def fit_powerlaw(data, plot=True, **kwargs):
"""Fit a powerlaw by doing a linear regression in log space."""
ys = pd.DataFrame(data)
x = pd.Series(data.index.values, index=data.index, dtype=np.float64)
values = pd.DataFrame(index=['n', 'A'])
fits = {}
for col in ys:
y = ys[col].dropna()
slope, intercept, r, p, stderr = \
stats.linregress(np.log(x), np.log(y))
values[col] = [slope, np.exp(intercept)]
fits[col] = x.apply(lambda x: np.exp(intercept) * x**slope)
values = values.T
fits = pd.concat(fits, axis=1)
if plot:
from trackpy import plots
plots.fit(data, fits, logx=True, logy=True, legend=False, **kwargs)
return values
def fit_powerlaw(data, plot=True, **kwargs):
"""Fit a powerlaw by doing a linear regression in log space."""
ys = pd.DataFrame(data)
x = pd.Series(data.index.values, index=data.index, dtype=np.float64)
values = pd.DataFrame(index=['n', 'A'])
fits = {}
for col in ys:
y = ys[col].dropna()
slope, intercept, r, p, stderr = \
stats.linregress(np.log(x), np.log(y))
values[col] = [slope, np.exp(intercept)]
fits[col] = x.apply(lambda x: np.exp(intercept)*x**slope)
values = values.T
fits = pd.concat(fits, axis=1)
if plot:
from trackpy import plots
plots.fit(data, fits, logx=True, logy=True, legend=False, **kwargs)
return values