Added extror from logs and namelist

pipeline.py

@@ -27,6 +27,9 @@ input_args.add_argument("-p", "--project",
                           help="specify project name (directory within the input directory)",
                     default="disk")
 
+input_args.add_argument("-c", "--config",
+                        help="Path of a default config file",
+                        default=None)
 
 input_args.add_argument("-wo", "--which_inputs",
                     choices=['all', 'id', 'time'],
@@ -77,15 +80,19 @@ output_args.add_argument("--tag",
                     help="Add a special tag on output filemanes",
                     default='')
 
+output_args.add_argument("--interactive",
+                    help="Interactive mode : do not save data",
+                    action='store_true')
+
 output_args.add_argument("-owr", "--overwrite",
                           help="Overwrite outputs",
                           action='store_true')
 
-
 output_args.add_argument("-owrd", "--overwrite_dependencies",
                          help="Overwrite outputs for dependencies",
                          action='store_true',
-                         default=None)
+                         default=False)
+
 
 pp_args = parser.add_argument_group('postproc', "Post Processing configuration")
 
@@ -118,7 +125,7 @@ pp_args.add_argument("--plot",
 
 pp_args.add_argument("-plargs", "--plot_args",
                      help="Args to give to plot rules",
-                     default=['x', 'y', 'z'],
+                     default=[None],
                      nargs='*')
 
 pp_args.add_argument("-d", "--disk",
@@ -174,11 +181,15 @@ runs = args.runs
 storage_in = args.input_path
 storage_out = args.output_path
 
-pp_params = Params()
+if args.config is None:
+     pp_params = default_params()
+else:
+     pp_params = load_params(args.config)
 
 pp_params.out.zoom = args.zoom
 pp_params.out.tag  = args.tag
 pp_params.out.map_size  = args.map_size
+pp_params.out.interactive = args.interactive
 
 pp_params.pymses.fft  = args.fft
 
@@ -208,7 +219,6 @@ P.rcParams["errorbar.capsize"] = 4
 # List of id that were successfully computed
 nums_success = dict()
 
-
 # Go through all runs
 for run in runs:
      path_suffix = project + '/' + run
@@ -249,15 +259,11 @@ for run in runs:
 
           while not success:
                try:
-                    if len(args.process) > 0 and len(args.plot) > 0:
+                    if len(args.process) > 0 :
                          pp = PostProcessor(run, num, pp_params=pp_params)
                          pp.process(args.process, args.process_args,
                                     overwrite=args.overwrite, overwrite_dep=args.overwrite_dependencies)
 
-                         pltter = Plotter(filename=pp.filename)
-                         pltter.plot(args.plot, args.plot_args,
-                                     overwrite=args.overwrite, overwrite_dep=args.overwrite_dependencies)
-
                     # If we are here, success !
                     success = True
                     nums_success[run].append(num)
@@ -274,10 +280,15 @@ for run in runs:
                     else:
                          raise
 
-if len(args.compare) > 0:
+path_in = storage_in + project
-     path_in = storage_in + project
+path_out = storage_out + project
-     path_out = storage_out + project
 
+if len(args.plot) > 0:
+     pl = Plotter(path_in, runs, nums_success, path_out=path_out,  pp_params=pp_params)
+     pl.process(args.plot, args.plot_args,
+             overwrite=args.overwrite, overwrite_dep=args.overwrite_dependencies)
+
+if len(args.compare) > 0:
      cc = Comparator(path_in, runs, nums_success, path_out=path_out,  pp_params=pp_params)
      cc.process(args.compare, args.compare_args,
                 overwrite=args.overwrite, overwrite_dep=args.overwrite_dependencies)

plotter.py

@@ -15,9 +15,10 @@ import matplotlib.patches as mpatches
 from matplotlib.collections import PatchCollection
 from functools import partial
 from numpy.polynomial.polynomial import polyfit
+from scipy.ndimage.filters import gaussian_filter1d
 
 from pp_params import *
-from postprocessor import Rule, BaseProcessor
+from postprocessor import *
 
 
 P.rcParams['image.cmap']='plasma'
@@ -29,18 +30,12 @@ P.rcParams.update(tex_params)
 
 class PlotRule(Rule):
 
-    def plot(self, arg):
+    def plot(self, save, arg, **kwargs):
-        return self.process_fn(arg)
+        self.postproc.save = save
+        return self.process_fn(arg, **kwargs)
 
     def is_valid(self, arg):
-        save = self.postproc.save
+        return arg in self.args_ok and self.is_valid_add(arg)
-        valid = True
-        for dep in self.dependencies:
-            if not arg is None:
-                valid = valid and dep + '_' + str(arg) in save
-            else:
-                valid = valid and dep in save
-        return arg in self.args_ok and valid and self.is_valid_add(arg)
 
 class Plotter(BaseProcessor):
     """
@@ -55,81 +50,139 @@ class Plotter(BaseProcessor):
 
     G = 1. # Gravitational constant
 
-    def __init__(self, filename=None, path_out='.', num=None, pp_params=Params()):
+    def __init__(self, path, runs, nums, path_out=None, pp_params=default_params(), tag=None):
 
+        if type(pp_params) == str:
+            self.pp_params = load_params(pp_params)
+        else :
             self.pp_params = pp_params
 
+        if tag is not None:
+             self.pp_params.out.tag = tag
+
         # Determining output directory
-        if path_out is None:
+        if (path_out is None):
-            if filename is None:
+            self.path_out = path
-                self.path_out='.'
-            else:
-                self.path_out = os.path.dirname(filename)
         else:
             self.path_out = path_out
 
-        # Find HDF5 file
+        # Get postprocesor objets for each run
-        if filename is None:
+        self.pp_runs = dict()
-            if not pp_params.out.tag == '':
+        if not type(nums) == dict:
-                tag_name = '_' + pp_params.out.tag
+            nums_tmp = nums
-            else :
+            nums = dict()
-                tag_name = ''
+            for run in runs:
-                self.filename = (self.path_out + '/postproc_' +
+                nums[run] = nums_tmp
-                                 tag_name + format(num,'05') + '.h5')
+
-        else:
+        for run in runs:
-            self.filename = filename
+            path_run = path + '/' + run
+            path_out_run = path_out + '/' + run
+            self.pp_runs[run] = dict()
+            for num in nums[run]:
+                self.pp_runs[run][num] = PostProcessor(path_run, num, path_out_run, pp_params)
+
+        # Get comparator object
+        self.comp = Comparator(path, runs, nums, path_out, pp_params)
+
+        # save infos
+        self.runs = runs
+        self.nums = nums
+
 
         self.log_id = "[plot {}] ".format(self.pp_params.out.tag)
         self.def_rules()
 
-    def plot(self, to_plot_list, args=[None], overwrite=False):
+    def _process_single(self, name, rule, arg, overwrite=False, just_done=[], **kwargs):
-        """
+        # Solve dependencies
-        Plot the data in to_plot_list and save them
+        for dep in rule.dependencies:
-        """
+            if dep in self.comp.rules:
-        self.process(to_plot_list, args, overwrite, False)
+                if type(rule.dependencies) == dict:
-
+                    self.comp.process([dep], [rule.dependencies[dep]],
-    def _process_single(self, name, rule, arg, overwrite=False, overwrite_dep=False, just_done=[]):
+                                      self.overwrite_dep)
-        done = self._plot_rule(name, rule, arg, overwrite)
-        return []
-
-    def _plot_rule(self, name, rule, arg, overwrite):
-        if not arg is None:
-            name_full = rule.group + '/' + name + '_' + str(arg)
                 else:
-            name_full = rule.group + '/' + name
+                    self.comp.process([dep], [arg], self.overwrite_dep, self.overwrite_dep)
+            else:
+                for run in self.runs:
+                    for i, num in enumerate(self.nums[run]):
+                        if type(rule.dependencies) == dict:
+                            dep_arg = rule.dependencies[dep]
+                            self.pp_runs[run][num].process([dep], [dep_arg],
+                                                           self.overwrite_dep)
+                        else:
+                            self.pp_runs[run][num].process([dep], [arg],
+                                                           self.overwrite_dep,
+                                                           self.overwrite_dep)
+
+        # Process rule
+        done = self._process_rule(name, rule, arg, overwrite, just_done, **kwargs)
+        return just_done + [done]
+
+    def _process_rule(self, name, rule, arg, overwrite=False, just_done=[], **kwargs):
+        if not arg is None:
+            name_full =  name + '_' + str(arg)
+        else:
+            name_full =  name
 
         if rule.is_valid(arg):
-            plot_filename = self._find_filename(name_full)
+            if rule.kind == 'classic':
-            if overwrite or not os.path.exists(plot_filename):
+                for run in self.runs:
-                rule.plot(arg)
+                    for i, num in enumerate(self.nums[run]):
-                P.tight_layout(pad=1)
+                       plot_filename = self._find_filename(name_full, run, num)
-                P.savefig(plot_filename)
+                       save = tables.open_file(self.pp_runs[run][num].filename)
-                P.close()
+                       try:
-                self._log("{} plotted".format(name_full), "SUCCESS")
+                           self._plot_rule(rule, save, arg, plot_filename, overwrite, **kwargs)
+                       finally:
+                           save.close()
             else:
-                self._log("Plot {} is already done, skipping...".format(name_full))
+                if rule.kind == 'series' and len(self.runs) == 1:
+                    run = self.runs[0]
+                    plot_filename = self._find_filename(name_full, run)
+                else:
+                    plot_filename = self._find_filename(name_full)
+                save = tables.open_file(self.comp.filename, 'r')
+                try :
+                    self._plot_rule(rule, save, arg, plot_filename, overwrite, **kwargs)
+                finally:
+                    save.close()
         else:
             self._log("{} is not valid in this context".format(name_full), "ERROR")
 
+    def _plot_rule(self, rule, save, arg, plot_filename, overwrite, **kwargs):
+         if overwrite or not os.path.exists(plot_filename):
+             if not self.pp_params.out.interactive:
+                 P.figure()
+             rule.plot(save, arg, **kwargs)
+             P.tight_layout(pad=1)
+             if not self.pp_params.out.interactive:
+                 P.savefig(plot_filename)
+                 P.close()
+                 self._log("{} plotted".format(plot_filename), "SUCCESS")
+             else:
+                 self._log("{} plotted".format(os.path.basename(plot_filename)), "SUCCESS")
+         else:
+             self._log("Plot {} is already done, skipping...".format(plot_filename))
 
-    def _find_filename(self, name_full):
+
+    def _find_filename(self, name_full, run=None, num=None):
         if not self.pp_params.out.tag == '':
             tag_name = '_' + self.pp_params.out.tag
         else :
             tag_name = ''
 
-        if 'num' in self.save.root._v_attrs:
+        if not run is None and not num is None:
-            num = self.save.root._v_attrs.num
+            return (self.path_out + '/' + run + '/' + name_full +
-            return (self.path_out + '/' + name_full +
                     tag_name + '_' + format(num,'05') +
                     self.pp_params.plot.out_ext)
+        elif not run is None:
+            return (self.path_out + '/' + run + '/'
+                    + name_full + tag_name + self.pp_params.plot.out_ext)
         else:
             return self.path_out + '/' + name_full + tag_name + self.pp_params.plot.out_ext
 
 
 
     def _plot_map(self, name, ax_los, label=None, cmap='plasma', vmin=None, vmax=None, overlays=[]):
-        P.figure()
+
         ax_h = self._axes_h[ax_los]
         ax_v = self._axes_v[ax_los]
         im_extent = self.save.root.maps._v_attrs.im_extent
@@ -201,8 +254,8 @@ class Plotter(BaseProcessor):
         P.quiverkey(Q, 0.6, 0.98, max_v,
                     r'$'+str(max_v)[0:4]+'$ (code)', labelpos='E', coordinates='figure')
 
-    def _plot_radial(self, name, ax_los='z', label=None, xlog=False, ylog=False):
+    def _plot_radial(self, name, label=None, xlog=False, ylog=False):
-        P.figure()
+
         radial_bins = self.save.get_node('/radial/radial_bins_' + ax_los).read()
         bin_centers =  0.5 * (radial_bins[1:] + radial_bins[:-1])
         mean_bin = self.save.get_node('/radial/{}_{}'.format(name, ax_los)).read()
@@ -220,7 +273,7 @@ class Plotter(BaseProcessor):
             P.ylabel(label)
 
     def _plot_hist(self, name, ax_los='z', label=None, ylog=False):
-        P.figure()
+
         pdf = self.save.get_node('/hist/' + name + '_' + ax_los)
         values, centers = pdf.read()
         width = centers[1] - centers[0]
@@ -236,106 +289,185 @@ class Plotter(BaseProcessor):
 
         P.ylim([None, 1.])
 
-    def _plot(self, name_x, name_y, xlabel=None, ylabel=None, linearfit=False):
+    def _plot(self, name_x, name_y, xlabel=None, ylabel=None,
-        P.figure()
+              xunit=None, yunit=None,
+              linearfit=False, smooth=0,
+              nml_key=None, runs=None, **kwargs):
+
         node_x = self.save.get_node(name_x)
         node_y = self.save.get_node(name_y)
 
         if xlabel is None:
-            if 'label' in node_x:
+            if 'label' in node_x._v_attrs:
-                xlabel = name_x._vattrs.label
+                xlabel = node_x._v_attrs.label
             else:
-                xlabel = name_x
+                xlabel = os.path.basename(name_x)
 
         if ylabel is None:
-            if 'label' in node_y:
+            if 'label' in node_y._v_attrs:
-                ylabel = name_y._vattrs.label
+                ylabel = node_y._v_attrs.label
             else:
-                ylabel = name_y
+                ylabel = os.path.basename(name_y)
 
-        x = node_x.read()
+        if 'unit' in node_x._v_attrs:
-        if node_y._v_attrs.CLASS == 'ARRAY':
+            xunit_old = node_x._v_attrs.unit
-            y = node_y.read()
-            P.plot(x, y, fmt='*')
         else:
-            y = node_y.mean.read()
+            xunit_old = cst.none
-            yerr = node_y.std.read()
+
-            P.errorbar(x, y, yerr=y, fmt='*')
+        if 'unit' in node_y._v_attrs:
+            yunit_old = node_y._v_attrs.unit
+        else:
+            yunit_old = cst.none
+
+        if xunit is None:
+            xunit = xunit_old
+        if yunit is None:
+            yunit = yunit_old
+
+        xlabel = xlabel + unit_str(xunit)
+        ylabel = ylabel + unit_str(yunit)
 
         P.xlabel(xlabel)
         P.ylabel(ylabel)
+        P.grid()
+
+        if node_y._v_attrs.CLASS == 'ARRAY':
+            x = node_x.read() * xunit_old.express(xunit)
+            y = node_y.read() * yunit_old.express(yunit)
+            if smooth > 0:
+                y = gaussian_filter1d(y, sigma=smooth)
+            yerr = None
+            P.plot(x, y, fmt='*', **kwargs)
+        elif 'mean' in node_y:
+            x = node_x.read() * xunit_old.express(xunit)
+            y = node_y.mean.read() * yunit_old.express(yunit)
+            if smooth > 0:
+                 y = gaussian_filter1d(y, sigma=smooth)
+            yerr = node_y.std.read()  * yunit_old.express(yunit)
+            P.errorbar(x, y, yerr=yerr, fmt='*', **kwargs)
+        else:
+            yerr = None
+            if runs is None:
+                runs = self.runs
+            for run in runs:
+                x_run, y_run = node_x[run], node_y[run]
+                x = x_run.read() * xunit_old.express(xunit)
+                y = y_run.read() * yunit_old.express(yunit)
+                if smooth > 0:
+                    y = gaussian_filter1d(y, sigma=smooth)
+                if nml_key is None:
+                    label_run = r"{}".format(self.save.root._v_attrs.attrs[y_run.name].label)
+                else:
+                    prop_name = os.path.basename(nml_key)
+                    prop_value = self.comp.get_nml(nml_key, run)
+                    label_run = r"{} = {}".format(prop_name, prop_value)
+                P.plot(x, y, label=label_run, **kwargs)
+                P.legend()
 
         if linearfit:
-            if node_y._v_attrs.CLASS == 'ARRAY':
+            _overlay_linearfit(x, y, yerr)
-                (a, b, rho, _, stderr) = linregress(node_x.read(), node_y.read())
+
+    def _overlay_linearfit(x, y, yerr=None):
+            if yerr is None:
+                (a, b, rho, _, stderr) = linregress(x, y)
             else:
-                c = polyfit(node_x.read(), node_y.mean.read(), 1,
+                c = polyfit(x, y, 1,
-                            w = [1.0 / ty for ty in node_y.std.read()], full=False)
+                            w = [1.0 / ty for ty in yerr], full=False)
                 b, a = c[0], c[1]
 
             P.plot(x, a*y + b, '--', linewidth=1.5)
 
-        P.grid()
-
-
-
     def def_rules(self):
         self.rules = {
             'coldens' : PlotRule(self, lambda ax_los:
-                             self._plot_map('coldens', ax_los, label=r'$\Sigma$ (code)', vmin=0.01, vmax=100),
+                             self._plot_map('coldens', ax_los,
-                             "Column density", ['/maps/coldens']),
+                                            label=r'$\Sigma$ (code)', vmin=0.01, vmax=100),
+                                 "Column density", dependencies=['coldens']),
             'rho' : PlotRule(self, lambda ax_los:
                          self._plot_map('rho', ax_los, label=r'$\rho$ (code)'),
-                         "Density slice", ['/maps/rho']),
+                         "Density slice", dependencies=['rho']),
             'coldens_l' : PlotRule(self, lambda ax_los:
-                               self._plot_map('coldens', ax_los, label=r'$\Sigma$ (code)',
+                                   self._plot_map('coldens', ax_los,
-                                              vmin=0.01, vmax=100, overlays=[self._overlay_levels]),
+                                                  label=r'$\Sigma$ (code)',
-                               "Column density", ['/maps/coldens', '/maps/levels']),
+                                                  vmin=0.01, vmax=100,
+                                                  overlays=[self._overlay_levels]),
+                                   "Column density",
+                                   dependencies=['coldens', 'levels']),
             'rho_v' : PlotRule(self, lambda ax_los:
                                self._plot_map('rho', ax_los, label=r'$\rho$ (code)',
                                               overlays=[self._overlay_speed]),
-                           "Density slice", ['/maps/rho', '/maps/speed_h', '/maps/speed_v']),
+                               "Density slice",
+                               dependencies= ['rho', 'speed_h', 'speed_v']),
             'jeans_ratio' : PlotRule(self, lambda ax_los:
                                  self._plot_map('jeans_ratio', ax_los, vmin=0.1, vmax=100,
                                                 cmap='RdBu_r',
                                                 overlays=[self._overlay_levels]),
                                  "Jeans' lenght divided by the max resolution",
-                                 dependencies=['/maps/jeans_ratio', '/maps/levels']),
+                                 dependencies=['jeans_ratio', 'levels']),
             'Q' : PlotRule(self, lambda ax_los:
-                       self._plot_map('rho', ax_los, label=r'$Q$', vmin=0.01, vmax=100, cmap='RdBu_r'),
+                       self._plot_map('rho', ax_los, label=r'$Q$',
+                                      vmin=0.01, vmax=100, cmap='RdBu_r'),
                        "Toomre Q parameter for a Keplerian disk",
-                       dependencies=['/maps/Q'], args_ok=['z'])
+                       dependencies=['Q'], args_ok=['z'])
 
         }
 
         averageables = ['coldens', 'rho', 'T', 'Q']
         for name in averageables:
-            self.rules['rad_' + name] = PlotRule(self, partial(self._plot_radial, 'rad_avg_' + name,
+            self.rules['rad_' + name] = PlotRule(self,
-                                                         label=name, xlog=True, ylog=True),
+                                                 partial(self._plot_radial,
+                                                         'rad_avg_' + name,
+                                                         label=name,
+                                                         xlog=True, ylog=True),
                                                  "Azimuthal average of {}".format(name),
-                                                 dependencies=['/radial/radial_bins', '/radial/rad_avg_' + name],
+                                                 dependencies=['radial_bins', 'rad_avg_' + name],
                                                  args_ok=['z'])
 
-            self.rules['fluct_' + name] = PlotRule(self, partial(self._plot_map, 'fluct_' + name,
+            self.rules['fluct_' + name] = PlotRule(self,
+                                                   partial(self._plot_map, 'fluct_' + name,
                                                            vmin=0.01, vmax=100, cmap='RdBu_r',
                                                            label='{}/avg({})'.format(name, name)),
                                                    "Fluctuation wrt to average of {}".format(name),
-                                               dependencies=['/maps/fluct_' + name],
+                                                   dependencies=['fluct_' + name],
                                                    args_ok=['z'])
             self.rules['pdf_' + name] = PlotRule(self, partial(self._plot_hist, 'pdf_' + name, ylog=True,
                                                      label='{}/avg({})'.format(name, name)),
                                              "Probability density function of {} fluctuations".format(name),
-                                             dependencies=['/hist/pdf_' + name],
+                                             dependencies=['pdf_' + name],
                                              args_ok=['z'])
 
 
         self.rules.update({
-            'kappa_beta' : PlotRule(self, partial(self._plot, '/comp/beta', '/comp/avg_pdf_slope_coldens',
+            'kappa_beta' : PlotRule(self, partial(self._plot,
+                                                  '/comp/beta',
+                                                  '/comp/avg_pdf_slope_coldens',
                                                   linearfit=True),
-                                    args_ok=[None], dependencies=['/comp/beta', '/comp/avg_pdf_slope_coldens']),
+                                    args_ok=[None], kind='comp',
-            'sink_mass' : PlotRule(self, partial(self._plot, '/series/time', '/series/sinks_mass',
+                                    dependencies=['beta', 'avg_pdf_slope_coldens']),
-                                                  linearfit=True),
+            'sink_mass' : PlotRule(self, partial(self._plot,
-                                args_ok=[None], dependencies=['/series/time', '/series/sinks_mass'])
+                                                 '/series/sinks_from_log/time',
+                                                 '/series/sinks_from_log/mass_sink',
+                                                 ylabel="Mass of sinks",
+                                                 xunit=cst.Myr,
+                                                 yunit=cst.Msun),
+                                   args_ok=[None], kind='series',
+                                   dependencies=['sinks_from_log']),
+            'assfr' : PlotRule(self, partial(self._plot,
+                                           '/series/sfr_from_log/time',
+                                           '/series/sfr_from_log/sfr',
+                                           ylabel="Averaged surfacic SFR",
+                                           xunit=cst.Myr,
+                                           yunit=cst.ssfr),
+                                   args_ok=[None], kind='series',
+                                   dependencies=['sfr_from_log']),
+            'issfr' : PlotRule(self, partial(self._plot,
+                                           '/series/sinks_from_log/time',
+                                           '/series/sinks_from_log/issfr',
+                                           ylabel="Surfacic SFR",
+                                           xunit=cst.Myr,
+                                           yunit=cst.ssfr),
+                                   args_ok=[None], kind='series',
+                                   dependencies=['issfr'])
          })
 
 

postprocessor.py

@@ -2,25 +2,48 @@
 
 import sys
 import os
+import glob as glob
+
 
 import tables
 import pymses
 import numpy as np
 from numpy.polynomial.polynomial import polyfit
+from scipy.stats import linregress
+
 from pymses.sources.ramses import output
 from pymses.sources.hop.file_formats import *
 from pymses.analysis import Camera, raytracing, slicing, splatting
 from pymses.filters import CellsToPoints
 from pymses.analysis import ScalarOperator, FractionOperator, MaxLevelOperator
-from scipy.stats import linregress
+import pymses.utils.constants as cst
+
+import f90nml
+import bunch
+
 from functools import partial
 from abc import ABCMeta, abstractmethod
 
 from pp_params import *
 
+def unit_str(unit):
+    if unit == cst.none:
+        return ''
+    elif len(unit.name) > 0 :
+        return  ' ({})'.format(unit.name)
+    else:
+        return ' ' + str(unit)
+
+cst.ssfr = cst.create_unit('Msun/yr/pc^2',
+                           base_unit=cst.Msun/cst.year/cst.pc**2,
+                           descr="Surfacic SFR",
+                           latex='M_{\astrosun}.yr^{-1}.pc^{-2}')
+
+
 class Rule:
-    def __init__(self, postproc, process, description='', group='', dependencies=[], args_ok=['x', 'y', 'z'],
+    def __init__(self, postproc, process, description='',
-                 is_valid=lambda arg:True):
+                 group='', dependencies=[], args_ok=['x', 'y', 'z'],
+                 is_valid=lambda arg:True, kind='classic', unit=cst.none):
         self.postproc = postproc
         self.process_fn = process
         self.dependencies = dependencies
@@ -28,12 +51,14 @@ class Rule:
         self.group = group
         self.args_ok = args_ok
         self.description = description
+        self.unit=unit
+        self.kind = kind
 
-    def process(self, arg):
+    def process(self, arg, **kwargs):
         if not arg is None:
-            return self.process_fn(arg)
+            return self.process_fn(arg, **kwargs)
         else:
-            return self.process_fn()
+            return self.process_fn(**kwargs)
 
     def is_valid(self, arg):
         save = self.postproc.save
@@ -55,7 +80,9 @@ class BaseProcessor:
 
     log_id = ""
     rules = dict()
+    data = dict()
     filename = ""
+    save = None
 
     @abstractmethod
     def __init__(self):
@@ -67,39 +94,45 @@ class BaseProcessor:
         else:
             print(self.log_id + string)
 
-    def process(self, to_process_list, args=[None], overwrite=False, overwrite_dep=None):
+    def open(self):
+        self.save = tables.open_file(self.filename, mode="a")
+
+    def close(self):
+        self.save.close()
+
+    def process(self, to_process_list, args=[None], overwrite=False, overwrite_dep=False, **kwargs):
         """
         Render the data in to_process_list and save them
         """
-        if overwrite_dep is None:
-            overwrite_dep = overwrite
-
         self.overwrite_dep = overwrite_dep
         just_done = [] # Computations done within this call
 
-        self.save = tables.open_file(self.filename, mode="a")
         for name in to_process_list:
             if name in self.rules:
                 rule = self.rules[name]
                 for arg in args:
-                  just_done = self._process_single(name, rule, arg, overwrite, just_done)
+                  just_done = self._process_single(name, rule, arg, overwrite, just_done, **kwargs)
             else:
                 self._log("{} is unknown, allowed rules are {}".format(name, self.rules.keys()), "ERROR")
-        self.save.close()
 
-    def _process_single(self, name, rule, arg, overwrite=False, just_done=[]):
+    def _process_single(self, name, rule, arg, overwrite=False, just_done=[], **kwargs):
         # Solve dependencies
         for dep in rule.dependencies:
             if dep in self.rules:
                 rule_dep = self.rules[dep]
-                just_done = self._process_single(dep, rule_dep, arg, self.overwrite_dep, just_done)
+                just_done = self._process_single(dep, rule_dep, arg, self.overwrite_dep,
+                                                 just_done)
             else:
                 self._log("Dependency {} for {} is unknown".format(dep, name), "ERROR")
         # Process rule
-        done = self._process_rule(name, rule, arg, overwrite, just_done)
+        self.open()
+        try:
+            done = self._process_rule(name, rule, arg, overwrite, just_done, **kwargs)
+        finally:
+            self.close()
         return just_done + [done]
 
-    def _process_rule(self, name, rule, arg, overwrite=False, just_done=[]):
+    def _process_rule(self, name, rule, arg, overwrite=False, just_done=[], **kwargs):
         if not arg is None:
             name_full = rule.group + '/' + name + '_' + str(arg)
         else:
@@ -107,10 +140,11 @@ class BaseProcessor:
 
         if rule.is_valid(arg):
             if not name_full in just_done:
-                if overwrite or not name_full in self.save:
+                if (overwrite or
+                    (not name_full in self.save and not name_full in self.data)):
                     self._log("Processing {}".format(name_full))
-                    data = rule.process(arg)
+                    data = rule.process(arg, **kwargs)
-                    self._save_data(name_full, data, rule.description)
+                    self._save_data(name_full, data, rule.description, rule.unit)
                     self._log("Data for {} computed".format(name_full), "SUCCESS")
                     return name_full
                 else:
@@ -119,24 +153,43 @@ class BaseProcessor:
             self._log("{} is not valid in this context".format(name_full), "ERROR")
 
 
-    def _save_data(self, name_full, data, description):
+    def _save_data(self, name_full, data, description, unit):
         """
         Save data in the HDF5 structure, overwrite if necessary
         """
         if name_full in self.save:
             self.save.remove_node(name_full, recursive=True)
 
-        if not type(data) == dict:
+        if type(data) == dict:
-            self.save.create_array(os.path.dirname(name_full), os.path.basename(name_full),
+            if type(description) == str:
-                                   data, description, createparents=True)
+                self.save.create_group(os.path.dirname(name_full),
+                                       os.path.basename(name_full),
+                                       description, createparents=True)
             else:
+                self.save.create_group(os.path.dirname(name_full),
+                                       os.path.basename(name_full),
+                                       '', createparents=True)
+
+            if not type(unit) == dict:
+                self.save.get_node(name_full)._v_attrs.unit = unit
+
             for key in data:
                 if type(description) == dict:
-                    self.save.create_array(name_full, key,
+                    if type(unit) == dict:
-                                           data[key], description[key], createparents=True)
+                        self._save_data(name_full + '/' + key, data[key], description[key], unit[key])
                     else:
-                    self.save.create_array(name_full, key,
+                        self._save_data(name_full + '/' + key, data[key], description[key], unit)
-                                           data[key], description, createparents=True)
+                else:
+                    if type(unit) == dict:
+                        self._save_data(name_full + '/' + key, data[key], '', unit[key])
+                    else:
+                        self._save_data(name_full + '/' + key, data[key], '', unit)
+        else:
+            self.save.create_array(os.path.dirname(name_full),
+                                   os.path.basename(name_full),
+                                   data, description, createparents=True)
+            self.save.get_node(name_full).attrs.unit = unit
+
 
     @abstractmethod
     def def_rules(self):
@@ -154,13 +207,17 @@ class PostProcessor(BaseProcessor):
 
     G = 1. # Gravitational constant
 
-    def __init__(self, path=None, num=None, path_out=None, pp_params=Params()):
+    def __init__(self, path=None, num=None, path_out=None, pp_params=default_params()):
         """
         Creates the basic structures needed for the outputs
         """
 
         if not path is None and not num is None:
             # TODO : Make possible to load the HDF5 file even without the original file
+
+            if type(pp_params) == str:
+                self.pp_params = load_params(pp_params)
+            else :
                 self.pp_params = pp_params
 
             # Determining output directory
@@ -175,6 +232,9 @@ class PostProcessor(BaseProcessor):
 
             self.filename = (path_out + '/postproc_' +
                              tag_name  + format(num,'05') + '.h5')
+
+            if not os.path.exists(path_out):
+                os.makedirs(path_out)
             self.save = tables.open_file(self.filename, mode="a",
                                          title=os.path.basename(path)+ '_' + format(num,'05'))
 
