Merge pull request #50 from harmsm/tweaks

Tweaks

pytc/experiments/base.py

@@ -22,13 +22,13 @@ class BaseITCExperiment:
     Class that holds an experimental ITC measurement and a model that describes it.
     """
 
-    AVAIL_UNITS = {"cal":1.9872036,
-               "kcal":0.0019872036,
-               "J":8.3144598,
-               "kJ":0.0083144598}
+    AVAIL_UNITS = {"cal/mol":1.9872036,
+                   "kcal/mol":0.0019872036,
+                   "J/mol":8.3144598,
+                   "kJ/mol":0.0083144598}
 
-    def __init__(self,dh_file,model,shot_start=1,units="cal",uncertainty=0.1,
-                 **model_kwargs):
+    def __init__(self,dh_file,model,shot_start=1,units="cal/mol",
+                 uncertainty=0.1,**model_kwargs):
         """
 
         Parameters
@@ -42,7 +42,7 @@ def __init__(self,dh_file,model,shot_start=1,units="cal",uncertainty=0.1,
             what shot to use as the first real point.  Shots start at 0, so
             default=1 discards first point.
         units : string
-            file units ("cal","kcal","J","kJ") 
+            file units ("cal/mol","kcal/mol","J/mol","kJ/mol") 
         uncertainty : float > 0.0
             uncertainty in integrated heats (set to same for all shots, unless
             specified in something like NITPIC output file). 
@@ -219,6 +219,24 @@ def units(self):
 
         return self._units
 
+    @units.setter
+    def units(self,units):
+        """
+        Change the units.
+        """
+
+        # Deal with units
+        self._units = units
+        try:
+            self._R = self.AVAIL_UNITS[self._units]
+        except KeyError:
+            err = "units must be one of:\n"
+            for k in self.AVAIL_UNITS.keys():
+                err += "    {}\n".format(k)
+            err += "\n"
+
+            raise ValueError(err)
+
     @property
     def R(self):
         """

pytc/experiments/chooser.py

@@ -1,7 +1,7 @@
 
 from . import OriginExperiment, NitpicExperiment
 
-def ITCExperiment(dh_file,model,shot_start=1,units="cal",uncertainty=0.1,
+def ITCExperiment(dh_file,model,shot_start=1,units="cal/mol",uncertainty=0.1,
                   **model_kwargs):
     """
     Wrapper that creates a BaseITCExperiment instance that that holds an ITC
@@ -19,7 +19,7 @@ def ITCExperiment(dh_file,model,shot_start=1,units="cal",uncertainty=0.1,
         what shot to use as the first real point.  Shots start at 0, so
         default=1 discards first point.
     units : string
-        file units ("cal","kcal","J","kJ") 
+        file units ("cal/mol","kcal/mol","J/mol","kJ/mol") 
     uncertainty : float > 0.0
         uncertainty in integrated heats (set to same for all shots, unless
         specified in something like NITPIC output file). 

pytc/fit_param.py

@@ -39,10 +39,15 @@ def __init__(self,name,guess=None,guess_range=None,fixed=False,bounds=None,
         self.fixed = fixed
         self.bounds = bounds        
         self.alias = alias
-        
-        self.value = self.guess
+
+        self._initialize_fit_results() 
 
-        # These will be filled in by the fitter
+    def _initialize_fit_results(self):
+        """
+        Set fit results to start (stdev, ninetyfive, value to guess).
+        """
+
+        self.value = self.guess
         self._stdev = np.inf
         self._ninetyfive = [-np.inf,np.inf]
 
@@ -158,6 +163,7 @@ def guess(self,g):
                 self._guess = 1.0
 
         self._value = self._guess
+        self._initialize_fit_results()
 
     #--------------------------------------------------------------------------
     # parameter guess_range
@@ -196,6 +202,8 @@ def guess_range(self,g):
             else:
                 self._guess_range = [-10000.0,10000.0]
 
+        self._initialize_fit_results()
+
     #--------------------------------------------------------------------------
     # parameter fixed-ness. 
 
