In [20]:

    

%matplotlib inline
import matplotlib.pyplot as plt, seaborn as sn, mpld3
import pandas as pd, os, glob, numpy as np, imp
import statsmodels.api as sm
from statsmodels.formula.api import ols
from sqlalchemy import create_engine
sn.set_context('notebook')


    

In [21]:

    

# Use custom RESA2 function to connect to db
r2_func_path = r'C:\Data\James_Work\Staff\Heleen_d_W\ICP_Waters\Upload_Template\useful_resa2_code.py'
resa2 = imp.load_source('useful_resa2_code', r2_func_path)

engine, conn = resa2.connect_to_resa2()


    

In [22]:

    

# Specify sites and parameters of interest
stn_list = ['2030-JAR-05',]
par_list = ['Co',]

# Period of interest
st_dt = '1990-01-01'   # yyyy-mm-dd
end_dt = '2017-01-10'  # yyyy-mm-dd


    

In [23]:

    

# Create plot
resa2.plot_resa2_ts(stn_list, par_list, st_dt, end_dt)


    

    
Out[23]:

In [24]:

    

# Create plot
resa2.plot_resa2_ts_single(stn_list, par_list, st_dt, end_dt)


    

    
Out[24]: