Added sbeta_onavg + Switched to densty PDF

comparator.py

@@ -367,6 +367,29 @@ class Comparator(Aggregator, HDF5Container):
             turb_power[run] = energy / dt
         return turb_power
 
+    def _sbeta_onavg(self):
+        """
+        [ismfeed] Compute the slope of the Sigma pdf as a function of the value of beta
+        """
+        col_pdf = self.get_value("/comp/avg_time_coldens_pdf_z")
+        beta = self.get_value("/comp/nml_cloud_params/beta_cool")
+
+        slope = np.zeros(len(col_pdf["runs"]))
+        origin = np.zeros(len(col_pdf["runs"]))
+        stderr = np.zeros(len(col_pdf["runs"]))
+
+        for i, run in enumerate(col_pdf["runs"]):
+            values, centers = col_pdf["mean"][i]
+            mask_fit = (
+                (centers > self.pp_params.pdf.xmin_fit)
+                & (centers < self.pp_params.pdf.xmax_fit)
+                & (values > np.max(values) * self.pp_params.pdf.fit_cut)
+            )
+            (slope[i], origin[i], correlation, _, stderr[i]) = linregress(
+                centers[mask_fit], np.log10(values[mask_fit])
+            )
+        return {"beta": beta, "slope": slope, "origin": origin, "stderr": stderr}
+
     def _gen_rule_time_global(
         self,
         glob_name,
@@ -584,6 +607,15 @@ class Comparator(Aggregator, HDF5Container):
                 group="/series",
                 dependencies={"time": None, "fit_pdf_coldens": "z"},
             ),
+            "sbeta_onavg": Rule(
+                self,
+                partial(self._sbeta_onavg),
+                group="/comp",
+                dependencies={
+                    "avg_time_coldens_pdf": "z",
+                    "nml": "cloud_params/beta_cool",
+                },
+            ),
             # namelist
             "nml": Rule(
                 self,