Improve log system

plotter.py

@@ -239,6 +239,10 @@ class Plotter(Aggregator, BaseProcessor):
         kwargs : Keyword arguments for RunSelector.
         """
 
+        # log info
+        self.log_id = "plot {}".format(tag)
+
+
         super(Plotter, self).__init__(path, path_out, params, tag)
 
         # Select runs
@@ -273,9 +277,6 @@ class Plotter(Aggregator, BaseProcessor):
         # Get postprocesor objets for each run
         self.snaps = self.study.snaps
 
-        # Define log prefix
-        self.log_id = "[plot {}] ".format(self.params.out.tag)
-
         # Define rules
         self.def_rules()
 
@@ -312,7 +313,7 @@ class Plotter(Aggregator, BaseProcessor):
                 try:
                     value = self.study.get_nml(param.key, run)
                 except KeyError as e:
-                    self._log("key {} not found".format(e), "WARNING")
+                    self.logger.warning("key {} not found".format(e))
 
                 if value is not None:
                     try:
@@ -858,7 +859,7 @@ class Plotter(Aggregator, BaseProcessor):
             plt.xlim(xlim)
             plt.ylim(ylim)
 
-        if self.params.astrophysics.generate:
+        if self.params.astrophysix.generate:
             return PlotInfo(
                 plot_type=PlotType.IMAGE,
                 xaxis_values=np.linspace(im_extent[0], im_extent[1], dmap.shape[0] + 1),
@@ -1203,7 +1204,7 @@ class Plotter(Aggregator, BaseProcessor):
             )
 
         # returns PlotInfo (for Galactica)
-        if self.params.astrophysics.generate:
+        if self.params.astrophysix.generate:
             edges = np.append(centers - width / 2.0, centers[-1] + width / 2.0)
             return PlotInfo(
                 plot_type=PlotType.HISTOGRAM,
@@ -1429,7 +1430,7 @@ class Plotter(Aggregator, BaseProcessor):
         if kind == "linear":
             if yerr is None or np.sum(np.abs(yerr)) == 0:
                 (a, b, rho, _map_rule, stderr) = linregress(x, y)
-                self._log(
+                self.logger.info(
                     "Linear fit y = {} x + {} with R^2 = {} and error is {}".format(
                         a, b, rho, stderr
                     )
@@ -1443,7 +1444,7 @@ class Plotter(Aggregator, BaseProcessor):
                 c = fit[0]
                 residual = fit[1][0][0]
                 b, a = c[0], c[1]
-                self._log(
+                self.logger.info(
                     "Linear fit y = {} x + {} with residual {}".format(a, b, residual)
                 )
                 if label is None:
@@ -1452,7 +1453,7 @@ class Plotter(Aggregator, BaseProcessor):
         elif kind == "power_law":
             if yerr is None or np.sum(np.abs(yerr)) == 0:
                 (a, b, rho, _map_rule, stderr) = linregress(np.log10(x), np.log10(y))
-                self._log(
+                self.logger.info(
                     "Power law fit y =  x^({}) *  {} with R^2 = {} and error is {}".format(
                         a, 10 ** b, rho, stderr
                     )
@@ -1476,7 +1477,7 @@ class Plotter(Aggregator, BaseProcessor):
                 c = out[0]
                 b, a = c[0], c[1]
                 residual = errfunc(c, np.log10(x), np.log10(y), yerr / y)
-                self._log(
+                self.logger.info(
                     "Power law fit y =   x^({}) * {}  with residual {}".format(
                         a, 10 ** b, residual
                     )