>>> outqueue = Queue()
>>> L,a = model.fit(outqueue=outqueue)
>>> fit_info = outqueue.get(block=False)
- >>> print L
+ >>> print(L)
3.5e-08
- >>> print a
+ >>> print(a)
2.5e-10
Fit the example data with a one-parameter fit (`L`). We introduce
>>> model = FJC(d_data, info=info, rescale=True)
>>> L, = model.fit(outqueue=outqueue)
>>> fit_info = outqueue.get(block=False)
- >>> print L # doctest: +ELLIPSIS
+ >>> print(L) # doctest: +ELLIPSIS
3.199...e-08
"""
def Lp(self, L):
>>> outqueue = Queue()
>>> N,a = model.fit(outqueue=outqueue)
>>> fit_info = outqueue.get(block=False)
- >>> print N
+ >>> print(N)
123.0
- >>> print a
+ >>> print(a)
7e-10
Fit the example data with a one-parameter fit (`N`). We introduce
>>> model = FJC_PEG(d_data, info=info, rescale=True)
>>> N, = model.fit(outqueue=outqueue)
>>> fit_info = outqueue.get(block=False)
- >>> print N # doctest: +ELLIPSIS
+ >>> print(N) # doctest: +ELLIPSIS
96.931...
"""
def Lr(self, L):
>>> outqueue = Queue()
>>> L,p = model.fit(outqueue=outqueue)
>>> fit_info = outqueue.get(block=False)
- >>> print L
+ >>> print(L)
3.5e-08
- >>> print p
+ >>> print(p)
2.5e-10
Fit the example data with a one-parameter fit (`L`). We introduce
>>> model = WLC(d_data, info=info, rescale=True)
>>> L, = model.fit(outqueue=outqueue)
>>> fit_info = outqueue.get(block=False)
- >>> print L # doctest: +ELLIPSIS
+ >>> print(L) # doctest: +ELLIPSIS
3.318...e-08
"""
def Lp(self, L):