{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison of 201905 Model Phytoplankton to HPLC Phytoplankton Abundances from Nina Nemcek" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "papermill": { "duration": 2.59497, "end_time": "2020-11-16T18:41:27.623510", "exception": false, "start_time": "2020-11-16T18:41:25.028540", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import numpy as np # this module handles arrays, but here we need it for its NaN value\n", "import pandas as pd # this module contains a lot of tools for handling tabular data\n", "from matplotlib import pyplot as plt\n", "from salishsea_tools import evaltools as et\n", "import datetime as dt\n", "import os\n", "import gsw\n", "import pickle\n", "import netCDF4 as nc\n", "import cmocean\n", "from scipy import stats as spst\n", "from pandas.plotting import register_matplotlib_converters\n", "register_matplotlib_converters()\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: crypto+hapto+prasino grouping was actually determined based on comparisons to 201812 model run" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load data and matched model output" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "papermill": { "duration": 0.021207, "end_time": "2020-11-16T18:41:27.664289", "exception": false, "start_time": "2020-11-16T18:41:27.643082", "status": "completed" }, "tags": [ "parameters" ] }, "outputs": [], "source": [ "modSourceDir= '/results2/SalishSea/nowcast-green.201905/'\n", "modver='201905'\n", "Chl_N=1.8 # Chl:N ratio\n", "startYMD=(2015,1,1)\n", "endYMD=(2018,12,31)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "papermill": { "duration": 0.021268, "end_time": "2020-11-16T18:41:27.741462", "exception": false, "start_time": "2020-11-16T18:41:27.720194", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "start_date = dt.datetime(startYMD[0],startYMD[1],startYMD[2])\n", "end_date = dt.datetime(endYMD[0],endYMD[1],endYMD[2]) #dt.datetime(2019,6,30)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "papermill": { "duration": 0.020773, "end_time": "2020-11-16T18:41:27.779566", "exception": false, "start_time": "2020-11-16T18:41:27.758793", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "datestr='_'+start_date.strftime('%Y%m%d')+'_'+end_date.strftime('%Y%m%d')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "papermill": { "duration": 0.056725, "end_time": "2020-11-16T18:41:29.562101", "exception": false, "start_time": "2020-11-16T18:41:29.505376", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "with nc.Dataset('/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702_noLPE.nc') as mesh:\n", " tmask=np.copy(mesh.variables['tmask'][0,:,:,:])\n", " navlat=np.copy(mesh.variables['nav_lat'][:,:])\n", " navlon=np.copy(mesh.variables['nav_lon'][:,:])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "papermill": { "duration": 0.022584, "end_time": "2020-11-16T18:41:27.819353", "exception": false, "start_time": "2020-11-16T18:41:27.796769", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def subval(idf,colList):\n", " # first value in colList should be the column you are going to keep\n", " # follow with other columns that will be used to fill in when that column is NaN\n", " # in order of precedence\n", " if len(colList)==2:\n", " idf[colList[0]]=[r[colList[0]] if not pd.isna(r[colList[0]]) \\\n", " else r[colList[1]] for i,r in idf.iterrows()]\n", " elif len(colList)==3:\n", " idf[colList[0]]=[r[colList[0]] if not pd.isna(r[colList[0]]) \\\n", " else r[colList[1]] if not pd.isna(r[colList[1]]) \\\n", " else r[colList[2]] for i,r in idf.iterrows()]\n", " else:\n", " raise NotImplementedError('Add to code to handle this case')\n", " idf.drop(columns=list(colList[1:]),inplace=True)\n", " return idf" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "papermill": { "duration": 0.032863, "end_time": "2020-11-16T18:41:27.869564", "exception": false, "start_time": "2020-11-16T18:41:27.836701", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "matched_201905_20150101_20181231_NewALLO.pkl\n" ] } ], "source": [ "if os.path.isfile('matched_'+modver+datestr+'_NewALLO.pkl'):\n", " data=pickle.load(open( 'matched_'+modver+datestr+'_NewALLO.pkl', 'rb' ) )\n", " print('matched_'+modver+datestr+'_NewALLO.pkl')\n", "else:\n", " # define paths to the source files and eventual output file\n", " flist=('/ocean/eolson/MEOPAR/obs/NemcekHPLC/bottlePhytoMerged2015_NewALLO.csv',\n", " '/ocean/eolson/MEOPAR/obs/NemcekHPLC/bottlePhytoMerged2016_NewALLO.csv',\n", " '/ocean/eolson/MEOPAR/obs/NemcekHPLC/bottlePhytoMerged2017_NewALLO.csv',\n", " '/ocean/eolson/MEOPAR/obs/NemcekHPLC/bottlePhytoMerged2018_NewALLO.csv')#,\n", " #'/ocean/eolson/MEOPAR/obs/NemcekHPLC/bottlePhytoMerged2019.csv')\n", "\n", " dfs=list()\n", " for fname in flist:\n", " idf=pd.read_csv(fname)\n", " print(fname,sorted(idf.keys()))\n", " dfs.append(idf)\n", " df=pd.concat(dfs,ignore_index=True,sort=False); # concatenate the list into a single table\n", "\n", " df.drop(labels=['ADM:MISSION','ADM:PROJECT','ADM:SCIENTIST','Zone','Zone.1','Temperature:Draw',\n", " 'Temperature:Draw [deg C (ITS90)]','Bottle:Firing_Sequence','Comments by sample_numbeR',\n", " 'File Name','LOC:EVENT_NUMBER','Number_of_bin_records'\n", " ],axis=1,inplace=True)\n", "\n", " #df=subval(df,('Dictyochophytes','Dictyo'))\n", " df=subval(df,('Chlorophyll:Extracted [mg/m^3]','Chlorophyll:Extracted'))\n", " #df=subval(df,('Dinoflagellates','Dinoflagellates-1'))\n", " df=subval(df,('Fluorescence [mg/m^3]','Fluorescence:URU:Seapoint [mg/m^3]','Fluorescence:URU:Seapoint'))\n", " df=subval(df,('Lat','LOC:LATITUDE'))\n", " df=subval(df,('Lon','LOC:LONGITUDE'))\n", " df=subval(df,('Nitrate_plus_Nitrite [umol/L]','Nitrate_plus_Nitrite'))\n", " df=subval(df,('PAR [uE/m^2/sec]','PAR'))\n", " df=subval(df,('Phaeo-Pigment:Extracted [mg/m^3]','Phaeo-Pigment:Extracted'))\n", " df=subval(df,('Phosphate [umol/L]','Phosphate'))\n", " df=subval(df,('Pressure [decibar]','Pressure'))\n", " #df=subval(df,('Raphidophytes','Raphido'))\n", " df=subval(df,('Salinity','Salinity [PSS-78]','Salinity:T1:C1 [PSS-78]'))\n", " df=subval(df,('Salinity:Bottle','Salinity:Bottle [PSS-78]'))\n", " df=subval(df,('Silicate [umol/L]','Silicate'))\n", " #df=subval(df,('TchlA (ug/L)','TchlA'))\n", " df=subval(df,('Temperature','Temperature [deg C (ITS90)]','Temperature:Secondary [deg C (ITS90)]'))\n", " df=subval(df,('Transmissivity [*/metre]','Transmissivity'))\n", "\n", " df['Z']=np.where(pd.isna(df['Depth [metres]']),\n", " -1*gsw.z_from_p(df['Pressure [decibar]'].values,df['Lat'].values),\n", " df['Depth [metres]'])\n", " df['p']=np.where(pd.isna(df['Pressure [decibar]']),\n", " gsw.p_from_z(-1*df['Depth [metres]'].values,df['Lat'].values),\n", " df['Pressure [decibar]'])\n", " df['SA']=gsw.SA_from_SP(df['Salinity'].values,df['p'].values,df['Lon'].values,df['Lat'].values)\n", " df['CT']=gsw.CT_from_t(df['SA'].values,df['Temperature'].values,df['p'].values)\n", " df.rename({'TchlA':'TchlA (ug/L)','Raphido':'Raphidophytes','Dinoflagellates-1':'Dinoflagellates',\n", " 'Dictyo':'Dictyochophytes'},axis=1, inplace=True, errors='raise')\n", " 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'] ]\n", "\n", " PATH= modSourceDir\n", "\n", " flen=1\n", " namfmt='nowcast'\n", " #varmap={'N':'nitrate','Si':'silicon','Ammonium':'ammonium'}\n", " 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'}\n", " #gridmap={'nitrate':'tmask','silicon':'tmask','ammonium':'tmask'}\n", " fdict={'ptrc_T':1,'grid_T':1}\n", "\n", " data=et.matchData(df,filemap,fdict,start_date,end_date,namfmt,PATH,flen)\n", "\n", " with open('matched_'+modver+datestr+'_NewALLO.pkl','wb') as f:\n", " pickle.dump(data,f)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['FIL:START TIME YYYY/MM/DD HH:MM:SS', 'LOC:STATION', 'Lat', 'Lon',\n", " 'LOC:WATER DEPTH', 'Sample_Number', 'Temperature', 'Salinity',\n", " 'Oxygen:Dissolved:CTD', 'pH:SBE:Nominal', 'Salinity:Bottle',\n", " 'Flag:Salinity:Bottle', 'Flag:Chlorophyll:Extracted',\n", " 'Flag:Nitrate_plus_Nitrite', 'Flag:Silicate', 'Flag:Phosphate',\n", " 'Cruise', 'Oxygen:Dissolved', 'Flag:Oxygen:Dissolved', 'Diatoms-1',\n", " 'Diatoms-2', 'Prasinophytes', 'Cryptophytes', 'Dinoflagellates',\n", " 'Haptophytes', 'Dictyochophytes', 'Raphidophytes', 'Cyanobacteria',\n", " 'TchlA (ug/L)', 'Pressure [decibar]', 'Transmissivity [*/metre]',\n", " 'PAR [uE/m^2/sec]', 'PAR:Reference [uE/m^2/sec]',\n", " 'Oxygen:Dissolved:SBE [mL/L]', 'Oxygen:Dissolved:SBE [umol/kg]',\n", " 'Chlorophyll:Extracted [mg/m^3]', 'Phaeo-Pigment:Extracted [mg/m^3]',\n", " 'Nitrate_plus_Nitrite [umol/L]', 'Silicate [umol/L]',\n", " 'Phosphate [umol/L]', 'Bottle_Number', 'Oxygen:Dissolved [mL/L]',\n", " 'Oxygen:Dissolved [umol/kg]', 'Depth [metres]', 'Fluorescence [mg/m^3]',\n", " 'Oxygen:Dissolved:CTD [mL/L]', 'Oxygen:Dissolved:CTD [umol/kg]',\n", " 'Depth:Nominal [metres]', 'Alkalinity:Total [umol/L]',\n", " 'Flag:Alkalinity:Total', 'Carbon:Dissolved:Inorganic [umol/kg]',\n", " 'Flag:Carbon:Dissolved:Inorganic', 'Z', 'p', 'SA', 'CT', 'dtUTC', 'j',\n", " 'i', 'mod_nitrate', 'mod_silicon', 'mod_ammonium', 'mod_diatoms',\n", " 'mod_ciliates', 'mod_flagellates', 'mod_vosaline', 'mod_votemper', 'k'],\n", " dtype='object')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.keys()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['SI', '59', '102', '75', '72', '69', 'ADCP', '65', '63', '62',\n", " '56', '46', '42', '39', 'GE01', '27', '2', '3', 'BS', '6', '9',\n", " '12', '14', '16', '22', '11', 'CPF2', 'CPF1', '24', '28', '38',\n", " '41', 'BS17', '19', 'GEO1', 'BS11', 'SC-04', '66', 'BI2', 'JF2',\n", " 'HARO59', 'SI03', '15', 'SC04', '40', 'qu39', 'Van1', 'BS-11',\n", " 'adcp', 'QU39', 'CPF-2', 'CPF-1', 'Haro 59', 'BS2', 'IS-2', 'PEN1',\n", " 'PEN2', 'PEN3'], dtype=object)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['LOC:STATION'].unique()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "papermill": { "duration": 0.026848, "end_time": "2020-11-16T18:41:27.913839", "exception": false, "start_time": "2020-11-16T18:41:27.886991", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "data['other']=0.0\n", "for el in ('Cryptophytes', 'Cyanobacteria', 'Dictyochophytes', 'Dinoflagellates',\n", " 'Haptophytes', 'Prasinophytes', 'Raphidophytes'):\n", " data['other']=data['other']+data[el]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "papermill": { "duration": 0.104595, "end_time": "2020-11-16T18:41:29.169217", "exception": false, "start_time": "2020-11-16T18:41:29.064622", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def yd(idt):\n", " if type(idt)==dt.datetime:\n", " yd=(idt-dt.datetime(idt.year-1,12,31)).days\n", " else: # assume array or pandas\n", " yd=[(ii-dt.datetime(ii.year-1,12,31)).days for ii in idt]\n", " return yd\n", "\n", "data['yd']=yd(data['dtUTC'])\n", "data['Year']=[ii.year for ii in data['dtUTC']]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "papermill": { "duration": 0.022354, "end_time": "2020-11-16T18:41:28.515306", "exception": false, "start_time": "2020-11-16T18:41:28.492952", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# define log transform function with slight shift to accommodate zero values\n", "def logt(x):\n", " return np.log10(x+.001)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Determine which HPLC groups have the highest biomass" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.1757399193548412" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Diatoms-1'].mean() ## Highest biomass" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.3043850806451614" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Diatoms-2'].mean() ## include" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.03817540322580645" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Cyanobacteria'].mean() ## exclude due to low biomass" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.4574556451612899" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Cryptophytes'].mean() ## include" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.21607862903225808" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Prasinophytes'].mean() ## include" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.23795766129032253" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Haptophytes'].mean() ## include" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.039802419354838664" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Dictyochophytes'].mean() ## exclude due to low biomass" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.10347580645161288" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Dinoflagellates'].mean() # exclude due to low biomass" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.40433064516129036" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Raphidophytes'].mean() ## Include" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "data['Month']=[ii.month for ii in data['dtUTC']]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "monthlymean=data.groupby(['Month']).mean()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Month\n", "2 