def weibull(self,
column=None,
ws_intervals=1,
method='EuroAtlas',
plot='matplotlib'):
'''Calculate distribution and weibull parameters from data
Parameters:
___________
column: tuple, default None
Column to perform weibull analysis on
ws_intervals: float, default=1
Wind Speed intervals on which to bin
method: string, default 'LeastSq'
Weibull calculation method.
plot: string, default 'matplotlib'
Choose whether or not to plot your data, and what method.
Currently only supporting matplotlib, but hoping to add
Bokeh as that library evolves.
Returns:
________
DataFrame with hourly data distributions
'''
ws_data = self.data[column]
ws_range = np.arange(0, ws_data.max() + ws_intervals, ws_intervals)
binned = pd.cut(ws_data, ws_range)
dist_10min = pd.value_counts(binned).reindex(binned.levels)
dist = pd.DataFrame({'Binned: 10Min': dist_10min})
dist['Binned: Hourly'] = dist['Binned: 10Min'] / 6
dist = dist.fillna(0)
normed = dist['Binned: 10Min'] / dist['Binned: 10Min'].sum()
ws_normed = normed.values
x = np.arange(0, len(ws_normed), ws_intervals)
if method == 'EuroAtlas':
A, k = west.euro_atlas(ws_data)
elif method == 'LeastSq':
A, k = west.least_sq(ws_normed, x)
A = round(A, 3)
k = round(k, 3)
rv = spystats.exponweib(1, k, scale=A, floc=0)
if plot == 'matplotlib':
smooth = np.arange(0, 100, 0.1)
plottools.weibull(smooth,
rv.pdf(smooth),
binned=True,
binned_x=x,
binned_data=dist['Binned: Hourly'],
align='edge')
return {'Weibull A': A, 'Weibull k': k, 'Dist': dist}
def weibull_hourly(k=None,
A=None,
Vmean=None,
bins=np.arange(0, 41, 1),
plot='matplotlib'):
'''Calculate weibull distribution and annual hours from weibull k and A or
Vmean parameters. This distribution is based on multiplying the
PDF by the annual hours for each wind speed bin. Defaults to Vmean for
calculation of A if both Vmean and A are provided.
Parameters:
----------
k: float, int
Weibull k parameters
A: float, int
Weibull A parameter
Vmean: float, int
Mean wind speed, for calculating weibull with Vmean and k only
bins: array, default np.arange(0, 41, 1)
Wind speed bins for estimating and plotting weibull
plot: string, default 'matplotlib'
Choose whether or not to plot your data, and what method.
Currently only supporting matplotlib, but hoping to add
Bokeh as that library evolves.
Returns:
________
Dataframe of wind-speed binned annual hours and normed values
'''
if Vmean:
A = Vmean / (gamma(1 + 1 / k))
step_size = bins[1] - bins[0]
rv = spystats.exponweib(1, k, scale=A, floc=0)
hourly = rv.pdf(bins) * 8760 * step_size
df_hourly = pd.DataFrame(
{
'Annual Hours': hourly,
'Normalized': hourly / hourly.sum()
},
index=bins)
cont_bins = np.arange(0, 100, 0.1)
if plot == 'matplotlib':
plottools.weibull(cont_bins,
rv.pdf(cont_bins),
binned=True,
binned_x=bins,
binned_data=hourly,
align='center')
return df_hourly
def weibull(self, column=None, ws_intervals=1, method='EuroAtlas',
plot='matplotlib'):
'''Calculate distribution and weibull parameters from data
Parameters:
___________
column: tuple, default None
Column to perform weibull analysis on
ws_intervals: float, default=1
Wind Speed intervals on which to bin
method: string, default 'LeastSq'
Weibull calculation method.
plot: string, default 'matplotlib'
Choose whether or not to plot your data, and what method.
Currently only supporting matplotlib, but hoping to add
Bokeh as that library evolves.
Returns:
________
DataFrame with hourly data distributions
'''
ws_data = self.data[column]
ws_range = np.arange(0, ws_data.max()+ws_intervals,
ws_intervals)
binned = pd.cut(ws_data, ws_range)
dist_10min = pd.value_counts(binned).reindex(binned.levels)
dist = pd.DataFrame({'Binned: 10Min': dist_10min})
dist['Binned: Hourly'] = dist['Binned: 10Min']/6
dist = dist.fillna(0)
normed = dist['Binned: 10Min']/dist['Binned: 10Min'].sum()
ws_normed = normed.values
x = np.arange(0, len(ws_normed), ws_intervals)
if method == 'EuroAtlas':
A, k = west.euro_atlas(ws_data)
elif method == 'LeastSq':
A, k = west.least_sq(ws_normed, x)
A = round(A, 3)
k = round(k, 3)
rv = spystats.exponweib(1, k, scale=A, floc=0)
if plot == 'matplotlib':
smooth = np.arange(0, 100, 0.1)
plottools.weibull(smooth, rv.pdf(smooth), binned=True,
binned_x=x, binned_data=dist['Binned: Hourly'],
align='edge')
return {'Weibull A': A, 'Weibull k': k, 'Dist': dist}
def weibull_hourly(k=None, A=None, Vmean=None, bins=np.arange(0, 41, 1),
plot='matplotlib'):
'''Calculate weibull distribution and annual hours from weibull k and A or
Vmean parameters. This distribution is based on multiplying the
PDF by the annual hours for each wind speed bin. Defaults to Vmean for
calculation of A if both Vmean and A are provided.
Parameters:
----------
k: float, int
Weibull k parameters
A: float, int
Weibull A parameter
Vmean: float, int
Mean wind speed, for calculating weibull with Vmean and k only
bins: array, default np.arange(0, 41, 1)
Wind speed bins for estimating and plotting weibull
plot: string, default 'matplotlib'
Choose whether or not to plot your data, and what method.
Currently only supporting matplotlib, but hoping to add
Bokeh as that library evolves.
Returns:
________
Dataframe of wind-speed binned annual hours and normed values
'''
if Vmean:
A = Vmean/(gamma(1+1/k))
step_size = bins[1]-bins[0]
rv = spystats.exponweib(1, k, scale=A, floc=0)
hourly = rv.pdf(bins)*8760*step_size
df_hourly = pd.DataFrame({'Annual Hours': hourly,
'Normalized': hourly/hourly.sum()},
index=bins)
cont_bins = np.arange(0, 100, 0.1)
if plot == 'matplotlib':
plottools.weibull(cont_bins, rv.pdf(cont_bins), binned=True,
binned_x=bins, binned_data=hourly, align='center')
return df_hourly