@@ -214,6 +222,7 @@ def fixed(self,bool_value):
         """
 
         self._fixed = bool(bool_value)
+        self._initialize_fit_results()
 
     #--------------------------------------------------------------------------
     # bounds for fit.
@@ -245,6 +254,8 @@ def bounds(self,b):
         else:
             self._bounds = (-np.inf,np.inf)
 
+        self._initialize_fit_results()
+
     #--------------------------------------------------------------------------
     # parameter alias
 
@@ -270,3 +281,5 @@ def alias(self,a):
             pass
 
         self._alias = a
+
+        self._initialize_fit_results()

pytc/fitters/base.py

@@ -172,8 +172,6 @@ def corner_plot(self,filter_params=(),*args,**kwargs):
 
         return fig
 
-        return fig
-
     @property
     def samples(self):
         """
@@ -184,4 +182,3 @@ def samples(self):
             return self._samples
         except AttributeError:
             return []
-

pytc/fitters/bayesian.py

@@ -62,6 +62,8 @@ def __init__(self,num_walkers=100,initial_walker_spread=1e-4,ml_guess=True,
             err = "multithreading has not yet been (fully) implemented.\n"
             raise NotImplementedError(err)
 
+        self._success = None
+
         self.fit_type = "bayesian"
 
     def ln_prior(self,param):

pytc/fitters/bootstrap.py

@@ -73,6 +73,8 @@ def fit(self,model,parameters,bounds,y_obs,y_err=None,param_names=None):
         self._bounds= bounds
         self._y_obs = y_obs
         self._y_err = y_err
+
+        self._success = None 
 
         if y_err is None or self._exp_err == False:
             self._y_err = np.array([self._perturb_size

pytc/fitters/ml.py

@@ -58,6 +58,8 @@ def fit(self,model,parameters,bounds,y_obs,y_err,param_names=None):
         self._y_obs = y_obs
         self._y_err = y_err
 
+        self._success = None
+
         # If no error is specified, assign the error as 1/N, identical for all
         # points 
         self._y_err = y_err

pytc/global_fit.py

@@ -63,6 +63,10 @@ def add_experiment(self,experiment):
         self._expt_dict[name] = experiment
         self._expt_list_stable_order.append(name)
 
+        # Delete the fitter if we remove an experiment.  It is no longer valid
+        self.delete_current_fit()
+
+
     def remove_experiment(self,experiment):
         """
         Remove an experiment from the analysis.
@@ -89,6 +93,9 @@ def remove_experiment(self,experiment):
         self._expt_dict.pop(expt_name)
         self._expt_list_stable_order.remove(expt_name)
 
+        # Delete the fitter if we remove an experiment.  It is no longer valid
+        self.delete_current_fit()
+
     def link_to_global(self,expt,expt_param,global_param_name):
         """
         Link a local experimental fitting parameter to a global fitting
@@ -450,6 +457,15 @@ def _parse_fit(self):
                 err = "Paramter type {} not recognized.\n".format(self._flat_param_type[i])
                 raise ValueError(err) 
 
+    def delete_current_fit(self):
+        """
+        Delete the current experiment (if it exists).
+        """
+
+        try:
+            del self._fitter
+        except AttributeError:
+            pass
 
     def plot(self,correct_molar_ratio=False,subtract_dilution=False,
              color_list=None,data_symbol="o",linewidth=1.5,num_samples=100):
@@ -493,9 +509,13 @@ def plot(self,correct_molar_ratio=False,subtract_dilution=False,
             ax[i].yaxis.set_ticks_position('left')
             ax[i].xaxis.set_ticks_position('bottom')
 
+        # Nothing to plot
+        if len(self._expt_list_stable_order) < 1:
+            return fig, ax
+
         # Add labels to top plot and remove x-axis
         u = self._expt_dict[self._expt_list_stable_order[0]].units
-        ax[0].set_ylabel("heat per shot ({}/mol)".format(u))
+        ax[0].set_ylabel("heat per shot ({})".format(u))
         plt.setp(ax[0].get_xticklabels(), visible=False)
 
         # Add labels to the residuals plot
@@ -586,7 +606,9 @@ def corner_plot(self,filter_params=("competent","dilution","intercept","heat")):
         try: 
             return self._fitter.corner_plot(filter_params)
         except AttributeError:
-            raise FitNotRunError("Fit has not been run yet\n")
+            # If the fit has not been done, return an empty plot
+            dummy_fig = plt.figure(figsize=(5.5,6))
+            return dummy_fig
 
     # -------------------------------------------------------------------------
     # Properties describing fit results
@@ -597,11 +619,14 @@ def fit_as_csv(self):
         Return a csv-style string of the fit.
         """
 
+        if len(self._expt_list_stable_order) < 1:
+            return "# No experiments loaded."
+
         out = ["# Fit successful? {}\n".format(self.fit_success)]
         out.append("# {}\n".format(datetime.datetime.now()))
-       
+
         u = self._expt_dict[self._expt_list_stable_order[0]].units
-        out.append("# Units: {}/mol\n".format(u))
+        out.append("# Units: {}\n".format(u))
 
         fit_stats_keys = list(self.fit_stats.keys())
         fit_stats_keys.sort()

setup.cfg

@@ -0,0 +1,2 @@
+[metadata]
+description-file = README.md