In [1]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime as dt
from salishsea_tools import evaltools as et, viz_tools
import os
import datetime as dt
import gsw
import matplotlib.gridspec as gridspec
import matplotlib as mpl
import matplotlib.dates as mdates
import cmocean as cmo
import scipy.interpolate as sinterp
import cmocean
import json
import f90nml
from collections import OrderedDict

fs=16
mpl.rc('xtick', labelsize=fs)
mpl.rc('ytick', labelsize=fs)
mpl.rc('legend', fontsize=fs)
mpl.rc('axes', titlesize=fs)
mpl.rc('axes', labelsize=fs)
mpl.rc('figure', titlesize=fs)
mpl.rc('font', size=fs)
mpl.rc('font', family='sans-serif', weight='normal', style='normal')

import warnings
#warnings.filterwarnings('ignore')
from IPython.display import Markdown, display

%matplotlib inline

Import Puget Sound data and create dataframe¶

In [2]:

df=pd.read_excel('/ocean/ksuchy/MOAD/observe/SSMSP_TransBoundary_Extract_2014-2019_Verticals_Keister_Lab.xlsx',engine='openpyxl',sheet_name='SSMSP 2014-2019 Density&Biomass')

In [3]:

df

Out[3]:

	BugSampleID	Project	Sample Code	Sampling Group	Station	Site Name	Basin	Sub Basin	Latitude	Longitude	...	Ind Carbon (ug)	Density (#/m3)	Final Carbon (mg/m3)	Species Size Category Aggregation	Size Category Aggregation	Collection Confidence	Carbon Regression EQ	C to DW multipier	DW Regression Equation	C/DW Source
0	93945	SSMSP	060817HCB004V1454	HCSEG	HCB004V	Sisters Point	Hood Canal	S Hood Canal	47.35620	-123.0249	...	122.333000	5.169013	3.185477	Amphipod	Amphipod	High	C(ug)=(0.0049([Length(mm)])^2.957)1000)*37%	C = DW * 37%	DW(ug)=0.0049([Length(mm)])^2.957)1000	Ikeda & Shiga, 1999
1	93946	SSMSP	060817HCB004V1454	HCSEG	HCB004V	Sisters Point	Hood Canal	S Hood Canal	47.35620	-123.0249	...	107.303200	0.068013	0.010533	Amphipod	Amphipod	High	C(ug)=(0.0049([Length(mm)])^2.957)1000)*37%	C = DW * 37%	DW(ug)=0.0049([Length(mm)])^2.957)1000	Ikeda & Shiga, 1999
2	93962	SSMSP	060817HCB004V1454	HCSEG	HCB004V	Sisters Point	Hood Canal	S Hood Canal	47.35620	-123.0249	...	17.764660	21.764267	0.397324	Amphipod	Amphipod	High	C(ug)=(0.0049([Length(mm)])^2.957)1000)*37%	C = DW * 37%	DW(ug)=0.0049([Length(mm)])^2.957)1000	Ikeda & Shiga, 1999
3	94018	SSMSP	030917HCB003V1218	HCSEG	HCB003V	Eldon	Hood Canal	S Hood Canal	47.53787	-123.0096	...	343.152100	1.730036	0.564992	Amphipod	Amphipod	High	C(ug)=((0.02 * [Length (mm)]^2.1)1000)37%	C = DW * 37%	DW(μg) = (0.02 * [Length(mm)]^2.1)*1000	Kafanov & Fedotov, 1982
4	94030	SSMSP	030917HCB003V1218	HCSEG	HCB003V	Eldon	Hood Canal	S Hood Canal	47.53787	-123.0096	...	17.764660	0.812593	0.090677	Amphipod	Amphipod	High	C(ug)=(0.0049([Length(mm)])^2.957)1000)*37%	C = DW * 37%	DW(ug)=0.0049([Length(mm)])^2.957)1000	Ikeda & Shiga, 1999
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
81582	31509	SSMSP	050615MUKV1200	TUL	MUKV	Mukilteo	Whidbey Basin	S Whidbey Basin	47.97166	-122.3222	...	7.450710	98.804469	0.252324	Unknown Egg	Unknown Egg	Moderate	C (ug) = 8.0935*[Length (mm)]-1.0883	C (ug) = 8.0935*[Length (mm)]-1.0883	NONE	Keister Lab estimate, 2016
81583	38434	SSMSP	081315MUKV1121	TUL	MUKV	Mukilteo	Whidbey Basin	S Whidbey Basin	47.97166	-122.3222	...	7.450710	8.634707	0.011568	Unknown Egg	Unknown Egg	Moderate	C (ug) = 8.0935*[Length (mm)]-1.0883	C (ug) = 8.0935*[Length (mm)]-1.0883	NONE	Keister Lab estimate, 2016
81584	38675	SSMSP	082615MUKV1116	TUL	MUKV	Mukilteo	Whidbey Basin	S Whidbey Basin	47.97166	-122.3222	...	2.868051	27.045302	0.077567	Unknown Egg	Unknown Egg	Moderate	C (ug) = 8.0935*[Length (mm)]-1.0883	C (ug) = 8.0935*[Length (mm)]-1.0883	NONE	Keister Lab estimate, 2016
81585	39535	SSMSP	050615CAMV1305	TUL	CAMV	Camano Head	Whidbey Basin	S Whidbey Basin	48.05901	-122.3873	...	7.450710	43.628610	0.325064	Unknown Egg	Unknown Egg	Moderate	C (ug) = 8.0935*[Length (mm)]-1.0883	C (ug) = 8.0935*[Length (mm)]-1.0883	NONE	Keister Lab estimate, 2016
81586	104254	SSMSP	080318HCB004V1114	HCSEG	HCB004V	Sisters Point	Hood Canal	S Hood Canal	47.35620	-123.0249	...	0.200000	4.105091	0.004105	Unknown Nauplius	Unknown Nauplius	High	NONE	NONE	NONE	Keister Lab estimate, 2016

