Prepare refactoring

comparator.py

@@ -127,14 +127,14 @@ class Comparator(Aggregator, HDF5Container):
         return series
 
     def _comp(self, getter, use_num=True):
-        prop = np.zeros(len(self.runs))
+        prop = []
         for i, run in enumerate(self.runs):
             if use_num:
                 num = self.nums[run][0]
-                prop[i] = getter(run, num)
+                prop.append(getter(run, num))
             else:
-                prop[i] = getter(run)
+                prop.append(getter(run))
-        return prop
+        return np.array(prop)
 
     def _time_avg(self, name, start=None, end=None, span=None, group="/series"):
         mean = np.zeros(len(self.runs))

plotter.py

@@ -805,6 +805,10 @@ class Plotter(Aggregator, BaseProcessor):
 
     def def_rules(self):
         self.rules = {
+            # Generic rules
+            "plot": PlotRule(
+                self, lambda arg, **kwargs: self._plot(*arg, **kwargs), kind="comp"
+            ),
             "coldens": PlotRule(
                 self,
                 partial(self._plot_map, "coldens", label=r"$\Sigma$", unit=cst.coldens),
@@ -1037,9 +1041,6 @@ class Plotter(Aggregator, BaseProcessor):
                     kind="series",
                     dependencies={"time_max_fluct_coldens": "z"},
                 ),
-                "plot": PlotRule(
-                    self, lambda arg, **kwargs: self._plot(*arg, **kwargs), kind="comp"
-                ),
             }
         )
 

postprocessor.py

@@ -245,7 +245,11 @@ class PostProcessor(HDF5Container):
         else:
             df["value"] = value
 
+        if self.pp_params.process.unload_cells:
+            self.unload_cells()
+
         df.sort_values("axis", inplace=True)
+
         return df.groupby("axis").mean().values[:, 0]
 
     def _vol_avg(self, getter, mass_weighted=True):
@@ -254,9 +258,13 @@ class PostProcessor(HDF5Container):
         if mass_weighted:
             mass = mass_func(self.cells)
             # Transpose (.T) is for vectorial values
-            return np.sum((mass * value.T).T, axis=0) / np.sum(mass)
+            data = np.sum((mass * value.T).T, axis=0) / np.sum(mass)
         else:
-            return np.sum(value, axis=0)
+            data = np.sum(value, axis=0)
+
+        if self.pp_params.process.unload_cells:
+            self.unload_cells()
+        return data
 
     def _vol_pdf(self, getter, bins=100, logbins=False, weight_func=vol_func):
         self.load_cells()
@@ -264,6 +272,8 @@ class PostProcessor(HDF5Container):
         if logbins:
             data = np.log10(data)
         weights = weight_func(self.cells)
+        if self.pp_params.process.unload_cells:
+            self.unload_cells()
 
         values, edges = np.histogram(data, bins, weights=weights)
         centers = 0.5 * (edges[1:] + edges[:-1])

pp_params.yml

@@ -66,6 +66,7 @@ process: # General setting of the post-processor module
     verbose : True                     # Give more infos on what is going on
     num_process : 1                    # Number of forks
     save_cells : True                  # Save cells structure on disk
+    unload_cells : True                # Save memory usage
 
 rules: # Specific rules parameters
     turb_energy_threshold : -1       # Remove invalid data (<0 = no threshold)