In [1]:

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import datetime as dt
import matplotlib.gridspec as gridspec
from matplotlib.colors import LogNorm
from salishsea_tools import evaltools as et

%matplotlib inline

In [2]:

df=et.loadPSFCTD()

3.894 34.086
5.35 32.915
1.504 32.291

In [3]:

# path to model files:
PATH= '/results/SalishSea/nowcast-green.201812/'

# start and end dates for analysis:
start_date = dt.datetime(2015,1,1)
end_date = dt.datetime(2018,1,1)

# number of days per model file:
flen=1

# dictionary mapping desired model variables to the file types where they are found
filemap={'vosaline':'grid_T','votemper':'grid_T'}

# dictionary mapping model file types to their time resolution in hours (1 is hourly files, 24 is daily)
fdict={'ptrc_T':1,'grid_T':1}

# results format
# -- nowcast: files like 01jan15/SalishSea_1h_20150101_20150101_ptrc_T.nc
# -- long: files like SalishSea_1h_20150206_20150804_ptrc_T_20150427-20150506.nc, all in one directory
namfmt='nowcast'

data=et.matchData(df,filemap,fdict,start_date,end_date,namfmt,PATH,flen,preIndexed=False)

(Lat,Lon)= 0.0 0.0  not matched to domain
(Lat,Lon)= 48.44 -123.34  not matched to domain
(Lat,Lon)= 48.44 -123.32  not matched to domain
(Lat,Lon)= 48.46 -123.34  not matched to domain
(Lat,Lon)= 49.29 -124.4598  not matched to domain
(Lat,Lon)= 49.2909 -124.3619  not matched to domain
(Lat,Lon)= 49.2915 -124.3626  not matched to domain
(Lat,Lon)= 49.855 -123.363333  not matched to domain
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In [4]:

fig = plt.figure(figsize = (7.5,3.25))
gs1=gridspec.GridSpec(1,4,left=.08,right=.91,bottom=.1,top=.97,
                      wspace=.2,width_ratios=[1,.1,1,.1])
axT=fig.add_subplot(gs1[0,0])
axS = fig.add_subplot(gs1[0,2])
gscb=gridspec.GridSpecFromSubplotSpec(7,1,subplot_spec=gs1[:,3])
axcb = fig.add_subplot(gscb[1:-1])

axS.plot((0,36),(0,36),'k-',alpha=.2)
axT.plot((0,25),(0,25),'k-',alpha=.2)

iiT=(~np.isnan(data['CT']))&(~np.isnan(data['mod_votemper']))
iiS=(~np.isnan(data['SA']))&(~np.isnan(data['mod_vosaline']))
counts, xedges, yedges, m1=axT.hist2d(data.loc[iiT,['CT']].values.flatten(),
                                      data.loc[iiT,['mod_votemper']].values.flatten(),bins=25*3,norm=LogNorm())
counts, xedges, yedges, m2=axS.hist2d(data.loc[iiS,['SA']].values.flatten(),
                                      data.loc[iiS,['mod_vosaline']].values.flatten(),bins=36*3,norm=LogNorm())

cb=fig.colorbar(m1,cax=axcb)
cb.set_label('Count')

ntick=np.arange(0,36,10)
ntickl=[str(i) for i in ntick]
axS.set_xlim((0,36))
axS.set_ylim((0,36))
axS.set_xticks(ntick)
axS.set_xticklabels(ntickl)
axS.set_yticks(ntick)
axS.set_yticklabels(ntickl)
    
stick=np.arange(0,25,10)
stickl=[str(i) for i in stick]
axT.set_xlim((0,25))
axT.set_ylim((0,25))
axT.set_xticks(stick)
axT.set_xticklabels(stickl)
axT.set_yticks(stick)
axT.set_yticklabels(stickl)
    
for ax in (axT,axS):
    ax.set_aspect(1, adjustable='box')
    
axT.set_ylabel('Modeled',fontsize=12)
axS.set_ylabel('Modeled',fontsize=12)
axT.set_xlabel('Observed',fontsize=12)
axS.set_xlabel('Observed',fontsize=12)
axS.set_xlim(5,35)
axS.set_ylim(5,35)

axS.set_title('S$_A$ (g/kg)',fontsize=12)
axT.set_title('$\Theta$ ($^{\circ}$C)',fontsize=12)

Out[4]:

Text(0.5, 1.0, '$\\Theta$ ($^{\\circ}$C)')

In [6]:

data.keys()

Out[6]:

Index(['ID', 'Patrol', 'chl', 'conductivity', 'datetime', 'Z', 'Lat', 'Lon',
       'o2SAT', 'o2uM', 'pressure', 'SA', 'station', 'temperature', 'dtUTC',
       'CT', 'j', 'i', 'mod_vosaline', 'mod_votemper', 'k'],
      dtype='object')

In [8]:

data[['station','ID', 'Patrol','Lat', 'Lon','Z','dtUTC','i', 'j', 'k',
       'pressure','chl', 'o2SAT', 'o2uM', 'conductivity', 'temperature', 'SA', 
      'CT']].to_csv('/data/eolson/results/MEOPAR/oldDBs/PSFCTD.csv',index=False)

In [12]:

test=pd.read_csv('/data/eolson/results/MEOPAR/oldDBs/PSFCTD.csv')

/home/eolson/anaconda3/envs/py39/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3165: DtypeWarning: Columns (1) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

In [13]:

test

Out[13]:

	station	ID	Patrol	Lat	Lon	Z	dtUTC	i	j	k	pressure	chl	o2SAT	o2uM	conductivity	temperature	SA	CT
0	PR-1	NaN	2	49.866525	-124.580281	1.0	2015-02-15 16:24:32	200	667	0	1.33	NaN	85.9	270.6	2.5319	8.431	23.394	8.602689
1	PR-1	NaN	2	49.866525	-124.580281	2.0	2015-02-15 16:24:32	200	667	1	1.99	NaN	85.2	264.6	2.6946	8.602	24.930	8.753593
2	PR-1	NaN	2	49.866525	-124.580281	3.0	2015-02-15 16:24:32	200	667	2	3.03	NaN	84.8	262.2	2.7500	8.636	25.469	8.779866
3	PR-1	NaN	2	49.866525	-124.580281	4.0	2015-02-15 16:24:32	200	667	3	4.04	NaN	84.5	260.8	2.7872	8.615	25.863	8.752570
4	PR-1	NaN	2	49.866525	-124.580281	5.0	2015-02-15 16:24:32	200	667	4	5.03	NaN	84.2	259.9	2.7960	8.597	25.966	8.732706
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
468328	BS-7	NaN	1	49.483333	-124.766667	37.0	2017-10-19 21:49:51	125	605	22	37.39	NaN	NaN	NaN	3.2862	10.025	29.801	10.107736
468329	BS-7	NaN	1	49.483333	-124.766667	38.0	2017-10-19 21:49:51	125	605	22	38.26	NaN	NaN	NaN	3.2860	10.021	29.808	10.103491
468330	BS-7	NaN	1	49.483333	-124.766667	39.0	2017-10-19 21:49:51	125	605	22	39.44	NaN	NaN	NaN	3.2860	10.007	29.816	10.089130
468331	BS-7	NaN	1	49.483333	-124.766667	40.0	2017-10-19 21:49:51	125	605	23	40.38	NaN	NaN	NaN	3.2859	10.001	29.821	10.082901
468332	BS-7	NaN	1	49.483333	-124.766667	41.0	2017-10-19 21:49:51	125	605	23	41.23	NaN	NaN	NaN	3.2861	9.999	29.823	10.080761

468333 rows × 18 columns

In [15]:

data.head()

Out[15]:

	ID	Patrol	chl	conductivity	datetime	Z	Lat	Lon	o2SAT	o2uM	...	SA	station	temperature	dtUTC	CT	j	i	mod_vosaline	mod_votemper	k
0	NaN	2	NaN	2.5319	15/02/2015 16:24:32	1.0	49.866525	-124.580281	85.9	270.6	...	23.394	PR-1	8.431	2015-02-15 16:24:32	8.602689	667	200	17.300295	6.104673	0
1	NaN	2	NaN	2.6946	15/02/2015 16:24:32	2.0	49.866525	-124.580281	85.2	264.6	...	24.930	PR-1	8.602	2015-02-15 16:24:32	8.753593	667	200	25.917959	8.178636	1
2	NaN	2	NaN	2.7500	15/02/2015 16:24:32	3.0	49.866525	-124.580281	84.8	262.2	...	25.469	PR-1	8.636	2015-02-15 16:24:32	8.779866	667	200	26.043879	8.188029	2
3	NaN	2	NaN	2.7872	15/02/2015 16:24:32	4.0	49.866525	-124.580281	84.5	260.8	...	25.863	PR-1	8.615	2015-02-15 16:24:32	8.752570	667	200	26.736950	8.213362	3
4	NaN	2	NaN	2.7960	15/02/2015 16:24:32	5.0	49.866525	-124.580281	84.2	259.9	...	25.966	PR-1	8.597	2015-02-15 16:24:32	8.732706	667	200	26.997450	8.230015	4

5 rows × 21 columns

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