

In [9]:

groups.keys()

Out[9]:

Index(['I', 'j', 'Cluster_ID'], dtype='object')

In [10]:

cset0=set([(ii,jj) for ii,jj in groups.loc[:,['I','j']].values])

In [11]:

pdlist=list()
cset1=set()
for i,row in groups.iterrows():
    for ii in range(-5,5):
        for jj in range(-5,5):
            if not (row['I']+ii,row['j']+jj) in cset0|cset1:
                pdlist.append((row['I']+ii,row['j']+jj,row['Cluster_ID']))
                cset1.add((row['I']+ii,row['j']+jj))

In [12]:

newpd=pd.DataFrame(columns=['I', 'j', 'Cluster_ID'],data=pdlist)

In [13]:

data.keys()

Out[13]:

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'],
      dtype='object')

In [14]:

fig,ax=plt.subplots(1,1,figsize=(6,12))
ax.plot(data['i'],data['j'],'b.')
ax.plot(groups.loc[groups['Cluster_ID']=='NORTH',['I']].values,groups.loc[groups['Cluster_ID']=='NORTH',['j']],'r.')
ax.plot(groups.loc[groups['Cluster_ID']=='SOUTH',['I']].values,groups.loc[groups['Cluster_ID']=='SOUTH',['j']],'m.')
ax.plot(newpd.loc[newpd['Cluster_ID']=='NORTH',['I']].values,newpd.loc[newpd['Cluster_ID']=='NORTH',['j']],'y.')
ax.plot(newpd.loc[newpd['Cluster_ID']=='SOUTH',['I']].values,newpd.loc[newpd['Cluster_ID']=='SOUTH',['j']],'c.')
ax.set_aspect(1)
ax.set_xlim(130,160)
ax.set_ylim(640,700)

Out[14]:

(640, 700)

In [15]:

data2=pd.merge(data,pd.concat((newpd,groups)),how='inner',left_on=['i','j'],right_on=['I','j'])

In [16]:

len(data),len(data2),len(groups)

Out[16]:

(5607, 1444, 146)

In [17]:

data2.loc[:,['i','j','Cluster_ID','mod_diatoms','Diatoms-1']]

Out[17]:

	i	j	Cluster_ID	mod_diatoms	Diatoms-1
0	245	487	SOUTH	1.346822	1.677
1	245	487	SOUTH	0.133013	0.710
2	245	487	SOUTH	0.380877	0.658
3	245	487	SOUTH	0.158944	0.218
4	245	487	SOUTH	0.159075	NaN
...	...	...	...	...	...
1439	146	735	NORTH	0.292580	NaN
1440	146	735	NORTH	0.023296	NaN
1441	222	606	SOUTH	0.085162	0.228
1442	222	606	SOUTH	0.054806	NaN
1443	222	606	SOUTH	0.033587	NaN

1444 rows × 5 columns

In [18]:

fig,ax=plt.subplots(1,1,figsize=(5,5))
ax.contour(navlon,navlat,tmask[0,:,:],(0.5,))
r1=data2.Cluster_ID=='NORTH'
r2=data2.Cluster_ID=='SOUTH'
ax.plot(data2.loc[r1,['Lon']].values,data2.loc[r1,['Lat']].values,'r.')
ax.plot(data2.loc[r2,['Lon']].values,data2.loc[r2,['Lat']].values,'b.')

Out[18]:

[<matplotlib.lines.Line2D at 0x7fd596cd6ca0>]

In [19]:

data2.loc[r1,['Diatoms-1','Diatoms-2','mod_diatoms']]

Out[19]:

	Diatoms-1	Diatoms-2	mod_diatoms
479	0.010	0.15	2.589933
480	NaN	NaN	1.019906
481	NaN	NaN	0.562324
482	NaN	NaN	0.393980
483	NaN	NaN	0.290062
...	...	...	...
1436	NaN	NaN	0.006073
1437	NaN	NaN	0.003910
1438	0.003	9.81	0.447318
1439	NaN	NaN	0.292580
1440	NaN	NaN	0.023296

462 rows × 3 columns

In [20]:

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

In [21]:

data2.keys()

Out[21]:

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', 'I',
       'Cluster_ID'],
      dtype='object')