0.120424\n", "3 3.233714\n", "4 4.943088\n", "5 2.529966\n", "6 2.168265\n", "7 0.551176\n", "8 2.249000\n", "9 0.941815\n", "10 1.593719\n", "11 0.785317\n", "Name: Diatoms-1, dtype: float64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "monthlymean['Diatoms-1']" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "monthlymean['HPLCDiatoms']=(monthlymean['Diatoms-1']+monthlymean['Raphidophytes']+monthlymean['Diatoms-2'])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "monthlymean['HPLCFlag']=(monthlymean['Cryptophytes']+monthlymean['Haptophytes']+monthlymean['Raphidophytes'])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Month\n", "2 0.337182\n", "3 3.697286\n", "4 5.422284\n", "5 3.205103\n", "6 3.780675\n", "7 0.780706\n", "8 4.946000\n", "9 1.361207\n", "10 2.194649\n", "11 0.897024\n", "Name: HPLCDiatoms, dtype: float64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "monthlymean['HPLCDiatoms']" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ksuchy/anaconda3/envs/py39/lib/python3.9/site-packages/pandas/core/groupby/groupby.py:1510: RuntimeWarning: divide by zero encountered in true_divide\n", " result.iloc[:, cols].values / np.sqrt(self.count().iloc[:, cols]).values\n" ] } ], "source": [ "monthlysem=logt(data.groupby(['Month']).sem())" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "monthlymean['L10mod_diatoms']=logt(monthlymean['mod_diatoms']*Chl_N)\n", "monthlymean['L10mod_flagellates']=logt(monthlymean['mod_flagellates']*Chl_N)\n", "monthlymean['L10Diatoms-1']=logt(monthlymean['Diatoms-1'])\n", "monthlymean['L10Diatoms-2']=logt(monthlymean['Diatoms-2'])\n", "monthlymean['L10Cryptophytes']=logt(monthlymean['Cryptophytes'])\n", "monthlymean['L10Prasinophytes']=logt(monthlymean['Prasinophytes'])\n", "monthlymean['L10Haptophytes']=logt(monthlymean['Haptophytes'])\n", "monthlymean['L10Raphidophytes']=logt(monthlymean['Raphidophytes'])\n", "monthlymean['L10TotalChla']=logt(monthlymean['TchlA (ug/L)'])\n", "\n", "monthlymean['L10HPLCDiatoms']=logt(monthlymean['HPLCDiatoms'])\n", "monthlymean['L10HPLCFlag']=logt(monthlymean['HPLCFlag'])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Month\n", "2 -2.432694\n", "3 -1.367311\n", "4 -0.199868\n", "5 0.144985\n", "6 -0.174303\n", "7 -0.161647\n", "8 -0.892479\n", "9 -1.197468\n", "10 -1.436750\n", "11 -1.700827\n", "Name: L10mod_diatoms, dtype: float64" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "monthlymean['L10mod_diatoms']" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# define inverse log transform with same shift\n", "def logt_inv(y):\n", " return 10**y-.001" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model vs Obs Plots for various model-obs groups" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.028767, "end_time": "2020-11-16T18:41:32.112120", "exception": false, "start_time": "2020-11-16T18:41:32.083353", "status": "completed" }, "tags": [] }, "source": [ "### Correlation Coefficient Matrix" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "papermill": { "duration": 0.040984, "end_time": "2020-11-16T18:41:31.918310", "exception": false, "start_time": "2020-11-16T18:41:31.877326", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "data['mod_diatoms_chl']=Chl_N*data['mod_diatoms']\n", "data['mod_flagellates_chl']=Chl_N*data['mod_flagellates']\n", "data['mod_ciliates_chl']=Chl_N*data['mod_ciliates']\n", "data['mod_TChl']=data['mod_diatoms_chl']+data['mod_flagellates_chl']+data['mod_ciliates_chl']\n", "data['CPH']=data['Cryptophytes']+data['Prasinophytes']+data['Haptophytes']\n", "data['DD']=data['Diatoms-1']+data['Diatoms-2']\n", "dfVars=data.loc[:,['Diatoms-1', 'Diatoms-2','Cyanobacteria','Cryptophytes', 'Prasinophytes', \n", " 'Haptophytes', 'Dictyochophytes','Dinoflagellates','Raphidophytes','DD','CPH','TchlA (ug/L)',\n", " 'mod_diatoms_chl','mod_flagellates_chl','mod_ciliates_chl','mod_TChl']]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "papermill": { "duration": 0.046279, "end_time": "2020-11-16T18:41:32.187547", "exception": false, "start_time": "2020-11-16T18:41:32.141268", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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Diatoms-1Diatoms-2CyanobacteriaCryptophytesPrasinophytesHaptophytesDictyochophytesDinoflagellatesRaphidophytesDDCPHTchlA (ug/L)mod_diatoms_chlmod_flagellates_chlmod_ciliates_chlmod_TChl
Diatoms-11.0000000.129174-0.044520-0.104293-0.155529-0.089528-0.0380140.0685070.1860190.986013-0.1433940.7990780.329545-0.1075240.0865550.240522
Diatoms-20.1291741.0000000.0870550.0657890.0418720.0454390.2042560.0301590.0894790.2926390.0671380.2789080.068727-0.136186-0.003254-0.009364
Cyanobacteria-0.0445200.0870551.0000000.4550470.6735850.2650690.1345170.1114270.471045-0.0282990.5653120.323326-0.0649530.2712490.0283730.083302
Cryptophytes-0.1042930.0657890.4550471.0000000.6297270.3308460.0591230.1050060.151756-0.0895120.8500310.1456220.1076190.2834300.0185420.238735
Prasinophytes-0.1555290.0418720.6735850.6297271.0000000.2737440.1154850.0438390.193154-0.1429390.7606890.115373-0.1140430.288055-0.0325310.043383
Haptophytes-0.0895280.0454390.2650690.3308460.2737441.0000000.028364-0.005455-0.014300-0.0786950.7188540.040637-0.0241290.3093590.0411820.139303
Dictyochophytes-0.0380140.2042560.1345170.0591230.1154850.0283641.0000000.0704780.073749-0.0023260.0796370.084752-0.0614540.083663-0.033596-0.014315
Dinoflagellates0.0685070.0301590.1114270.1050060.043839-0.0054550.0704781.0000000.2245670.0711300.0632640.2470060.0519620.1312460.0286590.114278
Raphidophytes0.1860190.0894790.4710450.1517560.193154-0.0143000.0737490.2245671.0000000.1944170.1303320.6972460.198755-0.0385710.0030860.153761
DD0.9860130.292639-0.028299-0.089512-0.142939-0.078695-0.0023260.0711300.1944171.000000-0.1269910.8174310.329332-0.1265750.0829190.230362
CPH-0.1433940.0671380.5653120.8500310.7606890.7188540.0796370.0632640.130332-0.1269911.0000000.1278980.0048460.3771300.0178820.196614
TchlA (ug/L)0.7990780.2789080.3233260.1456220.1153730.0406370.0847520.2470060.6972460.8174310.1278981.0000000.343112-0.0459380.0638790.281355
mod_diatoms_chl0.3295450.068727-0.0649530.107619-0.114043-0.024129-0.0614540.0519620.1987550.3293320.0048460.3431121.0000000.1883240.4627670.842638
mod_flagellates_chl-0.107524-0.1361860.2712490.2834300.2880550.3093590.0836630.131246-0.038571-0.1265750.377130-0.0459380.1883241.0000000.5521380.682743
