sort data by time only once in fitting functions

sncosmo/fitting.py

@@ -72,6 +72,7 @@ def chisq(data, model, modelcov=False):
     chisq : float
     """
     data = photometric_data(data)
+    data.sort_by_time()
 
     if data.fluxcov is None and not modelcov:
         mflux = model.bandflux(data.band, data.time,
@@ -385,6 +386,7 @@ def fit_lc(data, model, vparam_names, bounds=None, method='minuit',
 
     # Standardize data
     data = photometric_data(data)
+    data.sort_by_time()
 
     # Make a copy of the model so we can modify it with impunity.
     model = copy.copy(model)
@@ -751,6 +753,7 @@ def nest_lc(data, model, vparam_names, bounds, guess_amplitude_bound=False,
     tied = kwargs.get("tied", None)
 
     data = photometric_data(data)
+    data.sort_by_time()
     model = copy.copy(model)
     bounds = copy.copy(bounds)  # need to copy this b/c we modify it below
 
@@ -998,6 +1001,7 @@ def mcmc_lc(data, model, vparam_names, bounds=None, priors=None,
 
     # Standardize and normalize data.
     data = photometric_data(data)
+    data.sort_by_time()
 
     # Make a copy of the model so we can modify it with impunity.
     model = copy.copy(model)

sncosmo/photdata.py

@@ -87,10 +87,7 @@ def __init__(self, data):
                     "the data? Use ``sncosmo.select_data(data, mask)`` in "
                     "place of ``data[mask]`` to properly slice covariance.")
 
-        # ensure sorted by time
-        self._sort_by_time()
-
-    def _sort_by_time(self):
+    def sort_by_time(self):
         if not np.all(np.ediff1d(self.time) >= 0.0):
             idx = np.argsort(self.time)
             self.time = self.time[idx]
@@ -116,9 +113,6 @@ def __getitem__(self, key):
         newdata.fluxcov = (None if self.fluxcov is None else
                            self.fluxcov[np.ix_(key, key)])
 
-        # ensure sorted by time (key could have caused reordering)
-        newdata._sort_by_time()
-
         return newdata
 
     def normalized(self, zp=25., zpsys='ab'):

sncosmo/plotting.py

@@ -189,7 +189,9 @@ def plot_lc(data=None, model=None, bands=None, zp=25., zpsys='ab',
 
     # Standardize and normalize data.
     if data is not None:
-        data = photometric_data(data).normalized(zp=zp, zpsys=zpsys)
+        data = photometric_data(data)
+        data.sort_by_time()
+        data = data.normalized(zp=zp, zpsys=zpsys)
 
     # Bands to plot
     if data is None: