New probability plot in common.Util

pyFTS/common/Util.py

@@ -162,6 +162,63 @@ def plot_distribution(ax, cmap, probabilitydist, fig, time_from, reference_data=
     cb.set_label('Density')
 
 
+def plot_distribution2(probabilitydist, data, **kwargs): #ax, cmap, probabilitydist, time_from, data=None):
+    '''
+    Plot distributions over the time (x-axis)
+    :param probabilitydist:
+    :param data:
+    :param kwargs:
+    :return:
+    '''
+    import matplotlib.colorbar as cbar
+    import matplotlib.cm as cm
+
+    ax = kwargs.get('ax',None)
+    if ax is None:
+        fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15, 5])
+
+    l = len(probabilitydist)
+
+    cmap = kwargs.get('cmap','Blues')
+    cmap = plt.get_cmap(cmap)
+
+    start_at = kwargs.get('start_at',0)
+
+    x = [k + start_at for k in range(l + 1)]
+
+    qt = kwargs.get('quantiles',None)
+
+    if qt is None:
+        qt = [round(k, 2) for k in np.arange(.05, 1., .0)]
+        qt.insert(0, .01)
+        qt.append(.99)
+
+    lq = len(qt)
+
+    normal = plt.Normalize(min(qt), max(qt))
+    scalarMap = cm.ScalarMappable(norm=normal, cmap=cmap)
+
+    for ct in np.arange(1, int(lq / 2) + 1):
+        y = [[data[start_at], data[start_at]]]
+        for pd in probabilitydist:
+            qts = pd.quantile([qt[ct - 1], qt[-ct]])
+            y.append(qts)
+
+        ax.fill_between(x, [k[0] for k in y], [k[1] for k in y],
+                        facecolor=scalarMap.to_rgba(ct / lq))
+
+    y = [data[start_at]]
+    for pd in probabilitydist:
+        qts = pd.quantile(.5)
+        y.append(qts[0])
+
+    ax.plot(x, y, color='red')
+
+    cax, _ = cbar.make_axes(ax)
+    cb = cbar.ColorbarBase(cax, cmap=cmap, norm=normal)
+    cb.set_label('Density')
+
+
 def plot_interval(axis, intervals, order, label, color='red', typeonlegend=False, ls='-', linewidth=1):
     '''
     Plot forecasted intervals on matplotlib

pyFTS/models/ensemble/ensemble.py

@@ -261,30 +261,16 @@ class EnsembleFTS(fts.FTS):
 
         for k in np.arange(self.order, steps+self.order):
             forecasts = []
-            '''
-            lags = {}
-            for i in np.arange(0, self.order): lags[i] = sample[k-self.order]
 
-            # Build the tree with all possible paths
-
-            root = tree.FLRGTreeNode(None)
-
-            tree.build_tree_without_order(root, lags, 0)
-
-            for p in root.paths():
-                path = list(reversed(list(filter(None.__ne__, p))))
-'''
             lags = []
             for i in np.arange(0, self.order):
                 lags.append(sample[i - self.order])
 
-            print(k, lags)
-
             # Trace the possible paths
             for path in product(*lags):
                 forecasts.extend(self.get_models_forecasts(path))
 
-            sample.append(sampler(forecasts, np.arange(0.05, .99, 0.1)))
+            sample.append(forecasts)
 
             if alpha is None:
                 forecasts = np.ravel(forecasts).tolist()