mod_ciliates_chl0.086555-0.0032540.0283730.018542-0.0325310.041182-0.0335960.0286590.0030860.0829190.0178820.0638790.4627670.5521381.0000000.696784
mod_TChl0.240522-0.0093640.0833020.2387350.0433830.139303-0.0143150.1142780.1537610.2303620.1966140.2813550.8426380.6827430.6967841.000000
\n", "
" ], "text/plain": [ " Diatoms-1 Diatoms-2 Cyanobacteria Cryptophytes \\\n", "Diatoms-1 1.000000 0.129174 -0.044520 -0.104293 \n", "Diatoms-2 0.129174 1.000000 0.087055 0.065789 \n", "Cyanobacteria -0.044520 0.087055 1.000000 0.455047 \n", "Cryptophytes -0.104293 0.065789 0.455047 1.000000 \n", "Prasinophytes -0.155529 0.041872 0.673585 0.629727 \n", "Haptophytes -0.089528 0.045439 0.265069 0.330846 \n", "Dictyochophytes -0.038014 0.204256 0.134517 0.059123 \n", "Dinoflagellates 0.068507 0.030159 0.111427 0.105006 \n", "Raphidophytes 0.186019 0.089479 0.471045 0.151756 \n", "DD 0.986013 0.292639 -0.028299 -0.089512 \n", "CPH -0.143394 0.067138 0.565312 0.850031 \n", "TchlA (ug/L) 0.799078 0.278908 0.323326 0.145622 \n", "mod_diatoms_chl 0.329545 0.068727 -0.064953 0.107619 \n", "mod_flagellates_chl -0.107524 -0.136186 0.271249 0.283430 \n", "mod_ciliates_chl 0.086555 -0.003254 0.028373 0.018542 \n", "mod_TChl 0.240522 -0.009364 0.083302 0.238735 \n", "\n", " Prasinophytes Haptophytes Dictyochophytes \\\n", "Diatoms-1 -0.155529 -0.089528 -0.038014 \n", "Diatoms-2 0.041872 0.045439 0.204256 \n", "Cyanobacteria 0.673585 0.265069 0.134517 \n", "Cryptophytes 0.629727 0.330846 0.059123 \n", "Prasinophytes 1.000000 0.273744 0.115485 \n", "Haptophytes 0.273744 1.000000 0.028364 \n", "Dictyochophytes 0.115485 0.028364 1.000000 \n", "Dinoflagellates 0.043839 -0.005455 0.070478 \n", "Raphidophytes 0.193154 -0.014300 0.073749 \n", "DD -0.142939 -0.078695 -0.002326 \n", "CPH 0.760689 0.718854 0.079637 \n", "TchlA (ug/L) 0.115373 0.040637 0.084752 \n", "mod_diatoms_chl -0.114043 -0.024129 -0.061454 \n", "mod_flagellates_chl 0.288055 0.309359 0.083663 \n", "mod_ciliates_chl -0.032531 0.041182 -0.033596 \n", "mod_TChl 0.043383 0.139303 -0.014315 \n", "\n", " Dinoflagellates Raphidophytes DD CPH \\\n", "Diatoms-1 0.068507 0.186019 0.986013 -0.143394 \n", "Diatoms-2 0.030159 0.089479 0.292639 0.067138 \n", "Cyanobacteria 0.111427 0.471045 -0.028299 0.565312 \n", "Cryptophytes 0.105006 0.151756 -0.089512 0.850031 \n", "Prasinophytes 0.043839 0.193154 -0.142939 0.760689 \n", "Haptophytes -0.005455 -0.014300 -0.078695 0.718854 \n", "Dictyochophytes 0.070478 0.073749 -0.002326 0.079637 \n", "Dinoflagellates 1.000000 0.224567 0.071130 0.063264 \n", "Raphidophytes 0.224567 1.000000 0.194417 0.130332 \n", "DD 0.071130 0.194417 1.000000 -0.126991 \n", "CPH 0.063264 0.130332 -0.126991 1.000000 \n", "TchlA (ug/L) 0.247006 0.697246 0.817431 0.127898 \n", "mod_diatoms_chl 0.051962 0.198755 0.329332 0.004846 \n", "mod_flagellates_chl 0.131246 -0.038571 -0.126575 0.377130 \n", "mod_ciliates_chl 0.028659 0.003086 0.082919 0.017882 \n", "mod_TChl 0.114278 0.153761 0.230362 0.196614 \n", "\n", " TchlA (ug/L) mod_diatoms_chl mod_flagellates_chl \\\n", "Diatoms-1 0.799078 0.329545 -0.107524 \n", "Diatoms-2 0.278908 0.068727 -0.136186 \n", "Cyanobacteria 0.323326 -0.064953 0.271249 \n", "Cryptophytes 0.145622 0.107619 0.283430 \n", "Prasinophytes 0.115373 -0.114043 0.288055 \n", "Haptophytes 0.040637 -0.024129 0.309359 \n", "Dictyochophytes 0.084752 -0.061454 0.083663 \n", "Dinoflagellates 0.247006 0.051962 0.131246 \n", "Raphidophytes 0.697246 0.198755 -0.038571 \n", "DD 0.817431 0.329332 -0.126575 \n", "CPH 0.127898 0.004846 0.377130 \n", "TchlA (ug/L) 1.000000 0.343112 -0.045938 \n", "mod_diatoms_chl 0.343112 1.000000 0.188324 \n", "mod_flagellates_chl -0.045938 0.188324 1.000000 \n", "mod_ciliates_chl 0.063879 0.462767 0.552138 \n", "mod_TChl 0.281355 0.842638 0.682743 \n", "\n", " mod_ciliates_chl mod_TChl \n", "Diatoms-1 0.086555 0.240522 \n", "Diatoms-2 -0.003254 -0.009364 \n", "Cyanobacteria 0.028373 0.083302 \n", "Cryptophytes 0.018542 0.238735 \n", "Prasinophytes -0.032531 0.043383 \n", "Haptophytes 0.041182 0.139303 \n", "Dictyochophytes -0.033596 -0.014315 \n", "Dinoflagellates 0.028659 0.114278 \n", "Raphidophytes 0.003086 0.153761 \n", "DD 0.082919 0.230362 \n", "CPH 0.017882 0.196614 \n", "TchlA (ug/L) 0.063879 0.281355 \n", "mod_diatoms_chl 0.462767 0.842638 \n", "mod_flagellates_chl 0.552138 0.682743 \n", "mod_ciliates_chl 1.000000 0.696784 \n", "mod_TChl 0.696784 1.000000 " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfVars.corr()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Strongest correlations:\n", "Model diatoms and:\n", "- Total chla: 0.343112\n", "- Diatoms-1: 0.329545\n", "- Diatoms-1+Diatoms-2: 0.329332\n", "\n", "Model flagellates and:\n", "- crypto+hapto+prasino: 0.377130\n", "- haptophytes: 0.309359\n", "- prasinophytes: 0.288055\n", "- cryptophytes: 0.283430\n", "- cyanobacteria: 0.271249 (but remember that cyanobacteria abundances are low)" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.028561, "end_time": "2020-11-16T18:41:31.976144", "exception": false, "start_time": "2020-11-16T18:41:31.947583", "status": "completed" }, "tags": [] }, "source": [ "### Variance-Covariance Matrix" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "papermill": { "duration": 0.049078, "end_time": "2020-11-16T18:41:32.054432", "exception": false, "start_time": "2020-11-16T18:41:32.005354", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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Diatoms-1Diatoms-2CyanobacteriaCryptophytesPrasinophytesHaptophytesDictyochophytesDinoflagellatesRaphidophytesDDCPHTchlA (ug/L)mod_diatoms_chlmod_flagellates_chlmod_ciliates_chlmod_TChl
Diatoms-117.0302270.383433-0.014114-0.180451-0.176579-0.147258-0.0291330.1113242.29001817.413661-0.50428819.1959202.849766-0.5403110.0804262.389881
Diatoms-20.3834330.5173810.0048100.0198410.0082860.0130270.0272840.0085420.1919990.9008140.0411541.1678190.103589-0.119279-0.000527-0.016217
Cyanobacteria-0.0141140.0048100.0059010.0146560.0142360.0081160.0019190.0033710.107947-0.0093030.0370080.144586-0.0104560.0253730.0004910.015408
Cryptophytes-0.1804510.0198410.0146560.1757890.0726380.0552880.0046030.0173360.189807-0.1606100.3037150.3554110.0945520.1447000.0017500.241003
Prasinophytes-0.1765790.0082860.0142360.0726380.0756900.0300170.0059000.0047490.158524-0.1682930.1783450.184770-0.0657460.096499-0.0020150.028737
Haptophytes-0.1472580.0130270.0081160.0552880.0300170.1588610.002099-0.000856-0.017002-0.1342310.2441670.094285-0.0201530.1501410.0036960.133684
