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 IOS Zooplankton data and create dataframe

In [2]:

ls '/ocean/ksuchy/MOAD/observe/Data and Code files for KS 2020dec11/2020_05_21 1995-2011 SoG VNH.csv'

/ocean/ksuchy/MOAD/observe/Data and Code files for KS 2020dec11/2020_05_21 1995-2011 SoG VNH.csv

In [3]:

df=pd.read_csv('/ocean/ksuchy/MOAD/observe/Data and Code files for KS 2020dec11/2020_05_21 2012-2015 SoG VNH.csv',
               encoding = "ISO-8859-1")

In [4]:

df

Out[4]:

	Key	region_name	Station	PROJECT	lon	lat	Date	STN_TIME	Twilight	Net_Type	...	Phylum:	Class:	Order:	Family:	Name	Abundance(#/m3)	Biomass(mg/m3)	NumberOfSpecies	Station Diversity	Station Equitability
0	IOS2012005000901	Northern Strait of Georgia	22	Str. Geo.	-124.272	49.670	6/14/2012	7:32	Daylight	SCOR VNH	...	Arthropoda	Branchiopoda	Diplostraca	Podonidae	Podon *sp. s1	12.22541	0.06113	51	2.63	0.67
1	IOS2012005000901	Northern Strait of Georgia	22	Str. Geo.	-124.272	49.670	6/14/2012	7:32	Daylight	SCOR VNH	...	Arthropoda	Cirripedia	Thecostraca	NaN	Cirripedia *sp. nauplii s1	30.56351	1.22254	51	2.63	0.67
2	IOS2012005000901	Northern Strait of Georgia	22	Str. Geo.	-124.272	49.670	6/14/2012	7:32	Daylight	SCOR VNH	...	Arthropoda	Malacostraca	Amphipoda	Hyperiidae	Themisto pacifica juvenile s1	6.11270	1.44871	51	2.63	0.67
3	IOS2012005000901	Northern Strait of Georgia	22	Str. Geo.	-124.272	49.670	6/14/2012	7:32	Daylight	SCOR VNH	...	Arthropoda	Malacostraca	Decapoda	NaN	Caridea *sp. zoea s1	3.05635	0.07030	51	2.63	0.67
4	IOS2012005000901	Northern Strait of Georgia	22	Str. Geo.	-124.272	49.670	6/14/2012	7:32	Daylight	SCOR VNH	...	Arthropoda	Malacostraca	Decapoda	Hippolytidae	Hippolytidae *sp. mysis s2	0.09551	0.06017	51	2.63	0.67
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
60225	SOO2015095000801	Tidal Mixed	MAC41	Citizen Science	-123.409	48.402	7/27/2015	13:00	Daylight	SCOR VNH	...	Ctenophora	Tentaculata	Cydippida	Pleurobrachiidae	Pleurobrachia bachei s2	0.25316	0.31797	44	2.56	0.67
60226	SOO2015095000801	Tidal Mixed	MAC41	Citizen Science	-123.409	48.402	7/27/2015	13:00	Daylight	SCOR VNH	...	Ectoprocta	Gymnolaemata	NaN	NaN	Bryozoa *sp. cyphonautes s1	48.60759	0.06319	44	2.56	0.67
60227	SOO2015095000801	Tidal Mixed	MAC41	Citizen Science	-123.409	48.402	7/27/2015	13:00	Daylight	SCOR VNH	...	Mollusca	Bivalvia	Pholadomyoida	NaN	Bivalvia *sp. veligers s1	21.60338	0.01296	44	2.56	0.67
60228	SOO2015095000801	Tidal Mixed	MAC41	Citizen Science	-123.409	48.402	7/27/2015	13:00	Daylight	SCOR VNH	...	Mollusca	Cephalopoda	Teuthida	Gonatidae	Berryteuthis magister s2	0.08439	0.46414	44	2.56	0.67
60229	SOO2015095000801	Tidal Mixed	MAC41	Citizen Science	-123.409	48.402	7/27/2015	13:00	Daylight	SCOR VNH	...	Mollusca	Gastropoda	NaN	NaN	Gastropoda *sp. veligers s1	10.80169	0.02808	44	2.56	0.67

60230 rows × 29 columns

In [5]:

df.keys()

Out[5]:

Index(['Key', 'region_name', 'Station', 'PROJECT', 'lon', 'lat', 'Date',
       'STN_TIME', 'Twilight', 'Net_Type', 'Mesh_Size(um)', 'Net_Mouth_Dia(m)',
       'DEPTH_STRT1', 'DEPTH_END1', 'Bottom Depth(m)', 'Volume Filtered(m3)',
       'CTD', 'NOTES', 'PI', 'Phylum:', 'Class:', 'Order:', 'Family:', 'Name',
       'Abundance(#/m3)', 'Biomass(mg/m3)', 'NumberOfSpecies',
       'Station Diversity', 'Station Equitability'],
      dtype='object')

In [6]:

df['Abundance(#/m3)'][2200:2300]

Out[6]:

2200     5.51249
2201     2.75624
2202    33.07494
2203    49.61240
2204     5.51249
          ...   
2295    17.61982
2296    22.42523
2297    36.84145
2298     1.60180
2299     0.80090
Name: Abundance(#/m3), Length: 100, dtype: float64

Convert date to proper format

In [7]:

df['Date'][0],df['STN_TIME'][0]

Out[7]:

('6/14/2012', '7:32')

In [8]:

df['Date'][1000:1020]

Out[8]:

1000    6/15/2012
1001    6/15/2012
1002    6/15/2012
1003    6/15/2012
1004    6/15/2012
1005    6/15/2012
1006    6/15/2012
1007    6/15/2012
1008    6/15/2012
1009    6/15/2012
1010    6/15/2012
1011    6/15/2012
1012    6/15/2012
1013    6/15/2012
1014    6/15/2012
1015    6/15/2012
1016    6/15/2012
1017    6/15/2012
1018    6/15/2012
1019    6/15/2012
Name: Date, dtype: object

In [9]:

df['Date'][0].split('/')

Out[9]:

['6', '14', '2012']

In [10]:

dateslist=list()

In [11]:

for el in df['Date']:
    dateslist.append(el.split('/'))

In [12]:

timeslist=list()
for el in df['STN_TIME']:
    timeslist.append(el.split(':'))

In [13]:

dts=list()
for ii,jj in zip(dateslist,timeslist):
    dts.append(dt.datetime(int(ii[2]),int(ii[0]),int(ii[1]),int(jj[0]),int(jj[1])))

In [14]:

df.loc[df.Twilight=='Daylight']['STN_TIME'].unique()

Out[14]:

array(['7:32', '7:52', '3:30', '7:42', '7:49', '15:09', '16:30', '22:09',
       '22:27', '19:36', '19:53', '9:21', '9:35', '13:29', '13:45',
       '19:25', '19:43', '5:26', '5:43', '5:45', '5:47', '5:56', '5:59',
       '8:38', '8:39', '8:40', '8:41', '8:57', '11:10', '11:44', '11:45',
       '11:47', '11:48', '11:49', '12:40', '12:42', '15:14', '15:15',
       '15:16', '15:17', '15:18', '17:54', '17:55', '17:57', '17:58',
       '17:59', '5:39', '6:05', '14:47', '15:02', '19:13', '19:30',
       '14:32', '14:49', '20:04', '20:24', '20:27', '20:47', '9:37',
       '9:48', '13:57', '14:17', '19:41', '19:59', '7:18', '7:53', '9:25',
       '12:57', '13:09', '7:30', '7:45', '13:33', '13:36', '13:38',
       '10:00', '10:30', '11:00', '11:30', '12:30', '13:00', '13:30',
       '16:00', '17:00', '9:29', '10:26', '11:19', '13:39', '9:16',
       '10:04', '10:42', '11:03', '9:00', '9:41', '10:17', '10:40',
       '12:00', '9:33', '10:13', '11:11', '12:02', '12:32', '13:22',
       '9:57', '10:35', '10:02', '12:08', '13:18', '14:50', '17:45',
       '18:45', '8:30', '9:30', '11:41', '12:37', '13:03', '14:05',
       '9:20', '11:56', '13:43', '9:32', '11:22', '11:52', '7:27',
       '19:08', '9:45', '16:38', '16:57', '16:04', '16:26', '13:31',
       '13:35', '7:12', '16:12', '16:14', '16:18', '16:20', '16:22',
       '3:48', '4:04', '15:20', '15:35', '21:28', '21:42', '4:33', '4:45',
       '11:38', '11:55', '18:18', '18:29', '10:12', '12:55', '10:16',
       '11:20', '13:08', '11:07', '12:19', '12:58', '14:42', '15:37',
       '12:10', '12:23', '13:24', '14:33', '10:32', '12:39', '9:49',
       '10:53', '12:49', '11:36', '14:11', '9:51', '11:24', '13:04',
       '13:19', '9:24', '10:15', '11:53', '12:51', '12:50', '16:11',
       '6:15', '17:15', '11:16', '12:17', '13:10', '13:59', '11:21',
       '12:01', '12:35', '9:58', '10:41', '11:29', '9:05', '9:50', '8:49',
       '9:55', '11:14', '12:14', '9:10', '10:20', '10:50', '11:25',
       '11:50', '19:52', '20:32', '6:20', '5:58', '20:29', '5:20',
       '20:10', '15:52', '6:43', '20:12', '7:22', '21:03', '21:55',
       '14:56', '10:08', '20:01', '11:33', '15:22', '16:40', '23:15',
       '8:51', '12:12', '15:12', '10:57', '7:03', '8:03', '14:22',
       '23:06', '4:05', '6:25', '9:26', '11:28', '13:27', '15:11',
       '15:53', '15:54', '15:56', '15:59', '18:06', '19:45', '9:40',
       '20:11', '7:10', '14:34', '11:42', '13:28', '17:12', '9:04',
       '14:10', '16:17', '8:02', '10:31', '15:27', '17:01', '16:43',
       '10:46', '13:15', '14:43', '9:44', '13:55', '15:44', '16:59',
       '18:41', '8:18', '11:13', '8:28', '15:43', '18:08', '7:40',
       '10:56', '10:33', '12:52', '14:55', '7:43', '12:13', '13:44',
       '7:41', '16:56', '8:17', '9:13', '11:12', '15:40', '18:27',
       '10:07', '12:06', '16:07', '16:41', '10:52', '16:33', '17:48',
       '7:48', '11:09', '15:08', '8:31', '12:22', '14:39', '12:03',
       '14:30', '17:31', '14:15', '18:16', '7:55', '17:38', '6:36',
       '18:22', '7:15', '9:17', '12:44', '18:54', '12:43', '18:44',
       '7:08', '13:02', '7:19', '16:36', '8:25', '8:33', '13:25', '17:20',
       '17:26', '13:42', '18:37', '18:43', '13:21', '14:53', '17:24',
       '9:38', '7:06', '9:31', '12:20', '15:28', '11:01', '11:57',
       '14:12', '15:10', '7:35', '14:38', '16:06', '8:08', '16:05',
       '16:52', '17:47', '8:21', '9:19', '16:35', '8:10', '8:59', '10:21',
       '14:41', '16:09', '18:26', '11:23', '8:14', '10:10', '11:27',
       '13:26', '11:15', '13:11', '9:18', '12:04', '10:45', '12:25',
       '13:56', '10:22', '13:53', '9:28', '11:06', '11:32', '10:34',
       '14:08', '11:18', '12:48', '14:01', '12:07', '14:07', '11:08',
       '14:04', '10:06', '11:43', '13:40', '10:18', '13:34', '16:27',
       '10:28', '15:26', '9:14', '12:15', '15:23', '8:23', '14:58',
       '9:08', '10:43', '14:29', '8:44', '9:52', '8:13', '14:27', '14:46',
       '14:16', '10:11', '14:51', '8:32', '10:37'], dtype=object)

In [15]:

df['dtUTC']=et.pac_to_utc(dts) #convert from Pac time to UTC

In [16]:

df['Order:'].unique()

Out[16]:

array(['Diplostraca', 'Thecostraca', 'Amphipoda', 'Decapoda',
       'Euphausiacea', 'Calanoida', 'Cyclopoida', 'Poecilostomatoida',
       'Halocyprida', 'Aphragmophora', 'Copelata', 'Leptothecate',
       'Siphonophorae', 'Trachylina', 'Cydippida', nan, 'Pholadomyoida',
       'Neotaenioglossa', 'Thecosomata', 'Aciculata', 'Canalipalpata',
       'Osmeriformes', 'Perciformes', 'Beroida', 'Teuthida',
       'Gymnosomata', 'Isopoda', 'Siphonostomatoida', 'Anthoathecatae',
       'Scorpaeniformes', 'Phragmophora', 'Clupeiformes', 'Ophiurida',
       'Gadiformes', 'Semaeostomeae', 'Cumacea', 'Echinoida',
       'Harpacticoida', 'Pleuronectiformes', 'Tricladida', 'Myodocopida',
       'Phaeogromia', 'Noctilucales', 'Octopoda', 'Actiniaria',
       'Foraminiferida', 'Monstrilloida', 'Oligotrichida', 'Mysida',
       'Acariformes', 'Lophogastrida', 'Ophidiiformes',
       'Thalassocalycida', 'Doliolida', 'Lepadomorpha', 'Cephalaspidea',
       'Sygnathiformes'], dtype=object)

In [17]:

SpeciesNames = df['Name'].unique()[1:1000]
SpeciesNames.sort()
print(SpeciesNames)

['Acartia *sp.  1' 'Acartia *sp.  2' 'Acartia *sp.  3' 'Acartia *sp.  4'
 'Acartia hudsonica  5' 'Acartia hudsonica  6F' 'Acartia hudsonica  6M'
 'Acartia longiremis  3' 'Acartia longiremis  4' 'Acartia longiremis  5'
 'Acartia longiremis  6F' 'Acartia longiremis  6M' 'Aegina citrea  s1'
 'Aegina citrea  s2' 'Aegina citrea  s3' 'Aeginidae *sp.  s1'
 'Aequorea *sp.  s1' 'Aequorea *sp.  s2' 'Aequorea *sp.  s3'
 'Aetideidae *sp.  1' 'Aetideidae *sp.  2' 'Aetideidae *sp.  3'
 'Aetideus *sp.  1' 'Aetideus *sp.  2' 'Aetideus *sp.  3'
 'Aetideus *sp.  4' 'Aetideus *sp.  5' 'Aetideus divergens  2'
 'Aetideus divergens  3' 'Aetideus divergens  4' 'Aetideus divergens  5'
 'Aetideus divergens  6F' 'Aetideus divergens  6M' 'Aetideus pacificus  5'
 'Aetideus pacificus  6F' 'Aetideus pacificus  6M' 'Aglantha digitale  s1'
 'Aglantha digitale  s2' 'Aglantha digitale  s3' 'Alacia *sp.  s1'
 'Alacia minor  s1' 'Alacia pseudoalata  s1'
 'Ammodytes hexapterus flexion s2' 'Ammodytes hexapterus postflexion s3'
 'Amphicaryon acaule nectophore s1' 'Amphicaryon acaule nectophore s2'
 'Amphicaryon ernesti nectophore s1' 'Anomura *sp. megalops s1'
 'Anomura *sp. zoea s1' 'Anomura *sp. zoea s2'
 'Anoplarchus *sp. postflexion s3' 'Anthomedusae *sp.  s1'
 'Artedius fenestralis flexion s2' 'Artedius fenestralis preflexion s1'
 'Artedius harringtoni flexion s2' 'Artedius harringtoni preflexion s1'
 'Ascidiacea *sp. tadpole s1' 'Asteroidea *sp. bipinnaria s1'
 'Asteroidea *sp. brachiolaria s1' 'Asteroidea *sp. juvenile s1'
 'Atrophia minuta  5' 'Atrophia minuta  6F' 'Atrophia minuta  6M'
 'Augaptilus glacialis  6F' 'Aurelia *sp.  s2' 'Aurelia *sp.  s3'
 'Aurelia *sp. ephyra s1' 'Axiidae *sp.  s1' 'Axiidae *sp. mysis s2'
 'Axiidae *sp. zoea s1' 'Axiidae *sp. zoea s2'
 'Bathyagonus infraspinatus flexion s2' 'Bathylagus *sp. eggs s1'
 'Beroe *sp.  s3' 'Beroe abyssicola  s2' 'Beroe abyssicola  s3'
 'Beroe cucumis  s3' 'Berryteuthis *sp.  s1' 'Berryteuthis *sp.  s2'
 'Berryteuthis magister  s2' 'Bivalvia *sp. veligers s1'
 'Bosmina *.sp  s1' 'Bougainvillia *sp.  s1' 'Bougainvillia *sp.  s2'
 'Bougainvillia multitentaculata  s1' 'Bougainvillia multitentaculata  s2'
 'Bougainvillia superciliaris  s1' 'Brachyura *sp. megalops s1'
 'Brachyura *sp. zoea s1' 'Bradyidius *sp.  4' 'Bradyidius saanichi  6F'
 'Brosmophycis marginata postflexion s3' 'Bryozoa *sp. cyphonautes s1'
 'Calanoida *sp.  1' 'Calanoida *sp.  2' 'Calanoida *sp.  3'
 'Calanoida *sp.  4' 'Calanoida *sp.  6M' 'Calanus *sp.  1'
 'Calanus *sp.  2' 'Calanus *sp.  3' 'Calanus *sp.  4'
 'Calanus *sp. nauplii s1' 'Calanus marshallae  2' 'Calanus marshallae  3'
 'Calanus marshallae  4' 'Calanus marshallae  5' 'Calanus marshallae  6F'
 'Calanus marshallae  6M' 'Calanus pacificus  2' 'Calanus pacificus  3'
 'Calanus pacificus  4' 'Calanus pacificus  5' 'Calanus pacificus  6F'
 'Calanus pacificus  6M' 'Caligus clemensi  6F' 'Caligus clemensi  6M'
 'Caligus clemensi copepodite s1' 'Caligus clemensi subadult male 5M'
 'Calliopius *sp.  s1' 'Calliopius *sp.  s2' 'Calliopius *sp.  s3'
 'Calliopius pacificus  s1' 'Calliopius pacificus  s2'
 'Calliopius pacificus  s3' 'Cancer *sp. megalops s1'
 'Cancer *sp. zoea s1' 'Cancer *sp. zoea s2' 'Cancer magister megalops s2'
 'Cancer magister zoea s1' 'Cancer magister zoea s2'
 'Cancer oregonensis megalops s1' 'Cancer oregonensis megalops s2'
 'Cancer productus megalops s1' 'Cancer productus megalops s2'
 'Cancer productus zoea s1' 'Cancer productus zoea s2' 'Candacia *sp.  1'
 'Candacia *sp.  2' 'Candacia *sp.  3' 'Candacia *sp.  4'
 'Candacia bipinnata  6M' 'Candacia columbiae  5' 'Candacia columbiae  6F'
 'Candacia columbiae  6M' 'Caridea *sp.  s3' 'Caridea *sp. mysis s1'
 'Caridea *sp. mysis s2' 'Caridea *sp. mysis s3' 'Caridea *sp. zoea s1'
 'Caridea *sp. zoea s2' 'Castanellidae *sp.  s1' 'Catablema *sp.  s2'
 'Catablema *sp.  s3' 'Catablema multicirrata  s3'
 'Catablema nodulosa  s1' 'Catablema nodulosa  s2'
 'Catablema nodulosa  s3' 'Centropages *sp.  3'
 'Centropages abdominalis  1' 'Centropages abdominalis  2'
 'Centropages abdominalis  3' 'Centropages abdominalis  4'
 'Centropages abdominalis  5' 'Centropages abdominalis  6F'
 'Centropages abdominalis  6M' 'Cephalopoda *sp. eggs s1'
 'Chaetognatha *sp.  s3' 'Chaetognatha *sp. juvenile s1'
 'Chaetognatha *sp. juvenile s2' 'Chaetopterus *sp. larvae s1'
 'Chelophyes appendiculata nectophore s3' 'Chionoecetes *sp. megalops s1'
 'Chionoecetes *sp. megalops s2' 'Chionoecetes *sp. zoea s1'
 'Chiridius *sp.  2' 'Chiridius *sp.  3' 'Chiridius *sp.  4'
 'Chiridius gracilis  2' 'Chiridius gracilis  3' 'Chiridius gracilis  4'
 'Chiridius gracilis  5' 'Chiridius gracilis  6F' 'Chiridius gracilis  6M'
 'Chiridius pacificus  5' 'Chirundina streetsi  6F'
 'Cirripedia *sp. cyprids s1' 'Cirripedia *sp. nauplii s1'
 'Citharichthys *sp. eggs s1' 'Citharichthys *sp. preflexion s1'
 'Citharichthys sordidus flexion s2'
 'Citharichthys sordidus preflexion s1'
 'Citharichthys stigmaeus flexion s2'
 'Citharichthys stigmaeus preflexion s1' 'Clausocalanus arcuicornis  5'
 'Clausocalanus arcuicornis  6F' 'Clausocalanus arcuicornis  6M'
 'Clausocalanus lividus  6F' 'Clausocalanus parapergens  5'
 'Clausocalanus parapergens  6F' 'Clausocalanus pergens  5'
 'Clausocalanus pergens  6F' 'Cleonardo *sp.  s2'
 'Clevelandia ios flexion s2' 'Clevelandia ios preflexion s1'
 'Clione *sp. larvae s1' 'Clione limacina  s2' 'Clione limacina  s3'
 'Clione limacina juvenile s1' 'Clione limacina polytroch s0'
 'Clupea pallasii flexion s2' 'Clupea pallasii postflexion s3'
 'Clupea pallasii preflexion s1' 'Clupeiformes *sp. preflexion s1'
 'Clytemnestra scutellata  6F' 'Clytia gregaria  s1' 'Clytia gregaria  s2'
 'Clytia gregaria  s3' 'Conchoecia lophura  s1' 'Conchoecia rudjakovi  s1'
 'Conchoecinae *sp.  s1' 'Copepoda *sp. eggs s1'
 'Copepoda *sp. nauplii s1' 'Corycaeus anglicus  3'
 'Corycaeus anglicus  4' 'Corycaeus anglicus  5' 'Corycaeus anglicus  6F'
 'Corycaeus anglicus  6M' 'Cottidae *sp. flexion s2'
 'Cottidae *sp. preflexion s1' 'Crangonidae *sp. mysis s1'
 'Crangonidae *sp. mysis s2' 'Crangonidae *sp. mysis s3'
 'Crangonidae *sp. zoea s1' 'Crangonidae *sp. zoea s2'
 'Cryptacanthodes aleutensis postflexion s3' 'Ctenocalanus vanus  4'
 'Ctenocalanus vanus  5' 'Ctenocalanus vanus  6F' 'Ctenophora *sp.  s1'
 'Ctenophora *sp.  s2' 'Ctenophora *sp.  s3' 'Cumacea *sp.  s1'
 'Cumacea *sp.  s2' 'Cuninidae *sp.  s2' 'Cyanea *sp. ephrya s2'
 'Cyanea capillata  s3' 'Cyclopoida *sp.  3' 'Cyclopoida *sp.  5'
 'Cyclopoida *sp.  6F' 'Cyclopoida *sp.  6M' 'Cyphocaris *sp.  s1'
 'Cyphocaris challengeri  s1' 'Cyphocaris challengeri  s2'
 'Cyphocaris challengeri  s3' 'Decapoda *sp. zoea s1'
 'Dimophyes arctica eudoxid s1' 'Dimophyes arctica eudoxid s2'
 'Dimophyes arctica nectophore s1' 'Dimophyes arctica nectophore s2'
 'Dimophyes arctica nectophore s3' 'Discoconchoecia elegans  s1'
 'Dolioletta gegenbauri  s1' 'Dolioletta gegenbauri  s2'
 'Echinodermata *sp. auricularia s1' 'Echinodermata *sp. bipinaria s1'
 'Echinodermata *sp. juvenile s1' 'Echinodermata *sp. pluteus s1'
 'Echinoid *sp. pluteus s1' 'Engraulis mordax eggs s1'
 'Engraulis mordax flexion s2' 'Engraulis mordax postflexion s3'
 'Engraulis mordax preflexion s1' 'Epicarid *sp. larvae s1'
 'Epilabidocera *sp.  1' 'Epilabidocera *sp.  2' 'Epilabidocera *sp.  3'
 'Epilabidocera *sp.  4' 'Epilabidocera *sp. nauplii s1'
 'Epilabidocera longipedata  1' 'Epilabidocera longipedata  3'
 'Epilabidocera longipedata  4' 'Epilabidocera longipedata  5'
 'Epilabidocera longipedata  6F' 'Epilabidocera longipedata  6M'
 'Eualus *sp.  s2' 'Eualus *sp.  s3' 'Eucalanus *sp.  1'
 'Eucalanus *sp.  2' 'Eucalanus *sp.  3' 'Eucalanus *sp. nauplii s1'
 'Eucalanus bungii  3' 'Eucalanus bungii  4' 'Eucalanus bungii  4F'
 'Eucalanus bungii  4M' 'Eucalanus bungii  5F' 'Eucalanus bungii  5M'
 'Eucalanus bungii  6F' 'Eucalanus bungii  6M'
 'Eucalanus californicus  5F' 'Eucalanus californicus  6F'
 'Euchaetidae *sp.  1' 'Euchaetidae *sp.  2' 'Euchaetidae *sp.  3'
 'Euchaetidae *sp.  4' 'Euchaetidae *sp. nauplii s1' 'Euchirella *sp.  4'
 'Euchirella curticauda  5' 'Euchirella grandicornis  5'
 'Euchirella pseudopulchra  5' 'Euchirella pseudopulchra  6F'
 'Euchirella pseudopulchra  6M' 'Eucopia grimaldii  M'
 'Eukrohnia hamata  s2' 'Eukrohnia hamata  s3' 'Euphausia pacifica  F'
 'Euphausia pacifica  M' 'Euphausia pacifica  s2' 'Euphausia pacifica  s3'
 'Euphausia pacifica eggs s1' 'Euphausia pacifica nauplii s1'
 'Euphausia pacifica zoea s1' 'Euphausiacea *sp.  s2'
 'Euphausiidae *sp. eggs s1' 'Euphausiidae *sp. nauplii s1'
 'Euphausiidae *sp. protozoea (or calyptopis) s1'
 'Euphausiidae *sp. zoea (or furcilia) s1' 'Euphysa *sp.  s1'
 'Euphysa *sp.  s2' 'Euphysa japonica  s1' 'Euphysa japonica  s2'
 'Euphysa japonica  s3' 'Euphysa tentaculata  s2' 'Eurytemora *sp.  4'
 'Eurytemora *sp.  6M' 'Eurytemora americana  6M' 'Eutonina indicans  s2'
 'Eutonina indicans  s3' 'Evadne *sp.  s1' 'Fabia subquadrata megalops s1'
 'Facetotecta *sp. nauplii s1' 'Fish *sp. eggs s1' 'Fish *sp. flexion s2'
 'Fish *sp. juvenile s3' 'Fish *sp. preflexion s1'
 'Foersteria purpurea  s2' 'Foersteria purpurea  s3'
 'Foraminiferida *sp.  s1' 'Fritillaria borealis  s1'
 'Gadidae *sp. eggs s1' 'Gadidae *sp. flexion s2'
 'Gadidae *sp. preflexion s1' 'Gadus macrocephalus preflexion s1'
 'Gaetanus *sp.  1' 'Gaetanus *sp.  2' 'Gaetanus *sp.  3'
 'Gaetanus *sp.  4' 'Gaetanus *sp.  5' 'Gaetanus *sp.  6F'
 'Gaetanus minutus  3' 'Gaetanus minutus  4' 'Gaetanus minutus  5'
 'Gaetanus minutus  6F' 'Gaetanus minutus  6M' 'Gaetanus pungens  6F'
 'Gaetanus simplex  3' 'Gaetanus simplex  4' 'Gaetanus simplex  5'
 'Gaetanus simplex  6F' 'Gaetanus simplex  6M' 'Gaetanus tenuispinus  6F'
 'Galatheidae *sp. zoea s1' 'Galatheidae *sp. zoea s2'
 'Gammaridea *sp.  s1' 'Gammaridea *sp.  s2'
 'Gastropoda *sp. echinospira larvae s1'
 'Gastropoda *sp. prosobranch and ophistobranch s1'
 'Gastropoda *sp. veligers s1' 'Gastropteron pacificum  s1'
 'Gastropteron pacificum  s2' 'Geomackiea zephyrolata  s1'
 'Glyptocephalus *sp. eggs s1' 'Glyptocephalus *sp. flexion s2'
 'Gonatidae *sp. paralarvae s2' 'Gonatus *sp.  s2' 'Gonatus onyx  s2'
 'Grapsidae *sp. megalops s1' 'Grapsidae *sp. zoea s1'
 'Halacaridae *sp.  s1' 'Halitholus *sp.  s1' 'Halitholus *sp.  s2'
 'Halitholus *sp.  s3' 'Halocypris *sp.  s1' 'Harpacticoida *sp.  15'
 'Harpacticoida *sp.  56' 'Harpacticoida *sp.  6F'
 'Harpacticoida *sp.  6M' 'Hemicylops *sp.  2' 'Hemicylops *sp.  5'
 'Hemicylops *sp.  6F' 'Hemigrapsus *sp. megalops s1'
 'Hemigrapsus *sp. zoea s1' 'Hemilepidotus hemilepidotus flexion s2'
 'Heptacarpus stylus mysis s3' 'Heterorhabdus *sp.  3'
 'Heterorhabdus *sp.  4' 'Heterorhabdus *sp.  5'
 'Heterorhabdus tanneri  4' 'Heterorhabdus tanneri  5'
 'Heterorhabdus tanneri  6F' 'Heterorhabdus tanneri  6M'
 'Heterostylites longicornis  6F' 'Hippoglossoides *sp. eggs s1'
 'Hippoglossoides elassodon eggs s1'
 'Hippoglossoides elassodon flexion s2'
 'Hippoglossoides elassodon postflexion s3' 'Hippolytidae *sp. mysis s2'
 'Hippolytidae *sp. mysis s3' 'Hippolytidae *sp. zoea s1'
 'Holmesiella anomala  M' 'Holmesiella anomala  s2'
 'Holophryxus alaskensis  s1' 'Holothuroidea *sp. auricularia s1'
 'Holothuroidea *sp. doliolaria s1' 'Holothuroidea *sp. pentacula s1'
 'Hormiphora *sp.  s2' 'Hormiphora *sp.  s3' 'Hybocodon *sp.  s1'
 'Hybocodon prolifer  s1' 'Hyperia medusarum  F' 'Hyperia medusarum  M'
 'Hyperia medusarum  s1' 'Hyperia medusarum  s2' 'Hyperoche *sp.  s1'
 'Hyperoche mediterranea  F' 'Hyperoche mediterranea  M'
 'Hyperoche medusarum  F' 'Hyperoche medusarum  M'
 'Hyperoche medusarum  s1' 'Hyperoche medusarum  s2'
 'Insecta *sp. adult s1' 'Insecta *sp. adult s2' 'Insecta *sp. adult s3'
 'Insecta *sp. larvae s1' 'Insecta *sp. nymph s2' 'Insecta *sp. pupa s1'
 'Invertebrate *sp. eggs s1' 'Isopoda *sp.  s1'
 'Isopsetta isolepis preflexion s1' 'Labidocera *sp.  3'
 'Labidocera *sp.  4' 'Labidocera *sp.  5' 'Labidocera *sp.  6M'
 'Lebbeus *sp. mysis s2' 'Lensia achilles nectophore s2'
 'Lensia achilles nectophore s3' 'Lensia conoidea nectophore s2'
 'Lensia conoidea nectophore s3' 'Lepas *sp. cyprids s1'
 'Lepeophtheirus salmonis nauplii s1' 'Lepidogobius lepidus preflexion s1'
 'Lepidopsetta bilineata flexion s2'
 'Lepidopsetta bilineata postflexion s3'
 'Lepidopsetta bilineata preflexion s1' 'Leuckartiara *sp.  s1'
 'Leuckartiara *sp.  s2' 'Leuroglossus schmidti eggs s1'
 'Leuroglossus schmidti flexion s2' 'Leuroglossus schmidti postflexion s3'
 'Leuroglossus schmidti preflexion s1' 'Limacina helicina  s0'
 'Limacina helicina  s1' 'Limacina helicina  s2' 'Liparis *sp. flexion s2'
 'Liparis fucensis flexion s2' 'Liparis fucensis postflexion s3'
 'Liparis fucensis preflexion s1' 'Lithodidae *sp. megalops s1'
 'Lithodidae *sp. zoea s1' 'Lithodidae *sp. zoea s2'
 'Littorina *sp. egg cases s1' 'Loligo *sp.  s2'
 'Lophopanopeus *sp. megalops s1' 'Lubbockia *sp.  4' 'Lubbockia *sp.  5'
 'Lubbockia *sp.  6M' 'Lubbockia wilsonae  4' 'Lubbockia wilsonae  5'
 'Lubbockia wilsonae  6F' 'Lubbockia wilsonae  6M' 'Lucicutia ovalis  6F'
 'Lyopsetta exilis eggs s1' 'Lyopsetta exilis flexion s2'
 'Lyopsetta exilis postflexion s3' 'Lyopsetta exilis preflexion s1'
 'Lysianassidae *sp. flexion s2' 'Magelonidae *sp. larvae s1'
 'Magelonidae *sp. larvae s2' 'Majidae *sp. megalops s1'
 'Majidae *sp. megalops s2' 'Majidae *sp. zoea s1'
 'Mallotus villosus flexion s2' 'Maupasia coeca  s1' 'Medusae *sp.  s0'
