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 [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
In [ ]:
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)
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
In [30]:
df['Month']=[ii.month for ii in df['dtUTC']]
In [34]:
len(df.loc[df.Month==2]['Key'].unique())
Out[34]:
13
In [39]:
test=df.loc[(df.Month==2)&(df['Order:']=='Calanoida')].groupby(['Key']).sum()
In [49]:
test
Out[49]:
| lon | lat | Mesh_Size(um) | Net_Mouth_Dia(m) | DEPTH_STRT1 | DEPTH_END1 | Bottom Depth(m) | Volume Filtered(m3) | CTD | Abundance(#/m3) | Biomass(mg/m3) | NumberOfSpecies | Station Diversity | Station Equitability | Month | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Key | |||||||||||||||
| IOS2015001000201A | -4067.085 | 1604.262 | 7788 | 18.48 | 6732 | 0 | 7491 | 1574.43 | 99.0 | 228.25407 | 5.45896 | 1452 | 65.34 | 17.16 | 66 |
| IOS2015001000701A | -6820.110 | 2656.500 | 12980 | 30.80 | 9735 | 0 | 10285 | 1885.95 | 275.0 | 80.92749 | 3.33723 | 2915 | 176.00 | 44.55 | 110 |
| IOS2015008000501 | -5808.025 | 2292.190 | 9400 | 23.50 | 2773 | 0 | 2820 | 462.95 | 188.0 | 422.43662 | 3.71556 | 2538 | 94.00 | 23.97 | 94 |
| IOS2015008000801 | -3955.936 | 1560.000 | 6400 | 16.00 | 960 | 0 | 960 | 158.08 | 224.0 | 398.78547 | 1.77838 | 1152 | 72.00 | 20.16 | 64 |
| IOS2015008001201 | -5191.452 | 2047.416 | 8400 | 21.00 | 2226 | 0 | 2394 | 312.06 | 462.0 | 397.71195 | 4.57015 | 2016 | 77.28 | 20.16 | 84 |
| IOS2015008001601 | -5437.256 | 2144.472 | 8800 | 22.00 | 3080 | 0 | 3212 | 473.44 | 660.0 | 461.98892 | 5.28456 | 2332 | 87.12 | 22.00 | 88 |
| IOS2015099000101 | -6328.692 | 2528.733 | 12036 | 28.56 | 10200 | 0 | 13464 | 2135.37 | 102.0 | 262.04919 | 15.79196 | 2754 | 131.58 | 32.64 | 102 |
| IOS2015099000201 | -6558.644 | 2610.356 | 12508 | 29.68 | 13038 | 0 | 21200 | 3302.43 | 159.0 | 170.91952 | 14.82036 | 3286 | 126.67 | 30.74 | 106 |
| IOS2015099000301 | -7981.952 | 3181.120 | 15104 | 35.84 | 16000 | 0 | 19136 | 3584.64 | 0.0 | 205.39196 | 8.40212 | 4608 | 159.36 | 37.12 | 128 |
| PSF2015098000101 | -3119.175 | 1237.075 | 6250 | 12.50 | 1000 | 0 | 249975 | 173.25 | 0.0 | 300.14424 | 3.09494 | 800 | 64.00 | 18.25 | 50 |
| PSF2015098000201 | -4370.310 | 1736.140 | 8750 | 17.50 | 1400 | 0 | 1400 | 215.25 | 0.0 | 1202.27643 | 12.38677 | 1470 | 78.40 | 21.00 | 70 |
| PSF2015098000301 | -4731.000 | 1879.746 | 9500 | 19.00 | 4940 | 0 | 379962 | 772.16 | 0.0 | 154.37984 | 7.44550 | 1862 | 105.26 | 26.98 | 76 |
| PSF2015098000401 | -4237.692 | 1686.128 | 8500 | 17.00 | 4420 | 0 | 339966 | 737.80 | 0.0 | 166.26730 | 4.97040 | 1088 | 80.24 | 23.12 | 68 |
In [65]:
temp=df.loc[(df.Month==2)&(df['Order:']=='Calanoida'),['Name','Biomass(mg/m3)']].groupby('Name',as_index=False).sum().sort_values(['Biomass(mg/m3)'],ascending=False)
In [70]:
if 'Neocalanus plumchrus 1' in colList:
print('yes')
yes
In [72]:
np.sum(np.isnan(df['Biomass(mg/m3)']))
Out[72]:
0
In [74]:
temp=df.loc[(df.Month==2)&(df['Order:']=='Calanoida'),['Name','Biomass(mg/m3)']].groupby('Name',as_index=False).sum()\
.sort_values(['Biomass(mg/m3)'],ascending=False)
print('Total:',np.sum(temp['Biomass(mg/m3)']))
bsum=0
for i in range(0,len(temp)):
print(temp['Name'][i],np.sum(temp['Biomass(mg/m3)'][:(i+1)])/np.sum(temp['Biomass(mg/m3)']))
if temp['Name'][i] in colList:
bsum=bsum+temp['Biomass(mg/m3)'][(i+1)]
print('included:',bsum)
print('fraction included:',bsum/np.sum(temp['Biomass(mg/m3)']))
