# Read precip data from csv
crnt_path = os.path.dirname(os.path.abspath(__file__))
precip_file = os.path.join(crnt_path, '..', 'data', 'rainfall_test.csv')
rainfall_data = np.genfromtxt(precip_file, delimiter=',')
# Initialize SPI class
spi = SPI()
# Set rolling window parameters
spi.set_rolling_window_params(span=1, window_type=None, center=True)
# Set distribution parameters
spi.set_distribution_params(dist_type='gengamma')
# Calculate SPI
data = spi.calculate(rainfall_data, starting_month=1)
# Create date list for plotting
n_dates = np.shape(data)[0]
date_list = create_datelist(dt.date(2000, 1, 1), n_dates)
# Plot SPI
plot_index(date_list, data)
print np.squeeze(data)
# Test find best distribution fit
dist_list = ['gamma', 'gengamma', 'fisk', 'dweibull', 'logistic']
test_data = scipy.stats.gamma.rvs(10., size=100)
sse = spi.best_fit_distribution(test_data,
dist_list,