 'Medusae *sp.  s1' 'Medusae *sp.  s2' 'Medusae *sp.  s3'
 'Melicertum octocostatum  s1' 'Melicertum octocostatum  s2'
 'Melphidippa *sp.  s3' 'Merluccius productus eggs s1'
 'Merluccius productus flexion s2' 'Merluccius productus postflexion s3'
 'Merluccius productus preflexion s1' 'Mesosagitta decipiens  s2'
 'Mesosagitta decipiens  s3' 'Mesosagitta minima  s2'
 'Meterythrops robusta  M' 'Meterythrops robusta  s1' 'Metridia *sp.  1'
 'Metridia *sp.  2' 'Metridia *sp.  3' 'Metridia *sp.  4'
 'Metridia *sp.  5' 'Metridia *sp.  6F' 'Metridia *sp.  6M'
 'Metridia pacifica  3' 'Metridia pacifica  4' 'Metridia pacifica  5F'
 'Metridia pacifica  5M' 'Metridia pacifica  6F' 'Metridia pacifica  6M'
 'Metridia pseudopacifica  5' 'Metridia pseudopacifica  6F'
 'Metridia pseudopacifica  6M' 'Microcalanus *sp.  3'
 'Microcalanus *sp.  4' 'Microcalanus *sp.  5' 'Microcalanus pusillus  4'
 'Microcalanus pusillus  5' 'Microcalanus pusillus  6F'
 'Microcalanus pusillus  6M' 'Microcalanus pygmaeus  3'
 'Microcalanus pygmaeus  4' 'Microcalanus pygmaeus  5'
 'Microcalanus pygmaeus  6F' 'Microcalanus pygmaeus  6M'
 'Microgadus proximus flexion s2' 'Microgadus proximus preflexion s1'
 'Microniscus *sp. larvae s1' 'Microsetella *sp.  5'
 'Microsetella norvegica  6F' 'Microsetella rosea  5'
 'Microsetella rosea  6F' 'Microstomus pacificus eggs s1'
 'Microstomus pacificus flexion s2' 'Mikroconchoecia stigmatica  s1'
 'Mitrocoma cellularia  s2' 'Mitrocoma cellularia  s3'
 'Mitrocomella *sp.  s1' 'Mitrocomella *sp.  s2' 'Monstrilla wandelii  F'
 'Monstrilloida *sp.  56' 'Monstrilloida *sp.  F' 'Monstrilloida *sp.  M'
 'Muggiaea atlantica eudoxid s1' 'Muggiaea atlantica nectophore s1'
 'Muggiaea atlantica nectophore s2' 'Muggiaea atlantica nectophore s3'
 'Munida *sp. zoea s1' 'Munida *sp. zoea s2'
 'Munida quadrispina megalops s2' 'Munida quadrispina megalops s3'
 'Munnopsis *sp.  s1' 'Mysidacea *sp. juvenile s1'
 'Nanomia bijuga nectophore s1' 'Nanomia bijuga nectophore s2'
 'Nanomia bijuga pneumatophore s1' 'Nanomia bijuga pneumatophore s2'
 'Nanomia bijuga pneumatophore s3' 'Narcomedusae *sp. larvae s1'
 'Nectoliparis pelagicus flexion s2'
 'Nectoliparis pelagicus postflexion s3' 'Nematoda *sp.  s1'
 'Nematoda *sp.  s2' 'Nematoscelis difficilis  F'
 'Nematoscelis difficilis  M' 'Nematoscelis difficilis  s3'
 'Nemertea *sp. larvae s1' 'Nemertea *sp. larvae s2'
 'Nemertea *sp. pilidium s1' 'Neocalanus *sp.  4'
 'Neocalanus *sp. eggs s1' 'Neocalanus *sp. nauplii s1'
 'Neocalanus cristatus  2' 'Neocalanus cristatus  3'
 'Neocalanus cristatus  4' 'Neocalanus cristatus  5'
 'Neocalanus cristatus  6F' 'Neocalanus cristatus  6M'
 'Neocalanus plumchrus  1' 'Neocalanus plumchrus  2'
 'Neocalanus plumchrus  3' 'Neocalanus plumchrus  4'
 'Neocalanus plumchrus  5' 'Neocalanus plumchrus  6F'
 'Neocalanus plumchrus  6M' 'Neomysis *sp.  s2' 'Neomysis rayii  F'
 'Neomysis rayii  M' 'Neomysis rayii  s2' 'Neotrypaea *sp. mysis s1'
 'Neotrypaea *sp. mysis s2' 'Neotrypaea *sp. zoea s1'
 'Neotrypaea *sp. zoea s2' 'Neoturris breviconis  s2'
 'Neoturris breviconis  s3' 'Nereidae *sp. nectochaete s1'
 'Noctiluca *sp.  s1' 'Obelia *sp.  s1' 'Octopoda *sp. larvae s1'
 'Octopus rubescens larvae s1' 'Octopus rubescens larvae s2'
 'Odontopyxis trispinosa flexion s2' 'Oedicerotidae *sp.  s1'
 'Oedicerotidae *sp.  s2' 'Oikopleura *sp.  s1' 'Oikopleura *sp.  s2'
 'Oikopleura dioica  s1' 'Oikopleura dioica  s2'
 'Oikopleura labradoriensis  s1' 'Oikopleura labradoriensis  s2'
 'Oikopleura labradoriensis  s3' 'Oithona *sp.  1' 'Oithona *sp.  14'
 'Oithona *sp.  2' 'Oithona *sp.  3' 'Oithona *sp.  4'
 'Oithona atlantica  3' 'Oithona atlantica  4' 'Oithona atlantica  5'
 'Oithona atlantica  6F' 'Oithona atlantica  6M' 'Oithona similis  2'
 'Oithona similis  3' 'Oithona similis  4' 'Oithona similis  5'
 'Oithona similis  6F' 'Oithona similis  6M' 'Oncaea prolata  5'
 'Oncaea prolata  6F' 'Oncaea prolata  6M' 'Oncaeidae *sp.  2'
 'Oncaeidae *sp.  3' 'Oncaeidae *sp.  4' 'Oncaeidae *sp.  5'
 'Oncaeidae *sp.  6F' 'Oncaeidae *sp.  6M' 'Ophiuroid *sp. juvenile s1'
 'Ophiuroid *sp. pluteus s1' 'Orchomenella *sp.  s2'
 'Orchomenella *sp.  s3' 'Oregonia *sp. megalops s1'
 'Osmeridae *sp. flexion s2' 'Ostracoda *sp.  s1'
 'Oweniidae *sp. larvae s1' 'Pacifacanthomysis nephrophthalma  s2'
 'Paguridae *sp. megalops s1' 'Paguridae *sp. megalops s2'
 'Paguridae *sp. zoea s1' 'Paguridae *sp. zoea s2'
 'Pandalidae *sp. mysis s2' 'Pandalidae *sp. mysis s3'
 'Pandalidae *sp. zoea s1' 'Pandalidae *sp. zoea s2'
 'Pandalopsis dispar  s2' 'Pandalopsis dispar  s3' 'Pandalus danae  s2'
 'Pandalus danae  s3' 'Pandalus eous  s3' 'Pandalus jordani  s2'
 'Pandalus jordani  s3' 'Pandalus stenolepis  s1'
 'Pandalus stenolepis  s2' 'Pandalus stenolepis  s3'
 'Pandalus tridens  s2' 'Pandalus tridens  s3' 'Paracalanus *sp.  1'
 'Paracalanus *sp.  2' 'Paracalanus *sp.  3' 'Paracalanus *sp.  4'
 'Paracalanus *sp.  5' 'Paracalanus *sp.  6F' 'Paracalanus *sp.  6M'
 'Paracalanus indicus  3' 'Paracalanus indicus  4'
 'Paracalanus indicus  5' 'Paracalanus indicus  6F'
 'Paracalanus indicus  6M' 'Paracalanus parvus  4' 'Paracalanus parvus  5'
 'Paracalanus parvus  6F' 'Paracalanus parvus  6M'
 'Paracalanus quasimodo  5' 'Paracalanus quasimodo  6F'
 'Paracalanus quasimodo  6M' 'Paradulichia *sp.  s1'
 'Paraeuchaeta *sp.  4' 'Paraeuchaeta elongata  4'
 'Paraeuchaeta elongata  5F' 'Paraeuchaeta elongata  5M'
 'Paraeuchaeta elongata  6F' 'Paraeuchaeta elongata  6M'
 'Parasagitta elegans  s2' 'Parasagitta elegans  s3'
 'Parasagitta elegans juvenile s1' 'Parasagitta euneritica  s2'
 'Parasagitta euneritica  s3' 'Paroithona *sp.  5'
 'Parophrys vetulus eggs s1' 'Parophrys vetulus flexion s2'
 'Parophrys vetulus postflexion s3' 'Parophrys vetulus preflexion s1'
 'Pasiphaea pacifica  F' 'Pasiphaea pacifica  M' 'Pasiphaea pacifica  s2'
 'Pasiphaea pacifica  s3' 'Pasiphaea pacifica larvae s1'
 'Peachia *sp.  s1' 'Peachia *sp.  s2' 'Pectinariidae *sp. larvae s1'
 'Pelagobia longicirrata  s1' 'Perciformes *sp. postflexion s3'
 'Phalacrophorus pictus  s1' 'Phalacrophorus pictus  s2'
 'Philomedidae *sp.  s1' 'Pholidae *sp. postflexion s3'
 'Phoronidae *sp. actinotrocha s1' 'Phoxocephalidae *sp.  s1'
 'Phoxocephalidae *sp.  s2' 'Phronima sedentaria  M'
 'Phyllodocida *sp.  s2' 'Phyllodocida *sp. larvae s1'
 'Pinnixa *sp. megalops s1' 'Pinnotheres *sp. megalops s1'
 'Pinnotheridae *sp. megalops s1' 'Pinnotheridae *sp. megalops s2'
 'Pinnotheridae *sp. zoea s1' 'Pinnotheridae *sp. zoea s2'
 'Pisidae zoea zoea s1' 'Planaria *sp.  s1'
 'Plectobranchus evides flexion s2' 'Pleurobrachia bachei  s1'
 'Pleurobrachia bachei  s2' 'Pleurobrachia bachei  s3'
 'Pleuromamma abdominalis  6M' 'Pleuromamma indica  6F'
 'Pleuromamma scutullata  6F' 'Pleuronectidae *sp. eggs s0'
 'Pleuronectidae *sp. eggs s1' 'Pleuronectidae *sp. flexion s2'
 'Pleuronectidae *sp. preflexion s1' 'Pleuronichthys *sp. eggs s1'
 'Pleuronichthys coenosus eggs s1' 'Pleuronichthys coenosus preflexion s1'
 'Plotocnide borealis  s1' 'Polychaeta *sp.  s1' 'Polychaeta *sp.  s2'
 'Polychaeta *sp.  s3' 'Polychaeta *sp. larvae s1'
 'Polychaeta *sp. trochophores s1' 'Polygordius *sp. larvae s1'
 'Polynoidae *sp. larvae s1' 'Polynoidae *sp. larvae s2'
 'Porcellanidae *sp. megalops s1' 'Porcellanidae *sp. zoea s1'
 'Porcellanidae *sp. zoea s2' 'Primno *sp.  s1' 'Primno abyssalis  F'
 'Primno abyssalis  M' 'Primno abyssalis  s1' 'Primno abyssalis  s2'
 'Primno brevidens  s1' 'Proboscidactyla *sp.  s1'
 'Proboscidactyla flavicirrata  s1' 'Proboscidactyla flavicirrata  s2'
 'Proneomysis wailesi  s2' 'Psettichthys melanostictus postflexion s3'
 'Pseudocalanus *sp.  1' 'Pseudocalanus *sp.  2' 'Pseudocalanus *sp.  3'
 'Pseudocalanus *sp.  4' 'Pseudocalanus mimus  4'
 'Pseudocalanus mimus  5F' 'Pseudocalanus mimus  5M'
 'Pseudocalanus mimus  6F' 'Pseudocalanus mimus  6M'
 'Pseudocalanus minutus  4' 'Pseudocalanus minutus  5'
 'Pseudocalanus minutus  5F' 'Pseudocalanus minutus  5M'
 'Pseudocalanus minutus  6F' 'Pseudocalanus minutus  6M'
 'Pseudocalanus moultoni  4' 'Pseudocalanus moultoni  5'
 'Pseudocalanus moultoni  5F' 'Pseudocalanus moultoni  5M'
 'Pseudocalanus moultoni  6F' 'Pseudocalanus moultoni  6M'
 'Pseudocalanus newmani  4' 'Pseudocalanus newmani  5F'
 'Pseudocalanus newmani  5M' 'Pseudocalanus newmani  6F'
 'Pseudocalanus newmani  6M' 'Pseudosagitta *sp.  s3'
 'Pseudosagitta scrippsae  s2' 'Pseudosagitta scrippsae  s3'
 'Ptychogena lactea  s2' 'Ptychogena lactea  s3'
 'Pugettia *sp. megalops s1' 'Racovitzanus antarcticus  4'
 'Racovitzanus antarcticus  5' 'Racovitzanus antarcticus  6F'
 'Radulinus *sp. preflexion s1' 'Radulinus asprellus flexion s2'
 'Rathkea *sp.  s1' 'Rhincalanus nasutus  4' 'Rhincalanus nasutus  6F'
 'Rhinogobiops nicholsii flexion s2'
 'Rhinogobiops nicholsii preflexion s1'
 'Rhinolithodes wosnessenskii zoea s3' 'Rhizocephala *sp. nauplii s1'
 'Rhopalonematidae *sp.  s1' 'Rhynchonereella angelina  s2'
 'Rhynchonereella angelina  s3' 'Ronquilus jordani flexion s2'
 'Ronquilus jordani preflexion s1' 'Rotifera *sp.  s1'
 'Ruscarius meanyi flexion s2' 'Ruscarius meanyi postflexion s3'
 'Ruscarius meanyi preflexion s1' 'Sarsia *sp.  s1' 'Sarsia *sp.  s2'
 'Sarsia *sp.  s3' 'Sarsia japonica  s2' 'Sarsia princeps  s2'
 'Sarsia princeps  s3' 'Scaphocalanus *sp.  3' 'Scaphocalanus *sp.  4'
 'Scaphocalanus *sp.  5' 'Scaphocalanus *sp.  6M'
 'Scaphocalanus brevicornis  5' 'Scaphocalanus brevicornis  6F'
 'Scaphocalanus brevicornis  6M' 'Scaphocalanus echinatus  5'
 'Scaphocalanus echinatus  6F' 'Scina *sp.  s1' 'Scina *sp.  s2'
 'Scina borealis  F' 'Scina borealis  M' 'Scina borealis  s1'
 'Scina borealis  s2' 'Scleroconcha trituberculata  s1'
 'Scolecithricella globulosa  5' 'Scolecithricella minor  2'
 'Scolecithricella minor  3' 'Scolecithricella minor  4'
 'Scolecithricella minor  5' 'Scolecithricella minor  6F'
 'Scolecithricella minor  6M' 'Scolecithricidae *sp.  6M'
 'Scorpaenichthys marmoratus flexion s2' 'Scottocalanus persecans  6F'
 'Scyphozoa medusae  s3' 'Scyphozoa medusae ephyra s1'
 'Scyphozoa medusae ephyra s2' 'Sebastes *sp. flexion s2'
 'Sebastes *sp. postflexion s3' 'Sebastes *sp. preflexion s1'
 'Sergestes *sp. mysis s1' 'Sergestes *sp. mysis s2'
 'Sergestes *sp. zoea s1' 'Sergestes similis  F'
 'Siphonophora *sp. bracts s1' 'Siphonophora *sp. eudoxid s1'
 'Siphonophora *sp. gas floats s1' 'Siphonophora *sp. gas floats s2'
 'Siphonophora *sp. gas floats s3' 'Siphonophora *sp. larvae s1'
 'Siphonophora *sp. nectophore s1' 'Siphonophora *sp. nectophore s2'
 'Siphonophora *sp. nectophore s3' 'Solmissus *sp.  s3'
 'Spinocalanus *sp.  3' 'Spinocalanus *sp.  4' 'Spinocalanus *sp.  5'
 'Spinocalanus *sp.  6M' 'Spinocalanus brevicaudatus  5'
 'Spinocalanus brevicaudatus  6F' 'Spinocalanus brevicaudatus  6M'
 'Spinocalanus longicornis  5' 'Spionidae *sp.  s3'
 'Spionidae *sp. larvae s1' 'Spionidae *sp. larvae s2'
 'Spirontocaris *sp.  s2' 'Stichaeidae *sp. flexion s2'
 'Stichaeidae *sp. postflexion s3' 'Stilipes distinctus  s2'
 'Stilipes distinctus  s3' 'Stomotoca *sp.  s1' 'Stomotoca atra  s1'
 'Stomotoca atra  s2' 'Stomotoca atra  s3' 'Syllidae *sp.  s1'
 'Syllidae *sp.  s2' 'Syllidae *sp.  s3'
 'Syngnathus leptorhynchus postflexion s3' 'Tessarabrachion oculatum  F'
 'Teuthida *sp. larvae s1' 'Teuthida *sp. larvae s2'
 'Thalassocalyce inconstans  s2' 'Thaleichthys pacificus flexion s2'
 'Tharybis fultoni  3' 'Tharybis fultoni  4' 'Tharybis fultoni  5'
 'Tharybis fultoni  6F' 'Tharybis fultoni  6M' 'Themisto *sp.  s1'
 'Themisto pacifica  F' 'Themisto pacifica  M' 'Themisto pacifica  s2'
 'Themisto pacifica juvenile s1' 'Theragra chalcogramma eggs s1'
 'Theragra chalcogramma flexion s2' 'Theragra chalcogramma postflexion s3'
 'Theragra chalcogramma preflexion s1' 'Thysanoessa *sp.  s1'
 'Thysanoessa *sp.  s2' 'Thysanoessa *sp. nauplii s1'
 'Thysanoessa *sp. zoea s1' 'Thysanoessa longipes  F'
 'Thysanoessa longipes  M' 'Thysanoessa longipes  s2'
 'Thysanoessa longipes  s3' 'Thysanoessa longipes zoea s1'
 'Thysanoessa raschii  F' 'Thysanoessa raschii  M'
 'Thysanoessa raschii  s2' 'Thysanoessa raschii zoea s1'
 'Thysanoessa spinifera  F' 'Thysanoessa spinifera  M'
 'Thysanoessa spinifera  s2' 'Thysanoessa spinifera  s3'
 'Thysanoessa spinifera eggs s1' 'Thysanoessa spinifera nauplii s1'
 'Thysanoessa spinifera zoea s1' 'Tiaropsidium kelseyi  s2'
 'Tiaropsidium kelseyi  s3' 'Tintinnida *sp.  s1' 'Tomopteris *sp.  s1'
 'Tomopteris *sp.  s2' 'Tomopteris *sp.  s3' 'Tomopteris elegans  s1'
 'Tomopteris elegans  s2' 'Tomopteris pacifica  s2'
 'Tomopteris pacifica  s3' 'Tomopteris septentrionalis  s1'
 'Tomopteris septentrionalis  s2' 'Tomopteris septentrionalis  s3'
 'Tortanus *sp. eggs s1' 'Tortanus *sp. nauplii s1'
 'Tortanus discaudatus  1' 'Tortanus discaudatus  2'
 'Tortanus discaudatus  3' 'Tortanus discaudatus  4'
 'Tortanus discaudatus  5' 'Tortanus discaudatus  6F'
 'Tortanus discaudatus  6M' 'Triconia borealis  4' 'Triconia borealis  5'
 'Triconia borealis  6F' 'Triconia borealis  6M' 'Triconia conifera  6F'
 'Triconia conifera  6M' 'Trochophore *sp. larvae s1' 'Tryphana malmi  F'
 'Tryphana malmi  M' 'Turbellaria *sp.  s1' 'Typhloscolex muelleri  s1'
 'Typhloscolex muelleri  s2' 'Typhloscolex muelleri  s3'
 'Unclassified *sp. eggs s1' 'Xanthidae *sp. megalops s1'
 'Xanthidae *sp. zoea s1' 'Xenacanthomysis pseudomacropsis  F'
 'Xenacanthomysis pseudomacropsis  M'
 'Xenacanthomysis pseudomacropsis  s2' 'Xeneretmus latifrons flexion s2'
 'Xeneretmus leiops postflexion s3']