Total: 91.05689 Acartia *sp. 1 0.1524709442635258 Acartia *sp. 3 0.29484666124661185 Acartia *sp. 4 0.3680829643973125 Acartia hudsonica 6F 0.4329025513610228 Acartia hudsonica 6M 0.49399644551884 Acartia longiremis 5 0.5431409967988144 Acartia longiremis 6F 0.5886318981463128 Acartia longiremis 6M 0.6190386032292559 Aetideidae *sp. 1 0.6439498427850984 Aetideidae *sp. 2 0.6671245855201072 Aetideus divergens 4 0.6864790791778634 Aetideus divergens 5 0.7047988351018797 Aetideus divergens 6F 0.7226583293147834 Aetideus divergens 6M 0.7390363321216001 Calanoida *sp. 1 0.7541849935792889 Calanoida *sp. 2 0.7665354044048726 Calanus *sp. 1 0.7788231071805769 Calanus *sp. 2 0.790237839223369 Calanus *sp. nauplii s1 0.8015660319608983 Calanus marshallae 5 0.8112372386098404 Calanus marshallae 6F 0.8207127434288607 Calanus marshallae 6M 0.829321427516358 Calanus pacificus 4 0.8376354606444388 Calanus pacificus 5 0.8458247366014807 Calanus pacificus 6F 0.8535784606744201 Calanus pacificus 6M 0.8607734131925657 Candacia *sp. 1 0.8679344308816171 Candacia columbiae 6F 0.8747362225966646 Centropages abdominalis 1 0.880900720417752 Centropages abdominalis 2 0.8866486654661718 Centropages abdominalis 3 0.8916313746274446 Centropages abdominalis 4 0.8963357962258539 Centropages abdominalis 5 0.9007499597229819 Centropages abdominalis 6F 0.9046806891823342 Centropages abdominalis 6M 0.9085288329087452 Chiridius *sp. 3 0.9121159310404736 Chiridius gracilis 3 0.9155190782377918 Chiridius gracilis 4 0.9188578700634294 Chiridius gracilis 5 0.922149987771381 Chiridius gracilis 6F 0.9253754438571316 Clausocalanus arcuicornis 5 0.9284623052687172 Clausocalanus arcuicornis 6F 0.9314462639784864 Clausocalanus arcuicornis 6M 0.9343421458826454 Clausocalanus parapergens 5 0.9371864117037162 Clausocalanus parapergens 6F 0.9399879569794224 Clausocalanus pergens 6F 0.9427879647547812 Ctenocalanus vanus 4 0.9452954081783376 Ctenocalanus vanus 5 0.9475569613677779 Ctenocalanus vanus 6F 0.9498129136630957 Epilabidocera *sp. nauplii s1 0.9518977641340486 Epilabidocera longipedata 5 0.9538723538658086 Eucalanus *sp. nauplii s1 0.9558418918107131 Eucalanus bungii 5F 0.9577635476019443 Eucalanus bungii 5M 0.9596218364145757 Eucalanus bungii 6F 0.9614208216423822 Eucalanus bungii 6M 0.9630263014693342 Eucalanus californicus 5F 0.9645543571716539 Eucalanus californicus 6F 0.9659662217762982 Euchaetidae *sp. 1 0.9673497524459708 Euchaetidae *sp. 2 0.9685823884387001 Euchaetidae *sp. 3 0.9697042145849697 Euchaetidae *sp. nauplii s1 0.9707633326813598 Euchirella *sp. 4 0.9718003766656207 Euchirella pseudopulchra 6M 0.9727906367107421 Gaetanus minutus 4 0.973688427092118 Gaetanus minutus 5 0.9745852290804133 Gaetanus minutus 6F 0.9754191033759225 Heterorhabdus *sp. 3 0.9762425446333606 Heterorhabdus *sp. 4 0.9770157974866042 Heterorhabdus tanneri 6F 0.9777780681945101 Metridia *sp. 1 0.978522987112782 Metridia *sp. 2 0.9792526408490341 Metridia *sp. 3 0.979977901727151 Metridia *sp. 4 0.9807012956405606 Metridia *sp. 5 0.9813971243691719 Metridia *sp. 6F 0.9820906468472622 Metridia pacifica 5F 0.9827817532533781 Metridia pacifica 5M 0.9834220123265796 Metridia pacifica 6F 0.9839754026301578 Metridia pacifica 6M 0.9845213250749064 Metridia pseudopacifica 6F 0.9850584618033847 Metridia pseudopacifica 6M 0.9855917547809946 Microcalanus *sp. 3 0.9861073665046104 Microcalanus *sp. 4 0.9865972800081355 Microcalanus pusillus 4 0.9870838988680593 Microcalanus pusillus 5 0.9875657954054877 Microcalanus pusillus 6F 0.9880469231927423 Microcalanus pusillus 6M 0.9885272822298237 Microcalanus pygmaeus 3 0.9890037975160365 Microcalanus pygmaeus 4 0.9894687815496445 Microcalanus pygmaeus 5 0.9899276155818633 Microcalanus pygmaeus 6F 0.9903856808639082 Microcalanus pygmaeus 6M 0.9908161809611551 Neocalanus *sp. eggs s1 0.9912303176618485 Neocalanus cristatus 6F 0.991639292754233 Neocalanus plumchrus 4 0.9920401410590676 Neocalanus plumchrus 5 0.9924279206109499 Neocalanus plumchrus 6F 0.9927969206943044 