Dictyochophytes-0.0291330.0272840.0019190.0046030.0059000.0020990.0344870.0051540.040856-0.0018490.0126030.091620-0.0239150.018919-0.001405-0.006401
Dinoflagellates0.1113240.0085420.0033710.0173360.004749-0.0008560.0051540.1550560.2637920.1198660.0212290.5661880.0428760.0629300.0025410.108347
Raphidophytes2.2900180.1919990.1079470.1898070.158524-0.0170020.0408560.2637928.8990712.4820170.33132912.1078731.242441-0.1401060.0020731.104408
DD17.4136610.900814-0.009303-0.160610-0.168293-0.134231-0.0018490.1198662.48201718.314475-0.46313520.3637392.953355-0.6595900.0799002.373664
CPH-0.5042880.0411540.0370080.3037150.1783450.2441670.0126030.0212290.331329-0.4631350.7262270.6344660.0086530.3913400.0034310.403424
TchlA (ug/L)19.1959201.1678190.1445860.3554110.1847700.0942850.0916200.56618812.10787320.3637390.63446633.8858884.185330-0.3256190.0837263.943436
mod_diatoms_chl2.8497660.103589-0.0104560.094552-0.065746-0.020153-0.0239150.0428761.2424412.9533550.0086534.1853301.2052090.1600730.0764021.441683
mod_flagellates_chl-0.540311-0.1192790.0253730.1447000.0964990.1501410.0189190.062930-0.140106-0.6595900.391340-0.3256190.1600730.5994670.0642890.823829
mod_ciliates_chl0.080426-0.0005270.0004910.001750-0.0020150.003696-0.0014050.0025410.0020730.0799000.0034310.0837260.0764020.0642890.0226160.163307
mod_TChl2.389881-0.0162170.0154080.2410030.0287370.133684-0.0064010.1083471.1044082.3736640.4034243.9434361.4416830.8238290.1633072.428820
\n", "
" ], "text/plain": [ " Diatoms-1 Diatoms-2 Cyanobacteria Cryptophytes \\\n", "Diatoms-1 17.030227 0.383433 -0.014114 -0.180451 \n", "Diatoms-2 0.383433 0.517381 0.004810 0.019841 \n", "Cyanobacteria -0.014114 0.004810 0.005901 0.014656 \n", "Cryptophytes -0.180451 0.019841 0.014656 0.175789 \n", "Prasinophytes -0.176579 0.008286 0.014236 0.072638 \n", "Haptophytes -0.147258 0.013027 0.008116 0.055288 \n", "Dictyochophytes -0.029133 0.027284 0.001919 0.004603 \n", "Dinoflagellates 0.111324 0.008542 0.003371 0.017336 \n", "Raphidophytes 2.290018 0.191999 0.107947 0.189807 \n", "DD 17.413661 0.900814 -0.009303 -0.160610 \n", "CPH -0.504288 0.041154 0.037008 0.303715 \n", "TchlA (ug/L) 19.195920 1.167819 0.144586 0.355411 \n", "mod_diatoms_chl 2.849766 0.103589 -0.010456 0.094552 \n", "mod_flagellates_chl -0.540311 -0.119279 0.025373 0.144700 \n", "mod_ciliates_chl 0.080426 -0.000527 0.000491 0.001750 \n", "mod_TChl 2.389881 -0.016217 0.015408 0.241003 \n", "\n", " Prasinophytes Haptophytes Dictyochophytes \\\n", "Diatoms-1 -0.176579 -0.147258 -0.029133 \n", "Diatoms-2 0.008286 0.013027 0.027284 \n", "Cyanobacteria 0.014236 0.008116 0.001919 \n", "Cryptophytes 0.072638 0.055288 0.004603 \n", "Prasinophytes 0.075690 0.030017 0.005900 \n", "Haptophytes 0.030017 0.158861 0.002099 \n", "Dictyochophytes 0.005900 0.002099 0.034487 \n", "Dinoflagellates 0.004749 -0.000856 0.005154 \n", "Raphidophytes 0.158524 -0.017002 0.040856 \n", "DD -0.168293 -0.134231 -0.001849 \n", "CPH 0.178345 0.244167 0.012603 \n", "TchlA (ug/L) 0.184770 0.094285 0.091620 \n", "mod_diatoms_chl -0.065746 -0.020153 -0.023915 \n", "mod_flagellates_chl 0.096499 0.150141 0.018919 \n", "mod_ciliates_chl -0.002015 0.003696 -0.001405 \n", "mod_TChl 0.028737 0.133684 -0.006401 \n", "\n", " Dinoflagellates Raphidophytes DD CPH \\\n", "Diatoms-1 0.111324 2.290018 17.413661 -0.504288 \n", "Diatoms-2 0.008542 0.191999 0.900814 0.041154 \n", "Cyanobacteria 0.003371 0.107947 -0.009303 0.037008 \n", "Cryptophytes 0.017336 0.189807 -0.160610 0.303715 \n", "Prasinophytes 0.004749 0.158524 -0.168293 0.178345 \n", "Haptophytes -0.000856 -0.017002 -0.134231 0.244167 \n", "Dictyochophytes 0.005154 0.040856 -0.001849 0.012603 \n", "Dinoflagellates 0.155056 0.263792 0.119866 0.021229 \n", "Raphidophytes 0.263792 8.899071 2.482017 0.331329 \n", "DD 0.119866 2.482017 18.314475 -0.463135 \n", "CPH 0.021229 0.331329 -0.463135 0.726227 \n", "TchlA (ug/L) 0.566188 12.107873 20.363739 0.634466 \n", "mod_diatoms_chl 0.042876 1.242441 2.953355 0.008653 \n", "mod_flagellates_chl 0.062930 -0.140106 -0.659590 0.391340 \n", "mod_ciliates_chl 0.002541 0.002073 0.079900 0.003431 \n", "mod_TChl 0.108347 1.104408 2.373664 0.403424 \n", "\n", " TchlA (ug/L) mod_diatoms_chl mod_flagellates_chl \\\n", "Diatoms-1 19.195920 2.849766 -0.540311 \n", "Diatoms-2 1.167819 0.103589 -0.119279 \n", "Cyanobacteria 0.144586 -0.010456 0.025373 \n", "Cryptophytes 0.355411 0.094552 0.144700 \n", "Prasinophytes 0.184770 -0.065746 0.096499 \n", "Haptophytes 0.094285 -0.020153 0.150141 \n", "Dictyochophytes 0.091620 -0.023915 0.018919 \n", "Dinoflagellates 0.566188 0.042876 0.062930 \n", "Raphidophytes 12.107873 1.242441 -0.140106 \n", "DD 20.363739 2.953355 -0.659590 \n", "CPH 0.634466 0.008653 0.391340 \n", "TchlA (ug/L) 33.885888 4.185330 -0.325619 \n", "mod_diatoms_chl 4.185330 1.205209 0.160073 \n", "mod_flagellates_chl -0.325619 0.160073 0.599467 \n", "mod_ciliates_chl 0.083726 0.076402 0.064289 \n", "mod_TChl 3.943436 1.441683 0.823829 \n", "\n", " mod_ciliates_chl mod_TChl \n", "Diatoms-1 0.080426 2.389881 \n", "Diatoms-2 -0.000527 -0.016217 \n", "Cyanobacteria 0.000491 0.015408 \n", "Cryptophytes 0.001750 0.241003 \n", "Prasinophytes -0.002015 0.028737 \n", "Haptophytes 0.003696 0.133684 \n", "Dictyochophytes -0.001405 -0.006401 \n", "Dinoflagellates 0.002541 0.108347 \n", "Raphidophytes 0.002073 1.104408 \n", "DD 0.079900 2.373664 \n", "CPH 0.003431 0.403424 \n", "TchlA (ug/L) 0.083726 3.943436 \n", "mod_diatoms_chl 0.076402 1.441683 \n", "mod_flagellates_chl 0.064289 0.823829 \n", "mod_ciliates_chl 0.022616 0.163307 \n", "mod_TChl 0.163307 2.428820 " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfVars.cov()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### largest covariances:\n", "Model diatoms and:\n", "- TChlA: 4.185330\n", "- Diatoms-1+Diatoms-2: 2.953355\n", "- Diatoms-1: 2.849766\n", "- Raphidophytes: 1.242441\n", "\n", "Model flagellates and:\n", "- crypto+hapto+prasino: 0.391340\n", "- haptophytes: 0.150141\n", "- cryptophytes: 0.144700" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.030535, "end_time": "2020-11-16T18:41:32.395616", "exception": false, "start_time": "2020-11-16T18:41:32.365081", "status": "completed" }, "tags": [] }, "source": [ "### Corr Coeff matrix with log transformed values:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "papermill": { "duration": 0.047132, "end_time": "2020-11-16T18:41:32.473848", "exception": false, "start_time": "2020-11-16T18:41:32.426716", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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Diatoms-1Diatoms-2CyanobacteriaCryptophytesPrasinophytesHaptophytesDictyochophytesDinoflagellatesRaphidophytesCPHTchlA (ug/L)mod_diatoms_chlmod_flagellates_chlmod_ciliates_chlmod_TChl
