import pickle
import pandas as pd
import quandl
import matplotlib.pyplot as plt
from matplotlib import style

style.use("seaborn")

api_key = "rFsSehe51RLzREtYhLfo"


def state_list():
    fifty_states = pd.read_html("https://simple.wikipedia.org/wiki/List_of_U.S._states")
    return fifty_states[0][0][1:]


def initial_state_data():
    states = state_list()
    main_df = pd.DataFrame()

    for abbv in states:
        query = "FMAC/HPI_" + str(abbv)
        df = quandl.get(query, authtoken=api_key)
        df.columns = [str(abbv)]
        df[abbv] = (df[abbv] - df[abbv][0]) / df[abbv][0] * 100.0
        if main_df.empty:
            main_df = df
        else:
            main_df = main_df.join(df)

    pickle_out = open("fifty_states_pct.pickle", "wb")
    pickle.dump(main_df, pickle_out)
    pickle_out.close()


def HPI_Benchmark():
    df = quandl.get("FMAC/HPI_USA", authtoken=api_key)
    df["United States"] = (df["Value"] - df["Value"][0]) / df["Value"][0] * 100.0

    pickle_out = open("us_pct.pickle", "wb")
    pickle.dump(df, pickle_out)
    pickle_out.close()


ax1 = plt.subplot(2, 1, 1)
ax2 = plt.subplot(2, 1, 2, sharex=ax1)

# initial_state_data()

pickle_in = open("fifty_states_pct.pickle", "rb")
HPI_data = pickle.load(pickle_in)

# HPI_Benchmark()

pickle_in = open("us_pct.pickle", "rb")
benchmark = pickle.load(pickle_in)

# HPI_data = HPI_data.pct_change()

# HPI_data.plot(ax=ax1)
# benchmark['United States'].plot(ax=ax1, color='k', linewidth=10)
# plt.legend().remove()

TX1yr = HPI_data["TX"].resample("A").mean()
HPI_data["TX1yr"] = TX1yr
# print(HPI_data[['TX1yr','TX']])
print(HPI_data.isnull().values.sum())

HPI_data.fillna(method="bfill", inplace=True)
# HPI_data.dropna(inplace=True)
print(HPI_data.isnull().values.sum())

# print(HPI_data[['TX1yr','TX']])

# HPI_data[['TX1yr', 'TX']].plot(ax=ax1)
# plt.show()

# print(HPI_data['TX'].hasnans)

# rolling statistics
HPI_data["TX12MA"] = HPI_data["TX"].rolling(window=12, center=False).mean()
HPI_data["TX12STD"] = HPI_data["TX"].rolling(window=12, center=False).std()
# standard deviation is a measure of the volatility of the price
HPI_data.dropna(inplace=True)

TK_AK_12corr = HPI_data["TX"].rolling(window=12).corr(HPI_data["AK"])

HPI_data["TX"].plot(ax=ax1, label="TX HPI")
HPI_data["AK"].plot(ax=ax1, label="AK HPI")
ax1.legend(loc=4)

TK_AK_12corr.plot(ax=ax2, label="TK AK 12 month correlation")
ax2.legend(loc=4)

# HPI_data[['TX12MA','TX']].plot(ax=ax1)
# HPI_data['TX12STD'].plot(ax=ax2)
# print(HPI_data.head())
plt.show()