"""
Look at some macro plots, then do some VARs and IRFs.
"""

import numpy as np
import scikits.timeseries as ts
import scikits.timeseries.lib.plotlib as tplt

import statsmodels.api as sm

data = sm.datasets.macrodata.load()
data = data.data


# Create Timeseries Representations of a few vars

dates = ts.date_array(
    start_date=ts.Date("Q", year=1959, quarter=1),
    end_date=ts.Date("Q", year=2009, quarter=3),
)

ts_data = data[["realgdp", "realcons", "cpi"]].view(float).reshape(-1, 3)
ts_data = np.column_stack((ts_data, (1 - data["unemp"] / 100) * data["pop"]))
ts_series = ts.time_series(ts_data, dates)


fig = tplt.tsfigure()
fsp = fig.add_tsplot(221)
fsp.tsplot(ts_series[:, 0], "-")
fsp.set_title("Real GDP")
fsp = fig.add_tsplot(222)
fsp.tsplot(ts_series[:, 1], "r-")
fsp.set_title("Real Consumption")
fsp = fig.add_tsplot(223)
fsp.tsplot(ts_series[:, 2], "g-")
fsp.set_title("CPI")
fsp = fig.add_tsplot(224)
fsp.tsplot(ts_series[:, 3], "y-")
fsp.set_title("Employment")


# Plot real GDP
# plt.subplot(221)
# plt.plot(data['realgdp'])
# plt.title("Real GDP")

# Plot employment
# plt.subplot(222)

# Plot cpi
# plt.subplot(223)

# Plot real consumption
# plt.subplot(224)

# plt.show()
