Update ModelFitters in polymer_fit.py to match new diag/scale interpretation.

diff --git a/hooke/plugin/polymer_fit.py b/hooke/plugin/polymer_fit.py
index 88058a629a3d6f2c6d3f2e710629927c35642605..f4afb2a486210dc68c549a56ab8e24e322f29a88 100644 (file)
--- a/hooke/plugin/polymer_fit.py
+++ b/hooke/plugin/polymer_fit.py
@@ -233,21 +233,21 @@ class FJC (ModelFitter):
     ...     }
     >>> model = FJC(d_data, info=info, rescale=True)
     >>> outqueue = Queue()
-    >>> Lp,a = model.fit(outqueue=outqueue)
+    >>> L,a = model.fit(outqueue=outqueue)
     >>> fit_info = outqueue.get(block=False)
-    >>> model.L(Lp)  # doctest: +ELLIPSIS
-    3.500...e-08
-    >>> a  # doctest: +ELLIPSIS
-    2.499...e-10
+    >>> print L
+    3.5e-08
+    >>> print a
+    2.5e-10
 
     Fit the example data with a one-parameter fit (`L`).  We introduce
     some error in our fixed Kuhn length for fun.
 
     >>> info['Kuhn length (m)'] = 2*a
     >>> model = FJC(d_data, info=info, rescale=True)
-    >>> Lp = model.fit(outqueue=outqueue)
+    >>> L = model.fit(outqueue=outqueue)
     >>> fit_info = outqueue.get(block=False)
-    >>> model.L(Lp)  # doctest: +ELLIPSIS
+    >>> print L  # doctest: +ELLIPSIS
     3.199...e-08
     """
     def Lp(self, L):
@@ -339,6 +339,15 @@ class FJC (ModelFitter):
         self._model_data[:] = FJC_fn(x_data, T, L, a)
         return self._model_data
 
+    def fit(self, *args, **kwargs):
+        params = super(FJC, self).fit(*args, **kwargs)
+        if is_iterable(params):
+            params[0] = self.L(params[0])  # convert Lp -> L
+            params[1] = abs(params[1])  # take the absolute value of `a`
+        else:  # params is a float
+            params = self.L(params)  # convert Lp -> L
+        return params
+
     def guess_initial_params(self, outqueue=None):
         """Guess initial fitting parameters.
 
@@ -362,22 +371,6 @@ class FJC (ModelFitter):
             return [Lp]
         return [Lp, a]
 
-    def guess_scale(self, params, outqueue=None):
-        """Guess parameter scales.
-
-        Returns
-        -------
-        Lp_scale : float
-            A guess at the reparameterized contour length scale in meters.
-        a_scale : float (optional)
-            A guess at the Kuhn length in meters.  If the length of
-            `params` is less than 2, `a_scale` is not returned.
-        """
-        Lp_scale = 1.0
-        if len(params) == 1:
-            return [Lp_scale]
-        return [Lp_scale] + [x/10.0 for x in params[1:]]
-
 
 def inverse_FJC_PEG_fn(F_data, T=300, N=1, k=150., Lp=3.58e-10, Lh=2.8e-10, dG=3., a=7e-10):
     """Inverse poly(ethylene-glycol) adjusted extended FJC model.
@@ -503,21 +496,21 @@ class FJC_PEG (ModelFitter):
     ...     }
     >>> model = FJC_PEG(d_data, info=info, rescale=True)
     >>> outqueue = Queue()
-    >>> Nr,a = model.fit(outqueue=outqueue)
+    >>> N,a = model.fit(outqueue=outqueue)
     >>> fit_info = outqueue.get(block=False)
-    >>> model.L(Nr)  # doctest: +ELLIPSIS
-    122.999...
-    >>> a  # doctest: +ELLIPSIS
-    6.999...e-10
+    >>> print N
+    123.0
+    >>> print a
+    7e-10
 
     Fit the example data with a one-parameter fit (`N`).  We introduce
     some error in our fixed Kuhn length for fun.
 
     >>> info['Kuhn length (m)'] = 2*kwargs['a']
     >>> model = FJC_PEG(d_data, info=info, rescale=True)
-    >>> Nr = model.fit(outqueue=outqueue)
+    >>> N = model.fit(outqueue=outqueue)
     >>> fit_info = outqueue.get(block=False)
-    >>> model.L(Nr)  # doctest: +ELLIPSIS
+    >>> print N  # doctest: +ELLIPSIS
     96.931...
     """
     def Lr(self, L):
@@ -619,6 +612,15 @@ class FJC_PEG (ModelFitter):
         self._model_data[:] = FJC_PEG_fn(x_data, N=N, a=a, **kwargs)
         return self._model_data
 
+    def fit(self, *args, **kwargs):
+        params = super(FJC_PEG, self).fit(*args, **kwargs)
+        if is_iterable(params):
+            params[0] = self.L(params[0])  # convert Nr -> N
+            params[1] = abs(params[1])  # take the absolute value of `a`
+        else:  # params is a float
+            params = self.L(params)  # convert Nr -> N
+        return params
+
     def guess_initial_params(self, outqueue=None):
         """Guess initial fitting parameters.
 
@@ -646,19 +648,6 @@ class FJC_PEG (ModelFitter):
             return [Nr]
         return [Nr, a]
 
-    def guess_scale(self, params, outqueue=None):
-        """Guess parameter scales.
-
-        Returns
-        -------
-        N_scale : float
-            A guess at the section count scale in meters.
-        a_scale : float (optional)
-            A guess at the Kuhn length in meters.  If the length of
-            `params` is less than 2, `a_scale` is not returned.
-        """
-        return [x/10.0 for x in params]
-
 
 def WLC_fn(x_data, T, L, p):
     """The worm like chain interpolation model.
@@ -726,21 +715,21 @@ class WLC (ModelFitter):
     ...     }
     >>> model = WLC(d_data, info=info, rescale=True)
     >>> outqueue = Queue()
-    >>> Lp,p = model.fit(outqueue=outqueue)
+    >>> L,p = model.fit(outqueue=outqueue)
     >>> fit_info = outqueue.get(block=False)
-    >>> model.L(Lp)  # doctest: +ELLIPSIS
-    3.500...e-08
-    >>> p  # doctest: +ELLIPSIS
-    2.500...e-10
+    >>> print L
+    3.5e-08
+    >>> print p
+    2.5e-10
 
     Fit the example data with a one-parameter fit (`L`).  We introduce
     some error in our fixed persistence length for fun.
 
     >>> info['persistence length (m)'] = 2*p
     >>> model = WLC(d_data, info=info, rescale=True)
-    >>> Lp = model.fit(outqueue=outqueue)
+    >>> L = model.fit(outqueue=outqueue)
     >>> fit_info = outqueue.get(block=False)
-    >>> model.L(Lp)  # doctest: +ELLIPSIS
+    >>> print L  # doctest: +ELLIPSIS
     3.318...e-08
     """
     def Lp(self, L):
@@ -832,6 +821,15 @@ class WLC (ModelFitter):
         self._model_data[:] = WLC_fn(x_data, T, L, p)
         return self._model_data
 
+    def fit(self, *args, **kwargs):
+        params = super(WLC, self).fit(*args, **kwargs)
+        if is_iterable(params):
+            params[0] = self.L(params[0])  # convert Lp -> L
+            params[1] = abs(params[1])  # take the absolute value of `p`
+        else:  # params is a float
+            params = self.L(params)  # convert Lp -> L
+        return params
+
     def guess_initial_params(self, outqueue=None):
         """Guess initial fitting parameters.
 
@@ -856,23 +854,6 @@ class WLC (ModelFitter):
             return [Lp]
         return [Lp, p]
 
-    def guess_scale(self, params, outqueue=None):
-        """Guess parameter scales.
-
-        Returns
-        -------
-        Lp_scale : float
-            A guess at the reparameterized contour length scale in meters.
-        p_scale : float (optional)
-            A guess at the persistence length in meters.  If the
-            length of `params` is less than 2, `p_scale` is not
-            returned.
-        """
-        Lp_scale = 1.0
-        if len(params) == 1:
-            return [Lp_scale]
-        return [Lp_scale] + [x/10.0 for x in params[1:]]
-
 
 class PolymerFitPlugin (Plugin):
     """Polymer model (WLC, FJC, etc.) fitting.