fix: automatically reset kde/contour accuracy when scale (linear/log)…

… of an axis changes (finally close #231)

CHANGELOG

@@ -1,11 +1,10 @@
 0.9.2
  - Allow to set contour levels at quantiles instead of KDE fractions (#180)
  - Allow to manually select contour levels
+ - Log-scale contour plots are now available (#231)
  - Pin numpy version to <=1.13.0 to prevent ValueError caused
    by chaco testing whether an array object is contained in another
    object
- - Log-scale contour plots are now available in expert mode
-   (be careful when setting the contour accuracies)
  - Bugfixes:
    - Log-scale scatter plots did not show log-scale KDE data (#231)
    - Contour lines were shifted by a small amount due to an uncommon

shapeout/analysis.py

@@ -135,11 +135,14 @@ def _complete_config(self, measurements=None):
                     ["kde accuracy {}", self._doanes_formula_acc, 1/2],
                     ]
             pltng = mm.config["plotting"]
-            for kk in self.GetPlotAxes():
+            for kk, sc in zip(self.GetPlotAxes(), self.GetPlotScales()):
                 for d, l, mult in defs:
                     var = d.format(kk)
                     if var not in pltng:
-                        acc = l(mm[kk]) * mult
+                        data = mm[kk]
+                        if sc == "log":
+                            data = np.log(data)
+                        acc = l(data) * mult
                         # round to make it look pretty in the GUI
                         accr = float("{:.1e}".format(acc))
                         pltng[var] = accr
@@ -155,7 +158,7 @@ def _doanes_formula_acc(a):
         """Compute accuracy (bin width) based on Doane's formula"""
         # https://en.wikipedia.org/wiki/Histogram#Number_of_bins_and_width
         # https://stats.stackexchange.com/questions/55134/doanes-formula-for-histogram-binning
-        bad = np.isnan(a) + np.isinf(a)
+        bad = np.isnan(a) | np.isinf(a)
         data = a[~bad]
         n = data.size
         g1 = scipy.stats.skew(data)
@@ -371,6 +374,10 @@ def GetPlotGeometry(self, mid=0):
         return (int(p["Rows"]), int(p["Columns"]),
                 int(p["Contour Plot"]), int(p["Legend Plot"]))
 
+    def GetPlotScales(self, mid=0):
+        p = self.GetParameters("Plotting", mid)
+        return [p["scale x"].lower(), p["scale y"].lower()]
+
     def GetStatisticsBasic(self):
         """
         Computes Mean, Avg, etc for all data sets and returns two lists:
@@ -497,7 +504,7 @@ def reset_plot(self):
         self.reset_plot_accuracies()
         self.reset_plot_ranges()
 
-    def reset_plot_accuracies(self):
+    def reset_plot_accuracies(self, feature_names=None):
         """ Set initial (heuristic) accuracies for all plots.
 
         It is not always easy to determine the correct accuracy for
@@ -518,8 +525,11 @@ def reset_plot_accuracies(self):
                 self.measurements[0].config["plotting"]["contour fix scale"]):
             return
 
+        if feature_names is None:
+            feature_names = dfn.scalar_feature_names
+
         # Remove contour accuracies for the current plots
-        for key in dfn.scalar_feature_names:
+        for key in feature_names:
             for mm in self.measurements:
                 for var in ["contour accuracy {}".format(key),
                             "kde accuracy {}".format(key)]:
@@ -550,6 +560,21 @@ def PolygonFilterRemove(self, filt):
             except ValueError:
                 pass
 
+    def set_config_value(self, section, key, value):
+        """Set the section/key value for all measurements
+
+        Parameters
+        ----------
+        section: str
+            Configuration section, e.g. "imaging", "filtering", or "plotting"
+        key: str
+            Configuration key within `section`
+        value:
+            Value to set
+        """
+        for mm in self.measurements:
+            mm.config[section][key] = value
+
     def SetContourColors(self, colors=None):
         """ Sets the contour colors.
 
@@ -574,6 +599,9 @@ def SetContourColors(self, colors=None):
 
     def SetParameters(self, newcfg):
         """Update the RT-DC dataset configuration"""
+        scalex, scaley = self.GetPlotScales()
+        xax, yax = self.GetPlotAxes()
+
         upcfg = {}
         if "filtering" in newcfg:
             upcfg["filtering"] = newcfg["filtering"].copy()
@@ -588,14 +616,18 @@ def SetParameters(self, newcfg):
                     pops.append(skey)
             for skey in pops:
                 pl.pop(skey)
-            # Only plot log-space contours in expert mode
-            config = SettingsFile()
-            if ((("scale x" in pl and pl["scale x"] == "log")
-                  or ("scale y" in pl and pl["scale y"] == "log"))
-                    and not config.get_bool("expert mode")):
-                warnings.warn(
-                    "Disabling contour plots on log-scale (no expert mode)!")
-                pl["contour plot"] = False
+
+            # If the scale changed, recompute kde and contour accuracies.
+            if "scale x" in pl and pl["scale x"] != scalex:
+                self.set_config_value("plotting", "scale x", pl["scale x"])
+                self.reset_plot_accuracies(feature_names=[xax])
+                pl.pop("kde accuracy {}".format(xax))
+                pl.pop("contour accuracy {}".format(xax))
+            if "scale y" in pl and pl["scale y"] != scaley:
+                self.set_config_value("plotting", "scale y", pl["scale y"])
+                self.reset_plot_accuracies(feature_names=[yax])
+                pl.pop("kde accuracy {}".format(yax))
+                pl.pop("contour accuracy {}".format(yax))
             # check for inverted plotting ranges
             for feat in dfn.scalar_feature_names:
                 fmin = feat + " min"

shapeout/gui/controls.py

@@ -213,12 +213,12 @@ def OnChangePlot(self, e=None, updp=True):
                         mm.config["plotting"]["contour color"] = col.tolist()
 
         cfg = {"plotting": newfilt }
+
         self.analysis.SetParameters(cfg)
 
         # Update Plots
         self.frame.PlotArea.Plot(self.analysis)
 
-
         if updp:
             self.UpdatePages()
         wx.EndBusyCursor()

shapeout/gui/plot_contour.py

@@ -151,6 +151,9 @@ def set_contour_data(plot, analysis):
                                            kde_kwargs=kde_kwargs,
                                            )
 
+        if X.shape[0] == 1 or X.shape[1] == 1:
+            raise ValueError("Please decrease value for contour accuracy!")
+
         print("...KDE contour time {}: {:.2f}s".format(kde_type, time.time()-a))
 
         # contour widths