Time Series¶

In [22]:

fig,ax=plt.subplots(1,1,figsize=(15,4))
ax.plot(data2.loc[r1,['dtUTC']].values,1.8*(data2.loc[r1,['mod_diatoms']].values),'b.',label='Model North')
ax.plot(data2.loc[r1,['dtUTC']].values,
        data2.loc[r1,['Diatoms-1']].values+data2.loc[r1,['Diatoms-2']].values,
        'c.',label='Obs North')
ax.plot([pd.to_datetime(ii)+dt.timedelta(days=7) for ii in data2.loc[r2,['dtUTC']].values[:,0]],
        1.8*(data2.loc[r2,['mod_diatoms']].values),'m.',label='Model South (7 day shift)')
ax.plot([pd.to_datetime(ii)+dt.timedelta(days=7) for ii in data2.loc[r2,['dtUTC']].values[:,0]],
        data2.loc[r2,['Diatoms-1']].values+data2.loc[r2,['Diatoms-2']].values,
        '.',color='orange',label='Obs South (7 day shift)')
ax.set_ylabel('Diatoms (mg Chl/m3)')
ax.set_title('Diatoms');
ax.legend()

Out[22]:

<matplotlib.legend.Legend at 0x7fd596bfab50>

In [23]:

fig,ax=plt.subplots(1,1,figsize=(15,4))
ax.plot(data2.loc[r1,['dtUTC']].values,
        1.8*(data2.loc[r1,['mod_flagellates']].values+data2.loc[r1,['mod_ciliates']].values),'b.',label='Model North')
ax.plot(data2.loc[r1,['dtUTC']].values,
        data2.loc[r1,['other']].values,
        'c.',label='Obs North')
ax.plot([pd.to_datetime(ii)+dt.timedelta(days=7) for ii in data2.loc[r2,['dtUTC']].values[:,0]],
        1.8*(data2.loc[r2,['mod_flagellates']].values+data2.loc[r2,['mod_ciliates']].values),
        'm.',label='Model South (7 day shift)')
ax.plot([pd.to_datetime(ii)+dt.timedelta(days=7) for ii in data2.loc[r2,['dtUTC']].values[:,0]],
        data2.loc[r2,['other']].values,
        '.',color='orange',label='Obs South (7 day shift)')
ax.set_ylabel('Non-Diatoms (mg Chl/m3)')
ax.set_title('Non-Diatoms');
ax.legend()

Out[23]:

<matplotlib.legend.Legend at 0x7fd596b444f0>

In [24]:

fig,ax=plt.subplots(1,1,figsize=(15,4))
ax.plot(data2.loc[r1,['dtUTC']].values,
        1.8*(data2.loc[r1,['mod_diatoms']].values+data2.loc[r1,['mod_flagellates']].values+\
             data2.loc[r1,['mod_ciliates']].values),'b.',label='Model North')
ax.plot(data2.loc[r1,['dtUTC']].values,
        data2.loc[r1,['TchlA (ug/L)']].values,
        'c.',label='Obs North')
ax.plot([pd.to_datetime(ii)+dt.timedelta(days=7) for ii in data2.loc[r2,['dtUTC']].values[:,0]],
        1.8*(data2.loc[r2,['mod_diatoms']].values+data2.loc[r2,['mod_flagellates']].values+\
             data2.loc[r2,['mod_ciliates']].values),
        'm.',label='Model South (7 day shift)')
ax.plot([pd.to_datetime(ii)+dt.timedelta(days=7) for ii in data2.loc[r2,['dtUTC']].values[:,0]],
        data2.loc[r2,['TchlA (ug/L)']].values,
        '.',color='orange',label='Obs South (7 day shift)')
ax.set_ylabel('TChl (mg Chl/m3)')
ax.set_title('TChl');
ax.legend()

Out[24]:

<matplotlib.legend.Legend at 0x7fd596ad8040>

In [25]:

fig,ax=plt.subplots(10,1,figsize=(14,25))
ax=ax.flatten()

chplc=('Cyanobacteria','Prasinophytes', 'Cryptophytes', 'Diatoms-1', 'Diatoms-2',
       'Haptophytes', 'Dinoflagellates', 'Dictyochophytes','Raphidophytes','TchlA (ug/L)')
for ii in range(0,len(chplc)):
    ax[ii].plot(data2.loc[r1,['dtUTC']],data2.loc[r1,[chplc[ii]]],'.',color='purple',label='North')
    ax[ii].plot([pd.to_datetime(ii)+dt.timedelta(days=7) for ii in data2.loc[r2,['dtUTC']].values[:,0]],data2.loc[r2,[chplc[ii]]],'.',color='red',label='South 7 day shift')
    ax[ii].set_title(chplc[ii])
    ax[ii].set_ylim(0,10)
plt.tight_layout()
ax[ii].set_ylim(0,20)
ax[0].legend()

Out[25]:

<matplotlib.legend.Legend at 0x7fd59679b8b0>

Correlations¶

In [26]:

chplc2=list(chplc)+['other']

In [27]:

r0=~pd.isnull(data2['Diatoms-1'])
cc=np.corrcoef(data2.loc[r1&r0,['mod_diatoms',]+list(chplc)+['other']],rowvar=False)
print('Correlation Coefficients, North')
print('  Model Diatoms With:')
for ii in range(0,len(chplc2)):
    print('   ',chplc2[ii],': ','{:.2f}'.format(cc[0,ii+1]))
cc=np.corrcoef(data2.loc[r1&r0,['mod_ciliates',]+list(chplc)+['other']],rowvar=False)
print('  Model Ciliates With:')
for ii in range(0,len(chplc2)):
    print('   ',chplc2[ii],': ','{:.2f}'.format(cc[0,ii+1]))
cc=np.corrcoef(data2.loc[r1&r0,['mod_flagellates',]+list(chplc)+['other']],rowvar=False)
print('  Model Flagellates With:')
for ii in range(0,len(chplc2)):
    print('   ',chplc2[ii],': ','{:.2f}'.format(cc[0,ii+1]))

Correlation Coefficients, North
  Model Diatoms With:
    Cyanobacteria :  -0.29
    Prasinophytes :  -0.34
    Cryptophytes :  -0.26
    Diatoms-1 :  0.43
    Diatoms-2 :  -0.05
    Haptophytes :  -0.01
    Dinoflagellates :  0.03
    Dictyochophytes :  -0.19
    Raphidophytes :  -0.31
    TchlA (ug/L) :  0.22
    other :  -0.23
  Model Ciliates With:
    Cyanobacteria :  -0.02
    Prasinophytes :  -0.00
    Cryptophytes :  0.03
    Diatoms-1 :  0.25
    Diatoms-2 :  0.16
    Haptophytes :  0.13
    Dinoflagellates :  0.05
    Dictyochophytes :  -0.04
    Raphidophytes :  -0.03
    TchlA (ug/L) :  0.29
    other :  0.08
  Model Flagellates With:
    Cyanobacteria :  0.32
    Prasinophytes :  0.53
    Cryptophytes :  0.58
    Diatoms-1 :  -0.06
    Diatoms-2 :  0.01
    Haptophytes :  0.22
    Dinoflagellates :  0.27
    Dictyochophytes :  0.36
    Raphidophytes :  0.37
    TchlA (ug/L) :  0.10
    other :  0.55

In [28]:

r0=~pd.isnull(data2['Diatoms-1'])
cc=np.corrcoef(data2.loc[r2&r0,['mod_diatoms',]+list(chplc)+['other']],rowvar=False)
print('Correlation Coefficients, South')
print('  Model Diatoms With:')
for ii in range(0,len(chplc2)):
    print('   ',chplc2[ii],': ','{:.2f}'.format(cc[0,ii+1]))
cc=np.corrcoef(data2.loc[r2&r0,['mod_ciliates',]+list(chplc)+['other']],rowvar=False)
print('  Model Ciliates With:')
for ii in range(0,len(chplc2)):
    print('   ',chplc2[ii],': ','{:.2f}'.format(cc[0,ii+1]))
cc=np.corrcoef(data2.loc[r2&r0,['mod_flagellates',]+list(chplc)+['other']],rowvar=False)
print('  Model Flagellates With:')
for ii in range(0,len(chplc2)):
    print('   ',chplc2[ii],': ','{:.2f}'.format(cc[0,ii+1]))

Correlation Coefficients, South
  Model Diatoms With:
    Cyanobacteria :  -0.22
    Prasinophytes :  -0.27
    Cryptophytes :  -0.33
    Diatoms-1 :  0.72
    Diatoms-2 :  -0.03
    Haptophytes :  -0.13
    Dinoflagellates :  -0.08
    Dictyochophytes :  -0.10
    Raphidophytes :  -0.11
    TchlA (ug/L) :  0.60
    other :  -0.29
  Model Ciliates With:
    Cyanobacteria :  0.11
    Prasinophytes :  -0.01
    Cryptophytes :  -0.07
    Diatoms-1 :  0.46
    Diatoms-2 :  0.07
    Haptophytes :  0.13
    Dinoflagellates :  -0.04
    Dictyochophytes :  -0.12
    Raphidophytes :  -0.05
    TchlA (ug/L) :  0.44
    other :  -0.03
  Model Flagellates With:
    Cyanobacteria :  0.37
    Prasinophytes :  0.47
    Cryptophytes :  0.47
    Diatoms-1 :  -0.20
    Diatoms-2 :  -0.01
    Haptophytes :  0.39
    Dinoflagellates :  -0.10
    Dictyochophytes :  0.00
    Raphidophytes :  -0.07
    TchlA (ug/L) :  -0.08
    other :  0.32

In [29]:

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

chplc=('Cyanobacteria','Prasinophytes', 'Cryptophytes', 'Diatoms-1', 'Diatoms-2',
       'Haptophytes', 'Dinoflagellates', 'Dictyochophytes','Raphidophytes','TchlA (ug/L)')

mvar1=1.8*data2.loc[r1,['mod_diatoms']].values
mvar2=1.8*data2.loc[r2,['mod_diatoms']].values

for ii in range(0,len(chplc)):
    ax[ii].plot(logt(mvar1),logt(data2.loc[r1,[chplc[ii]]].values),'.',color='blue',label='North')
    ax[ii].plot(logt(mvar2),logt(data2.loc[r2,[chplc[ii]]].values),'.',color='red',label='South')
    ax[ii].set_xlabel('Model Diatoms')
    ax[ii].set_ylabel(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[29]:

<matplotlib.legend.Legend at 0x7fd5943de460>

In [30]:

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

chplc=('Cyanobacteria','Prasinophytes', 'Cryptophytes', 'Diatoms-1', 'Diatoms-2',
       'Haptophytes', 'Dinoflagellates', 'Dictyochophytes','Raphidophytes','TchlA (ug/L)')

mvar1=1.8*data2.loc[r1,['mod_flagellates']].values
mvar2=1.8*data2.loc[r2,['mod_flagellates']].values

for ii in range(0,len(chplc)):
    ax[ii].plot(logt(mvar1),logt(data2.loc[r1,[chplc[ii]]].values),'.',color='blue',label='North')
    ax[ii].plot(logt(mvar2),logt(data2.loc[r2,[chplc[ii]]].values),'.',color='red',label='South')
    ax[ii].set_xlabel('Model Flagellates')
    ax[ii].set_ylabel(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[30]:

<matplotlib.legend.Legend at 0x7fd594120040>

In [31]:

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

chplc=('Cyanobacteria','Prasinophytes', 'Cryptophytes', 'Diatoms-1', 'Diatoms-2',
       'Haptophytes', 'Dinoflagellates', 'Dictyochophytes','Raphidophytes','TchlA (ug/L)')

mvar1=1.8*data2.loc[r1,['mod_ciliates']].values
mvar2=1.8*data2.loc[r2,['mod_ciliates']].values

for ii in range(0,len(chplc)):
    ax[ii].plot(logt(mvar1),logt(data2.loc[r1,[chplc[ii]]].values),'.',color='blue',label='North')
    ax[ii].plot(logt(mvar2),logt(data2.loc[r2,[chplc[ii]]].values),'.',color='red',label='South')
    ax[ii].set_xlabel('Model Ciliates')
    ax[ii].set_ylabel(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[31]:

<matplotlib.legend.Legend at 0x7fd58cfac5b0>

Focus on diatoms vs non-diatoms¶

In [32]:

fig,ax=plt.subplots(2,2,figsize=(12,8))
ax=ax.flatten()

iax=ax[0]; title='Diatoms (mg Chl/m$^3$)'
iax.plot(data2.loc[r1,['Diatoms-1']].values+data2.loc[r1,['Diatoms-2']].values,1.8*data2.loc[r1,['mod_diatoms']],'c.',label='Northern')
iax.plot(data2.loc[r2,['Diatoms-1']].values+data2.loc[r2,['Diatoms-2']].values,1.8*data2.loc[r2,['mod_diatoms']],'m.',label='Southern')
iax.set_title(title)
iax.set_xlabel('HPLC')
iax.set_ylabel('model')
iax.plot((0,12),(0,12),'k-',alpha=.3)
iax.legend()


iax=ax[1]; title='Non-Diatoms (mg Chl/m$^3$)'
iax.plot(data2.loc[r1,['other']].values,1.8*(data2.loc[r1,['mod_flagellates']].values+data2.loc[r1,['mod_ciliates']].values),'c.')
iax.plot(data2.loc[r2,['other']].values,1.8*(data2.loc[r2,['mod_flagellates']].values+data2.loc[r2,['mod_ciliates']].values),'m.')
iax.set_title('non-Diatoms (mg Chl/m$^3$)')
iax.set_xlabel('HPLC')
iax.set_ylabel('model')
iax.plot((0,8),(0,8),'k-',alpha=.3)


iax=ax[2]; title='log10(Diatoms (mg Chl/m$^3$)+.001)'
iax.plot(logt(data2.loc[r1,['Diatoms-1']].values+data2.loc[r1,['Diatoms-2']].values),logt(1.8*data2.loc[r1,['mod_diatoms']]),'c.',label='Northern')
iax.plot(logt(data2.loc[r2,['Diatoms-1']].values+data2.loc[r2,['Diatoms-2']].values),logt(1.8*data2.loc[r2,['mod_diatoms']]),'m.',label='Southern')
iax.set_title(title)
iax.set_xlabel('HPLC')
iax.set_ylabel('model')
iax.plot((-2,3),(-2,3),'k-',alpha=.3)

iax=ax[3]; title='log10(Non-Diatoms (mg Chl/m$^3$)+.001)'
iax.plot(logt(data2.loc[r1,['other']].values),logt(1.8*(data2.loc[r1,['mod_flagellates']].values+data2.loc[r1,['mod_ciliates']].values)),'c.')
iax.plot(data2.loc[r2,['other']].values,logt(1.8*(data2.loc[r2,['mod_flagellates']].values+data2.loc[r2,['mod_ciliates']].values)),'m.')
iax.set_title('non-Diatoms (mg Chl/m$^3$)')
iax.set_xlabel('HPLC')
iax.set_ylabel('model')
iax.plot((-2,3),(-2,3),'k-',alpha=.3)

Out[32]:

[<matplotlib.lines.Line2D at 0x7fd58cc45400>]

In [33]:

fig,ax=plt.subplots(2,2,figsize=(12,8))
ax=ax.flatten()

iax=ax[0]; title='TChl (mg Chl/m$^3$)'
iax.plot(data2.loc[r1,['TchlA (ug/L)']].values,
        1.8*(data2.loc[r1,['mod_flagellates']].values+\
             data2.loc[r1,['mod_ciliates']].values+\
             data2.loc[r1,['mod_diatoms']].values),'c.',label='Northern')
iax.plot(data2.loc[r2,['TchlA (ug/L)']].values,
        1.8*(data2.loc[r2,['mod_flagellates']].values+\
             data2.loc[r2,['mod_ciliates']].values+\
             data2.loc[r2,['mod_diatoms']].values),'m.',label='Southern')
iax.set_title(title)
iax.set_xlabel('HPLC')
iax.set_ylabel('model')
iax.plot((0,12),(0,12),'k-',alpha=.3)
iax.legend()


iax=ax[2]; title='log10(Diatoms (mg Chl/m$^3$)+.001)'
iax.plot(logt(data2.loc[r1,['TchlA (ug/L)']].values),
        1.8*(data2.loc[r1,['mod_flagellates']].values+\
             logt(data2.loc[r1,['mod_ciliates']].values+\
             data2.loc[r1,['mod_diatoms']].values)),'c.',label='Northern')
iax.plot(logt(data2.loc[r2,['TchlA (ug/L)']].values),
        logt(1.8*(data2.loc[r2,['mod_flagellates']].values+\
             data2.loc[r2,['mod_ciliates']].values+\
             data2.loc[r2,['mod_diatoms']].values)),'m.',label='Southern')
iax.set_title(title)
iax.set_xlabel('HPLC')
iax.set_ylabel('model')
iax.plot((-2,3),(-2,3),'k-',alpha=.3)
iax.legend()

Out[33]:

<matplotlib.legend.Legend at 0x7fd58cb34ee0>