Start by creating a group of zooplankton taxa of interest¶

In [18]:

colList=('Anomura *sp. megalops s1',
 'Anomura *sp. zoea s1' ,'Anomura *sp. zoea s2', 'Axiidae *sp.  s1', 'Axiidae *sp. mysis s2',
 'Axiidae *sp. zoea s1' ,'Axiidae *sp. zoea s2','Brachyura *sp. zoea s1',
'Calanoida *sp.  1', 'Calanoida *sp.  2' ,'Calanoida *sp.  3',
 'Calanoida *sp.  4' ,'Calanoida *sp.  6M', 'Calanus *sp.  1',
 'Calanus *sp.  2', 'Calanus *sp.  3' ,'Calanus *sp.  4',
 'Calanus *sp. nauplii s1', 'Calanus marshallae  2' ,'Calanus marshallae  3',
 'Calanus marshallae  4' ,'Calanus marshallae  5' ,'Calanus marshallae  6F',
 'Calanus marshallae  6M' ,'Calanus pacificus  2' ,'Calanus pacificus  3',
 'Calanus pacificus  4' ,'Calanus pacificus  5', 'Calanus pacificus  6F',
 'Calanus pacificus  6M','Calliopius *sp.  s1' ,'Calliopius *sp.  s2', 'Calliopius *sp.  s3',
 'Calliopius pacificus  s1' ,'Calliopius pacificus  s2',
 'Calliopius pacificus  s3', 'Cancer *sp. megalops s1',
 'Cancer *sp. zoea s1' ,'Cancer *sp. zoea s2', 'Cancer magister megalops s2',
 'Cancer magister zoea s1' ,'Cancer magister zoea s2',
 'Cancer oregonensis megalops s1', 'Cancer oregonensis megalops s2',
 'Cancer productus megalops s1' ,'Cancer productus megalops s2',
 'Cancer productus zoea s1' ,'Cancer productus zoea s2','Caridea *sp.  s3' ,'Caridea *sp. mysis s1',
 'Caridea *sp. mysis s2', 'Caridea *sp. mysis s3' ,'Caridea *sp. zoea s1',
 'Caridea *sp. zoea s2', 'Chaetognatha *sp.  s3' ,'Chaetognatha *sp. juvenile s1',
 'Chaetognatha *sp. juvenile s2','Chionoecetes *sp. megalops s1',
 'Chionoecetes *sp. megalops s2', 'Chionoecetes *sp. zoea s1','Corycaeus anglicus  3',
 'Corycaeus anglicus  4' ,'Corycaeus anglicus  5' ,'Corycaeus anglicus  6F',
 'Corycaeus anglicus  6M' ,'Crangonidae *sp. mysis s1',
 'Crangonidae *sp. mysis s2' ,'Crangonidae *sp. mysis s3',
 'Crangonidae *sp. zoea s1', 'Crangonidae *sp. zoea s2','Cyphocaris *sp.  s1',
 'Cyphocaris challengeri  s1' ,'Cyphocaris challengeri  s2',
 'Cyphocaris challengeri  s3', 'Decapoda *sp. zoea s1','Eucalanus *sp.  1',
 'Eucalanus *sp.  2' ,'Eucalanus *sp.  3' ,'Eucalanus *sp. nauplii s1',
 'Eucalanus bungii  3', 'Eucalanus bungii  4', 'Eucalanus bungii  4F',
 'Eucalanus bungii  4M', 'Eucalanus bungii  5F', 'Eucalanus bungii  5M',
 'Eucalanus bungii  6F', 'Eucalanus bungii  6M',
 'Eucalanus californicus  5F', 'Eucalanus californicus  6F','Euphausia pacifica  F',
 'Euphausia pacifica  M' ,'Euphausia pacifica  s2', 'Euphausia pacifica  s3',
 'Euphausia pacifica eggs s1' ,'Euphausia pacifica nauplii s1',
 'Euphausia pacifica zoea s1', 'Euphausiacea *sp.  s2',
 'Euphausiidae *sp. eggs s1', 'Euphausiidae *sp. nauplii s1',
 'Euphausiidae *sp. protozoea (or calyptopis) s1',
 'Euphausiidae *sp. zoea (or furcilia) s1','Galatheidae *sp. zoea s1', 'Galatheidae *sp. zoea s2',
 'Gammaridea *sp.  s1', 'Gammaridea *sp.  s2','Grapsidae *sp. megalops s1', 'Grapsidae *sp. zoea s1','Hemigrapsus *sp. megalops s1'
 ,'Hemigrapsus *sp. zoea s1','Hyperia medusarum  F', 'Hyperia medusarum  M',
 'Hyperia medusarum  s1', 'Hyperia medusarum  s2' ,'Limacina helicina  s0',
 'Limacina helicina  s1' ,'Limacina helicina  s2','Lithodidae *sp. megalops s1',
 'Lithodidae *sp. zoea s1', 'Lithodidae *sp. zoea s2','Lophopanopeus *sp. megalops s1','Majidae *sp. megalops s1',
 'Majidae *sp. megalops s2', 'Majidae *sp. zoea s1','Metridia *sp.  1',
 'Metridia *sp.  2', 'Metridia *sp.  3' ,'Metridia *sp.  4',
 'Metridia *sp.  5', 'Metridia *sp.  6F', 'Metridia *sp.  6M',
 'Metridia pacifica  3' ,'Metridia pacifica  4' ,'Metridia pacifica  5F',
 'Metridia pacifica  5M' 'Metridia pacifica  6F' 'Metridia pacifica  6M'
 'Metridia pseudopacifica  5', 'Metridia pseudopacifica  6F',
 'Metridia pseudopacifica  6M' ,'Munida *sp. zoea s1' ,'Munida *sp. zoea s2',
 'Munida quadrispina megalops s2', 'Munida quadrispina megalops s3','Neocalanus *sp.  4',
 'Neocalanus *sp. eggs s1', 'Neocalanus *sp. nauplii s1',
 'Neocalanus cristatus  2', 'Neocalanus cristatus  3',
 'Neocalanus cristatus  4', 'Neocalanus cristatus  5',
 'Neocalanus cristatus  6F' ,'Neocalanus cristatus  6M',
 'Neocalanus plumchrus  1', 'Neocalanus plumchrus  2',
 'Neocalanus plumchrus  3', 'Neocalanus plumchrus  4',
 'Neocalanus plumchrus  5', 'Neocalanus plumchrus  6F',
 'Neocalanus plumchrus  6M' ,'Neotrypaea *sp. mysis s1',
 'Neotrypaea *sp. mysis s2', 'Neotrypaea *sp. zoea s1',
 'Neotrypaea *sp. zoea s2', 'Oikopleura *sp.  s1', 'Oikopleura *sp.  s2',
 'Oikopleura dioica  s1' ,'Oikopleura dioica  s2',
 'Oikopleura labradoriensis  s1', 'Oikopleura labradoriensis  s2',
 'Oikopleura labradoriensis  s3', 'Oregonia *sp. megalops s1','Paguridae *sp. megalops s1' ,'Paguridae *sp. megalops s2',
 'Paguridae *sp. zoea s1', 'Paguridae *sp. zoea s2',
 'Pandalidae *sp. mysis s2', 'Pandalidae *sp. mysis s3',
 'Pandalidae *sp. zoea s1', 'Pandalidae *sp. zoea s2',
 'Pandalopsis dispar  s2' ,'Pandalopsis dispar  s3' ,'Pandalus danae  s2',
 'Pandalus danae  s3', 'Pandalus eous  s3', 'Pandalus jordani  s2',
 'Pandalus jordani  s3', 'Pandalus stenolepis  s1',
 'Pandalus stenolepis  s2', 'Pandalus stenolepis  s3',
 'Pandalus tridens  s2' ,'Pandalus tridens  s3','Parasagitta elegans  s2', 'Parasagitta elegans  s3',
 'Parasagitta elegans juvenile s1', 'Parasagitta euneritica  s2',
 'Parasagitta euneritica  s3','Pinnixa *sp. megalops s1', 'Pinnotheres *sp. megalops s1',
 'Pinnotheridae *sp. megalops s1', 'Pinnotheridae *sp. megalops s2',
 'Pinnotheridae *sp. zoea s1' ,'Pinnotheridae *sp. zoea s2',
 'Pisidae zoea zoea s1', 'Porcellanidae *sp. megalops s1', 'Porcellanidae *sp. zoea s1',
 'Porcellanidae *sp. zoea s2', 'Primno *sp.  s1', 'Primno abyssalis  F',
 'Primno abyssalis  M', 'Primno abyssalis  s1', 'Primno abyssalis  s2',
 'Primno brevidens  s1' ,'Pugettia *sp. megalops s1', 'Themisto *sp.  s1',
 'Themisto pacifica  F', 'Themisto pacifica  M', 'Themisto pacifica  s2',
 'Themisto pacifica juvenile s1', 'Thysanoessa *sp.  s1',
 'Thysanoessa *sp.  s2', 'Thysanoessa *sp. nauplii s1',
 'Thysanoessa *sp. zoea s1', 'Thysanoessa longipes  F',
 'Thysanoessa longipes  M', 'Thysanoessa longipes  s2',
 'Thysanoessa longipes  s3' ,'Thysanoessa longipes zoea s1',
 'Thysanoessa raschii  F', 'Thysanoessa raschii  M',
 'Thysanoessa raschii  s2' ,'Thysanoessa raschii zoea s1',
 'Thysanoessa spinifera  F', 'Thysanoessa spinifera  M',
 'Thysanoessa spinifera  s2', 'Thysanoessa spinifera  s3',
 'Thysanoessa spinifera eggs s1', 'Thysanoessa spinifera nauplii s1',
 'Thysanoessa spinifera zoea s1','Xanthidae *sp. megalops s1',
 'Xanthidae *sp. zoea s1')

In [19]:

df.keys()

Out[19]:

Index(['Key', 'region_name', 'Station', 'PROJECT', 'lon', 'lat', 'Date',
       'STN_TIME', 'Twilight', 'Net_Type', 'Mesh_Size(um)', 'Net_Mouth_Dia(m)',
       'DEPTH_STRT1', 'DEPTH_END1', 'Bottom Depth(m)', 'Volume Filtered(m3)',
       'CTD', 'NOTES', 'PI', 'Phylum:', 'Class:', 'Order:', 'Family:', 'Name',
       'Abundance(#/m3)', 'Biomass(mg/m3)', 'NumberOfSpecies',
       'Station Diversity', 'Station Equitability', 'dtUTC'],
      dtype='object')

In [20]:

dtsutc=et.pac_to_utc(dts)

In [21]:

df.loc[0]

Out[21]:

Key                                                      IOS2012005000901
region_name                                    Northern Strait of Georgia
Station                                                                22
PROJECT                                                         Str. Geo.
lon                                                              -124.272
lat                                                                 49.67
Date                                                            6/14/2012
STN_TIME                                                             7:32
Twilight                                                         Daylight
Net_Type                                                         SCOR VNH
Mesh_Size(um)                                                         236
Net_Mouth_Dia(m)                                                     0.56
DEPTH_STRT1                                                            50
DEPTH_END1                                                              0
Bottom Depth(m)                                                       352
Volume Filtered(m3)                                                 10.47
CTD                                                                   8.0
NOTES                   Flowmeter reading may be incorrect. Value ente...
PI                                                            Dave Mackas
Phylum:                                                        Arthropoda
Class:                                                       Branchiopoda
Order:                                                        Diplostraca
Family:                                                         Podonidae
Name                                                       Podon *sp.  s1
Abundance(#/m3)                                                  12.22541
Biomass(mg/m3)                                                    0.06113
NumberOfSpecies                                                        51
Station Diversity                                                    2.63
Station Equitability                                                 0.67
dtUTC                                                 2012-06-14 14:32:00
Name: 0, dtype: object

In [22]:

towIDlist=['Key', 'region_name', 'Station',  'lon', 'lat','Date', 'dtUTC', 'Twilight', 'Net_Type', 'Mesh_Size(um)', 'DEPTH_STRT1', 'DEPTH_END1', 'Bottom Depth(m)']

In [23]:

towIDlist2=['Key', 'region_name', 'Station',  'lon', 'lat','Date', 'dtUTC', 'Twilight', 'Net_Type', 'Mesh_Size(um)', 'DEPTH_STRT1', 'DEPTH_END1', 'Bottom Depth(m)','CTD']

In [24]:

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

Out[24]:

(654, 12694, 654)

In [25]:

# Key is a unique identifier for each tow
# do not group by CTD due to NaN values

Create an abundance dataframe¶

In [26]:

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

In [27]:

biomassDF

Out[27]:

	Key	region_name	Station	lon	lat	Date	dtUTC	Twilight	Net_Type	Mesh_Size(um)	DEPTH_STRT1	DEPTH_END1	Bottom Depth(m)
0	IOS2012005000901	Northern Strait of Georgia	22	-124.272	49.670	6/14/2012	2012-06-14 14:32:00	Daylight	SCOR VNH	236	50	0	352
1	IOS2012005001001	Northern Strait of Georgia	22	-124.272	49.670	6/14/2012	2012-06-14 14:52:00	Daylight	SCOR VNH	236	345	0	352
2	IOS2012005002101	Northern Strait of Georgia	11	-124.722	49.710	6/14/2012	2012-06-14 07:00:00	Night	SCOR VNH	236	50	0	307
3	IOS2012005002201	Northern Strait of Georgia	11	-124.722	49.710	6/14/2012	2012-06-14 07:05:00	Night	SCOR VNH	236	300	0	307
4	IOS2012005002901	Northern Strait of Georgia	CPF2	-124.499	49.466	6/15/2012	2012-06-15 10:00:00	Night	SCOR VNH	236	50	0	325
...	...	...	...	...	...	...	...	...	...	...	...	...	...
649	SOO2015095000101	Tidal Mixed	CLO-42	-123.345	48.394	4/27/2015	2015-04-27 17:37:00	Daylight	SCOR VNH	236	58	0	72
650	SOO2015095000401	Tidal Mixed	CB01	-123.318	48.344	4/30/2015	2015-04-30 19:00:00	Daylight	SCOR VNH	236	54	0	64
651	SOO2015095000501	Tidal Mixed	CLO-41	-123.345	48.395	7/9/2015	2015-07-09 19:25:00	Daylight	SCOR VNH	236	42	0	64
652	SOO2015095000701	Tidal Mixed	CB01	-123.318	48.344	7/27/2015	2015-07-27 19:06:00	Daylight	SCOR VNH	236	28	0	60
653	SOO2015095000801	Tidal Mixed	MAC41	-123.409	48.402	7/27/2015	2015-07-27 20:00:00	Daylight	SCOR VNH	236	49	0	62

654 rows × 13 columns

In [28]:

#### Get abundance column to input into biomassDF as these abundance values will be multiplied by carbon conversion equations

In [29]:

#for icol in colList:
#    biomassDF[icol]=[getabundance(icol,ikey,df,'Name') for ikey in biomassDF['Key']]

In [30]:

#for icol in colList:
#    biomassDF[icol]=[getabundance(icol,ikey,df) for ikey in biomassDF['Key']]

In [32]:

def getabundance(colname,key,origdf):        
    abundanceArray=origdf.loc[(origdf.Key==key)&(origdf['Name']==colname),
                    ['Abundance(#/m3)']]
    if len(abundanceArray)>1:
        print(len(abundanceArray),colname,key)
    abundance=np.nansum(abundanceArray)
    
    return abundance

In [33]:

for icol in colList:
    biomassDF[icol]=[getabundance(icol,ikey,df) for ikey in biomassDF['Key']]

In [ ]:

In [38]:

biomassDF.loc[biomassDF.Key=='IOS2012005000901',['Calanus *sp.  1']]

Out[38]:

	Calanus *sp. 1
0	3.05635

In [37]:

biomassDF

Out[37]:

	Key	region_name	Station	lon	lat	Date	dtUTC	Twilight	Net_Type	Mesh_Size(um)	...	Thysanoessa raschii zoea s1	Thysanoessa spinifera F	Thysanoessa spinifera M	Thysanoessa spinifera s2	Thysanoessa spinifera s3	Thysanoessa spinifera eggs s1	Thysanoessa spinifera nauplii s1	Thysanoessa spinifera zoea s1	Xanthidae *sp. megalops s1	Xanthidae *sp. zoea s1
0	IOS2012005000901	Northern Strait of Georgia	22	-124.272	49.670	6/14/2012	2012-06-14 14:32:00	Daylight	SCOR VNH	236	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	IOS2012005001001	Northern Strait of Georgia	22	-124.272	49.670	6/14/2012	2012-06-14 14:52:00	Daylight	SCOR VNH	236	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	IOS2012005002101	Northern Strait of Georgia	11	-124.722	49.710	6/14/2012	2012-06-14 07:00:00	Night	SCOR VNH	236	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	IOS2012005002201	Northern Strait of Georgia	11	-124.722	49.710	6/14/2012	2012-06-14 07:05:00	Night	SCOR VNH	236	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	IOS2012005002901	Northern Strait of Georgia	CPF2	-124.499	49.466	6/15/2012	2012-06-15 10:00:00	Night	SCOR VNH	236	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
649	SOO2015095000101	Tidal Mixed	CLO-42	-123.345	48.394	4/27/2015	2015-04-27 17:37:00	Daylight	SCOR VNH	236	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
650	SOO2015095000401	Tidal Mixed	CB01	-123.318	48.344	4/30/2015	2015-04-30 19:00:00	Daylight	SCOR VNH	236	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
651	SOO2015095000501	Tidal Mixed	CLO-41	-123.345	48.395	7/9/2015	2015-07-09 19:25:00	Daylight	SCOR VNH	236	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
652	SOO2015095000701	Tidal Mixed	CB01	-123.318	48.344	7/27/2015	2015-07-27 19:06:00	Daylight	SCOR VNH	236	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
653	SOO2015095000801	Tidal Mixed	MAC41	-123.409	48.402	7/27/2015	2015-07-27 20:00:00	Daylight	SCOR VNH	236	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

654 rows × 250 columns

In [283]:

biomassDF.keys()[1:100]

Out[283]:

