%pylab inline
import windng.io as wio
import windng.plot.windplot as wplot
import windng.weibull as weibull
import windng.wind as wind

with open('sample_data_80m.txt') as f:
    for i in range(10):
        print f.readline(),

df = wio.read_wind('sample_data_80m.txt', freq='10T', index_name=None)

df.head()

df.tail()

df.describe()

df['1-aug-2010':'10-aug-2010'][['speed', 'gust']].plot(figsize=(14, 4))
plt.grid(True, which='both')

monthly = wind.monthly_means(df['speed'])
monthly

monthly.describe()

wind.downsample(df['speed'], freq='M')

wind.downsample(df['speed'], freq='D').head()

wplot.plot_means(monthly, style='-o')

plt.figure(figsize=(14, 4))
wplot.plot_means(wind.downsample(df['speed'], freq='W'), title='Weekly means')

periods = wind.reference_periods(monthly, years=1)
periods

rp_index = 1
start = periods['start'][rp_index].start_time
end = periods['end'][rp_index].end_time
rp_df = df[start:end]

rp_df.head()

rp_df.tail()

rp_df.describe()

rp_monthly = monthly[start:end]
rp_monthly

wplot.plot_means(monthly, style='-o')
wplot.plot_means(rp_monthly['mean'], style='-o', lw=2)

with open('sample_lt_data.txt') as f:
    for i in range(10):
        print f.readline(),

lt_df = wio.read_wind('sample_lt_data.txt', freq='H', index_name=None)

lt_df.head()

lt_df.tail()

lt_df.describe()

lt_monthly = wind.monthly_means(lt_df['M(m/s)'])

lt_monthly.head()

lt_monthly.tail()

lt_monthly.describe()

model = wind.lt_analysis(monthly, lt_monthly)
print model.summary_as_series()
model.used_data

model.used_data.reindex(monthly.index).plot(style='-o')
plt.grid(True, which='both')

wplot.plot_regression_scatter(model.used_data['lt_source'], model.used_data['mast'], model.slope, model.intercept)

df['speed'] *= (model.lt_speed / rp_df['speed'].mean())
assert abs(rp_df['speed'].mean() - model.lt_speed) < 1e-7

speed_dist = wind.speed_hist(rp_df['speed'], dataframe=True)
speed_dist

shape, scale = weibull.weibull_fit(rp_df['speed'])
print 'Weibull fit'
print 'Shape = {}'.format(shape)
print 'Scale = {}'.format(scale)

wplot.plot_speed_hist(speed_dist, weibull=(shape, scale))

freq_rose = wind.freq_rose(rp_df['dir'], dataframe=True)
freq_rose

wplot.bar_rose(freq_rose['freq'])
plt.title('Frequency rose')

power_rose = wind.power_rose(rp_df['speed'], rp_df['dir'], dataframe=True)
power_rose

wplot.bar_rose(power_rose['freq'], color='r')
plt.title('Power rose')

ti = wind.turbulence(rp_df['speed'], rp_df['std_speed'])
ti

wplot.plot_char_ti(ti, lw=2)

wplot.plot_repr_ti(ti, lw=2)

kwargs = dict()
kwargs['mm'] = 1
kwargs['lon'] = 0
kwargs['lat'] = 40
kwargs['hubheight'] = 80
kwargs['shortname'] = 'Sample mast'
kwargs['longname'] = 'Sample mast 80 m'
kwargs['speedcol'] = 'speed'
kwargs['dircol'] = 'dir'
rp_df['TI'] = 100 * rp_df['std_speed'] / rp_df['speed']
rp_df['Temperature'] = 12
rp_df['Density'] = 1.225
wio.write_openwind(rp_df, 'sample_data_80m_LT_ow.csv', **kwargs)
with open('sample_data_80m_LT_ow.csv') as f:
    for i in range(10):
        print f.readline(),

ftable = wind.freq_table(rp_df['speed'], rp_df['dir'], nsectors=12, dataframe=True)
from IPython.core.display import HTML
display(HTML(ftable.to_html()))

wio.make_tab('sample_data_80m_LT.tab', ftable, desc='Sample data', height=80)
with open('sample_data_80m_LT.tab') as f:
    print f.read()