Diatoms-11.0000000.158963-0.183906-0.200691-0.330748-0.178564-0.0227530.257098-0.044532-0.2406310.6207410.381332-0.1216850.0980880.158253
Diatoms-20.1589631.000000-0.070727-0.017724-0.081120-0.2069870.2608790.110120-0.069205-0.0956800.117311-0.054775-0.295843-0.045642-0.179840
Cyanobacteria-0.183906-0.0707271.0000000.3086770.3912660.3226850.3412060.0010060.3237200.3872150.114417-0.1773850.4238840.1623950.141110
Cryptophytes-0.200691-0.0177240.3086771.0000000.7050890.3284450.3373500.2566640.3837610.8722840.077971-0.0579140.2904010.0360450.120324
Prasinophytes-0.330748-0.0811200.3912660.7050891.0000000.2996010.4018700.1408070.4624230.698793-0.023818-0.2899770.3400490.0215570.023916
Haptophytes-0.178564-0.2069870.3226850.3284450.2996011.0000000.2042900.0472970.1890560.5766960.063931-0.1023670.2508560.0627340.100466
Dictyochophytes-0.0227530.2608790.3412060.3373500.4018700.2042901.0000000.2671180.3371070.3288080.131390-0.2290190.120689-0.086112-0.078591
Dinoflagellates0.2570980.1101200.0010060.2566640.1408070.0472970.2671181.0000000.3546250.2469110.4162440.1761410.1096160.0392430.135781
Raphidophytes-0.044532-0.0692050.3237200.3837610.4624230.1890560.3371070.3546251.0000000.3708690.227029-0.1027500.2872050.0397280.080884
CPH-0.240631-0.0956800.3872150.8722840.6987930.5766960.3288080.2469110.3708691.0000000.125576-0.0841750.3241590.0599140.130502
TchlA (ug/L)0.6207410.1173110.1144170.077971-0.0238180.0639310.1313900.4162440.2270290.1255761.0000000.4059820.1607210.2084560.367118
mod_diatoms_chl0.381332-0.054775-0.177385-0.057914-0.289977-0.102367-0.2290190.176141-0.102750-0.0841750.4059821.0000000.6743530.7490270.831184
mod_flagellates_chl-0.121685-0.2958430.4238840.2904010.3400490.2508560.1206890.1096160.2872050.3241590.1607210.6743531.0000000.9143870.947829
mod_ciliates_chl0.098088-0.0456420.1623950.0360450.0215570.062734-0.0861120.0392430.0397280.0599140.2084560.7490270.9143871.0000000.959662
mod_TChl0.158253-0.1798400.1411100.1203240.0239160.100466-0.0785910.1357810.0808840.1305020.3671180.8311840.9478290.9596621.000000
\n", "
" ], "text/plain": [ " Diatoms-1 Diatoms-2 Cyanobacteria Cryptophytes \\\n", "Diatoms-1 1.000000 0.158963 -0.183906 -0.200691 \n", "Diatoms-2 0.158963 1.000000 -0.070727 -0.017724 \n", "Cyanobacteria -0.183906 -0.070727 1.000000 0.308677 \n", "Cryptophytes -0.200691 -0.017724 0.308677 1.000000 \n", "Prasinophytes -0.330748 -0.081120 0.391266 0.705089 \n", "Haptophytes -0.178564 -0.206987 0.322685 0.328445 \n", "Dictyochophytes -0.022753 0.260879 0.341206 0.337350 \n", "Dinoflagellates 0.257098 0.110120 0.001006 0.256664 \n", "Raphidophytes -0.044532 -0.069205 0.323720 0.383761 \n", "CPH -0.240631 -0.095680 0.387215 0.872284 \n", "TchlA (ug/L) 0.620741 0.117311 0.114417 0.077971 \n", "mod_diatoms_chl 0.381332 -0.054775 -0.177385 -0.057914 \n", "mod_flagellates_chl -0.121685 -0.295843 0.423884 0.290401 \n", "mod_ciliates_chl 0.098088 -0.045642 0.162395 0.036045 \n", "mod_TChl 0.158253 -0.179840 0.141110 0.120324 \n", "\n", " Prasinophytes Haptophytes Dictyochophytes \\\n", "Diatoms-1 -0.330748 -0.178564 -0.022753 \n", "Diatoms-2 -0.081120 -0.206987 0.260879 \n", "Cyanobacteria 0.391266 0.322685 0.341206 \n", "Cryptophytes 0.705089 0.328445 0.337350 \n", "Prasinophytes 1.000000 0.299601 0.401870 \n", "Haptophytes 0.299601 1.000000 0.204290 \n", "Dictyochophytes 0.401870 0.204290 1.000000 \n", "Dinoflagellates 0.140807 0.047297 0.267118 \n", "Raphidophytes 0.462423 0.189056 0.337107 \n", "CPH 0.698793 0.576696 0.328808 \n", "TchlA (ug/L) -0.023818 0.063931 0.131390 \n", "mod_diatoms_chl -0.289977 -0.102367 -0.229019 \n", "mod_flagellates_chl 0.340049 0.250856 0.120689 \n", "mod_ciliates_chl 0.021557 0.062734 -0.086112 \n", "mod_TChl 0.023916 0.100466 -0.078591 \n", "\n", " Dinoflagellates Raphidophytes CPH TchlA (ug/L) \\\n", "Diatoms-1 0.257098 -0.044532 -0.240631 0.620741 \n", "Diatoms-2 0.110120 -0.069205 -0.095680 0.117311 \n", "Cyanobacteria 0.001006 0.323720 0.387215 0.114417 \n", "Cryptophytes 0.256664 0.383761 0.872284 0.077971 \n", "Prasinophytes 0.140807 0.462423 0.698793 -0.023818 \n", "Haptophytes 0.047297 0.189056 0.576696 0.063931 \n", "Dictyochophytes 0.267118 0.337107 0.328808 0.131390 \n", "Dinoflagellates 1.000000 0.354625 0.246911 0.416244 \n", "Raphidophytes 0.354625 1.000000 0.370869 0.227029 \n", "CPH 0.246911 0.370869 1.000000 0.125576 \n", "TchlA (ug/L) 0.416244 0.227029 0.125576 1.000000 \n", "mod_diatoms_chl 0.176141 -0.102750 -0.084175 0.405982 \n", "mod_flagellates_chl 0.109616 0.287205 0.324159 0.160721 \n", "mod_ciliates_chl 0.039243 0.039728 0.059914 0.208456 \n", "mod_TChl 0.135781 0.080884 0.130502 0.367118 \n", "\n", " mod_diatoms_chl mod_flagellates_chl mod_ciliates_chl \\\n", "Diatoms-1 0.381332 -0.121685 0.098088 \n", "Diatoms-2 -0.054775 -0.295843 -0.045642 \n", "Cyanobacteria -0.177385 0.423884 0.162395 \n", "Cryptophytes -0.057914 0.290401 0.036045 \n", "Prasinophytes -0.289977 0.340049 0.021557 \n", "Haptophytes -0.102367 0.250856 0.062734 \n", "Dictyochophytes -0.229019 0.120689 -0.086112 \n", "Dinoflagellates 0.176141 0.109616 0.039243 \n", "Raphidophytes -0.102750 0.287205 0.039728 \n", "CPH -0.084175 0.324159 0.059914 \n", "TchlA (ug/L) 0.405982 0.160721 0.208456 \n", "mod_diatoms_chl 1.000000 0.674353 0.749027 \n", "mod_flagellates_chl 0.674353 1.000000 0.914387 \n", "mod_ciliates_chl 0.749027 0.914387 1.000000 \n", "mod_TChl 0.831184 0.947829 0.959662 \n", "\n", " mod_TChl \n", "Diatoms-1 0.158253 \n", "Diatoms-2 -0.179840 \n", "Cyanobacteria 0.141110 \n", "Cryptophytes 0.120324 \n", "Prasinophytes 0.023916 \n", "Haptophytes 0.100466 \n", "Dictyochophytes -0.078591 \n", "Dinoflagellates 0.135781 \n", "Raphidophytes 0.080884 \n", "CPH 0.130502 \n", "TchlA (ug/L) 0.367118 \n", "mod_diatoms_chl 0.831184 \n", "mod_flagellates_chl 0.947829 \n", "mod_ciliates_chl 0.959662 \n", "mod_TChl 1.000000 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflog=pd.DataFrame()\n", "for el in ['Diatoms-1', 'Diatoms-2','Cyanobacteria','Cryptophytes', 'Prasinophytes', \n", " 'Haptophytes', 'Dictyochophytes','Dinoflagellates','Raphidophytes','CPH','TchlA (ug/L)',\n", " 'mod_diatoms_chl','mod_flagellates_chl','mod_ciliates_chl','mod_TChl']:\n", " dflog[el]=logt(data[el])\n", "dflog.corr()" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.030079, "end_time": "2020-11-16T18:41:32.247688", "exception": false, "start_time": "2020-11-16T18:41:32.217609", "status": "completed" }, "tags": [] }, "source": [ "### Cov matrix with log transformed values:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "papermill": { "duration": 0.056303, "end_time": "2020-11-16T18:41:32.334217", "exception": false, "start_time": "2020-11-16T18:41:32.277914", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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Diatoms-1Diatoms-2CyanobacteriaCryptophytesPrasinophytesHaptophytesDictyochophytesDinoflagellatesRaphidophytesCPHTchlA (ug/L)mod_diatoms_chlmod_flagellates_chlmod_ciliates_chlmod_TChl
