In [1]:

import matplotlib.pyplot as plt
import numpy as np
import netCDF4 as nc
import datetime as dt
from salishsea_tools import evaltools as et, viz_tools, geo_tools
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

%matplotlib inline

In [2]:

df1=et.loadDFO()
df1.head()

Out[2]:

	Year	Month	Day	Hour	Lat	Lon	Pressure	Depth	Ammonium	Ammonium_units	Chlorophyll_Extracted	Chlorophyll_Extracted_units	N	Si	Silicate_units	AbsSal	ConsT	Z	dtUTC
0	1982.0	7.0	16.0	1.35	49.25	-123.943	NaN	12.0	NaN	None	5.28	mg/m^3	6.6	13.6	umol/L	NaN	NaN	12.0	1982-07-16 01:21:00
1	1982.0	7.0	16.0	1.35	49.25	-123.943	NaN	21.5	NaN	None	0.61	mg/m^3	21.2	45.0	umol/L	NaN	NaN	21.5	1982-07-16 01:21:00
2	1982.0	7.0	16.0	1.35	49.25	-123.943	NaN	30.5	NaN	None	NaN	mg/m^3	23.5	47.4	umol/L	NaN	NaN	30.5	1982-07-16 01:21:00
3	1982.0	7.0	16.0	1.35	49.25	-123.943	NaN	52.3	NaN	None	NaN	mg/m^3	28.0	50.2	umol/L	NaN	NaN	52.3	1982-07-16 01:21:00
4	1982.0	7.0	16.0	1.35	49.25	-123.943	NaN	75.4	NaN	None	NaN	mg/m^3	26.5	49.1	umol/L	NaN	NaN	75.4	1982-07-16 01:21:00

In [3]:

fig,ax=plt.subplots(1,1,figsize=(24,4))
ax.hist(df1['dtUTC'].values)

Out[3]:

(array([ 4421.,     0., 10641.,   871.,  1004.,  2699.,   232.,  3028.,
        11517., 13849.]),
 array([704863.75416667, 708060.60233565, 711257.45050463, 714454.29867361,
        717651.14684259, 720847.99501157, 724044.84318056, 727241.69134954,
        730438.53951852, 733635.3876875 , 736832.23585648]),
 <a list of 10 Patch objects>)

In [4]:

df1.loc[df1.Si>=0.0]['dtUTC'].max()

Out[4]:

Timestamp('2018-05-18 05:39:38')

In [5]:

dfam=df1.loc[(df1.Ammonium>=0)]
dfam

Out[5]:

	Year	Month	Day	Hour	Lat	Lon	Pressure	Depth	Ammonium	Ammonium_units	Chlorophyll_Extracted	Chlorophyll_Extracted_units	N	Si	Silicate_units	AbsSal	ConsT	Z	dtUTC
6731	1995.0	7.0	5.0	23.216667	48.693000	-123.505	0.2	NaN	0.20	umol/L	NaN	None	0.2	5.6	umol/L	27.996839	17.559562	0.198309	1995-07-05 23:13:00
6732	1995.0	7.0	5.0	23.216667	48.693000	-123.505	5.2	NaN	0.26	umol/L	NaN	None	5.6	18.3	umol/L	28.561329	14.012140	5.155979	1995-07-05 23:13:00
6733	1995.0	7.0	5.0	23.216667	48.693000	-123.505	10.2	NaN	0.24	umol/L	NaN	None	8.6	21.8	umol/L	28.733937	13.288506	10.113529	1995-07-05 23:13:00
6734	1995.0	7.0	5.0	23.216667	48.693000	-123.505	15.1	NaN	0.20	umol/L	NaN	None	11.2	25.4	umol/L	28.903706	13.070437	14.971810	1995-07-05 23:13:00
6735	1995.0	7.0	5.0	23.216667	48.693000	-123.505	20.2	NaN	0.18	umol/L	NaN	None	13.0	27.8	umol/L	29.068438	12.762858	20.028267	1995-07-05 23:13:00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
38129	2003.0	2.0	28.0	15.860556	49.007167	-123.385	125.4	NaN	0.00	umol/L	NaN	mg/m^3	27.3	51.5	umol/L	30.585648	8.557020	124.298603	2003-02-28 15:51:38
38130	2003.0	2.0	28.0	15.860556	49.007167	-123.385	161.5	NaN	0.01	umol/L	NaN	mg/m^3	29.3	56.0	umol/L	30.861703	8.875833	160.067538	2003-02-28 15:51:38
38131	2003.0	2.0	28.0	15.860556	49.007167	-123.385	174.9	NaN	0.00	umol/L	NaN	mg/m^3	29.8	57.5	umol/L	30.939630	8.951475	173.343060	2003-02-28 15:51:38
38132	2003.0	2.0	28.0	15.860556	49.007167	-123.385	199.6	NaN	0.00	umol/L	NaN	mg/m^3	30.6	60.6	umol/L	31.084543	9.059098	197.811359	2003-02-28 15:51:38
38133	2003.0	2.0	28.0	15.860556	49.007167	-123.385	257.0	NaN	0.02	umol/L	NaN	mg/m^3	30.5	61.7	umol/L	31.185513	9.132899	254.661639	2003-02-28 15:51:38

97 rows × 19 columns

In [6]:

with nc.Dataset('/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702_noLPE.nc') as mesh:
    navlon=mesh.variables['nav_lon'][:,:]
    navlat=mesh.variables['nav_lat'][:,:]
    tmask=mesh.variables['tmask'][0,:,:,:]

In [7]:

m=plt.scatter(dfam.Lon,dfam.Lat,c=dfam.Ammonium)
plt.contour(navlon,navlat,tmask[0,:,:],levels=[.5,])
plt.colorbar(m)
plt.xlim((-124,-123))
plt.ylim(48,49.5)

Out[7]:

(48, 49.5)

In [8]:

dfam2=dfam.loc[(dfam.Lat<49.2)]
dfam3=dfam.loc[(dfam.Lat<49.2)&(dfam.dtUTC>dt.datetime(2003,1,1))]
plt.scatter(dfam3.dtUTC,dfam3.Ammonium,c=dfam3.Lat)

Out[8]:

<matplotlib.collections.PathCollection at 0x7f926d9c3bb0>

In [9]:

plt.scatter(dfam2.Ammonium,dfam2.Z,c=dfam2.Lat)

Out[9]:

<matplotlib.collections.PathCollection at 0x7f926c3e0d60>

In [10]:

np.unique(dfam2.loc[dfam2.Lat<49,['Lat','Lon']])

Out[10]:

array([-123.505     , -123.50466667, -123.50416667, -123.50383333,
       -123.50283333, -123.50183333, -123.43666667,   48.391     ,
         48.6925    ,   48.69266667,   48.693     ,   48.69333333,
         48.69366667,   48.69433333])

In [11]:

j,i=geo_tools.find_closest_model_point(-123.43666667,   48.391,navlon,navlat)

In [12]:

j,i

Out[12]:

(299, 184)

In [13]:

sourceloc='/results/SalishSea/hindcast.201905/'
start=dt.datetime(2014,1,1)
end=dt.datetime(2016,1,1)
flist=et.index_model_files(start,end,sourceloc,'nowcast',1,'ptrc_T',1)

In [14]:

data=list()
for ind, row in flist.iterrows():
    with nc.Dataset(row['paths']) as f:
        data.append(np.mean(f.variables['ammonium'][:,:,j,i],0,keepdims=True))

In [15]:

data=np.concatenate(data,axis=0)

In [16]:

times=[start+dt.timedelta(days=ii) for ii in \
       range(0,int((end-start).total_seconds()/(24*3600)+1))]

In [19]:

fig,ax=plt.subplots(1,1,figsize=(16,3))
ax.plot(times,data);

In [ ]: