Resolve "histogram of inputs and targets"

Closes #299 (closed)

mlair/plotting/data_insight_plotting.py

@@ -454,6 +454,8 @@ class PlotDataHistogram(AbstractPlotClass):  # pragma: no cover
         self.window_dim = window_dim
         self.inputs, self.targets = self._get_inputs_targets(generators, self.variables_dim)
         self.bins = {}
+        self.interval_width = {}
+        self.bin_edges = {}
 
         # input plots
         self._calculate_hist(generators, self.inputs, input_data=True)
@@ -476,38 +478,58 @@ class PlotDataHistogram(AbstractPlotClass):  # pragma: no cover
         return inputs, targets
 
     def _calculate_hist(self, generators, variables, input_data=True):
+        n_bins = 100
         for set_type, generator in generators.items():
-            bins = {}
-            n_bins = 100
-            interval_width = None
-            bin_edges = None
+            tmp_bins = {}
+            tmp_edges = {}
+            end = {}
+            start = {}
             f = lambda x: x.get_X(as_numpy=False)[0] if input_data is True else x.get_Y(as_numpy=False)
             for gen in generator:
                 w = min(abs(f(gen).coords[self.window_dim].values))
                 data = f(gen).sel({self.window_dim: w})
-                res, interval_width, bin_edges = f_proc_hist(data, variables, n_bins, self.variables_dim)
+                res, _, g_edges = f_proc_hist(data, variables, n_bins, self.variables_dim)
                 for var in variables:
-                    n_var = bins.get(var, np.zeros(n_bins))
-                    n_var += res[var]
-                    bins[var] = n_var
+                    b = tmp_bins.get(var, [])
+                    b.append(res[var])
+                    tmp_bins[var] = b
+                    e = tmp_edges.get(var, [])
+                    e.append(g_edges[var])
+                    tmp_edges[var] = e
+                    end[var] = max([end.get(var, g_edges[var].max()), g_edges[var].max()])
+                    start[var] = min([start.get(var, g_edges[var].min()), g_edges[var].min()])
+            # interpolate and aggregate
+            bins = {}
+            edges = {}
+            interval_width = {}
+            for var in variables:
+                bin_edges = np.linspace(start[var], end[var], n_bins + 1)
+                interval_width[var] = bin_edges[1] - bin_edges[0]
+                for i, e in enumerate(tmp_bins[var]):
+                    bins_interp = np.interp(bin_edges[:-1], tmp_edges[var][i][:-1], e, left=0, right=0)
+                    bins[var] = bins.get(var, np.zeros(n_bins)) + bins_interp
+                edges[var] = bin_edges
+
             self.bins[set_type] = bins
-            self.interval_width = interval_width
-            self.bin_edges = bin_edges
+            self.interval_width[set_type] = interval_width
+            self.bin_edges[set_type] = edges
 
     def _plot(self, add_name, subset):
         plot_path = os.path.join(os.path.abspath(self.plot_folder), f"{self.plot_name}_{subset}_{add_name}.pdf")
         pdf_pages = matplotlib.backends.backend_pdf.PdfPages(plot_path)
         bins = self.bins[subset]
+        bin_edges = self.bin_edges[subset]
+        interval_width = self.interval_width[subset]
         colors = self.get_dataset_colors()
         for var in bins.keys():
             fig, ax = plt.subplots()
             hist_var = bins[var]
             n_var = sum(hist_var)
-            weights = hist_var / (self.interval_width * n_var)
-            ax.hist(self.bin_edges[:-1], self.bin_edges, weights=weights, color=colors[subset])
+            weights = hist_var / (interval_width[var] * n_var)
+            ax.hist(bin_edges[var][:-1], bin_edges[var], weights=weights, color=colors[subset])
             ax.set_ylabel("probability density")
-            ax.set_xlabel(f"values ({subset})")
-            ax.set_title(f"Histogram ({var}, n={int(n_var)})")
+            ax.set_xlabel(f"values")
+            ax.set_title(f"histogram {var} ({subset}, n={int(n_var)})")
             pdf_pages.savefig()
         # close all open figures / plots
         pdf_pages.close()
@@ -522,14 +544,16 @@ class PlotDataHistogram(AbstractPlotClass):  # pragma: no cover
             fig, ax = plt.subplots()
             for subset in self.bins.keys():
                 hist_var = self.bins[subset][var]
+                interval_width = self.interval_width[subset][var]
+                bin_edges = self.bin_edges[subset][var]
                 n_var = sum(hist_var)
-                weights = hist_var / (self.interval_width * n_var)
-                ax.plot(self.bin_edges[:-1] + 0.5 * self.interval_width, weights, label=f"{subset}",
+                weights = hist_var / (interval_width * n_var)
+                ax.plot(bin_edges[:-1] + 0.5 * interval_width, weights, label=f"{subset}",
                         c=colors[subset])
             ax.set_ylabel("probability density")
-            ax.set_xlabel(f"{var}")
+            ax.set_xlabel("values")
             ax.legend(loc="upper right")
-            ax.set_title(f"Histogram")
+            ax.set_title(f"histogram {var}")
             pdf_pages.savefig()
         # close all open figures / plots
         pdf_pages.close()
@@ -815,9 +839,10 @@ def f_proc_2(g, m, pos, variables_dim, time_dim):
 
 def f_proc_hist(data, variables, n_bins, variables_dim):
     res = {}
+    bin_edges = {}
+    interval_width = {}
     for var in variables:
         d = data.sel({variables_dim: var}).squeeze() if len(data.shape) > 1 else data
-        hist, bin_edges = np.histogram(d.values, n_bins, range=(-4, 4))
-        interval_width = (bin_edges[1] - bin_edges[0])
-        res[var] = hist
+        res[var], bin_edges[var] = np.histogram(d.values, n_bins)
+        interval_width[var] = bin_edges[var][1] - bin_edges[var][0]
     return res, interval_width, bin_edges