#!/usr/bin/env python3

# -*- coding: utf-8 -*-

"""Due March 27, 2020

Created on Thu Mar 19 15:43:52 2020

by Hannah Walcek

Assignment 08 - Time Series Analysis With Pandas

Script that produces various plots from monthly arctic oscillation (AO) and

north atlantic oscillation (NAO)

"""

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from pandas import Series, DataFrame

#loading data

ao = np.loadtxt('monthly.ao.index.b50.current.ascii')

#head and tail shows us that the start date is 1950-01 most current date is from 2020-02

dates = pd.date_range(start='1950-01', end='2020-02', freq='M')

#other way of writing without specifying the end

dates = pd.date_range(start='1950-01', periods=ao.shape[0], freq='M')

dates

AO = Series(ao[:,2], index=dates)

#plot total time series

AO.plot()

plt.savefig('DailyAtlanticOscillation.png')

plt.close()

#plot smaller portion

AO['1980':'1990'].plot()

plt.close()

#plot even smaller portion

AO['1980-05':'1981-03'].plot()

plt.close()

#individual value

AO[120]

#individual by index

AO['1960-01']

#one year

AO['1960']

#where values are greater than 0

AO[AO >0]

#loading new data and creating series same way as for AO

nao = np.loadtxt('norm.nao.monthly.b5001.current.ascii')

dates_nao = pd.date_range('1950-01', periods=nao.shape[0], freq='M')

NAO = Series(nao[:,2], index=dates_nao)

NAO.index

#create data frame that will contain both AO and NAO data - different lengths are fine

#this will not work

aonao = DataFrame({'AO':AO, 'NAO':NAO})

#plot data

aonao.plot(subplots=True)

plt.close()

#look at first rows of aonao

aonao.tail()

#add column to data frame

aonao['Diff'] = aonao['AO'] - aonao['NAO']

aonao.head()

#deleting said column

del aonao['Diff']

aonao.head()

import datetime

aonao.loc[(aonao.AO > 0) & (aonao.NAO < 0)

& (aonao.index > datetime.datetime(1980,1,1))

& (aonao.index < datetime.datetime(1989,1,1)),

'NAO'].plot(kind='barh')

plt.close()

#let's do some statistics

aonao.mean()

aonao.max()

aonao.min()

#mean row-wise

aonao.mean(1)

#gets most statistical information

aonao.describe()

#annual ('A') mean

AO_mm = AO.resample("A").mean()

AO_mm.plot(style='g--')

plt.close()

#median

AO_mm = AO.resample("A").median()

AO_mm.plot()

plt.savefig('AnnualMedianValues.png')

plt.close()

#rolling mean

aonao.rolling(window=12, center=False).mean().plot(style='-g')

plt.savefig('RollingMean.png')

plt.close()