Oxygen in the Strait of Georgia¶

In [1]:

import cmocean.cm as cm
import datetime as dt
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import numpy as np
import pandas as pd
# import statfieldodels.api as sm
import xarray as xr

from salishsea_tools import viz_tools
import netCDF4 as nc

# import glob

North¶

In [2]:

def read_pieces(pieces):
    temp1 = pd.read_csv(pieces[0])
    for piece in pieces[1:]:
        nextpiece = pd.read_csv(piece)
        if 'ferry' in piece:
            nextpiece['k'] = 0
        temp1 = pd.concat([temp1, nextpiece], ignore_index=True)
    return temp1

In [9]:

y = [2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019]
# y = [2018, 2019]
years = np.array(y)
pieces = []
df = pd.DataFrame()
for year in years:
        pieces.append(
              f'/data/sallen/results/MEOPAR/202111/ObsModel/ObsModel_202111_bot_{year}0101_{year}1231.csv')
data_bot = read_pieces(pieces)
        #df = pd.concat([data_bot, df_csv_append], ignore_index=True)
data_bot

Out[9]:

	Unnamed: 0	Year	Month	Day	Hour	Lat	Lon	Pressure	Depth	Chlorophyll_Extracted	...	i	mod_nitrate	mod_silicon	mod_ammonium	mod_diatoms	mod_flagellates	mod_vosaline	mod_votemper	mod_dissolved_oxygen	k
0	0	2007.0	2.0	23.0	5.530278	49.319167	-123.8010	6.0	NaN	7.79	...	246	25.040506	50.541100	1.127255	0.482299	0.196455	28.584118	7.920136	274.199707	5
1	1	2007.0	2.0	23.0	5.530278	49.319167	-123.8010	21.3	NaN	3.06	...	246	26.823246	53.314888	0.967661	0.255242	0.159323	29.460060	8.236219	240.519928	19
2	2	2007.0	2.0	23.0	5.530278	49.319167	-123.8010	51.5	NaN	0.11	...	246	27.905815	52.202267	0.798150	0.079532	0.059894	30.236198	8.781296	216.537003	24
3	3	2007.0	2.0	23.0	5.530278	49.319167	-123.8010	101.5	NaN	0.10	...	246	26.866858	48.162453	0.856218	0.111528	0.073811	30.385254	8.206964	228.185669	26
4	4	2007.0	2.0	23.0	5.530278	49.319167	-123.8010	151.1	NaN	NaN	...	246	27.156851	48.782696	0.748487	0.098728	0.055468	30.776199	8.576658	202.310898	28
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
19415	1592	2019.0	10.0	6.0	2.446389	49.295333	-123.0545	5.1	5.0	3.07	...	353	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-1
19416	1593	2019.0	10.0	6.0	2.446389	49.295333	-123.0545	10.1	10.0	2.39	...	353	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-1
19417	1594	2019.0	10.0	6.0	2.446389	49.295333	-123.0545	20.0	19.8	1.07	...	353	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-1
19418	1595	2019.0	10.0	6.0	2.446389	49.295333	-123.0545	30.1	29.9	NaN	...	353	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-1
19419	1596	2019.0	10.0	6.0	2.446389	49.295333	-123.0545	31.7	31.5	NaN	...	353	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-1

19420 rows × 31 columns

In [8]:

fig, ax = plt.subplots(1,1,figsize = (6,6))
with nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc') as grid:
    viz_tools.plot_coastline(ax, grid, coords = 'map',isobath=.1)
    

ax, plt.plot(data_bot.Lon, data_bot.Lat,'.')

#ax.set_ylim(48.5, 48.78)
ax.legend(bbox_to_anchor=[1,.6,0,0])
#ax.set_xlim(-123.65, -123.44);
ax.set_title('Observation Locations - Bot');

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

In [ ]:

# Concat all bot data
# np.savetxt('/ocean/atall/MOAD/ObsModel/202111/bot/ObsModel_202111_bot_all_200201_20191231.csv',  data_bot, fmt='%s')

In [ ]:

# date_range=(dt.datetime(2015,1,1),dt.datetime(2017,12,31))

In [ ]:

# SMOOTH data
DO = data_bot.Oxygen_Dissolved
dtime = data_bot.dtUTC
# dtime = data_bot.D
def smooth(DO, box_pts):
    box = np.ones(box_pts)/box_pts
    DO_smooth = np.convolve(DO, box, mode='same')
    return DO_smooth

plt.plot(dtime[:365], DO[:365], '.')
plt.plot(dtime[:365], smooth(DO[:365],3), 'r-')

#plot(x, smooth(y,3), 'r-', lw=2)
#plot(x, smooth(y,19), 'g-', lw=2)
DO.shape

Out[ ]:

(14061,)