81587 rows × 38 columns

In [4]:

df.keys()

Out[4]:

Index(['BugSampleID', 'Project', 'Sample Code', 'Sampling Group', 'Station',
       'Site Name', 'Basin', 'Sub Basin', 'Latitude', 'Longitude',
       'Date Category', 'Sample Date', 'Sample Year', 'Sample Month',
       'Sample Time', 'Tow Type', 'Mesh Size', 'Diameter (cm)',
       'Station Depth (m)', 'Max Tow Depth (m)', 'Min Tow Depth (m)',
       'Vol Filtered (m3)', 'Broad Group', 'Mid-Level Group', '1st Word Taxa',
       'Genus species', 'Life History Stage', 'Total Ct', 'Ind Carbon (ug)',
       'Density (#/m3)', 'Final Carbon (mg/m3)',
       'Species Size Category Aggregation', 'Size Category Aggregation',
       'Collection Confidence', 'Carbon Regression EQ', 'C to DW multipier',
       'DW Regression Equation', 'C/DW Source'],
      dtype='object')

In [6]:

df.drop(columns=['BugSampleID','Project','Sampling Group','Date Category','Tow Type','Species Size Category Aggregation','Collection Confidence','Carbon Regression EQ','C to DW multipier','DW Regression Equation','C/DW Source'
                       ],inplace=True)

In [7]:

df

Out[7]:

	Sample Code	Station	Site Name	Basin	Sub Basin	Latitude	Longitude	Sample Date	Sample Year	Sample Month	...	Broad Group	Mid-Level Group	1st Word Taxa	Genus species	Life History Stage	Total Ct	Ind Carbon (ug)	Density (#/m3)	Final Carbon (mg/m3)	Size Category Aggregation
0	060817HCB004V1454	HCB004V	Sisters Point	Hood Canal	S Hood Canal	47.35620	-123.0249	2017-06-08	2017	JUN	...	Amphipod	Amphipoda-Hyperiidea	HYPEROCHE	HYPEROCHE	Unknown	76.0	122.333000	5.169013	3.185477	Amphipod
1	060817HCB004V1454	HCB004V	Sisters Point	Hood Canal	S Hood Canal	47.35620	-123.0249	2017-06-08	2017	JUN	...	Amphipod	Amphipoda-Hyperiidea	HYPERIA	HYPERIA	Unknown	1.0	107.303200	0.068013	0.010533	Amphipod
2	060817HCB004V1454	HCB004V	Sisters Point	Hood Canal	S Hood Canal	47.35620	-123.0249	2017-06-08	2017	JUN	...	Amphipod	Amphipoda-Hyperiidea	THEMISTO	THEMISTO PACIFICA	Unknown	320.0	17.764660	21.764267	0.397324	Amphipod
3	030917HCB003V1218	HCB003V	Eldon	Hood Canal	S Hood Canal	47.53787	-123.0096	2017-03-09	2017	MAR	...	Amphipod	Amphipoda-Cyphocarididae	CYPHOCARIS	CYPHOCARIS CHALLENGERI	Unknown	66.0	343.152100	1.730036	0.564992	Amphipod
4	030917HCB003V1218	HCB003V	Eldon	Hood Canal	S Hood Canal	47.53787	-123.0096	2017-03-09	2017	MAR	...	Amphipod	Amphipoda-Hyperiidea	THEMISTO	THEMISTO PACIFICA	Unknown	31.0	17.764660	0.812593	0.090677	Amphipod
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
81582	050615MUKV1200	MUKV	Mukilteo	Whidbey Basin	S Whidbey Basin	47.97166	-122.3222	2015-05-06	2015	MAY	...	Unknown Egg	Unknown	UNKNOWN	UNKNOWN	EGG Lg	4500.0	7.450710	98.804469	0.252324	Unknown Egg
81583	081315MUKV1121	MUKV	Mukilteo	Whidbey Basin	S Whidbey Basin	47.97166	-122.3222	2015-08-13	2015	AUG	...	Unknown Egg	Unknown	UNKNOWN	UNKNOWN	EGG Lg	385.0	7.450710	8.634707	0.011568	Unknown Egg
81584	082615MUKV1116	MUKV	Mukilteo	Whidbey Basin	S Whidbey Basin	47.97166	-122.3222	2015-08-26	2015	SEP	...	Unknown Egg	Unknown	UNKNOWN	UNKNOWN	EGG Med	1000.0	2.868051	27.045302	0.077567	Unknown Egg
81585	050615CAMV1305	CAMV	Camano Head	Whidbey Basin	S Whidbey Basin	48.05901	-122.3873	2015-05-06	2015	MAY	...	Unknown Egg	Unknown	UNKNOWN	UNKNOWN	EGG Lg	2025.0	7.450710	43.628610	0.325064	Unknown Egg
81586	080318HCB004V1114	HCB004V	Sisters Point	Hood Canal	S Hood Canal	47.35620	-123.0249	2018-08-03	2018	AUG	...	Unknown Nauplius	Unknown	UNKNOWN	UNKNOWN	Nauplius	50.0	0.200000	4.105091	0.004105	Unknown Nauplius

81587 rows × 27 columns

In [8]:

df.keys()

Out[8]:

Index(['Sample Code', 'Station', 'Site Name', 'Basin', 'Sub Basin', 'Latitude',
       'Longitude', 'Sample Date', 'Sample Year', 'Sample Month',
       'Sample Time', 'Mesh Size', 'Diameter (cm)', 'Station Depth (m)',
       'Max Tow Depth (m)', 'Min Tow Depth (m)', 'Vol Filtered (m3)',
       'Broad Group', 'Mid-Level Group', '1st Word Taxa', 'Genus species',
       'Life History Stage', 'Total Ct', 'Ind Carbon (ug)', 'Density (#/m3)',
       'Final Carbon (mg/m3)', 'Size Category Aggregation'],
      dtype='object')

In [9]:

df['Broad Group'].unique()

Out[9]:

array(['Amphipod', 'Amphipod Egg', 'Anthozoan', 'Arachnida', 'Barnacles',
       'Bivalve', 'Bryozoan', 'Cephalopod', 'Chaetognath',
       'Chaetognath Egg', 'Cladocera', 'Copepod', 'Copepod Egg',
       'Copepod Nauplius', 'Crabs', 'Ctenophore', 'Cumacea',
       'Dinoflagellate', 'Echinoderm', 'Fish', 'Fish Egg', 'Gastropod',
       'Hydrozoan', 'Insect', 'Isopod', 'Krill Egg', 'Krill Nauplius',
       'Krill-Adult Juvenile', 'Krill-Calyptopis', 'Krill-Furcilia',
       'Larvacea', 'Mollusk Egg', 'Mysid', 'Mysid Egg', 'Nematode',
       'Nemertea', 'Nudibranch', 'Ostracod', 'Phoronid',
       'Platyhelminthes', 'Polychaete', 'Pycnogonida', 'Scyphozoan',
       'Shrimp', 'Siphonophore', 'Siphonophore Gonophore', 'Tunicate',
       'Tunicate Egg', 'Unknown', 'Unknown Egg', 'Unknown Nauplius'],
      dtype=object)

In [10]:

colListBroad=('Amphipoda', 'Crabs','Krill-Adult Juvenile','Krill-Calyptopis','Krill-Furcilia','Copepod', 'Larvacea',)

In [39]:

colListGenus=('METRIDIA PACIFICA','NEOCALANUS PLUMCHRUS','CALANOIDA','EUCALANUS BUNGII','CALANUS PACIFICUS','CALANUS MARSHALLAE','NEOCALANUS CRISTATUS','EUCALANUS CALIFORNICUS','EUCALANUS','METRIDIIDAE','NEOCALANUS')

In [11]:

#l1=set(df['order'].values)
#l2=set(df['genus species'].values)
#l3=l1|l2

In [12]:

df['Genus species'].unique()

Out[12]:

array(['HYPEROCHE', 'HYPERIA', 'THEMISTO PACIFICA',
       'CYPHOCARIS CHALLENGERI', 'PRIMNO MACROPA', 'CALLIOPIUS PACIFICUS',
       'HYPERIIDEA', 'GAMMARIDEA', 'AMPHIPODA', 'COROPHIUM',
       'CAPRELLIDAE', 'CALLIOPIUS CARINATUS', 'PARAPHRONIMA CRASSIPES',
       'Anthozoa', 'PEACHIA', 'MITES', 'Arachnidae', 'BARNACLES',
       'BIVALVIA', 'BRYOZOA', 'OCTOPODA', 'Teuthida', 'CHAETOGNATHA',
       'Chaet/Euphaus Egg', 'EVADNE', 'PODON', 'Evadne', 'CLADOCERA',
       'ACARTIA HUDSONICA', 'ACARTIA LONGIREMIS', 'AETIDEUS',
       'CENTROPAGES ABDOMINALIS', 'DITRICHOCORYCAEUS ANGLICUS',
       'METRIDIA PACIFICA', 'MICROCALANUS', 'OITHONA SIMILIS',
       'PARACALANUS', 'PSEUDOCALANUS', 'SCOLECITHRICELLA MINOR',
       'PARAEUCHAETA ELONGATA', 'CANDACIA COLUMBIAE',
       'CHIRIDIUS GRACILIS', 'OITHONA ATLANTICA', 'PSEUDOCALANUS NEWMANI',
       'PSEUDOCALANUS MOULTONI', 'PSEUDOCALANUS Sm', 'THARYBIS FULTONI',
       'TRICONIA MINUTA', 'Monstrilloida', 'HARPACTICOIDA',
       'GAETANUS PUNGENS', 'NEOCALANUS PLUMCHRUS', 'CALANOIDA',
       'TRICONIA CONIFERA', 'EUCALANUS BUNGII', 'TRICONIA BOREALIS',
       'OITHONA', 'CALANUS PACIFICUS', 'CALANUS MARSHALLAE',
       'TORTANUS DISCAUDATUS', 'PSEUDOCALANUS MIMUS', 'ACARTIA TONSA',
       'ONCAEA SUBTILIS', 'Siphonostomatoida', 'ONCAEIDAE',
       'BRADYIDIUS SAANICHI', 'PSEUDOCALANUS Lg', 'TRICONIA CANADENSIS',
       'NEOCALANUS CRISTATUS', 'PSEUDOCALANUS MINUTUS',
       'RACOVITZANUS ANTARCTICUS', 'ACARTIA', 'HEMICYCLOPS',
       'ONCAEA PROLATA', 'EPILABIDOCERA AMPHITRITES',
       'CTENOCALANUS VANUS', 'SCOLECITHRICELLA OVATA', 'HETERORHABDUS',
       'EUCALANUS CALIFORNICUS', 'CYCLOPOIDA', 'TRICONIA SIMILIS',
       'SCOLECITHRICELLA Minor', 'Chiridius gracilis', 'GAETANUS SIMPLEX',
       'CANDACIA BIPINNATA', 'CANDACIA', 'EUCHIRELLA PULCHRA',
       'AETIDEUS ARMATUS', 'TRICONIA', 'GAETANUS MINUTUS', 'EURYTEMORA',
       'MESOCALANUS TENUICORNIS', 'EUCALANUS', 'CLAUSOCALANUS',
       'SCOLECITHRICELLA DENTATA', 'SCOLECITHRICELLA',
       'PLEUROMAMMA ABDOMINALIS', 'Poecilostomatoida',
       'EUCHIRELLA PSEUDOPULCHRA', 'EUCHAETIDAE',
       'SCAPHOCALANUS BREVICORNIS', 'METRIDIIDAE', 'COPEPODA',
       'NEOCALANUS', 'PLEUROMAMMA', 'GAETANUS MILES', 'TRICONIA (tiny)',
       'AETIDEUS DIVERGENS', 'CALIGIDAE', 'FARRANULA', 'TORTANUS',
       'Tortanus', 'METACARCINUS MAGISTER', 'GLEBOCARCINUS OREGONENSIS',
       'METACARCINUS GRACILIS', 'PINNIXA', 'PAGURUS', 'HEMIGRAPSUS',
       'GALATHEIDAE', 'PUGETTIA', 'LOPHOPANOPEUS BELLUS',
       'CANCER PRODUCTUS', 'FABIA SUBQUADRATA', 'LITHODIDAE',
       'OREGONIA GRACILIS', 'PINNOTHERES', 'BRACHYURA', 'Galatheidae',
       'Cancridae Sm', 'PINNOTHERIDAE', 'CANCRIDAE',
       'Telmessus cheiragonus', 'XANTHOIDEA', 'PACHYCHELES',
       'PETROLISTHES', 'Cancridae Lg', 'MAJOIDEA', 'LOPHOLITHODES',
       'CHIONOECETES', 'PORCELLANIDAE', 'PAGURIDAE', 'RHINOLITHODES',
       'GRAPSIDAE', 'CRYPTOLITHODES', 'DIOGENIDAE', 'ANOMURA',
       'PLEUROBRACHIA BACHEI', 'CTENOPHORA', 'Pleurobrachia bachei',
       'BEROE', 'CUMACEA', 'NOCTILUCA', 'ECHINODERMATA', 'HOLOTHUROIDEA',
       'OPHIUROIDEA', 'FISH', 'Forage FISH', 'Syngnathidae', 'Fish',
       'LIMACINA HELICINA', 'CLIONE LIMACINA', 'GASTROPODA',
       'GASTROPTERON PACIFICUM', 'Limacina helicina', 'AEQUOREA VICTORIA',
       'Mitrocoma cellularia', 'AGLANTHA DIGITALE', 'PANDEIDAE',
       'EUPHYSA', 'PROBOSCIDACTYLA FLAVICIRRATA', 'HALITHOLUS', 'RATHKEA',
       'OBELIA', 'EUTONINA INDICANS', 'CLYTIA GREGARIA', 'SARSIA',
       'LEPTOTHECATA', 'LEUCKARTIARA', 'ANTHOATHECATA', 'HYDROMEDUSA',
       'Clytia gregaria', 'Aglantha digitale', 'HYDROID',
       'Geomackiea zephyrolata', 'AMPHINEMA', 'AEGINA CITREA',
       'STOMOTOCA ATRA', 'BOUGAINVILLIA', 'Euphysa', 'SOLMISSUS',
       'HYBOCODON', 'NARCOMEDUSIDA', 'MITROCOMELLA', 'TIAROPSIS',
       'HYDROZOA', 'Insecta', 'ISOPODA', 'EUPHAUSIIDAE',
       'EUPHAUSIA PACIFICA', 'THYSANOESSA', 'THYSANOESSA RASCHII',
       'NEMATOSCELIS DIFFICILIS', 'THYSANOESSA SPINIFERA',
       'THYSANOESSA LONGIPES', 'FRITILLARIA', 'OIKOPLEURA', 'LARVACEA',
       'LITTORINA', 'ALIENACANTHOMYSIS MACROPSIS',
       'METERYTHROPS ROBUSTUS', 'PACIFACANTHOMYSIS NEPHROPHTHALMA',
       'MYSIDELLA AMERICANA', 'MYSIDACEA',
       'XENACANTHOMYSIS PSEUDOMACROPSIS', 'NEOMYSIS',
       'Exacanthomysis davisi', 'PSEUDOMMA', 'NEOMYSIS MERCEDIS',
       'NEMATODA', 'Nemertea', 'NUDIBRANCHIA', 'OSTRACODA', 'PHORONIDA',
       'PLATYHELMINTHES', 'POLYCHAETA', 'RHYNCHONEREELLA ANGELINI',
       'OWENIA', 'TOMOPTERIS', 'PYCNOGONIDA', 'SCYPHOMEDUSA',
       'HIPPOLYTIDAE', 'NEOTRYPAEA CALIFORNIENSIS', 'CRANGONIDAE',
       'PANDALUS STENOLEPIS', 'PASIPHAEA PACIFICA', 'CALLIANASSIDAE',
       'PANDALIDAE', 'CARIDEA', 'UPOGEBIIDAE', 'Caridea',
       'SERGESTES SIMILIS', 'DECAPODA', 'MUGGIAEA ATLANTICA',
       'Nanomia bijuga', 'Muggiaea atlantica', 'Physonect', 'DIMOPHYES',
       'CALYCOPHORAE', 'SIPHONOPHORA', 'ASCIDIACEA', 'SALPA', 'UNKNOWN',
       'Unknown'], dtype=object)

In [ ]:

Convert date to proper format¶

In [11]:

df['Sample Date'][0],df['Sample Time'][0]

Out[11]:

(Timestamp('2017-06-08 00:00:00'), datetime.time(14, 54))

In [12]:

df['Sample Date'][1000:1020]

Out[12]:

1000   2015-08-04
1001   2015-08-04
1002   2015-08-04
1003   2015-08-04
1004   2015-08-17
1005   2015-08-17
1006   2015-08-17
1007   2015-08-18
1008   2015-08-18
1009   2015-08-18
1010   2015-08-18
1011   2015-08-18
1012   2015-07-20
1013   2015-07-20
1014   2015-07-20
1015   2015-08-03
1016   2015-08-03
1017   2015-08-03
1018   2015-07-06
1019   2015-07-06
Name: Sample Date, dtype: datetime64[ns]

In [13]:

df['dtPac']=[dt.datetime.combine(idate, itime) for idate, itime \
                 in zip(df['Sample Date'],df['Sample Time'])]

In [14]:

df['dtUTC']=[et.pac_to_utc(ii) for ii in df['dtPac']]

In [17]:

df['dtUTC']

Out[17]:

0       2017-06-08 21:54:00
1       2017-06-08 21:54:00
2       2017-06-08 21:54:00
3       2017-03-09 20:18:00
4       2017-03-09 20:18:00
                ...        
81582   2015-05-06 19:00:00
81583   2015-08-13 18:21:00
81584   2015-08-26 18:16:00
81585   2015-05-06 20:05:00
81586   2018-08-03 18:14:00
Name: dtUTC, Length: 81587, dtype: datetime64[ns]

In [15]:

df.rename(columns={'Sample Code':'Key','Latitude':'Lat','Longitude':'Lon'},inplace=True)

In [16]:

df.loc[0]

Out[16]:

Key                             060817HCB004V1454
Station                                   HCB004V
Site Name                           Sisters Point
Basin                                  Hood Canal
Sub Basin                            S Hood Canal
Lat                                       47.3562
Lon                                      -123.025
Sample Date                   2017-06-08 00:00:00
Sample Year                                  2017
Sample Month                                  JUN
Sample Time                              14:54:00
Mesh Size                                     200
Diameter (cm)                                  60
Station Depth (m)                              52
Max Tow Depth (m)                              47
Min Tow Depth (m)                               0
Vol Filtered (m3)                          14.703
Broad Group                              Amphipod
Mid-Level Group              Amphipoda-Hyperiidea
1st Word Taxa                           HYPEROCHE
Genus species                           HYPEROCHE
Life History Stage                        Unknown
Total Ct                                       76
Ind Carbon (ug)                           122.333
Density (#/m3)                            5.16901
Final Carbon (mg/m3)                      3.18548
Size Category Aggregation                Amphipod
dtPac                         2017-06-08 14:54:00
dtUTC                         2017-06-08 21:54:00
Name: 0, dtype: object

In [17]:

towIDlist=['Key','Station', 'Site Name', 'Basin', 'Sub Basin', 'Lat',
       'Lon', 'Sample Date', 'Sample Year', 'Sample Month',
       'Sample Time', 'Mesh Size', 'Diameter (cm)', 'Station Depth (m)',
       'Max Tow Depth (m)', 'Min Tow Depth (m)','dtUTC']

In [18]:

towIDlist2=['Key', 'Station', 'Sample Date','Sample Time','dtUTC']

In [22]:

len(np.unique(df['Key']))

Out[22]:

In [19]:

len(df.groupby(towIDlist)),len(df.groupby(towIDlist2)),len(df.groupby(['Key']))

Out[19]:

(2168, 1159, 1159)

Create a biomass dataframe¶

In [23]:

biomassDF=df.groupby(towIDlist2,as_index=False).first()\
     .loc[:,towIDlist].copy(deep=True)

In [26]:

biomassDF.keys()

Out[26]:

Index(['Key', 'Station', 'Site Name', 'Basin', 'Sub Basin', 'Lat', 'Lon',
       'Sample Date', 'Sample Year', 'Sample Month', 'Sample Time',
       'Mesh Size', 'Diameter (cm)', 'Station Depth (m)', 'Max Tow Depth (m)',
       'Min Tow Depth (m)', 'dtUTC'],
      dtype='object')

In [27]:

biomassDF

Out[27]:

	Key	Station	Site Name	Basin	Sub Basin	Lat	Lon	Sample Date	Sample Year	Sample Month	Sample Time	Mesh Size	Diameter (cm)	Station Depth (m)	Max Tow Depth (m)	Min Tow Depth (m)	dtUTC
0	010218ELIV1151	ELIV	Eliza Island	Bellingham Bay	Bellingham Bay	48.63795	-122.5694	2018-01-02	2018	JAN	11:51:00	200	60.0	120.700	110.0	0	2018-01-02 19:51:00
1	010818SKETV1058	SKETV	South Ketron/Solo Point	South Sound	South Sound	47.15243	-122.6586	2018-01-08	2018	JAN	10:58:00	200	60.0	132.000	122.0	0	2018-01-08 18:58:00
2	011119ELIV1052	ELIV	Eliza Island	Bellingham Bay	Bellingham Bay	48.63795	-122.5694	2019-01-11	2019	JAN	10:52:00	200	60.0	124.054	114.0	0	2019-01-11 18:52:00
3	011315CAMV1330	CAMV	Camano Head	Whidbey Basin	S Whidbey Basin	48.05901	-122.3873	2015-01-13	2015	JAN	13:30:00	200	60.0	175.000	170.0	0	2015-01-13 21:30:00
4	011315MUKV1415	MUKV	Mukilteo	Whidbey Basin	S Whidbey Basin	47.97166	-122.3222	2015-01-13	2015	JAN	14:15:00	200	60.0	200.000	190.0	0	2015-01-13 22:15:00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1154	121917LSNT01V1000	LSNT01V	Point Williams	Central Basin	N Central Basin	47.53333	-122.4333	2017-12-19	2017	DEC	10:00:00	200	60.0	210.000	200.0	0	2017-12-19 18:00:00
1155	121917NSEX01V1224	NSEX01V	East Passage	Central Basin	S Central Basin	47.35862	-122.3871	2017-12-19	2017	DEC	12:24:00	200	60.0	180.000	170.0	0	2017-12-19 20:24:00
1156	121918TDBV1327	TDBV	Thorndyke Bay	Hood Canal	N Hood Canal	47.78297	-122.7330	2018-12-19	2018	DEC	13:27:00	200	60.0	112.000	109.0	0	2018-12-19 21:27:00
1157	122116LSNT01V1057	LSNT01V	Point Williams	Central Basin	N Central Basin	47.53333	-122.4333	2016-12-21	2016	DEC	10:57:00	200	60.0	210.000	200.0	0	2016-12-21 18:57:00
1158	122116NSEX01V1237	NSEX01V	East Passage	Central Basin	S Central Basin	47.35862	-122.3871	2016-12-21	2016	DEC	12:37:00	200	60.0	180.000	170.0	0	2016-12-21 20:37:00

1159 rows × 17 columns

In [30]:

biomassDF2=biomassDF.loc[biomassDF['Sample Year']==2015].copy(deep=True)

In [31]:

df2=df.loc[df['Sample Year']==2015].copy(deep=True)

In [35]:

def getbiomass(colname,key,origdf,matchcol):        
    biomassArray=origdf.loc[(origdf.Key==key)&(origdf[matchcol]==colname),
                    ['Final Carbon (mg/m3)']]
    biomass=np.nansum(biomassArray)
    
    return biomass

In [37]:

for icol in colListBroad:
    biomassDF[icol]=[getbiomass(icol,ikey,df,'Broad Group') for ikey in biomassDF['Key']]
 

In [41]:

colListGenus

Out[41]:

('METRIDIA PACIFICA',
 'NEOCALANUS PLUMCHRUS',
 'CALANOIDA',
 'EUCALANUS BUNGII',
 'CALANUS PACIFICUS',
 'CALANUS MARSHALLAE',
 'NEOCALANUS CRISTATUS',
 'EUCALANUS CALIFORNICUS',
 'EUCALANUS',
 'METRIDIIDAE',
 'NEOCALANUS')

In [42]:

for icol in colListGenus:
    biomassDF[icol]=[getbiomass(icol,ikey,df,'Genus species') for ikey in biomassDF['Key']]

In [43]:

biomassDF

Out[43]:

	Key	Station	Site Name	Basin	Sub Basin	Lat	Lon	Sample Date	Sample Year	Sample Month	...	NEOCALANUS PLUMCHRUS	CALANOIDA	EUCALANUS BUNGII	CALANUS PACIFICUS	CALANUS MARSHALLAE	NEOCALANUS CRISTATUS	EUCALANUS CALIFORNICUS	EUCALANUS	METRIDIIDAE	NEOCALANUS
0	010218ELIV1151	ELIV	Eliza Island	Bellingham Bay	Bellingham Bay	48.63795	-122.5694	2018-01-02	2018	JAN	...	0.000000	0.000000	0.000000	0.107332	0.000000	0.0	0.0	0.0	0.0	0.0
1	010818SKETV1058	SKETV	South Ketron/Solo Point	South Sound	South Sound	47.15243	-122.6586	2018-01-08	2018	JAN	...	0.000000	0.000000	0.000000	0.146470	0.001061	0.0	0.0	0.0	0.0	0.0
2	011119ELIV1052	ELIV	Eliza Island	Bellingham Bay	Bellingham Bay	48.63795	-122.5694	2019-01-11	2019	JAN	...	0.251016	0.030672	0.018811	0.372864	0.034060	0.0	0.0	0.0	0.0	0.0
3	011315CAMV1330	CAMV	Camano Head	Whidbey Basin	S Whidbey Basin	48.05901	-122.3873	2015-01-13	2015	JAN	...	0.000000	0.000000	0.000000	1.702296	0.036034	0.0	0.0	0.0	0.0	0.0
4	011315MUKV1415	MUKV	Mukilteo	Whidbey Basin	S Whidbey Basin	47.97166	-122.3222	2015-01-13	2015	JAN	...	0.000000	0.000000	0.000000	1.012093	0.000000	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1154	121917LSNT01V1000	LSNT01V	Point Williams	Central Basin	N Central Basin	47.53333	-122.4333	2017-12-19	2017	DEC	...	0.000000	0.000000	0.000000	0.395991	0.000000	0.0	0.0	0.0	0.0	0.0
1155	121917NSEX01V1224	NSEX01V	East Passage	Central Basin	S Central Basin	47.35862	-122.3871	2017-12-19	2017	DEC	...	0.000000	0.000000	0.000000	0.491356	0.000000	0.0	0.0	0.0	0.0	0.0
1156	121918TDBV1327	TDBV	Thorndyke Bay	Hood Canal	N Hood Canal	47.78297	-122.7330	2018-12-19	2018	DEC	...	0.000000	0.000000	0.000000	0.232957	0.000000	0.0	0.0	0.0	0.0	0.0
1157	122116LSNT01V1057	LSNT01V	Point Williams	Central Basin	N Central Basin	47.53333	-122.4333	2016-12-21	2016	DEC	...	0.000000	0.000000	0.000000	0.749717	0.202440	0.0	0.0	0.0	0.0	0.0
1158	122116NSEX01V1237	NSEX01V	East Passage	Central Basin	S Central Basin	47.35862	-122.3871	2016-12-21	2016	DEC	...	0.000000	0.000000	0.000000	0.645485	0.011819	0.0	0.0	0.0	0.0	0.0

1159 rows × 35 columns

In [ ]:

biomassDF.keys()

In [ ]:

import netCDF4 as nc

In [ ]:

#ftemp=nc.Dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')

In [ ]:

#ftemp.variables.keys()

In [ ]:

#ftemp.variables['e3t_0']

In [ ]:

#ftemp.variables['e3t_1d'][:]

In [ ]:

fdict={'ptrc_T':1,'grid_T':1}
start_date = dt.datetime(2015,1,1)
end_date = dt.datetime(2016,12,31)
flen=1 # number of days per model output file. always 1 for 201905 and 201812 model runs
namfmt='nowcast' # for 201905 and 201812 model runs, this should always be 'nowcast'
# filemap is dictionary of the form variableName: fileType, where variableName is the name
# of the variable you want to extract and fileType designates the type of 
# model output file it can be found in (usually ptrc_T for biology, grid_T for temperature and 
# salinity)
filemap={'microzooplankton':'ptrc_T','mesozooplankton':'ptrc_T'}
# fdict is a dictionary mappy file type to its time resolution. Here, 1 means hourly output
# (1h file) and 24 means daily output (1d file). In certain runs, multiple time resolutions 
# are available
fdict={'ptrc_T':1,'grid_T':1}

In [ ]:

PATH= '/results2/SalishSea/nowcast-green.201905/'

In [ ]:

biomassDF.keys()

In [ ]:

biomassDF.rename(columns={'Max Tow Depth (m)':'Z_lower','Min Tow Depth (m)':'Z_upper','Station Depth (m)':'Z'},inplace=True)

In [ ]:

# define log transform function with slight shift to accommodate zero values
def logt(x):
  return np.log10(x+.001)

In [ ]:

biomassDF['Year']=[ii.year for ii in biomassDF['dtUTC']]
biomassDF['YD']=et.datetimeToYD(biomassDF['dtUTC'])

In [ ]:

biomassDF

In [ ]:

biomassDF.keys()

In [ ]:

data=et.matchData(biomassDF,filemap,fdict,start_date,end_date,'nowcast',PATH,1,quiet=False,method='vertNet');

In [ ]:

data

In [ ]:

data.keys()

In [ ]:

data['L10Amphipoda']=logt(data['Amphipoda'])
data['L10Crabs']=logt(data['Crabs'])
data['L10Krill-Adult Juvenile']=logt(data['Krill-Adult Juvenile'])
data['L10Krill-Calyptopis']=logt(data['Krill-Calyptopis'])
data['L10Krill-Furcilia']=logt(data['Krill-Furcilia'])
data['L10Copepod']=logt(data['Copepod'])
data['L10Larvacea']=logt(data['Larvacea'])
data['L10mod_microzooplankton']=logt(data['mod_microzooplankton'])
data['L10mod_mesozooplankton']=logt(data['mod_mesozooplankton'])

In [ ]:

cm1=cmocean.cm.thermal
with nc.Dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/bathymetry_201702.nc') as bathy:
    bathylon=np.copy(bathy.variables['nav_lon'][:,:])
    bathylat=np.copy(bathy.variables['nav_lat'][:,:])
    bathyZ=np.copy(bathy.variables['Bathymetry'][:,:])

In [ ]:

fig, ax = plt.subplots(1,1,figsize = (6,6))
with nc.Dataset('/ocean/ksuchy/MOAD/NEMO-forcing/grid/bathymetry_201702.nc') as grid:
    viz_tools.plot_coastline(ax, grid, coords = 'map',isobath=.1)
colors=('teal','green','firebrick','darkorange','darkviolet','fuchsia',
        'royalblue','darkgoldenrod','mediumspringgreen','deepskyblue')
datreg=dict()
for ind, iregion in enumerate(data.Basin.unique()):
    datreg[iregion] = data.loc[data.Basin==iregion]
    ax.plot(datreg[iregion]['Lon'], datreg[iregion]['Lat'],'.',
            color = colors[ind], label=iregion)
ax.set_ylim(47,51)
ax.legend(bbox_to_anchor=[1,.6,0,0])
ax.set_xlim(-126, -120);
ax.set_title('Observation Locations');
ax.legend(bbox_to_anchor=(1.1, 1.05))

In [ ]:

data.keys()

In [ ]:

def byRegion(ax,obsvar,modvar,lims):
    PS=[]
    for ind, iregion in enumerate(data.Basin.unique()):
        #ax.plot(datreg[iregion]['Lon'], datreg[iregion]['Lat'],'.',
                #color = colors[ind], label=iregion)
        PS0=et.varvarPlot(ax,datreg[iregion],obsvar,modvar,
                          cols=(colors[ind],),lname=iregion)
        PS.append(PS0)
    l=ax.legend(handles=[ip[0][0] for ip in PS])
    ax.set_xlabel('Obs')
    ax.set_ylabel('Model')
    ax.plot(lims,lims,'k-',alpha=.5)
    ax.set_xlim(lims)
    ax.set_ylim(lims)
    ax.set_aspect(1)
    return PS,l

In [ ]:

data['Month']=[ii.month for ii in data['dtUTC']]
JF=data.loc[(data.Month==1)|(data.Month==2)]
MAM=data.loc[(data.Month==3)|(data.Month==4)|(data.Month==5)]
JJA=data.loc[(data.Month==6)|(data.Month==7)|(data.Month==8)]
SOND=data.loc[(data.Month==9)|(data.Month==10)|(data.Month==11)|(data.Month==12)]

In [ ]:

def bySeason(ax,obsvar,modvar,lims):
    for axi in ax:
        axi.plot(lims,lims,'k-')
        axi.set_xlim(lims)
        axi.set_ylim(lims)
        axi.set_aspect(1)
        axi.set_xlabel('Obs')
        axi.set_ylabel('Model')
    PS=et.varvarPlot(ax[0],JF,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
    ax[0].set_title('Winter')
    PS=et.varvarPlot(ax[1],MAM,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
    ax[1].set_title('Spring')
    PS=et.varvarPlot(ax[2],JJA,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
    ax[2].set_title('Summer')
    PS=et.varvarPlot(ax[3],SOND,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
    ax[3].set_title('Autumn')
    return 

In [ ]:

fig, ax = plt.subplots(1,1,figsize = (16,7))     
PS,l=byRegion(ax,'L10Crabs','L10mod_mesozooplankton',(-2,2))
ax.set_title('Crabs ($\mu$M) By Region')
ax.legend(bbox_to_anchor=(1.1, 1.05))

fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,'L10Crabs','L10mod_mesozooplankton',(-2,2))

In [ ]:

fig, ax = plt.subplots(1,1,figsize = (16,7))     
PS,l=byRegion(ax,'L10Krill-Adult Juvenile','L10mod_mesozooplankton',(-3,3))
ax.set_title('L10Krill-Adult Juveniles ($\mu$M) By Region')
ax.legend(bbox_to_anchor=(1.1, 1.05))

fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,'L10Krill-Adult Juvenile','L10mod_mesozooplankton',(-3,3))

In [ ]:

fig, ax = plt.subplots(1,1,figsize = (16,7))     
PS,l=byRegion(ax,'L10Copepod','L10mod_mesozooplankton',(-1.5,1.5))
ax.set_title('L10Copepod($\mu$M) By Region')
ax.legend(bbox_to_anchor=(1.1, 1.05))

fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,'L10Copepod','L10mod_mesozooplankton',(-1.5,1.5))

In [ ]:

fig, ax = plt.subplots(1,1,figsize = (16,7))     
PS,l=byRegion(ax,'L10Larvacea','L10mod_mesozooplankton',(-1.5,1.5))
ax.set_title('L10Larvaceans ($\mu$M) By Region')
ax.legend(bbox_to_anchor=(1.1, 1.05))

fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,'L10Larvacea','L10mod_mesozooplankton',(-1.5,1.5))

In [ ]:

fig,ax=plt.subplots(2,4,figsize=(15,7))
fig.subplots_adjust(wspace=.4)
fig.subplots_adjust(hspace=.6)
ax=ax.flatten()

chplc=('Amphipoda', 'Crabs','Krill-Adult Juvenile', 'Krill-Calyptopis', 'Krill-Furcilia','Copepod','Larvacea')

mvar1=data['mod_microzooplankton']
mvar2=data['mod_mesozooplankton']

for ii in range(0,len(chplc)):
    ax[ii].plot(logt(data.loc[:,[chplc[ii]]].values),logt(mvar1),'.',ms=1,color='blue',label='Microzoop')
    ax[ii].plot(logt(data.loc[:,[chplc[ii]]].values),logt(mvar2),'.',ms=1,color='red',label='Mesozoop')
    ax[ii].set_ylabel('Model Class')
    ax[ii].set_xlabel(chplc[ii])
    ax[ii].set_title('log10[$\mu$M +.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(loc=2, fontsize = 'small')

In [ ]: