Notebook to play with WOD data using the wodpy package

WOD data

http://www.nodc.noaa.gov/OC5/WOD/datageo.html

wodpy package

https://github.com/BillMills/wodpy

Playing with new pandas supprot

In [1]:

from wodpy import wod

from mpl_toolkits.basemap import Basemap

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import netCDF4 as nc
from salishsea_tools import viz_tools, tidetools
import numpy as np
import glob
import os
import datetime
from salishsea_tools.nowcast import analyze

%matplotlib inline

In [2]:

#make plots pretty
sns.set_style('darkgrid')

Load Model Grid¶

In [3]:

f=nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')
bathy=f.variables['Bathymetry'][:]
X=f.variables['nav_lon'][:]
Y=f.variables['nav_lat'][:]

Load WOD observations¶

Experiment with functions to manipulate the data.

In [4]:

def extract_data(filename, lat_min, lat_max, lon_min, lon_max, start_date, end_date ):
    """Reads a WOD file (filename). 
    Returns a list of dataframes with profiles in the region defined by lat_min, lat_max, lon_min, lon_max 
    and between start_date, end_date (datetime objects)
    """
    
    file = open(filename)
    
    #empty list for gatherthing profiles.
    list_data=[]
    
    #loop through profiles
    profile = wod.WodProfile(file)
    while not profile.is_last_profile_in_file(file):
        df=profile.df()
        profile_date = datetime.datetime(df.year, df.month, df.day)
        #isolate
        if profile_date >= start_date and profile_date <= end_date:
            if df.latitude >= lat_min and df.latitude <= lat_max:
                if df.longitude >= lon_min and df.longitude <= lon_max:
                    list_data.append(df)
        profile = wod.WodProfile(file)
        
    #again for last profile
    df=profile.df()
    profile_date = datetime.datetime(df.year, df.month, df.day)
    #isolate
    if profile_date >= start_date and profile_date <= end_date:
        if df.latitude >= lat_min and df.latitude <= lat_max:
            if df.longitude >= lon_min and df.longitude <= lon_max:
                list_data.append(df)
   
    
    return list_data

In [5]:

#define region
lon_min=-123.5; lat_min=49;
lon_max=-123.2; lat_max=49.2;

#define time period
sdt = datetime.datetime(2000,1,1)
edt = datetime.datetime(2015,12,31)

data = extract_data('/ocean/nsoontie/MEOPAR/WOD/CTDS7412', lat_min, lat_max, lon_min, lon_max, sdt, edt)    

In [10]:

data[0].latitude, data[0].longitude, data[0].year, data[0].month, data[0].day, data[0].uid

Out[10]:

(49.0302, -123.4368, 2000, 4, 27, 10499682)

In [7]:

data[0]

Out[7]:

	depth	salinity	salinity_qc_flag	temperature
0	0	NaN	NaN	10.378
1	5	NaN	NaN	10.042
2	10	NaN	NaN	9.026
3	15	NaN	NaN	8.760
4	20	NaN	NaN	8.596
5	25	NaN	NaN	8.478
6	30	NaN	NaN	8.429
7	35	NaN	NaN	8.417
8	40	NaN	NaN	8.370
9	45	NaN	NaN	8.248
10	50	NaN	NaN	8.220
11	55	NaN	NaN	8.192
12	60	NaN	NaN	8.151
13	65	NaN	NaN	8.127
14	70	NaN	NaN	8.097
15	75	NaN	NaN	8.079
16	80	NaN	NaN	8.064
17	85	NaN	NaN	8.049
18	90	NaN	NaN	8.043
19	95	NaN	NaN	8.029
20	100	NaN	NaN	8.012
21	125	NaN	NaN	8.099
22	150	NaN	NaN	8.276
23	175	NaN	NaN	8.360
24	200	NaN	NaN	8.538
25	225	NaN	NaN	8.651
26	250	NaN	NaN	8.806
27	275	NaN	NaN	8.908
28	300	NaN	NaN	8.900

Examine data by plotting

In [8]:

fig,axm = plt.subplots(1,figsize=(5,5))
for df in data:
    axm.plot(df.longitude, df.latitude, 'o')
viz_tools.plot_coastline(axm,f,coords='map')
axm.set_xlim([-124.1,-122.5])
axm.set_ylim([48.4,49.5])

Out[8]:

(48.4, 49.5)

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