@@ -184,6 +244,13 @@ class PostProcessor(BaseProcessor):
             self.num  = num
             self._ro  = pymses.RamsesOutput(path, num, order=pp_params.pymses.order)
             self._amr = self._ro.amr_source(["rho","vel","P"])
+            self.info = self._ro.info.copy()
+
+            # Delete specific info
+            self.info.pop('dom_decomp_Hilbert_keys')
+            self.info.pop('nstep_coarse')
+            self.info.pop('dom_decomp')
+            self.info.pop('time')
 
             # Density operator
             self._rho_op = ScalarOperator(lambda dset: dset["rho"], self._ro.info["unit_density"])
@@ -196,7 +263,7 @@ class PostProcessor(BaseProcessor):
 
             # Set the extend of the image
             self._radius = 0.5 / pp_params.out.zoom
-            self._lbox = self._ro.info['boxlen']
+            self._lbox = self.info['boxlen']
             center = pp_params.out.center
             im_extent = [(- self._radius + center[0]) * self._lbox,
                          (  self._radius + center[0]) * self._lbox,
@@ -219,7 +286,6 @@ class PostProcessor(BaseProcessor):
                 self.save.root.maps._v_attrs.radius = self._radius
                 self.save.root.maps._v_attrs.im_extent = im_extent
 
-
             # Initialize cameras
             self._cam = dict()
             for ax_los in self._ax_nb : # los = line of sight
@@ -246,12 +312,15 @@ class PostProcessor(BaseProcessor):
         Add additional metadata to the file
         """
 
-        # Beta for the beta cooling
+        # Label of the run in the label.txt file
-        if not (self.pp_params.disk.beta is None or self.pp_params.disk.beta == False):
+        label_filename = self.path + '/' + self.pp_params.input.label_filename
-            if type(self.pp_params.disk.beta) == int:
+        if os.path.exists(label_filename):
-                self.save.root._v_attrs.beta = self.pp_params.disk.beta
+            label_file = open(label_filename, 'r')
+            self.label = label_file.readline()
+            label_file.close()
         else:
-                self.save.root._v_attrs.beta = int(self.save.root._v_attrs.run.split('_')[1][4:])
+            self.label = self.run
+        self.save.root._v_attrs.label = self.label
 
     def _coldens(self, ax_los):
          datamap  = self._rt.process(self._cam[ax_los], surf_qty=True)
@@ -482,27 +551,39 @@ class PostProcessor(BaseProcessor):
             # Base rules
             'coldens' : Rule(self, self._coldens, "Column density", '/maps'),
             'rho' : Rule(self, self._rho, "Density slice", '/maps'),
-            'speed_h' : Rule(self, self._speed_h, "Horizontal speed slice wrt the line of sight", '/maps'),
+            'speed_h' : Rule(self, self._speed_h,
-            'speed_v' : Rule(self, self._speed_v, "Vertical speed slice wrt the line of sight", '/maps'),
+                             "Horizontal speed slice wrt the line of sight", '/maps'),
-            'T' : Rule(self, self._temperature, "Temperature slice", '/maps', dependencies=['rho']),
+            'speed_v' : Rule(self, self._speed_v,
-            'levels' : Rule(self, self._levels, "Max level within line of sight", '/maps'),
+                             "Vertical speed slice wrt the line of sight", '/maps'),
-            'jeans' : Rule(self, self._jeans, "Jeans lenght slice", '/maps', dependencies=['rho', 'T']),
+            'T' : Rule(self, self._temperature,
-            'jeans_ratio' : Rule(self, self._jeans_ratio, "Jeans' lenght divided by the max resolution",
+                       "Temperature slice", '/maps', dependencies=['rho']),
+            'levels' : Rule(self, self._levels,
+                            "Max level within line of sight", '/maps'),
+            'jeans' : Rule(self, self._jeans,
+                           "Jeans lenght slice", '/maps', dependencies=['rho', 'T']),
+            'jeans_ratio' : Rule(self, self._jeans_ratio,
+                                 "Jeans' lenght divided by the max resolution",
                                  '/maps', dependencies=['jeans', 'levels']),
-            'Q' : Rule(self, self._toomreQ_disk, "Toomre Q parameter for a Keplerian disk", '/maps',
+            'Q' : Rule(self, self._toomreQ_disk,
+                       "Toomre Q parameter for a Keplerian disk", '/maps',
                        dependencies=['coldens'], args_ok=['z'],
                        is_valid=lambda _: self.pp_params.disk.on),
             'sinks' :  Rule(self, self._sinks, group="/datasets", args_ok=[None],
                             description={'Id': '', 'M':'[Msol]', 'dmf':'[Msol]',
-                                         'x': '', 'y': '', 'z': '', 'vx': '', 'vy': '', 'vz': '',
+                                         'x': '', 'y': '', 'z': '',
-                                         'rot_period':'[y]', 'lx':'|l|', 'ly':'|l|', 'lz':'|l|',
+                                         'vx': '', 'vy': '', 'vz': '',
-                                         'acc_rate':'[Msol/y]', 'acc_lum':'[Lsol]', 'age':'[y]',
+                                         'rot_period':'[y]',
+                                         'lx':'|l|', 'ly':'|l|', 'lz':'|l|',
+                                         'acc_rate':'[Msol/y]', 'acc_lum':'[Lsol]',
+                                         'age':'[y]',
                                          'int_lum':'[Lsol]', 'Teff':'[K]'}),
 
             # Helpers
-            'radial_bins' : Rule(self, self._radial_bins, "Radial bins", '/radial', args_ok=['z']),
+            'radial_bins' : Rule(self, self._radial_bins,
+                                 "Radial bins", '/radial', args_ok=['z']),
             'rr' : Rule(self, self._rr, "Coordinate map", '/maps', args_ok=['z']),
-            'bins_on_map' : Rule(self, self._bins_on_map, "Convert map coordinates to bins", '/maps',
+            'bins_on_map' : Rule(self, self._bins_on_map,
+                                 "Convert map coordinates to bins", '/maps',
                                  dependencies=['radial_bins', 'rr'], args_ok=['z'])
         }
 
@@ -542,11 +623,14 @@ class Comparator(BaseProcessor):
     Do comparaison between outputs and runs
     """
 
-    def __init__(self, path, runs, nums, path_out=None, pp_params=Params()):
+    def __init__(self, path, runs, nums, path_out=None, pp_params=default_params()):
         """
         Creates the basic structures needed for the outputs
         """
 
+        if type(pp_params) == str:
+            self.pp_params = load_params(pp_params)
+        else :
             self.pp_params = pp_params
 
         # Determining output directory
@@ -570,16 +654,31 @@ class Comparator(BaseProcessor):
             for run in runs:
                 nums[run] = nums_tmp
 
+        attrs = dict()
+        self.namelist = dict()
+
         for run in runs:
             path_run = path + '/' + run
             path_out_run = path_out + '/' + run
             self.pp_runs[run] = dict()
             for num in nums[run]:
-                self.pp_runs[run][num] = PostProcessor(path_run, num, path_out=path_out_run, pp_params=pp_params)
+                self.pp_runs[run][num] = PostProcessor(path_run, num, path_out=path_out_run,
+                                                       pp_params=pp_params)
+
+            h5file = tables.open_file(self.pp_runs[run][nums[run][0]].filename, 'r')
+            attrs[run] = h5file.root._v_attrs
+            h5file.close()
+            path_nml = path_run + '/' + self.pp_params.input.nml_filename
+            self.namelist[run] = bunch.bunchify(f90nml.read(path_nml))
+
+
+        self.info = self.pp_runs[runs[0]][nums[runs[0]][0]].info
 
         # save metadata
         self.save.root._v_attrs.runs = runs
         self.save.root._v_attrs.nums = nums
+        self.save.root._v_attrs.attrs = attrs
+        self.save.root._v_attrs.namelist = bunch.unbunchify(self.namelist)
 
         # log info
         self.log_id = "[comp {}] ".format(self.pp_params.out.tag)
@@ -593,7 +692,7 @@ class Comparator(BaseProcessor):
         for run in self.save.root._v_attrs.runs:
             series[run] = np.zeros(len(nums[run]))
             for i, num in enumerate(nums[run]):
-                series[run][i] = getter(self.pp_runs[run][num])
+                series[run][i] = getter(run, num)
         return series
 
     def _comp(self, name, getter):
@@ -602,7 +701,7 @@ class Comparator(BaseProcessor):
         prop = np.zeros(len(runs))
         for i, run in enumerate(runs):
             num = nums[run][0]
-            prop[i] = getter(self.pp_runs[run][num])
+            prop[i] = getter(run, num)
         return prop
 
     def _time_avg(self, name):
@@ -615,13 +714,21 @@ class Comparator(BaseProcessor):
             std[i] = np.std(serie)
         return {"mean": mean, "std": std}
 
-    def _get_attr(self, attr_name, pp):
+    def get_attr(self, attr_name, run, num):
+        pp = self.pp_runs[run][num]
         h5file = tables.open_file(pp.filename, "r")
         attr = h5file.root._v_attrs[attr_name]
         h5file.close()
         return attr
 
-    def _get_pdf_slope(self, name, pp):
+    def get_nml(self, nml_key, run):
+        res = self.namelist[run]
+        for key in nml_key.split('/'):
+            res = res[key]
+        return res
+
+    def get_pdf_slope(self, name, run, num):
+        pp = self.pp_runs[run][num]
         pp.process(['fit_pdf_' + name], ['z'], overwrite=self.overwrite_dep)
         h5file = tables.open_file(pp.filename, "r")
         pdf = h5file.get_node('/hist/pdf_' + name +'_z')
@@ -629,34 +736,136 @@ class Comparator(BaseProcessor):
         h5file.close()
         return slope
 
-    def _get_sinks_mass(self, pp):
+    def get_sinks_mass(self, run, num):
+        pp = self.pp_runs[run][num]
         pp.process(['sinks'], overwrite=self.overwrite_dep)
         h5file = tables.open_file(pp.filename, "r")
         sinks_mass = h5file.get_node('/datasets/sinks/M').read()
         h5file.close()
         return np.sum(sinks_mass)
 