Neocalanus plumchrus 6M 0.9931437368440763 Paracalanus indicus 4 0.9934752878118285 Paracalanus indicus 5 0.9937899262757602 Paracalanus indicus 6F 0.9940953397376081 Paracalanus indicus 6M 0.994397019270041 Paracalanus parvus 5 0.9946788211194123 Paracalanus parvus 6F 0.9949577676109959 Paracalanus quasimodo 5 0.9952290266008427 Paracalanus quasimodo 6F 0.9954999561263295 Paracalanus quasimodo 6M 0.9957663829722276 Paraeuchaeta *sp. 4 0.9960321508894056 Paraeuchaeta elongata 4 0.9962944045200753 Paraeuchaeta elongata 5F 0.9965557795791181 Paraeuchaeta elongata 5M 0.9967816823087192 Paraeuchaeta elongata 6F 0.9969847421760175 Paraeuchaeta elongata 6M 0.9971824207921003 Pleuromamma indica 6F 0.9973601119036681 Pseudocalanus *sp. 1 0.9975172664034538 Pseudocalanus *sp. 2 0.9976716753669052 Pseudocalanus *sp. 3 0.9978228995082085 Pseudocalanus *sp. 4 0.9979725861491645 Pseudocalanus mimus 5F 0.9981151343956508 Pseudocalanus mimus 5M 0.9982552665701628 Pseudocalanus mimus 6F 0.9983936416014209 Pseudocalanus minutus 5F 0.9985239996665821 Pseudocalanus minutus 5M 0.9986469996943669 Pseudocalanus minutus 6F 0.9987699997221517 Pseudocalanus minutus 6M 0.9988843238551196 Pseudocalanus moultoni 5F 0.9989867872711226 Pseudocalanus moultoni 5M 0.9990847480075368 Pseudocalanus moultoni 6F 0.9991825989224978 Pseudocalanus moultoni 6M 0.9992677105488669 Pseudocalanus newmani 5F 0.9993470016382066 Pseudocalanus newmani 5M 0.9994149811178484 Pseudocalanus newmani 6F 0.9994764811317408 Pseudocalanus newmani 6M 0.9995352356092989 Racovitzanus antarcticus 4 0.9995854240134933 Rhincalanus nasutus 6F 0.9996324275955395 Scaphocalanus brevicornis 6M 0.9996775642128783 Scolecithricella globulosa 5 0.9997201749367896 Scolecithricella minor 3 0.9997611383389 Scolecithricella minor 4 0.9997937553105537 Scolecithricella minor 5 0.9998251642462203 Scolecithricella minor 6F 0.999854925860086 Scolecithricella minor 6M 0.9998805142587233 Spinocalanus brevicaudatus 5 0.9999061026573607 Spinocalanus brevicaudatus 6F 0.9999260901618757 Tortanus discaudatus 1 0.9999438812373231 Tortanus discaudatus 2 0.9999592562407963 Tortanus discaudatus 3 0.9999718857079348 Tortanus discaudatus 4 0.9999794633882181 Tortanus discaudatus 5 0.9999866017826877 Tortanus discaudatus 6F 0.9999937401771575 Tortanus discaudatus 6M 1.0 included: 45.85667 fraction included: 0.5036046146535424
In [66]:
temp['Name'][0]
Out[66]:
'Acartia *sp. 1'
In [45]:
10**(np.sum(np.log10(test['Biomass(mg/m3)']))/13)
Out[45]:
5.784098014243127
In [ ]:
In [43]:
10**(np.sum(np.log10(test['Abundance(#/m3)']))/13)
Out[43]:
273.85192568125007
In [44]:
10**(np.sum(np.log10(test['Abundance(#/m3)']))/13)*(4.13*(1.8**3.92))*0.45/1000
Out[44]:
5.097374999675256
In [48]:
10**(np.sum(np.log10(test['Abundance(#/m3)']))/13)*4.13*(0.5**3.28)*0.45/1000
Out[48]:
0.05239622263482247
In [ ]:
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 [ ]:
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In [ ]:
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 [ ]:
In [ ]:
In [ ]:
In [ ]:
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 [227]:
# define log transform function with slight shift to accommodate zero values
def logt(x):
return np.log10(x+.001)
In [228]:
# 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 [ ]:
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¶
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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¶
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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))
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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))
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