Diatoms-11.7070490.223426-0.191222-0.156507-0.337293-0.224417-0.0229760.263191-0.054699-0.1696670.3648060.476603-0.0848670.0389200.104432
Diatoms-20.2234261.157251-0.060550-0.011380-0.068113-0.2141880.2169060.092818-0.069990-0.0555470.056765-0.056367-0.169885-0.014911-0.097714
Cyanobacteria-0.191222-0.0605500.6333470.1466250.2430420.2470240.2098720.0006280.2421990.1663020.040958-0.1350420.1800730.0392490.056720
Cryptophytes-0.156507-0.0113800.1466250.3562590.3284850.1885750.1556260.1200320.2153410.2809740.020934-0.0330670.0925250.0065340.036274
Prasinophytes-0.337293-0.0681130.2430420.3284850.6092230.2249420.2424330.0861120.3393210.294348-0.008362-0.2165120.1416800.0051100.009428
Haptophytes-0.224417-0.2141880.2470240.1885750.2249420.9252890.1518810.0356470.1709670.2993720.027662-0.0941950.1288080.0183260.048811
Dictyochophytes-0.0229760.2169060.2098720.1556260.2424330.1518810.5973600.1617600.2449450.1371470.045678-0.1693240.049793-0.020213-0.030679
Dinoflagellates0.2631910.0928180.0006280.1200320.0861120.0356470.1617600.6139040.2612170.1044030.1466990.1320200.0458460.0093380.053734
Raphidophytes-0.054699-0.0699900.2421990.2153410.3393210.1709670.2449450.2612170.8838230.1881600.096005-0.0924040.1441300.0113430.038407
CPH-0.169667-0.0555470.1663020.2809740.2943480.2993720.1371470.1044030.1881600.2912390.030483-0.0434550.0933820.0098200.035571
TchlA (ug/L)0.3648060.0567650.0409580.020934-0.0083620.0276620.0456780.1466990.0960050.0304830.2023290.1746890.0385900.0284760.083405
mod_diatoms_chl0.476603-0.056367-0.135042-0.033067-0.216512-0.094195-0.1693240.132020-0.092404-0.0434550.1746890.9967900.5479350.5286310.718548
mod_flagellates_chl-0.084867-0.1698850.1800730.0925250.1416800.1288080.0497930.0458460.1441300.0933820.0385900.5479350.6623370.5260450.667923
mod_ciliates_chl0.038920-0.0149110.0392490.0065340.0051100.018326-0.0202130.0093380.0113430.0098200.0284760.5286310.5260450.4996970.587392
mod_TChl0.104432-0.0977140.0567200.0362740.0094280.048811-0.0306790.0537340.0384070.0355710.0834050.7185480.6679230.5873920.749744
\n", "
" ], "text/plain": [ " Diatoms-1 Diatoms-2 Cyanobacteria Cryptophytes \\\n", "Diatoms-1 1.707049 0.223426 -0.191222 -0.156507 \n", "Diatoms-2 0.223426 1.157251 -0.060550 -0.011380 \n", "Cyanobacteria -0.191222 -0.060550 0.633347 0.146625 \n", "Cryptophytes -0.156507 -0.011380 0.146625 0.356259 \n", "Prasinophytes -0.337293 -0.068113 0.243042 0.328485 \n", "Haptophytes -0.224417 -0.214188 0.247024 0.188575 \n", "Dictyochophytes -0.022976 0.216906 0.209872 0.155626 \n", "Dinoflagellates 0.263191 0.092818 0.000628 0.120032 \n", "Raphidophytes -0.054699 -0.069990 0.242199 0.215341 \n", "CPH -0.169667 -0.055547 0.166302 0.280974 \n", "TchlA (ug/L) 0.364806 0.056765 0.040958 0.020934 \n", "mod_diatoms_chl 0.476603 -0.056367 -0.135042 -0.033067 \n", "mod_flagellates_chl -0.084867 -0.169885 0.180073 0.092525 \n", "mod_ciliates_chl 0.038920 -0.014911 0.039249 0.006534 \n", "mod_TChl 0.104432 -0.097714 0.056720 0.036274 \n", "\n", " Prasinophytes Haptophytes Dictyochophytes \\\n", "Diatoms-1 -0.337293 -0.224417 -0.022976 \n", "Diatoms-2 -0.068113 -0.214188 0.216906 \n", "Cyanobacteria 0.243042 0.247024 0.209872 \n", "Cryptophytes 0.328485 0.188575 0.155626 \n", "Prasinophytes 0.609223 0.224942 0.242433 \n", "Haptophytes 0.224942 0.925289 0.151881 \n", "Dictyochophytes 0.242433 0.151881 0.597360 \n", "Dinoflagellates 0.086112 0.035647 0.161760 \n", "Raphidophytes 0.339321 0.170967 0.244945 \n", "CPH 0.294348 0.299372 0.137147 \n", "TchlA (ug/L) -0.008362 0.027662 0.045678 \n", "mod_diatoms_chl -0.216512 -0.094195 -0.169324 \n", "mod_flagellates_chl 0.141680 0.128808 0.049793 \n", "mod_ciliates_chl 0.005110 0.018326 -0.020213 \n", "mod_TChl 0.009428 0.048811 -0.030679 \n", "\n", " Dinoflagellates Raphidophytes CPH TchlA (ug/L) \\\n", "Diatoms-1 0.263191 -0.054699 -0.169667 0.364806 \n", "Diatoms-2 0.092818 -0.069990 -0.055547 0.056765 \n", "Cyanobacteria 0.000628 0.242199 0.166302 0.040958 \n", "Cryptophytes 0.120032 0.215341 0.280974 0.020934 \n", "Prasinophytes 0.086112 0.339321 0.294348 -0.008362 \n", "Haptophytes 0.035647 0.170967 0.299372 0.027662 \n", "Dictyochophytes 0.161760 0.244945 0.137147 0.045678 \n", "Dinoflagellates 0.613904 0.261217 0.104403 0.146699 \n", "Raphidophytes 0.261217 0.883823 0.188160 0.096005 \n", "CPH 0.104403 0.188160 0.291239 0.030483 \n", "TchlA (ug/L) 0.146699 0.096005 0.030483 0.202329 \n", "mod_diatoms_chl 0.132020 -0.092404 -0.043455 0.174689 \n", "mod_flagellates_chl 0.045846 0.144130 0.093382 0.038590 \n", "mod_ciliates_chl 0.009338 0.011343 0.009820 0.028476 \n", "mod_TChl 0.053734 0.038407 0.035571 0.083405 \n", "\n", " mod_diatoms_chl mod_flagellates_chl mod_ciliates_chl \\\n", "Diatoms-1 0.476603 -0.084867 0.038920 \n", "Diatoms-2 -0.056367 -0.169885 -0.014911 \n", "Cyanobacteria -0.135042 0.180073 0.039249 \n", "Cryptophytes -0.033067 0.092525 0.006534 \n", "Prasinophytes -0.216512 0.141680 0.005110 \n", "Haptophytes -0.094195 0.128808 0.018326 \n", "Dictyochophytes -0.169324 0.049793 -0.020213 \n", "Dinoflagellates 0.132020 0.045846 0.009338 \n", "Raphidophytes -0.092404 0.144130 0.011343 \n", "CPH -0.043455 0.093382 0.009820 \n", "TchlA (ug/L) 0.174689 0.038590 0.028476 \n", "mod_diatoms_chl 0.996790 0.547935 0.528631 \n", "mod_flagellates_chl 0.547935 0.662337 0.526045 \n", "mod_ciliates_chl 0.528631 0.526045 0.499697 \n", "mod_TChl 0.718548 0.667923 0.587392 \n", "\n", " mod_TChl \n", "Diatoms-1 0.104432 \n", "Diatoms-2 -0.097714 \n", "Cyanobacteria 0.056720 \n", "Cryptophytes 0.036274 \n", "Prasinophytes 0.009428 \n", "Haptophytes 0.048811 \n", "Dictyochophytes -0.030679 \n", "Dinoflagellates 0.053734 \n", "Raphidophytes 0.038407 \n", "CPH 0.035571 \n", "TchlA (ug/L) 0.083405 \n", "mod_diatoms_chl 0.718548 \n", "mod_flagellates_chl 0.667923 \n", "mod_ciliates_chl 0.587392 \n", "mod_TChl 0.749744 " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "dflog.cov()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Individual phytoplankton groups compared to model groups (1:1 correspondence not expected)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.001584893192461114, 10, 'r = 0.33')" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,ax=plt.subplots(1,4,figsize=(20,4))\n", "fig.subplots_adjust(wspace=.3)\n", "#ax[0].plot(logt(data['Diatoms-1']+data['Raphidophytes']+.5*data['Cryptophytes']),logt(data['mod_diatoms']),'r.')