Index(['region_name', 'Station', 'Lon', 'Lat', 'Date', 'dtUTC', 'Twilight',
       'Net_Type', 'Mesh_Size(um)', 'Z_lower', 'Z_upper', 'Bottom Depth(m)',
       'Anomura *sp. megalops s1', 'Anomura *sp. zoea s1',
       'Anomura *sp. zoea s2', 'Axiidae *sp.  s1', 'Axiidae *sp. mysis s2',
       'Axiidae *sp. zoea s1', 'Axiidae *sp. zoea s2',
       'Brachyura *sp. zoea s1', 'Calanoida *sp.  1', 'Calanoida *sp.  2',
       'Calanoida *sp.  3', 'Calanoida *sp.  4', 'Calanoida *sp.  6M',
       'Calanus *sp.  1', 'Calanus *sp.  2', 'Calanus *sp.  3',
       'Calanus *sp.  4', 'Calanus *sp. nauplii s1', 'Calanus marshallae  2',
       'Calanus marshallae  3', 'Calanus marshallae  4',
       'Calanus marshallae  5', 'Calanus marshallae  6F',
       'Calanus marshallae  6M', 'Calanus pacificus  2',
       'Calanus pacificus  3', 'Calanus pacificus  4', 'Calanus pacificus  5',
       'Calanus pacificus  6F', 'Calanus pacificus  6M', 'Calliopius *sp.  s1',
       'Calliopius *sp.  s2', 'Calliopius *sp.  s3',
       'Calliopius pacificus  s1', 'Calliopius pacificus  s2',
       'Calliopius pacificus  s3', 'Cancer *sp. megalops s1',
       'Cancer *sp. zoea s1', 'Cancer *sp. zoea s2',
       'Cancer magister megalops s2', 'Cancer magister zoea s1',
       'Cancer magister zoea s2', 'Cancer oregonensis megalops s1',
       'Cancer oregonensis megalops s2', 'Cancer productus megalops s1',
       'Cancer productus megalops s2', 'Cancer productus zoea s1',
       'Cancer productus zoea s2', 'Caridea *sp.  s3', 'Caridea *sp. mysis s1',
       'Caridea *sp. mysis s2', 'Caridea *sp. mysis s3',
       'Caridea *sp. zoea s1', 'Caridea *sp. zoea s2', 'Chaetognatha *sp.  s3',
       'Chaetognatha *sp. juvenile s1', 'Chaetognatha *sp. juvenile s2',
       'Chionoecetes *sp. megalops s1', 'Chionoecetes *sp. megalops s2',
       'Chionoecetes *sp. zoea s1', 'Corycaeus anglicus  3',
       'Corycaeus anglicus  4', 'Corycaeus anglicus  5',
       'Corycaeus anglicus  6F', 'Corycaeus anglicus  6M',
       'Crangonidae *sp. mysis s1', 'Crangonidae *sp. mysis s2',
       'Crangonidae *sp. mysis s3', 'Crangonidae *sp. zoea s1',
       'Crangonidae *sp. zoea s2', 'Cyphocaris *sp.  s1',
       'Cyphocaris challengeri  s1', 'Cyphocaris challengeri  s2',
       'Cyphocaris challengeri  s3', 'Decapoda *sp. zoea s1',
       'Eucalanus *sp.  1', 'Eucalanus *sp.  2', 'Eucalanus *sp.  3',
       'Eucalanus *sp. nauplii s1', 'Eucalanus bungii  3',
       'Eucalanus bungii  4', 'Eucalanus bungii  4F', 'Eucalanus bungii  4M',
       'Eucalanus bungii  5F', 'Eucalanus bungii  5M', 'Eucalanus bungii  6F',
       'Eucalanus bungii  6M'],
      dtype='object')

In [177]:

# define log transform function
def log(x):
  return np.log10(x)

In [ ]:

Convert Abundances to new Biomass values using Puget Sound LW Regressions¶

Copepod Conversions¶

Neocalanus conversion C (ug):¶

In [39]:

biomassDF['Neocalanus plumchrus  1']=(biomassDF['Neocalanus plumchrus  1']*(4.13*(0.9**3.28))*0.45)/1000
biomassDF['Neocalanus plumchrus  2']=(biomassDF['Neocalanus plumchrus  2']*(4.13*(1.1**3.28))*0.45)/1000
biomassDF['Neocalanus plumchrus  3']=(biomassDF['Neocalanus plumchrus  3']*(4.13*(1.75**3.28))*0.45)/1000
biomassDF['Neocalanus plumchrus  4']=(biomassDF['Neocalanus plumchrus  4']*(4.13*(2.6**3.28))*0.45)/1000
biomassDF['Neocalanus plumchrus  5']=(biomassDF['Neocalanus plumchrus  5']*(2.00*(3.8**3.92))*0.5)/1000
biomassDF['Neocalanus plumchrus  6F']=(biomassDF['Neocalanus plumchrus  6F']*(2.00*(4.5**3.92))*0.5)/1000
biomassDF['Neocalanus plumchrus  6M']=(biomassDF['Neocalanus plumchrus  6M']*(2.00*(4.5**3.92))*0.5)/1000

Calanus Conversions C (ug):¶

In [40]:

#check these lengths/regression equations
biomassDF['Calanoida *sp.  1']=(biomassDF['Calanoida *sp.  1']*(4.13*(0.5**3.28))*0.45)/1000
biomassDF['Calanoida *sp.  2']=(biomassDF['Calanoida *sp.  2']*(4.13*(0.8**3.28))*0.45)/1000
biomassDF['Calanoida *sp.  3']=(biomassDF['Calanoida *sp.  3']*(4.13*(1.2**3.28))*0.45)/1000
biomassDF['Calanoida *sp.  4']=(biomassDF['Calanoida *sp.  4']*(4.13*(1.4**3.28))*0.45)/1000
biomassDF['Calanoida *sp.  6M']=(biomassDF['Calanoida *sp.  6M']*(2.00*(2.1**3.92))*0.45)/1000

In [41]:

#check these lengths/regression equations
biomassDF['Calanus *sp.  1']=(biomassDF['Calanus *sp.  1']*(4.13*(0.5**3.28))*0.45)/1000
biomassDF['Calanus *sp.  2']=(biomassDF['Calanus *sp.  2']*(4.13*(0.8**3.28))*0.45)/1000
biomassDF['Calanus *sp.  3']=(biomassDF['Calanus *sp.  3']*(4.13*(1.2**3.28))*0.45)/1000
biomassDF['Calanus *sp.  4']=(biomassDF['Calanus *sp.  4']*(4.13*(1.4**3.28))*0.45)/1000
biomassDF['Calanus *sp. nauplii s1']=(biomassDF['Calanus *sp. nauplii s1']*(4.13*(0.4**3.28))*0.45)/1000

In [42]:

biomassDF['Calanus pacificus  2']=(biomassDF['Calanus pacificus  2']*(4.13*(0.8**3.28))*0.45)/1000
biomassDF['Calanus pacificus  3']=(biomassDF['Calanus pacificus  3']*(4.13*(1.1**3.28))*0.45)/1000
biomassDF['Calanus pacificus  4']=(biomassDF['Calanus pacificus  4']*(4.13*(1.4**3.28))*0.45)/1000
biomassDF['Calanus pacificus  5']=(biomassDF['Calanus pacificus  5']*(2.00*(1.8**3.92))*0.45)/1000
biomassDF['Calanus pacificus  6F']=(biomassDF['Calanus pacificus  6F']*(2.00*(2.2**3.92))*0.45)/1000
biomassDF['Calanus pacificus  6M']=(biomassDF['Calanus pacificus  6M']*(2.00*(2.1**3.92))*0.45)/1000

In [43]:

biomassDF['Calanus marshallae  2']=(biomassDF['Calanus marshallae  2']*(4.13*(0.8**3.28))*0.45)/1000
biomassDF['Calanus marshallae  3']=(biomassDF['Calanus marshallae  3']*(4.13*(1.6**3.28))*0.45)/1000
biomassDF['Calanus marshallae  4']=(biomassDF['Calanus marshallae  4']*(4.13*(2.0**3.28))*0.45)/1000
biomassDF['Calanus marshallae  5']=(biomassDF['Calanus marshallae  5']*(2.00*(2.5**3.92))*0.45)/1000
biomassDF['Calanus marshallae  6F']=(biomassDF['Calanus marshallae  6F']*(2.00*(2.8**3.92))*0.45)/1000
biomassDF['Calanus marshallae  6M']=(biomassDF['Calanus marshallae  6M']*(2.00*(2.6**3.92))*0.45)/1000

Metridia conversions C (ug):¶

need lengths for these groups. Should they be included?¶

'Metridia sp. 2', 'Metridia sp. 3' ,'Metridia sp. 4', 'Metridia sp. 5', 'Metridia sp. 6F', 'Metridia sp. 6M', 'Metridia pseudopacifica 5', 'Metridia pseudopacifica 6F', 'Metridia pseudopacifica 6M'

In [44]:

biomassDF['Metridia *sp.  2']=(biomassDF['Metridia *sp.  2']*(4.13*(0.4**3.28))*0.45)/1000
biomassDF['Metridia *sp.  3']=(biomassDF['Metridia *sp.  3']*(4.13*(0.7**3.28))*0.45)/1000
biomassDF['Metridia *sp.  4']=(biomassDF['Metridia *sp.  4']*(4.13*(1.1**3.28))*0.45)/1000
biomassDF['Metridia *sp.  5']=(biomassDF['Metridia *sp.  5']*(4.13*(1.3**3.28))*0.45)/1000
biomassDF['Metridia *sp.  6F']=(biomassDF['Metridia *sp.  6F']*(4.13*(2.0**3.28))*0.45)/1000
biomassDF['Metridia *sp.  6M']=(biomassDF['Metridia *sp.  6M']*(4.13*(1.4**3.28))*0.45)/1000
biomassDF['Metridia pacifica  3']=(biomassDF['Metridia pacifica  3']*(4.13*(0.7**3.28))*0.45)/1000
biomassDF['Metridia pacifica  4']=(biomassDF['Metridia pacifica  4']*(4.13*(1.1**3.28))*0.45)/1000
biomassDF['Metridia pacifica  5F']=(biomassDF['Metridia pacifica  5F']*(4.13*(1.5**3.28))*0.45)/1000
#biomassDF['Metridia pacifica  5M']=biomassDF['Metridia pacifica  5M']*(4.13*(1.3**3.28))*0.45
#biomassDF['Metridia pacifica  6F']=biomassDF['Metridia pacifica  6F']*(4.13*(2.0**3.28))*0.45
#biomassDF['Metridia pacifica  6M']=biomassDF['Metridia pacifica  6M']*(4.13*(1.4**3.28))*0.45

In [45]:

biomassDF['Metridia *sp.  2'].unique()[1:20]

Out[45]:

array([0.00260935, 0.02090429, 0.0086791 , 0.01314684, 0.00035168,
       0.01081596, 0.00156882, 0.01201997, 0.00216238, 0.02722169,
       0.00376946, 0.0137952 , 0.0034529 , 0.00916302, 0.00267991,
       0.01073472, 0.00145261, 0.00274524, 0.00100394])

Eucalanus conversions C (ug):¶

need lengths for these groups if I will use them. Not including them for now.¶

'Eucalanus sp. 1', 'Eucalanus sp. 2' ,'Eucalanus sp. 3' ,'Eucalanus sp. nauplii s1', 'Eucalanus californicus 5F', 'Eucalanus californicus 6F'

In [46]:

biomassDF['Eucalanus bungii  3']=(biomassDF['Eucalanus bungii  3']*(4.13*(2.6**3.28))*0.45)/1000
biomassDF['Eucalanus bungii  4']=(biomassDF['Eucalanus bungii  4']*(4.13*(3.2**3.28))*0.45)/1000
biomassDF['Eucalanus bungii  4F']=(biomassDF['Eucalanus bungii  4F']*(4.13*(3.2**3.28))*0.45)/1000
biomassDF['Eucalanus bungii  4M']=(biomassDF['Eucalanus bungii  4M']*(4.13*(3.2**3.28))*0.45)/1000
biomassDF['Eucalanus bungii  5F']=(biomassDF['Eucalanus bungii  5F']*(4.13*(4.6**3.28))*0.45)/1000
biomassDF['Eucalanus bungii  5M']=(biomassDF['Eucalanus bungii  5M']*(4.13*(4.4**3.28))*0.45)/1000
biomassDF['Eucalanus bungii  6F']=(biomassDF['Eucalanus bungii  6F']*(4.13*(6.2**3.28))*0.45)/1000
biomassDF['Eucalanus bungii  6M']=(biomassDF['Eucalanus bungii  6M']*(4.13*(6.1**3.28))*0.45)/1000

In [47]:

biomassDF['Calanus pacificus  5'].unique()[1:20]

Out[47]:

array([0.03194719, 0.20475132, 0.05426117, 0.0515078 , 0.        ,
       0.02195154, 0.02452752, 0.02647487, 0.56557544, 0.08391082,
       0.1501332 , 0.13528053, 0.04986061, 0.00547231, 0.02444432,
       0.00898015, 0.03032253, 0.00576887, 0.01201845])

Euphausiid Conversions¶

Euphausia pacifica¶

Include these groups too?¶

'Euphausiacea *sp. s2',

'Thysanoessa sp. s1', 'Thysanoessa sp. s2', 'Thysanoessa *sp. zoea s1',

'Thysanoessa longipes F', 'Thysanoessa longipes M', 'Thysanoessa longipes s2', 'Thysanoessa longipes s3' ,'Thysanoessa longipes zoea s1',

'Thysanoessa raschii F', 'Thysanoessa raschii M', 'Thysanoessa raschii s2' ,'Thysanoessa raschii zoea s1',

'Thysanoessa spinifera F', 'Thysanoessa spinifera M', 'Thysanoessa spinifera s2', 'Thysanoessa spinifera s3', 'Thysanoessa spinifera zoea s1'

multiply by 3 to adjust for nighttime abundance¶

In [48]:

#Double check these regressions for the calyptopis and furcilia with BethElLee(4.13*[Length (mm)^3.28)*46.9%
biomassDF['Euphausiidae *sp. protozoea (or calyptopis) s1']=(biomassDF['Euphausiidae *sp. protozoea (or calyptopis) s1']*(4.13*(1.3**3.28)*.469))/1000
biomassDF['Euphausiidae *sp. zoea (or furcilia) s1']=(biomassDF['Euphausiidae *sp. zoea (or furcilia) s1']*(4.13*(3.0**3.28)*.469))/1000

biomassDF['Euphausia pacifica zoea s1']=(biomassDF['Euphausia pacifica zoea s1']*(4.13*(4.7**3.28)*.469))/1000
biomassDF['Euphausia pacifica  s2']=(biomassDF['Euphausia pacifica  s2']*(4.13*(8.0**3.28)*.469))/1000
biomassDF['Euphausia pacifica  s3']=(biomassDF['Euphausia pacifica  s3']*(4.13*(12.0**3.28)*.469))/1000
biomassDF['Euphausia pacifica  F']=(biomassDF['Euphausia pacifica  F']*(4.13*(18.1**3.28)*.469))/1000
biomassDF['Euphausia pacifica  M']=(biomassDF['Euphausia pacifica  M']*(4.13*(15.1**3.28)*.469))/1000

In [ ]:

Amphipod Conversions¶

Decide later if we include Calliopius (not in Puget Sound?)¶

'Calliopius sp. s1' ,'Calliopius sp. s2', 'Calliopius *sp. s3', 'Calliopius pacificus s1' ,'Calliopius pacificus s2', 'Calliopius pacificus s3',

Hyperiids¶

In [49]:

biomassDF['Themisto *sp.  s1']=biomassDF['Themisto *sp.  s1']*(0.0049*(3.5**2.957)*1000)*0.37/1000
biomassDF['Themisto pacifica juvenile s1']=biomassDF['Themisto pacifica juvenile s1']*(0.0049*(4.3**2.957)*1000)*0.37/1000
biomassDF['Themisto pacifica  s2']=biomassDF['Themisto pacifica  s2']*(0.0049*(6.2**2.957)*1000)*0.37/1000
biomassDF['Themisto pacifica  F']=biomassDF['Themisto pacifica  F']*(0.0049*(7.5**2.957)*1000)*0.37/1000
biomassDF['Themisto pacifica  M']=biomassDF['Themisto pacifica  M']*(0.0049*(5.9**2.957)*1000)*0.37/1000

In [50]:

biomassDF['Hyperia medusarum  s1']=biomassDF['Hyperia medusarum  s1']*(0.0049*(3.0**2.957)*1000)*0.37/1000
biomassDF['Hyperia medusarum  s2']=biomassDF['Hyperia medusarum  s2']*(0.0049*(6.0**2.957)*1000)*0.37/1000
biomassDF['Hyperia medusarum  F']=biomassDF['Hyperia medusarum  F']*(0.0049*(10.8**2.957)*1000)*0.37/1000
biomassDF['Hyperia medusarum  M']=biomassDF['Hyperia medusarum  M']*(0.0049*(10.4**2.957)*1000)*0.37/1000

In [51]:

biomassDF['Primno *sp.  s1']=biomassDF['Primno *sp.  s1']*(0.0049*(3.0**2.957)*1000)*0.37/1000
biomassDF['Primno brevidens  s1']=biomassDF['Primno brevidens  s1']*(0.0049*(3.0**2.957)*1000)*0.37/1000
biomassDF['Primno abyssalis  s1']=biomassDF['Primno abyssalis  s1']*(0.0049*(3.0**2.957)*1000)*0.37/1000
biomassDF['Primno abyssalis  s2']=biomassDF['Primno abyssalis  s2']*(0.0049*(7.5**2.957)*1000)*0.37/1000
biomassDF['Primno abyssalis  F']=biomassDF['Primno abyssalis  F']*(0.0049*(12.3**2.957)*1000)*0.37/1000
biomassDF['Primno abyssalis  M']=biomassDF['Primno abyssalis  M']*(0.0049*(11.8**2.957)*1000)*0.37/1000

Gammariids¶

In [52]:

biomassDF['Gammaridea *sp.  s1']=biomassDF['Gammaridea *sp.  s1']*(0.0049*(4.0**2.957)*1000)*0.37/1000
biomassDF['Gammaridea *sp.  s2']=biomassDF['Gammaridea *sp.  s2']*(0.0049*(6.5**2.957)*1000)*0.37/1000

Cyphocariids¶

In [53]:

biomassDF['Cyphocaris *sp.  s1']=biomassDF['Cyphocaris *sp.  s1']*((0.02 * (3.3)**2.1)*1000)*0.37/1000
biomassDF['Cyphocaris challengeri  s1']=biomassDF['Cyphocaris challengeri  s1']*((0.02 * (3.2)**2.1)*1000)*0.37/1000
biomassDF['Cyphocaris challengeri  s2']=biomassDF['Cyphocaris challengeri  s2']*((0.02 * (8.6)**2.1)*1000)*0.37/1000
biomassDF['Cyphocaris challengeri  s3']=biomassDF['Cyphocaris challengeri  s3']*((0.02 * (11.3)**2.1)*1000)*0.37/1000

Chaetognath Conversions¶

In [54]:

biomassDF['Chaetognatha *sp. juvenile s1']=biomassDF['Chaetognatha *sp. juvenile s1']*0.0956*(3.5**2.9093)/1000
biomassDF['Chaetognatha *sp. juvenile s2']=biomassDF['Chaetognatha *sp. juvenile s2']*0.0956*(6.0**2.9093)/1000
biomassDF['Chaetognatha *sp.  s3']=biomassDF['Chaetognatha *sp.  s3']*0.0956*(15.0**2.9093)/1000