+    def _extract_sinks_from_log(self, series, log_filename, run):
+        cmd_grep = "grep 'Number of sink' {} -A 2".format(log_filename)
+        content = os.popen(cmd_grep).readlines()
+        for i in range(0, len(content), 4):
+            series['nb_sink'][run].append(np.int(content[i].split('=')[1]))
+            series['mass_sink'][run].append(np.float(content[i + 1].split('=')[1]))
+            series['time'][run].append(np.float(content[i + 2].split('=')[1]))
+        return series
+
+    def _extract_sfr_from_log(self, series, log_filename, run):
+        cmd_grep = "grep '\[SFR' {} ".format(log_filename)
+        content = os.popen(cmd_grep).readlines()
+        for i in range(0, len(content)):
+            time = np.float(content[i].split(']')[0].split('=')[1].split()[0])
+            sfr = np.float(content[i].split(']')[1].split('=')[1].split()[0])
+            series['time'][run].append(time)
+            series['sfr'][run].append(sfr)
+        return series
+
+    def _from_log(self, keys, extractor):
+        nums = self.save.root._v_attrs.nums
+
+        # Initialize series
+        series = dict()
+        for key in keys:
+            series[key] = dict()
+
+        for run in self.save.root._v_attrs.runs:
+            # Initialize list
+            for key in keys:
+                series[key][run] = []
+
+            # get one preprocessor
+            num = nums[run][0]
+            pp = self.pp_runs[run][num]
+
+            # Get list of run files
+            log_files = (pp.path + '/'
+                         + self.pp_params.input.log_prefix + '*')
+
+            # Parse files
+            for log_filename in glob.glob(log_files):
+                series = extractor(series, log_filename, run)
+
+            # Numpify the lists
+            for key in series:
+                series[key][run] = np.array(series[key][run])
+
+            # Sort the arrays
+            ind_sort = series['time'][run].argsort()
+            for key in series:
+                series[key][run] = series[key][run][ind_sort]
+        return series
+
+
+    def _ssfr_from_mass_sink(self):
+        time_unit = self.save.get_node('/series/sinks_from_log/time')._v_attrs.unit
+        mass_unit = self.save.get_node('/series/sinks_from_log/mass_sink')._v_attrs.unit
+        # Surface of the box in pc^2
+        surface = (self.info['unit_length'].express(cst.pc))**2
+        # WARNING : We do not multiply by boxlen since already done in 'unit_length'
+        ssfr = dict()
+        for run in self.save.root._v_attrs.runs:
+            time = self.save.get_node('/series/sinks_from_log/time/' + run).read()
+            time = time * time_unit.express(cst.year)
+            mass_sink = self.save.get_node('/series/sinks_from_log/mass_sink/' + run).read()
+            mass_sink = mass_sink * mass_unit.express(cst.Msun)
+            sfr = (mass_sink[1:] - mass_sink[:-1]) / (time[1:] - time[:-1])
+            ssfr[run] = np.zeros(len(mass_sink))
+            ssfr[run][1:] = sfr / surface
+        return ssfr
+
+
     def def_rules(self):
         averageables = ['coldens', 'rho', 'T', 'Q']
         self.rules = {
-            'beta' : Rule(self, lambda arg: self._comp("beta", partial(self._get_attr, 'beta')), group='/comp',
+            # Read from log
-                          args_ok = [None]),
+            'sinks_from_log' : Rule(self,
+                                    partial(self._from_log,
+                                                  ['time', 'mass_sink', 'nb_sink'],
+                                                  self._extract_sinks_from_log),
+                                    group='/series', args_ok=[None],
+                                    unit={'time' : self.info['unit_time'],
+                                          'mass_sink' : cst.Msun,
+                                          'nb_sink' : cst.none}),
+            'issfr' : Rule(self,
+                           self._ssfr_from_mass_sink,
+                           group='/series/sinks_from_log', args_ok=[None],
+                           unit=cst.ssfr,
+                           description="Instantaneous surfacic star formation rate",
+                           dependencies=['sinks_from_log']),
+            'sfr_from_log' : Rule(self,
+                                  partial(self._from_log,
+                                                  ['time', 'sfr'],
+                                                  self._extract_sfr_from_log),
+                                  group='/series', args_ok=[None],
+                                  unit={'time' : cst.year,
+                                        'sfr' : cst.ssfr},
+                                  description={'time': 'Time',
+                                               'sfr' : 'Averaged surfacic star formation rate'}),
+            # Read from outputs
+            'time' : Rule(self, partial(self._time_series, "time",
+                                        partial(self.get_attr, 'time')),
+                          group='/series', args_ok=[None]),
             'time_pdf_slope' : Rule(self,
-                                    lambda name: self._time_series("pdf_slope_" + name,
+                                    lambda name:
-                                                                   partial(self._get_pdf_slope, name)),
+                                    self._time_series("pdf_slope_" + name,
+                                                      partial(self.get_pdf_slope,
+                                                              name)),
                                     group='/series', args_ok = averageables),
-            'time_sinks_mass' : Rule(self, partial(self._time_series, "sinks", self._get_sinks_mass),
+            # Averages and run properties
-                                     group='/series', args_ok=[None]),
-            'time' : Rule(self, partial(self._time_series, "time", partial(self._get_attr, 'time')),
-                                     group='/series', args_ok=[None]),
             'avg_pdf_slope' : Rule(self,
-                                   lambda name: self._time_avg("time_pdf_slope_" + name),
+                                   lambda name:
+                                   self._time_avg("time_pdf_slope_" + name),
                                    group='/comp', dependencies=['time_pdf_slope'],
                                    args_ok=averageables,
-                                   description={"mean": "Temporal average", "std": "Standard deviation"})
+                                   description={"mean": "Temporal average",
+                                                "std": "Standard deviation"}),
         }
 
 
+
 def get_time(path, num):
     """
     Return the time of the output (code units)

pp_params.py

@@ -1,69 +1,31 @@
 # coding: utf-8
 
 import numpy as np
+import re
+import bunch
+import yaml
+import os
 
-class PlotParams:
+_dir_path = os.path.dirname(os.path.realpath(__file__))
-    """
+
-    Plot parameters
+# Add support for '1e3' kind of float
-    """
+_loader = yaml.SafeLoader
-    out_ext   =  '.jpeg'               # extension for plots
+_loader.add_implicit_resolver(
-    put_title = False                  # Add a title to plot
+    u'tag:yaml.org,2002:float',
-    ntick     = 6                      # Number of ticks for maps
+    re.compile(u'''^(?:
-    set_lim   = True                   # Set default limits
+     [-+]?(?:[0-9][0-9_]*)\\.[0-9_]*(?:[eE][-+]?[0-9]+)?
-    vel_red   = 40                     # Take point each vel_red for velocities
+    |[-+]?(?:[0-9][0-9_]*)(?:[eE][-+]?[0-9]+)
-    put_title = False
+    |\\.[0-9_]+(?:[eE][-+][0-9]+)?
+    |[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\\.[0-9_]*
+    |[-+]?\\.(?:inf|Inf|INF)
+    |\\.(?:nan|NaN|NAN))$''', re.X),
+    list(u'-+0123456789.'))
 
 
-class DiskParams:
+def load_params(filename):
-    """
+    with open(filename) as f:
-    Disk speficic parameters
+        para_disk = yaml.load(f, Loader=_loader)
-    """
+    return bunch.bunchify(para_disk)
-    on       = False                   # Enable specific disk analysis
-    pos_star = np.array([1., 1., 1.])  # Position of the central star
-    binning  = "log"                   # Kind of binning (lin = linear, log = logarithmic)
-    nb_bin   = 100                     # Number of bins for averaged quantities
-    bin_in   = 1e-3                    # Outer radius of the inner bin
-    bin_out  = 0.25                    # Inner radius of the outer bin
-    rmin_pdf = 0.075                   # Inner radius for PDF computation
-    rmax_pdf = 0.3                     # Outer radius for PDF computation
-
-    beta     = False                   # Beta cooling. Do nothing if False.
-                                       # If true, beta will be parsed,
-                                       # otherwise the value is read therre
-
-class PdfParams:
-    """
-    parameters for probability density functions
-    """
-    nb_bin    = 50                    # Number of bins for the PDF
-    xmin_fit  = 0.                     # Lower boundary of the fit
-    xmax_fit  = 1.25                   # Upper boundary of the fit
-
-
-class PymsesParams:
-    """
-    Parameters for Pymses reader
-    """
-    order = '<'                        # In which order the output are read
-    fft   = False                      # Quick and dirty rendering using FFT
-
-class OutputParams:
-    """
-    Parameters for post processing
-    """
-    center   =  [0.5, 0.5, 0.5]        # Center of the image
-    zoom     = 1.                      # Zoom  of the image
-    map_size = 512                     # Size of the computed maps in pixel
-
-    tag      = ""                      # Tag for the image
-
-class Params:
-    """
-    Strutured parameters for the post processing
-    """
-    disk   = DiskParams()
-    pdf    = PdfParams
-    pymses = PymsesParams()
-    out    = OutputParams()
-    plot   = PlotParams()
 
+def default_params():
+    return load_params(_dir_path + '/pp_params.yml')