\n", "ax[0].plot((data['Diatoms-1']),(data['mod_diatoms']),'g.',ms=5)\n", "ax[0].set_ylabel('Model Diatoms \\n log10 Chl(mg m$^{-3}$)+.001',fontsize=14)\n", "ax[0].set_xlabel('Diatoms-1 \\n log10 Chl(mg m$^{-3}$)+.001',fontsize=14)\n", "ax[0].set_title('')\n", "ax[0].plot((10**-3,10**1.5),(10**-3,10**1.5),'k-',alpha=.2)\n", "ax[0].set_xlim((10**-3,10**1.5))\n", "ax[0].set_ylim((10**-3,10**1.5))\n", "ax[0].set_aspect(1)\n", "ax[0].set_xscale('log')\n", "ax[0].set_yscale('log')\n", "ax[0].xaxis.set_tick_params(labelsize=12)\n", "ax[0].yaxis.set_tick_params(labelsize=12)\n", "ax[0].text(10**-2.8,10**1, 'r = 0.33', fontsize=12, color='k')\n", "\n", "ax[1].plot((data['Diatoms-2']),(data['mod_diatoms']),'g.',ms=5)\n", "ax[1].set_ylabel('')\n", "ax[1].set_xlabel('Diatoms-2 \\n log10 Chl(mg m$^{-3}$)+.001',fontsize=14)\n", "ax[1].plot((10**-3,10**1.5),(10**-3,10**1.5),'k-',alpha=.2)\n", "ax[1].set_xlim((10**-3,10**1.5))\n", "ax[1].set_ylim((10**-3,10**1.5))\n", "ax[1].set_aspect(1)\n", "ax[1].set_xscale('log')\n", "ax[1].set_yscale('log')\n", "ax[1].xaxis.set_tick_params(labelsize=12)\n", "ax[1].yaxis.set_tick_params(labelsize=12)\n", "ax[1].text(10**-2.8,10**1, 'r = 0.07', fontsize=12, color='k')\n", "\n", "ax[2].plot((data['Raphidophytes']),(data['mod_diatoms']),'g.',ms=5)\n", "ax[2].set_ylabel('')\n", "ax[2].set_xlabel('Raphidophytes \\n log10 Chl(mg m$^{-3}$)+.001',fontsize=14)\n", "ax[2].set_title('')\n", "ax[2].plot((10**-3,10**1.5),(10**-3,10**1.5),'k-',alpha=.2)\n", "ax[2].set_xlim((10**-3,10**1.5))\n", "ax[2].set_ylim((10**-3,10**1.5))\n", "ax[2].set_aspect(1)\n", "ax[2].set_xscale('log')\n", "ax[2].set_yscale('log')\n", "ax[2].xaxis.set_tick_params(labelsize=12)\n", "ax[2].yaxis.set_tick_params(labelsize=12)\n", "ax[2].text(10**-2.8,10**1, 'r = 0.20', fontsize=12, color='k')\n", "\n", "ax[3].plot((data['Diatoms-1']+data['Diatoms-2']),(data['mod_diatoms']),'g.',ms=5)\n", "ax[3].set_ylabel('')\n", "ax[3].set_xlabel('Diatoms-1 + Diatoms-2 \\n log10 Chl(mg m$^{-3}$)+.001',fontsize=14)\n", "ax[3].set_title('')\n", "ax[3].plot((10**-3,10**1.5),(10**-3,10**1.5),'k-',alpha=.2)\n", "ax[3].set_xlim((10**-3,10**1.5))\n", "ax[3].set_ylim((10**-3,10**1.5))\n", "ax[3].set_aspect(1)\n", "ax[3].set_xscale('log')\n", "ax[3].set_yscale('log')\n", "ax[3].xaxis.set_tick_params(labelsize=12)\n", "ax[3].yaxis.set_tick_params(labelsize=12)\n", "ax[3].text(10**-2.8,10**1, 'r = 0.33', fontsize=12, color='k')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.001584893192461114, 10, 'r = 0.38')" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,ax=plt.subplots(1,4,figsize=(20,4))\n", "fig.subplots_adjust(wspace=.3)\n", "#ax[0].plot(logt(data['Diatoms-1']+data['Raphidophytes']+.5*data['Cryptophytes']),logt(data['mod_diatoms']),'r.')\n", "ax[0].plot((data['Cryptophytes']),(data['mod_flagellates']),'m.',ms=5)\n", "ax[0].set_ylabel('Model Nanoflagellates \\n log10 Chl(mg m$^{-3}$)+.001',fontsize=14)\n", "ax[0].set_xlabel('Cryptophytes \\n log10 Chl(mg m$^{-3}$)+.001',fontsize=14)\n", "ax[0].set_title('')\n", "ax[0].plot((10**-3,10**1.5),(10**-3,10**1.5),'k-',alpha=.2)\n", "ax[0].set_xlim((10**-3,10**1.5))\n", "ax[0].set_ylim((10**-3,10**1.5))\n", "ax[0].set_aspect(1)\n", "ax[0].set_xscale('log')\n", "ax[0].set_yscale('log')\n", "ax[0].xaxis.set_tick_params(labelsize=12)\n", "ax[0].yaxis.set_tick_params(labelsize=12)\n", "ax[0].text(10**-2.8,10**1, 'r = 0.28', fontsize=12, color='k')\n", "\n", "ax[1].plot((data['Prasinophytes']),(data['mod_flagellates']),'m.',ms=5)\n", "ax[1].set_ylabel('')\n", "ax[1].set_xlabel('Prasinophytes \\n log10 Chl(mg m$^{-3}$)+.001',fontsize=14)\n", "ax[1].plot((10**-3,10**1.5),(10**-3,10**1.5),'k-',alpha=.2)\n", "ax[1].set_xlim((10**-3,10**1.5))\n", "ax[1].set_ylim((10**-3,10**1.5))\n", "ax[1].set_aspect(1)\n", "ax[1].set_xscale('log')\n", "ax[1].set_yscale('log')\n", "ax[1].xaxis.set_tick_params(labelsize=12)\n", "ax[1].yaxis.set_tick_params(labelsize=12)\n", "ax[1].text(10**-2.8,10**1, 'r = 0.29', fontsize=12, color='k')\n", "\n", "ax[2].plot((data['Haptophytes']),(data['mod_flagellates']),'m.',ms=5)\n", "ax[2].set_ylabel('')\n", "ax[2].set_xlabel('Haptophytes \\n log10 Chl(mg m$^{-3}$)+.001',fontsize=14)\n", "ax[2].set_title('')\n", "ax[2].plot((10**-3,10**1.5),(10**-3,10**1.5),'k-',alpha=.2)\n", "ax[2].set_xlim((10**-3,10**1.5))\n", "ax[2].set_ylim((10**-3,10**1.5))\n", "ax[2].set_aspect(1)\n", "ax[2].set_xscale('log')\n", "ax[2].set_yscale('log')\n", "ax[2].xaxis.set_tick_params(labelsize=12)\n", "ax[2].yaxis.set_tick_params(labelsize=12)\n", "ax[2].text(10**-2.8,10**1, 'r = 0.31', fontsize=12, color='k')\n", "\n", "ax[3].plot((data['Cryptophytes']+data['Prasinophytes']+data['Haptophytes']),(data['mod_flagellates']),'m.',ms=5)\n", "ax[3].set_ylabel('')\n", "ax[3].set_xlabel('Crypto + Prasino + Hapto \\n log10 Chl(mg m$^{-3}$)+.001',fontsize=14)\n", "ax[3].set_title('')\n", "ax[3].plot((10**-3,10**1.5),(10**-3,10**1.5),'k-',alpha=.2)\n", "ax[3].set_xlim((10**-3,10**1.5))\n", "ax[3].set_ylim((10**-3,10**1.5))\n", "ax[3].set_aspect(1)\n", "ax[3].set_xscale('log')\n", "ax[3].set_yscale('log')\n", "ax[3].xaxis.set_tick_params(labelsize=12)\n", "ax[3].yaxis.set_tick_params(labelsize=12)\n", "ax[3].text(10**-2.8,10**1, 'r = 0.38', fontsize=12, color='k')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { "display_name": "Python (py39)", "language": "python", "name": "py39" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.15" }, "papermill": { "duration": 11.766062, "end_time": "2020-11-16T18:41:35.052382", "environment_variables": {}, "exception": null, "input_path": "compHPLCModelFirstLook-Regress-Base.ipynb", "output_path": "compHPLCModelFirstLook-Regress-201812.ipynb", "parameters": { "Chl_N": 1.8, "fname": "compHPLCModelFirstLook-Regress-201812.ipynb", "modSourceDir": "/results/SalishSea/nowcast-green.201812/", "modver": "201812" }, "start_time": "2020-11-16T18:41:23.286320", "version": "2.0.0" } }, "nbformat": 4, "nbformat_minor": 4 }