In [ ]:

In [55]:

biomassDF['Parasagitta elegans juvenile s1']=biomassDF['Parasagitta elegans juvenile s1']*0.0956*(3.5**2.9093)/1000
biomassDF['Parasagitta elegans  s2']=biomassDF['Parasagitta elegans  s2']*0.0956*(7.5**2.9093)/1000
biomassDF['Parasagitta elegans  s3']=biomassDF['Parasagitta elegans  s3']*0.0956*(20.0**2.9093)/1000
biomassDF['Parasagitta euneritica  s2']=biomassDF['Parasagitta euneritica  s2']*0.0956*(7.5**2.9093)/1000
biomassDF['Parasagitta euneritica  s3']=biomassDF['Parasagitta euneritica  s3']*0.0956*(20.0**2.9093)/1000

Gastropod - Limacina Conversions¶

In [56]:

biomassDF['Limacina helicina  s0']=biomassDF['Limacina helicina  s0']*(2.6*(1.0**2.659))*0.22/1000
biomassDF['Limacina helicina  s1']=biomassDF['Limacina helicina  s1']*(2.6*(2.0**2.659))*0.22/1000                                                                       
biomassDF['Limacina helicina  s2']=biomassDF['Limacina helicina  s2']*(2.6*(6.0**2.659))*0.22/1000    

Larvaceans - Oikopleura Conversions¶

Need length for this category: 'Oikopleura *sp. s2',¶

In [57]:

biomassDF['Oikopleura *sp.  s1']=biomassDF['Oikopleura *sp.  s1']*((38.8*(1.3**2.574))*0.46)/1000
biomassDF['Oikopleura dioica  s1']=biomassDF['Oikopleura dioica  s1']*((38.8*(4.6**2.574))*0.46)/1000
biomassDF['Oikopleura dioica  s2']=biomassDF['Oikopleura dioica  s2']*((38.8*(8.5**2.574))*0.46)/1000
biomassDF['Oikopleura labradoriensis  s1']=biomassDF['Oikopleura labradoriensis  s1']*((38.8*(4.6**2.574))*0.46)/1000
biomassDF['Oikopleura labradoriensis  s2']=biomassDF['Oikopleura labradoriensis  s2']*((38.8*(8.5**2.574))*0.46)/1000
biomassDF['Oikopleura labradoriensis  s3']=biomassDF['Oikopleura labradoriensis  s3']*((38.8*(18.5**2.574))*0.46)/1000

In [ ]:

Import model data for comparison with observations¶

In [199]:

import netCDF4 as nc

In [200]:

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

In [201]:

ftemp.variables.keys()

Out[201]:

dict_keys(['nav_lon', 'nav_lat', 'time_counter', 'tmask', 'umask', 'vmask', 'fmask', 'tmaskutil', 'umaskutil', 'vmaskutil', 'fmaskutil', 'glamt', 'glamu', 'glamv', 'glamf', 'gphit', 'gphiu', 'gphiv', 'gphif', 'e1t', 'e1u', 'e1v', 'e1f', 'e2t', 'e2u', 'e2v', 'e2f', 'ff', 'mbathy', 'misf', 'isfdraft', 'e3t_0', 'e3u_0', 'e3v_0', 'e3w_0', 'gdept_0', 'gdepu', 'gdepv', 'gdepw_0', 'gdept_1d', 'gdepw_1d', 'e3t_1d', 'e3w_1d'])

In [202]:

ftemp.variables['e3t_0']

Out[202]:

<class 'netCDF4._netCDF4.Variable'>
float64 e3t_0(t, z, y, x)
    _FillValue: nan
    standard_name: e3t_0
    long_name: grid spacing on T-grid in w direction
    units: m
unlimited dimensions: t
current shape = (1, 40, 898, 398)
filling on

In [203]:

ftemp.variables['e3t_1d'][:]

Out[203]:

masked_array(data=[[ 1.00000115,  1.00000501,  1.00001253,  1.00002718,
                     1.0000557 ,  1.00011125,  1.00021946,  1.0004302 ,
                     1.00084067,  1.00164012,  1.0031971 ,  1.00622914,
                     1.01213271,  1.02362358,  1.04597551,  1.08940061,
                     1.17356428,  1.33592899,  1.64636781,  2.22990285,
                     3.29248567,  5.11998508,  7.97451506, 11.8252972 ,
                    16.10792044, 19.95870258, 22.81323256, 24.64073198,
                    25.70331479, 26.28684983, 26.59728865, 26.75965336,
                    26.84381704, 26.88724213, 26.90959407, 26.92108493,
                    26.9269885 , 26.93002054, 26.93157752, 26.93237697]],
             mask=False,
       fill_value=1e+20)

In [204]:

fdict={'ptrc_T':1,'grid_T':1}
start_date = dt.datetime(2014,1,1)
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 [205]:

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

In [206]:

biomassDF.rename(columns={'lon':'Lon','lat':'Lat'},inplace=True)

In [207]:

biomassDF.keys()

Out[207]:

Index(['Key', 'region_name', 'Station', 'Lon', 'Lat', 'Date', 'dtUTC',
       'Twilight', 'Net_Type', 'Mesh_Size(um)',
       ...
       'OtherCalanoids', 'Calanoids', 'CalanoidsDiaRemoved', 'Euphausiids',
       'Themisto', 'Hyperia', 'Primno', 'Hyperiids', 'Gammariids', 'Total'],
      dtype='object', length=272)

In [208]:

biomassDF.rename(columns={'DEPTH_STRT1':'Z_lower','DEPTH_END1':'Z_upper'},inplace=True)

In [209]:

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

Need to convert biomass from mg C to N using a known C:N

In [210]:

#biomassDF['Amphipoda']=biomassDF['Amphipoda']*0.45/8.5

#biomassDF['Euphausiacea']=biomassDF['Euphausiacea']*0.45/8.5
#biomassDF['Calanoida']=biomassDF['Calanoida']*0.45/8.5



#biomassDF['Cyclopoida']=biomassDF['Cyclopoida']*0.45/8.5
#biomassDF['Poecilostomatoida']=biomassDF['Poecilostomatoida']*0.45/8.5
#biomassDF['Copelata']=biomassDF['Copelata']*0.45/8.5
#biomassDF['Harpacticoida']=biomassDF['Harpacticoida']*0.45/8.5
#biomassDF['Decapoda']=biomassDF['Decapoda']*0.45/8.5

In [58]:

biomassDF['Neocalanus']=(biomassDF['Neocalanus plumchrus  1']+biomassDF['Neocalanus plumchrus  2']+biomassDF['Neocalanus plumchrus  3']+biomassDF['Neocalanus plumchrus  4']+biomassDF['Neocalanus plumchrus  5']+biomassDF['Neocalanus plumchrus  6F']+biomassDF['Neocalanus plumchrus  6M'])
biomassDF['NeocalanusDiaRemoved']=(biomassDF['Neocalanus plumchrus  1']+biomassDF['Neocalanus plumchrus  2']+biomassDF['Neocalanus plumchrus  3']+biomassDF['Neocalanus plumchrus  4'])

In [261]:

biomassDF['Neocalanus'].unique()[1:20]

Out[261]:

array([1.16228376e+00, 4.25664753e+00, 6.76832999e+00, 1.67322568e-02,
       5.19182122e+00, 1.56416337e-02, 5.70440844e-02, 5.50394930e+00,
       5.24904520e-02, 2.44223301e+00, 9.88737895e-02, 2.72848784e-01,
       2.11242203e+01, 1.60857534e-02, 3.26710947e-01, 1.80099161e+00,
       2.83673986e+01, 3.99770194e+01, 3.16960664e+01])

In [59]:

biomassDF['CalPacificus']=(biomassDF['Calanus pacificus  2']+biomassDF['Calanus pacificus  3']+biomassDF['Calanus pacificus  4']+biomassDF['Calanus pacificus  5']+biomassDF['Calanus pacificus  6F']+biomassDF['Calanus pacificus  6M'])
biomassDF['CalPacificusDiaRemoved']=(biomassDF['Calanus pacificus  2']+biomassDF['Calanus pacificus  3']+biomassDF['Calanus pacificus  4'])

In [60]:

biomassDF['CalMarsh']=(biomassDF['Calanus marshallae  2']+biomassDF['Calanus marshallae  3']+biomassDF['Calanus marshallae  4']+biomassDF['Calanus marshallae  5']+biomassDF['Calanus marshallae  6F']+biomassDF['Calanus marshallae  6M'])
biomassDF['CalMarshDiaRemoved']=(biomassDF['Calanus marshallae  2']+biomassDF['Calanus marshallae  3']+biomassDF['Calanus marshallae  4']+biomassDF['Calanus marshallae  5'])

In [61]:

biomassDF['Metridia']=(biomassDF['Metridia *sp.  2']+biomassDF['Metridia *sp.  3']+biomassDF['Metridia *sp.  4']+biomassDF['Metridia *sp.  5']+biomassDF['Metridia *sp.  6F']+biomassDF['Metridia *sp.  6M']+biomassDF['Metridia pacifica  3']+biomassDF['Metridia pacifica  4']+biomassDF['Metridia pacifica  5F'])
biomassDF['Eucalanus']=(biomassDF['Eucalanus bungii  3']+biomassDF['Eucalanus bungii  4']+biomassDF['Eucalanus bungii  4F']+biomassDF['Eucalanus bungii  4M']+biomassDF['Eucalanus bungii  5F']+biomassDF['Eucalanus bungii  5M']+biomassDF['Eucalanus bungii  6F']+biomassDF['Eucalanus bungii  6M'])
biomassDF['EucalanusDiaRemoved']=(biomassDF['Eucalanus bungii  3']+biomassDF['Eucalanus bungii  4']+biomassDF['Eucalanus bungii  4F']+biomassDF['Eucalanus bungii  4M'])

In [62]:

biomassDF['Metridia'].unique()[1:20]

Out[62]:

array([0.05328713, 0.15333201, 0.15656956, 0.47845137, 0.04713698,
       0.11257754, 0.0756802 , 0.11441692, 0.0672129 , 0.2762964 ,
       0.09263692, 0.07144549, 0.01643963, 0.07175813, 0.03725966,
       0.06370595, 0.03816424, 0.08821852, 0.04425084])

In [63]:

biomassDF['OtherCalanoids']=(biomassDF['Calanoida *sp.  1']+biomassDF['Calanoida *sp.  2']+biomassDF['Calanoida *sp.  3']+biomassDF['Calanoida *sp.  4']+biomassDF['Calanoida *sp.  6M']+biomassDF['Calanus *sp.  1']+biomassDF['Calanus *sp.  2']+biomassDF['Calanus *sp.  3']+biomassDF['Calanus *sp.  4'])

In [64]:

biomassDF['Calanoids']=biomassDF['Neocalanus']+biomassDF['CalPacificus']+biomassDF['CalMarsh']+biomassDF['Metridia']+biomassDF['Eucalanus']+biomassDF['OtherCalanoids']
biomassDF['CalanoidsDiaRemoved']=biomassDF['NeocalanusDiaRemoved']+biomassDF['CalPacificusDiaRemoved']+biomassDF['CalMarshDiaRemoved']+biomassDF['EucalanusDiaRemoved']+biomassDF['OtherCalanoids']

In [65]:

biomassDF['Euphausiids']=(biomassDF['Euphausiidae *sp. protozoea (or calyptopis) s1']+biomassDF['Euphausiidae *sp. zoea (or furcilia) s1']+biomassDF['Euphausia pacifica zoea s1']+biomassDF['Euphausia pacifica  s2']+biomassDF['Euphausia pacifica  s3']+biomassDF['Euphausia pacifica  F']+biomassDF['Euphausia pacifica  M'])

In [66]:

biomassDF['Themisto']=(biomassDF['Themisto *sp.  s1']+biomassDF['Themisto pacifica juvenile s1']+biomassDF['Themisto pacifica  s2']+biomassDF['Themisto pacifica  F']+biomassDF['Themisto pacifica  M'])
biomassDF['Hyperia']=(biomassDF['Hyperia medusarum  s1']+biomassDF['Hyperia medusarum  s2']+biomassDF['Hyperia medusarum  F']+biomassDF['Hyperia medusarum  M'])
biomassDF['Primno']=(biomassDF['Primno *sp.  s1']+biomassDF['Primno brevidens  s1']+biomassDF['Primno abyssalis  s1']+biomassDF['Primno abyssalis  s2']+biomassDF['Primno abyssalis  F']+biomassDF['Primno abyssalis  M'])

In [67]:

biomassDF['Hyperiids']=biomassDF['Themisto']+biomassDF['Hyperia']+biomassDF['Primno']

In [68]:

biomassDF['Gammariids']=(biomassDF['Gammaridea *sp.  s1']+biomassDF['Gammaridea *sp.  s2'])

In [69]:

biomassDF['Total']=biomassDF['Calanoids']+biomassDF['Euphausiids']+biomassDF['Hyperiids']+biomassDF['Gammariids']

In [ ]:

In [263]:

biomassDF['Hyperiids'].unique()[1:20]

Out[263]:

array([0.36245896, 1.87300085, 3.0219189 , 8.89761441, 0.88179773,
       2.20092374, 1.05748946, 2.39226072, 0.14470814, 1.42228345,
       0.81166803, 0.79332675, 0.66087864, 0.17989049, 0.37087584,
       1.05846975, 0.57754293, 0.05944203, 0.2487363 ])

In [ ]:

In [70]:

biomassDF

Out[70]:

	Key	region_name	Station	lon	lat	Date	dtUTC	Twilight	Net_Type	Mesh_Size(um)	...	OtherCalanoids	Calanoids	CalanoidsDiaRemoved	Euphausiids	Themisto	Hyperia	Primno	Hyperiids	Gammariids	Total
0	IOS2012005000901	Northern Strait of Georgia	22	-124.272	49.670	6/14/2012	2012-06-14 14:32:00	Daylight	SCOR VNH	236	...	0.115408	0.445351	0.237637	0.448822	0.827555	0.000000	0.000000	0.827555	0.0	1.721729
1	IOS2012005001001	Northern Strait of Georgia	22	-124.272	49.670	6/14/2012	2012-06-14 14:52:00	Daylight	SCOR VNH	236	...	0.015486	3.666233	0.604391	0.172039	0.278596	0.000000	0.083863	0.362459	0.0	4.200731
2	IOS2012005002101	Northern Strait of Georgia	11	-124.722	49.710	6/14/2012	2012-06-14 07:00:00	Night	SCOR VNH	236	...	0.004346	12.477846	1.859686	0.000000	1.635304	0.000000	0.237697	1.873001	0.0	14.350847
3	IOS2012005002201	Northern Strait of Georgia	11	-124.722	49.710	6/14/2012	2012-06-14 07:05:00	Night	SCOR VNH	236	...	0.010369	14.173214	2.698241	12.270955	2.562381	0.000000	0.459538	3.021919	0.0	29.466088
4	IOS2012005002901	Northern Strait of Georgia	CPF2	-124.499	49.466	6/15/2012	2012-06-15 10:00:00	Night	SCOR VNH	236	...	0.051333	0.747524	0.051333	39.755211	7.895820	0.197026	0.804768	8.897614	0.0	49.400349
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
649	SOO2015095000101	Tidal Mixed	CLO-42	-123.345	48.394	4/27/2015	2015-04-27 17:37:00	Daylight	SCOR VNH	236	...	0.000146	0.496019	0.056654	0.000000	0.304123	0.000000	0.000000	0.304123	0.0	0.800142
650	SOO2015095000401	Tidal Mixed	CB01	-123.318	48.344	4/30/2015	2015-04-30 19:00:00	Daylight	SCOR VNH	236	...	0.006763	1.091657	0.180086	0.023474	1.761051	0.000000	0.000000	1.761051	0.0	2.876183
651	SOO2015095000501	Tidal Mixed	CLO-41	-123.345	48.395	7/9/2015	2015-07-09 19:25:00	Daylight	SCOR VNH	236	...	0.010097	0.141755	0.075749	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.141755
652	SOO2015095000701	Tidal Mixed	CB01	-123.318	48.344	7/27/2015	2015-07-27 19:06:00	Daylight	SCOR VNH	236	...	0.029189	0.029189	0.029189	0.003526	0.168176	0.000000	0.000000	0.168176	0.0	0.200890
653	SOO2015095000801	Tidal Mixed	MAC41	-123.409	48.402	7/27/2015	2015-07-27 20:00:00	Daylight	SCOR VNH	236	...	0.399796	0.893605	0.655242	0.000000	0.731182	0.000000	0.000000	0.731182	0.0	1.624787

654 rows × 269 columns

In [73]:

biomassDF['Month']=[ii.month for ii in biomassDF['dtUTC']]

In [75]:

biomassDF.loc[biomassDF.Month==2,['Calanoids']]

Out[75]:

	Calanoids
239	0.231678
240	0.262995
241	0.045065
242	0.011634
243	0.263927
244	0.210876
457	0.910112
458	1.208902
459	1.319525
589	0.150697
590	0.294798
591	0.441176
592	0.343120

In [81]:

logt_inv(np.mean(logt(biomassDF.loc[biomassDF.Month==2,['Calanoids']])))

Out[81]:

Calanoids    0.25467
dtype: float64

In [ ]:

In [224]:

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

Warning: lower limit is not an ocean value:  i=265, j=388, k_upper=0, k_lower=27, k_seafloor=27 Lon=-123.311, Lat=48.898, dtUTC=2014-03-09 18:57:00
Warning: lower limit is not an ocean value:  i=265, j=388, k_upper=0, k_lower=27, k_seafloor=27 Lon=-123.311, Lat=48.898, dtUTC=2014-03-09 18:57:00
Warning: lower limit is not an ocean value:  i=159, j=645, k_upper=0, k_lower=34, k_seafloor=34 Lon=-124.72200000000001, Lat=49.706, dtUTC=2014-04-06 07:14:00
Warning: lower limit is not an ocean value:  i=159, j=645, k_upper=0, k_lower=34, k_seafloor=34 Lon=-124.72200000000001, Lat=49.706, dtUTC=2014-04-06 07:14:00
Warning: lower limit is not an ocean value:  i=201, j=382, k_upper=0, k_lower=22, k_seafloor=11 Lon=-123.62299999999999, Lat=48.751000000000005, dtUTC=2014-04-23 18:49:00
Warning: lower limit is not an ocean value:  i=201, j=382, k_upper=0, k_lower=22, k_seafloor=11 Lon=-123.62299999999999, Lat=48.751000000000005, dtUTC=2014-04-23 18:49:00
Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=22, k_seafloor=17 Lon=-123.62100000000001, Lat=48.751000000000005, dtUTC=2014-04-29 18:20:00
Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=22, k_seafloor=17 Lon=-123.62100000000001, Lat=48.751000000000005, dtUTC=2014-04-29 18:20:00
Warning: lower limit is not an ocean value:  i=205, j=381, k_upper=0, k_lower=23, k_seafloor=23 Lon=-123.605, Lat=48.755, dtUTC=2014-04-29 19:40:00
Warning: lower limit is not an ocean value:  i=205, j=381, k_upper=0, k_lower=23, k_seafloor=23 Lon=-123.605, Lat=48.755, dtUTC=2014-04-29 19:40:00
Warning: lower limit is not an ocean value:  i=203, j=378, k_upper=0, k_lower=22, k_seafloor=22 Lon=-123.602, Lat=48.74100000000001, dtUTC=2014-04-29 20:36:00
Warning: lower limit is not an ocean value:  i=203, j=378, k_upper=0, k_lower=22, k_seafloor=22 Lon=-123.602, Lat=48.74100000000001, dtUTC=2014-04-29 20:36:00
Warning: lower limit is not an ocean value:  i=201, j=382, k_upper=0, k_lower=19, k_seafloor=11 Lon=-123.62299999999999, Lat=48.751000000000005, dtUTC=2014-05-07 18:07:00
Warning: lower limit is not an ocean value:  i=201, j=382, k_upper=0, k_lower=19, k_seafloor=11 Lon=-123.62299999999999, Lat=48.751000000000005, dtUTC=2014-05-07 18:07:00
Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.574, Lat=48.77, dtUTC=2014-05-07 21:42:00
Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.574, Lat=48.77, dtUTC=2014-05-07 21:42:00
Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=18, k_seafloor=17 Lon=-123.62299999999999, Lat=48.751999999999995, dtUTC=2014-05-15 18:20:00
Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=18, k_seafloor=17 Lon=-123.62299999999999, Lat=48.751999999999995, dtUTC=2014-05-15 18:20:00
Warning: lower limit is not an ocean value:  i=205, j=381, k_upper=0, k_lower=23, k_seafloor=23 Lon=-123.605, Lat=48.755, dtUTC=2014-05-15 20:24:00
Warning: lower limit is not an ocean value:  i=205, j=381, k_upper=0, k_lower=23, k_seafloor=23 Lon=-123.605, Lat=48.755, dtUTC=2014-05-15 20:24:00
Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.572, Lat=48.771, dtUTC=2014-05-28 16:49:00
Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.572, Lat=48.771, dtUTC=2014-05-28 16:49:00
Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=21, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751999999999995, dtUTC=2014-05-28 19:49:00
Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=21, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751999999999995, dtUTC=2014-05-28 19:49:00
Warning: lower limit is not an ocean value:  i=207, j=376, k_upper=0, k_lower=25, k_seafloor=25 Lon=-123.575, Lat=48.736999999999995, dtUTC=2014-06-05 21:11:00
Warning: lower limit is not an ocean value:  i=207, j=376, k_upper=0, k_lower=25, k_seafloor=25 Lon=-123.575, Lat=48.736999999999995, dtUTC=2014-06-05 21:11:00
Warning: lower limit is not an ocean value:  i=122, j=628, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.86, Lat=49.568999999999996, dtUTC=2014-06-20 21:38:00
Warning: lower limit is not an ocean value:  i=122, j=628, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.86, Lat=49.568999999999996, dtUTC=2014-06-20 21:38:00
Warning: lower limit is not an ocean value:  i=120, j=621, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.84200000000001, Lat=49.537, dtUTC=2014-06-20 23:06:00
Warning: lower limit is not an ocean value:  i=120, j=621, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.84200000000001, Lat=49.537, dtUTC=2014-06-20 23:06:00
Warning: lower limit is not an ocean value:  i=123, j=606, k_upper=0, k_lower=23, k_seafloor=19 Lon=-124.77600000000001, Lat=49.483999999999995, dtUTC=2014-06-21 16:45:00
Warning: lower limit is not an ocean value:  i=123, j=606, k_upper=0, k_lower=23, k_seafloor=19 Lon=-124.77600000000001, Lat=49.483999999999995, dtUTC=2014-06-21 16:45:00
Warning: lower limit is not an ocean value:  i=136, j=606, k_upper=0, k_lower=23, k_seafloor=22 Lon=-124.713, Lat=49.50899999999999, dtUTC=2014-06-22 19:39:00
Warning: lower limit is not an ocean value:  i=136, j=606, k_upper=0, k_lower=23, k_seafloor=22 Lon=-124.713, Lat=49.50899999999999, dtUTC=2014-06-22 19:39:00
Warning: lower limit is not an ocean value:  i=134, j=639, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.831, Lat=49.635, dtUTC=2014-06-22 23:04:00
Warning: lower limit is not an ocean value:  i=134, j=639, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.831, Lat=49.635, dtUTC=2014-06-22 23:04:00
Warning: lower limit is not an ocean value:  i=125, j=635, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.866, Lat=49.604, dtUTC=2015-02-18 08:00:00
Warning: lower limit is not an ocean value:  i=125, j=635, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.866, Lat=49.604, dtUTC=2015-02-18 08:00:00
Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.575, Lat=48.77, dtUTC=2015-02-19 19:16:00
Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.575, Lat=48.77, dtUTC=2015-02-19 19:16:00
Warning: lower limit is not an ocean value:  i=201, j=381, k_upper=0, k_lower=21, k_seafloor=18 Lon=-123.62299999999999, Lat=48.75, dtUTC=2015-02-19 20:17:00
Warning: lower limit is not an ocean value:  i=201, j=381, k_upper=0, k_lower=21, k_seafloor=18 Lon=-123.62299999999999, Lat=48.75, dtUTC=2015-02-19 20:17:00
Warning: lower limit is not an ocean value:  i=207, j=376, k_upper=0, k_lower=25, k_seafloor=25 Lon=-123.574, Lat=48.738, dtUTC=2015-02-19 21:59:00
Warning: lower limit is not an ocean value:  i=207, j=376, k_upper=0, k_lower=25, k_seafloor=25 Lon=-123.574, Lat=48.738, dtUTC=2015-02-19 21:59:00
Warning: lower limit is not an ocean value:  i=202, j=379, k_upper=0, k_lower=22, k_seafloor=22 Lon=-123.60700000000001, Lat=48.74100000000001, dtUTC=2015-03-12 19:01:00
Warning: lower limit is not an ocean value:  i=202, j=379, k_upper=0, k_lower=22, k_seafloor=22 Lon=-123.60700000000001, Lat=48.74100000000001, dtUTC=2015-03-12 19:01:00
Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=22, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751000000000005, dtUTC=2015-03-12 20:36:00
Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=22, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751000000000005, dtUTC=2015-03-12 20:36:00
Warning: lower limit is not an ocean value:  i=124, j=605, k_upper=0, k_lower=24, k_seafloor=24 Lon=-124.76799999999999, Lat=49.483000000000004, dtUTC=2015-03-17 22:27:00
Warning: lower limit is not an ocean value:  i=124, j=605, k_upper=0, k_lower=24, k_seafloor=24 Lon=-124.76799999999999, Lat=49.483000000000004, dtUTC=2015-03-17 22:27:00
Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.573, Lat=48.769, dtUTC=2015-03-19 16:58:00
Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.573, Lat=48.769, dtUTC=2015-03-19 16:58:00
Warning: lower limit is not an ocean value:  i=202, j=381, k_upper=0, k_lower=23, k_seafloor=23 Lon=-123.618, Lat=48.75, dtUTC=2015-03-19 19:51:00
Warning: lower limit is not an ocean value:  i=202, j=381, k_upper=0, k_lower=23, k_seafloor=23 Lon=-123.618, Lat=48.75, dtUTC=2015-03-19 19:51:00
Warning: lower limit is not an ocean value:  i=149, j=594, k_upper=0, k_lower=25, k_seafloor=25 Lon=-124.60600000000001, Lat=49.485, dtUTC=2015-03-21 17:18:00
Warning: lower limit is not an ocean value:  i=149, j=594, k_upper=0, k_lower=25, k_seafloor=25 Lon=-124.60600000000001, Lat=49.485, dtUTC=2015-03-21 17:18:00
Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.575, Lat=48.769, dtUTC=2015-03-26 16:05:00
Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.575, Lat=48.769, dtUTC=2015-03-26 16:05:00
Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.574, Lat=48.769, dtUTC=2015-04-02 15:49:00
Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.574, Lat=48.769, dtUTC=2015-04-02 15:49:00
Warning: lower limit is not an ocean value:  i=203, j=378, k_upper=0, k_lower=22, k_seafloor=22 Lon=-123.60600000000001, Lat=48.74, dtUTC=2015-04-02 17:41:00
Warning: lower limit is not an ocean value:  i=203, j=378, k_upper=0, k_lower=22, k_seafloor=22 Lon=-123.60600000000001, Lat=48.74, dtUTC=2015-04-02 17:41:00
Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=20, k_seafloor=17 Lon=-123.62299999999999, Lat=48.751999999999995, dtUTC=2015-04-02 19:14:00
Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=20, k_seafloor=17 Lon=-123.62299999999999, Lat=48.751999999999995, dtUTC=2015-04-02 19:14:00
Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=19, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751000000000005, dtUTC=2015-04-16 19:10:00
Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=19, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751000000000005, dtUTC=2015-04-16 19:10:00
Warning: lower limit is not an ocean value:  i=126, j=636, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.867, Lat=49.608000000000004, dtUTC=2015-05-13 18:06:00
Warning: lower limit is not an ocean value:  i=126, j=636, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.867, Lat=49.608000000000004, dtUTC=2015-05-13 18:06:00
Warning: lower limit is not an ocean value:  i=149, j=594, k_upper=0, k_lower=25, k_seafloor=25 Lon=-124.605, Lat=49.485, dtUTC=2015-05-17 15:59:00
Warning: lower limit is not an ocean value:  i=149, j=594, k_upper=0, k_lower=25, k_seafloor=25 Lon=-124.605, Lat=49.485, dtUTC=2015-05-17 15:59:00
Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=19, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751999999999995, dtUTC=2015-05-19 18:50:00
Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=19, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751999999999995, dtUTC=2015-05-19 18:50:00
Warning: lower limit is not an ocean value:  i=159, j=646, k_upper=0, k_lower=34, k_seafloor=34 Lon=-124.72399999999999, Lat=49.707, dtUTC=2015-06-28 06:15:00
Warning: lower limit is not an ocean value:  i=159, j=646, k_upper=0, k_lower=34, k_seafloor=34 Lon=-124.72399999999999, Lat=49.707, dtUTC=2015-06-28 06:15:00
Warning: lower limit is not an ocean value:  i=239, j=329, k_upper=0, k_lower=31, k_seafloor=29 Lon=-123.249, Lat=48.615, dtUTC=2015-08-20 02:52:00
Warning: lower limit is not an ocean value:  i=239, j=329, k_upper=0, k_lower=31, k_seafloor=29 Lon=-123.249, Lat=48.615, dtUTC=2015-08-20 02:52:00
Warning: lower limit is not an ocean value:  i=149, j=594, k_upper=0, k_lower=25, k_seafloor=25 Lon=-124.605, Lat=49.485, dtUTC=2015-09-14 17:11:00
Warning: lower limit is not an ocean value:  i=149, j=594, k_upper=0, k_lower=25, k_seafloor=25 Lon=-124.605, Lat=49.485, dtUTC=2015-09-14 17:11:00
Warning: lower limit is not an ocean value:  i=251, j=457, k_upper=0, k_lower=28, k_seafloor=28 Lon=-123.61399999999999, Lat=49.141999999999996, dtUTC=2015-10-04 07:32:00
Warning: lower limit is not an ocean value:  i=251, j=457, k_upper=0, k_lower=28, k_seafloor=28 Lon=-123.61399999999999, Lat=49.141999999999996, dtUTC=2015-10-04 07:32:00

In [225]:

data

Out[225]:

	Key	region_name	Station	Lon	Lat	Date	dtUTC	Twilight	Net_Type	Mesh_Size(um)	...	Primno	Hyperiids	Gammariids	Total	j	i	mod_microzooplankton	mod_mesozooplankton	k_upper	k_lower
0	PBS2014016000801	Juan de Fuca	JF03	-124.051	48.388	3/7/2014	2014-03-07 19:01:00	Daylight	Bongo VNH	253	...	0.000000	0.092604	0.0	10.554291	342	94	0.049066	0.286660	0	23
1	PBS2014016004401	Central Strait of Georgia	GS04	-123.311	48.898	3/9/2014	2014-03-09 18:57:00	Daylight	Bongo VNH	253	...	0.000000	0.025194	0.0	0.335986	388	265	0.065740	0.263313	0	27
2	PBS2014016005201	Central Strait of Georgia	GS08	-123.580	49.045	3/9/2014	2014-03-09 23:57:00	Daylight	Bongo VNH	253	...	0.158000	0.261419	0.0	1.316260	436	244	0.045737	0.193986	0	28
3	IOS2014003000101	Central Strait of Georgia	GEO1	-123.744	49.250	3/9/2014	2014-03-10 02:08:00	Daylight	SCOR VNH	236	...	0.068851	0.068851	0.0	1.469906	487	246	0.021994	0.094057	0	37
4	PBS2014016011401	Central Strait of Georgia	GS20	-123.484	49.068	3/13/2014	2014-03-13 21:12:00	Daylight	Bongo VNH	253	...	0.118801	0.286148	0.0	1.654707	433	261	0.059742	0.211711	0	28
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
461	PSF2015097005001	Northern Strait of Georgia	IS-3	-124.287	49.655	10/5/2015	2015-10-05 18:43:00	Daylight	Ring VNH	250	...	0.289586	0.623782	0.0	3.103821	604	217	0.057682	0.424223	0	28
462	PSF2015098005701	Northern Strait of Georgia	BS-3	-124.666	49.692	10/5/2015	2015-10-05 19:15:00	Daylight	Ring VNH	250	...	0.667453	1.180838	0.0	76.616760	639	166	0.058821	0.396426	0	28
463	PSF2015098005801	Baynes Sound	BS-1	-124.867	49.608	10/5/2015	2015-10-05 20:38:00	Daylight	Ring VNH	250	...	0.000000	0.000000	0.0	0.075827	636	126	0.194869	1.209480	0	22
464	PSF2015097005101	Nearshore-North East	IS-2	-124.083	49.637	10/5/2015	2015-10-05 20:40:00	Daylight	Ring VNH	250	...	0.000000	4.332255	0.0	4.409816	587	245	0.159315	1.060016	0	21
465	PSF2015098005901	Baynes Sound	BS-7	-124.767	49.483	10/5/2015	2015-10-05 21:51:00	Daylight	Ring VNH	250	...	0.000000	1.026601	0.0	1.921206	605	125	0.183379	1.091250	0	23

466 rows × 278 columns

In [226]:

cm1=cmocean.cm.thermal
with nc.Dataset('/ocean/ksuchy/MOAD/NEMO-forcing/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 [78]:

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

In [79]:

# define inverse log transform with same shift
def logt_inv(y):
    return 10**y-.001

In [266]:

data['Euphausiids'].unique()[20:40]

Out[266]:

array([3.5717944 , 4.38464013, 1.8619691 , 0.77795756, 0.07166609,
       4.9216666 , 1.30426199, 0.04374841, 0.03221038, 1.42433184,
       3.62682488, 6.55181683, 0.08520765, 1.18366988, 1.02939106,
       0.29600866, 0.85211712, 1.79831119, 0.50258672, 0.07633187])

In [230]:

data['L10Calanoids']=logt(data['Calanoids'])
data['L10CalanoidsDiaRemoved']=logt(data['CalanoidsDiaRemoved'])
data['L10Euphausiids']=logt(data['Euphausiids'])
data['L10Hyperiids']=logt(data['Hyperiids'])
data['L10Gammariids']=logt(data['Gammariids'])
data['L10Neocalanus']=logt(data['Neocalanus'])

data['L10Total']=logt(data['Total'])

data['L10mod_mesozooplankton']=logt(data['mod_mesozooplankton']*5.7*14)
data['L10mod_microzooplankton']=logt(data['mod_microzooplankton']*5.7*14)

In [231]:

data['L10Neocalanus'].unique()[1:20]

Out[231]:

array([-0.52201674, -1.2195666 , -1.11382969, -1.38807898, -1.43340323,
       -0.85816102, -1.08101945, -0.58318385, -0.48369823,  0.01373667,
        0.08996821, -0.60699648, -0.23719985,  0.25433288, -0.02657255,
       -2.14076674, -1.40852969, -1.66178233, -1.95689915])

Figure showing observation locations of IOS zooplankton sampling

In [232]:

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.region_name.unique()):
    datreg[iregion] = data.loc[data.region_name==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))

Out[232]:

<matplotlib.legend.Legend at 0x7ff1a5e72310>

In [233]:

#look at data for individual years
View2012=data.loc[data.Year==2012]
View2013=data.loc[data.Year==2013]
View2014=data.loc[data.Year==2014]
View2015=data.loc[data.Year==2015]

In [234]:

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

In [235]:

#look at data for a specific region
ViewCentralSoG=data.loc[data.region_name=='Central Strait of Georgia']

In [236]:

#look at data for an individual station
ViewGEO1=data.loc[data.Station=='GEO1']

Calculate Mean and SEMs¶

In [237]:

#monthly mean and SEM for entire SoG
monthlymean=data.groupby(['Month']).mean()

In [238]:

monthlysem=data.groupby(['Month']).sem()

In [239]:

#monthly mean and SEM for Central SoG Only
monthlymeanCentral=ViewCentralSoG.groupby(['Month']).mean()

In [240]:

monthlysemCentral=ViewCentralSoG.groupby(['Month']).sem()

In [241]:

#monthly mean and SEM for station GEO1 only
monthlymeanGEO1=ViewGEO1.groupby(['Month']).mean()

In [242]:

monthlysemGEO1=ViewGEO1.groupby(['Month']).sem()

In [243]:

#look at data for an individual station
ViewBaynes=data.loc[data.Station=='BS-1']

In [244]:

#monthly mean and SEM for Baynes Sound Only
monthlymeanBaynes=ViewBaynes.groupby(['Month']).mean()

In [245]:

monthlymean2012=View2012.groupby(['Month']).mean()
monthlymean2013=View2013.groupby(['Month']).mean()
monthlymean2014=View2014.groupby(['Month']).mean()
monthlymean2015=View2015.groupby(['Month']).mean()

In [246]:

monthlysem2012=View2012.groupby(['Month']).sem()
monthlysem2013=View2013.groupby(['Month']).sem()
monthlysem2014=View2014.groupby(['Month']).sem()
monthlysem2015=View2015.groupby(['Month']).sem()

In [275]:

fig,ax=plt.subplots(1,4,figsize=(20,5))
fig.suptitle('Strait of Georgia', fontsize=24)
fig.subplots_adjust(top=0.8)
#ax[0].errorbar(monthlymean.index,logt_inv(monthlymean['L10Calanoids']),
#                  yerr=logt_inv(np.array([monthlymean['L10Calanoids']-monthlysem['L10Calanoids'],
#                 monthlymean['L10Calanoids']+monthlysem['L10Calanoids']])),
#               fmt='ro',capsize=5)
ax[0].plot(logt_inv(monthlymean['L10Calanoids']),'b--')
#ax[1].errorbar(monthlymean.index,logt_inv(monthlymean['L10CalanoidsDiaRemoved']),
#               yerr=logt_inv(np.array([monthlymean['L10CalanoidsDiaRemoved']-monthlysem['L10CalanoidsDiaRemoved'],
#                 monthlymean['L10CalanoidsDiaRemoved']+monthlysem['L10CalanoidsDiaRemoved']])),
#               fmt='ro',capsize=5)
ax[1].plot(logt_inv(monthlymean['L10CalanoidsDiaRemoved']),'b--')
#ax[2].errorbar(monthlymean.index,logt_inv(monthlymean['L10Euphausiids']),
#               yerr=logt_inv(np.array([monthlymean['L10Euphausiids']-monthlysem['L10Euphausiids'],
#                 monthlymean['L10Euphausiids']+monthlysem['L10Euphausiids']])),
#               fmt='ro',capsize=5)
ax[2].plot(logt_inv(monthlymean['L10Euphausiids']),'b--')
#ax[3].errorbar(monthlymeanCentral.index,logt_inv(monthlymeanCentral['L10Hyperiids']),
#               yerr=logt_inv(np.array([monthlymeanCentral['L10Hyperiids']-monthlysemCentral['L10Hyperiids'],
#                 monthlymeanCentral['L10Hyperiids']+monthlysemCentral['L10Hyperiids']])),
#               fmt='ro',capsize=5)
ax[3].plot(logt_inv(monthlymean['L10Hyperiids']),'b--')
ax[0].set_title('Calanoids')
ax[1].set_title('Calanoids no Diapausers')
ax[2].set_title('Euphausiids')
ax[3].set_title('Amphipods')
ax[0].set_ylabel('Mean Biomass (mg/m3)')
ax[0].set_xlim(0,12)
ax[1].set_xlim(0,12)
ax[2].set_xlim(0,12)
ax[3].set_xlim(0,12)
ax[0].set_ylim(0,5)
ax[1].set_ylim(0,5)
ax[2].set_ylim(0,5)
ax[3].set_ylim(0,5)

fig,ax=plt.subplots(1,1,figsize=(12,2.5))
ax.plot(logt_inv(monthlymean['L10Total']),'--',color='blue',label='Total')
ax.plot(logt_inv(monthlymean['L10CalanoidsDiaRemoved']),'--',color='lightblue',label='Calanoids No Diapause')
ax.set_ylim(0,20)
ax.set_xlim(0,12)
ax.set_title('Strait of Georgia Observation Zooplankton Seasonal Cycle')
ax.set_ylabel('Mean Biomass (mg/m3)',fontsize=10)
ax.legend(fontsize=10)

Out[275]:

<matplotlib.legend.Legend at 0x7ff1a5007850>

In [277]:

fig,ax=plt.subplots(1,4,figsize=(20,5))
fig.suptitle('Central', fontsize=24)
fig.subplots_adjust(top=0.8)
ax[0].plot(logt_inv(monthlymeanCentral['L10Calanoids']),'b--')
ax[1].plot(logt_inv(monthlymeanCentral['L10CalanoidsDiaRemoved']),'b--')
ax[2].plot(logt_inv(monthlymeanCentral['L10Euphausiids']),'b--')
ax[3].plot(logt_inv(monthlymeanCentral['L10Hyperiids']),'b--')
ax[0].set_title('Calanoids')
ax[1].set_title('Calanoids no Diapausers')
ax[2].set_title('Euphausiids')
ax[3].set_title('Amphipods')
ax[0].set_ylabel('Mean Biomass (mg/m3)')
ax[0].set_xlim(0,12)
ax[1].set_xlim(0,12)
ax[2].set_xlim(0,12)
ax[3].set_xlim(0,12)
ax[0].set_ylim(0,5)
ax[1].set_ylim(0,5)
ax[2].set_ylim(0,50)
ax[3].set_ylim(0,5)

Out[277]:

(0.0, 5.0)

In [268]:

fig,ax=plt.subplots(1,4,figsize=(20,5))
fig.suptitle('Baynes', fontsize=24)
fig.subplots_adjust(top=0.8)
ax[0].plot(logt_inv(monthlymeanBaynes['L10Calanoids']),'b--')
ax[1].plot(logt_inv(monthlymeanBaynes['L10CalanoidsDiaRemoved']),'b--')
ax[2].plot(logt_inv(monthlymeanBaynes['L10Euphausiids']),'b--')
ax[3].plot(logt_inv(monthlymeanBaynes['L10Hyperiids']),'b--')
ax[0].set_title('Calanoids')
ax[1].set_title('Calanoids no Diapausers')
ax[2].set_title('Euphausiids')
ax[3].set_title('Amphipods')
ax[0].set_ylabel('Mean Biomass (mg/m3)')
ax[0].set_xlim(0,12)
ax[1].set_xlim(0,12)
ax[2].set_xlim(0,12)
ax[3].set_xlim(0,12)
ax[0].set_ylim(0,20)
ax[1].set_ylim(0,20)
ax[2].set_ylim(0,20)
ax[3].set_ylim(0,20)

Out[268]:

(0.0, 20.0)

In [272]:

fig,ax=plt.subplots(1,1,figsize=(12,2.5))
ax.plot(logt_inv(monthlymean['L10Total']),'--',color='blue',label='Total')
ax.plot(logt_inv(monthlymean['L10CalanoidsDiaRemoved']),'--',color='lightblue',label='Calanoids No Diapause')
ax.set_ylim(0,60)
ax.set_xlim(0,12)
ax.set_title('Strait of Georgia Observation Zooplankton Seasonal Cycle')
ax.set_ylabel('Mean Biomass (mg/m3)',fontsize=10)
ax.legend(fontsize=10)

fig,ax=plt.subplots(1,1,figsize=(12,2.5))
ax.plot(logt_inv(monthlymean['L10mod_mesozooplankton']),'--',color='red',label='Model Microzoop')
ax.set_ylim(0,60)
ax.set_xlim(0,12)
ax.set_title('Model Mesozooplankton Seasonal Cycle')
ax.set_ylabel('Mean Biomass (mg/m3)',fontsize=10)

fig,ax=plt.subplots(1,1,figsize=(12,2.5))
ax.plot(logt_inv(monthlymean['L10mod_microzooplankton']),'--',color='darkorange',label='Model Microzoop')
ax.set_ylim(0,60)
ax.set_xlim(0,12)
ax.set_title('Model Microzooplankton Seasonal Cycle')
ax.set_ylabel('Mean Biomass (mg/m3)',fontsize=10)

Out[272]:

Text(0, 0.5, 'Mean Biomass (mg/m3)')

In [ ]:

In [251]:

data.keys()

Out[251]:

Index(['Key', 'region_name', 'Station', 'Lon', 'Lat', 'Date', 'dtUTC',
       'Twilight', 'Net_Type', 'Mesh_Size(um)',
       ...
       'k_lower', 'L10Calanoids', 'L10CalanoidsDiaRemoved', 'L10Euphausiids',
       'L10Hyperiids', 'L10Gammariids', 'L10Neocalanus', 'L10Total',
       'L10mod_mesozooplankton', 'L10mod_microzooplankton'],
      dtype='object', length=287)

In [252]:

data['Calanoids']

Out[252]:

0      1.157396
1      0.310793
2      0.292791
3      1.391521
4      0.855453
         ...   
461    0.346093
462    0.390772
463    0.075827
464    0.077561
465    0.894605
Name: Calanoids, Length: 466, dtype: float64

In [253]:

fig,ax=plt.subplots(2,2,figsize=(10,10))
fig.subplots_adjust(hspace=1)
ax=ax.flatten()
ax[0].plot(data['Calanoida']+data['Euphausiacea']+data['Amphipoda'],data['mod_mesozooplankton'],'k.')
ax[0].set_title('Copepods Euphausiids Amphipods ($\mu$M)')
ax[0].set_xlabel('Obs')
ax[0].set_ylabel('Model')
ax[0].plot((0,10),(0,10),'r-',alpha=.3)

ax[1].plot(logt(data['Calanoida']+data['Euphausiacea']+data['Amphipoda']),logt(data['mod_mesozooplankton']),'k.')
ax[1].set_title('Log10 Copepods Euphausiids Amphipods ($\mu$M)')
ax[1].set_xlabel('Obs')
ax[1].set_ylabel('Model')
ax[1].plot((-3,3),(-3,3),'r-',alpha=.3)

ax[2].plot(data['Calanoida']+data['Euphausiacea']+data['Amphipoda']+data['Decapoda']+data['Copelata'],data['mod_mesozooplankton'],'k.')
ax[2].set_title('Total ($\mu$M)')
ax[2].set_xlabel('Obs')
ax[2].set_ylabel('Model')
ax[2].plot((0,10),(0,10),'r-',alpha=.3)

ax[3].plot(logt(data['Calanoida']+data['Euphausiacea']+data['Amphipoda']+data['Decapoda']+data['Copelata']),logt(data['mod_mesozooplankton']),'k.')
ax[3].set_title('Log10 Total ($\mu$M)')
ax[3].set_xlabel('Obs')
ax[3].set_ylabel('Model')
ax[3].plot((-3,3),(-3,3),'r-',alpha=.3)

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~/anaconda3/envs/py39/lib/python3.9/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   2897             try:
-> 2898                 return self._engine.get_loc(casted_key)
   2899             except KeyError as err:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'Calanoida'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
<ipython-input-253-3e1f8b5907be> in <module>
      2 fig.subplots_adjust(hspace=1)
      3 ax=ax.flatten()
----> 4 ax[0].plot(data['Calanoida']+data['Euphausiacea']+data['Amphipoda'],data['mod_mesozooplankton'],'k.')
      5 ax[0].set_title('Copepods Euphausiids Amphipods ($\mu$M)')
      6 ax[0].set_xlabel('Obs')

~/anaconda3/envs/py39/lib/python3.9/site-packages/pandas/core/frame.py in __getitem__(self, key)
   2904             if self.columns.nlevels > 1:
   2905                 return self._getitem_multilevel(key)
-> 2906             indexer = self.columns.get_loc(key)
   2907             if is_integer(indexer):
   2908                 indexer = [indexer]

~/anaconda3/envs/py39/lib/python3.9/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   2898                 return self._engine.get_loc(casted_key)
   2899             except KeyError as err:
-> 2900                 raise KeyError(key) from err
   2901 
   2902         if tolerance is not None:

KeyError: 'Calanoida'

In [ ]:

#fig,ax=plt.subplots(1,1,figsize=(10,10))
#ax.plot((data['L10Calanoida']),(data['L10mod_mesozooplankton']),'k.')
#ax.set_title('Log10 Total ($\mu$M)')
#ax.set_xlabel('Obs')
#ax.set_ylabel('Model')
#ax.plot((-3,3),(-3,3),'r-',alpha=.3)

In [ ]:

def yd(idt):
    if type(idt)==dt.datetime:
        yd=(idt-dt.datetime(idt.year-1,12,31)).days
    else: # assume array or pandas
        yd=[(ii-dt.datetime(ii.year-1,12,31)).days for ii in idt]
    return yd

data['yd']=yd(data['dtUTC'])
data['Year']=[ii.year for ii in data['dtUTC']]

By Day of Year¶

In [ ]:

fig,ax=plt.subplots(1,1,figsize=(7,7))
m=ax.scatter(logt(data['Calanoida']+data['Euphausiacea']+data['Amphipoda']),logt(data['mod_mesozooplankton']),
          c=data['yd'],s=5,cmap=cmocean.cm.phase)

ax.set_title('log10[ Copepods Amphipods Euphausiids + 0.001] By Year Day')
ax.set_xlabel('Obs')
ax.set_ylabel('Model')
ax.plot((-6,5),(-6,5),'r-',alpha=.3)
ax.set_xlim(-1.5,1.5)
ax.set_ylim(-1.5,1.5);
fig.colorbar(m)

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 byRegion(ax,obsvar,modvar,lims):
    SoG=[]
    for ind, iregion in enumerate(data.region_name.unique()):
        #ax.plot(datreg[iregion]['Lon'], datreg[iregion]['Lat'],'.',
                #color = colors[ind], label=iregion)
        SoG0=et.varvarPlot(ax,datreg[iregion],obsvar,modvar,
                          cols=(colors[ind],),lname=iregion)
        SoG.append(SoG0)
    l=ax.legend(handles=[ip[0][0] for ip in SoG])
    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 SoG,l

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')
    SoG=et.varvarPlot(ax[0],JF,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
    ax[0].set_title('Winter')
    SoG=et.varvarPlot(ax[1],MAM,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
    ax[1].set_title('Spring')
    SoG=et.varvarPlot(ax[2],JJA,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
    ax[2].set_title('Summer')
    SoG=et.varvarPlot(ax[3],SOND,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
    ax[3].set_title('Autumn')
    return 

In [ ]:

#obsvar='L10Calanoida'
#modvar='L10mod_mesozooplankton'

Plots of Calanoid copepods vs. model mesozooplankton 2015¶

In [ ]:

fig, ax = plt.subplots(1,1,figsize = (16,7))

             
SoG,l=byRegion(ax,'L10Calanoida','L10mod_mesozooplankton',(-1.5,1.5))
ax.set_title('Log 10 Calanoida + 0.001 ($\mu$M) By Region')
ax.legend(bbox_to_anchor=(1.1, 1.05))

fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,'L10Calanoida','L10mod_mesozooplankton',(-1.5,1.5))

In [ ]:

¶

In [ ]:

#obsvar='Euphausiacea'
#modvar='mod_mesozooplankton'

Plots of Euphausiids vs. model mesozooplankton 2015¶

In [ ]:

fig, ax = plt.subplots(1,1,figsize = (16,7))
SoG,l=byRegion(ax,'L10Euphausiacea','L10mod_mesozooplankton',(-1.5,1.5))
ax.set_title('Log 10 Euphausiacea + 0.001 ($\mu$M) By Region');
ax.legend(bbox_to_anchor=(1.1, 1.05))

fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,'L10Euphausiacea','L10mod_mesozooplankton',(-1.5,1.5))

Plots of Amphipods vs. model mesozooplankton 2015¶

In [ ]:

fig, ax = plt.subplots(1,1,figsize = (16,7))
SoG,l=byRegion(ax,'L10Amphipoda','L10mod_mesozooplankton',(-1.5,1.5))
ax.set_title('Log 10 Amphipoda + 0.001 ($\mu$M) By Region');
ax.legend(bbox_to_anchor=(1.1, 1.05))

fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,'L10Amphipoda','L10mod_mesozooplankton',(-1.5,1.5))

Decapods vs. model mesozooplankton 2015¶

In [ ]:

fig, ax = plt.subplots(1,1,figsize = (16,7))
SoG,l=byRegion(ax,'L10Decapoda','L10mod_mesozooplankton',(-1.5,1.5))
ax.set_title('Log 10 Decapoda + 0.001 ($\mu$M) By Region');
ax.legend(bbox_to_anchor=(1.1, 1.05))

fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,'L10Decapoda','L10mod_mesozooplankton',(-1.5,1.5))

Larvaceans vs. model mesozooplankton 2015¶

In [ ]:

fig, ax = plt.subplots(1,1,figsize = (16,7))
SoG,l=byRegion(ax,'L10Copelata','L10mod_mesozooplankton',(-1.5,1.5))
ax.set_title('Log 10 Larvaceans + 0.001 ($\mu$M) By Region');
ax.legend(bbox_to_anchor=(1.1, 1.05))

fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,'L10Copelata','L10mod_mesozooplankton',(-1.5,1.5))

Plots of Cyclopoid copepods vs. model mesozooplankton 2015¶

In [ ]:

fig, ax = plt.subplots(1,1,figsize = (16,7))

SoG,l=byRegion(ax,'L10Cyclopoida','L10mod_mesozooplankton',(-1.5,1.5))
ax.set_title('Log 10 Cyclopoida + 0.001 ($\mu$M) By Region');
ax.legend(bbox_to_anchor=(1.1, 1.05))

fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,'L10Cyclopoida','L10mod_mesozooplankton',(-1.5,1.5))

Calanoid copepods vs. model microzooplankton from Mar-Jun 2015¶

In [ ]:

fig, ax = plt.subplots(1,1,figsize = (16,7))

SoG,l=byRegion(ax,'L10Calanoida','L10mod_microzooplankton',(-1.5,1.5))
ax.set_title('Log10 Calanoida +0.001 ($\mu$M) By Region');
ax.legend(bbox_to_anchor=(1.1, 1.05))

fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,'L10Cyclopoida','L10mod_mesozooplankton',(-1.5,1.5))

In [ ]:

fig, ax = plt.subplots(1,1,figsize = (16,7))

SoG,l=byRegion(ax,'L10Copelata','L10mod_microzooplankton',(-1.5,1.5))
ax.set_title('Log10 Larvaceans +0.001 ($\mu$M) By Region');
ax.legend(bbox_to_anchor=(1.1, 1.05))

fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,'L10Copelata','L10mod_microzooplankton',(-1.5,1.5))

In [ ]: