diff --git a/pyFTS/benchmarks/benchmarks.py b/pyFTS/benchmarks/benchmarks.py
index b89f10d..3ca2ae0 100644
--- a/pyFTS/benchmarks/benchmarks.py
+++ b/pyFTS/benchmarks/benchmarks.py
@@ -90,7 +90,7 @@ def run_point(mfts, partitioner, train_data, test_data, window_key=None, transfo
             mfts.append_transformation(transformation)
 
     _start = time.time()
-    mfts.train(train_data, partitioner.sets, order=mfts.order)
+    mfts.train(train_data, sets=partitioner.sets, order=mfts.order)
     _end = time.time()
     times = _end - _start
 
@@ -273,7 +273,7 @@ def all_point_forecasters(data_train, data_test, partitions, max_order=3, statis
         #print(model)
         if transformation is not None:
             model.append_transformation(transformation)
-        model.train(data_train, data_train_fs.sets, order=model.order)
+        model.train(data_train, sets=data_train_fs.sets, order=model.order)
         objs.append(model)
         lcolors.append( colors[count % ncol] )
 
@@ -385,7 +385,7 @@ def interval_sliding_window(data, windowsize, train=0.8, models=None, partitione
                             mfts.append_transformation(transformation)
 
                         _start = time.time()
-                        mfts.train(training, data_train_fs.sets)
+                        mfts.train(training, sets=data_train_fs.sets)
                         _end = time.time()
                         _tdiff = _end - _start
 
@@ -419,7 +419,7 @@ def interval_sliding_window(data, windowsize, train=0.8, models=None, partitione
                                     mfts.append_transformation(transformation)
 
                                 _start = time.time()
-                                mfts.train(training, data_train_fs.sets, order=order)
+                                mfts.train(training, sets=data_train_fs.sets, order=order)
                                 _end = time.time()
 
                                 _tdiff = _end - _start
@@ -476,7 +476,7 @@ def all_interval_forecasters(data_train, data_test, partitions, max_order=3,save
     for count, model in Util.enumerate2(models, start=0, step=2):
         if transformation is not None:
             model.append_transformation(transformation)
-            model.train(data_train, data_train_fs, order=model.order)
+            model.train(data_train, sets=data_train_fs, order=model.order)
         objs.append(model)
         lcolors.append( colors[count % ncol] )
 
@@ -635,7 +635,7 @@ def ahead_sliding_window(data, windowsize, train, steps, models=None, resolution
                             mfts.append_transformation(transformation)
 
                         _start = time.time()
-                        mfts.train(train, data_train_fs.sets)
+                        mfts.train(train, sets=data_train_fs.sets)
                         _end = time.time()
 
                         _tdiff = _end - _start
@@ -670,7 +670,7 @@ def ahead_sliding_window(data, windowsize, train, steps, models=None, resolution
                                     mfts.append_transformation(transformation)
 
                                 _start = time.time()
-                                mfts.train(train, data_train_fs.sets, order=order)
+                                mfts.train(train, sets=data_train_fs.sets, order=order)
                                 _end = time.time()
 
                                 _tdiff = _end - _start
@@ -705,7 +705,7 @@ def all_ahead_forecasters(data_train, data_test, partitions, start, steps, resol
         if not mfts.is_high_order:
             if transformation is not None:
                 mfts.append_transformation(transformation)
-            mfts.train(data_train, data_train_fs)
+            mfts.train(data_train, sets=data_train_fs.sets)
             objs.append(mfts)
             lcolors.append( colors[count % ncol] )
         else:
@@ -714,7 +714,7 @@ def all_ahead_forecasters(data_train, data_test, partitions, start, steps, resol
                     mfts = model(" n = " + str(order))
                     if transformation is not None:
                         mfts.append_transformation(transformation)
-                    mfts.train(data_train, data_train_fs, order=order)
+                    mfts.train(data_train, sets=data_train_fs.sets, order=order)
                     objs.append(mfts)
                     lcolors.append(colors[count % ncol])
 
@@ -896,7 +896,7 @@ def SelecaoSimples_MenorRMSE(original, parameters, modelo):
     for p in parameters:
         sets = Grid.GridPartitioner(original, p).sets
         fts = modelo(str(p) + " particoes")
-        fts.train(original, sets)
+        fts.train(original, sets=sets)
         # print(original)
         forecasted = fts.forecast(original)
         forecasted.insert(0, original[0])
@@ -936,7 +936,7 @@ def SelecaoSimples_MenorRMSE(original, parameters, modelo):
     for p in parameters:
         sets = Grid.GridPartitionerTrimf(difffts, p)
         fts = modelo(str(p) + " particoes")
-        fts.train(difffts, sets)
+        fts.train(difffts, sets=sets)
         forecasted = fts.forecast(difffts)
         forecasted.insert(0, difffts[0])
         ax2.plot(forecasted, label=fts.name)
@@ -1050,7 +1050,7 @@ def simpleSearch_RMSE(train, test, model, partitions, orders, save=False, file=N
         for oc, o in enumerate(orders, start=0):
             fts = model("q = " + str(p) + " n = " + str(o))
             fts.append_transformation(transformation)
-            fts.train(train, sets, o, parameters=parameters)
+            fts.train(train, sets=sets, order=o, parameters=parameters)
             if not intervals:
                 forecasted = fts.forecast(test)
                 if not fts.has_seasonality:
@@ -1128,7 +1128,7 @@ def sliding_window_simple_search(data, windowsize, model, partitions, orders, sa
             _error = []
             for ct, train, test in Util.sliding_window(data, windowsize, 0.8):
                 fts = model("q = " + str(p) + " n = " + str(o))
-                fts.train(data, sets, o, parameters=parameters)
+                fts.train(data, sets=sets, order=o, parameters=parameters)
                 if not intervals:
                     forecasted = fts.forecast(test)
                     if not fts.has_seasonality:
@@ -1191,7 +1191,7 @@ def pftsExploreOrderAndPartitions(data,save=False, file=None):
     for order in np.arange(1, 6):
         fts = pwfts.ProbabilisticWeightedFTS("")
         fts.shortname = "n = " + str(order)
-        fts.train(data, data_fs1, order=order)
+        fts.train(data, sets=data_fs1.sets, order=order)
         point_forecasts = fts.forecast(data)
         interval_forecasts = fts.forecast_interval(data)
         lower = [kk[0] for kk in interval_forecasts]
@@ -1213,7 +1213,7 @@ def pftsExploreOrderAndPartitions(data,save=False, file=None):
         data_fs = Grid.GridPartitioner(data, partitions).sets
         fts = pwfts.ProbabilisticWeightedFTS("")
         fts.shortname = "q = " + str(partitions)
-        fts.train(data, data_fs, 1)
+        fts.train(data, sets=data_fs.sets, order=1)
         point_forecasts = fts.forecast(data)
         interval_forecasts = fts.forecast_interval(data)
         lower = [kk[0] for kk in interval_forecasts]
diff --git a/pyFTS/common/fts.py b/pyFTS/common/fts.py
index 9d1ed84..a9d0377 100644
--- a/pyFTS/common/fts.py
+++ b/pyFTS/common/fts.py
@@ -148,7 +148,7 @@ class FTS(object):
         """
         raise NotImplementedError('This model do not perform multi step ahead distribution forecasts!')
 
-    def train(self, data, sets, order=1, parameters=None):
+    def train(self, data, **kwargs):
         """
         
         :param data: 
@@ -166,7 +166,7 @@ class FTS(object):
         :param kwargs:
         :return:
         """
-        self.train(data, sets=None)
+        self.train(data, **kwargs)
 
     def append_transformation(self, transformation):
         if transformation is not None:
diff --git a/pyFTS/data/artificial.py b/pyFTS/data/artificial.py
index 03a774b..4ef4c9b 100644
--- a/pyFTS/data/artificial.py
+++ b/pyFTS/data/artificial.py
@@ -28,6 +28,7 @@ def generate_gaussian_linear(mu_ini, sigma_ini, mu_inc, sigma_inc, it=100, num=1
         sigma += sigma_inc
     return ret
 
+
 def generate_uniform_linear(min_ini, max_ini, min_inc, max_inc, it=100, num=10, vmin=None, vmax=None):
     """
     Generate data sampled from Uniform distribution, with constant or  linear changing bounds
@@ -53,4 +54,21 @@ def generate_uniform_linear(min_ini, max_ini, min_inc, max_inc, it=100, num=10,
         ret.extend(tmp)
         _min += min_inc
         _max += max_inc
-    return ret
\ No newline at end of file
+    return ret
+
+
+def white_noise(n=500):
+    return np.random.normal(0, 1, n)
+
+
+def random_walk(n=500, type='gaussian'):
+    if type == 'gaussian':
+        tmp = generate_gaussian_linear(0, 1, 0, 0, it=1, num=n)
+    else:
+        tmp = generate_uniform_linear(-1, 1, 0, 0, it=1, num=n)
+    ret = [0]
+    for i in range(n):
+        ret.append(tmp[i] + ret[i])
+
+    return ret
+
diff --git a/pyFTS/models/chen.py b/pyFTS/models/chen.py
index 6b2aa01..319f86a 100644
--- a/pyFTS/models/chen.py
+++ b/pyFTS/models/chen.py
@@ -36,7 +36,7 @@ class ConventionalFTS(fts.FTS):
         self.detail = "Chen"
         self.flrgs = {}
 
-    def generateFLRG(self, flrs):
+    def generate_flrg(self, flrs):
         flrgs = {}
         for flr in flrs:
             if flr.LHS.name in flrgs:
@@ -46,12 +46,13 @@ class ConventionalFTS(fts.FTS):
                 flrgs[flr.LHS.name].append(flr.RHS)
         return (flrgs)
 
-    def train(self, data, sets,order=1,parameters=None):
-        self.sets = sets
+    def train(self, data, **kwargs):
+        if kwargs.get('sets', None) is not None:
+            self.sets = kwargs.get('sets', None)
         ndata = self.apply_transformations(data)
-        tmpdata = FuzzySet.fuzzyfy_series_old(ndata, sets)
+        tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
         flrs = FLR.generate_non_recurrent_flrs(tmpdata)
-        self.flrgs = self.generateFLRG(flrs)
+        self.flrgs = self.generate_flrg(flrs)
 
     def forecast(self, data, **kwargs):
 
@@ -74,6 +75,6 @@ class ConventionalFTS(fts.FTS):
 
                 ret.append(_flrg.get_midpoint())
 
-        ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]])
+        ret = self.apply_inverse_transformations(ret, params=[data])
 
         return ret
diff --git a/pyFTS/models/cheng.py b/pyFTS/models/cheng.py
index 45b8fbc..e56cfd1 100644
--- a/pyFTS/models/cheng.py
+++ b/pyFTS/models/cheng.py
@@ -16,28 +16,31 @@ class TrendWeightedFLRG(yu.WeightedFLRG):
     """
     def __init__(self, LHS, **kwargs):
         super(TrendWeightedFLRG, self).__init__(LHS, **kwargs)
+        self.w = None
 
     def weights(self):
-        count_nochange = 0.0
-        count_up = 0.0
-        count_down = 0.0
-        weights = []
+        if self.w is None:
+            count_nochange = 0.0
+            count_up = 0.0
+            count_down = 0.0
+            weights = []
 
-        for c in self.RHS:
-            tmp = 0
-            if self.LHS.centroid == c.centroid:
-                count_nochange += 1.0
-                tmp = count_nochange
-            elif self.LHS.centroid > c.centroid:
-                count_down += 1.0
-                tmp = count_down
-            else:
-                count_up += 1.0
-                tmp = count_up
-            weights.append(tmp)
+            for c in self.RHS:
+                tmp = 0
+                if self.LHS.centroid == c.centroid:
+                    count_nochange += 1.0
+                    tmp = count_nochange
+                elif self.LHS.centroid > c.centroid:
+                    count_down += 1.0
+                    tmp = count_down
+                else:
+                    count_up += 1.0
+                    tmp = count_up
+                weights.append(tmp)
 
-        tot = sum(weights)
-        return np.array([k / tot for k in weights])
+            tot = sum(weights)
+            self.w = np.array([k / tot for k in weights])
+        return self.w
 
 
 class TrendWeightedFTS(yu.WeightedFTS):
diff --git a/pyFTS/models/hofts.py b/pyFTS/models/hofts.py
index a1522dc..72a1ae8 100644
--- a/pyFTS/models/hofts.py
+++ b/pyFTS/models/hofts.py
@@ -16,19 +16,19 @@ class HighOrderFLRG(flrg.FLRG):
         self.RHS = {}
         self.strlhs = ""
 
-    def appendRHS(self, c):
+    def append_rhs(self, c):
         if c.name not in self.RHS:
             self.RHS[c.name] = c
 
-    def strLHS(self):
+    def str_lhs(self):
         if len(self.strlhs) == 0:
             for c in self.LHS:
                 if len(self.strlhs) > 0:
                     self.strlhs += ", "
-                self.strlhs = self.strlhs + str(c)
+                self.strlhs = self.strlhs + str(c.name)
         return self.strlhs
 
-    def appendLHS(self, c):
+    def append_lhs(self, c):
         self.LHS.append(c)
 
     def __str__(self):
@@ -37,7 +37,7 @@ class HighOrderFLRG(flrg.FLRG):
             if len(tmp) > 0:
                 tmp = tmp + ","
             tmp = tmp + c
-        return self.strLHS() + " -> " + tmp
+        return self.str_lhs() + " -> " + tmp
 
 
     def __len__(self):
@@ -51,7 +51,7 @@ class HighOrderFTS(fts.FTS):
         self.name = "High Order FTS"
         self.shortname = "HOFTS" + name
         self.detail = "Chen"
-        self.order = 1
+        self.order = kwargs.get('order',1)
         self.setsDict = {}
         self.is_high_order = True
 
@@ -83,13 +83,13 @@ class HighOrderFTS(fts.FTS):
             flrg = HighOrderFLRG(self.order)
 
             for kk in np.arange(k - self.order, k):
-                flrg.appendLHS(flrs[kk].LHS)
+                flrg.append_lhs(flrs[kk].LHS)
 
-            if flrg.strLHS() in flrgs:
-                flrgs[flrg.strLHS()].appendRHS(flrs[k].RHS)
+            if flrg.str_lhs() in flrgs:
+                flrgs[flrg.str_lhs()].append_rhs(flrs[k].RHS)
             else:
-                flrgs[flrg.strLHS()] = flrg;
-                flrgs[flrg.strLHS()].appendRHS(flrs[k].RHS)
+                flrgs[flrg.str_lhs()] = flrg;
+                flrgs[flrg.str_lhs()].append_rhs(flrs[k].RHS)
         return (flrgs)
 
     def generate_flrg(self, data):
@@ -118,23 +118,25 @@ class HighOrderFTS(fts.FTS):
                 flrg = HighOrderFLRG(self.order)
                 path = list(reversed(list(filter(None.__ne__, p))))
 
-                for lhs in enumerate(path, start=0):
-                    flrg.appendLHS(lhs)
+                for lhs in path:
+                    flrg.append_lhs(lhs)
 
-                if flrg.strLHS() not in flrgs:
-                    flrgs[flrg.strLHS()] = flrg;
+                if flrg.str_lhs() not in flrgs:
+                    flrgs[flrg.str_lhs()] = flrg;
 
                 for st in rhs:
-                    flrgs[flrg.strLHS()].appendRHS(st)
+                    flrgs[flrg.str_lhs()].append_rhs(st)
 
         return flrgs
 
-    def train(self, data, sets, order=1,parameters=None):
+    def train(self, data, **kwargs):
 
         data = self.apply_transformations(data, updateUoD=True)
 
-        self.order = order
-        self.sets = sets
+        self.order = kwargs.get('order',2)
+
+        if kwargs.get('sets', None) is not None:
+            self.sets = kwargs.get('sets', None)
         for s in self.sets:    self.setsDict[s.name] = s
         self.flrgs = self.generate_flrg(data)
 
@@ -153,12 +155,12 @@ class HighOrderFTS(fts.FTS):
             tmpdata = FuzzySet.fuzzyfy_series_old(ndata[k - self.order: k], self.sets)
             tmpflrg = HighOrderFLRG(self.order)
 
-            for s in tmpdata: tmpflrg.appendLHS(s)
+            for s in tmpdata: tmpflrg.append_lhs(s)
 
-            if tmpflrg.strLHS() not in self.flrgs:
+            if tmpflrg.str_lhs() not in self.flrgs:
                 ret.append(tmpdata[-1].centroid)
             else:
-                flrg = self.flrgs[tmpflrg.strLHS()]
+                flrg = self.flrgs[tmpflrg.str_lhs()]
                 ret.append(flrg.get_midpoint())
 
         ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]])
diff --git a/pyFTS/models/hwang.py b/pyFTS/models/hwang.py
index b0c033e..dbd7651 100644
--- a/pyFTS/models/hwang.py
+++ b/pyFTS/models/hwang.py
@@ -50,6 +50,7 @@ class HighOrderFTS(fts.FTS):
 
         return ret
 
-    def train(self, data, sets, order=1, parameters=None):
-        self.sets = sets
-        self.order = order
\ No newline at end of file
+    def train(self, data, **kwargs):
+        if kwargs.get('sets', None) is not None:
+            self.sets = kwargs.get('sets', None)
+        self.order = kwargs.get('order', 1)
\ No newline at end of file
diff --git a/pyFTS/models/ifts.py b/pyFTS/models/ifts.py
index a77ca50..7cfcb08 100644
--- a/pyFTS/models/ifts.py
+++ b/pyFTS/models/ifts.py
@@ -24,16 +24,16 @@ class IntervalFTS(hofts.HighOrderFTS):
         self.is_high_order = True
 
     def get_upper(self, flrg):
-        if flrg.strLHS() in self.flrgs:
-            tmp = self.flrgs[flrg.strLHS()]
+        if flrg.str_lhs() in self.flrgs:
+            tmp = self.flrgs[flrg.str_lhs()]
             ret = tmp.get_upper()
         else:
             ret = flrg.LHS[-1].upper
         return ret
 
     def get_lower(self, flrg):
-        if flrg.strLHS() in self.flrgs:
-            tmp = self.flrgs[flrg.strLHS()]
+        if flrg.str_lhs() in self.flrgs:
+            tmp = self.flrgs[flrg.str_lhs()]
             ret = tmp.get_lower()
         else:
             ret = flrg.LHS[-1].lower
@@ -93,7 +93,7 @@ class IntervalFTS(hofts.HighOrderFTS):
                 for p in root.paths():
                     path = list(reversed(list(filter(None.__ne__, p))))
                     flrg = hofts.HighOrderFLRG(self.order)
-                    for kk in path: flrg.appendLHS(self.sets[kk])
+                    for kk in path: flrg.append_lhs(self.sets[kk])
 
                     affected_flrgs.append(flrg)
 
@@ -115,7 +115,7 @@ class IntervalFTS(hofts.HighOrderFTS):
 
                 for kk in idx:
                     flrg = hofts.HighOrderFLRG(self.order)
-                    flrg.appendLHS(self.sets[kk])
+                    flrg.append_lhs(self.sets[kk])
                     affected_flrgs.append(flrg)
                     affected_flrgs_memberships.append(mv[kk])
 
diff --git a/pyFTS/models/ismailefendi.py b/pyFTS/models/ismailefendi.py
index ebcebfb..c314955 100644
--- a/pyFTS/models/ismailefendi.py
+++ b/pyFTS/models/ismailefendi.py
@@ -17,6 +17,7 @@ class ImprovedWeightedFLRG(flrg.FLRG):
         self.RHS = {}
         self.rhs_counts = {}
         self.count = 0.0
+        self.w = None
 
     def append(self, c):
         if c.name not in self.RHS:
@@ -27,7 +28,9 @@ class ImprovedWeightedFLRG(flrg.FLRG):
         self.count += 1.0
 
     def weights(self):
-        return np.array([self.rhs_counts[c] / self.count for c in self.RHS.keys()])
+        if self.w is None:
+            self.w = np.array([self.rhs_counts[c] / self.count for c in self.RHS.keys()])
+        return self.w
 
     def __str__(self):
         tmp = self.LHS.name + " -> "
@@ -50,7 +53,7 @@ class ImprovedWeightedFTS(fts.FTS):
         self.detail = "Ismail & Efendi"
         self.setsDict = {}
 
-    def generateFLRG(self, flrs):
+    def generate_flrg(self, flrs):
         flrgs = {}
         for flr in flrs:
             if flr.LHS.name in flrgs:
@@ -60,8 +63,9 @@ class ImprovedWeightedFTS(fts.FTS):
                 flrgs[flr.LHS.name].append(flr.RHS)
         return (flrgs)
 
-    def train(self, data, sets, order=1, parameters=None):
-        self.sets = sets
+    def train(self, data, **kwargs):
+        if kwargs.get('sets', None) is not None:
+            self.sets = kwargs.get('sets', None)
 
         for s in self.sets:    self.setsDict[s.name] = s
 
@@ -69,7 +73,7 @@ class ImprovedWeightedFTS(fts.FTS):
 
         tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
         flrs = FLR.generate_recurrent_flrs(tmpdata)
-        self.flrgs = self.generateFLRG(flrs)
+        self.flrgs = self.generate_flrg(flrs)
 
     def forecast(self, data, **kwargs):
         l = 1
@@ -95,6 +99,6 @@ class ImprovedWeightedFTS(fts.FTS):
 
                 ret.append(mp.dot(flrg.weights()))
 
-        ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]])
+        ret = self.apply_inverse_transformations(ret, params=[data])
 
         return ret
diff --git a/pyFTS/models/nonstationary/honsfts.py b/pyFTS/models/nonstationary/honsfts.py
index 3c34687..0bede52 100644
--- a/pyFTS/models/nonstationary/honsfts.py
+++ b/pyFTS/models/nonstationary/honsfts.py
@@ -88,7 +88,7 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
                     flrgs[flrg.strLHS()] = flrg;
 
                 for st in rhs:
-                    flrgs[flrg.strLHS()].appendRHS(st)
+                    flrgs[flrg.strLHS()].append_rhs(st)
 
         # flrgs = sorted(flrgs, key=lambda flrg: flrg.get_midpoint(0, window_size=1))
 
@@ -144,7 +144,7 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
             affected_flrgs.append(flrg)
             # affected_flrgs_memberships.append(flrg.get_membership(sample, disp))
 
-            #                print(flrg.strLHS())
+            #                print(flrg.str_lhs())
 
             # the FLRG is here because of the bounds verification
             mv = []
@@ -196,14 +196,14 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
                 tmp.append(common.check_bounds(sample[-1], self.sets, tdisp))
             elif len(affected_flrgs) == 1:
                 flrg = affected_flrgs[0]
-                if flrg.strLHS() in self.flrgs:
-                    tmp.append(self.flrgs[flrg.strLHS()].get_midpoint(tdisp))
+                if flrg.str_lhs() in self.flrgs:
+                    tmp.append(self.flrgs[flrg.str_lhs()].get_midpoint(tdisp))
                 else:
                     tmp.append(flrg.LHS[-1].get_midpoint(tdisp))
             else:
                 for ct, aset in enumerate(affected_flrgs):
-                    if aset.strLHS() in self.flrgs:
-                        tmp.append(self.flrgs[aset.strLHS()].get_midpoint(tdisp) *
+                    if aset.str_lhs() in self.flrgs:
+                        tmp.append(self.flrgs[aset.str_lhs()].get_midpoint(tdisp) *
                                    affected_flrgs_memberships[ct])
                     else:
                         tmp.append(aset.LHS[-1].get_midpoint(tdisp)*
@@ -250,19 +250,19 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
                 upper.append(aset.get_upper(tdisp))
             elif len(affected_flrgs) == 1:
                 _flrg = affected_flrgs[0]
-                if _flrg.strLHS() in self.flrgs:
-                    lower.append(self.flrgs[_flrg.strLHS()].get_lower(tdisp))
-                    upper.append(self.flrgs[_flrg.strLHS()].get_upper(tdisp))
+                if _flrg.str_lhs() in self.flrgs:
+                    lower.append(self.flrgs[_flrg.str_lhs()].get_lower(tdisp))
+                    upper.append(self.flrgs[_flrg.str_lhs()].get_upper(tdisp))
                 else:
                     lower.append(_flrg.LHS[-1].get_lower(tdisp))
                     upper.append(_flrg.LHS[-1].get_upper(tdisp))
             else:
                 for ct, aset in enumerate(affected_flrgs):
-                    if aset.strLHS() in self.flrgs:
-                        lower.append(self.flrgs[aset.strLHS()].get_lower(tdisp) *
-                                   affected_flrgs_memberships[ct])
-                        upper.append(self.flrgs[aset.strLHS()].get_upper(tdisp) *
-                                   affected_flrgs_memberships[ct])
+                    if aset.str_lhs() in self.flrgs:
+                        lower.append(self.flrgs[aset.str_lhs()].get_lower(tdisp) *
+                                     affected_flrgs_memberships[ct])
+                        upper.append(self.flrgs[aset.str_lhs()].get_upper(tdisp) *
+                                     affected_flrgs_memberships[ct])
                     else:
                         lower.append(aset.LHS[-1].get_lower(tdisp) *
                                    affected_flrgs_memberships[ct])
diff --git a/pyFTS/models/pwfts.py b/pyFTS/models/pwfts.py
index ca0839e..a3161b0 100644
--- a/pyFTS/models/pwfts.py
+++ b/pyFTS/models/pwfts.py
@@ -19,7 +19,7 @@ class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
         self.frequency_count = 0.0
         self.Z = None
 
-    def appendRHS(self, c):
+    def append_rhs(self, c):
         self.frequency_count += 1.0
         if c.name in self.RHS:
             self.rhs_count[c.name] += 1.0
@@ -91,7 +91,7 @@ class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
             if len(tmp2) > 0:
                 tmp2 = tmp2 + ", "
             tmp2 = tmp2 + "(" + str(round(self.rhs_count[c] / self.frequency_count, 3)) + ")" + c
-        return self.strLHS() + " -> " + tmp2
+        return self.str_lhs() + " -> " + tmp2
 
 
 class ProbabilisticWeightedFTS(ifts.IntervalFTS):
@@ -111,20 +111,22 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
         self.interval_method = kwargs.get('interval_method','extremum')
         self.alpha = kwargs.get('alpha', 0.05)
 
-    def train(self, data, sets, order=1,parameters='Fuzzy'):
+    def train(self, data, **kwargs):
 
         data = self.apply_transformations(data, updateUoD=True)
 
-        self.order = order
-        if sets is None and self.partitioner is not None:
+        parameters = kwargs.get('parameters','Fuzzy')
+
+        self.order = kwargs.get('order',1)
+        if kwargs.get('sets',None) is None and self.partitioner is not None:
             self.sets = self.partitioner.sets
             self.original_min = self.partitioner.min
             self.original_max = self.partitioner.max
         else:
-            self.sets = sets
+            self.sets = kwargs.get('sets',None)
         for s in self.sets:    self.setsDict[s.name] = s
         if parameters == 'Monotonic':
-            tmpdata = FuzzySet.fuzzyfy_series_old(data, sets)
+            tmpdata = FuzzySet.fuzzyfy_series_old(data, self.sets)
             flrs = FLR.generate_recurrent_flrs(tmpdata)
             self.flrgs = self.generateFLRG(flrs)
         else:
@@ -162,15 +164,15 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
                 tmp_path = []
                 for c, e in enumerate(path, start=0):
                     tmp_path.append(  e.membership( sample[c] )  )
-                    flrg.appendLHS(e)
+                    flrg.append_lhs(e)
 
                 lhs_mv = np.prod(tmp_path)
 
-                if flrg.strLHS() not in flrgs:
-                    flrgs[flrg.strLHS()] = flrg;
+                if flrg.str_lhs() not in flrgs:
+                    flrgs[flrg.str_lhs()] = flrg;
 
                 for st in idx:
-                    flrgs[flrg.strLHS()].appendRHSFuzzy(self.sets[st], lhs_mv*mv[st])
+                    flrgs[flrg.str_lhs()].appendRHSFuzzy(self.sets[st], lhs_mv * mv[st])
 
                 tmp_fq = sum([lhs_mv*kk for kk in mv if kk > 0])
 
@@ -186,14 +188,14 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
             flrg = ProbabilisticWeightedFLRG(self.order)
 
             for kk in np.arange(k - self.order, k):
-                flrg.appendLHS(flrs[kk].LHS)
+                flrg.append_lhs(flrs[kk].LHS)
                 if self.dump: print("LHS: " + str(flrs[kk]))
 
-            if flrg.strLHS() in flrgs:
-                flrgs[flrg.strLHS()].appendRHS(flrs[k-1].RHS)
+            if flrg.str_lhs() in flrgs:
+                flrgs[flrg.str_lhs()].append_rhs(flrs[k - 1].RHS)
             else:
-                flrgs[flrg.strLHS()] = flrg
-                flrgs[flrg.strLHS()].appendRHS(flrs[k-1].RHS)
+                flrgs[flrg.str_lhs()] = flrg
+                flrgs[flrg.str_lhs()].append_rhs(flrs[k - 1].RHS)
             if self.dump: print("RHS: " + str(flrs[k-1]))
 
             self.global_frequency_count += 1
@@ -205,34 +207,34 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
         flrg = ProbabilisticWeightedFLRG(self.order)
 
-        for k in np.arange(0, self.order): flrg.appendLHS(fzzy[k])
+        for k in np.arange(0, self.order): flrg.append_lhs(fzzy[k])
 
-        if flrg.strLHS() in self.flrgs:
-            self.flrgs[flrg.strLHS()].appendRHS(fzzy[self.order])
+        if flrg.str_lhs() in self.flrgs:
+            self.flrgs[flrg.str_lhs()].append_rhs(fzzy[self.order])
         else:
-            self.flrgs[flrg.strLHS()] = flrg
-            self.flrgs[flrg.strLHS()].appendRHS(fzzy[self.order])
+            self.flrgs[flrg.str_lhs()] = flrg
+            self.flrgs[flrg.str_lhs()].append_rhs(fzzy[self.order])
 
         self.global_frequency_count += 1
 
     def add_new_PWFLGR(self, flrg):
-        if flrg.strLHS() not in self.flrgs:
+        if flrg.str_lhs() not in self.flrgs:
             tmp = ProbabilisticWeightedFLRG(self.order)
-            for fs in flrg.LHS: tmp.appendLHS(fs)
-            tmp.appendRHS(flrg.LHS[-1])
-            self.flrgs[tmp.strLHS()] = tmp;
+            for fs in flrg.LHS: tmp.append_lhs(fs)
+            tmp.append_rhs(flrg.LHS[-1])
+            self.flrgs[tmp.str_lhs()] = tmp;
             self.global_frequency_count += 1
 
     def get_flrg_global_probability(self, flrg):
-        if flrg.strLHS() in self.flrgs:
-            return self.flrgs[flrg.strLHS()].frequency_count / self.global_frequency_count
+        if flrg.str_lhs() in self.flrgs:
+            return self.flrgs[flrg.str_lhs()].frequency_count / self.global_frequency_count
         else:
             self.add_new_PWFLGR(flrg)
             return self.get_flrg_global_probability(flrg)
 
     def get_midpoint(self, flrg):
-        if flrg.strLHS() in self.flrgs:
-            tmp = self.flrgs[flrg.strLHS()]
+        if flrg.str_lhs() in self.flrgs:
+            tmp = self.flrgs[flrg.str_lhs()]
             ret = tmp.get_midpoint() #sum(np.array([tmp.get_RHSprobability(s) * self.setsDict[s].centroid for s in tmp.RHS]))
         else:
             pi = 1 / len(flrg.LHS)
@@ -241,8 +243,8 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
     def get_conditional_probability(self, x, flrg):
 
-        if flrg.strLHS() in self.flrgs:
-            _flrg = self.flrgs[flrg.strLHS()]
+        if flrg.str_lhs() in self.flrgs:
+            _flrg = self.flrgs[flrg.str_lhs()]
             cond = []
             for s in _flrg.RHS:
                 _set = self.setsDict[s]
@@ -258,8 +260,8 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
         return ret
 
     def get_upper(self, flrg):
-        if flrg.strLHS() in self.flrgs:
-            tmp = self.flrgs[flrg.strLHS()]
+        if flrg.str_lhs() in self.flrgs:
+            tmp = self.flrgs[flrg.str_lhs()]
             ret = tmp.get_upper()
         else:
             pi = 1 / len(flrg.LHS)
@@ -267,8 +269,8 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
         return ret
 
     def get_lower(self, flrg):
-        if flrg.strLHS() in self.flrgs:
-            tmp = self.flrgs[flrg.strLHS()]
+        if flrg.str_lhs() in self.flrgs:
+            tmp = self.flrgs[flrg.str_lhs()]
             ret = tmp.get_lower()
         else:
             pi = 1 / len(flrg.LHS)
@@ -324,7 +326,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
                 for p in root.paths():
                     path = list(reversed(list(filter(None.__ne__, p))))
                     flrg = hofts.HighOrderFLRG(self.order)
-                    for kk in path: flrg.appendLHS(self.sets[kk])
+                    for kk in path: flrg.append_lhs(self.sets[kk])
 
                     assert len(flrg.LHS) == subset.size, str(subset) + " -> " + str([s.name for s in flrg.LHS])
 
@@ -350,7 +352,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
                 for kk in idx:
                     flrg = hofts.HighOrderFLRG(self.order)
-                    flrg.appendLHS(self.sets[kk])
+                    flrg.append_lhs(self.sets[kk])
                     affected_flrgs.append(flrg)
                     affected_flrgs_memberships.append(mv[kk])
 
@@ -446,7 +448,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
             for p in root.paths():
                 path = list(reversed(list(filter(None.__ne__, p))))
                 flrg = hofts.HighOrderFLRG(self.order)
-                for kk in path: flrg.appendLHS(self.sets[kk])
+                for kk in path: flrg.append_lhs(self.sets[kk])
 
                 assert len(flrg.LHS) == subset.size, str(subset) + " -> " + str([s.name for s in flrg.LHS])
 
@@ -473,7 +475,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
             for kk in idx:
                 flrg = hofts.HighOrderFLRG(self.order)
-                flrg.appendLHS(self.sets[kk])
+                flrg.append_lhs(self.sets[kk])
                 affected_flrgs.append(flrg)
                 affected_flrgs_memberships.append(mv[kk])
         for count, flrg in enumerate(affected_flrgs):
diff --git a/pyFTS/models/sadaei.py b/pyFTS/models/sadaei.py
index 0813d40..5428170 100644
--- a/pyFTS/models/sadaei.py
+++ b/pyFTS/models/sadaei.py
@@ -8,6 +8,8 @@ refined exponentially weighted fuzzy time series and an improved harmony search,
 import numpy as np
 from pyFTS.common import FuzzySet,FLR,fts, flrg
 
+default_c = 1.1
+
 
 class ExponentialyWeightedFLRG(flrg.FLRG):
     """First Order Exponentialy Weighted Fuzzy Logical Relationship Group"""
@@ -16,16 +18,19 @@ class ExponentialyWeightedFLRG(flrg.FLRG):
         self.LHS = LHS
         self.RHS = []
         self.count = 0.0
-        self.c = kwargs.get("c",2.0)
+        self.c = kwargs.get("c",default_c)
+        self.w = None
 
     def append(self, c):
         self.RHS.append(c)
         self.count = self.count + 1.0
 
     def weights(self):
-        wei = [self.c ** k for k in np.arange(0.0, self.count, 1.0)]
-        tot = sum(wei)
-        return np.array([k / tot for k in wei])
+        if self.w is None:
+            wei = [self.c ** k for k in np.arange(0.0, self.count, 1.0)]
+            tot = sum(wei)
+            self.w = np.array([k / tot for k in wei])
+        return self.w
 
     def __str__(self):
         tmp = self.LHS.name + " -> "
@@ -50,9 +55,9 @@ class ExponentialyWeightedFTS(fts.FTS):
         super(ExponentialyWeightedFTS, self).__init__(1, "EWFTS", **kwargs)
         self.name = "Exponentialy Weighted FTS"
         self.detail = "Sadaei"
-        self.c = 1
+        self.c = kwargs.get('c', default_c)
 
-    def generateFLRG(self, flrs, c):
+    def generate_flrg(self, flrs, c):
         flrgs = {}
         for flr in flrs:
             if flr.LHS.name in flrgs:
@@ -62,13 +67,14 @@ class ExponentialyWeightedFTS(fts.FTS):
                 flrgs[flr.LHS.name].append(flr.RHS)
         return (flrgs)
 
-    def train(self, data, sets,order=1,parameters=1.05):
-        self.c = parameters
-        self.sets = sets
+    def train(self, data, **kwargs):
+        self.c = kwargs.get('parameters', default_c)
+        if kwargs.get('sets', None) is not None:
+            self.sets = kwargs.get('sets', None)
         ndata = self.apply_transformations(data)
-        tmpdata = FuzzySet.fuzzyfy_series_old(ndata, sets)
+        tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
         flrs = FLR.generate_recurrent_flrs(tmpdata)
-        self.flrgs = self.generateFLRG(flrs, self.c)
+        self.flrgs = self.generate_flrg(flrs, self.c)
 
     def forecast(self, data, **kwargs):
         l = 1
@@ -95,6 +101,6 @@ class ExponentialyWeightedFTS(fts.FTS):
 
                 ret.append(mp.dot(flrg.weights()))
 
-        ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]])
+        ret = self.apply_inverse_transformations(ret, params=[data])
 
         return ret
diff --git a/pyFTS/models/song.py b/pyFTS/models/song.py
index 70fec5a..5a1cf27 100644
--- a/pyFTS/models/song.py
+++ b/pyFTS/models/song.py
@@ -37,9 +37,9 @@ class ConventionalFTS(fts.FTS):
 
         return r
 
-    def train(self, data, sets,order=1,parameters=None):
-        if sets != None:
-            self.sets = sets
+    def train(self, data, **kwargs):
+        if kwargs.get('sets', None) is not None:
+            self.sets = kwargs.get('sets', None)
         ndata = self.apply_transformations(data)
         tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
         flrs = FLR.generate_non_recurrent_flrs(tmpdata)
@@ -71,3 +71,7 @@ class ConventionalFTS(fts.FTS):
         ret = self.apply_inverse_transformations(ret, params=[data])
 
         return ret
+
+    def __str__(self):
+        tmp = self.name + ":\n"
+        return tmp + str(self.R)
diff --git a/pyFTS/models/yu.py b/pyFTS/models/yu.py
index 23a67c9..d241e9e 100644
--- a/pyFTS/models/yu.py
+++ b/pyFTS/models/yu.py
@@ -56,10 +56,11 @@ class WeightedFTS(fts.FTS):
                 flrgs[flr.LHS.name].append(flr.RHS)
         return (flrgs)
 
-    def train(self, data, sets,order=1,parameters=None):
-        self.sets = sets
+    def train(self, data, **kwargs):
+        if kwargs.get('sets', None) is not None:
+            self.sets = kwargs.get('sets', None)
         ndata = self.apply_transformations(data)
-        tmpdata = FuzzySet.fuzzyfy_series_old(ndata, sets)
+        tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
         flrs = FLR.generate_recurrent_flrs(tmpdata)
         self.flrgs = self.generate_FLRG(flrs)
 
@@ -88,6 +89,6 @@ class WeightedFTS(fts.FTS):
 
                 ret.append(mp.dot(flrg.weights()))
 
-        ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]])
+        ret = self.apply_inverse_transformations(ret, params=[data])
 
         return ret
diff --git a/pyFTS/notebooks/Benchmarks.ipynb b/pyFTS/notebooks/Benchmarks.ipynb
new file mode 100644
index 0000000..4b33af9
--- /dev/null
+++ b/pyFTS/notebooks/Benchmarks.ipynb
@@ -0,0 +1,315 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/lib/python3/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['plt']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n",
+      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pylab as plt\n",
+    "\n",
+    "%pylab inline\n",
+    "\n",
+    "from pyFTS.data import Enrollments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f63a0ff1f60>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD7CAYAAABjVUMJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f63a1264908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "enrollments = Enrollments.get_data()\n",
+    "\n",
+    "plot(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model\t\t& Order     & RMSE\t\t& SMAPE      & Theil's U\t\t\\\\ \n",
+      "FTS \t\t& 1\t\t& 845.73\t\t& 2.19\t\t& 1.38\t\\\\ \n",
+      "CFTS \t\t& 1\t\t& 517.09\t\t& 1.29\t\t& 0.84\t\\\\ \n",
+      "WFTS \t\t& 1\t\t& 524.74\t\t& 1.35\t\t& 0.85\t\\\\ \n",
+      "IWFTS \t\t& 1\t\t& 528.07\t\t& 1.33\t\t& 0.86\t\\\\ \n",
+      "TWFTS \t\t& 1\t\t& 555.11\t\t& 1.39\t\t& 0.9\t\\\\ \n",
+      "EWFTS\t\t& 1\t\t& 525.82\t\t& 1.33\t\t& 0.85\t\\\\ \n",
+      "HOFTS\t\t& 1\t\t& 655.18\t\t& 1.79\t\t& 1.06\t\\\\ \n",
+      "HOFTS\t\t& 2\t\t& 649.75\t\t& 1.73\t\t& 1.04\t\\\\ \n",
+      "HOFTS\t\t& 3\t\t& 666.63\t\t& 1.82\t\t& 1.08\t\\\\ \n",
+      "Hwang\t\t& 2\t\t& 3064.83\t\t& 8.38\t\t& 4.57\t\\\\ \n",
+      "Hwang\t\t& 3\t\t& 3144.45\t\t& 8.82\t\t& 4.79\t\\\\ \n",
+      "PWFTS \t\t& 1\t\t& 519.23\t\t& 1.29\t\t& 0.84\t\\\\ \n",
+      "PWFTS \t\t& 2\t\t& 410.08\t\t& 1.04\t\t& 0.65\t\\\\ \n",
+      "PWFTS \t\t& 3\t\t& 304.71\t\t& 0.72\t\t& 0.5\t\\\\ \n",
+      "\n",
+      "Model\t\t& Order     & Mean      & STD       & Box-Pierce    & Box-Ljung & P-value \\\\ \n",
+      "FTS \t\t& 1\t\t& 488.46\t\t& 690.41\t\t& 12.4\t\t& 15.96\t\t& 0.10066138603814807\t\\\\ \n",
+      "CFTS \t\t& 1\t\t& -15.3\t\t& 516.87\t\t& 17.14\t\t& 23.55\t\t& 0.00888612379757273\t\\\\ \n",
+      "WFTS \t\t& 1\t\t& -101.66\t\t& 514.8\t\t& 22.29\t\t& 29.71\t\t& 0.0009556271526125373\t\\\\ \n",
+      "IWFTS \t\t& 1\t\t& -40.49\t\t& 526.52\t\t& 24.19\t\t& 32.34\t\t& 0.00035170094875716196\t\\\\ \n",
+      "TWFTS \t\t& 1\t\t& -27.67\t\t& 554.42\t\t& 25.9\t\t& 34.86\t\t& 0.00013170699014792897\t\\\\ \n",
+      "EWFTS\t\t& 1\t\t& -48.03\t\t& 523.62\t\t& 23.8\t\t& 31.8\t\t& 0.0004320959330739345\t\\\\ \n",
+      "HOFTS\t\t& 1\t\t& 261.77\t\t& 600.61\t\t& 10.47\t\t& 13.71\t\t& 0.18662179852621738\t\\\\ \n",
+      "HOFTS\t\t& 2\t\t& 237.08\t\t& 604.96\t\t& 11.34\t\t& 15.05\t\t& 0.13038783472681856\t\\\\ \n",
+      "HOFTS\t\t& 3\t\t& 249.47\t\t& 618.19\t\t& 11.21\t\t& 15.02\t\t& 0.1313121680254091\t\\\\ \n",
+      "Hwang\t\t& 2\t\t& 2617.38\t\t& 1594.52\t\t& 29.24\t\t& 30.93\t\t& 0.0006028624694539923\t\\\\ \n",
+      "Hwang\t\t& 3\t\t& 2755.05\t\t& 1515.67\t\t& 29.13\t\t& 30.41\t\t& 0.0007334346784103673\t\\\\ \n",
+      "PWFTS \t\t& 1\t\t& -27.93\t\t& 518.48\t\t& 19.0\t\t& 23.68\t\t& 0.008484467982225652\t\\\\ \n",
+      "PWFTS \t\t& 2\t\t& -59.76\t\t& 405.71\t\t& 13.77\t\t& 18.14\t\t& 0.052616020141970136\t\\\\ \n",
+      "PWFTS \t\t& 3\t\t& -60.36\t\t& 298.67\t\t& 12.43\t\t& 18.18\t\t& 0.051986792908239585\t\\\\ \n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "cannot convert float NaN to integer",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2216\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1299\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2437\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1131\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1135\u001b[0m                                                                 renderer)\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m         \u001b[0minterval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 974\u001b[0;31m         \u001b[0mtick_tups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    975\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m             \u001b[0;31m# handle inverted limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m         \"\"\"\n\u001b[0;32m--> 917\u001b[0;31m         \u001b[0mmajorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1951\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1952\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1953\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1955\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1959\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[1;32m   1960\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[0;32m-> 1961\u001b[0;31m         \u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0mprune\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'auto'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1902\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1903\u001b[0;31m                 nbins = np.clip(self.axis.get_tick_space(),\n\u001b[0m\u001b[1;32m   1904\u001b[0m                                 max(1, self._min_n_ticks - 1), 9)\n\u001b[1;32m   1905\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2060\u001b[0m         \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m31\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f63a1227128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABOsAAAE/CAYAAAAXC0faAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3Xl4VNXh//H3nUkmC9kX1iRE1pCA\nUUhdMMENVNAAVUFlUWKhrbXa2qqt/hAVrbW4tPitUtwQUVRcAhg2UQQSkR0DJKCQsCRhCYGQyUKW\nmbm/PyaBgKwhIYCf1/Pcztxz7z3n3KHlefj0LIZpmoiIiIiIiIiIiEjzszR3B0RERERERERERMRN\nYZ2IiIiIiIiIiMh5QmGdiIiIiIiIiIjIeUJhnYiIiIiIiIiIyHlCYZ2IiIiIiIiIiMh5QmGdiIiI\niIiIiIjIeUJhnYiIiIiIiIiIyHlCYZ2IiIiIiIiIiMh5QmGdiIiIiIiIiIjIeUJhnYiIiIiIiIiI\nyHnCo7k70BTCwsLM6Ojo5u6GiIiIiIiIiMhFY82aNUWmaYY3dz8udhdlWBcdHc3q1aubuxsiIiIi\nIiIiIhcNwzB2NHcffgmaLKwzDOO3tV87mqb5t9qyO4GDQE/TNCecbZmIiIiIiIiIiMjFpEnCOsMw\n+gJfm6aZaxjGp7XnBwBM0/zaMIwOhmH0rLu/IWWmaa5tir6LiIiIiIiIiIg0l6baYKID0Lf2e27t\n+V24R8bVlfU9yzIREREREREREZGLSpOMrDNN8816pz2BT4Be1I6uqxUKBJ1FmYiIiIiIiIiINKM1\na9a09PDweBvoTtMNCrvYuICNDodjdK9evQqPvdikG0zUTmFda5rmWsMwmrKpujXyfgsQFRXVpG2J\niIiIiIiIiAh4eHi83bp1627h4eHFFovFbO7+XAhcLpexb9++2D179rwNDDz2elMnnn3rNpfAPY01\npPZ7ELD/LMuOYprmm6ZpJpimmRAerl2ERURERERERETOge7h4eF2BXWnz2KxmOHh4SW4RyP+TJPu\nBltvJ9e+uKfCJtRe7gB8Xfv9bMpERERERERERKT5WBTUnbna3+y4g+iaZGRdbTj3L8MwcgzDKAao\n27219tpB0zTXnk1ZU/RbREREREREREQuLNnZ2bbevXt3jouL6xYXF9dt2LBh7YuKiqzH3jdlypTg\nsWPHtjpRPae6frLnHnjggXZn+tyJNNUGE18Dwccpf7Mxy0REREREREREpPnV1NSQm5tra4q6O3To\nUO3p6Xnca0VFRdabb765y0cffZSbmJhYAfDyyy+HXXvttV2ysrI21b83JSWl+GTtnOr6udKkG0yI\niIiIiIiIiMjFLzc31xYTE9OjKerevHnzhq5du1Yf79p//vOfsPvuu29fXVAH8OijjxZNmTIlPCMj\nw3fLli1eCxcuDEhPT/d/4IEH9ubl5dkmTZpU0L9//w4lJSXW6Ojo6szMTN+srKxNU6ZMCV65cqXv\nzTffbJ88eXJ4SUmJtaSkxOPRRx/dUxfk9e7du3NdO2PGjClqioBPW+qKiIiIiIiIiMgFKTc317tj\nx44/C/Li4+MrtmzZ4gWQmZnpm5eXt7FNmzYOgAceeKBdr169ypctW7Zl6NChB+x2+8+mzO7cudNr\n2bJlW5YsWfLTuHHj2oF7uu2YMWOKli1btmXChAkFb731VlhTvJNG1omIiIiIiIiIyFnp0KFD9ebN\nmzc0Vd0nuVaZk5Pzs+m327dvt1155ZXlK1asaNGnTx/7Mde8hg8fXgwwePDg0oceeuhn9dY9ExYW\n5qwra9mypXPhwoUBCxcuDDiL1zklhXUiIiIiIiIiInJWPD09OdFU1ab05z//uejyyy/vdsstt5TW\nX7MOIDY2tnrFihUtjn0mOjq6av78+f6JiYkVM2fO9D/dtp566qnWPXv2LH/00UeLZs6c6T9hwoTW\njfcmRyisExERERERERGRC1JYWJhzwYIFP40ePbp9SUmJB7inwM6ePTv3RM8899xzewYOHNihd+/e\nAfHx8RUnuu9Yw4cPL3788cfbffPNNwHR0dFVeXl5XhkZGb6N8R71GaZpNnadzS4hIcFcvXp1c3dD\nREREREREROSiYRjGGtM0E+qXZWZmbo+Pjy9qrj41RN1ousGDB5dmZGT4Pv744+2WLVu25Vz3IzMz\nMyw+Pj762HKNrBMRERERERERkV+MxMTEipEjR7av2/H17bff3tHcfapPYZ2IiIiIiIiIiPxihIWF\nOefNm3fCabLNzdLcHRARERERERERERE3hXUiIiIiIiIiIiLnCYV1IiIiIiIiIiIi5wmFdSIiIiIi\nIiIiIucJbTAhIiIiIiIiIiIXpKKiImt4ePhlsbGxFXVl8fHxFQCZmZm+drvdWlJS4hEZGVkVFRVV\nNW/evNyxY8e2Sk1NDam7f/LkyTsSExMrjld/c1BYJyIiIiIiIiIiF6yIiIiqrKysTce79vLLL4fl\n5OR4TZo0qQAgIyPDd+rUqeF5eXkbAbKzs21DhgzpeKLnm4OmwYqIiIiIiIiIyC9CTExMVUlJicfM\nmTP9AWJjY6uXLFnyU3P3qz6NrBMRERERERERkbM2a9asyMLCQt/GrLNly5YVgwYNyjvZPfn5+V5x\ncXHd6s5PNq01LCzMOXfu3J/eeOON8Iceeqh9ZGRk1YQJEwo0DVZEREREREREGpXL5cLlcmGa5jn5\nbMq6W7duTa9evbBarc39s8oF4GTTYI+VnZ1tCwkJcUyfPn0HuKfFDhgwoIvdbv+haXt5+hTWiYiI\niIiIiFxgTNNkx44dpKenHz42b97c3N1qVC1btiQ5OZmBAwfSt29ffH0bdcCWNIFTjYA7H6xYsaLF\nW2+9FbZs2bItAImJiRWBgYGOoqIia1hYmLO5+wcK60RERERERETOey6Xi02bNrF06dLD4Vx+fn5z\nd6vBLBYLFosFwzB+9mkYBmVlZRQWFvLOO+/wzjvv4OPjw0033cTAgQO57bbbaNmyZXO/glygUlJS\ninNycmz1p82OHz++4HwJ6gAM0zSbuw+NLiEhwVy9enVzd0NERERERESkQWpqali7du3hYC4jI4MD\nBw787D5/f3+uueYakpKSSEhIwMfH57gB2MnCsYZ8NvTZuuNUcnJymD17NrNnzyY9PR2n80iOYhgG\nvXv3ZtCgQQwcOJCuXbs26m8vJ2YYxhrTNBPql2VmZm6Pj48vaq4+XcgyMzPD4uPjo48tV1gnIiIi\nIiIi0swqKipYvnw56enpLF26lOXLl1NR8fP17lu2bEmfPn246pqbaBd1LTVmFNt2GOzYDkX7wGox\n8PQwsHmAh9WCpxVsngaeVgOr1cBqBYuFw59N/b2hz4WEQMuWYBiwf/9+5s6dy6xZs5g/fz7l5eVH\n/SZdu3Y9HNxdddVVWueuCSmsa1wK60RERERERETOEwcOHCAjI+PwyLk1a9bgcDgOX/cPuoTA0ATa\nRSURGpqAabSntCSIwt2e7Mm3Yi+2HFWf1cMkKNQFgNMFZu3hchm46n03XWCa4HLiLjdPPcqtuYSG\nQlzc0UfHjpVs3Pgts2bNYvbs2ezevfuoZ8LDww+vc9evXz+tc9fIFNY1LoV1IiIiIiIiIs0kPz//\ncDC3dGk6BXtNAkJ64esXh4dnZ0wziuqqVlSUBnOg0IdDZUeHcTYvk1btnLSJMIls7yIqCi6JNuh0\niUGXjhbaR1jw8DAwTROHaVLlcFHpcFHldNZ+uqh0OGs/Xe7rTidOF/XCvLqQz8DDsGCzWPA0rHga\nFmyGBQ/DgodhxdNwX/fEitUwDgeCLhc4a0PAs/nudMLevZCVdeQoKTnyW4SHu4O72FgTP78d7Nr1\nNWvWvM+mTelH/WY+Pj7069ePgQMHkpycrHXuGoHCusZ1orBOG0yIiIiIiIjIL1LR9wvwTr0NzxaN\nu658jenJ9ppLyansSW55LNvtndh5MJKCouvYs28Qe/d4U11p4WC9eMPXz0mrVhVEt9xPYsxuooJ2\nEO2/hY6+G+jivZo2tlzqlnpzYVDh6UvZQT/Ks1pwYIsf6Z1bsLHKB5dpAO4bzcNrwx0ZPVdXZtaW\nWQ1fbB4BeHoEYrME4GkNxOYRgM0agI1AbFZ/bBb3ucXw/Nm7ukwnNa5SqlylVDvtVDlLqXaWUl13\n7iqjyml3n7sOuR+yGGA50oef9xdoCfSAWKCbCRVFwRTviKB4ezuKt0eQvT2C71ZGUFMRDYwGRuMd\nVEJwdH7tUUBw+3wCovNZHrCI5RsXNfSPs1EYpon7jU0M03XUOWZduYmB66jzI8+4n6t/Tr1n6p83\n5BmDet9N11Hn9Z+Rc6NJwzrDMHqaprm23vnjQC4QYprmm7VldwIHgZ6maU44kzIRERERERGRhnBU\nV+Mz6zZ84xxUbrGc+oF6DpktyK3pSU7lZeSWd2OHvQP5B9tRsD+cPYX+FO31wuE4enppQLCDlq0q\niIg6wOUJBwgJ209gyyJ8wwvxarUPR4hJuc2PMpsf5TY/frK1YJ3tV5TZrnOXe/lR7tmCcpsf5V5+\njflTnB7TxNdlEuxwEeh0EuRwEeR0EeRwEuT0I9DR6vB5S4eLn8d64ABKPCwctFo46GHloNXiPq/9\nfrD2WomHlQpLbTR0ss0oTCAfyHIflVmB7M4KZPfXcVBW777WQNxxjqDG+GF+YYadv9OmLyZNFtYZ\nhtEXmAx0rHeOaZqfGYbxL8MwOlD7Pw3TNL82DKODYRg9654/VVn9EFBERERERETkTOx6pAdR1zgo\nSI+m3aRt1ADluDOevWUmP+U62L7NJC/XZPcO2LcT9uVZKMqzUFxoOWqtN8MwCQh3EtDWQUC8k+Co\nMsxog8pOHpR18uBgByt2fw/sBLCVACDiuH3yBvyOOVoArY5T5gdU7f2YTevm4LKXkdA5kIRL38DD\n6l6j7dgxdfUjlhNdO+m9hgFWA8NqATxOWh+mictl4nQ4cTpdOB0uHE4XDoeT0NrvTocTR1UNDseZ\njdaqn90ZdS12dB/GQHcnTNOgaJeFvK0e7NziPnb8ZCF/mSdVh44Es4Gh1bSOPkTb6EO07+KkQ6xB\nlx4e+AcaR7dxVJtH/sM4qrDebYZx1O/j6WHF5uWBzeY+rB4WTKNu7NrPD9cJyk927Vw9M+o4fybS\n+JosrKsN1nLrFfUDVtV+zwH64v6f1MLastzastDTLFNYJyIiIiIiImds21tPE/2rn1ifF8OYHj9Q\n/ocqanYaVOQblORZKT1ggXpjwyxWk+A2LvwiXIT3cdI6qgZX2xoOtaqkNLSUg/4HKKkupcrppFWL\nFkQEB9OhZUsig1vgbxiHw7XjBW71z093D1On4wCLvurOslW7uSoYbk8eR7tLnm3EX+gs1QZ7WE89\nYtE0TapdJlWOo9fWc7jc6+ubh+8D8/CZ+/zo60fuN4H2/tCzK4ATEyeY4HQdYneBQebqCtavOcTW\nTbA7z4+c9aF8W3Pkzzso9BBRnWroGmehUzeI7uokupMTX7+f96mu7aP7U9tT0/1ZeqiampIjffcw\nDPy8PPC31T+s+Nk8sJxsJOF5YFRzd+AX4lyuWbcfCKn9HoQ7gAsCDtS750zKRERERERERM5IyfYt\nROwfT5WfB8O+X0vWLC88bSZh7ZyERrjodEsNIe0ceLbYR3n1T+zYvoSNP6Sxv7SY/T+WwZoyqKkB\nIDY2lluSkkiqPaKiopq8//t2TeKLWQ+yp9Ck56VB3Nx/LTbvS5q83aZiGAZeVgMvq4UAr3PQYDsY\ndEXg4dNt27Yxc+ZHzJixkpUry3G5Yji4P46D++NYv6Ib4HP43qgok7g4o3ZzCw5/+p1iVrJpmlQ5\nXZRWO9xHlfuzqKKKPPuhw/cZgF9taOdv8yDA5uH+7uWBp+XMpmr/0mRnZ9tGjx7dvqSkxMNut1tv\nu+224kmTJhUUFRVZw8PDL4uNja2ouzc+Pr4CIDMz09dut1tLSko8IiMjq6KioqrmzZuXO3bs2Fap\nqal1+RWTJ0/ekZiYWHG8dpvKuQzrPgN+V/u9I+7RdZohLiIiIiIiIueE0+Gg+qVf4XOlJ1e/m0/W\nEh/u+n+VPDsyn2XL0g/v1rp469afPWu1WunZsydJSUn06dOHa665hrCwsHPWd9PlZNWyX7FwyTps\nNrj713fS9dJPz1n7F6tLLrmERx75E488AsXFxcydO5dZs2Yxb94fKCurAC6hbpG74uKrWL06gW++\naU119ZHwrH17d3BX/+jWDVq0cF83DANvDyveHlbCfY9OJB0u11EBnvtwsqesqt44QvDxsBw9Eq92\nZJ6X1eKeovwLd/PNN3dZsGDBT7GxsdUAvXv37jxlypTg5ORke0RERFVWVtam4z338ssvh+Xk5HhN\nmjSpACAjI8N36tSp4Xl5eRvBHQIOGTKk44mebyrnLKwzTTPXMIxPategO4h7OmsoR4+221/7/XTL\nDjMM47fAb4Fz8v9miIiIiIiIyIXDNE22/r+BtP9VJdf+ez0//NCSvk8Wk/5eD2L+UfCz+318fLjq\nqqsOj5q76qqr8DvVEKomUnZwEbO+7M/W3Go6dbAxKHkefkE3NEtfLmbBwcEMHz6c4cOHU1VVxeLF\ni5k1axazZ8+moGA2paVQWgpgJTi4F5ddNoI2bW6kuroLP/7owddfQ3W1uy7DgOhod3B31VVwww2Q\nkACex+y84WGxEOxtI9jbdlS5yzQpr3EeE+I52GE/dHiKMICnxThuiNfC09osId7KlSsj7Xa7b2PW\nGRAQUHHFFVfknej6lClTgpOSkkrrgjqA2bNn557o/pOJiYmpKikp8Zg5c6b/4MGDS2NjY6uXLFny\nU0PqOhvnLKyrDekSTNN80zCM39VuNJELJNTe0gH4uvb76ZYdVru77JsACQkJ5rHXRURERERE5Jdr\n+5yPadt6CX1fXs7yrBhu+YedrybF4ypwB3VBQUEkJiYeDud69eqFzWY7Ra1N78f1Q5i94DOqq6H/\njZfzq96rMCynu7qdNJSXlxc333wzN998M6+//jpr165l1qxZzJo1i/Xr11NcvJJvv10JgLe3N337\n9uXBBwcTGzuQwsJwsrIgOxs2bIC0NHedfn6QlOQO7m64AeLjwXqCP0qLcSSEq880TSodrqMCvNJq\nB3vKq9hRb0qtxQA/zyPhXd3hZ/PAw3JxjcTLycmxdejQobJ+WVhYmBOgqKjImp+f7xUXF9et7trJ\nprWGhYU5586d+9Mbb7wR/tBDD7WPjIysmjBhQsFFMw3WMIw7gQTDMO40TfMz0zTX1u7keifuXWKp\nLUuo3Sn2YN0Or6dbJiIiIiIiInIq9j178F70IDctWMaKzZdy+yulpGX9Bcue3Tz40EOMGTOGuLg4\nLOfRumDVldtYMK8na9cfpHVLg9sHvUZ42z82d7d+kQzDoFevXvTq1Yvx48ezfft2Zs+ezaxZs1iy\nZAmVlZWkpaWRlpaGYRhceeWVDBo0iKefHkRMTAz79xssXgzffguLFsFjj7nrDQqC665zB3fXX+8e\nhXeqwXCGYeDjacXH00rLFkdPqa12uiirF+DZqxyUVNZQUHpUjoWvp/WYzS1qp9R6nP1//082Aq6p\ndOzYsXrhwoUB9csyMjJ8ly9f7jtq1Kjik02DPVZ2drYtJCTEMX369B119QwYMKCL3W7/oSn6fiJG\n3Y4pF5OEhARz9erVzd0NERERERERaWY1lZVk/eFqxnz3HmtzLmX4qyV80el7+r83hX++8AKdOnVq\n7i7+zK5tz/JF2jPsPwC9f9WG6/tm4mELb+5uyXEUFxczb9682nXu5lHqnid7WKdOnRg0aBDjxo0j\nIMCdJ+3aBYsXu4O7RYtg2zb3vS1bukO76693B3idOp06vDsdTpdJWY2DstoAry7MK6t24KwXCdms\nFvxt1p9Nq/X1ODKl1jCMNaZpJtSvPzMzc3t8fHzR2fe04SIjI7t/9NFHuXUj4Hr37t15zJgxRcnJ\nyfbLL7+8W90adMc6ds26KVOmBL/11lthy5Yt21K/7nXr1m2qG63XmDIzM8Pi4+Ojjy0/lxtMiIiI\niIiIiJwzO3fuZMXzT/GPxdPIyotl9KtFpN5r4+PcVtw2Y0Zzd+9nXM5DZCzuwZJlOfi1gHvveoBL\nYt5o7m7JSQQHBzNs2DCGDRtGVVUVS5YsObzOXX5+Plu3buW9997jxRdfPPxM27YwbJj7ANi+/cio\nu0WL4JNP3OUREUdG3d1wAzR0eX6rxSDQy5NAL0/a+R8pN02TCoez3gYX7u+7yqqodh6ZUms1OLxD\n7fnqo48+yn388cfb1d8NNiUlpbioqOiM5oynpKQU5+Tk2OpPmx0/fnxBUwR1J6ORdSIiIiIiInJR\nKSkp4cUXXyR/xRbWbn6OrQe68tCre5k+OpwXPa3cex7unllc+Amps4eTV+Cke4wvA279Dh+/y5q7\nW9JApmmybt06Zs2ahdVqZdy4caf5HGzZ4g7t6gK8otoxax07Hgnurr8eWrduuv5XHWddvNJqB/07\ntjovR9ZdqDSyTkRERERERC5q1dXVTJo0ieeee442Ad04dPA98g9F8+R/9/Dx8DB+ZfNgZHN38him\ny8n6Nf2Y+/W3GAb8+tYb6dFzgTaRuMAZhkHPnj3p2bPnGT4HXbq4j9//HlwuyMo6Etx9+im8/bb7\n3m7djmxWce21EBraeP338rDg5WEjzLf5N1n5JVJYJyIiIiIiIhc0l8vFp59+ypNPPklubi6hAT0o\n3v8Jxc5wxr25j8w7QtnrY2MRcD6NqTtU9gNz0nqT9eMhoiKs/Dr5I4JaDmnubsl5xGKBHj3cx8MP\ng9MJ69YdCe/eew9ef90d8sXHHxl116cPBAScsno5TymsExERERERkQvW4sWLefzxx1m1ahUANs9u\nWIzFlDhbMO7dA3j3Npnh68WHQJvm7epRtm3+PTPnTqasHG5I6sQ1167HYvVp7m7Jec5qhYQE9/HY\nY1BTA6tWHVnv7vXX4dVXj9xXF95dcw34+jZ37+V0nT/7UouIiIiIiIicpqysLJKTk7n++usPB3X9\n+j1EgN8aqk0fnn7fzqUt0vlbRBsGA/c0b3cPc1Tv46s5rXn/k8l4esJvRjxD0g1bFNRJg3h6Qu/e\nMHasO6w7eND9+cQT4OEBL70EN90EQUHuqbLPPANLl0JVVXP3XE5GI+tERERERETkgrFr1y6efvpp\n3n33XVwuFwBXX301v//96/z1Lz1wuap45sMKrt7zGg+Pfg4/4H+cH9NfCwv+jy9m/4m9hSa94oO5\n6ZY12Lwvae5uyUXE29s9ku76693nZWWQkXFk2uxzz8Gzz4KPj3u0Xd2ad716ucM9OT/oj0JERERE\nRETOe3a7nZdeeolXXnmFQ4cOAdC5c2defPFFoqJ+zU03mViNQzz7cTm9D01m/oj/x0rgI6BVs/bc\nvYnEyu968fXSTGw2uPvXQ+l66SfN3Cv5JfDzg1tucR/gHnm3dOmRabNPPuku9/d3r3NXF95deql7\nvTxpHgrrRERERERE5LxVU1PDm2++ybPPPsu+ffsACA8P55lnnmHMmDGsXu1J374mPl6VPPVhOb/y\nnIHdGsez3t7cAdzVvN2ntHghs768lZxtNXTuYGNg8gL8gq5r9HY2bNjAt99+e3i0ocjJREfD/fdD\nWZkPW7dGsGVLJKtWRTBnTggAvr6H6NQpn86d8+jUKZ9WrQ5gnA/DU4+jf//+HYYOHVqckpJSDBAQ\nEHDZxIkTd9Q/X7VqVXZMTEyP2NjYirrn4uPjKwAyMzN97Xa7taSkxCMyMrIqKiqqat68ebljx45t\nlZqaGlJ3/+TJk3ckJiZWHNt+U1BYJyIiIiIiIucd0zT5/PPPefLJJ9myZQsAvr6+/PWvf+Wxxx7D\n39+fpUvh1ltNggIreer9EuL8viFo2gz+8H9LCQDeoHmnv25efwez539BTQ0M6NuThKtXYlisjd7O\npk2bSE1NpXXr1rRq1dzjCOVC06OHA9gGbGP/fm82bAhjw4Yw1q9vx/r1nQEICqqkR48i4JHm7Opx\n3XjjjfaFCxcGpKSkFGdkZPgGBgY6ZsyYEZySklKcnZ1tCwwMdISGhjojIiKqsrKyNh2vjpdffjks\nJyfHa9KkSQUAGRkZvlOnTg3Py8vbCJCdnW0bMmRIxxM939gU1omIiIiIiMh5JSMjg8cee4zly5cD\nYLFY+M1vfsMzzzxD27ZtAVi4EAYNgjatq3hyyn46+q2j27sP87//K2I1MANo2Uz9r67MYf68Xqxb\nX0KbVga/Hvg64W0faJK2cnJy+Pzzz2nXrh0jR47EZrM1STvyy7Rtm3u67LfferNoUcQp78/+6qvI\n8v37G3Xf2RahoRWxN92Ud6Lro0aNKp44cWJrgPnz5/uPHz++YNy4ce0A5s6dG5CUlFR6pm3GxMRU\nlZSUeMycOdN/8ODBpbGxsdVLliz5qeFvcWY0A1lERERERETOC5s3b2bw4MEkJSUdDuoGDhzIhg0b\nePPNNw8HdWlpkJwM0dE1jJ2yjyi/rVy55F42P7yUZywWhgBDmukdCnKfYvKbnVi3voRrrmjLb0YX\nNVlQt3PnTj7++GPCwsIYNmyYgjppdJdcAr/5DXzwARQUNHdvji8sLMwJUFRUZE1NTQ1JTk62d+/e\nvSIjI8N37dq1Lfr162cHyM/P94qLi+tWd2RkZJwwVAwLC3POnTv3pxkzZoRERkZ27927d+fNmzd7\nnat30sg6ERERERERaVZ79uzh2Wef5a233sLpdAJwxRVX8NJLL9GnT5+j7v3iC7j7bojr7uSR13YT\n5lPINT8OI698KA937U4Q8HozvIPLWUb6t5eyZNk2Avzhvnv+SHSX/2uy9nbv3s306dMJDAxk5MiR\n+Pj4NFlbIsBprVl3shFwTSkpKan0vffeCwZ30DZ06NDiDz/8MDg9Pd3/tddeywc42TTYY2VnZ9tC\nQkIc06dP3wHuabEDBgzoYrfbf2i6tzhCI+tERERERESkWZSVlfHss8/SqVMn/ve//+F0OunYsSMz\nZsxg+fLlPwvqpk+HoUOhV4KLx/8vj0C/cpK23419vpMZT01mDTAJCD/H71G8dzrvTQlk8Xfb6B7j\ny+9/m9mkQd2+ffuYNm0a3t7qSASdAAAgAElEQVTejBw5khYtWjRZWyIXgn79+tnHjx8f0adPHztA\ncnKyPS0tLTggIMBZN/LuTKxYsaLF6NGj29edJyYmVgQGBjqKiooaf9HJ49DIOhERERERETmnHA4H\n77zzDk8//TR79+4FIDQ0lKeffprf/e53x53OOWWKezpenz4mD72Uh8UHkjbdg211Lj9O3M944G7g\njnP4HqbLSebqG5j3zVIMA26/tS89EhY2aZvFxcVMmzYNq9XKvffeS2BgYJO2J3IhSE5Ott9///3W\n4cOHF4N7dF1AQICzLrw7UykpKcU5OTm2uLi4bnVl48ePL2hI8NcQhmma56KdcyohIcFcvXp1c3dD\nRERERERE6jFNk1mzZvH3v/+dH3/8EQBvb28eeeQR/va3v50weHrjDXjwQejXz+ThCbuo9jK5YvkI\n2lUuYbvff7lj5IMUAFlA2Dl6l0Nla0hLSyT7x0raR1oZPPATgsKaNiq02+1MmTKFqqoqRo0aRcuW\nzbWFhvxSGYaxxjTNhPplmZmZ2+Pj44uaq08XsszMzLD4+PjoY8s1sk5ERERERESa3Pfff89jjz3G\nd999B4BhGKSkpPDss88SEXHiXSZffRX++le4LdnkzxMKKcGgy6IniAhawvallzFtyoOsA77g3AV1\nuZvGMHPu25RXwI1Jnel9bSYWa9OuGVdeXs60adOoqKjg3nvvVVAnchFTWCciIiIiIiJNZsuWLTzx\nxBN8/vnnh8sGDBjAiy++SI8ePU767D/+AWPHwp13mjwyoZg91S7Cl0+ne9A07N/aOPDmSp4DhgG/\nbtrXAMBRvYdvvopn+ZpCQkPgnjufo037sU3ebmVlJR988AEHDx5kxIgRtGvXrsnbFJHmo7BORERE\nREREGl1hYSHjx49n8uTJOBwOAHr27MlLL73EDTfccNJnTRPGjYPnn4cRI+Av/yplW2kVPptX0rvi\nMVx2KL9/Ifd7ehIGvHYu3qfgP3w+6y8U7jNJuCyEm25Zh6dXVJO3W11dzfTp0yksLOTuu++mffv2\np35IRC5oCutERERERESk0ZSXl/Pvf/+bf/3rX5SVlQEQHR3NCy+8wF133YXFYjnp86YJjz0Gr7wC\no0fDX14oY9OBcmw7ttB73TCs0ZC77R6mXt2HTGAWENqE72O6qlmRkcDXSzfg7Q333HEPXbpPb8IW\nj3A4HMyYMYP8/HzuuOMOOnfufE7aFZHmpbBOREREREREzprD4eC9995j3Lhx7N69G4Dg4GCeeuop\n/vCHP+Dl5XXKOlwuePhheP11+OMf4S/jK1hXWIrnnjxi0x8nsFsFe78M4eBH03kBGAkMbMJ3Kj2w\ngJlf3kbudgddOtoYmLyQFoF9mrDFI1wuF59//jk5OTkMHDiQuLi4c9KuiDQ/hXUiIiIiIiLSYKZp\nMnfuXP72t7+RlZUFgJeXF3/605/4+9//TnBw8GnV43TC734H77zjHln356cqWbGrBFtxEa3XfMwl\nHVdQ/YOB18vrGQWEAxOb7K1gU+Zgvlwwi5oauLXfr+h11fcYFmsTtnhE3a65mzdv5pZbbuHyyy8/\nJ+2KyPlBYZ2IiIiIiIg0yKpVq3jsscdYsmQJ4N7hdeTIkTz33HNERZ3+em4OB4waBR9+6F6r7o+P\nV/NdQTGeh8rwXf0NPVyvwSHYfck/ebtdOzYAXwKnFwOemerKHObN7ckPG+y0aW3h9oH/I6zNmCZo\n6fjqws/169dz/fXXc+WVV56ztkUuRIZh9DJNc03//v07DB06tDglJaUYICAg4LKJEyfuqH++atWq\n7JiYmB6xsbEVdc/Hx8dXAGRmZvra7XZrSUmJR2RkZFVUVFTVvHnzcseOHdsqNTU1pO7+yZMn70hM\nTKw4th+NSWGdiIiIiIiInJHc3FyefPJJPvnkk8NlN910E//617+47LLLzqiu6moYPhw++wxeeAH+\n8EgNS/MO4Omowbbsa7rvm4hXVyfbv+xG0Ud/45/AfcBtjftKAOTnPMEXc16kuBgSr2zHdX3XY/UI\nOfWDjeibb75h9erV9O7dm6SkpHPatsiF7MYbb7QvXLgwICUlpTgjI8M3MDDQMWPGjOCUlJTi7Oxs\nW2BgoCM0NNQZERFRlZWVtel4dbz88sthOTk5XpMmTSoAyMjI8J06dWp4Xl7eRoDs7GzbkCFDOp7o\n+caisE5ERERERERO2yeffMJ9991HVVUVAPHx8UyYMIGbbrrpjOuqrIShQ+HLL+Hf/4Yxf3CweOcB\nDJcL29L5RFetoGXX7ZR+40H4lLXcBrQC/tOobwQuZxlLF/Vg6ffbCQiAUcP+RPvOjd3KqaWnp/Pd\nd9/Rq1cv+vbti2EY57wPImdj1v33RxZu3OjbmHW27N69YtC77+ad6r5Ro0YVT5w4sTXA/Pnz/ceP\nH18wbty4dgBz584NSEpKKj3TtmNiYqpKSko8Zs6c6T948ODS2NjY6iVLlvx05m9xZk6+Dc9ZMgyj\n5zHndxqG0dcwjN8ep+zxMy0TERERERGRc8M0TV544QXuvvtuqqqqiIiI4P3332ft2rUNCuoqKmDQ\nIHdQ98Yb8Ps/OsnIP4DL5cJnxbcEeZbRwWcqrp1QMexL/uHtTRbwNhDUiO91YO8HTHk3kCXLttOj\nWwt+P2ZDswR1K1euZNGiRVx66aXceuutCupEzlBYWJgToKioyJqamhqSnJxs7969e0VGRobv2rVr\nW/Tr188OkJ+f7xUXF9et7sjIyDhhuBgWFuacO3fuTzNmzAiJjIzs3rt3786bN28+9W45Z6nJRtYZ\nhtEXmAx0rD3vCeSaprm2NnQ7HOSZpvm1YRgdzqTMNM21TdV3EREREREROaKmpobf//73vPvuuwD0\n6dOH1NRUQkIaNkW0rAySk2HJEnj3XRhxr4uleQeodDgJ3bwWR9lBuh54FmsE5OQP4sDfb+FfwP1A\n/0Z6J9Pl5IdV1zF/UQaGAXfcdjPde81vpNrPzA8//MC8efOIiYlh0KBBCurkgnU6I+CaUlJSUul7\n770XDO6gbejQocUffvhhcHp6uv9rr72WD3CyabDHys7OtoWEhDimT5++A9zTYgcMGNDFbrf/0HRv\n0YQj60zT/BrIPab4X7WfHWrDtruAg7VluUDfMygTERERERGRJnbw4EH69+9/OKgbMWIEX331VYOD\nupISuOkmSE93byhx730m3xcUY69y0HbfTqpyt9Cl8ksCOtgpnBNIu//O5D6gLfBqI71TRelKPv3Y\nj9nzM2jTysoDo79otqAuOzub2bNn06FDB+644w4sliadACdyUevXr599/PjxEX369LEDJCcn29PS\n0oIDAgKcdSPvzsSKFStajB49un3deWJiYkVgYKCjqKioSbeGPmdr1tWOqMs1DKMYqNtKJwg4UO+2\n0DMoExERERERkSa0fft2br31VrKzswF4+umnefrppxs88uvAAbj5ZsjMhBkz4Ne/Nlmxq5iiQ9V0\ncpWzb9X3RLdz0aZ8ATXrwPufq3kG2ATMBwIb4Z1yNv2GmXPfpaIC+vbpytV91mGx+jRCzWdu69at\nfP7550RERHDXXXfh4aFl5UXORnJysv3++++3Dh8+vBjco+sCAgKcdeHdmUpJSSnOycmxxcXFdasr\nGz9+fEFDgr8zcc7+JjAMIwj36Lh/Am8ZhqFprCIiIiIiIueplStXMnDgQPbu3YunpyfvvPMOI0eO\nbHB9hYXQrx/8+COkpsKAASbr9pawq6yKrr4WCmfNJ7htOBE7R4EPFLQex55OnXgJGA3cfJbv46je\nw9dfXcqKNfsIC4Vhd75Am/ZPnGWtDbdjxw4++eQTWrZsybBhw7DZbM3WF5ELmWmaa+q+h4WFOeuf\nA9Sf8hoWFuas29n1eB599NGiY8uef/75vc8///zexurv6TiXsf1vgX+apnnQMIxc4E7c4V3d2Okg\nYH/t99MtO6x204rfAkRFRTV650VERERERH4pUlNTGT58OIcOHSIoKIjU1FSuu+66Bte3axf07Qvb\nt0Namvt71r5StpcconOgNyXzZ+Fhs9E+9x94RTnY8VlHWn3xLLcA7YBXzvJ99ua9whdfPkbhPpNf\nXR5Kv5vX4unVfP9u3LVrF9OnTycoKIgRI0bg7e3dbH0RkfNPs4yxNU3zs9pw7Wsgoba4Q+05Z1BW\nv843gTcBEhISzCbotoiIiIiIyEXNNE1effVVHnvsMUzTpEOHDsyZM4eYmJgG17lzJ9x4I+zZA/Pn\nQ58+sPVAOT8eKCc60AfXqnQqS0qIbVtIqN8myhdaafv+DzwJ/Ah8BQQ09H1c1SxP78k36Vl4e8Ow\nO0fQOW5ag9+lMRQWFvLBBx/g6+vLyJEjadGiRbP2R0TOP025G+ydQIJhGHeapvmZaZoTDMN4vHZU\nXUhtuIZhGAm1O8cerNvh9XTLREREREREpHE4HA4efvhhJk2aBMDVV1/NrFmzCA8Pb3Cdublwww1w\n8CAsXAhXXQU77YdYv89OWz9vQgpyyMnJofOvuhC+4RFcxVAy+GO2+/nxCu6pU/0a2Lb9wFxmzR5E\n7g4HXTraGJi8kBaBfRr8Lo3hwIEDTJs2DavVysiRIwkIaGgMKSIXsyYL60zT/Az47JiyCce5782G\nlomIiIiIiMjZKy0t5a677mLevHkADBkyhKlTp+Lj0/CNF3780T2i7tAhWLQIevaEPWWVrNl9kHBf\nG51c5azPyCC8Y0eC00dgbW2y5ad+tHviTkYBUcDLDWw7e90g0hbOxuGA2266gp5XLsOwNOnmjadk\nt9uZNm0aTqeTUaNGNXg3XRG5+GmrGRERERERkV+w/Px8br31VtavXw/AE088wfPPP4/FYmlwnRs3\nutelM01YvBh69ID9h6pZsauYAC8PLg/yYt1Hn+MTFETbwg/wjypm36ct6PjZPB4FtuBe+8j/DNut\nqviJ+fMS+GFjKW1bW7h94JuEtvlNg9+jsZSXlzNt2jQqKiq47777aNmyZXN3SUTOYwrrRERERERE\nfqHWrVvHbbfdxq5du7Barfzvf/9j9OjRZ1mne9dXLy/45huIiQF7VQ3L8g/g7WHl6jZBZM9KxVFd\nTfdeLQnaMAPHWrCN/55lViv/AR4AbjzDdvO2Pk7qnJc4WAJJV0Vw7Y0bsXoEntW7NIbKyko++OAD\nDh48yIgRI2jbtm1zd0lEznMK60RERERERH6B0tLSuPvuuykvLycgIIDPPvuMfv0aukKc24oVcMst\nEBDgnvrasSNU1DjIyD+AxTBIjAihYPkySnbtIu6m6/BZcCWGF+zwe4Q2PXqQArQHfrZ+0kk4HSUs\nXXQp6ct3EhgAo4Y9QlSnV8/qPRpLdXU1H374IYWFhdxzzz20b9++ubskctEZNmxY+8zMTF+73W4t\nKSnxiIyMrIqKiqoCGDp0aHFKSkoxQEBAwGUTJ07cUf981apV2TExMT1iY2Mr6uqLj4+vADhenfPm\nzcsdO3Zsq9TU1MPz2CdPnrwjMTGxgkaksE5EREREROQX5r///S9/+tOfcLlcREVFMWfOHLp3735W\ndaanw4AB0KqVe0Rd+/ZQ5XCSkXcAp8ukT2QoZTu2sXPtWiLi47EtSMG7VTU7pkfSMe1V/gxsBRYB\nfqfZ5oE97/PFlykU7HIRH+fHLf2/x7vF2b1HY3E4HHz88ccUFBQwZMgQOnXq1NxdErkoTZ8+fQfA\nyy+/HJaTk+M1adKkgrrzhQsXBqSkpBRnZGT4BgYGOmbMmBGckpJSnJ2dbQsMDHSEhoY6IyIiqrKy\nsjYdr+5j68zIyPCdOnVqeF5e3kaA7Oxs25AhQzqe6PmGUlgnIiIiIiLyC+F0OvnrX//KxIkTAUhI\nSODLL7+kdevWZ1XvN9/AwIEQFQVffw3t2kGNy8V3BcVUOJwkRoTieaiMdQsXEtC6Na35gcBWP1Cx\nwELraZksBSYCDwLXn0Z7psvJupVJzF/0PVYr3Jk8gLiec87qHRqT0+nks88+Y9u2bQwePJhu3bo1\nd5dEzo37749k40bfRq2ze/cK3n0370wfGzVqVPHEiRNbA8yfP99//PjxBePGjWsHMHfu3ICkpKTS\nM60zJiamqqSkxGPmzJn+gwcPLo2Nja1esmTJT2daz6k0fMVQERERERERuWCUl5dz++23Hw7qBg8e\nzOLFi886qJs7F2691T3ldckSd1DndJksLyimpLKGK9sGE+xpsD4tDYvFQtx1l+P3418xd8D+m9/B\nERxMCtABePE02quwL2fGxy34csH3RLT14IHRX55XQZ1pmsyaNYsff/yR/v37Ex8f39xdEvlFCgsL\ncwIUFRVZU1NTQ5KTk+3du3evyMjI8F27dm2Lfv362QHy8/O94uLiutUdGRkZJwwbw8LCnHPnzv1p\nxowZIZGRkd179+7defPmzV6N3XeNrBMREREREbnI7d69m+TkZNasWQPAX/7yFyZMmIDVaj2relNT\n4a674NJLYcECCA11h1Wr9xxkX0U1vVoH0rqFF9kLFlC+fz+XDR6Mc9oVWMNNtmQm0vmJUTwE5AKL\nOfX0163Zo5g1dyoVh6DftTFc3ScTw2I7q3doTKZpMmfOHDZs2MANN9zAFVdc0dxdEjm3GjACrikl\nJSWVvvfee8HgDtqGDh1a/OGHHwanp6f7v/baa/kAJ5sGe6zs7GxbSEiIo27qbUZGhu+AAQO62O32\nHxqz3xpZJyIiIiIichHbsGEDV155JWvWrMFisfD666/zyiuvnHVQ9/HHMGQIJCS4p77WBXU/FNop\nKK2ke7g/7QN9KVi/nj2bN9Ph6quxfP8M/u32sf8Lbzp+uJjFwH+Bh4FrT9JWTVUB874M58NPp+Lj\nYzDmvhfpfd2m8y6o+/rrr1mzZg3XXHMNSUlJzd0lkV+8fv362cePHx/Rp08fO0BycrI9LS0tOCAg\nwFk38u5MrFixosXo0aMP7xSTmJhYERgY6CgqKjq7v1CPoZF1IiIiIiIiF6mvvvqKO++8k9LSUvz8\n/Pjkk08YMGDAWdf77rswZgwkJkJaGvj7u8s37S9j28EKOge3oEuIHyV79vDTkiWERkfTtnUNts1T\ncWwA8/8tpsJqJQXoCLxwkrb27JzAF1/+nX1FJlf0DKXvTZl4erU763dobOnp6SxbtoyEhARuvPHG\n5u6OiOAO5+6//37r8OHDi8E9ui4gIMBZF96dqZSUlOKcnBxbXFzc4YUox48fX9CQ4O9kDNM0G7O+\n80JCQoK5evXq5u6GiIiIiIhIs3nrrbd44IEHcDqdtGvXjrS0NC677LKzqrOsDB5+GKZMgX79YOZM\n8K1d3SmnuJzMQjvtA3zo2TqQmspKVk6fjgFccfftON9qg7etip+2/ZYuL03mQWASsAQ43hg001XN\n90svY1HGJny8YVD/e+kUN/Ws+t9UVqxYwfz587n00ksZPHgwhmE0d5dEmoRhGGtM00yoX5aZmbk9\nPj6+qLn6dCHLzMwMi4+Pjz62XCPrRERERERELiIul4snnniCCRMmABAfH09aWhoRERFnVe+qVTBs\nGOTmwlNPwbhx4FH7L8p8+yEyC+208fPi8taBYJpkzZtHdUUFCUOHUvbezQS3rCLvvVZ0+Woyi4A3\ngD9z/KDOvj+NmV/+mm07HHTt5EXybd/QIvCas+p/U1m3bh3z588nJiaGQYMGKagTkbOmsE5ERERE\nROQicejQIe69914+++wzAAYMGMDHH3+Mf9081QZwOuGll9wBXZs2sHgx1F+ObW95Fat2HyTUx5Mr\n2gRjMQxyly/nwM6dxNx4I9bczwkIXsWhBQZh7/9AKXA/0Bn4x3Hay1p3G2lfzcHphOSbr+byK9Ix\nLI26HFSjycrK4ssvv6Rjx47ccccdWCxaFl5Ezp7COhERERERkYtAYWEhgwYNYvny5QA8+OCD/Oc/\n/8HDo+H/7CsogJEj4dtvYehQ+N//IDj4yPUDh6pZXlBMgJcHV7cLwWoxKNq2jW0rVtAmNpbW0cEw\n/Y+YB6Dwmv+jfevW/B7YCaQDvvXaqqrYxLy5V5CZVUa7NhZ+PfAdQluPanDfm9qWLVv44osviIyM\nZOjQoWf1O4uI1Ke/TURERERERC5wmzZt4tZbb2Xbtm0YhsErr7zCn//857OakpmaCqNHQ1WVe0OJ\nUaOgfnWlVQ6WFRzAy8NC74gQbFYLh0pKyJo/H7+wMLpedy0V/22Pf4jJluU96TzrQRYCk4G/AvUn\nte7c+iipaa9QYoc+V0XS58YNWD0CG9z3prZ9+3ZmzJhBq1atuOeee7DZzp9daUXkwqewTkRERERE\n5AL27bffcvvtt3Pw4EF8fHyYPn06gwcPbnB95eXwl7/Am29CQgJMnw6dOx99T0WNk4z8/RgYJEaE\n4ONhxelwsGHOHDBNetx2G2Wz/0hg670UT/fiks++ww78BugKPFdbj9NRwpJvepCxIo/AQEgZ9hiR\nnSY0uO/nQkFBAR999BHBwcGMGDECb2/v5u6SiFxkFNaJiIiIiIhcoKZOncqYMWOoqamhVatWpKWl\nkZCQcOoHT2DdOvcmEj/+CH/7G4wfD8cOGiurdrAs/wA1LpM+kaH42dz/rPxp8WJKCwu5NDkZz/LN\n+JS/jXM91Dw6Hw9vbx4FCoDvAB9g/+53+OLL37Jrt4v4OH/6D1iBl2+3Bvf9XCgsLOTDDz+kRYsW\njBw5El9f31M/JCJyhhTWiYiIiIiIXGBM0+Tpp5/muefcY9Ti4uKYM2cO7du3b1B9LhdMnAh//zuE\nhcHXX8MNN/z8vsLyKlbsKsYwDK5pF0KQtycAu7Ky2LVxI+0TEghv35bKN7rjaYWt5ffQ9brrWAC8\nBTwGXOlysmZFIgu+XY7VCncOvJW4y9Ma9kOcQwcOHGDatGl4eHgwcuTIs9q0Q0QaT1FRkTU8PPyy\n2NjYirqywMBAx7Jly7b079+/w9ChQ4tTUlKKAQICAi6bOHHijvrnq1atyo6JielR//n4+PgKgMzM\nTF+73W4tKSnxiIyMrIqKiqqaN29e7tixY1ulpqaG1N0/efLkHYmJiRU0EoV1IiIiIiIiF5Cqqiru\nv/9+pk+fDkC/fv349NNPCQxs2Bpve/bAfffBV1/B4MHw9tsQGnr0PaZpknuwgvWFdvxtHlwdEUwL\nT/c/J0v37ePHRYsIjoigQ+/elLydRGBYJflvh9F10XRKgNFADPC3kmV8knYDP26t4pL2HgxOTiUg\n9LaG/xjnSElJCe+//z4ul4tRo0YRXH+XDRFpdhEREVVZWVmbji2/8cYb7QsXLgxISUkpzsjI8A0M\nDHTMmDEjOCUlpTg7O9sWGBjoCA0NdZ7oeYCXX345LCcnx2vSpEkFABkZGb5Tp04Nz8vL2wiQnZ1t\nGzJkSMcTPd8Q2ldaRETkF6C02kFxZXVzd0NERM7S/v376dev3+GgbvTo0cyZM6fBQd2cOXDppZCe\nDpMnwxdf/Dyoc5kmP+y1k1lop7WfF9e2Dz0c1NVUVrIhLQ1Pb2/i+venct27BPoto3K+QdC7qwD4\nC7ALeD73Wd576xq2bqvipuviGHlv+QUR1JWXlzNt2jQqKysZMWIE4eHhzd0lETlNo0aNKk5PT/cH\nmD9/vv/48eMLNm7c6Aswd+7cgKSkpNIzrTMmJqaqpKTEY+bMmf4AsbGx1UuWLPmpMfutkXUiIiIX\nsbqREBv22XGZEBXgQ/dwf7w9rM3dNREROUNbt25lwIABbNmyBYAXX3yRxx9/vEE7vh46BI8/Dv/9\nL8THw0cfQbfjLBdX5XSxoqCYokPVdAlpQVyY/+H2TNMk+6uvqCwtpeedd+JlKcWx5neY+6Hg0hfo\nGB3NPOBdYMiW/7Bx+jOEhxkMH/oiraMeP4tf4tw5dOgQ06ZNw263M2LECNq0adPcXRI5vy2/P5KD\nGxt3Mceg7hVc9W7eyW7Jz8/3iouLO/y3WHx8fMX06dN3hIWFOcE9VTY1NTVkyZIlP82YMSM4IyPD\nd+3atS369etnP97zJ5vWGhYW5pw7d+5Pb7zxRvhDDz3UPjIysmrChAkFmgYrIiIip1TtdLF2Twm7\nyipp1cKLQC8PthaXs6usktgwfzoE+WJpwD/wRETk3MvIyGDw4MHs378fLy8vpk2bxpAhQxpU18aN\ncM897s9HHoF//hO8vH5+n73q/7N353FR1fsfx18HhmHfUXZUVAQEEXOPXFKT3Nc0+XkzzdLMNK83\n08yStFtezdTSzMosRS1TyxW3chc1FYdFUXBBRGUf9mFmzu+PIwSuYJhW3+fjwSM9Z86Z74w9Zjif\n8/1+3mUcTsuhWG+gpZs9PvZVr78v//YbmSkpNO7QAQd3N/IXeWNrZ+T8r01pFP0WucBIfT5ueZdp\nsnYKbVq40LW7BpXa7YHG/WfT6XRERUWRmZnJ888/j4+Pz6MekiAId3GvZaxPPfVU/jfffOMISqHt\nueeey1m1apXj/v37bRcuXHjlfsffKiEhQe3k5KSPioq6BMqy2B49evhptdpTtfV6RLFOEARBEP6G\nsop1HL2aS4neQHAdWxrYmlOi1VLHwYwzBXpO39CSkpVPgI0pjmZ/ra4YkiRh5eCAZPLXGrcgCMKD\nWr16NSNGjECn0+Hi4sLPP/9Mu3btanweWYbPPoPJk8HBAbZvh+7d7/zYawUlHE3PxVSS6ODtjJNl\n1UjYnNRUzh88SN3GjfEODSV/82vYulwld6UZ9TccRTbqiLi+mRuufXht2whGDIygYcDXD/LyHwm9\nXs+aNWtIS0tj8ODBNGzY8FEPSRD+Gu4zA+5R6Natm3bChAn1IiIiMgB69+6tnTFjhqednZ3BxcXF\nkJmZWaMlJzExMdbLli1zOXTo0DmAsLCwInt7e31mZqZp+Uy+P0oU6wRBEAThb0SWZZKyC0nIzMfK\nzJSOPs5YlBRycMV6inLyUJsZkAErV08KA0I5ZrDG7MoFLM7GYqIrfdTDrzZ7Dw+a9e6N2tLyUQ9F\nEAThoZFlmQ8++IDp0xog4TwAACAASURBVKcD0KRJE7Zs2fJAhaOMDBg5EjZvhh49YPlyqFv3zs95\nLqeQuIx87M1VtPN0wsqs6nVsdmoqms2bsXJwIKBbNwzXj2GTvRjDaSh+dT1S8S7eO/glW7v9TL+4\nj/hvn0VY2bV9oPfgUTAYDPzwww9cuHCB/v37E3Cn9cGCIDxWbl3GCrB3794kFxcXQ+/evbUjR440\njYiIyAFldp2dnZ2hQ4cO2gd5rhdffDEnOTlZXfn5IiMj02qrUAcgybJcW+d6bLRs2VI+fvz4ox6G\nIAiCIPypSvQGjqfncqNIh5etBaGu9qSfv8bsty7z/e4Q9EY1I/8vl5dH5OLiZMAgwxXUpKHGBPCh\nFHfKeNxXxpYWFJB88CDmtrY079sXK5HIJwjC35BOp2PMmDEsX74cgI4dO7J+/XqcnJxqfK4dO5S0\n15wc+N//4LXXuONnvcEoc/J6Hpe1xXjaWPCEuz2qW2YxX42P58zu3Vg6ONC8b18srdWUfO6ChVRE\n4pE+GCaW8sP+GOaPisfVtBiNRX0sTP46fVKNRiMbNmwgLi6OHj160KpVq0c9JEF4rEiS9Jssyy0r\nb4uNjb0YEhKS+ajG9FcWGxvrEhISUv/W7WJmnSAIgiD8DVwvLOV4ei56o5FQV3scseSdSRks/tKR\n/CIPunYpw9HJlEVfOPPld8688gr85z/Q2l1Jio29nseFIolscxtC6trjYqW+/5M+QvZubsRu2sSx\ntWtp1qsXjl5ej3pIgiAItSY3N5eBAweyZ88eAIYPH86XX36JWl2zz+bSUpg2DT7+GJo2VYp2wcF3\nfmyJ3sCRtByyS8oIcLbB39mmSnCFLMukHD7MxaNHcfT2JrhnT8wsLNB+0xE7pyIurbMl8fndaDYX\nsmPYtxTZuLFGMsHigd+FP58sy2zZsoW4uDi6du0qCnWCIDwyD7XZiyRJLSr/WZIkWZKk5Js/S29u\nHyRJUldJkt6s9NhqbRMEQRCEfzqjLBOXoeXglWzMTU1obufCik8sqedj4KOFdWkekMPBfaXs3GXG\n999DQgIMHAgLF0KDBjBuHORcU/GklxNtPBzQGYzsS83ieLrS7+5xZe/hQauhQ1FbWnJy/XrSE6vV\nD1gQBOGxd+HCBdq3b19RqJs5cyYrVqyocaHuzBlo21Yp1I0bB8eO3b1Ql1tSxi+XssgrLaO1hwMB\nlRJfAQx6PXHbtnHx6FE8mjaleb9+mFlYUHJ6BXbqfSRnwYYO+cQlFqLqM4ojjYczTTLhiQd+F/58\nsiyzc+dOTpw4wVNPPcWTTz75qIckCMI/2EMr1kmS1BX4odImJ1mWJVmWGwKDgY/Ki3myLO8Ccm8W\n9Kq17WGNWxAEQRD+Kgp1evZdziIpuxBHgzW/fOVCsyYqZs6UCK5/ke8/PcyvMa60f+r3iD9/f/j2\nWzh7FoYPh2XLoGFDGD1aouSGJd0a1MHPyZpUbTE7LmSQnFOI8TFtmWFpb0/LIUNw8PQkITqalMOH\n+Tu29xAE4Z8jJiaGtm3bkpiYiFqt5rvvvmPGjBlVCmf3I8vwxRfQogVcuQI//wyffgp3a/GZll/C\n3stZyMh08HHBy7bqA3VFRZz88UduJCXR8Mkn8e/aFRNTUyi+hnR0BLvNYFUOmEjQ/1/v8mnolzQD\npv+B9+FR2LdvH4cPH6Z169Z07tz5UQ9HEIR/uIe2DFaW5V2SJKVU/nul3S1lWf5CkqSPgJ03t6UA\nXQHnam478bDGLgiCIAiPu7T8Yk5cyyM3WyLmexdWfGFGQQF0aZvKc2F7eWawH/VatVUu8Ix68g+O\nICd7c8XxZsA7vWBkGy8+3/A6K7/9F8uXm9H3qXWMGzSPpg11XK77NrHGMJIuH8Xn+vvYFj8eX70S\nEnXqTcQiZAZmFhY079ePM3v2cCEmhqLcXAK6dcNUJTp9CILw1/Ljjz/yf//3f5SUlODo6MjGjRvp\n0KFDjc6RlQWjR8OGDdCtG6xYAe7ud36sLMuczS4gIbMARwsz2no6Yqmq2luuMDub2J9+orSggKCe\nPXFt3FjZUaYl7Sd/turhagI0D7Ij/NljjLLyIxPYCjzezRR+J8syR44c4ddff6V58+aEh4fXqDgq\nCILwMPzpv8nenHH3/c2/OgDZlXY712CbIAiCIPzjGIwyp29oOXG+hOhv7diyypLiYomB/XX0Dd2K\np30qgc88g2uTJsoBpdmc2eHLlrN5FBTe6Yx5+LZ+hdcC3uXQoclsPjSGDXsHExi4jg4d3sevpTfu\noTM5W28VORfWcu3UbPSlj75/cJ0r79Hr2hp8up3CxNScgK5dsbK3J/nQIUry80VSrCAIfxmyLDNv\n3jzefPNNZFmmYcOGbNmyhSbln+PV9MsvyozpGzdg7lx44w0wucs6KoNR5rdruVzJL8Hb1oIWbg6Y\nmlQtUJUnvkqmprQYNAj7m1U/Y+4Zju9tyq4UIyoVDOwRTlCrbWwEVgHvAqE1fxv+dJmZmWg0GuLi\n4sjOziYgIIDevXuLQp0gCI+FR3Hbudsts+wEQRAEQagGbWkZW09qWbnEgl3f16VMBxEREuNGXqPk\nzEYkSaJZ74E4eHgAUHx9F7/u68bRBKjjDGHOnpiYWt31/EOaLSVH+z2rd4zgh53D+TzhOTqE7mJE\nv3k4h7WAekNw8u6B/ZWvsbn+ExKPpqedTp/Jkewcvj1+ho6lNjzZNQUTa2/qt26NpYMDCdHRHF+z\nhub9+omkWEEQHmt6vZ7XXnuNpUuXAtC+fXt++uknXFxcqn2OsjKYMQM++gj8/JRlry3u0TSoWG/g\ncFoOuSVlNHWxxc/J+rYCVXniq5WDAyF9+2Jpbw9A9tmPiI55i6QL0MATOvt9iHerKWQCrwDNgWk1\nfA/+TFqtlvj4eDQaDenp6QA0aNCAsLAwmjVrhsndqpuCIAh/skdRrKv81ZELlGePOwBZN/9c3W0V\nJEl6GXgZwMfHpxaHKwiCIAiPlizLHI4vYfaHRnb+4ITRAP/6l8TUqWCjTyRx504s7e1p3q9fxQXV\npd+GseXIajIyoXUDaOP9PU6dB1fr+Z55DT7OgUWL4JNPujLy3a507w7/fkuPZcM8btSfgNzk3zSv\na4/zI0qNrbdiCgeNc9hzSk9Kpg/PdviIuo3fxNXPDwtbW2J//plja9bQrHdvkRQrCMJjSavV8txz\nzxEdHQ3AkCFD+Oabb7CwqH5+6vnzMGyYEh4xejTMnw/W1nd/fHaxjiNXc9AbZNp6OuJhU/W5ZFkm\n5dAhLh47hpOPD0E9emBmYQGyTNyvzdh+PI6SUujiIdGqy1nMGyjLYsejLIPaweO3/LWkpISEhATi\n4uK4cOECAB4eHnTv3p2mTZtia2v7iEcoCIJwO+lhNmKWJGmnLMvdKv3dF1havu1mUER5/7o3gfIZ\nd/fdJsvyXRvntGzZUj5+/PhDeU2CIAiC8Gc6l2xkyntlbFqrXP4M/5fM9GkmNGggc+HIES7ExODo\n7U1wz56YWVhQXJhHzC/1OXAqF0sLCC804YTJe6SamuLk5ET//v1xdXWt9vNrtbBkCcybBxkZ0KmT\nzNh/67Dxz6XEYMTHzpKgOrZY3NLnqDaUAanAxVt+UoEngJEH9lN0ugM7csHUFLq1CaRFp3gAivPy\nOLVxI8V5eQR07Yp7YGCtj08QBOFBpaam0rNnTzQaDQDTpk3j/fffr/bMLllW+tG99hqo1UpY0MCB\n93lObTG/XcvFQmVKO09H7M3Nquw36PUk7NjBjaQkPIKCaNK5MyamphRrz7AruhknEsqo6wLdExxo\n8OFlpJtFrh+BQUAk8E4N34eHpaysjHPnzqHRaDh37hwGgwEnJyeCg4MJDg7G2Vl0VRKEByVJ0m+y\nLLesvC02NvZiSEjII+uTkpCQoB48eHDD+Pj4xPJtY8eO9WzYsGHp5MmTMxMSEtQvvfRSvby8PBVA\nSEhI0cKFC6+4uLgYMjMzTevUqdM8MDCwqPzYkJCQIoDY2FgrrVZrmpeXp/L29i718fEp3bZtW8r0\n6dNdN2zYUD6hjKVLl14KCwurOH7u3Lkuubm5prNmzbp+v7HHxsa6hISE1L91+0ObWSdJ0iCgpSRJ\ng2RZXldpV+XQiROSJLW82ccut7wAV91tgiAIgvB3df68zPR3daz7Xo0kqenWK5PuHQ8iSZf5dkUW\n9VQqfGxtiU1NZfXKldyYNIl2T+QT3jOHS1fAvz4EzYcOsUaymVFx3tdee42uXbsybNgw+vfvf98Z\nBXZ2MGWKckG4bBnMmSMxpLc5bdvVZfj4YgjNI72ghEAXWxo4WGFSg14/euAKSgHuArcX5a4AxkqP\nNwG8ADfgE2B+2FP0bpbH8PkDyHbezaa9CVxIN6N7j+PY2IfQcsgQNFu2kLBjB0V5efi2bSt6EQmC\n8MidOHGCXr16kZ6ejkqlYunSpYwcObLax+fmwpgxsHYtdOqkJHx7e9/98bIsk5BVwNmsApwt1bT1\ncMD8lhssuqIiTm/aRF56Oo3CwvB54gkkSSIlfhLb9swnMxvaNoKW37bEedNBpUIIZABjUZZOvVXj\nd6J2GY1GLly4gEajITExEZ1Oh42NDa1atSI4OBh3d3fxHSAI/0CZmZmm3bt391u9enVKeUFt7ty5\nLh07dvQrL+55eXmVVi70VTZ37lyX5ORk8yVLlqQBHDhwwGrFihV1UlNT4+D2QmH79u0bHz582O7t\nt9++8kfG/VBn1j0qYmadIAiC8DjR6/VkZ2eTlZVV5SczM/O2bVev2pCWNoLS0oGo1BJhPTK4lDSK\nlPgtANhZWzNz5EiCfX35avNmVu/eDcDyxbZc1+ZjMEC4BxS8CD0AMwcHnJ2dcXJyIikpiby8vIpx\nWVpa0qdPH4YNG0Z4eDhq9f0XL5WUwPLl8OGHcPkyNA+VGTy2AL+wAhwtVVWWxuqBNKoW4CoX5a5A\nla53EuAJNADq3+HHGyXFlpvHLga+QOmNEZQYT7j2Yyx2R+FiVcIznUYR0PxLjAYDZ/bsIT0+Htcm\nTURSrCAIj9T69esZPnw4RUVF2Nvb8+OPP9KlS5dqH3/gAEREwNWrEBkJb76pzCy+G73RyPH0XK4W\nlFLP3pJQV/vbbqoUZmdzauNGdIWFNA0Pp27jxujLCtm3y4+Dx69ibQX99OB2cghWq1ZVecIhwAbg\nNyC4Zm9FrZBlmbS0NDQaDfHx8RQWFmJubk5AQADBwcHUr19f9KEThFp235l16SO9KY27e5PkB2Ee\nVIT716l3232vmXW5ubmmALfOcmvatGnA0qVLL/n7+5eGhoYGlBffbnVrsS4zM9PU19c3+Ntvv03u\n169ffvk2FxcXQ+VjHtuZdYIgCILwd1RUVHTXQtvdCnGVC2R3FwC8DQxFZSbT64VivBr+zHdzx1GQ\nl4tKpSKocWOmDBmCo40N0YmJWDdowOxZo2ns+w0JSfl4usOAEyBHd8B4JYpcV1dUlQpTJSUlbN26\nlaioKDZv3kxxcTFr165l7dq1ODk5MXjwYIYNG0ZYWNhdL24sLGDsWBg1Cr5dCbM+kHj7ZVu8Aq0Z\n8LKW7G5ZJDlasqaOLYkq09uKcR4ohbcwbi/KeVP9XkdewAcoS65WAQsaN2Gu6iscgz+iZdxSUn9Z\nzDOpdejcPUlJinVwIPngQSUptlcv1Fa1+zukIAjCvZw7d4433niDLVuUGy/16tVj69atBFZzib5e\nD++/D7NmQYMGcPAgtG5972OKyvQcTsshr1RPs7p2NHSwum1mWXZqKppNm5BUqorE1+tXVrM1OoLL\nV2QCfKHXclC1/A/q1R9BpeN/AL4HZvHnF+rKk1w1Gg05OTmYmpri5+dHcHAwjRs3rvLdJwjCP0NC\nQoJV06ZNA8r/npqaaj5jxowrKSkpFt26ddPe+viQkJCic+fOmfv7+5deuXLFvPKxty5rrczFxcWw\ndevWpMWLF9cZP358PW9v79I5c+ak3e3xD0p8igmCIAjCHRiNRr788ku+//57MjIyKgpvJSUlf/jc\nNjY2ODs74+zsjFrdkrS0F0lNbY1abSB8WD59RxVR1+Qqrmo//hNxAmdnZ/R5eWg2b0YyMSGkd2+6\ne3hwRjOO6N2LSTwHTwVBx6kgd3wJ1Z7P7zjVwsLCggEDBjBgwAByc3NZv349UVFR7Nmzh+zsbJYu\nXcrSpUvx8fHh+eefZ8iwYdRp1uyOs+IuquHySND/C1gLV2absHCiAysa2hLxUj5T+mSQ6W6LjYMV\n9SWpohhn/offvaosgZeAUSoVvxYXM//EGTa3n8qelm8SHb+Onlt78mpIBPVbjcPS3l5Jil27lpC+\nfbF2crrf6QVBEP6Q/Px8Zs2axfz58ykrKwOgS5curFy5Ejc3t2qd48IFZTbd4cPwwgtK+M/9MhGy\ninUcScvBKMs86eWEq/Xtn753Snw9eqADew7sR5ahVwCEjgYiP8FkwoSK45KB2cC3KP1Dp1TrVfxx\nWq2WuLg44uLiSE9PR5IkGjRoQIcOHfD3969RMIcgCA/RPWbAPUyBgYFFt86sA/D19S1JTk6+7X7w\nxYsX1W3atCmEey+DvVVCQoLayclJHxUVdQmUZbE9evTw02q1p2rnlShEsU4QBEEQbhEXF8crr7zC\noUOH7vk4ExMTHB0dcXZ2xsXFpaIAd6ef8v1OTk6Ym5tz8qQyS2LDBrC1lXl5oo52Q3LwcDWhtXsd\nHCw8Kp4nPSGBxF27KhJfVZZGoje7EXPiOg728H8+4DsCjJNmYPree1VmPtyNg4MDI0aOpPvIkRzP\nzOSHY8f45cIFrpqZcblBAz6qX5+P7pCu7o4yC64NyvKn+ipoEAE+z8Nv62HOLFMWT3Vgw2IDfV7K\np++QTEK9H35qrAR0trSkc5s2nJswgc+b1mfZyFF8FPw8K68eZWTCNKY1iaTFoEHEbtrE8bVradar\nF473avQkCILwgIxGIytXrmTKlClcu3YNgPr16zNv3jz69+9f7d5pUVHKbGaA1ath6ND7H3Mpr4iT\n1/OwVJnS3tMZW/Oql3yyLJN86BCXbia+BvfsSVFRHJvXhJFwtgQvd+iRBa7/MkFavhLp+ecBOI8y\ni24lykXkOJT54A/zgrK4uJjExEQ0Gg0XL14ERJKrIAg1M3HixMzQ0NCA8PDw/Mo96wACAwN1mZmZ\nNUpJi4mJsV62bJnLoUOHzgGEhYUV2dvb629dCvtHiWKdIAiCINxUXFzMrFmzmDNnDnq9HoChQ4fS\nunXrOxbgHBwcatwL59gxpUi3aRPY28O06UbaDs6lzLwUHztLmrvaobp5TlmWSTlyhIuVEl+vpX/F\n9h3juXpNpnkTCDsETkuABZ9h8uqrd3xOGTgI7KNq/7hLgA7AxQWefRYAF70e8/R08k6douCHH+Di\nxYqf9l5eDB88mMGDB9+epGcCTQbB8wNhyxZ4/30Tlr7rwI9LDPQdVcCIkUW09Hk4qbFVmJnReMEC\n5r7xBjM9vVgY9QKLWr/O+x4fsKgonTF2xbwydCjpGzdycsMGkRQrCEKtO3bsGOPHjycmJgZQ+oNO\nnTqVyZMnY2lpWa1zaLVKsM9330H79rBqFdSvf+9jZFkmLiOfczmF1LFS08bDEbVp1e8og15PQnQ0\nN86dq0h8PRs/lujdy9DmQ8cQaPkVWO0yx+Tnn+GZZ0hCKdKtQmlVMB54E+XmzcNQVlZGUlJSRZKr\n0WjE2dmZTp06ERQUJJJcBUGoERcXF0N0dHTSrWmwP//8c8r9jr2TF198MSc5OVldedlsZGRkWm0W\n6kAETAiCIAgCAHv27OGVV17h/PnzADRq1IilS5fy9NNP18r5Dx9WinTbtoGjI0yaBINeLOF8US5G\nGZq72uFj/3sfNYNeT+LOnVw/exb3pk3x69iBIwfbsO9wLKam0DMQfBZK2P5qirRyJQwZcttzlgHr\ngI+B8m/Fuigz4+4U4lAPZVkpKBd9R48eJSoqijVr1nDjxo2K86pUKsLDw4mIiKBPnz5Y3aH/myzD\nrl0Q+b7Mgf0SDi4G+o8qZOI4U4K8apYa+0BkGT74AN6ZTsYwiZXjnmFJnQmca/QsZsYyhsomhP/y\nC65xcdRv3Rrfdu1ESqAgCH/ItWvXmDZtGsuXL6/YNnToUObMmYN3DWbxxsTAsGHKfZIZM+Dtt+F+\nLdjKDEaOpudyvbAUXwcrmtW1u+1zVldUROzPP6O9do1GYWG4BfmwZ0cQR09m4OgAfbzBLdIE9SU7\nTLZv50ybNswCVqO0MBgL/Aclkbu23S3JNSgoSCS5CsJj5r4BE0KN3C1gQhTrBEEQhH+0zMxMJk+e\nzIoVKwClEDVlyhTefvvtas+AuJf9+5XEvl27lAls//43vDJG5nKpluTcIhzMVbT2cMRG/fuVmK64\nmNObNpF39SoNn3wS23r5REeHcy6ljAY+EG4GVnNUWCerkTZsgGeeqfKcecAyYCGQCvgBbwARwIMs\nGNLr9ezevZuoqCjWr19PQUFBxT5ra2v69+9PREQEXbt2vWNT73374L2ZRn7ZY4Ktg5FBo4p5Z7IZ\nDdwe7tJYAJYuRR47lpwOJpRMMLC0qAnb/MajCX2BEpUNLXJy6HXwIP1NTQkWSbGCIDwAnU7HwoUL\niYyMJD8/H4CQkBAWLlxIhw4dqn0eg0FJ2n73XfDyUmbTPfnk/Y8r0ClBEgU6PSF17fB1tL7tMbcm\nvpaYbGXbzje4dl0m1B/aXgeHOaaY4caZvXt5v2FD1qDcwHkVmAy4VvuVVM/dklwDAwMJDg6mXr16\nIslVEB5DolhXu0SxThAEQRAqkWWZlStXMmnSJDIzld8t2rVrxxdffEFQUNAfPDf8+qtSpPv1V6hb\nF/7zHxgzBmS1nqNXlXS+ho5WBLnYYWry+2yBwuxsYn/6idKCAgK7d+d63jR27P2RkhJ4uhn4xYPN\nZ2rMi6yRtm2DNm0qjr2AUqD7EigAOgOTgB5AbV3uFBUVsWnTJqKioti2bVtFw3SAOnXqMGTIECIi\nImjTps1tsyAOH5Z5Z6aR3dGmWNkaeX6UjplvmeHp+pCXxq5bhxwRgbaRhPx6KUec4dfz9iSFjeRY\n63e4onakrlbLkAsXmNa4MW4iKVYQhGratm0bEydOJCkpCQBnZ2dmzZrF6NGjMb1D0M/dpKbC//2f\ncnNj6FBYsgQcHO5/XEZRKTFpOchAGw9H6t4hSCL78mU0mzdjolIR3LMHmrM92HdYg5kZ9A4Eu73g\nttSUxA7hzFq7lu+trbFC6Un3b5QZ2bUpIyMDjUZDXFxcRZJrkyZNCAoKEkmugvAXIIp1tUsU6wRB\nEAThpuTkZMaMGcOuXbsAsLOz46OPPuLll1/+Q3fxK5Z+RsKBA+DuDm++CS+/DFZWcDmviJPXtZhK\n0MLdAQ+bqsl1OampnL6Z+OrfvQVHTnTjpCaPOi4w0AtKfgXPr9WY2tVF2rEDApRWGYdRlrquRynK\nDUWZSdfigV9J9WRlZbFu3TqioqLYt29flX2+vr4MGzaMiIgI/P39q+w79puRae8a2LXFDEtrI8NH\n6Zk51Qw3t4e4xGn3buR+/Si2N6B9pZjCUPjpLOQXm2DSYxTRTT7moLUN5no9w/R6/m1hQdOHNxpB\nEP7izp07x6RJk9i8eTMApqamvPrqq7z33ns41TBpet06GD0a9Hr47DMYPrxaOUGk5BYSe12LjVpF\nO8+qM7TLXY2L48yePVg5OlL/KXt27xvE+Qtl+PpAPzu4tl6iOD6IWXM/Zl2XLlhLEq+hFOlcavQq\n7q08yVWj0XDt2rWKJNfg4GCR5CoIfzGiWFe7RLFOEARB+McrKytj7ty5REZGUlJSAsCgQYNYsGAB\nHh4e9zn67mRZ6UUXGan0GvLygrfeglGjwMIC9EYjp65ruawtxtlSTSt3B6zMqs64qEh8dXDAOTSB\nnXs/IjMLWjc1pVuZgbMbTQjYaIKJbyOIjsbg48MGlCLdYcABeAWl8bfnA7+SB3f58mXWrFnDqlWr\nOH36dJV9oaGhREREMHToUDw9fx/d0ZN6pkca2P2zGjM1jBhl5J2ppng+rBdw/Djys89SZijm0tBC\nPDvDplKJM8kyPt6muHZcw+KyIHY2aoROpaIr8DrQk9qbmSgIwl9bfn4+s2bNYv78+RUzi59++mkW\nLFhQ41nZBQUwcSJ89RW0aqUkvzZqdP/jjLLM6RtaUnKLcLU2p7W7A2a3BEnIskzywYNcOn4cJx8f\n8FjO7gObKSmBLsEmtMwxsvlQK1Z2n8L6gQOxlWVelyTeAGorvqG4uJiEhATi4uIqklw9PT0JCgoi\nKCgIGxubWnomQRD+TKJYV7tEsU4QBEH4Rzty5Agvv/wyGo0GAG9vbz777DN69+79wOeUZSXVNTIS\nfvsNfHxg2jQYMQLMb65Eyi0p4+jVHArKDPg72+DvbFOl6bcsy6QcPszFo0dx8HIlz+ZtDh67gLUV\n9GpiRoOMMuJX2hCytxjpiSfI37qVr52dWYCy7NUXZRbdCOBxueyJi4sjKiqKqKgoLl26VLFdkiQ6\ndepEREQEAwcOxMHBAVmWOXCylPf/K7NnowUmJjBihMy0qSb3TT58IElJ0K0bhowbnA7XE9Jfz0lH\nC3ZoSjAxgU7tniQ7fTw/enuzpVUrrpmZ0RClCPoiYPcQhiQIwuPPaDSycuVKpkyZwrVr1wCoV68e\n8+bNY8CAATUOP/jtNyVE4tw5mDoV3nsPzMzuf5zOYOTo1RxuFOlo7GhNUB3b2567cuKra9M6JGtf\n5VSclrp1oJ+HCcmlzZjpOotdnXpiV1TEBLWaiSoVNZsPeGd3S3INDg4mODi4xrMOBUF4/IhiXe0S\nxTpBEAThH0mr1TJt2jQWL16MLMtIksTrr7/O+++/j63tg8QtgNEIGzcq6a6nToGvr1KkGz4c1Dcz\nE2RZJiW3CE2GFrWpCa3cHahjVbWXkEGvJ3HHDq4nJeESkM2JtNlcTjXSpKGKnmYGjNdlUle7E3Qk\nndRhw1i4fDlf51Jb6AAAIABJREFUqNVogTCUfnR9gIfc8e2BGY1GDh06RFRUFN9//z1ZWVkV+9Rq\nNT179iQiIoKePXuiUqvZfbKQ+XNN2P2jFchK/6Zp0yQaN67lgaWlwTPPIJ8/z7EnrQiJyCXX0YJN\nmaWkpcs0bWJJffNZZKWqONu9O2v9/DgkSdigFOzGA7U9JEEQHl/Hjh1j/PjxxMTEAGBpacnUqVOZ\nPHlyjYOIjEaYN09JeK1bF1auhE6dqndsfqmew2nZFJYZCHWzp7797f01Kye+uoTGsf/0V2RlQ5tm\nljhZ+vNOg3fZ0aQv9rm5TDxwgAnPPotjDXrr3fk1GUlJSUGj0XDmzBl0Oh22trYVSa5ubm4iyVUQ\n/kZEsa52iWKdIAiC8I+zYcMGxo8fT1paGqCk8y1btoxWrVo90PkMBqW30KxZEBcHjRvD9OnK7IjK\n/bB1BiO/XcslvaAUV2tzWrrZY66qejGkKypSEl/T07EMXMn+k79hNEKXFm60yrxGahLot/uTbbDm\n408+4fsnnwRJYhBKka51DceemZlZpVj2Z9Pr9Zw8eZK9e/cSExODTqer2GdpaUn79u3p2LEjfsEh\nxN2wZc3X9uxca4W+DJ59Np8xY7Jo3Pj3Y9zd3bGz+wPz3LKzoWdP5KNHOdW+Hj79L2BfF361d+DQ\nyVzs7ODJgN5oE5+mrp8fhc88w2cqFWsBPUpoxwSgKyAuQQXh7+n69etMnTqV5cuXV2wbMmQIc+bM\nwcfHp0bn0mqVwtySJcr3x4ABsGwZVHei2fXCUo5ezcFEkmjr4Yiz1e1p2gVZWUpAUVEuxvrzOXzy\nCtbW4NPuWVb4jGWzZ2/stXlMmjuP162scJgypXrN8e6gPMn19OnTxMfHU1RUhIWFBQEBASLJVRD+\n5h7HYl1CQoJ68ODBDePj4xPLt40dO9azYcOGpZMnT85MSEhQv/TSS/Xy8vJUACEhIUULFy684uLi\nYsjMzDStU6dO88DAwKLyY0NCQooAYmNjrbRarWleXp7K29u71MfHp3Tbtm0p06dPd92wYUPFJ/jS\npUsvhYWFFQEMGzas3sWLF9WpqanmkZGRaS+++GLOvcZ+t2KdiNoRBEEQ/nauXLnC+PHj2bhxI6AU\ngyIjI5k4ceIDpczp9bB2rVKkO3MG/P1h1SoYMgRunZCQVaTjaHoOJXojwXVsaeRofduMgvLE1zJ9\nEjmun3DgcAme7hK9fPxxy0rk+D4T4h0m8vWcPuzr2BFbWWaiJDEeqFeDcd/a0Ptx4OXlhZeX1x33\nJSYmkpio/I7VpWMDeg0PI3ptXbattGbLFjsCAhLo0GE/7u7Ka6lfvz5BQUEEBgbWeHYLTk6waxfS\noEGEbt9OkqoluW2P0zU4lwZhwWzRaNgWs4lWzQ5x4/xE7PPz+ap3b+ZYWfE58DnwDBCA0tduOGD9\ngO+JIAiPF51Ox6JFi4iMjESr1QLKzZ4FCxbQsWPHGp3r9GmlQLdypdKjLjRU+fOwYdWrk8myTHJu\nEadvaLE3V4IkrMxu/x4rT3xV2Z/iovErUn8zomrTnu3NI9np1gXb/GxmLFzIpBkzsJ81C157rUav\no1x5kqtGoyE3NxeVSoWfnx/BwcE0atRIJLkKgvDYyczMNO3evbvf6tWrU8oLanPnznXp2LGjX3lx\nz8vLq7Ryoa+yuXPnuiQnJ5svWbIkDeDAgQNWK1asqJOamhoHVQuFGzdutAU4dOjQuczMTFNfX9/g\n+xXr7kZ8mgqCIAh/GwaDgcWLF/P222+Tn58PQHh4OIsXL6ZBgwY1Pl9BAfz4I8yerfQVCgpSinYD\nB95epJNlmbPZBSRmFmBlZkqnes44Wtw+86E88VVV9wdOpv6CNh3aP+HKU0XmlOVcYnz+BLZN/zfJ\n3t7Uy83lY1lmlCRVu1dacXExiYmJaDSaiobeHh4edO/eHR8fn8duKVJ2dja7du1i69atFf0EyzXy\na8KwCW/Sb2Rztq60Yet3/ixdGkiXLsU891wC+fmH2bx5M1u3bqVx48YEBwfj5+eHWXUaPwFYW8NP\nP8GIEfitXs01dRdiru6hTXcNI3ybs7skiaOxWbi7ziBAP4Tja4sI6duXmU5OTAPWAguAscBUYDQw\njpoVVAVBeLxs27aNN954g7NnzwLg5OTE7NmzGT16NKbVXC5aWqrMwl6yBA4eVIKGhgyBsWOhdevq\nT2YzyjKnrudxMa8YdxtzWrk7oLrDbLW0uDjO7tmDqt5S9icmcMm9DbGv/Y/Dzk/hVJLFC1/PZt7i\nDTjHxsK338Lzz1f7/QDIy8sjLi6OuLi4iiRXX19fOnbsSEBAAObm5vc/iSAI/yAjvSHu9nX6f0hQ\nEXyd+iBHfvLJJy4vvPBCRnmhDmDy5MmZy5cvr3PgwAErf3//0pqcz9/fvzQvL0+1ceNG2379+uUH\nBgbq9u7dmwTg5+dXOn369HQAFxcXg729vf5BxgyiWCcIgiD8TZw+fZrRo0dz9OhRAOrUqcOCBQsY\nOnRotQpUsgwpKXD4MBw6pPz39Gmlv1Dz5krRrl8/uNOqnmK9gePpuWQU6fCytSDU1f62ZD64mfi6\nZxMl7h9yPD4XB3sY9uwA1KmxvNN4JJ83HEuBnSOtY2L4ID6eAeHh1fqivltD706dOhEUFISzc21l\n+9U+d3d3mjZtyoQJE0hJSWH16tWsWrWKxMRE0tPT2b/3Vzx9GzHpf4vo9WJzdkdZs+lbG3bvfoKh\nQ1swfvwN8vNPERcXx9mzZ1Gr1QQEBBAUFISvr+/9l2Gp1co0FxcX3BYtwqZ7d7avOkC3Iafoqfag\nfpfm7Dx0iH1Za2gXeIhjawpp1rsvTt7e/AtlRt1BYCFKMu88oB/KEtmnEEtkBeGv4vz587zxxhts\n3rwZABMTE1599VVmzpxZ7VCECxdg6VL4+mvIyFCSXefOVUKHavoxXKo3EnM1h8xiHU2cbAh0sbnt\nu6w88TU17mcynRcQnfkEMf8XTbzXMziXZPLO/rdoMX8vfU5dx+T6ddi8Gbp3r9bzlye5ajSaiqAg\nT09PwsPDadq0qUhyFQThsZOQkGDVtGnTgPK/p6amms+YMeNKSkqKRbdu3bS3Pj4kJKTo3Llz5v7+\n/qVXrlwxr3xs5WWtt3JxcTFs3bo1afHixXXGjx9fz9vbu3TOnDlpYWFhRYGBgbqbY1EPHjy44YQJ\nEx54aUu1i3WSJNUHWgCtgGPACVmWLz7oEwuCIAhCbSgqKiIyMpJ58+ah1ys3r0aNGsWcOXPueYFV\nXAzHj1ctzt24oeyzsYG2bZUG4B07wtNP330mxLXCEn5Lz0NvNNLCzZ56dpZ3vKBKOXyYq8kLOK/6\ngfQ4meBAa9xC/kukyo41LVajl1T027OHyZGRtHv1VaShQ+/5ussbesfFxZGYmIhOp8PGxobWrVsT\nHByMu7v7YzeL7n58fX15++23mTZtGqdOnSIqKorVq1eTlnKefw98lnbde/Hi1Jl0jfDg5yUqflrt\nzI8/1mXMmO68/XY3ioouodFoSEhIIDY2FmtrawIDA2nWrBmenp53fz9MTGDBAqhbF5t33uHpp59m\nS9QFnu57gWB9Nl69prPt6H/Zf/oyjepPRNqRROO2r+DRtCkSSthHGHAZWAwsA9YDzVGKdkMBiz/j\nDRQEocby8/OZPXs28+fPr+il2blzZxYsWEBwcPB9jzcYYPt2ZRbd1q3Kd0WfPsosuq5d73yD537y\nSss4nJZDid5AK3cHvO1uX+ZfnviaW/gh6x1ltrbaTIpvN+rotMw5+R+6b1xCQXI32h2+iKTTwe7d\nyhfbPZSVlXH27Fk0Gg3nz5+vcuNHJLkKglB9DzYD7o8KDAwsurVnHYCvr29JcnLybctdLl68qG7T\npk0h3HsZ7K0SEhLUTk5O+qioqEugLIvt0aOHn1arPQVQ3s/uXgW/6rhvsU6SpFCU1R1ZwAlgF+AL\nvCVJkiPwX1mWTz3oAARBEAThQe3cuZMxY8aQkpICQJMmTVi6dOltPYVkGVJTqxbmTp5UetGBEhQR\nHg7t2kH79tC06e3LXG9llGXiM/I5l1OInVrFU95O2JnfvvzSoNcTH72NzNJxHElNxUQl4TB4Gp96\nRPCLQyBWpQX0/eUX/vvBRzSKiYENG+CZZ+74nOUNvTUaDfHx8RQWFmJubk7Tpk3/Vg29JUkiNDSU\n0NBQPvzwQ/bt20dUVBTr1q3j9R5P0XfUq3R/IYLuLxj44TMbFi+24ptvJN58swFvvNGAHj16cP78\neTQaDSdOnODYsWM4OjpWJBPWqVPnTk+qpIW4uKB+9VV6tW3L9j31CW3+C27SLIaFvstBrzXsP3KW\ndPNFSIkHKc5diG/79hVFQB/gQ2AGsAplieyLwJvAKyjLZT3+nLdQEIT7MBqNrFy5krfeeov09HQA\n6tWrx7x58xgwYMB9b3bcuKHMoFu6FC5eBDc35SNk9Gjw9n7wcV0tKOH41VxUJhIdvJ1xsry9nUJp\nYSGxm6PY47qBpYEzudjgaZzLtHyUsIhxmrfQbC3GzGUC7TcvV+4+7d8PgYF3fR/ulOTapk0bkeQq\nCMLfwsSJEzNDQ0MDwsPD8yv3rAMIDAzUZWZm1igSOyYmxnrZsmUuhw4dOgcQFhZWZG9vr8/MzDQ9\ncOCA1Z49e+yqW/i7l+rMrHtCluXnbtm2G+XGMZIkjQZEsU4QBEH402RkZDBp0iRWrlwJgJmZGdOm\nTWPq1KmYm5tTWqoU4yoX524GwmJpqfQMmjxZKcy1bQt3qt3cS6FOz9H0XHJKymjgYEWzOnaYmtx+\nMaMrKiJ224ckFXxA4hUz0jq/wsFWH3HOzB7PoiuMjZrCv1IdaPvJQtDpYM8eaNPmjq9Xo9EQFxdH\nTk4OpqamNGnShKCgIBo3bvy3buhtampK586d6dy5M59++inbtm1j0aJFvLLkE57o2JXwYS/Q419P\ns2q+He+8Y8Gnn8m8954pL43yx9/fn9LS0ooefgcOHGD//v24ublVFO5uS5QdMwacnTGJiODZAn8O\ne40l6/oSgqSZPOk2iHrPRbBt17v8En+CEMMz5G/9lODuwzGt9G9ghdK/7iVgD0rRbjZKIW8wymy7\n2/+VBUH4sxw7dozXX3+dI0eOAEoI0VtvvcV//vOfe4bVyLLSg27JEvjhBygrg06dYM4cpU1Cddtl\n3vncMknZhcRn5uNgbkY7T0cszW6/fizIyuKb05+zMOxJznluxaH4Gh/mJTBmZx9sipLZsdaa0Pbv\n4zZrFtSrBzt2wC3JtbIsc+XKlYobP+VJruWfiz4+Pn+LGz+CIAigLFuNjo5OujUN9ueff055kPO9\n+OKLOcnJyerKy2YjIyPTbj6PXVxcnLW3t3dQ+b7yIIqakmRZvvcDJGmtLMtDHuTkj0rLli3l48eP\nP+phCIIgCLVMlmW++eYbJk+eTHZ2NgBPPfUUs2Z9RWZm44ri3G+/KQ2+QblWad/+91lzzZr9sQuq\nK9piTlzPQwJauNnjaXvnC7vC7GxO7nuWnWkX2Rf8KifbjidP7URodiwT4uZgP2c77Xq8j+u0aWBr\nC9HRVWY+3JrkKkkSDRo0IDg4GH9/fyws/rkLK2VZ5rvvvmPSpElkZWXh7ObByElTcPF6nuVz7Tlz\nQk39RgZmfwDPDzKtWMJcUFBAfHw8Go2GtJvV23r16hEcHHx7ouyuXcrVd926JE14nasH/kOn/np0\nFkHInb9l196uHI/NxtkJWtUbRGjXFait7t5LORn4FPga0KIU6yYAg4A/8L+jIAg1cP36daZNm8bX\nX39dse25557jf//7Hz63FLQqy89XWlsuWQIaDdjZwQsvKLX9u0xYqxGDUebE9TxStcV42VrQws0B\n1S03gGRgQ/pVZppd5rRLW2wLrvJK5iZmqrwx/3UAxfml7PipAd27jMV66lQldnbr1ip3ozIyMjh9\n+jRxcXEiyVUQhAcmSdJvsiy3rLwtNjb2YkhISOajGtNfWWxsrEtISEj9W7dXp1h3TJblVg9rYA+D\nKNYJgiD8/SQlJTFmzBh++WU/0AwLi6dp1uwVbtxoyMWLykWNWg0tWyqFufIfj1pad6g3ypy+oaTy\nOVmY0crDAWuzO1/YXD1/kHXnJvCN21g0zf4Pvak5vYvSmXhgOE8k7Wb3Fj+6P/cW1q++WmXmw90a\negcHB4uG3neQkZHBG2+8wapVqwCwsLTkgwWL0ZkMYNEHVqSlqAh6ooz3Zhvo1828yuzH7OxsNBoN\nGo2GrKwsTExMaNy4MUFBQTRp0kRJlD12DHr0AFNTbnz8MUdWvE74kCwkC0fMntlOfOoion9ZSWEh\ntAmqS5v2x7F3vff6t3xgBUogxTnAHXgOGAi0B2q0DkMQhGrR6XQsWrSIyMhItFqlx3izZs1YuHDh\nbW0TKtNolALdd98p6eChoUovumHDlDDp2lCiN3A4LYeckjICnG3wd64aJCGj9CB6K/8aJ2zdsNWm\n0TdxDh/UfxbPGyeQTr/NtYtwVNOFnsGdUU2fDl26KC0VbG0rklw1Gg3Xr1+vSHItv/EjklwFQagp\nUayrXX+kWJcNLL3TPlmWp9bK6GqZKNYJgiD8fVy9quPNNzewZs0lDIbWKDlHylWSh0fVWXOhofAw\nrju0pWUcvZqLVqfHz8maQBdbTO7Qw0cGlsUt4BPbJiTWC0etL2KEbGTiua8JODWBK+fh5LlePNuh\nD6qxYyE0lLKffuJsdjZxcXFVklyDg4NFQ+9qio6OZsyYMVy8eBGAoKAgPv38K/YdbcYnH5qRfcOU\nNl1KmPJuGU+3tsC+Um9BWZa5du1axTLj/Px81Go1/v7+BAcH46vTYRIeDnl5FC1fzvavZ9K562ns\nXUyR2n9Nnn0jtm/vzNnzOnw8JTq2XoRvs3H3HbMR2I7yC1Y0UAq4Av2BAUAnxIw7QagN27dvZ+LE\niZw9exYAJycnZs2axejRo+84k6y0FNavh8WL4cAB5TtlyBB49VWlhUJttm/LKSnjSFo2OoNMS/eq\nM7VlYAcwU5Y5LEnYa1PpcOi/jDc7SJewAxgPvojq2o/EHYEMm3/TyWCKNGcODBpE0RdfkJCcTFxc\nXMWNHy8vL4KCgsSNH0EQ/jBRrKtdf6RYdx746E77ZFleViujq2WiWCcIgvDXZDBAfPzvveb27Cnm\nypXyi5cy1OoEwsPteP75BrRvrzTxfph9r2VZ5mJeMbE38jAzMaGluwOu1rdXA0uB7/QlzC66wEW7\nAGwL0nkhbx/vuvbC/tfnMbuxidj9kOcxnaesHJAmT6aofXt2jxtH3KVLFQ29y/sFiYbeNVdYWMiM\nGTP45JNPMBqNSJLEuHHjmDZtNgs/t2Lhx6aUFMHTA4t5eXIJrfws8LKzQFWpL5PRaOTSJSVRNjEx\nkZKSEqysrHjC1ZWn3n8fVWoqxu++Izp6HYFu31M/EAyN3kAK/S9HDoSx98hxJAk6PPEE7btV//eQ\nfGAr8OPN/xYCTkAflBl33QAx90UQaub8+fNMmjSJTZs2AWBiYsLYsWOJjIy8402QixeVsIivvoKM\nDGjYUFnm+uKL4Oxc++NLyy/meHoualMT2nk64WChlOdllEL+TCAGcClMpc0vs+metpw+Xd6nnkcE\nZdHdMSuO59f1Kpz7fkXw3n3w1VdkPfccO/v04VxKCkajERcXF4KDgwkKChI3fgRBqDWiWFe7/kix\n7vit/xCPO1GsEwRB+GvIzYUjR34vzsXEKL2BACws8ikp2QMcQpJiGD++HR98MB3r2lp7VIlRlinQ\n6cnX6dGWKv/N1+kp0OkxyFDXSk1LdwcsVFUXKWYCnwMLDcVkmFpS9/ppBiQvYEajkbhbe1O2vRum\nxUnsWWeGx7CVeO3di93ixZwNDuaHvn0xs7EhICDgb5Xk+qgdP36c0aNHc+qUkn3l5eXF4sWLadeu\nNzMjZZZ+Diam0POFQga9XEgTDwsaOFjiYG5WpUCq1+srEmWTkpIw02oZvnYtbqmp5M+bR7xBhyp+\nCq26QpljZ8y6bCD1ylp27B7DlasygY3NeSb8APZONfsVqhhlNs2PwM9AHmAL9EQp3D1L+bxSQRDu\nJD8/n9mzZzN//nx0Oh0AnTp1YsGCBTRr1qzKYw0GpV3o4sVKezdJgt69laWu3brBw/hIlmWZM1kF\nJGYV4GRhRltPRyxUpsgoxfpI4CjgocvmqQNv0fjgN7QNsqFLeDwWhamU7QhHLslh2xpHWry9AcdZ\ns7DZtYsDnTuzu0MHbO3sxI0fQRAeKlGsq11/pFj3uSzLYx7WwB4GUawTBEF4/BiNkJT0ezrroUOQ\nkKDsMzGB4GBo105GpTrKmjUTyMyMAeCJJ57giy++oEWLFn94DGUGY0UhruKnVE9hmYHK34ZWKlNs\nzVXYqlU4WpjhZWtR5YLnLDAfpfdYCeCXvJWwox8zwvEG7bv+hml2DPpdPdEXadmyxhn1oFk0WLGC\noCNH+K11a1ImTSI4JEQ09H5IysrKmD9/Pu+++y4lJSUADB48mIULF1JU5Mb06TKrV0vYOxoZNDaf\nbkOLcLFVUd/eCh87S8xMq16hl5aWcubMGRKPHaPlRx/R6Px5jvTvz/X27THf+w7dnivBaF4Ps/Cd\n6NQu/BLdhJjYDOxsoVunkTQN/eqBXocOJU32R2AjSnHYEghHWSrbG7B/0DdJEP5mjEYjq1atYsqU\nKaSnpwPg4+PDvHnzGDhwYJXP8IwM+Ppr+PxzZUadqyuMHq383CNn4oHJskxOSRnpBaWkF5Sg1enx\nsbMk1NUeExOJzShFuuNAfdlIf80UbDctwMGyjM6tB9E87AfklO+QD40kL1PP9i1NsB30Bs3mzMEn\nJYVdvXtTPHp0xY0fUaATBOFhEsW62vVHinUfAmtkWT51h32hwHN3610nSVILWZZPVP474Asgy/K6\nm9sGAblAC1mW59Rk292IYp0gCMKjl58PR4/+Xpg7cgRycpR9jo7Qtu3v/eZat4acnMuMGzeOzZs3\nA2BlZcWsWbMYP358jQpasixTerMoV3mWXL5OT4neWPE4CbBRKwU5W7WqojhnqzatsjSy4rzAr8DH\nwGbAXDbyZMo3+EfPo4Uqkada/Be/llOQzy1DjhlDznUj637wJqPdQAZt347/mTNce/llHBcswPwf\nnOT6Z0pOTuaVV15h9+7dADg4OPC///2PUaNGceKExJQpsHs3eNczMnxSAaHdCjEzBU9bSxrYW+Fk\naXbbRW9BdjYlzz+Py44dHGrfnp3/z955x1lR3f3/PXP73Xu33K2wfem7dFGKKBiVZo8l+alPDD42\nHksUMTaQSEhiN5pEopKYqFFiomIXsCEbFBEQWZYO23u7vUz7/TFbYRcWxNjm/XqdPTNzZ85OuXfm\nzOd8y9ixFGx5gQuvaMPqcmL+0Uq0jDP4Yu0VFH/5HC2tcNK4FE6fWYrVltrbbvYLGShGF+5eAWrQ\nY9qdgW5xdx6QcsytGxh8t9m4cSM33XQTn376KQAOh4M77riD2267rTPLs6bpz6Jly+Bf/4JYDKZN\n02PRnX++nqDoeCKrKvXBGHWBCHXBKFFFf/6kOKzkxDvISXDwhiCwBNiM/oJ0Q8t64l49jdqqGMMH\nWzh10msMyJ+B8vkCTHt/T1kpvPrRGJQJ07n8pZdIa2ig7r77SLv5ZmPgx8DA4L+GIdYdX45ZrAMQ\nBOE29JAprUALkIw+mLtG07SH+tjmDOBJTdMGdVv2L03TLhYE4ZfoiY0ACjRN+7cgCNegDyj1a1l3\nEfBgDLHOwMDAoP8oCoRCEA7rdUf5KvNtbbBrl25NB1BY2JUEYvJkGDasy71IURT+8Ic/sHDhQoLB\nIABnnXUWf/rTn8jNze1zvzVNIygpPSzkOqYltevZZhaEbkJclzAXZzH1miTiYGLAS+gi3RYgFbik\n7nXSV16F1tDISSOTOfHE9Tg9A/C+P5eMwMvs/RJeXjuK9Evm8uPnnyd+82Z4/HG48cZjukYGx46m\naTz77LPMnz+flpYWAKZNm8ZTTz3FkCFDWbMGfvlL2LoVxoxTufaOEJljA8iahttqJi/BQU68E5u5\nm4CrqvCLX8Af/0jdrFn8e9o0lH89wU8vqSQ1U6B2wALSTllK/e4P2VhyPl/uipCRLjD7zEfJGfSL\nr3xMKnosq1fQxbsDgAhMQxfuLgCOUxJkA4NvNfX19dx1110888wzdLzTXHLJJTz44IPktJvI+f3w\nj3/oIt2XX0J8PFxxhR6PrrDw+O5PSFKobRfnGkNRVA0sokB6nI0Ml52MOBtmk8hr6JZ0XwCDgbtU\nhaHrT2Nd8ToAJhcO58RT/oOkBJDeu4AUdTMb18C7NaczcuYc5vz+91ibmxFefhlmzTq+B2FgYGBw\nBL6NYl1paan14osvHrR9+/YdHcvmzZuXOWjQoOiCBQuaSktLrVdddVWu1+s1A4wZMyb0+OOPV6Wk\npChNTU2m1NTUsYWFhaGObceMGRMC2Lp1q9Pn85m8Xq85Ozs7mpOTE33nnXf2L1y4MP3VV1/tDAb6\n5JNPlk+dOjUEMHv27AKv12vyer3m7sv7oi+xrl9DMJqmPQg8KAhCAvrAz35N07xH2OY9QRD2d8y3\nW8ZtbP+sw1rufmBN+yr70QeIk/u5rE+xzsDAwOD7gCQdP/HscPPtIX2OGqdTLw5H17TTCXFxkJYG\nF12ki3MTJ+qWdL2xZcsWrrnmGjoGWNLT03n88ce5+OKLOy2aFLVbPLluwlxAkummyWEzibitZrLi\nHcRbzZ1Wcw6zeESXoDBQDVR1qzumN6BbMY0Alkl+MlYVsXVTJYlJcMKos4jLWsxH69YyzreUgvgK\nPnkbyjzXc/Vfr8dz2WWwbRu88AL8v/93bCfa4CshCAJXXHEFs2fP5pZbbuGFF15g7dq1jB49mkWL\nFnHbbbexebOVF16AhQtF/u8nLs48M46bF0Uw5wTZ1uhne5OfgS47eQlOUp1WBFHUxdfUVDIWL+Z6\ni4WaFe9DMe+kAAAgAElEQVTz1l23MLnuLUac+CBfLF9DecadDBn0Mcm2a/h0zxc8t+Jmpk1ezpRp\nWxBNx24FIwKT28sD6C/8L7eXG4Ab2z+7EN1dNu+rnUIDg28dsViMP/7xj9x77734fD4ARo8ezWOP\nPcb06dMBKCnRBbrnntMFu7Fj4amn9Fvx8UqG2uneGoxSF4jgjcoAxFlMFCTGkRFnI8VpRRMEioGH\ngVeBSmAo8Cwwp/Uz3n/3VN7bHSV7oMCotJsg7ae889pTnCrdh8fu5a1/mNFm3M+t152M84ILIBKB\n997TR78MDAwMDA5LU1OTaebMmUNffPHF/R3C2UMPPZQybdq0oR3iXlZWVrS70Nedhx56KGXfvn22\nZcuWVQMUFxc7//73v6dWVlaWQE+h8KGHHkrJy8uLLlu2rLq4uNj5y1/+MnP9+vV7jmW/j9hTFARh\nmaZp89pn8zVN23Is/wg4sb298cAZ7YJdIrqlXgfJR7HMwMDA4HuBpsGbb8KDD+ox3TrENEU5+rZE\nsadw1l1MS0yEgQMPFdeOZd5u/2pZWIPBIIsXL+b3v/89SvuBXn/TL7jt7kVoNgcljf5OK7mg1PNE\nxFlMuK1m0uNsPSzmrKbeXVe9HCrAHTzdcsiWuvl4JvrD61pgxL7HWLXmFrbWa4waascdupN1O8y4\ndv6NS9Oex+3w8eazDobPX8nkIUNgxgyoqYE33jAsH74FpKWl8Y9//IPLL7+cefPmUV5ezsKFC1mx\nYgXLly/n8ssnctFFeqD53/xG4OxpDi67zMFtCyW0hBAVvjBV/ghxFhN5CU5yExzY77kHUlMRrr+e\nzLY2rnz9dTa9tIKPX7ueU8/7gpSmW1hReikW26WclD6VCnUZ768roazKwexZb5KcNvMrH5cAjGsv\nS4FSuizubm0v49GFuwuBYV/5PxoYfLO8++673HzzzezatQsAj8fD0qVLufrqq1FVMytW6L/jdevA\nZoNLLtFdXSdOPD7Zw2VVpSEYozYYoS7Q5d6a7LAyMtVNRpwNt9VMTBB4H/33+Bp6zEk7MBN4ELgI\n2PXFlTz74TMEAjBpZDLRpsW8+2UbefYnuDjtn6hqjFeeyWD6H98ntaVFf5bExekHV1T01Q/GwMDA\n4GvgtdeuzG5oKHEezzbT0kaGzjvvr5XHsu3vf//7lCuuuKKxu4XbggULmp555pnU4uJi5/Dhw6NH\n097w4cOjXq/XvHLlSvf555/vLywsjK1du3Y3wJw5c3zd101ISDiGNzqd/gzrdjdvfJp20e0YadY0\nbbMgCGe0W9oZGBgY/GBRVXjlFVi6VHfBy8uD887rW2zrz7zFcnxeRr4uNE3jnTXv8+iyJ7G44rny\n7l8zdNRYBheNRBXNbGqNATFEAVwWM4l2C9ntlnLudms5k6gfoAo0AmX0bRVXBQR72Y80IAvIBU5G\nF+Wy2ktme3G3r6sqMuvXjuMfn5RgscD0kaOp3Hspe4HpI4KcEPwLEV+UlS/mcNoTa0iORuHkkw3L\nh28ps2fPpqSkhHvuuYfHHnuMkpISJk+ezA033MBvfvMb5s93c+WVcN998Nhj8NJLFm64IYHb74gn\nYg1T5g2zvclPaZOfDJeN/P+ZS3pyMsLll8P06Zzw7ruUjyjitSVnM+eyGm7I+wvF1jv4dE8Ggyz3\ncvKoJ/i0tIa//n0WZ047l7EnvXZcj6+wvSwE9tEl3N3dXorQre0uBEaji30GBt8F9u7dy/z583nj\njTcAEEWRefPmsWTJEvx+D4sXw/Ll0NAABQXwwAMwdy6kHIdgjiFJoS4Qobabe6u53b11QJyNdJcd\nm0kkALyD/rt7C/AD8cDZ6O7pswAXEA3XsWr1SDZ+0YzHA9MHz2Dn7ukE7BIXj6pmWPB5Gio1/vPJ\nZM761xs4P/1UN1fPyYHVq+EwISIMDAwMfqiUlpY6i4qKRnTMV1ZW2u65556q/fv3288880zfweuP\nGTMmtGfPHtvw4cOjVVVVtu7bHs59NSUlRXn77bd3P/HEE6k33nhjbnZ2dvSBBx6onjp1aqiwsDAG\ncOmll+a++OKLKevWrevVWq8/9CfBxOcd/sjdp/vVuCCs0TTtzPbpX6K7z3bEneuIZbem3WX2InQX\n2+T+LDtckgkjZp2BwQ8DTdPYse1+/L6NnDDpn9+Z4MqyDCtWwG9/Czt2wNChcNddcOmlutj2Xcfn\nrWHLxvOJCm5USw6KJRfVktteZ4Opm/+R4sckVSBK5ZikcsRYOaJUjqo00WJLpsmeQaM9gyZbOk32\nDJrs6fq8PZ1mWxqy2DMiuEmV8EQbSY3UkRKpIyVaT2qknpRIPSmROlKjdXgiDVg1qd/HU1t/gH1l\nMvk5JnKVq/CZJjJ44kSGSK9gKllE7QH4dMt05vx1JfaSEjj7bN3yYdUqw/LhW87GjRu5+uqr2bp1\nKwDZ2dk88cQTnH322QBUVcHixfC3v4HbDXfcoYeqk00yZW26tV1UUXGYRXKb68i79CKcmgqrV9Nm\nNrPmqjOZcc4eXMkWlIl/pdRfRMWGDSS5VrPd9yr1jTC6MI6pp/6H1PQxX+uxVqG7370MrEMXuwfR\n5Sp7EoZwB3pG0S0br8VscjNq/EOIvSSbMfjv0trayu9+9zsee+wxYu1xG6ZPn86jjz5Gbe1onngC\n3n5bX/fss2HePN2w+atcug731rqgnr21u3trhsvGgDg7KU4roiDQDLyBLtCtBqLosU3PRxfofgTY\nurVb+uWvWLf+19Q3aIwe7sDdNJ9Y8kgKTxpHVuUSzNUvsGMjVKjXcMZDf8T04otw5ZW6H+8770Dq\nsSeqMTAwMDgefNdi1rW1tZkAli5dWt99mylTpgxZvnx5eVpamjJu3LgRHW6tB3OwG2xpaakVoEOY\nKy4uds6ZM2eoz+frkZS1tLTUOnPmzKF9tdvBV4lZp/UxfbT8G93iG3S31o3o8ec6LnIBXUkn+rus\nk3YB8BqgM6CsgYHB9xOv10tZ2X7qK3/Mxq1lyDJUVLnJyv4r+YNOIy0t7Vv5ghWL6bFzfvc72LcP\nRo7URbuLLgKT6ZveuyOjaRoxRSOiKERllaiiEpEVoopKKCbjC4YJRhuJaRpi5ssIYtcjRgrVEG3b\nR8T3CaFAGS2Kl3oxRqPDjc+diS8+C1/SePzuc/DFZxFwpYPQ8xqapRDxviri26pJ95UyxF+F21et\nL/NVEe+vJi7YgKipB+86oLvDHjbYah+YzXDyqIFQeQuDZ5xF5pA85HU/w1z7b7Z9AvWOmzj/pUcQ\n333XsHz4jnHiiSeyceNGHnnkEX71q19RWVnJOeecw09+8hMee+wxsrLS+ctf4JZb4M479fLHP8KS\nJWauuCKeolQ3tYEIB9pC7ExIY+eba0n/tJi8OxYy4O7bOe/Vzbx99SWMH/oOOcL/MHr4HYy5Zil7\nPx1NbNN4sgofYFOpn+q6sYwacTmZeYvJzs7GZrMdeeePkiz0WHY3Ag3ASnRx4RH0uHdZdFncnQx8\nB25Jxw1N02hqauLA/s3UVf2ELSVeBAGqqp4jPetl8vOL8Hg8R4x9aXB8iUaj/OlPf2Lp0qW0tqcS\nz8nJYfHiP9DYeA4//rHAgQN6jNQ774RrrtFvv8eKrGo0hPTYc7U93FstFKW4GeDS3VsFQaAaWIb+\nG1oLKEAOcB367+jg31BbWxvl5Qc6+y1WK0wfORa14RrGXnAeSUki0uo5mINb+HilQNwZTzDzmuvg\nkUfg1lvh9NPh1Vf1UQMDAwMDg6Pi5ptvbho3btyIWbNm+bvHrANdcGtqajqqbs+GDRvinn766ZSO\nWHRTp04NJSQkyE1NTaZFixZldCS1SEtLUzoSWhwL/dnwBEEQ9qAPuBZ0m9Y0TRvS10btFnATBEG4\nSNO0f2uatl8QhLb25cndkkxMaM8c29aR4bW/y7qjadpTwFOgW9YdxTkwMDD4DhAMBqmoqKCivBwp\n8DGtgQfYX65QkGvC43azaXsbNXWXEmyeTVC5muzcXHJzc78VL1iRCPzlL3D//VBZCSecoPe5zz33\nq438Hw80TSOmakRlhchBAtzB81FZ7X3ERtPQpCgmZT9t3hrCWgRrYgJtYibVJihVZKpdbloHTqQt\nfjZBx6EvG/GxMKkRH7lhH6kttaRV7yIt7CMt4uus3VKkF8ufFHCkgGMspB//8wMQqhcRmzIY/ZNz\nSYhXkN44EXNwOx+8YsJz3nLOuOLnugo7dy6MG6ebeBiWD98ZLBYLt99+OxdeeCHXXnstH3zwAf/8\n5z9ZvXo1Dz30EHPnzmXkSIE33oCPP9Yzx/7v/+rv0PfdJ3DWWQ4y3Q6CMZkyX5jySSezYfIp2Joa\nyN1dzunPrGTTfffS9OFvGc99yE1fMOS0l0hIS2P7u4mcPuqfbCwr5uNPnmdCYA1bPn+A9IF55OXl\nMXDgwK/FYjgNfXTzGqAV3SroZeBJ4PH2zy9AFx1OA74HBr+90tbWRnlFBRUVlViEVVS2fIQ3MJLR\nE1ORJT9bd+0ireE0miquQbWeRU5eHjk5OcTHx3/Tu/69RlVVVqxYwd13301ZWRkg4nLl8NOfLiEQ\nuIx588zEYjBtmj74dcEFYLUeqdXeCUtKZ+y5ht7cW+PsnZmg9wJ/RhfoNrRvPwK4Hf23Mp6e1ql9\n9VsG5ZrIVuZhD09kzGXnYpf3Ir82EyJNvPa8izGL3iTv1FN1BfK++/RBoOef1wPwGRgYGBgcNSkp\nKcqqVat2H5wN9vXXX99/pG17Y+7cua379u2zdnebXbJkSXVKSory61//uu7cc88teOaZZ1IBnn32\n2X3Hut/9cYNN6OuzI2WE/aYw3GANDPpBXZ2eovNb2vmTwmHa6uup3r+f5tpaYn4/JknCNuAvfHZg\nK7EYTBqRiaVpIWaLDcX5HJtrP8TrgxMLEzHX34kiuBAcDhLT0xmYn09KZiZ2t/u/Jt4Fg/Dkk3ri\niLo6PTPqokUwc2YvceVUFaqr9QB0yclfKfCcpmlIqtZTdJPVTou4iKLq4txhBDgBsJtFbCaTXptF\nbKKAEIvS0NrCAb+XckWm2mqhOlGjPCFCtSMff3wWWjeLOEFVcQcCJLa1kRYOk6NppEejpIVCegmH\nSQ2FcBxLNo3/Elank2GnnYYjtgN5zRyUcAtv/yOBCUvfIXvyZHj0UZg/37B8+B6gaRp///vfmT9/\nfqclz2mnncaTTz7JkCFD2tfRY03eeSfs2QOnnqrHxpo4UW9D1TTqy6o5ULyBuhMmgslEqtOKeceX\neP90DjMuCqI4B2OZuRpfwMGujz4iHNhCrf1Rdu6TyM4UGGy+gnBwAqrFQpzHQ3pODgPy8ojzeDB9\nje7+fuBtdOHubfR4j0nAuegWd2eiB8jvlbY28HohK+u/airccb+TVBVJ0euY0jGv6p8pKjFVJRaT\nCUUiRGISsgaayQzmw0uRqhJGChzARR00pUM0glVT8MS7yMrLxTNgAFan8xsfFOoVSdJHiKJHFTf7\na0HTIBwRaPWZaPHqpdUr6nX7slafiT37W/lyey2+oBUFDypJqCSioT9X3G6NK64QuO66Y4syoGka\nbVGJ2kBP91anxcSAOBsDXF3urRrwJbo49yqwrb2NE9DFuQvQxboODum3+HyYZPmgfksWlsa7SM7N\nZ9hpp2GqfRX1Pz8j0Cyx+o3BnPHUapKys3Vf3uXL4dpr4U9/+m6Y3xsYGPxg+Da6wX6X6csN9ohi\n3XcRQ6wzMDgMmgYLFuhmIaKoZzUYOhSGDNHrjpKd/bV3DlVVJeL1EmxtJdTSQqi1lUBLC4HmZtT2\nuDQAmiBgiQ/S4lzKlzvDpKfBtIkLSRl8BzVhGV9MRtA0pGgrLc1rafOGcdhiJFkKUAIeBEXWxTBV\nRdBUrHY7dqcTu9OBPS4Oh8uFw+3CYjEjCkJ7AVO3aVEQ2uc54kuZz6f3rR95BJqa4Ec/goULYfo0\nDaG5SU/52lH27OmqIxG9gaSkQ66HNnQo0qDBRGz2Q63eOgW4LkGuLwHOZhaxm0RsZlN7LWI3m7CZ\nxHZxTkSWYuwN+NkdibBHljkAlFksVDud1CYkED7IhCHeX01S636GUEdcYy7RXXvZ/uabVBYXQ20t\n+dnZLFu2jJkzv3rmy28Kbf/f0T65irYGmfffGcGMv6wiISsL7r5bN+0wLB++VzQ0NHDzzTfz4osv\nAmCz2Vi8eDELFizA0h5YUpL0d+l774X6erjwQj0O5dCh7Y00NxO+7HLKB42g7JobCNkdmDUV6f3n\nOTVhOUliPebTX4f06WiaRtjbxqbPfkTxpi8QBJhUOAK1+ka0g4QWq8uFKzmZOI8HZ1IScUlJOD2e\n4y4YhdHjb70MvI7uQu7SNM7yerlw505mFxfj2r696x7W2Ni+g1YYNKj350pGxiEDEZqmIasHi2xd\ndUxVD1nWo1aP3I8VZAlBioEUQ5BiCJKEoEiYzc20RdfS3NRGcnyQwsFXkTFwMjaLBVlV8YZj1DR8\nQU3bAVRzAVZ3LoLQJZYK0Qhi0IcpFMSBissikuC0k5gQj8uThDMxEfHrFllUVc863dtzZf9+PUjq\ncUTGRBuJtJJEC55D6sMti/Yt9WJCJolWPLQcUndMD6CWs4R3cOWnHlW/RVY1GkO6OFcXiBJpd2/1\n2C0McNl7uLeqwKfo4twr6PF6BOAUdIHuXFUlvVu/JdjaSrCf/ZbpExcxuOhuzDYbaCrqlrsQd95P\nxS7YtHMmc5b/C5vFogewffVVveOwZMm3O2uUgYHBDxJDrDu+GGKdgYGBLtTdcoue4vCKK/SYWt07\n+IFA17pWKwwe3LMj3NExTk8/qs6jFIkQau/QhlpbO6fDXi+a2i2+mMWCLIrIJhMmh4PkzExyBg+m\nufUR3l/7GC2tMH7CWPLGv0VNSCQoa4hoxFsUBERURBREwpE6ojEVRCsWix1E51cKuHkwAh3inV6L\noi7iBb0iK59x8srf7AR8IpMm+5n70zLGZlYitrYgNjVjCvgRYzHEWBRRljG5XYiJiYgeD2JqCrKq\nEQ2GdBFONBOJcxFNSSOanIxqPVQIEjQVG2CzWbBbzJ2im91kahfidAHOZjZhFQWEdmuBOkVhRzDI\nrnCYvbLMfqDcYqHK6aQpLg6t2/W1SRIZfj/pfj/ZksRAScJRv4cBocU0bdnJwMQI27bE8dzzQeJs\n6MUOZpPIz372M6677jocdsdxvAL/XdS9zyDu+T0HtsO28vOY/dQLWGw23fLh6acNy4fvMW+//Tbz\n5s2joqICgNGjR/P0009z0kknda4TCMDDD+sWtJGIHjdr8WL9NkkgABdeiLZmDQ1/fZayM8+iJhBB\nA+KbP2Ow7wUy807BkjkLzC4wx1FZtYLV711LVY3G8CE2TvvRe/ibEqkpK8PX0IAgSVgUBVFuH4Ro\nR7RaO4W7uKQkHIlJOD1JOOITEMxmOsIOd3T7Ou6JWrc/GoAko1VW6kLPgQNoBw4gVVaw3uPh3ZMm\nsmbGDFqSk7FHIpy6YQMzSks5rbkZ94ABqPEJyPUNxJqakLw+pFAYyRmHFJ9ALD4BKcmDlJaO5PEg\nueOR7A4kk/mIzxOTKGASRUSTgCCKaKKAikpMlolJEmEpSigWJRiNEJRiRICwKBASBUIWM1GTiYjF\ngmq3Y3K7McXH0xrcTJ0vgCTacCQmYXUXEQGiaETRSNJUijSZIgSGaxD45Fwqi/+Dx5PJ+BP/h7jk\na2hs8xKSNVSbA7rfn1UVMRRADPmxyjHiBHDbzCS644hPTMTlScLicPRfXNU0aG7uKcR1lL17IdQt\nWZ3DoT+rO57XgwfrCW8Oai4YNdMasNAStLXXVloCNlqDFloCVlqDVloC1h7TrUEr3tDh/U1ddgmP\nK4bHFSMpLoYnLkaSS687l3WrBbmRD1e9yIbidzq+gQwbOpTLL7+coZ3KdzuRiP697KvfYrN1CsXh\nseOonTCJuux8GmwuVMAsCKTF2RjgspERZ8Nm1u/ZEvARukC3EqgFLJrG9GiUM1taOKWyEmdDQ//6\nLQMHkjNkSI9+ywljPMyYuQ2ryQFyECQf8me3Ym58l80fgjfjdqbf+1uEQADOPx8+/FDvp910U/++\nHwYGBgb/ZQyx7vhiiHUGBj90NA1uvhkef1xPZ/jooz1fkDRN99U8+EVgzx79ZaDbiDFu9yGj2trg\nwYQzMgipqi7GNTcTbq0n2laPGmnDJMQwCVHMJgmHy4LDISIIMSTJTyTcBloYqyjhsutij1WUUCUf\nn5lLWb87ibTB55E56ELC9rGgqUhyGV84JP4xYASVznRsSoSkWCueaAueWAtuuQ1/oAm5uZVsRwsn\nCgLpMZF4WcYeCmMJRrBGJeyygNWeiNmWgCY6ickikaiArJpRBDuKaAWTEzEuAbMzHrPThdnhQDRb\nMEUiaP4ATZURnl+RxstvZxOKmDnlhCouu2AfQ4aHUa1WVGccitOJarPr8yYziijQV+5FAdoFNhG7\nALZgAHtLE7aaauzlZdh27sC+bSu2PXuwelsRNK3LSrL9mkRGjKBs1Ch25+ayy+Vij6JwQBAoN5up\ncjoJH5R2Ni3YRm6wmdxwM9n+Wga0VTCgrYwcfxkpvipiwSYC3gY0KUDcUPgkAE3NcGIhnBIC9/c1\nqFU7n62C6JBfMfXuexCiUbjsMt0X0rB8+N4TCARYtGgRjz/+OKqqIggCN910E0uXLsXl6spsXF+v\nfxWeekrXDBYs0OPCu20x+NnP4J//hNtuI/Kb31LWGmDHvgo0TyqiGsashgHQ9KEANEFAQUBWBQRB\nxCQKCIiAoLsDCh3rCockYvk2IqEhoRFVVaKqTFhRCGsqIQECZhMBk4jPasVnt+K12WizWWizWAia\nRMKigPoVfl8mVcGmxLBpMexKDKsaQZH9yKEoDjFKhimKW4piV8LYlCh2NYJNidJgT2N7QhH7XQWd\nrv0WNUZK6248tdsZJ5Qyy9fKyGAjOYEGQrKDZjII2LKIObNR4rKJOdKJWD1o3ZLtCFIEU9CLORLA\nrqo4zRYSnHY8CfG4rBYcDQ2I+/YdIsxpra3EsBLARcCUSCBzGIGs4QQGDCGQmk8gKYuAeyABSyKB\noEggQGdpbdVLS0tXLR0mIbbFoht4ezxddffpvpYlJfU/o7nP5+P+++/n0UcfJRzWv/9Dhw7l/vvv\n57zzzuufkNmt36Lt3k1bcxu1cQnUFQylbZAu9DmrKhjw4RoyPi0mJdCGqaAAhgwhPGIEq8aP518D\nB/K2w0GbyYRDUZhSU8Upu0uYtGcLCZK/R7/F7hARhRgxyU+0R79FIM6m9ei3rNum4HLBuYNN5PtB\npGeoB1WBNSvMZP7Pc4z86U+hoQFmz4Yvv9TTT192Wf9OpIGBgcE3gCHWHV8Msc7A4IeMpukC3R/+\nALfcgvrAA1R/9hlRnw8lGkaL+dEkP1rMD3IA5CCCHAQlhKCGEJQglnAbtogXa8yHVQlh0SJYRAmz\nVUWwAe1Fs4NmA8F2dO+QsgRSFGJRiEWgNTGODWmzUBN+jCvjFATBTLPkZa3byTsDM2mxmPA01TDy\n8/cZeGAH0YQEQkkeggkeAvF68ccl4Y33ELG4+vy/gqaSGGvDE9NFvg6xLynW2ut8or8Fj78Fj6+V\n5toUHvpwAU9+fi0R2c5Fw/7FrSc+zOC0cmJWFzFbAlFHEqrFhSY6weREM8WBOQ4sLrC40WwJYI1H\nsMchWG2IqgLhEFos0HldkAL6dZGCoAQRlBCoYbxmEzXOeGrdHqoSM6jwZFGenEtZcj7ViVk9jtMp\nBSkI7KcguF+vu5W8YBkOJXLo10bVr4fUfk2iEdg1BNbtBKcTTkkEcb3+Wcc6HetL0R5GP99pYrKT\nCfc8z4gLLtB9nA3Lhx8kGzdu5KqrruLLL78E9KyUy5YtY86cOT3W27NH947+17/0LJX33APX/K+C\n5dab4Ikn9EQkTz2FKop8/Nw/aGg5gNlhQRQ1TKKGaNIwmTRMogppGjWaRiQCA5JUUr0aZlHDZFIx\nmTXMJg0ETRfs0dDtZjUETQW6LdP0WlM1kDUESUWQNQRJA0lDkzVURUDVRBTMKKIV2WxHtthRTXYQ\nLCBa0QQrgmgFkw3BZEM12alKSGGbJ40vPUk02GwERZGgSSSKhqwpKJqCWZWxyhI2JYpVjmGVo9jk\nCPZYCEcsiD0WxBkLYlfCOOUQDi2CjSh2ItiEKDYiWMUoTjHSQ1CzqVHsSs9ldiXSuVyMRFEjaue9\nqSkd3muD1jaYOAzSPoaYv9uzJwpS+7qgG8upCQ7qCoZTNaiIirwiyvKK2J1XRL2roPOaW+Uog9t2\nUdi2nVG+7RT5Silo2E9acwOhsIsmpYAGdQjNSgHNSi6tchZtUga+aCKRkEAkJBINgeQPI/mixPwS\n0aBGOCgSClsIxpwEpThktf+jIg6bjNOh4LSrJLhl4t0SiW6ZBLdEglsmsb3unI/vmJdx2pWvbfxB\nliTefPNN/v73v+P1+QBITExk7s9/zuw5cw6bUEVTVVRJ6uy3KHKQoMNNIDGDgCcH2eYGTcXRVoa7\nbhNJ5etIbNiGNebHogQJ26ysKZrB62PO551hswlZ40iKtHBO2RucX/kKM5tW49QOfRb2hSJ3Pfdi\nEfC5Ya0JKmugsAAGbQO5vuu71fmsjEJYzuBHT7zJwBNOgAMHYMYMPW7tyy/rop2BgYHBtxhDrDu+\nGGKdgcEPFU2DG2/U3fTmz0f+zW/4/I5TKRy8EacLzEeZQS0WBSkmIEUFpJiALIkQExFiIuYYmCNg\nCatYgzKWgAJR9BKBkGYlIDrwmuOIuFKQUzMhawhyRi6iw4VosxH2pNKQ0UbMNQWzaKPFpPJhgpuP\n4x34zCJTQiGmRSL8SJYZIYqY2+OnqZKEEov1Wip9y1lb8zbV4SQGDhuA03ILflMSbWYzXpOIX1Pw\naQo+swm/04Hf5cYbn0ibOwmtt3StZcD9wF81UCDholYKrt1DZnY1nlgrKXIzKUoLybHehT+XHDjE\npmtC7cIAACAASURBVE6R9XMrimCx6XXYZKcsLo/9roJeS8jc07Up019JjvcAOa0HyG4+QF5TOXkN\nBxhUW8bAxnqsQQVrQEKM0HldYjGBoODAb46jWbCzzy/xRaOPLS0hGmIgWixkZGYyZWYcqRNKqazW\nGJJnYuzAh4iPn4zJau29WCzfG2szs82GyWo1LB8MkCSJhx9+mHvvvZdIe4zJn/70pzz22GOkpaX1\nWPezz/TMsWvX6p6Iv/2NxkWlSxDu/ZWeCnrFCnA4kMJhpFCoz/tXKLiXbfVXUbI7QnoqjEu9DEds\ndvvnUfC3Yq7ai1a9D0tLLa5IG/FyELcWwWJW9IEUO6g2kNwWYk4TskNAsoFq01CtGqJFxWJVMVtU\nrDYNi/WIeRd6oKl6DgOv6CbOHMNpiiH2M/iApuqG253PFUlEjokIMQExJmKOdnuuBCTMIbXzmaLG\nIKTZ8IsOfBY3kfhUlLRshNxhyCkDMdnjMFmtaGaJGssCPt/RRrwbJg2ZTaZnYa/3LlmzsnOfndo6\nEb9Xxu9V8ftU/H6VgB/8AY1gAOp9uyj3mQnKKchaEtFwIrGwCTUo6pk5+okoKDhtERx2GXuchi1O\nxOo2Y3eJ2J0adqeGwxEj3tJCoq2RJHMtSZYqkk1lpJj2kWhtwmUP6MWm105bCJOodg6Cqd/e/D1H\nhSCC4kqjKfUMahNm0Bh/CorowKwESPOtZYB3Dene97HLzZ3bNNhSeTnjPF7N/jEfZZ6OZLKS7q9h\n1s43mLPtdU7ZtQ5HSO6734IVv+DA19lvyULIHoLU3m8xWa2IZpEm+6/YsHu/Hm9yxAgKUp7u+/lo\ntWJzuxHNZti2Tc86FYnAW2/B5Mnf2Pk1MDAw6C+GWHd8McQ6A4MfIpoGN9ygW3MsWEB00SK2LTqB\nCRP34otmo6VMRVYsSLKJmCQQi4pEohqRkIqi2VAFB6poR7AlYE/MwJ6UjtPTFdjckZDQZ/DsaDRK\n1Y4dNH/6KcqOHbjr6khpaSGxoQFbRQVCW5u+i0D9pJPZfP3N+MdNwGK24RcF1rsdfOK2kuqwcYYo\ncjowHjjWPIiRmhLWFE9i844gKclwTtsIcl6x62Yw3WLeaDYbcl4evvR0GjweavLzaRk6FCZMoME8\ngn89Fse65/WXhqK5MOR2kPKhRdNoUVWaNY1WUSTWm8jXjlmRiY+FSIwFSZKCeOQASVKAJMmLz+Li\ngDODMmcadY6kHts5YhEGepsZ6GtjgM9HTkwiTzUx2GpniMNBcmIiTo+HiCSxfft2SkpKepSGhgaK\nhg/nnDFjmDZgAKmtrVgrKtB27SKxoYFMSaL7XsseD6bhw9kydy9rmhtQFDh19DCmTN+I6PqBZTwt\nK4MzzzQsHwwA2LNnD9deey0ffvghAElJSTz88MP8/Oc/7+G+p2nw9ttwxx1QUgInnQQPTFnJtMd+\nDKecAq+/DgkJR/6H0SibPjqdD7b+h5gEp2UnMOmvoxB374Pa2h6rqpmZBLOyaPZ4aEtNJZiZSdy4\ncaSddBIZ2dmIfdyb5GhUD2HQEVe0uYFoWx1RbwOiHETQIpi0MBazjN0hYrMLWK0aVouCxSRjEmKA\njIoNWbUiSSIxSSQWFYhENCJhDQUbquhEFWyYncnYEtNxJKXjTE7W4+0lJWGPj+/TBTIQCFD95Ze0\nbtiAsHcv8bW1pLS1kVBXh6WsDKHdnRIAux2GDKF+lok3B31BVR0UDrYyY9JbJBScDoJAOAxbt8Lm\nzV2lpKRvF1GbDVyujqJhN7UQkb9AVvx4EgMMCzpwN4YJW6L40l205HhoKEilZmgmTfmp4AJcYLHG\nyFJ9jHDBiWnxjLdZKQLyARE96UZEUfFHJFp8AbzBMP6YTFgTkMzWHgMhYsSPJdiILdKMU2rGJTXi\nVhtxq01YTDEENQx8NTNnDQFNMKEKJjRMqIK5a15on+eg+W6f+wNhKmvqCEUkEC0IZgtJyWmkpA9A\ntNjQ6KW9Xv5fzOzGb8/Rr0WslYS2XcQ17cbUcIBYKNbZb6lJyODjYSewbshIvkhNQxMEciWJ82WZ\ni61WJptM9PYriEQiVO/c2aPfktzSQtJB/RYAzGYoKCBwcjJvT9vAjjKV7EyBmSc8QubYX/RvsOo/\n/4Gzz9ZN1VevPrb0tgYGBgbfAIZYd3wxxDoDgx8aqqoLdcuWwW23EZx/C3sfGMeYCfU0hkayR7ie\nsL+bu4cg4EhI6Hxh6ghS7kzqfyBsSZKoqamhoqKCuro6NE0jPj6e3NxcsrOzO+M7qZrGxqYWSrwh\nTKqJeE0gKsDncTa2y1WkNb/N/9y5hqkfr8chiocmuuiIl5ec3LNDHArp8fV6y4rXpD879i2AlRn6\nqtOz7Zy85UrEoYVd8fe6ZZOTJInq6mo++qiR5cvTWb8+C4tF4yc/8bJokYMhQ3pPmqChZ1JsBVqA\nRlmmLhCgLhKhMRKhSZZp1jRaAL/Vit9u7yzOWIwBXm9nyQqFKAAGm81ku1yd18SRkEBMkti5c+ch\nolx5eXmP/cnPz+fkk09mypQpJCcnE41Gqa6uBiAvL4+RI0cydOhQrJrWI3h3oHo9q8a9RkmZRlY6\nXPAKeNa0N5qd3Xvykby8/gct+q5QUqK7KBmWDwbd0DSNv/3tb9x66620trYCcPrpp/PnP/+ZwYMH\n91hXUeC552DRIqiqgrPG1fC7bWczqkiFd9/Vs6QqClRW9rx/ddzDyspAVQmMh9fmwd5qGJQFZ+06\nnaSk07t+g4MH6y/+7fvX1NREeXk5VVVVxGIxrFYr2dnZ5OTkkJKS0q/7uqaqRPz+zgRB3RMFxbol\nNhBEEZPFgtwte61gMuFMTOwc4OnMXpuUpGfE7AeRSITKykoqKipobtYtplJSUsjJySE7OxtbRzsd\nWVHb7/nqrlI2jXiG95r8CAKcFnYRWzKWzYxns2USm80nsiOSj6Lp9/vkBJnx42H8SWZOOEG/lbnd\n4FK8uOr3EVe5E8v+XT2fK34/GrD5EVgV1h8ds+KTGXPgyp73xowM/IJAKbAlFuOzQIBtqsoBp5Pm\n9usF4NA0RggCRUARUNhe50GnuKSoGkFJpi0YptUXxBeOElQ0IqIZ1dRtOEtVEEMBzJGQbsktCHrs\nPUFP0IGgx0bU58Wuz8WO9brmv/a4iKqqu2pr7bWqgqbq7t2aiqC217KE2FCLpaEGMeBFFATs7f2W\nmsxMPsrOZlVSElvbLe5HoWdw/XH7dG/f9o5+S3l5OfX19X32W3ok+Wgvu1Ke4y2qCAbhtFSYchOI\nEvpvsOOZeHDG2uRkvb233oKLL4asLF2oy8v7es+xgYGBwXHk2yjWlZaWWi+++OJB27dv39GxbN68\neZmDBg2KLliw4L+yX5deemluWVmZtbKy0rZkyZLquXPntvZnO0OsMzD4IaGq8H//B08+CbffTts1\nV1L/1ASGjfZT6T2J3dGfkDAgk5T8fJwdL1EJCbpLxlGiKAr19fVUVFRQXV2Noig4nU5ycnLIyckh\nMTERDdgHfCgrVPjCeHxhcqMyCrDfaaE28hlNW55lVOu7nDf5SoYn3dJ71rsDB0CWu/55UpLe+XU6\n9XWrqnru3MCBh4pJQ4fS5K5g9Qdns2e/RE6WyMwZzzEw+9JDjm3zZvjNb/Q8AnFxGpde2sqZZ5ag\naXUApKamkpOTQ1ZWVtcL41GgaRpRv7/TkiXU2opoNndmdHQmJWF1OJBlmX379nWKcdu2baOkpIQ9\ne/ag9hEYLj09nVmzZjFlyhT9GmgacXFxDBs2jPz8/MPGBQIo2Xw1761djs8PJ43L4IyTP8dcVt/7\ndenF2oBBg/SshN8HPvjAsHww6JP6+npuvvlmVqxYAYDdbudXv/oV8+fPx3KQcB0O66FDf/tb8Pk0\nrjA9z5Lkx8lODh2ayMfl6lUQVwvyWbtpKus/343VCtNPnsWJU9457D523KfLy8upqanp9T59LHRk\n+u6wyJMjkS5RzuPB7nYjHMbKuM92exFQEhISOvc37qDspgezb/d6nnnmV3y2aTQ+7wk0Ns7hQFk8\nmqbLNekOHyc4d3CC9CnjfR8yns1kU6mLORkZ+vlWFP3+1tjY1bAg6KJKL8+VMmklqz+8ldo6jaLh\nDmbM/Iz4xJGH3U+/38/2qirWt7Wx22ymKiGBhtRUKt1u6rrdo53ACOgU8TpKDvSwEIsqKr5IjFav\nn7ZgmEBMJqzqkQwF2mMbtscvFLT20hHT8ODlHXEQe/msr+UdbcUiUfbt3kVFeTmqIqMpCgkJ8Ywq\nKiI1OeXQtvrxnQBdEHYmJOjPR4+H0oQEVppMvArsbF9nEro4dwEwuI92jvR7SEhIOKyQHQnX8t67\nRWz6spVkD5w57VaGJf2i//2WIUNg0yYYMwbeeUcPbmlgYGDwHcIQ6w5l5cqV7pdeesnzwgsvlDc1\nNZkKCgpG+Xy+L/qzrSHWGRj8UFBVuO46ePppuPNOGn9yPsFXTiVvWJRdDdOp4lzyTppI/qRJfbpD\nHYkOi42KigoqKyt7tdioFwQ+ANYqKs2BCEW+MCNDMUxAm82CI95OZvMLbF9zLQ2NMLowjjNnbsYV\nP7TvfyxJuoXJwVYnweCho9eDB+svu32dJkWm+KMJFG/YiskEp046mcnTigH45BNdpHvrLd1D7aab\n9PwcHQPigUCAiooKysvL8fv9iKJIRkYGOTk5DBw48IhC2OHOa0VFxSGWcjt27CDazVrlYJKSkhg1\nahTjxo1jzJgxJCYmIrX7cR2toBiLtvDeu8P4fGsTCQlwxrTrKBq77HA73dPaoONFZd++w6cb/C6R\nkaH/ngzLB4PD8NZbbzFv3jwqKysBGDNmDMuXL2fChAmHrNvSogt2f3hcRZVVCuLqKUj1k5+rUjDC\nTv74JArGJ5JfINCXjrZv132s+eBO6hv6ef9s56sKYV8XhxNQcnNzSejDXbixscuFddMm+PSTeqpr\n0js/z8lRGT9e1K3m2suAAd0aCIf1+9XB9zBRPNSiu6BA94Xtg1i0hfdWDefzLxr1++ep11I07s9H\nPHZN02hra6O8vJzKykrC4TBRh4NIQQHerCwq4+MpFQS2A92dnuPosr7rXrLpK9f410sgEODhhx/m\nwQcfJBjUA/cNGjSI3/3ud1x00UX9y/B6GBSgGHgFeBWoBEzAdHRx7nwgs49tj5el6b6dv2X1B3e3\n91tczJj1BXHuQX1v0Fe/JSNDD1ESH38UZ8DAwMDg28F3Tax75plnUteuXbsbIDU1dez27du3FRYW\nxrKzs0dWVlaWTJkyZUjHNldffXXT3LlzW1euXOl+8sknU71er8nr9ZoXLFhQ12EpN3v27AKv12vK\ny8uLbd261bl9+/YdpaWlVoDCwsIYQEfb/dl3Q6wzMPghoKpw7bWwfDncdRfVs6YgFp9HepbCtto5\neBPOpWjWLJJzc4+6aU3T8Hq9VFRUUFFRQSgUwmQykZmZSU5ODo70dIpNJt4DPtI0bIEop/rDTAhE\nsGogW0wMiHcwxu3AZYaP3ivkk8/3YLXCaSfPYcKUt47/+egHFfueYNX7N1Bdo4FyJh+te5m1a90k\nJ8P8+XD99X2HlOp4weo4J+FwGLPZ3HlO0tPT+44PJcvs2rWLzZs3s2XLls7a154drzecTidFRUWM\nGjWKkSNHMnLkSIYPH44sy1RUVNDQ0ICmaSQmJna+eDu7uVgdiQO7H2XNB7dSW99/yxADA4Mu/H4/\nCxcu5A9/+AOapiGKIr/4xS9YsmRJlztdN8rL4amn9Pf3/ft1I5zWgxwmkpIgP1/XiQ6u09Nq+fiD\nkWz6sgWPB86cPp/hox7u9/5GIhGqqqooLy8/vIvp10RvAorNZiMrK4vc3FySk5M7BRRN08PzdRfm\nNm/uaVCdkbEfj+dzBg/azLlnD+XcC64kNfVrPYReKdl8De9//DReH5w4No3TZ+7AavP0a1tVVXuc\nE0mSsNlsZGdnk5ubi+DxdAp3HaUUqOvWhosuEa8QXdRT0CPXqf2c7u96CiCrKnv27mVrSQmRWAxE\nEavDwfDCQnLy80EUj/r/97YvzejhJWzATHSB7hwguY9z2dFv6RBBD+63pKenY+oj7u4h10WRvzX9\nFgMDA4NvmiOJdVe+dmV2SUNJ/19C+sHItJGhv57318q+Pi8tLbUWFRWNKiws7IzRUVlZabvnnnuq\n2traTImJiUpycrLy0EMPZVxwwQUtEyZMCL300kuehQsX1m7YsCFu7ty5rcXFxc5f/vKXmevXr9+z\ncuVK99133521ffv2HU1NTaZx48aNqKysLJk3b15mcnKyvHTp0vqVK1e6b7zxxtzuolyHaDh37tzG\n/lr0GWKdgcH3HVWFa66Bv/wFFi7kwEl5JOy9GncSfFF/CWLBhRTNno39MNZmvREMBikvL6eiogKf\nz4cgCGRkZJCek0NFZiZrzWbeAz7XNIaEJU7zhTnZH8auaogmkVy3nZx4Bx67BUEQqK95jVWrL+RA\nucKgPDMzZrxO2oBvLli/psFbb/m54/btbC+dhNtdx7X/+zGLf33J4QzzDqHjBavD2rD7C9bAgQOp\nqanpFOU2b97M1q1bCXcPht4Ni8XC8OHDOwW5jpKXl4coiiiKQl1dHeXl5dTW1qIoCnFxcT1ceI4G\nVZH5+IMxrN9YitkM0yZPZ+IpHx5VGwYGBl1s2LCBq6++mm3btgGQm5vLn//8Z2bNmnXEbdvadNGu\nQ7zrXpeV9fSUFQQ95FVayg5E86e4XAeYMK6Vs859gKFDHWRk9D8pcyAQoLKykvLy8h73+g6L4YNd\neo+VvgY5Bg4cSG5uLunp6QiCSEVFT1Fu82aor+867mHDuizl0pOfpb7xJgIhL2OK3MyYtQWn6zDW\nTv8FfG1bWfXuJEp3RRg4QGDm6X8kZ9D/HVUbh7vX5+bmEt/NKqsZXbTbflBpOMr9Fg8qpsNNaxrR\ncBhvSwtSNAqqHmfOk5hIWnIyZpPp8Nv39/+0lzhgBjAbXZDsiw7r94P7Lcf6Xf629VsMDAwMvmm+\nrWJdX5Z1c+bM8d16661ZSUlJyiWXXNLywAMPZIwZMyY0c+ZM39SpU0M33XRTVsc2ZWVl1g6xbtWq\nVfHLli2rhi5LudmzZxfcfffddVOnTg11Xw6wcOHC9FdffdXz5JNPlnd83h/6EuuONbGigYHBtwlV\nhauugmeegUWL2D3UysDKqzC5RT5vuJK0qT8nf/Lkfru99hbQOyklBevkyZRkZPC0xUIxehKF3KjE\nJb4wN/ki2GQFkyAw0GUjO95BWpwNsdub4mfFZ/LR+veQJJg2eTinnr4N0fTN3IY0Dd54A5YuhY0b\n3WRlTeKu21eQmDyXcDjCx+9fzxmzdvXbGkIURdLS0nC5XEjt2VhDoRCBQIC9e/fS0NBAcXExxcXF\nnckdABITExk/fnxnGT16NEOHDj3kZaIva4v8/Hxyc3PxeDzH5GLUUL+K1avOZt8BmbwckRln/pMB\nWRcddTsGBgZdTJw4kU2bNvHggw+yZMkSysvLmT17NpdeeimPPvooaYeJUZWYCOPG6eVgOnIoHCrm\njWDf3kHU1ln58EN48BF9fbtdt8LrzTIvP7+nB57L5WLEiBEMHz68hxX1hg0belgjZWRkHFMIhb4E\nlFGjxhCJDGTrVjOvvtolzLW06NuZTFBYCLNmdYlzY8fqUQ5UJcpHawpZ//l+7HY4a+Y5TJj0+lHv\n29dBfOIYLv5pmE8+PpWPP1nH8/+8nqkn/Zmpp23u93Ov47xnZmZ2JjwqLy9n586d7Nixo4cVdbLT\nySnAKQe10QrE6J84JtB/99kNGzZw2223sW7dOgAEQeDnP/85S5YsIesbiMHWVyKS8ePHk5WVhd1u\nP6Z2u/dbpk8ZwSk/+vIb67cYGBgYfFc4nKj2TVBYWBirqKiwVVRU8MILL5TffffdWR9//HH8smXL\nqufNm5c5fvz44IIFC5pWrlzpfuCBBzIO11ZeXl703XffdU+dOjW0cuVKd8fylStXuj/44IP47mLh\nV8V42hgYfNdRFF2o+9vfYPFidqQ3URD9E1HBTEnoVoZech3J/Yi1dXAcI1XTCAwYQPmp/5+9O4+r\nus4eP/66O+tlR5FVNEEQUSFXsFwQtTTbbFErmqmmZhpnMb+VRpM2LeZvZpolx2nTMi1bdMxRFjUX\n3BHFEE3FhUUUUeQCFy7cez+/PxBCw0S9ist5Ph734b3v3p/P51xmlM899/0+ZzC5vr5s0Gpp2p01\noMHGjKpawk21YLGiAvxdDQT7utHJ3QnteR/kqk17SU+LJ2+vmY4dIGnoO4R3m+Lon0Sb2Gzw9deN\nNel272784Pr++/DYY6DXP4zpTA8y0vuyfVc5xaW+jBj+V8K6Tm71XJWVlezatat5tVxOTg779u07\np+mDk5MTt99+OwkJCdxzzz3cd9991NbW4uLiQkxMDBERERdMsrVWx6hpm21oaCj+/v6XXXcQIHvL\nWNZu+JY6CyT2C+fOpHzUmqu77U2IW4VOp+Pll1/mgQce4JlnnmHt2rUsXLiQtLQ0/vKXv/DYY49d\ncoJdrW5cSRcUBInnZ2XQY66xsvTrCWR+Z+LUqXBcnQZQ1/AoR46oycqC83fZ+/i0tr1WRefOnnTv\n7klMTEzzlwRNiRCtVou7uzt6vR6r1YrZbMZsNlNTU4Ner6dfv3507doVlUr1kwSK3a7CbA6moqI3\nhYU+5OZq2bnzx7h0OoiJgfvug7i4xsRcTEzrvWpOlCwhLeMBjhTa6dJZy4jk5fh3SL6kn+e1MGDw\nesI6LyY98xG+2/g9xaUujEhOx9d/yCWdR6fTERYWRlhYGHV1dc2Jz927d7N79+4L1if1cvD7OXjw\nIC+//DJffvll89ioUaN466236Nmzp4Ov9vMuVH8xJibmiusvnn/fMmLoO3Rup/sWIYQQVy42NtZc\nUVGhOf/5hAkTKqZOnRq4evVqY1hYmKWoqMiQlZV1wVWBM2fOPD527NjwgQMHGmNjY5tXz6Wnpxvz\n8vJcg4ODm2sItbVm3YXINlghbmQ2G/ziFzB/Psqrr5LnnEtUwFLOnDZw1HcWUfek4OTu/pPD7HY7\nxcXFuLu7YzabKSws5NixY5zU6dgfHExBaCjZnp4cO5sICgFG2uwMrarDp6oWk7lxH5aXk45gozNB\n7k44aVuv+5Kf+1tWrfsHFRUQH+tD0qi96A3XvoiQ1QqLFjUWdd+3DyIjYdo0ePjhxgam59u6YQjr\nNq/FaoVBt0cTGZNJ7u7vm5NyO3fu5ODBgxe8XkhICH369KF3797Nq+a8vLwoLi4+55v/1j5gVVVV\nNX8Qa9nAIjQ0lICAgMtuYNHEXF1ARlovcvdU4+8LSUNfo2v31Cs6pxDiwhRF4aOPPmLKlCmcOds9\nefjw4fz73/+mSxfHbNe0Wq3U1NRQU1PD8ZIF7Nz1IsXHFLp10eHi8jqmmk6cPGmlqEjHsWMGTpxw\npbzcnYoKT0wmH2pqfFGUlit6bajVJcBh7PaDqFRHCQ21ER/vw+DBQfj7Q3n5STZu3EhWVlZzYw0n\nJyeGDRvG0KHJWK3dOHzYi5KSDhQX+/HDD86Yzaqz8xqbYTYl5fr0aWy2rNdf/L2e/+9z4tBd1/1q\nJ7vNwpqMSLbsOIKzEwxJuI8+/b++4vNe6PfFlTY8Ot/JkyeZOXMmc+bMwXq2u2nv3r155513GDZs\nmEOu0RZXq7NxS+fct/TyIWlk+9y3CCHE9ep6bDBxrTStphs3blxVyxp3V3JOqVknxM3GZoOUFPj0\nU+yvvkqefSUx3bZRWuqOOe4zwu8Yjfq8wsl2u52lS5cye/ZsoqOj6Tl4MIWdO7PT25tcX1/Kz25d\ncTGb6VFWxsDaWoZpDBj1rpyut2NXwFWnIcToTLDRGTf9hT8EWBtMrEqLYNvO4xjdYejgx+gZN/+q\n/khaU18P8+fDW281bhfr2ROmT29cudFaXWlFUTh27Bg5OTkUH/kcZ9fPOVpkp3Oomn/9Q8e2nJ92\nZe3ates5W1l79+6Nr6/vz8Z1/pYwtVpNhw4dsFgsnD67/8vPz4/Q0FCCgoLQt+UTbBv8sOclMte8\nxanT0DvGgxEj83ByCbr4gUKIK3b8+HEmT57M4sWLAXB2duaVV14hNja2OdF2OQ+z2Ux9y2J2NCa9\n5v3HiYOFdbi5QlWFG6++Xv0z0alp7KXZGQhv5c+Ac2era/H2riQ42EbHjjUYDKWYzadpaIimsNCH\no0c9sFqbfkdU4el5mIgIM3fc4c7993emTx+XVr8o+TmmM3lkpPdlz75aAjqofnbl8/Vq/55XyPzu\ndcpPQa8eRpKSc3FxC7vi815oJXanTp2uaIVZQ0MD27dvZ9OmTc1dyT08PLjzzjuJjo6+4g6vl6Ku\nro6SkpLL7uR6MT+9b3mCnnEfOyByIYS4udzKybry8nLNpEmTQgEqKys1H3zwwdGmDrCXS5J1QtxM\nbDZ44glYsABb6ivsrV9Aj5jDHC30x+WB7/DrGnXOdEVRWL58OampqTg5OTHg1VdZFhfHIR8fFLUa\nampg/XpYvRrV6tVE6V1IvPteBiTfhZuHJ2fKT7IlYzk/bN2IrbqSgIAAAgIC6NSp00/+9PLyoujw\nB2SseoaSUoXutxkYkbwBT5/br+mPqLa2sdfG2283dgu8/fbGJN3ddzduJWv6uRw5cuScbaw5OTmU\nlf1YklujgXnvO3OkpBYnJ3DVdWDzjhHNibnY2NhLbujQUsti68XFxej1+uZOjJfSyfVirA01fJfR\nnS05Rbi6wNDE8fTq+4XDzi+EaLvly5fz7LPPUtyylelV8vvfuNP5tipOV0D32wz8472u2BUPXF1d\nf/JwcXFpddzV1RWNxp0zZzwpL3fnxAlXjh0zUFSk4/BhhUOHoKam8R9WN7cGgoLKUKl2cfJkOuXl\n6cAB4Mf7TY1GQ69evUhISCAhIYFBgwYREBDQ+hs4a8+uZ1m17t9UVkJ8rO8l1RS93pirj5CZhR2o\n8AAAIABJREFUHsuuPBN+vjD8zul0i57psPO3bHjUVOP0cs9jt9tp+qygUqlQq9VXVH7hSmg0Gjp1\n6nRFtRMvpPDQ+2SsfoaSY+133yKEEDeKWzlZdzVIsk6Im4XNBo8/Dp99Rv20Fzls/w8RPU5TUHgb\ngc9tw8n44xYQRVFIT08nNTWVvLw8Jj3zDEefe470224jyGxmZEUF4YcP456fT1VNLTrfAPy7RePm\n6Y2l1szWzJWsW/YVuzdnYbfZ2hTeh3NcOVFRg0oFoZ0C2LT9/lYTez4+PlflG/nqavj3v2H27MbO\ngQkJ8MorMHSojYMHD5yTlNu5c2fzlrTz6XQ6YmJimpNy0d1y2bNvLmXlXDfdBtuqpPAzMjIfo7DY\nTrcuOkaMWIWP/+D2DkuIW1pVVRXTpk3jww8/xG63XzBJ1tZk2oXm6/V6aqoOkJneh935NXTwh6Sh\nb9Il4kWHvRdFgVOnGr/3CQk5twNtcXFx81bZrKwscnNzae3es0uXLs3Ju4SEhOZ6no2rnbqxbecJ\nPIwwbPAv6dHnfYfF3p5yttzPd1nfUFsH/ePCGDpiX7vXDVUUhbS0NKZOnUpeXmOpHb1ez/PPP8/L\nL7+Mt/eNmSC9ELvNyqZ1/diwNQeVCgb3H8DAOze1d1hCCHFdk2SdY0myToibgdXamKhbuJCaKc9T\n5vIBnSNqOViWSPhv1qA+u6dIURRWr15NamoqmzdvJiIigjEzZvDZyJGUGo1MVhTeVKlQGmwUmWop\nMtViqm9sFNHBtbGTa4CbAex2ysrKKC0t5dixY5SWlp7zvOnP48eP0zNayx//aKXgiJ2gTiqWfuPE\n8rTaC74VnU53wRV6LZ/7+vq26dvzykr45z/hr39t/NDYr18Vd965gZqaleTk5JCbm0tNTU2rxzo7\nO9OrV69zasxFR0f/ZOtpnbmYjLQe7Py+El8fGD7kRSKi32z7/37tYNPagazfshlFgcR+vRl4x7br\nvraTELcSRVGu2VbC7ZtGsXZjGvX1MCD+Nu4cnn/N/z2orKxk8+bNzcm7rVu3UldX95N5vr6+/OZX\nnekUlM2x4wpR3ZwYMXIjHl59rmm8V1t52XdkpCdz4FADocFqkpMWERA8vl1i2bFjB1OnTmXNmjXN\nYxMmTOD1118nrA2Nqm40p09uJD19CPsLGggJUpOUNJ+gkIntHZYQQlz3JFnnWJKsE+JGZ7XCpEnw\n+eecenYilqDP6RhipdA6kbAnPm2etm7dOlJTU1m/fj0ajYb7HnoI+0svsSQqig4NCpPXqdn5qY11\na1VY7QpqNei0Kgy6xodOq0Kjofmh1XLO69bGqio3cvLUYex2G77eeiwNPamtraW2thqzuQqzuYqa\nmkpqakwoSgNgA6xn/2z5OH/MiloNnp7ueHkZ8fY24u3tgY+PJ76+nvj6euPn58X27S4sWuRLba0B\nD48N1NS8jNWa1eqP0Wg0ntP0oU+fPkRERKBprYDdBezc+iDfZX1FjRn69wlmyIi9aHWXXxPoajhz\najsZ6YnsPWAhMEDFiOHvERL+q/YOSwjRzspKV5KeMZZDR6x0DtWQPOJrOnS6p93iqa+vJycnpzl5\nl5WVxalTp/jo366UnqpBowGjkyt/fMlOv379mlfeDRgwAKPR2G5xO5LdZmXj2jg2bN2NRgN3DBhM\n/8Hrrtn1jxw5wrRp01i4cGHz2NChQ3nnnXfo0+fmSo422bXtIdZsWNz4ezwumCFJ19/vcSGEuF5J\nss6xJFknxI3MaoWJE+GLLzg2cSSG3hl4+Ngp806l05jXANi0aROpqamsXr0agICAAJ58+20WjRrN\noQM+RCy0U75YxakyFc6uduIG1+ProcFJrUGlqLFaG3fYtnxcbMza0IDJVECNWY1apUFv8EKt9v6Z\nYxVsNrDbr8Yqkq+APwO7mkd8fX3PScr16dOHzp07O6TOzamy9WRkDG/+Rn5E0icEhky44vM6wu4d\nKaxZPw9TFfTt3ZHhI39Aq7s5PtQKIa6c3WZl/eoYNm7fh14PdwwcQd9B6e0dFgAny9aQmZ7MgUNW\nQoJUrFkdxIJFRT+Zp1ar6dmz5zlbZwMDA9shYsc5cvAfZK6e3LiSMMKJ5JFbMHrGXrXrnT59mjfe\neIN//OMfzQ1KYmJimDVrFsnJyde0ecS1UmcuJjMthpzvz+DjDUlDphLR4+32DksIIW4okqxzLEnW\nCXGjslphwgRYvJgj4/vjc8dWdHqo7v4BvoOeZNu2bbz66qukpaU1H/LLp56ifPjvWJYTCYtV2A+r\n0OkV+txRR/K4eiber6eLvxPqK7gRP7h3JplrUs/WcHNjxMhdba7hpihgt184IVhfb6e8vILS0jJK\nS8s4caKc48fLKSs71fwoL6/g5MkKbDYFOEZgoOUnibnAwMCr+mGjsdZNXzZs3Xld1Lqpt5wkc2V3\nsnNP4eUJw+/4NVG9/tlu8Qghrm+HfphFxpr/40QZ9OjuQvLIbNyM3dstnp1bH2BN1teYzTAgPpSh\nI35ArTFw7Nixc+re7dq1C7vd/pPjw8LCSExMbE7eRUZGtlszhMtVbznN6vTubN9VdrZG31P06PMf\nh16jrq6Of/7zn/z5z39urtsaGBjIzJkzeeyxxy5ppfmN5Nz7lhur9qwQQlxPJFnnWJKsE+JG1NDQ\nmKj78ksO3R9Fp5H5NDRosA9ezqH6DqSmprJ8+fLm6bffPp7A7jNI2xZO3T4dKo1CrwH1DLqrluS7\nbMR1dqWjq+GKElh2m4W1mdFsyi7AyQB3JowhfsAyR7zbS6YoCqdPn0ZRFHx9fdslBoDCQ/8mY9Vz\nzd1vk0dtuuZ1lQ7vn03Gmhc4fgJ6RLqQPKp9P3QLIW4MjUn+SLJzT+PlBcPvnExUz79d0xjqzMVk\nrIxmZ54JXx9IGvIy3aL/fMH5VVVVbNmypTl5t2XLFsxm80/meXt7M2jQoObkXVxcHAZD+zZwaKtz\nut/28mN48r4r7n5rt9tZuHAh06ZNo7CwEAB3d3deeuklJk+e7NAO5NeTn9y3JI4hvn/73LcIIcTN\n4HpM1uXn5+sffPDBLnv27NnbNPbss88GdunSxTJlypRrEteoUaPCKysrNZWVldq5c+ceTUhI+OnN\nSSskWSfEjaahAeXhh1F98w2HHg4nJPkQJpOBoqj5vPbeFyxZsuTsxA4EBv4BV9dfsn9/44283+1W\nxo4yMyi5ltuCtUT4uOHrrL/iVWYnSpaQlvEARwrtdOmsZUTycvw7JF/hG705NHYsjGDbzuMY3WHo\n4CfoGffxVb9uy+1sOh3cOXAYfRNWXfXrCiFuLrt3PM7q9Z9QVQX9+gQwLHnfNdk+fyD/T2R+9xon\ny6FXDyNJybm4uIVd0jkaGhrYuXPnOXXvTp48+ZN5BoOBvn37NifvBg4ciKenZytnvD6YzuSRkd6X\nPftqCeioYsSwvxLWdfJlnWv16tW88MIL7Ny5EwCtVstzzz3H9OnT8fPzc2TY1xW5bxFCCMeTZN1P\nzZ4927egoMAwZ86ckqysLJepU6cGbtq06UBbjr1Qsk5aAgpxPWpowHb//Wi+/Zajj3Wic9Ihjh93\n5ZVtd/Dh5IcBDyAFV9enqK3tT0mJCrfIeuKm1/H4EBOBgTY6uRno5u2Ft7P+Yldrk61Zw1i3aQ0N\nDXDHwCgGD82VrqItaHVGRo4pJST4N6xa9y+WLJ9HUfG3JI3ci95wdT4IlZ1IJyPtbgqOWAkL0TBy\nxJd0CLz3qlxLCHFz6xk3n5Cw35CensiWHaUUl3oyYvj7BHf+xVW5nt1m4buM7mzecRhnJxiTPI4+\n/Zdc/MBW6HQ6+vbtS9++ffnDH/6AoigcOHDgnOTdgQMHsFgsbNiwgQ0bNgCgUqmIiYk5p+5dcHCw\nI9/mFTF69uCBh8wErb+TdZvXsXDx70jo+wEJQ3ai1mix2+3YbLaffRQXF/+kVMaDDz7IG2+8Qdeu\nXdvvzV0Dct8ihBDt48l5TwbnleQ5dLl2j8Ae5o+e+OinRWzbIDo6uvu6dev2A/j5+fXas2fP91FR\nUfXBwcE9ioqK8gYOHHhb09ynnnqqPCUlpWLp0qXuc+fO9WtaKTdlypTjKSkpFfDjCrqwsLD63Nxc\nlz179uwdPXq0qeU1PTw8bFfyfkGSdUJcf+rrsdx9N4bMTEp+5UNo4jHy853oN8tOdYMr8A0q1V0o\nih4/Pxtdhh4nYIITdwfVoVUg2OhMpLcrRoPOIeGYzuSRmd6XvH21dOygYsTQ/0fnbr93yLlvRlG9\n/klI+K9JXxlP9q5TFJd2YMTQWXTuNsWh18nedBdrN66gzgKJ/bty5/A81JobY3uXEOL65OlzOw89\nWsemtQNYv2ULCz7/JYn93mPgHVsdmuQoLVpMeuYjHC2y07WzlhHJK/HrMNxh51epVHTr1o1u3brx\n5JNPAnDixInmuncbNmxg586d2Gw2du/eze7du3nvvfcACAkJISEhgfDw8HOSXlar9aKJsSuZf7Fj\nEgZ4ct/9lXy3MY+jxQb+9U89W3fUXdLPJTExkXfeeYd+/fo57Gd9Pao27SV9ZRx5+2oJ6KAiSe5b\nhBDilpCfn+8SHR3dXAeoqKjIkJqaWnzvvfeenjdvnpePj48tKirKvHDhQq/4+HhzYmJiVX5+vr4p\nQde0Iq4pKVdYWGjYs2fP3vLyck3v3r27p6SkVDz77LOBcXFxNa+//vqJpUuXum/YsMEdICoqqh7g\n0UcfDV20aJHvhg0b9rYeZdtd1W2wKpWqj6IoOS1ev60oyv+pVKqnFUX5z9mxB4AzQB9FUWZdytiF\nyDZYcaOy19ZiHjECt01ZnPidEe9YM7MWDeeVtEdRGAe44+FRy2OPGeiVWMzJGAhX6bCrwMvDhUFe\nrrjqHfeBKn9X4yqxijNwey9fho/Mv2qrxG5G27KGs3bTahoaIKFvFIkO+FbfXF1Axspe5OZX4+8H\nSUNm0LX7Kw6KWAghGhUfnU9G5pMUldiJ6KpnxIg1ePsNuuLzbll/B+s2r8dmg4S+MSQMyWmX1U7V\n1dVs3bq1eeXd5s2bqampueZxXAqNBua/78zhklqcncBW58b/vVJ90eMiIyN5++23GTNmzE3Z4bWl\npvuWM5UQH+vL8JE/XHGtPyGEEOe60bbBjh492vTHP/4xyMvLyzZ+/PjTs2bN6hgbG2tOTk42JSQk\nmH/7298GNR1z5MgR/aZNmw4sXbrUPT093ThnzpwSgKZVeKNGjQqfNm3a8aZ6dE3j58eSnJzc7fzx\nC7nm22BVKtVwYC7Qss3S02eTbs+cndMHQFGUVSqVKrzpdVvGWiYBhbgZ1J48Sd2oUXjszmHZo0NZ\nmfsAC/75INX1vqjVlSQMKuWVV1yI7N1A1vFSDDpnDCoVxR5OPOHjjpfWcd3bzq+/dt/dTxIT96HD\nzn+r6JuwitDOS0jLeJC1m/IpOubMiJGXXy/nh+9fIHPtbE6dhj49PUkamY+Tc4CDoxZCCAgKfZzH\nHn+A7zIi2ZJTzLFPExiS8BC9+35+WeczncklI60/e36oO1t/7V3Cuj7v4Kjbzs3NjWHDhjFs2DAA\nrFYrubm5ZGVlsXHjRk6dOoVGo7nsh1arvaLjf+48HTp+wf5D8zlZU83XX7ji3/ETdIbAVo/X6/WE\nhYXdcF1xL9X59y333iX3LUIIIRpFRUXVFxYWGgoLC1m4cOHRadOmBa1fv944Z86ckmeffTawT58+\nNVOmTClfunSp+6xZszr+3LnCwsIsaWlp7gkJCealS5e6N423rI/n7+9vq6ysvOJc21VL1p1NrB06\nb/gpRVG+avH6ISDz7PNDwHDAp41jkqwTN42yPfnk3fMqKwsfYpHzN5QsCEGnNqM1rOK55wy8+ead\n1GlC2XXsONtPqak3uJDp4czD3m5McGCSDs7tbBrVzcCIkde+s+nNpEPgvUx6rIa1q3qwaftBPvlk\nJHcOGk38wP+1+RzWhhrWZESwNacEV1cYO+ryPzALIURbaXWuJN1VREhI4xcFy1Z+QXFJ+iV/UZCX\n8zSr179PpQn69vZnWPLe6261k1arJS4ujri4OCZPvrwmDtfOKBLMr5Oxsgc78yrxLbv/oh10b2Zy\n3yKEEOJiYmNjzRUVFZrzn0+YMKFi6tSpgatXrzaGhYVZioqKDFlZWRestzdz5szjY8eODR84cKAx\nNjbWfP74xx9/7AfwySefFFxpzFd7G2ymoihJLV5PpTHJ1kdRlFkqlWouMFdRlJyzK/GSAM+2jCmK\n8n8Xuq5sgxU3in17bbw78wCrvtZzsD4crbqBETHpGBq+JuEXA3juucc43QD7TlVxxmLFpIZvfNxx\nd9XzT4MeLwfGYrdZ2bj2drK27UKthsH9BzLgjo0OvII4uHcmmd+lUnYSYqPcGDFqFy5uXX72mKu1\nFU0IIS7F5WzBr7ecZlV6JNm7TuJhhGGDn6JHn/9co4hvDTu3PsiarK8wm2FAXAhDk/ffMvVL5b5F\nCCHax/W4DfZaaVpNN27cuKpL7fp6IddFN9gW9eeSzibdhLjlFBfDF1/A/HkWvs8zoKIbg/XrmfzQ\nW9zb/2uySvpz92tfcsqqIqu0ClO9FbNi4zN/D7a5OfEvrYaHHbyd5VTZetIzhnOgoIGQIDXJIz6l\nU/CjDr2GgK7dXyEo7JdkpkWRs/sMxce7knTnFCJi3ml1/sa1/dmwZSuKAsMT4xhwxxbpZCeEaBcu\nbl0Y92AVQZvu4ruNK/j861QGxs/nzqQ9rSaHjhz8B5mrJ3PsuEJ0hBMjRm7B6BnbDpHf3Hr3+5KQ\nzo2/wzduL6So1OWW+B0u9y1CCCHaQ0JCgnnSpEmhTZ1iP/jgg6NX61rX7FOfSqV6Gjh9dhvsKSCc\nxoYRTfsgPM+OcwljQtwQTp2Cr7+GhQth/XoFRVHRJeAUf/L7hF+6/QO/l45haYCT4XO5/YkJbDhe\njbnBhlqx8Y2LhkXBgdzZ0MBuvY5AB8fW8lv5QbeHMGTED2i0Tg6+imji5BzAmHsrCAp8mO+yvmDx\n0tn0L/ycISP2odW5AnDm1HbS0xPZd8BCYCcVycPfJ7jzL9o5ciGEgPiB/yOk80oyMsayYWsBRaWu\njEz+mg6d7gEaVztlfdebrG15aDSQPGQw/Qeva+eob24+/oN5+BFz8yqzTxdOYHD/f920q8zOv2+5\nlVYTCiGEaF++vr62lStXnl/u7aq4lks0smmsNweNTSfmnh1rWj4ZDqw6+7ytY83OJgOfBggJCXFk\n3EJclupqWLasMUGXng5WK/j4nCQ+cjW/ubuaO796gxDvw9h+A6eq3CkZvIZS12DqTpgw6tQUNFQy\no2sYDTodf7Pb+Y1ejyN7uNWZi3+sd+MDd498mYhbtN5Ne+jd93NCOz9PRsZQNmUXU1RqZETSR5wu\nX8Pq9Z9QVQX94wIYlrwPrc7Y3uEKIUQz/4BRPDqxlg1rYsnals/8BeO4c+AwwrtNIyM9mQOHGggN\nVpOctIiA4PHtHe4tQa3RkjhsJ6GdG+u3ZazdRPExp5uqftv59y1jRt66dfqEEELc/K5azbqzXV/f\np0VTiabVdUB4iy2xT9OYxAtXFOU/lzJ2IVKzTrQXi6UxMbdwYWOirrYWAgPthIZu4WjBTKaM70Zs\ncBABf/0rkZGlWJ7yZLfLU5RGTsaKGj9nPar6Kl7SqckKDaFXfT2f6/VEODjOQz/8mbRV0zlZDr16\nGElKzsXFLczBVxFtYbdZ2byuP+u37gCgvh68vWDYnZOJ6vm3do5OCCF+3uH9s8lY8wLHT4DB0PjF\n1IC4zgwZsVdWO7WT8zujBgXcHKvlT5TXUX5K7luEEKK93co1666GC9Wsu6oNJtqLJOvEtWSzwbp1\njQm6r7+GM2fAxwfGjWtArf6cr776PV07+PHihAk4AdHz5+E/sIqDTz5Dgc9j2HWuBLgZCHPT80Xh\nYWZ26cIZZ2detlp5Vadz6PJXxV7PxnWxrN24DycnGDJoLHED/uvAK4jLVXT4Q75b9wxGV2eGJ2/D\nzdi9vUMSQog2qbecZFVaFMdPniZhwMt0i57Z3iEJIH/Xb8ja+h5m881xr2/QQ98+ct8ihBDtTZJ1\njiXJOiEcSFFg+/bGBN3ixVBaCm5uMG4c3HdfHfv3v8fs2W9y+vRpUkaN4pHhw6kxm0nIzubo+L4c\njX0IOzo6OWvpHuDDqdPlTDabWd6lC53r6/lcp6OvypGbXsF0egVLl93D4aNWuoRpSBj0FWFdxzn0\nGkIIIYQQQgghbl6SrHOs66IbrBA3uvx8WLSo8VFQAHo9jB4Njz4Kw4bV8emnc3n22Tc5ceIEPh4e\n/OU3v6FH5864d7kN98oa1t07CRU2jAXp9Bn8KG4uziw6cIAXAwM5FhDA0xYLfzUYcHF03DvvYXnm\nMqxW6BUVRM+4tYSFd3HwVYQQQgghhBBCiGurvLxc4+fn1ysqKsoMYDKZNJMnTz4+ZcqU8lGjRoWP\nHz++IiUlpQLAaDT2evfdd4+2fL19+/b8yMjImKbjAWJjY80Aubm5LiaTSVNZWakNDg62hISEWFau\nXHlo+vTpHZYsWdLUCJW5c+ceTUhIMNOK2bNn+545c0bz+uuvn2jre5JknRBtVFQE0dGgVsPQofDy\ny3DffeDsbOHDDz8kJubPHDt2DID+PXrwyhNPoPXriCZuIMVaJzRWM11Ofoht01Zip3zLqaoqUg4d\nYlFEBD5WKyttNkYaHFvfx2LeT9rKeHblVRHQUYVWNZaoXu/QWRJ1QgghhBBCCCFuEkFBQZY9e/bs\nhR+Td1OmTCkfNmyYKTMz05iSklKRlZXl4uHhYV28eLFXSkpKRX5+vt7Dw8Pq4+Nja3n8+WbPnu1b\nUFBgmDNnTglAVlaWy/z58/2KioryAPLz8/UPPvhgl9aOHzhw4G2bN282Tps2rfhS3o8k64Roo+Dg\nxm2vQ4ZAx47Q0NDAvHnzeP311yksLATAYDDw/154ga63RWKNjKXawweduZqIg38lvOZDDhwdRM+p\ny0k/coTJnp4c6NaNe2tr+dDZGS8Hx1t0cCpL/vcOZyqhT4w3u/OfZty9E7ntttscfCUhhBBCCCGE\nEAKehOA8HLtZrAeYP4Kits4vKyvTND1/4oknKt59992OAGlpae4zZswoSU1NDQRYsWKFMTExsepS\n44mMjLRUVlZqly5d6j5u3LiqqKio+nXr1u1vbe6mTZsONK2su5RrSLJOiEvwyCNgtVr5+ONPmTlz\nJocPHwZAo9Hw7FNPce/osVT5BVHj5YtBDdHfzqOz4Q1UTtXsr3ma8N/+hT8ePMi/u3RBD3zS0MAk\nZ2eHxmizVrJ+TU82bCnEwwj9+9zN5h3xjB07lujoaIdeSwghhBBCCCGEaG/FxcWG6Ojo5i59H330\n0SEAX19fGzSutluyZIn3unXr9i9evNgrKyvLJScnxzUpKcnU2vE/t63V19fXtmLFiv3vvfee3/PP\nPx8aHBxsmTVrVsmF5l8OSdYJ0UY2m41Fixbx2muvcfDgQQDUajWPPf44T/36d5xEzxk3DwyKjR6e\nTgT/dgS6odlYbHBU+yfM9/+SwVVV7IyIIKG2lkVOTgQ5uInE6eOf8M23KZQcs9Mz2g2d/s9s3lHB\nyJEj6d27t0OvJYQQQgghhBBCtHQpK+Ac6ee2sSYmJlbNmzfPCxoTbePHj6/47LPPvDZs2OD+97//\nvfhix58vPz9f7+3tbV24cOFRaNwWO3r06G4mk2mXo96P2lEnEuJmlpmZSVxcHJMmTeLgwYOoVCom\nPfYYG/MPcP+UP3HczQ+VRksPdy2jgjwJ+90AdCOzqamBQv9/8dWARxjq60u+nx+zLRbWOTs7NFGn\n2G3kbBnIvz96nFOn7dw/ZhR658/ZsbOCIUOG0K9fP4ddSwghhBBCCCGEuFEkJSWZZsyYETR48GAT\nwJgxY0zLly/3MhqNtqaVd5di69atrr/85S9Dm14nJCSYPTw8rOXl5Ze01fXnyMo6IX7G999/z9Sp\nU0lLS2see/jRCfx6+muYdK4csyloqk8TaKkirl8ftPX1WH8dg2b0ESqLVOyN/YyXYxNZGxRED7OZ\nxTod3R3cRMJs2sK3y+9k3wELnUO1jBuzhG07ncjO3sjAgQNJTEx06PWEEEIIIYQQQogbxZgxY0xP\nPvmkZsKECRXQuLrOaDTampJ3lyolJaWioKBA33Lb7IwZM0ouJ/F3ISpFURx1rutGfHy8kp2d3d5h\niBvYsWPHSE1N5eOPP8Zut6NSqbh/4uM8M+VF6lw8qLcp6M6UYyjYS3RsNIHR0VBVhfWFCLSDSynL\n17Jo+Apm9h1EhZMTf6yr488uLugcHOfB/Cf474r51NbB0ITuDBi8i6yNW1mzZg1xcXHcddddqBy8\n1VYIIYQQQgghxK1JpVLtUBQlvuVYbm7ukdjY2PL2iulGlpub6xsbGxt2/risrBOiherqat555x1m\nz56Nq5c3Q+9/mITku4kZkAAaLSbAWFeDLnszRrWdmLvuws3HB0yVWP8UjnbwaQ7mevDHx7NYFtWD\nkLo6ltntDHRxaDMcGiwlrMroxbaccvx8VUwY/xYdQ6aybds21qxZQ0xMjCTqhBBCCCGEEEKIG5Ak\n68RVVVdVRdGuXZgrKto7lJ+l2O0cLS2l2FxPcPfefLBqCwZvXwA0DfU4V5/BxWxCc+IY5tISgrp3\nJ2LIELR6PVScxPpOONr4apYdGshzLy6nxMuLx6urec/NzbE9q4HjhbP45tsXOVmu0LePL8NH7EJn\nCGTXrl2sXLmSiIgI7rnnHknUCSGEEEIIIYQQNyBJ1omroub0aY5mZ3N83z5QFFx9fbneUkeKSoXF\n1YjJ4IrZ1QNNxO0EqNUoDfWoTp/EePQHnKvOoK2raY5dpVbTPSmJgKioxmTYqSKsc7pBTAOTNTP5\n18sv49nQwH8tFsa6uTk2Xns9m9f3Yk3WXpydYMKDj9M1ah4A+fn5LFu2jPDwcB544AErqzoHAAAg\nAElEQVQ0GofVtRRCCCGEEEIIIcQ1JMk64VCm48c5kp3NyYMHUWu1BPbsSUifPjgbje0dGoqiUF1v\no8xs4USNhRPVtSgqNTabjUN5uez+ZjEBbs789qknCe0Rf/HzHd+HfUFPDkeHcH/U53wfEc/oqio+\ncXPDx8Gr2kynlrP023s5fNRK5G0Gxty9FhdjfwAOHjzI119/TVBQEA899BBarfy1FkIIIYQQQggh\nblTyqV5cMUVRqCgq4sj27VQUFaE1GAjr25fgXr3QO7hW26WyWG2UmespM1soq7FQa7UDUFNxiqz0\n/7F703q+37KR/rfHM3v2bHr16tWm8yrF2diW9OODAb/k93F/RavR8YHZzC/c3R3+HvbsvJvlGf/D\nZoMxyQPp3Xc9KnXjyrmjR4/yxRdf4O/vz6OPPoper3f49YUQQgghhBBCCHHtSLJOXDbFbqesoICj\n27dTVVaG3tWVromJBMbENNZyawc2u8Kp2vrm1XOVFisAOrUKT52avRvW8LfXX6Oo4AAA0dHRfPn5\nIkaOHNnmGm+2A+kc35LC02OXsSL0LvpVVfGFzkCogxOTFvNeVq7oS+6eagID1Nw79kN8Oj7R/N+P\nHTvGwoUL8fT0ZOLEiTg5OTn0+kIIIYQQQgghhLj2JFknLpndZuP43r0c3bEDc0UFzp6eRA4fTkBk\nJOrWtmDWV4D52FWJRVGg0qqhrF5HWb2O8notdlSoUPDRWYlybcBbU8uqpZ/z9nv/oqLiDO7A4Fgf\nnn/+ecaNG4dWo4XK/DZdr/7QBv5bkcmzD+ymSu3O6yYTLxmNqB38vgoPTmHJ8v9HpQkGDwhh8NDd\naLQezf+9rKyMBQsW4OLiwqRJk3B1dXVwBEIIIYQQQgghxPWvvLxc4+fn1ysqKsoMYDKZNJMnTz4+\nZcqU8lGjRoWPHz++IiUlpQLAaDT2evfdd4+2fL19+/b8yMjImKbjAWJjY80Aubm5LiaTSVNZWakN\nDg62hISEWFauXHlo+vTpHZYsWeLdNH/u3LlHExISzJzn0UcfDT1y5Ii+qKjIMGPGjJKm616MJOtE\nm1nr6zmWl0dhTg6W6mrc/fzoMXo0/l27olKfl66y1WE78g21q17D1WU/Kgf2OzDrAigzDqbMOJiT\n7olYdI1/P9xrf6CzaT3+pvX4VW9Ga//x78mjHvDoSy3Pcgr4E6T/CQA7Kqq1blTqPTij86RS70Gl\nzoMzek8qdT8+3+cfybI+XxN56ABrgoz0dHAtPpu1knWrY8jaWoSHB6RMmEpwl7fPmXP69Gk+/fRT\nNBoNkyZNwngd1AMUQgghhBBCCCHaS1BQkGXPnj174cfk3ZQpU8qHDRtmyszMNKakpFRkZWW5eHh4\nWBcvXuyVkpJSkZ+fr/fw8LD6+PjYWh5/vtmzZ/sWFBQY5syZUwKQlZXlMn/+fL+ioqI8gPz8fP2D\nDz7Y5fzjly5d6g6wadOmA+Xl5Zrw8PAYSdYJh6mvraV41y6Kdu3CarHgFRRE96QkvENCzt06qtix\nH19LTdoruChb0DjZcbOCNQ1MRToU5fKaLlgNrpyJ7M+ZqAQqohIxB3QFQFd5Eq8dG/DMz8L5YDZ1\nNgsmN092uxoxuQ3hjKsRk6sHJjcjVa4eVLp7UuXmgcnVg0o3j7PPjVS6eVLlakQ5P+F4Hl1DPZ6m\n0zyb/j/+NmI0egc3kTh1fB5Llv2CklI7vXq4M3LUdgwuEefMMZlMfPrpp9hsNp544gm8vb0vcDYh\nhBBCCCGEEOLa2lF6JthUb3VojSijXmuOC/Asauv8srKy5uVCTzzxRMW7777bESAtLc19xowZJamp\nqYEAK1asMCYmJlZdajyRkZGWyspK7dKlS93HjRtXFRUVVb9u3br958/r1q2bZfr06aUAvr6+Ng8P\nD2tbryHJOnFBdSYThTk5lOTlYbda8evShdD4eDwCAs6dWLkXU9o0nCr+h95Yj7sC9m1Q+r07FdHj\n6fzXN/D297/gdeqByrOPM8AZRcFU10BtjQWVuR5DbT0qwKaCE84GDrvqyXcxcMDQkcq+PTnDc1zs\n//FqRcGoUuEJeJx9dGnxvOX4+a+bnjvp9Kh8OkLyXZf2g7wIxW4jZ2sC6d9tQaOBB8feTVTvb38y\nr6amhk8//RSz2czjjz+O/8/8TIUQQgghhBBCiFtFcXGxITo6unvT648++ugQNCbJoHG13ZIlS7zX\nrVu3f/HixV5ZWVkuOTk5rklJSabWjr/Qttamc65YsWL/e++95/f888+HBgcHW2bNmlVy/vyoqKh6\n+HHl3eTJk4+39f1Isk78RM3p0xzNzub4vn0AdIyIICQ+Hjcfnx8n1R6neu2baA58hLNvNUY7KIfh\n1HYDx32TCZo5iw4REZQCfwcOcm5CruXzOkWhY4ON2BoLPc0WYsz1uNoVDMBhg5bd3q7sdzFQ5qzH\nVa1qTqD15ceEmt5sZtOKFaxdtgxbeTlUVhIREMCffvc77kpIwE2lwrHr4ByjpnIj3y4fxg8HLXQO\n1TJu7H8xeo/+yby6ujoWLFjAmTNnmDhxIp06dWqHaIUQQgghhBBCiAu7lBVwjvRz21gTExOr5s2b\n5wWNibbx48dXfPbZZ14bNmxw//vf/158sePPl5+fr/f29rYuXLjwKDRuix09enQ3k8m06/y5TbXt\nfi751xpJ1olmlcePc3T7dk4WFKDWagns2ZOQPn1wbqqJZq3BnP1vbJvewc3/BG5qwASmTA3H7P3w\neeltGt5OIBt4A8gETp49dwfOXa12m81OZ7OFgBoL3uZ69A22xolaDQZ3JzxcDHRwMTBMq8YIXKjk\nncVi4V//+hevv/46FRWNW7+DgoJ44403mDBhAuqLbG1tTwf3PM5/V35CbR2MuLMH/RN3oFL/tItu\nfX09n332GWVlZTzyyCOEhoa2Q7RCCCGEEEIIIcSNJykpyTR58uTQCRMmnAQYM2aMKTU1NdBoNNp8\nfX1t5eXll1Rlf+vWra7vv/++76ZNmw4AJCQkmD08PKzl5eWappV80Fizbs2aNca2JgFbkmTdLU5R\nFE4XFnJ0+3YqiovRGgyE9etHcK9e6J2dwW7DsvdLale+jNHrIC4GQAu1y1WUnO4Ov5pJwfT7yAAy\ngLyz5/UHkoERwHDA365wuq6eEzUWysz1nKlrAECrVuHnosffyxV/VwNuOs25dfB+Ju7Fixfz0ksv\ncfjwYQDc3d15+eWXmTx5Ms7Ozg7/WTlKg6WEzPRYtu88hb+fiokPvUOH4D+2OtdqtfL5559TUlLC\nAw88QNeuXa9xtEIIIYQQQgghxI1rzJgxpieffFIzYcKECmhcXWc0Gm2DBw82Xc75UlJSKgoKCvQt\nt83OmDGjpGWiDiA9Pd2Yl5fnGhwc3KNprKkpxcWoFEW5nNiua/Hx8Up2dnZ7h3FdU+x2yg4e5Gh2\nNlVlZRhcXQnu04fAmBi0Oh3WE9swLZ6MUb8drdEONdCwDYqPBLE35S32PPwImWo16wELoAcS+TFB\nFwM0rWnbXnqGY1V12BQFFeDtrMPfxYC/qwEvJx3qS2zUkJWVxZQpU9i6dSsAWq2WX/3qV6SmpuLn\n5+egn9DVcbzwbb5e9iLlp6BfnB/DR+xGq+/Y6lybzcaXX37JDz/8wD333EOvXr2ucbRCCCGEEEII\nIcSPVCrVDkVR4luO5ebmHomNjS1vr5huZLm5ub6xsbFh54/LyrpbjN1qpXTfPo5mZ1N75gzOnp50\nHz6cjpGRUFtCxfwHcK1Ox6lDPd6eYNsFuccjyLzvNXbNuZ/VWi1NFRGjgedoTM4NBi7U7sVJoybU\nwxl/VwN+znp0msvbmrp//35efPFFlixZ0jx277338tZbb9GtW7fLOue1otjr2bQuljVZ+3BxgYnj\nn6RL9w8vPF9R+O9//8sPP/zAqFGjJFEnhBBCCCGEEELcIiRZd4uw1tdT8v33FOXkYKmpwd3fnx53\n3YV/iB8V/52GefmnuAZV4eMKtScMfL3vLtISn2XrIyP4XqcDwAdIonH1XBIQ2MZrx/gbryj2kydP\n8tprrzF37lys1sa+r3379mX27NkkJiZe0bmvhcpTy1i67H6OFFrpfpsTd49Zh4t73wvOVxSF//3v\nf3z//fcMHTqUvn0vPFcIIYQQQgghhBA3l6uarFOpVH0URclpZXyqoiizzj5/gMamoH0udUxcXL3Z\nTNGuXRTn5mK1WPAKDqb78CFoDnwNn/8egsvw0sOegCiW2UeT1nM82Q/HUatWowMG0dgsYgTQmx+3\ntl4LtbW1/O1vf+PNN9+kqqoKgLCwMN566y3Gjx/fptp27S0vZxT/y0zDZoOxIwfR6/Z1qNQXrl2p\nKAqZmZns2LGDQYMG3RDJSCGEEEIIIYQQQjjOVUvWqVSq4cBcoEsr40nALJVK1QdAUZRVKpUqvOl1\nW8ZaSwKKH9WZTBzNyeFYXh52qxW/8HA6up1AkzUVz/JDVPj6sGrAcFY4JZPRfTQnfDoAEAE8RWNy\n7g7ArR1it9vtLFiwgOnTp1NU1Nj12dPTk1deeYVf//rXGAyGdojq0tTV5LFyxQB251cT1EnNvWPn\n491h4kWP27BhA5s3byY+Pp5hw4Zdg0iFEEIIIYQQQghxPblqybqzibVDF5n2EJB59vkhGhuH+rRx\nTJJ1rag+dYqj2dmc+OEHADp1UOG1bz6uh/eSHd2fjHG/IN1zBDuD+qCo1XjR+MMcQWMGNbQdYwdY\nvXo1L7zwAjt37gRAp9Px/PPPM23aNLy9vds5urYpPPgHvln+V0wmuGNgGIOHfo9ac/G059atW/nu\nu+/o2bMno0ePviFWDgohhBBCCCGEEMKxrmnNurMr4lapVKr/OzvkCZxuMcXnEsZEC5WlpRzZvp3y\nQ4fQK9WEn/oflYHVZHa5k4yEGXznO4Qagxtam40BajUzVCpGAHHAhTdlXjt79uxh6tSprFixonns\noYce4o033iA8PLwdI2s7m7WStaui2bitBE8PSJk4leDwt9t07M6dO0lLSyMyMpJ77rlHEnVCCCGE\nEEIIIcQt6lo3mLgxlkbdIBRF4fTRoxzJzuZM4SGcT6+jONqLDfF3kNnpcwpdG9fJhZ46ySSNEyOB\nIRoNV9buwbFKS0t59dVX+fDDD7Hb7QAkJCQwe/Zs+vXr187RtV156fssWfYrjh230yvGyMiR2zG4\ntK1D7Z49e/j222/p0qUL999/P2r1tawMKIQQQgghhBBC3LjKy8s1fn5+vaKioswAJpNJM3ny5ONT\npkwpHzVqVPj48eMrUlJSKgCMRmOvd99992jL19u3b8+PjIyMaToeIDY21gyQm5vrYjKZNJWVldrg\n4GBLSEiIZeXKlYemT5/eYcmSJc05rrlz5x5NSEgwc55Ro0aFV1ZWaiorK7UXmtOaa5asa1pVd97w\nGX5M4HkCp84+b+tYy/M/DTwNEBIS4qCor0+K3U7ZwYMUZG9jb8X37O3qTdaEO9ju90fsag1G8xkG\nHNzHC+E+jHZzI9zHr71D/omamhpmz57NO++8Q01NDQC33XYbb7/9NuPGjbthVpYpdhs7tgwgY+12\ntFoYP+4euscubfPxBw4c4JtvviE4OJjx48ej1UqDZiGEEEIIIYQQ4lIEBQVZ9uzZsxd+TN5NmTKl\nfNiwYabMzExjSkpKRVZWlouHh4d18eLFXikpKRX5+fl6Dw8Pq4+Pj63l8eebPXu2b0FBgWHOnDkl\nAFlZWS7z58/3KyoqygPIz8/XP/jgg13OP3727Nm+YWFhljlz5pRkZWW5TJ06NXDTpk0H2vJ+rmVm\nIFylUoXTmHTzPts44gsgvum/A03JvLaONVMU5T/AfwDi4+MVh0d/HbBbrWwuKODbQzvZ1cnIpvvH\nUmWYiNpu4/aj23h+5XzuihnKkJAwtD37t3e4rbLZbHz88cekpqZSWloKgI+PD3/605945pln0Ol0\n7Rxh29VUrmfZt0nsL6gnPEzLuDHLcfdObvPxR44cYfHixXTo0IFHHnkEvV5/FaMVQgghhBBCCCGu\nriefJDgvDxdHnrNHD8wffURRW+eXlZU1V/t64oknKt59992OAGlpae4zZswoSU1NDQRYsWKFMTEx\nsepS44mMjLRUVlZqly5d6j5u3LiqqKio+nXr1u0/f97o0aNNLV97eHjY2nqNq9kN9gEgXqVSPaAo\nyleKonx1dvxpGlfHoShKjkqlij/bIfZMU4fXto7dCiqBzIYGvikqYKO7M4URERARQVj1YR7atYjb\nd+YzvPf9hPcbDJ0HtHe4F6QoCmlpaUydOpW8vDwADAYDv//973nxxRfx8PBo5wgvzYG8Cfw3bSF1\ndZA8JIZ+Cdmo1G1PtpWUlLBo0SK8vLyYOHEiTk5OVzFaIYQQQgghhBDi5lVcXGyIjo7u3vT6o48+\nOgTg6+trg8bVdkuWLPFet27d/sWLF3tlZWW55OTkuCYlJZlaO/7ntqz6+vraVqxYsf+9997ze/75\n50ODg4Mts2bNKjl/flRUVD3Ao48+Grpo0SLfDRs2tLpyrzVXsxvsV8BXrYw3r4Br8bq1ORcdu5l9\nDvytzky2To9Np8MtOJChJ9bwx/RZxGV9T3jvxwm47xm4zsu6WSwWtm/fzmuvvcaqVT8uiJw0aRKv\nv/76DbdlucFSSEZab7J3ncbfT8Wkh2bTIfgPl3SOEydOsGDBAlxdXZk0aRIuLg790kEIIYQQQggh\nhGgXl7ICzpF+bhtrYmJi1bx587ygMdE2fvz4is8++8xrw4YN7n//+9+LL3b8+fLz8/Xe3t7WhQsX\nHoXGbbGjR4/uZjKZdrU2f+HChUenT59empyc3K1p6+zFSIGs69TBz1NRht7FS2UZDN+XQbfvfsAW\n+ys6/eLvqJOvh/6tP9XQ0MCePXvIzs5ufpQU5jLqb1YsD0Piw6ACVCoVhaoFPLZ6QXuHfEmsVsjb\nq1BnAa1Ghdbkzkt/mwHMaPM5FEWhvr4eAL1eT+qfUq9StEIIIYQQQgghhEhKSjJNnjw5dMKECScB\nxowZY0pNTQ00Go02X19fW3l5+SUlWbZu3er6/vvv+zbVn0tISDB7eHhYy8vLNU0r+QCeffbZwC5d\nulimTJlS7u/vb6usrGxzDk6SddepR7PX85vNsznjO46gKeloH7i+Vl9ZrVb27t17TmIuNzcXi8XS\nPGfEIPj2a3jzJFhNoNVqUanP/h24AasKatQKanU9er0etdr5ko8/P1F3ozTREEIIIYQQQgghblRj\nxowxPfnkk5oJEyZUQOPqOqPRaBs8eLDpYse2JiUlpaKgoEDfctvsjBkzSlom6gBmzpx5fOzYseEf\nf/yxH8Ann3xS0NZrqBTlBsyaXER8fLySnZ3d3mHcNGw2G/v37z8nMbdz505qa2tbnR8a7M/fX3Fh\nbOIRTJYQLD4f4hc0/BpHfX2prq5m3rx5VFdX8/jjjxMQENDeIQkhhBBCCCGEEJdEpVLtUBQlvuVY\nbm7ukdjY2PL2iulGlpub6xsbGxt2/risrBPnsNvtHDx48CeJuerq6lbn+/j4cPvttxMfH098fDz9\neznjb/sDqvo94DUZo9+bcBmr0G4mtbW1LFiwAJPJxMSJEyVRJ4QQQgghhBBCiAuSZN0tTFEUDh8+\nfE5ibseOHZhMra8E9fT0bE7KNT1CQkIat3Mqdjj9Vyh/GdTeEJQGbsnX+B1df+rr61m4cCHl5eU8\n8sgjN1xDDSGEEEIIIYQQQlxbkqy7RSiKQmFh4TlJuezsbCoqKlqdbzQaiYuLIz4+vvnP8PDw1uus\nNRRD6eP8//buPjaKO7/j+Oe3tjG2obbBhKeeczFYiQmYxHZcJRFRUB2OEFGCsXelcKHKNQeq2lNP\nVyVUuj6cTuoDqK2uUnW60EhtT4nKriGALPLAQ0NdEEj4nCq1REMVUGgwCzaOIcSAsffXPzw2a98a\nY+z1zOy8X5LlnfmNd7/Ll4HffDw7o95/l2a9LC34Jym7JM3vyPv6+/u1e/duXbx4UY2NjVqyZInb\nJQEAAAAAAI8jrMtA1lp1dHSMOGOutbVVXV2pP0JeUFCgqqqqEWfMLV26VKFQaPwXu94kxbdJtk9a\n8LZU+D2JGydoYGBATU1NOn/+vDZu3KiKiorxfwgAAAAAAP9JJBIJEwqFMu+mCGmUSCSMpESqMcK6\nDBCPx3/to6zxeDzltnl5eXryySeHQ7nq6mo9+uijysqa0J2KpYHr0uUfSNd/Kc2slRa9I80on4J3\n43+JREL79+/X2bNntW7dOlVWVrpdEgAAAAAA6dLe2dm5bN68edcI7O5PIpEwnZ2dhZLaU40T1vlM\nZ2fn8EdYh74uXryYctvc3FytXLlyxBlzFRUVys6eZNt7T0iXvivduSDN/XOp5E8lkzO558wQ1lod\nPHhQ7e3tqqur01NPPeV2SQAAAAAApE1/f//r8Xj87Xg8vlzSfXxEDxo8o669v7//9VSDhHUe1t3d\nPRzMDX3/4osvUm6bk5OjysrKEWfMPf7445oxY8bUFWTvSF0/la7+lZTzbenh41Le01P3/D5nrdWh\nQ4fU1tamVatW6dlnn3W7JAAAAAAA0qq6uvqKpN9xu45MQljnMefPn1csFlM0GtUnn3yScpusrCwt\nX758xBlzK1asUG5ubvoK6/tfqWOzdOu0VPia9NA/SFmz0/d6PtTS0qJTp06ptrZWq1evdrscAAAA\nAADgQ4R1HnDhwgU1NTUpGo3q9OnTI8ZCoZCWLVs24oy5lStXKi8vb3qKs1a69rZ0+YeSyZUWNUm/\n0TA9r+0jJ0+e1LFjx/TEE09o7dq1qe+aCwAAAAAAMA7COpd0dHQMB3QnT54cMTZ//nw1Njaqvr5e\ntbW1KigocKfI/k4p/n3pxgEp/7elhf8q5Sx2pxYPa2tr06FDh1RRUaH169cT1AEAAAAAgAdGWDeN\nLl++rD179igajer48eOy9u5NUkpKStTQ0KBwOKznnntu4ndnnWo3PpQuvSYluqWH/l4q/iPJcJ3I\n0drb29Xc3KylS5eqvr5eoRB/RgAAAAAA4MER1qVZV1eX9u7dq1gspmPHjimRSAyPFRcXq76+XpFI\nRKtXr578XVqnQuKmdOVNqecfpdzl0sKPpJmVblflSWfPntW+fftUWlqqcDjsjf4BAAAAAABfI11I\ng+7ubu3bt0+xWExHjx7VwMDA8FhhYaFefvllRSIR1dXVKScnx8VKR7n1X1LHK1LfGan4h9K8v5ZC\nM92uypOGbgSyYMECvfLKK97qIwAAAAAA8C3Cuily7do1HThwQNFoVIcPH9adO3eGx2bNmqUNGzYo\nEolozZo16b1r64OwCan776TOH0vZJdK3DkkFL7hdlWd9+eWX2r17t+bMmaPNmzd7r58AAAAAAMC3\nCOsm4euvv1Zzc7Oi0ag+/PBD9fX1DY/l5+dr/fr1ikQiWrt27fTdvXWi7vyfdGmL1HtMmlUvLdwl\nZc11uyrPunz5st59910VFBTo1VdfVX5+vtslAQAAAACADEJYN0HffPONDh48qGg0qvfff1+3bt0a\nHps5c6ZeeuklRSIRrVu3blJ3cW1padGJEyemouQxVSz+VN95Yr9CJqHDn27Sf1+okrQrra/pd/39\n/SooKNCWLVs0e/Zst8sBAAAAAAAZhrDuPty8eVMffPCBYrGYmpub1dvbOzw2Y8YMvfjiiwqHw1q/\nfv2UBTgLFy5UVVXVlDzXaNnmGz025+daPOuoem4/pk8731ROySJVlaTl5TJKKBRSdXW1ioqK3C4F\nAAAAAABkIMK6Mdy+fVsfffSRYrGYDhw4oBs3bgyP5eTkaM2aNQqHw9qwYYMKCwun/PXLy8tVXl4+\n5c+r3uNSxzap/0up5CcqmvtjPWf4awAAAAAAAOAFpDRJ+vr6dPToUUWjUe3fv1/Xrl0bHsvKylJd\nXZ3C4bA2btyo4uJiFyt9APaO1PUT6erfSDnflh7+TynvaberAgAAAAAAQJLAh3X9/f36+OOPFY1G\n9d577+mrr74aHguFQnr++ecViURUX1+vkhKffk709mfSpe9Kt1qlwu9JD/1MyuJ6awAAAAAAAF4T\nyLBuYGBALS0tikaj2rt3r7q6uobHjDFatWqVIpGINm3apPnz57tY6SRZK/Xskq78SDIzpcV7pdn1\nblcFAAAAAACAMQQmrEskEjpx4oRisZj27NmjeDw+YvyZZ55RJBJRQ0ODFi1a5FKVU6j/ihR/XbrR\nLOW/IC38FyknA94XAAAAAABABktrWGeMqbLWtiUt1zkPX7DWbnfWNUjqkVRlrd05kXXjsdbq1KlT\nisViampq0sWLF0eM19bWDgd0paWlk3uzXnLjfenSa1Li2uBHXot/IJmQ21UBAAAAAABgHGkL65xg\n7i1JS5KWG62124wx240xVUPbWmuPGGPKJrIuOQQcrbe3V2+88YZisZguXLgwYqyqqkrhcFjhcFiP\nPPLI1L1hL0j0SlfekHp+LuWukBYekWaucLsqAAAAAAAA3Ke0hXVOsHYueVnSEWexzFrbZozZIemw\ns+6cpDpJc+9z3Zhh3ZkzZ3TmzJnh5crKyuGArry8fNLvzZNufSJ1bJb6zkjFP5Lm/aUUmul2VQAA\nAAAAAJiAab9mnTHmTUnbnMUiSd1Jw3MnsO6eKioqFIlEFA6HVVFRMbmivcwOSN1/K3X+mZQ9T/rW\nYamgbvyfAwAAAAAAgOdMe1hnrd1pjGkyxrSm6zWWLVum9vZ2GWPS9RLecOeC1LFFuvkf0uxN0oK3\npKxxc0wAAAAAAAB41LSFdUPXnnOuNXdO0lYN3jBijrNJkaSrzuP7XZf8/Fud51RpaWnmB3XX/02K\n/76kAWnBP0uFvytl+nsGAAAAAADIcNN5Zl3ydeaKJJ3W4DXsapx1Zbp7Tbv7XTfMWrtL0i5Jqqmp\nsVNZuKcM9EiX/1C6/q6U97S08B1pRpnbVQEAAAAAAGAKhNL1xMaYBkk1zndpMPAFUTAAAAYhSURB\nVEgrc86Ak7V2z9AdXZ07xfZYa9vud1266va03hbp/Erp+m6p5KdSaQtBHQAAAAAAQAYx1mbeSWg1\nNTW2tTVtl8SbfrZP6vwLqXuHlLNEWvSOlPdbblcFAAAAAAACxBjzK2ttzfhbYjKm/QYTmKDb/yN1\nbJZut0mFvyfN/5kUmuV2VQAAAAAAAEgDwjqvslbq+YV05Y+lUL60+D1p9ka3qwIAAAAAAEAaEdZ5\nVeefSN07pYI1g3d7zVnkdkUAAAAAAABIM8I6ryp8Tcr+Tan4DySTtvuAAAAAAAAAwEMI67wq97HB\nLwAAAAAAAAQGp2wBAAAAAAAAHkFYBwAAAAAAAHgEYR0AAAAAAADgEYR1AAAAAAAAgEcQ1gEAAAAA\nAAAeQVgHAAAAAAAAeARhHQAAAAAAAOARhHUAAAAAAACARxDWAQAAAAAAAB5BWAcAAAAAAAB4BGEd\nAAAAAAAA4BGEdQAAAAAAAIBHENYBAAAAAAAAHkFYBwAAAAAAAHgEYR0AAAAAAADgEYR1AAAAAAAA\ngEcQ1gEAAAAAAAAeQVgHAAAAAAAAeARhHQAAAAAAAOARxlrrdg1TzhjztaTP3K4D065EUpfbRcAV\n9D646H1w0fvgovfBRN+Di94HF733poettfPcLiLTZbtdQJp8Zq2tcbsITC9jTCt9DyZ6H1z0Prjo\nfXDR+2Ci78FF74OL3iPI+BgsAAAAAAAA4BGEdQAAAAAAAIBHZGpYt8vtAuAK+h5c9D646H1w0fvg\novfBRN+Di94HF71HYGXkDSYAAAAAAAAAP8rUM+sAAIDPGWOqRi03GGPqjDFvjrH9PcfhHyl6v9X5\n2jHG9juGtpuO+pA+KXp/z96y32eG5L4bY6qMMdYY87nz9VaK7dnnAWQ0X4d1TNqDi0l7cDFpDyYm\n7sFjjKmT1JS0XCVJ1tojknpSHNDfcxz+kaL3dZKOWGt3SSpzlkfbaoz5XNK5aSoTaTC6944xe8t+\nnxlS9H2OtdZYa5dIapSUar7PPp8BUh3TcYwPDPJtWMekPbiYtAcek/ZgYuIeMM5+nNzLiKQe5/E5\nSaP/7R9vHD6RovdlutvPc87yaN+31i5xfhY+laL30r17y36fAUb3fVSva6y1qf5fZ5/3uVTHdBzj\nA3f5NqwTk/YgY9IebEzaA4iJOyQVSepOWp47wXH4lLV2l3MwJ0lVklpTbFbGmRYZ6169Zb/PYE6Y\nExtjmH3e/1Id03GMDzj8HNYxaQ8oJu2Bx6Q9wJi4A8HlnEHRZq1tGz1mrd3pBPVzxzjjHj5FbwPt\nBWttT6oB/l743xjHdBzjAw4/h3UIOCbtwURvA4+Je3D1SJrjPC6SdHWC4/C/Omvt9tErnesdNTiL\nV5X6jHv40H30lv0+s6X8iCP7fGa51zEdEGR+DuuYtINJe8AwaYeYuAdZVHf7WibpiCQZY4ruNY7M\nYIzZaq3d6Tyuc74P9b5Vd/u9RKnPuIc/pewt+33mM8b82v/j7PMZK/mYjmN8wOHnsI5Je4AxaQ8s\nJu0BxsQ9WJzwtWYohB36jbvzb35P0m/gj44zDp8Z3XunpzucO0F/lbRpcu/Dzvaf03v/GmO/T9Vb\n9vsMMrrvSUZfn5Z9PsOkOKbjGB9wGGut2zU8MGPMVjkXoxz6vLsx5lfW2uqxxuF/Sbd379bgb1Ya\nrbVHUvS+W4O93+letZhqqXrLfh8MTli33Vq7LWkd+z0AAIDP3OOYjmN8QD4P6wAAAAAAAIBM4ueP\nwQIAAAAAAAAZhbAOAAAAAAAA8AjCOgAAAAAAAMAjCOsAAAAAAAAAjyCsAwAAAAAAADwi2+0CAAAA\ngsQY85akGklFkuZIOifpnLW20dXCAAAA4AnGWut2DQAAAIFjjNkqaYm1drvbtQAAAMA7+BgsAAAA\nAAAA4BGEdQAAAAAAAIBHENYBAAAAAAAAHkFYBwAAAAAAAHgEYR0AAAAAAADgEdwNFgAAAAAAAPAI\nzqwDAAAAAAAAPIKwDgAAAAAAAPAIwjoAAAAAAADAIwjrAAAAAAAAAI8grAMAAAAAAAA8grAOAAAA\nAAAA8AjCOgAAAAAAAMAjCOsAAAAAAAAAj/h/9qJUOO1h+I8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f63a32e07b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.benchmarks import benchmarks as bchmk\n",
+    "\n",
+    "models = bchmk.all_point_forecasters(enrollments, enrollments, 10, series=True )\n",
+    "\n",
+    "#bchmk.plot_compared_series(enrollments, models, bchmk.colors, intervals=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model\t\t& Order     & RMSE\t\t& SMAPE      & Theil's U\t\t\\\\ \n",
+      "FTS \t\t& 1\t\t& 845.73\t\t& 2.19\t\t& 1.38\t\\\\ \n",
+      "CFTS \t\t& 1\t\t& 517.09\t\t& 1.29\t\t& 0.84\t\\\\ \n",
+      "WFTS \t\t& 1\t\t& 524.74\t\t& 1.35\t\t& 0.85\t\\\\ \n",
+      "IWFTS \t\t& 1\t\t& 528.07\t\t& 1.33\t\t& 0.86\t\\\\ \n",
+      "TWFTS \t\t& 1\t\t& 555.11\t\t& 1.39\t\t& 0.9\t\\\\ \n",
+      "EWFTS\t\t& 1\t\t& 525.82\t\t& 1.33\t\t& 0.85\t\\\\ \n",
+      "HOFTS\t\t& 1\t\t& 655.18\t\t& 1.79\t\t& 1.06\t\\\\ \n",
+      "HOFTS\t\t& 2\t\t& 649.75\t\t& 1.73\t\t& 1.04\t\\\\ \n",
+      "HOFTS\t\t& 3\t\t& 666.63\t\t& 1.82\t\t& 1.08\t\\\\ \n",
+      "Hwang\t\t& 2\t\t& 3064.83\t\t& 8.38\t\t& 4.57\t\\\\ \n",
+      "Hwang\t\t& 3\t\t& 3144.45\t\t& 8.82\t\t& 4.79\t\\\\ \n",
+      "PWFTS \t\t& 1\t\t& 519.23\t\t& 1.29\t\t& 0.84\t\\\\ \n",
+      "PWFTS \t\t& 2\t\t& 410.08\t\t& 1.04\t\t& 0.65\t\\\\ \n",
+      "PWFTS \t\t& 3\t\t& 304.71\t\t& 0.72\t\t& 0.5\t\\\\ \n",
+      "\n",
+      "Model\t\t& Order     & Mean      & STD       & Box-Pierce    & Box-Ljung & P-value \\\\ \n",
+      "FTS \t\t& 1\t\t& 488.46\t\t& 690.41\t\t& 12.4\t\t& 15.96\t\t& 0.10066138603814807\t\\\\ \n",
+      "CFTS \t\t& 1\t\t& -15.3\t\t& 516.87\t\t& 17.14\t\t& 23.55\t\t& 0.00888612379757273\t\\\\ \n",
+      "WFTS \t\t& 1\t\t& -101.66\t\t& 514.8\t\t& 22.29\t\t& 29.71\t\t& 0.0009556271526125373\t\\\\ \n",
+      "IWFTS \t\t& 1\t\t& -40.49\t\t& 526.52\t\t& 24.19\t\t& 32.34\t\t& 0.00035170094875716196\t\\\\ \n",
+      "TWFTS \t\t& 1\t\t& -27.67\t\t& 554.42\t\t& 25.9\t\t& 34.86\t\t& 0.00013170699014792897\t\\\\ \n",
+      "EWFTS\t\t& 1\t\t& -48.03\t\t& 523.62\t\t& 23.8\t\t& 31.8\t\t& 0.0004320959330739345\t\\\\ \n",
+      "HOFTS\t\t& 1\t\t& 261.77\t\t& 600.61\t\t& 10.47\t\t& 13.71\t\t& 0.18662179852621738\t\\\\ \n",
+      "HOFTS\t\t& 2\t\t& 237.08\t\t& 604.96\t\t& 11.34\t\t& 15.05\t\t& 0.13038783472681856\t\\\\ \n",
+      "HOFTS\t\t& 3\t\t& 249.47\t\t& 618.19\t\t& 11.21\t\t& 15.02\t\t& 0.1313121680254091\t\\\\ \n",
+      "Hwang\t\t& 2\t\t& 2617.38\t\t& 1594.52\t\t& 29.24\t\t& 30.93\t\t& 0.0006028624694539923\t\\\\ \n",
+      "Hwang\t\t& 3\t\t& 2755.05\t\t& 1515.67\t\t& 29.13\t\t& 30.41\t\t& 0.0007334346784103673\t\\\\ \n",
+      "PWFTS \t\t& 1\t\t& -27.93\t\t& 518.48\t\t& 19.0\t\t& 23.68\t\t& 0.008484467982225652\t\\\\ \n",
+      "PWFTS \t\t& 2\t\t& -59.76\t\t& 405.71\t\t& 13.77\t\t& 18.14\t\t& 0.052616020141970136\t\\\\ \n",
+      "PWFTS \t\t& 3\t\t& -60.36\t\t& 298.67\t\t& 12.43\t\t& 18.18\t\t& 0.051986792908239585\t\\\\ \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<pyFTS.models.song.ConventionalFTS at 0x7f63a26295f8>,\n",
+       " <pyFTS.models.chen.ConventionalFTS at 0x7f63a2629630>,\n",
+       " <pyFTS.models.yu.WeightedFTS at 0x7f63a26294a8>,\n",
+       " <pyFTS.models.ismailefendi.ImprovedWeightedFTS at 0x7f63a2629518>,\n",
+       " <pyFTS.models.cheng.TrendWeightedFTS at 0x7f63a2629320>,\n",
+       " <pyFTS.models.sadaei.ExponentialyWeightedFTS at 0x7f63a26293c8>,\n",
+       " <pyFTS.models.hofts.HighOrderFTS at 0x7f63a25d9da0>,\n",
+       " <pyFTS.models.hofts.HighOrderFTS at 0x7f63a2e2e5f8>,\n",
+       " <pyFTS.models.hofts.HighOrderFTS at 0x7f63a261bf28>,\n",
+       " <pyFTS.models.hwang.HighOrderFTS at 0x7f63a261bdd8>,\n",
+       " <pyFTS.models.hwang.HighOrderFTS at 0x7f63a25d9780>,\n",
+       " <pyFTS.models.pwfts.ProbabilisticWeightedFTS at 0x7f63a261bc50>,\n",
+       " <pyFTS.models.pwfts.ProbabilisticWeightedFTS at 0x7f63a261bf60>,\n",
+       " <pyFTS.models.pwfts.ProbabilisticWeightedFTS at 0x7f63a261bcf8>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "cannot convert float NaN to integer",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2216\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1299\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2437\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1131\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1135\u001b[0m                                                                 renderer)\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m         \u001b[0minterval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 974\u001b[0;31m         \u001b[0mtick_tups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    975\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m             \u001b[0;31m# handle inverted limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m         \"\"\"\n\u001b[0;32m--> 917\u001b[0;31m         \u001b[0mmajorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1951\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1952\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1953\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1955\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1959\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[1;32m   1960\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[0;32m-> 1961\u001b[0;31m         \u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0mprune\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'auto'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1902\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1903\u001b[0;31m                 nbins = np.clip(self.axis.get_tick_space(),\n\u001b[0m\u001b[1;32m   1904\u001b[0m                                 max(1, self._min_n_ticks - 1), 9)\n\u001b[1;32m   1905\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2060\u001b[0m         \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m31\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f63a2e6bd30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABOsAAAE/CAYAAAAXC0faAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f63a195cbe0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bchmk.all_point_forecasters(enrollments, enrollments, 10, statistics=True,series=True, residuals=True )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyFTS/notebooks/Chen - ConventionalFTS.ipynb b/pyFTS/notebooks/Chen - ConventionalFTS.ipynb
new file mode 100644
index 0000000..edc6847
--- /dev/null
+++ b/pyFTS/notebooks/Chen - ConventionalFTS.ipynb	
@@ -0,0 +1,462 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# First Order Conventional Fuzzy Time Series by Chen (1996)\n",
+    "\n",
+    "S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Common Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
+      "  from pandas.core import datetools\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/lib/python3/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['plt']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n",
+      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pylab as plt\n",
+    "from pyFTS.benchmarks import benchmarks as bchmk\n",
+    "from pyFTS.models import chen\n",
+    "\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyFTS.data import Enrollments\n",
+    "\n",
+    "enrollments = Enrollments.get_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAF+CAYAAACI8nxKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEYQBAAAwKxGEAQAAMCsRhAEAADAr/f+Vdy8WZ1x9jQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f802603f9b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, chen.ConventionalFTS, range(1,40), [1], tam=[10, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAF+CAYAAACI8nxKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAARiSAMAACAiEQQBgAAQET6/5uSNpCir/MIAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8023fce208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.common import Transformations\n",
+    "diff = Transformations.Differential(1)\n",
+    "\n",
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, chen.ConventionalFTS, \n",
+    "                              range(1,20), [1], transformation=diff, tam=[10, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the partitioning schemas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAALICAYAAABijlFfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8023fa65f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.partitioners import Grid, Util as pUtil\n",
+    "\n",
+    "fuzzy_sets = Grid.GridPartitioner(enrollments, 10)\n",
+    "fuzzy_sets2 = Grid.GridPartitioner(enrollments, 3, transformation=diff)\n",
+    "\n",
+    "pUtil.plot_partitioners(enrollments, [fuzzy_sets,fuzzy_sets2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conventional FTS:\n",
+      "A1 -> A2\n",
+      "A2 -> A2,A3\n",
+      "A3 -> A4\n",
+      "A4 -> A4,A5\n",
+      "A5 -> A4,A5,A7\n",
+      "A7 -> A8\n",
+      "A8 -> A7,A8\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model1 = chen.ConventionalFTS(\"FTS\", partitioner=fuzzy_sets)\n",
+    "model1.fit(enrollments)\n",
+    "\n",
+    "print(model1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conventional FTS:\n",
+      "A0 -> A1\n",
+      "A1 -> A0,A1,A2\n",
+      "A2 -> A1,A2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model2 = chen.ConventionalFTS(\"FTS Diff\", partitioner=fuzzy_sets2)\n",
+    "model2.append_transformation(diff)\n",
+    "model2.fit(enrollments)\n",
+    "\n",
+    "print(model2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[13653.740000000002,\n",
+       " 14129.800000000003,\n",
+       " 14129.800000000003,\n",
+       " 15557.980000000003,\n",
+       " 16034.040000000005,\n",
+       " 16034.040000000005,\n",
+       " 16034.040000000005,\n",
+       " 16034.040000000005,\n",
+       " 16827.47333333334,\n",
+       " 16827.47333333334,\n",
+       " 16827.47333333334,\n",
+       " 16034.040000000005,\n",
+       " 16034.040000000005,\n",
+       " 16034.040000000005,\n",
+       " 16034.040000000005,\n",
+       " 16034.040000000005,\n",
+       " 16827.47333333334,\n",
+       " 19366.460000000006,\n",
+       " 18890.400000000005,\n",
+       " 18890.400000000005,\n",
+       " 18890.400000000005,\n",
+       " 19366.460000000006]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model1.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12870.2,\n",
+       " 12966.433333333332,\n",
+       " 13270.433333333332,\n",
+       " 14099.433333333332,\n",
+       " 14863.433333333332,\n",
+       " 15126.2,\n",
+       " 15006.433333333332,\n",
+       " 15264.433333333332,\n",
+       " 16210.433333333332,\n",
+       " 16734.2,\n",
+       " 16203.2,\n",
+       " 15248.2,\n",
+       " 15312.2,\n",
+       " 14960.2,\n",
+       " 14978.2,\n",
+       " 15387.433333333332,\n",
+       " 16262.433333333332,\n",
+       " 17553.433333333334,\n",
+       " 18373.433333333334,\n",
+       " 18731.433333333334,\n",
+       " 19152.2,\n",
+       " 18691.2]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model2.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQIAAAE/CAYAAAAdcr6hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8023df3ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bchmk.plot_compared_series(enrollments, [model1, model2], bchmk.colors, intervals=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model\t\t& Order     & RMSE\t\t& SMAPE      & Theil's U\t\t\\\\ \n",
+      "CFTS FTS\t\t& 1\t\t& 643.93\t\t& 1.58\t\t& 1.05\t\\\\ \n",
+      "CFTS FTS Diff\t\t& 1\t\t& 979.77\t\t& 2.67\t\t& 1.6\t\\\\ \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "bchmk.print_point_statistics(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Residual Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "cannot convert float NaN to integer",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2216\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1299\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2437\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1131\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1135\u001b[0m                                                                 renderer)\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m         \u001b[0minterval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 974\u001b[0;31m         \u001b[0mtick_tups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    975\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m             \u001b[0;31m# handle inverted limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m         \"\"\"\n\u001b[0;32m--> 917\u001b[0;31m         \u001b[0mmajorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1951\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1952\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1953\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1955\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1959\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[1;32m   1960\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[0;32m-> 1961\u001b[0;31m         \u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0mprune\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'auto'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1902\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1903\u001b[0;31m                 nbins = np.clip(self.axis.get_tick_space(),\n\u001b[0m\u001b[1;32m   1904\u001b[0m                                 max(1, self._min_n_ticks - 1), 9)\n\u001b[1;32m   1905\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2060\u001b[0m         \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m31\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8023d90160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.benchmarks import ResidualAnalysis as ra\n",
+    "\n",
+    "ra.plot_residuals(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyFTS/notebooks/Cheng - TrendWeightedFTS.ipynb b/pyFTS/notebooks/Cheng - TrendWeightedFTS.ipynb
new file mode 100644
index 0000000..dfb3311
--- /dev/null
+++ b/pyFTS/notebooks/Cheng - TrendWeightedFTS.ipynb	
@@ -0,0 +1,462 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Trend Weighted Fuzzy Time Series by Cheng, Chen and Wu (2009)\n",
+    "\n",
+    "C.-H. Cheng, Y.-S. Chen, and Y.-L. Wu, “Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model,” \n",
+    "Expert Syst. Appl., vol. 36, no. 2, pp. 1826–1832, 2009.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Common Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
+      "  from pandas.core import datetools\n",
+      "/usr/lib/python3/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['plt']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n",
+      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pylab as plt\n",
+    "from pyFTS.benchmarks import benchmarks as bchmk\n",
+    "from pyFTS.models import cheng\n",
+    "\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyFTS.data import Enrollments\n",
+    "\n",
+    "enrollments = Enrollments.get_data()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAF+CAYAAACPsKJfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff88b392668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, cheng.TrendWeightedFTS, \n",
+    "                              range(4,20), [1], tam=[10, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAF+CAYAAACI8nxKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3XdU1Ff6BvDnDlV6x4JIE1CxUBRL\nYqLBgkk2McEaNVU0PdkYk+27vy0xpscURVM2xljTNhvBiDHNLqAyKtJURBl6R+p8f38wZAlRQWXm\nTnk+53j8zp32nGOZlzv3vlcoigIiIiIiIkujkh2AiIiIiEgGFsJEREREZJFYCBMRERGRRWIhTERE\nREQWiYUwEREREVkkFsJEREREZJFYCBMR9RIhRKUQQrnELzcDvHeiECJP9355QohEfb8nEZGps5Yd\ngIjIzEQripJuyDcUQiwHsET36zCAGABbhRAViqJsM2QWIiJTwhlhIqLeVXWpQSFEkBBipxBiuRAi\nrett3WMSdLO5lUKIrR0zyZd6bKfXdQPwIoApiqKkKopSpShKKoDnAEzRPSaq8/N0t3de4rVrO88k\n68bW6K7jLpWNiMiUsRAmIjKcGADBABZ3vS2ECAKwFu2zuoG6+1+8wnM7j6cripLfeVBRlCRFUZZc\nZa43oSuedeagfWbZDcDWTtkqdFmJiEwal0YQEfWuPCFE51nhCkVRgnXXbh3Fqa7w7Xx7OYAtutlc\nCCGeA5CG9uLzF8/tIgjthen1cFMUZYmu4K3Uvb8bgCBFUVJ1s8SpHdkALBFCVF7nexIRScdCmIio\nd01B+zrdS8m/wm1PAHkdNxRFye+y/KDrczuPe3Qd1D13tqIoSZd4TtfH5+ves0oIkS6EiEN7gb1F\nd78bgIQuxS+XRhCRyePSCCKi3pWvW6f7869O93VdP9z5djnalycA+LmQvdJzOxwGEKWbYe5sNv43\nm9xV1yK282tvRnsxPwvAmk73b1MUxb3jV+esRESmioUwEVHvutaZ0m0AZus2srmhfQ3ulm6eA12h\n/RyAnboNbW5CiAS0ry/uXMhG6TbGuQH4XTc5EtG+LKKj+8UWAHGdXn9Np9cmIjJZLISJiHpX2iX6\nCMd19yTdZrfFaN+U1rEE4bmevKGiKCvRXpiu0T33RQDPdSyL0L12EtqXXuwC8EI3OSrQXhB3jFXh\nfzPElWhfNjGrJ9mIiIyZUBRFdgYiIiIiIoPjjDARERERWSQWwkRERERkkVgIExEREZFFYiFMRERE\nRBbJpA/U8PLyUgICAmTHICIiIiIjkZaWVqYoindPHmvShXBAQAAOH77cAU5EREREZGmEEGd7+lgu\njSAiIiIii8RCmIiIiIgsEgthIiIiIrJILISJiIiIyCKxECYiIiIii8RCmIiIiIgsEgthIiIiIrJI\nLISJiIiIyCKxECYiIiIii8RCmIiIiIgsEgthIiIiIrJILISJiIiIqNc1tbbJjtAtFsJERERE1Kta\n27S4/4ND+Nf2k7KjXBELYSIiIiLqVSuSs7A3rxyhvs6yo1wRC2EiIiIi6jWfZxRi3U+ncd/4ACRE\n+8mOc0UshImIiIioV6jPV+P5TzMRG+iBP9w6RHacbrEQJiIiIqLrVl7XhCXr0+DpaIu374mCjZXx\nl5nWsgMQERERkWlradPi0U/SUVbXhG1Lx8PLyU52pB5hIUxERERE1+Vf209if34FXpszEsP9XGXH\n6THjn7MmIiIiIqP1aVohPthzBg/eEIiZkca9Oa4rFsJEREREdE2OFVbhd59nYnywJ34XHy47zlXT\nWyEshIjqcjtBCBEnhFh+tWNEREREZFxKa9s3x3k72eGt+VGwNoHNcV3pJbEQIg7A1k63owBAUZRU\nAFVCiKiejukjHxERERFdu5Y2LR7dkI7KhmYkLYqGh6Ot7EjXRC+FsK6Qze80NAdAle46H0DcVYwR\nERERkRH5+39P4OCZCrx49wgM6286m+O6MtQcthuAik63Pa9ijIiIiIiMxJZD5/DRvrNInBiEO0YN\nkB3nupjeYg4iIiIikiKjoBJ//EKNG0K8sHxamOw4181QfYSrAHjort0AlOuuezr2MyFEIoBEAPD3\n99dHViIiIiLqoqS2EUs/ToOvqx1WzYs0yc1xXRmqEN4MIEZ3HQQgVXfd07GfKYqSBCAJAGJiYhR9\nhCUiIiKi/2lu1eKRj9NRc7EVnz0yHu4mujmuK311jUgAEKP7HYqipOvG4wBUKYqS3tMxfeQjIiIi\nop7721fHcfhsJV6aNQJD+rnIjtNr9DIjrCjKNgDbuowlXeJxPRojIiIiIjk2HizAhgMFWHpTMG4b\n0V92nF5l+os7iIiIiEgv0s5W4s9fqjEx1BvPmsHmuK5YCBMRERHRrxTXNOLhj9PQ360PVs2NhJVK\nyI7U61gIExEREdEvNLW2YenHaahrakXSwhi4OtjIjqQXhuoaQUREREQm4q//OY6Mgiq8e08Uwvo6\ny46jN5wRJiIiIqKfbThwFhsPnsOjk4IRP7yf7Dh6xUL4KjS2tOH11Gz8lFMmOwoRERFRrzt8pgJ/\n/c9xTArzxm+nmN/muK5YCF8FlRD4NL0Q/9x+Elotz/IgIiIi86GpbsTSj9Ph5+6A1810c1xXLISv\ngq21CsumhuFkUQ3+c/SC7DhEREREvaKxpQ1LPk7DxeZWJC2Mhmsf89wc1xUL4at0+4j+GNbfBS9/\ncwpNrW2y4xARkYXSahXszS1DYws/i+j6KIqCP3+pxtFzVXh1zigM9jXfzXFdsRC+SiqVwPPx4Sis\nvIgN+wtkxyEiIguk1Sr4wxeZmL/uAO59/yBqG1tkRyITtn7/WWw5XIgnJodg2rC+suMYFAvha3Dj\nYG9MCPHEqm9zUMP/fIiIyIDatAqWf3oMGw+ew/RhfZF2thL3rDuAivpm2dHIBB3IL8f/fXUCt4T7\n4Km4UNlxDI6F8DV6bno4KhtasPaHfNlRiIjIQrS2afHMliPYllaIp+NCsXphNJIWReOUphazVu9F\nUfVF2RHJhFyouohHNqTD39MBr80dBZUFbI7rioXwNRrh54bbRvTDuh9Po6SmUXYcIiIycy1tWjy1\n+Qi+OHIBz04Lw5NxgwEAk8N98dEDY1BS04SEd/fhdFm95KRkChpb2k+Oa2rVImlhDFzsLWNzXFcs\nhK/DsqlhaGnT4o1dObKjEBGRGWtu1eLxTzLw32NF+P2McDw6KeQX98cGeWJj4lg0trRh1uq9OH6h\nWlJSMgWKouAPn6txrLAar80ZhRAfJ9mRpGEhfB0CvBwxP9Yfmw6dQ35pnew4RERkhppa2/DIhjSk\nHNfgz7cNReLE4Es+LmKAK7YsHQdbKxXmJu3HoTMVBk5KpuLDvWfwaXr78popQ31lx5GKhfB1enzy\nYNhZq/DyN6dkRyEiIjPT2NKGJevTkHqyBH+/YxgeuCHwio8P9nbC1ofHw9vJDgvfO4Ddp0oMlJRM\nxb68cvzj65OYOtQXj08O6f4JZo6F8HXydrbD4huDsD1Tg4yCStlxiIjITFxsbsPijw7j++xSrLhr\nOBaOC+jR8wa49cGWpeMQ7O2Exf8+jK94ABTpFFY24NFP0hHo5YhXZo+0yM1xXbEQ7gWLJwbB09EW\nL6ZkQVF49DIREV2fhuZWPPDhIfyUW4aXEkZi7hj/q3q+l5MdNiaORZS/O57YlIFPDrDvvaW72Nz+\n7UJLmxZJC6PhbKGb47piIdwLnOys8cQtg7E/vwLfZ5fKjkNEvUSrVVDVwN6sZFh1Ta247/1DOHC6\nHK/NHoWEaL9reh0Xexv8+4ExuDnUG7//PBPvfpfXy0nJVCiKgt99dgwnimrwxtxRCPK23M1xXbEQ\n7iXzxvjD38MBK5KzoNVyVpjIHKz5IR8x/0jF1sPnZEchC1HT2IJF7x1AWkEl3pwXiTsjB1zX6/Wx\ntULSohj8ZmR/vJiShReST/KbSwv03k+n8cWRC3hmSigmh1v25riuWAj3EltrFZZNC0OWphZfHj0v\nOw4R9YLPMwqhVRQ8u+0YXt5xij/kkl5VN7Rg4boDOFZYjbfnR+K2Ef175XVtrFR4fc4oLBjrjzXf\n5+P3n2eijX+XLcae3DK8kJyF6cP6/qrtHrEQ7lW3De+HiAEueHlHNppa22THIaLrkFtSh+ziOvx+\nxhDMiRmIt3bn4snNR9DYwn/b1Psq65txz3v7cbKoFqsXRGN6RL9efX2VSuDvd0TgsUkh2HjwHJ7Y\nmIHmVm2vvgcZn3MVDXjsk3QEezvi5dkjIQQ3x3XFQrgXqVQCz08fgvNVF/Hxfm5MIDJlKeoiAMBt\nI/pjxd3D8dz0cHx19ALuWXcA5XVNktOROSmva8K8tfuRXVyHNYuiEaenvq5CCCybFoY/zBiCrzOL\n8NBHh9HQ3KqX9yL5Lja3IXF9Gtq0CpIWxsDJzlp2JKPEQriX3TDYCzcO9sJb3+agprFFdhwiukbb\nMzWIHuSOvq72EELg4ZuD8fb8KKjPV2PmO3uRx0N0qBeU1rYXwafL6vHevTGYFOaj9/dcPDEIL949\nHD/llGLhewdRfZGfVeZGURQs//QYsjQ1eHNeJAK8HGVHMloshPXguenhqGxoQdL3+bKjENE1OFte\njxNFNYiP6PuL8VtH9MPGxLGob2rFXe/sxb68ckkJyRwU1zRibtI+nKu4iA/uH40bB3sb7L3njPbH\nW/OjcKywCnOT9qO0lt9ymJO1P+bjq6MX8Oy0MNxsgB+uTBkLYT2IGOCK34zsj3U/5aOkplF2HCK6\nSslqDQBgepdCGACi/N3xxaMT4O1sh0XvH8C2tEJDxyMzUFR9EXOT9kNT3Yh/PzAG44O9DJ5hxvB+\neP++0ThTVo9Zq/fiXEWDwTNQ7/sxpxQrkrNw6/B+ePimSx/HTf/DQlhPlk0NQ5tWweu7cmRHIaKr\nlJxZhBF+rvBzd7jk/QM9HPDpw+MxOsADy7YexSvfnGJLKuqxwsoGzFmzH2W1TfjowViMCfSQluXG\nwd74+KFYVNQ3Y9bqfcgprpWWha5fQXkDHvskA6G+zliZMIKb43qAhbCe+Hs64J7YQdh86BzXEhKZ\nkMLKBhwtrEZ8N7v2XfvY4MP7x2B2jB9WfZuLJzaxowR1r6C8vQiuamjG+odiET3IXXYkRA9yx5al\n49CmKJi9Zh+OnquSHYmuQUNzKxLXHwYAJC2MgSM3x/UIC2E9emxyCOytVXh5xynZUYioh1J0yyK6\nrg++FFtrFV68ewSWTw9jRwnq1umyesxJ2of65lZ8sngsRg10kx3pZ+F9XbBt6Tg42Vtj/tr92JtX\nJjsSXQVF1+88u7gWq+ZFwt/z0t9m0a+xENYjLyc7JE4MRrJag/SCStlxiKgHUtQaDOnn0uNd1kII\nPHJzCN6eH4VMdpSgy8gtqcOcNfvQ1KrFJw+NRcQAV9mRfmWQpyO2LR2P/m59cN8Hh/DNcY3sSNRD\nq7/Px9fHivDc9HBMDDXcpktzYLBCWAixXAiRIIRI7DSWIISIu8zYckNl06eHbgyEl5MtViRncQ0h\nkZErrmnE4bOVPZoN7urWEf2wiR0l6BKyi2sxN2k/tAqwKXEshvZ3kR3psnxd7LFlyTgM6eeChzek\n47N0bgY1dt+dKsHKHVm4fWR/JE4Mkh3H5BikEBZCxAGAoijbAAQLIYKEEFEA8hVFSQWQL4SI0o1B\nN1bVcduUOdpZ48lbBuPg6Qp8d6pUdhwiuoIduhmwGcOvvhAG/tdRwsvJFoveP4BP2VHC4p0sqsHc\npP1QifYiONTXWXakbrk72mLDQ7GIDfTAb7ccxYd7TsuORJdxpqweT2zMQHhfF6y8m5vjroWhZoSn\nAOhoqpsHIE53/aLu9yBFUdIBzAHQsUo/v9PjTNrcMf4I8HTAiylZPN+dyIhtzyxCiI8TQnyuvVgZ\n6OGAzx6ZgNEBHnhm61G8yo4SFkt9vhrz1u6HnbUKm5eMQ4iPk+xIPeZkZ4337xuNqUN98devTuCN\n1Bz+PTYydU3tm+NUKoGkhdHoY2slO5JJMlQhXA6goz+MG4BgXeGbL4TIA1DR6b6KTs/z7PpCQohE\nIcRhIcTh0lLTmGG1sVJh2bQwZGlq8UXGedlxiOgSyuqacPB0BWZcw7KIrjp3lHjz21w8yY4SFufo\nuSrMX7sfjrbW2Jw4DoEmeLKXvY0V3rknCgnRfngtNRv/998T0HIyxygoioJlW44it6QOb8+PwkAP\nbo67VoYqhLcB6Ojq7AmgXAjhhvbZ3zUA1goherSwRVGUJEVRYhRFifH2Np0F4TMi+mGEnyte3ZnN\nD0QiI/TN8WJoFSB++JXbpvVUR0eJZ6eF4T9HL2DBugOoqG/uldcm45Z2thIL1h2Aq4MNNi8Za9I7\n+K2tVFh59wg8MCEQH+w5g2e3HUNrm1Z2LIv39u5cpBzX4PczhmBCiOEPYzEnBimEFUXJB7C505rf\nfACJAF5QFGUlgFkAEtBeGHeeOTab3SYqlcDz08NxvuoiPt5/VnYcIuoiWV2EAE8HhPftvTWcQgg8\nOikEb82PxLHz1Zj5zh52lDBzh85UYNF7B+DpZIstS8Zd9lAWU6JSCfzptiH47ZRQfJpeiIc3pHNC\nR6Jvs4rxys5s3DmqPx68IVB2HJNnqM1yUQBidMsh3HSb5n7WsTkOwGYAHTPDQQBSDZHPUMaHeGFi\nqDfe2p2L6ostsuMQkU5lfTP25pUjfng/vWw2uW1Ef2xcPBZ1je0dJfbnm83P+NTJvrxyLHrvIHxd\n7bF5yTj0c+0jO1KvEULgiVsG46+3D8XOE8W4/4NDqGtqlR3L4uSX1uHJjUcwtJ8LXriLm+N6g6Fm\nhNMBVAghEtC+FAK6meDEjpZquiUP6cDPXSaqOm6bk+emh6GqoQVrvs+THYWIdHaeLEabVrmmtmk9\nFT3IHZ8/0t5RYuF77Chhbn7KKcP9Hx7EQI8+2Jw4Dr4u9rIj6cV9EwLx2pyROHimAves3Y9KLvcx\nmNrGFiSuT4ONtQpruDmu1xisj7CiKNt0v9I7ja3UjSV1GktSFCW185g5GdbfFXeO6o/395yGprpR\ndhwiQvshGgPc+mC4ng858Pd0wGcPd+oosTObO/HNwHenSvDAvw8hwNMRGxePhbeznexIejUz0g9r\nFkTjpKYWs9fs42eZAWi1Cp7ZchSny+rx9vwos1hyYyx4spwEz0wNQ5tWwRu7smVHIbJ4NY0t+DGn\nFPERfQ3yNaOrQ3tHiVnRfnhzVw6e2syOEqYs9UQxEj9Kw2AfJ2xcPBaeTuZdBHeIG+qLf98/BkXV\njUhYvRdnyuplRzJrq77NxTcnivHHW4dgXPCvGmrRdWAhLMFADwcsGDsImw+dQ24JN84QyfTtyRK0\ntCm91i2iJ2ytVViZ0N5R4ssjF7DwPXaUMEUpag2WfpyGIf2c8clDY+HuaCs7kkGNC/bEJ4tjUd/U\nioTV+3CyqEZ2JLO080QxXkvNxl1RA3Df+ADZccwOC2FJHpsUAgdba7y0I0t2FCKLtj2zCL4udogc\n6GbQ9+3oKLFqXiSOFrZ3lMhnRwmT8d9jF/DoJ+kY4eeK9Q/FwtXBRnYkKUb4uWHr0nGwsRKYs2Yf\n0s5WdP8k6rHckjo8vfkIhg9wxb9mDufmOD1gISyJp5MdlkwMwo7jxUg7Wyk7DpFFqm9qxffZpYiP\n6AeVSs4HzO0j+2Pj4ljUNrZi5jt7cYAdJYzeFxnn8cTGDET5u+GjB2PhYm+ZRXCHEB9nbF06Dh6O\ntliw7iC+zzaNw66MXU1jCxLXH4adbnOcvQ03x+kDC2GJHrwxEF5OdngxOYsbZogk2H2qBE2tWkzX\nY7eInoge5IEvdB0lFrx3AJ+ls6OEsdqWVointxzBmEAP/PuBMXCys5YdySj4uTtg69LxCPByxEP/\nPoSvjxXJjmTStFoFT286goLyBrxzTxT6u5lPKz5jw0JYIgdbazwVNxgHz1Tg26wS2XGILE6yWgMv\nJ1uMDvDo/sF61tFRImaQB367hR0ljNGmgwV4dttRTAj2wgf3jYGDLYvgzryd7bApcSxG+rnh8Y3p\n2HSwQHYkk/X6rhzsyirBn28fitggbo7TJxbCks0ZPRCBXo54MSULbTzDnchgGlvasDurBFOH9YWV\npGURXbk62ODfD/yyo0RTKztKGIP1+8/i+c8ycVOoN9bdG8Merpfh2scG6x+MxY2DvfH8Z5nsmX8N\ndhzX4M1dOZgV7YeFYwfJjmP2WAhLZmOlwrPTwpBdXMevQ4kM6PvsUjQ0t2FGhOG6RfRE144SC9ax\no4RsH+w5jT99oUbcEB+u1eyBPrZWWLsoBreN6IcXkrOwMoXL/3oqp7gWv918BCMHuuHvd0Zwc5wB\nsBA2AvERfTFyoBte3ZnNfqJEBpKcWQR3BxvEBslfFtFV144Sd7GjhDRrf8jH3746gWnDfPHOPdGw\ns2YR3BO21iq8MTcS82P98c53efjjF2p+69mN6ovtJ8f1sbXGmgX8gctQWAgbASEEnp8ejqLqRny0\n74zsOERmr6m1DbtOlmDKUF/YWBnvf4MdHSVqGltx17vsKGFob+/OxT+3n8StI/rhrflRsLU23r8r\nxshKJfDPOyPw8M3B2HCgAE9tPoLmVq3sWEapTavgqU0ZOFfRgHcXRKGvq3ke0W2M+K/aSIwL9sTN\nYd54e3ceqhtaZMchMmt7cstQ29Rq0EM0rlX0IA98/sj49tZU7ChhMG+k5uClHadwx6j+eGPOKKP+\ngcmYCSHw3PRwPDc9HF8dvYDE9YdxsZnffHb12s5s7D5Vir/+ZphRbN61JPyXbUSWTwtHTWML3uXm\nAiK92p6pgbO9NSYEe8mO0iODPB3xeaeOEq+xo4TeKIqCV745hddSs3F3lB9enT0K1iyCr9vDNwfj\nhbuG4/vsUix6/wCqL3LCp0NyZhHe2p2LuaMH4p5Yf9lxLA7/dRuRof1dMHPUAHyw5zSKqi/KjkNk\nllratNh5ohhThvia1FfdHR0lEqL98MauHDzNjhK9TlEUrEjJwqpv24uSlxJGGE1HEXMwb4w/Vs2L\nxJFzVZiXtB+ltU2yI0l3SlOLZ7YeRaS/G/52xzBujpPAdD4FLMTTU0KhKMDrO3NkRyEyS/vyylF9\nsUX6IRrXwtZahZd0HSW+OHIBC9cdRCU7SvQKRVHw9/+exJrv87FgrD/+NXO4tNMGzdltI/pj7aIY\n5JfVYfaafThfZbmTPtUN7SfHOdpZY/UCbsSUhYWwkRno4YCF4wZha9o55BTXyo5DZHaS1Ro42lph\nYqi37CjXpKOjxJvzInGksAoz39mD02X1smOZNK1WwV/+cxzv7zmN+ycE4O93RLAI1qObw3zw8YOx\nKKtrQsK7e5FbYnkdUdq0Ch7flIELVRexekE0fF24OU4WFsJG6NFJIXC0tcbKHadkRyEyK21aBd8c\n12BSuI/Jtyb6TaeOEjPf2cOOEtdIq1Xwhy/U+GjfWSRODMKfbxvKr6cNICbAA5sTx6GlTcHsNfuQ\nWVgtO5JBvfzNKfyQXYr/uyMC0YPcZcexaCyEjZCHoy2W3hyMnSeKcfhMhew4RGbj4OkKlNc3Y4YJ\ndIvoia4dJT7PYEeJq9GmVfDcp8ew8WABHp0UjN/Fh7MINqCh/V2wdek49LGxwry1+7HfQn6Y+++x\nC3j3uzzcE+uPeWO4OU42FsJG6v4JAfBxtsOKZJ7IQ9RbktVFsLdR4eYw01wWcSkdHSWiB7nj6c3s\nKNFTrW1aLNt6FFvTCvHkLYOxbGoYi2AJAr0cse3hcejrao973z+IXSeLZUfSq5NFNXh26zHEDHLH\nX24fJjsOgYWw0XKwtcZTcaE4fLYSqSdLZMchMnlarYIUtQY3h/rAwdZadpxe5epgg48eiMXdUe0d\nJX675Sg7SlxBa5sWT285is8zzmPZ1FA8PSWURbBE/Vz7YMuScQjr64zE9Wn4IuO87Eh6UVnfjMT1\nh+HSxxrvLOABLcaCfwpGbHaMH4K8HLEyJQutbTyNh+h6pBdUoqS2CfHDTa9bRE/YWqvw8qwRWDY1\nFJ9nnGdHictoadPi8Y0Z+OroBfwuPhyPTR4sOxKhfUnghodiMTrAHU9tPmJ2p6y2tmnxxKYMFFc3\nYfWCaPg4c3OcsWAhbMSsrVRYPj0MOSV1+CzdPH9CJjKUZLUGtlYqTA73kR1Fb4QQeGzyYLwxdxSO\nFFbhrnf3sqNEJ02tbXhkQzqS1Rr86bahWHJTsOxI1ImzvQ0+vH8M4ob44s9fHseqXTlms8xn5Y5T\n+DGnDP+4MwKR/twcZ0xYCBu5acP6YtRAN7y6MxuNLfyqk+haKIqC5MwiTAz1grO9jew4enfHqAH4\n5KFYVDU0Y+Y7e3DwNDfdNra0Yen6NOw8UYz/u2MYHrwhUHYkugR7GyusXhCFuyIH4JWd2fjn1ydN\nvhj+8sh5JP2Qj0XjBmH26IGy41AXLISNnBACz8eHQ1PTiA/3npEdh8gkHS2sxoXqRkyPMI9uET0R\nE+CBzx+ZAA8HWyxYZ9kdJRpb2rD4o8PYfaoU/5o5HIvGBciORFdgbaXCy7NG4r7xAVj302ks33bM\nZJcHHr9Qjec+PYYxAR74021DZcehS2AhbALGBnlicrgP3tmdi6oGrvkjulrJ6iJYqwSmDPGVHcWg\nArwc8dkj4xE1yA1Pbz6K11Mtr6NEQ3MrHvjwEH7KLcPKhBGYH8t2VaZApRL4y+1D8eQtg7E1rRCP\nfZJhchtAK+qbkfhRGtwdbPH2PVGwsWLJZYz4p2Iilk8PQ21TK979Lk92FCKT0r4sQoPxIV5wdTD/\nZRFduTnY/txR4vVUy+ooUdfUivs+OIT9+eV4dfZIzI7h19KmRAiBp6eE4s+3DUXKcQ0e+PAQ6pta\nZcfqkdY2LR77JB2ldU1YszAa3s52siPRZbAQNhHhfV0wM3IAPth7Bhcs+Gx2oqt1oqgGBRUNmBFh\nnt0ieqKjo8QzUyyno0RtYwvuff8g0s5W4o25kZgZ6Sc7El2jB24IxMuzRmJ/fgXuWXfAJL4ZfSE5\nC3vzyvGvmcMxws9Ndhy6AhbCJuS3U0IBBXhtZ7bsKEQmIzlTA5UApgy1rGURXQkh8Pgtuo4S58y7\no0T1xRYseO8gjp6rwlvzInE0lGj2AAAgAElEQVT7yP6yI9F1Soj2wzv3ROHEhRrMXrMPxTWNsiNd\n1ucZhXjvp9O4b3wAEqL5A5ixYyFsQvzcHbBo3CB8ml6IU5pa2XGIjJ6iKNiuLsLYIE94OvGrSUDX\nUWKx+XaUqGpoxj3r9uPEhWq8uyAa8WZynDa1d1H68P7ROF95EQmr96KgvEF2pF9Rn6/G859mIjbQ\nA3+4dYjsONQDBiuEhRDLhRAJQojETmNRurGETmMJQog4IcRyQ2UzJY9OCoGjrTVe2pElOwqR0csp\nqUN+aT3iLXhZxKV07ShhLid5VdQ3Y97aA8gurkPSwhiL/xbAHI0P8cKGxWNR29iKhNV7jWpSqKyu\nCYkfHYaXkx3e4eY4k2GQPyUhRBwAKIqyDUCwECJId9cS3ViQriiO0j0uFUBVx236H3dHWyy9ORip\nJ0vMbiaHqLclZ2ogRPtMEv1SR0eJSH83PLX5CN5INe3DC0prmzAvaT/yS+uwblEMJpnxwSmWbtRA\nN2xZMg5CALPX7EN6QaXsSGhp0+LRDekor2/GmoXR/AbKhBjqx5UpAPJ113kA4nSzwHkAoCjKSkVR\n0gHMAVCle1w+gDgD5TMpD0wIhI+zHVYkm36jcSJ9SlYXIWaQO3xceJzppbg52GL9g7G4K2oAXkvN\nxjMm2lGipKYRc5P2oaCiAR/cNxoTQ71lRyI9C/V1xral4+HmYIMF6w7gp5wyqXn++fVJHDhdgRV3\nD0fEAFepWejqGKoQLgfgobt2AxAMYDQAT91M8PJO93We5vQ0UD6T0sfWCk9PCUV6QRW+OVEsOw6R\nUcovrUOWphbxFnSIxrWwtVbhlVkj8dspofgs4zwWvnfQJHbldyiqvog5SftRVN2ID+8fjfEhXrIj\nkYEM9HDA1iXj4O/hgAc+PIQUdZGUHNvSCvHh3jN48IZAdicxQYYqhLehvfgF2ovbct11uW4mGJ3X\nCV+JECJRCHFYCHG4tLS095OaiFnRfgjydsTKlCyTPXGHSJ+S1RoAwHSuD+6WEAJPdHSUKKjCzHf2\n4owJdJQorGzAnDX7UVrbhPUPjkFsEOdOLI2Piz02J45DxAAXPLIhHVsOnTPo+x89V4Xff56J8cGe\n+F18uEHfm3qHQQphRVHyAWzutOY3H+3LIvI73R6N9mURnWeOy9GFoihJiqLEKIoS4+1tuV9/WVup\nsHxaOPJK67EtzXKPTiW6nGR1EUYNdEN/tz6yo5iMO0YNwIZOHSUOnTHefQjnKtqL4MqGZnz8UCyi\nB3l0/yQyS64ONvj4oVhMCPHC8k+PYd2P+d0/qReU1jZh6cdp8Hayw1vzo2DNzXEmyVCb5aIAxOhm\nf910G+RSAXRsmgsCcAjA5i5jqYbIZ6qmDfNFpL8bXkvNxsVm01vXR6Qv5yoaoD5fgxnDORt8tUbr\nOkq4OdjinrUH8OUR4+socaasHnPW7ENdUys2Lh6LUQN5YIGlc7C1xrp7Y3Dr8H74x9cn8fKOU3rd\nQ9Pc2r45rrKhGUmLouHhaKu39yL9MtSMcDqACt3yhzW6sXy0d4ZI0N3e1mmZRByAqo7bdGlCCDw/\nPRzFNU34YO9p2XGIjEaybq0g1wdfmwAvR3z28HiM8nfDk5uO4M1dxtNRIq+0DnOS9qGxVYuNi8dy\nYxL9zM7aCm/Oi8ScmIF4a3cu/vKf49Bq9fP39h9fn8DBMxV48e4RGNaffwdNmbWh3kg3C9x1LKkn\nY3R5sUGeuCXcB+9+l4d5o/3hzp9KiZCs1iBigAsGejjIjmKy3B1tsf7BMfjdp5l4dWc2zpTX44W7\nhsPO2kpappziWsxbewCAgo2LxyKsr7O0LGScrFQCK+4eDlcHGyT9kI/qiy14edbIXu3pu+XQOXy0\n7ywSJwbhjlEDeu11SQ4uaDEDy6eHo66pFe98lys7CpF0RdUXkVFQxdngXmBnbYVXZo/E03Gh+Cz9\nPBZJ7CiRpanB3KT9EALYlMgimC5PCIHfxYfj2Wlh+PLIBSxZn4bGlt5ZPphRUIk/fqHGjYO9sHxa\nWK+8JsnFQtgMhPV1xt1Rfvj33rMorDS+IyeJDClF1y2Cp8n1DiEEnowbjNfnjEJGQRXuktBRQn2+\nGvOS9sPGSoXNiWMR4sMimK5MCIFHJ4XgH3dGYPepEix6/yBqGluu6zVLahux9OM0+LraYdW8SG6O\nMxP8UzQTT08JBQTw2s4c2VGIpErO1CDM1xlB3k6yo5iVOyMH4OOHYlGp6yhx2EAdJY4VVmH+2v1w\nsLXG5iVj+edKV2XB2EF4Y24k0s9WYv7a/Siva7qm12lu1eKRj9NRc7EVSQtj4ObAZYjmgoWwmRjg\n1gf3jQ/AZxmFyNLUyI5DJEVJbSMOna1APLtF6MWYwP91lJhvgI4S6QWVuGftAbj0scGmxLEY5Omo\n1/cj8/Sbkf2xdlEMcorrMGvNPpyvunjVr/G3r47j8NlKvDRrBIb0c9FDSpKFhbAZeeTmYDjZWWNl\nyinZUYik2HG8GIrCbhH6ZKiOEofOVGDRewfh4WSLLUvGceMjXZdJ4T5Y/2AsSmuaMOvdvcgrrevx\nczceLMCGAwVYelMwbhvRX48pSQYWwmbEzcEWj9wcgm+zSnAg/1dnkRCZvRR1EYK8HRHqy6/P9amj\no8TMyAF4dWc2ntl6FM2tvXfC5f78ctz7/kH4ONthc+I4HopCvWJMoAc2Jo5FU6sWs1fvg/p8dbfP\nSTtbgT9/qcbEUG88y81xZomFsJm5f0IA+rrYY0VKltH0/SQyhIr6ZuzPr8CMiH4QQsiOY/bsrK3w\naqeOEgvfO9ArHSX25Jbhvg8OYoBbH2xaMhZ9Xe17IS1Ru4gBrti6dBzsrFWYl7QfB09ffq17cU0j\nln6cjv5ufbBqbiSsVPx/xRyxEDYz9jZWeHrKYGQUVGHH8WLZcYgMZucJDdq0CqazW4TBXKqjxNny\na+8o8d2pEjzw4SEEeDpiY+JY+DizCKbeF+TthG0Pj4e3ix0WvncAu7NKfvWYptY2LP04DfVN7Zvj\nXB1sJCQlQ2AhbIbujvJDiI8TVu7IQmtb731dSWTMtmdq4O/hgGH9uZHF0Do6SlQ0NOPOt6+to8Su\nk8VI/CgNwd5O+GTxWHg52ekhKVG7/m59sHXJOAz2dcLijw7/YuOnoij4y5fHkVFQhVdmjWTPajPH\nQtgMWVupsHxaGPJL67E1rVB2HCK9q25owZ7cMsRH9OWyCEmup6PEjuMaLP04DeH9nPHJ4lh48IRM\nMgBPJztsXDwWUYPc8dTmI1i//ywAYMOBAmw6dA6PTQpB/HBuvDV3LITN1JShvoge5I7XdmbjYnPv\nnKhDZKxSTxajVavwQ0uywI6OEgPbO0qs6kFHia+PFeHRDemIGOCKjx+KZX9WMihnext89MAYTA7z\nwZ++UOP5T4/hb18dx6Qw7/b+/GT2WAibKSEEno8PR0ltE97fc1p2HCK9SlYXob+rPUb6ucqOYvHc\nHW2x/qH2jhKv7MzGsq3HLttR4ssj5/HEpgyMGuiGjx4YAxd7rsMkw7O3scLqhdG4Y1R/bDp0Dn7u\nDnidm+MshrXsAKQ/owM8EDfEF6u/y8P8Mf5w59eNZIZqG1vwQ04ZFsQO4rIII9HRUWKQpwNeT83B\n+aoGrF4Q/YvZ3k/TCvHstqMYHeCB9+8bDUc7fhyRPDZWKrw2exTGB3tifLAXXPvwhzJLwRlhM7d8\nehjqm1vx9u5c2VGI9OLbrBI0t2p5mpyREULgqbhQvDZnJNLPVuGud//XUWLLoXNYtu0oxgd74cP7\nx7AIJqOgUgnMGe3Pw1ssDAthMxfq64yEaD98tO8sCisbZMch6nUpag18nO0Q7e8uOwpdwsxIP6x/\ncAwq6psx8529eGH7SSz/9BgmDvbGuntj0MfWSnZEIrJgLIQtwFNxoRACeHVntuwoRL2qobkVu0+V\nYNqwvlBxPZ/Rig3yxOePTICLvTXW/JCPW8J9sGZhNOxtWAQTkVwshC1Af7c+uG9CAD7POI+TRTWy\n4xD1mu9PlaKxhcsiTEGglyM+f2QCXkoYgXcXsAgmIuPAQthCPHJTCJztrLEyJUt2FKJes12tgaej\nLcYEeMiOQj3g7miLWTEDYWvNjx4iMg7838hCuDrY4NFJIdh9qhT78splxyG6bo0tbfj2ZDGmDvOF\ntRX/KyMioqvHTw8Lcu/4APRztceKlKxum9wTGbsfc8pQ39yG+AgeokFERNeGhbAFsbexwtNTQnH0\nXBVS1BrZcYiuS3JmEVz72GBcsKfsKEREZKJYCFuYu6P8EOrrhJd2nEJL26VPeyIyds2tWuw8WYwp\nQ31hw2URRER0jfgJYmGsVALLp4Ujv6weWw6fkx2H6JrsyStDbWMr4iPYLYKIiK4dC2ELdMsQH4wO\ncMfrqTloaG6VHYfoqqVkauBkZ40bBnvJjkJERCaMhbAFEkLg+fhwlNY24f2fTsuOQ3RVWtu0+OaE\nBrcM8YGdNXvREhHRtWMhbKGiB3lg6lBfrP4+HxX1zbLjEPXYgdMVqGxoYbcIIiK6biyELdjy6WFo\naG7FW9/myo5C1GPbM4vQx8YKN4V6y45CREQmzmCFsBBiuRAiQQiReKn7Ol0nCCHiOo+RfoT4OGN2\nzECs338G5yoaZMch6labVsGO48WYHO6DPrZcFkFERNfHIIWwECIOABRF2QYgWAgR1OW+0brrKN3j\nUgFUddwm/XkqLhQqIfDqzmzZUYi6dfhMBcrqmjCd3SKIiKgXGGpGeAqAfN11HoC4yzxuDoAq3XX+\nFR5HvaSvqz0euCEQXxw5j+MXqmXHIbqiZLUGdtYqTAr3kR2FiIjMgKEK4XIAHrprNwDBQPsMsG72\nF53uq+h0m0dGGcDSm4LhYm+DlSmnZEchuiytVkGKWoObQr3hZGctOw4REZkBQxXC26ArftFe3Jbr\nrj0u/XAyJNc+NnhsUgi+zy7F3twy2XGILinjXBU0NY2IH85lEURE1DsMUggripIPYHOnNb/5l5gN\nBtqXRXSeOS7vcj+EEIlCiMNCiMOlpaX6C21hFo4bhP6u9liRkgVFUWTHIfqVFHURbKwEbhniKzsK\nERGZCUNtlosCEKMoSjoAN92muSBdh4gE3XUUgM0AOjbSBQHoWihDUZQkRVFiFEWJ8fZm+6TeYm9j\nhd9ODcOxwmpsz9TIjkP0C4qiYHumBjeEeMHF3kZ2HCIiMhOGmhFOB1ChK3rX6Ma26QpiD7TP/nY8\nrqOTRFXHbTKMmZEDEObrjJd2ZKGlTSs7DtHP1OdrcL7qIuKH8xANIiLqPQbrI9xR+HYtbnUzvMEd\n47rbqYqiJBkqG7WzUgk8Fx+GM+UN2HTonOw4RD/bri6ClUpgCpdFEBFRL+LJcvQLk8J8MCbQA2+k\n5qC+qVV2HCIoioLkzCKMD/aEu6Ot7DhERGRGWAjTLwgh8Hx8OMrqmvDeT6dlxyFClqYWZ8obeIgG\nERH1OhbC9CtR/u6YPqwv1nyfh/K6JtlxyMIlqzVQCWDqUBbCRETUu1gI0yU9Oz0Mja1arPo2V3YU\nsnDJmUUYHeABb2c72VGIiMjMXFMhLIRw6e0gZFyCvZ0wO8YPGw6cRUF5g+w4ZKFyS2qRU1KHGewW\nQUREenDFQlgIsaPT9bud7tqlt0RkNJ68JRRWKoFXdvLoZZIjWdfTmuuDiYhIH7qbERadroMvM05m\nqq+rPR6YEIgvj1yA+ny17DhkgZLVGkQPcoevi73sKEREZIaudY0wz+C1EEtuCoabgw1eTMmSHYUs\nzJmyepwoqkE8Z4OJiEhPuiuElctck4Vw7WODxyaF4MecMvyUUyY7DlmQZDWXRRARkX51VwhPEULk\nCCFyu1xHGSAbGYkFYwdhgFsfvJiSBa2WPw+RYaSoizDSzxV+7g6yoxARkZnqrhB2BxADILrLtYee\nc5ERsbexwm+nhCLzfDW+ziySHYcsQGFlA44WVmN6BLtFEBGR/lyxEFYUpfpyvwwVkIzDnZEDEN7X\nGS9/cwrNrVrZccjMpeiWRXB9MBER6VN37dMihRCHhBAuuusK3fKImYYKSMbBSiXw3PRwnC1vwKZD\nBbLjkJlLVmswpJ8LArwcZUchIiIz1t3SiCQAsxRFqQGwAsAtiqIMBvB7vScjo3NzmDdiAz3w5q4c\n1DW1yo5DZkpT3Yi0s5WYwdlgIiLSs277CCuKckZ37akoSkbHuP4ikbESQuD5+HCU1TVj3Y/5suOQ\nmdpxXLcsYjgLYSIi0q8e9REWQkwGcFjPWcgERPq7Iz6iL9b+kI/S2ibZccgMJauLMNjHCSE+zrKj\nEBGRmeuuEN6ia5e2FcBqIUSgEOIbAJv1H42M1bJpYWhs1eKtb3NkRyEzU1bXhIOnK7hJjoiIDKK7\nrhErAcwCEKQoyhG0H6qxRlGUlwwRjoxTsLcT5oweiA0HCnC2vF52HDIj3xwvhlYB4oezbRoREemf\n9ZXuFEK82+m606WIUxTlYX0GI+P21C2D8Xn6ebz8TTZWzYuUHYfMRLK6CIFejgjvy2URRESkf90t\njZgKYAqAKrQvj9jW6XeyYD4u9njwhkB8dfQCMgvZVpquX2V9M/bmlWN6RN/OP3gTERHpTXdLI4LR\nvjTCHcBKAHEA8hRF2WWAbGTkEm8KgruDDV5MyZIdhczAzpPFaNMqmMHT5IiIyEC67RqhKEqGoihL\nFUWJAZAK4EUhBHdJEVzsbfDY5MH4KbcMP+aUyo5DJi45swh+7n0QMcBFdhQiIrIQPWqfBvzcQm0W\ngGC0H7RBhAVj/THArQ9WJGdBq1VkxyETVdPYgp9yyxDPZRFERGRA3R2xPEoI8YIQ4hDa1wqvVhQl\nhl0jqIOdtRWWTQvF8Qs1+OrYBdlxyETtOlmMljYF07ksgoiIDKi7GeF0AAkATqN9nfASIcS7nbtJ\nEN0xcgCG9HPBy9+cQnOrVnYcMkHJmRr0dbFH5EA32VGIiMiCXLF9GoDoy4zzO3D6mUol8Nz0MNz3\nwSF8cuAs7psQKDsSmZD6plZ8n12KeWP8oVJxWQQRERlOd10jMtBeDLvrrisBBAJYYoBsZEJuCvXG\nuCBPvPltLmobW2THIROy+1QJmlq1PE2OiIgMrrs1wjvQ3kv4eSHEZrT3D54KIN8A2ciECCHwfHw4\nKuqbsfbH07LjkAlJztTAy8kWMQEesqMQEZGF6W5pRLCiKCEAIISoUBSFn1R0WSMHuuHW4f2w7sd8\nLBjrDx9ne9mRyMhdbG7D7lMlmBk5AFZcFkFERAbW3Wa5zjO/h/UZhMzDsmlhaGrVYtWuXNlRyAR8\nn12KhuY2zBjObhFERGR43RXCymWur5oQYrkQIkEIkdhpLFH368VOYwlCiDghxPLreT+SI9DLEfPG\nDMTGgwU4XVYvOw4ZuRR1EdwdbBAbyC+biIjI8LorhKcIIXKEELmdr6/2ZDkhRBwAKIqyDUCwECJI\nN5aqKEoSgCBd8Rule1wqgKqO22RanrhlMGysVHj5m1Oyo5ARa2ptQ+rJEkwd2hfWVj0+24eIiKjX\ndPfp4w4gBrrOEZ2uY67yfabgf8ss8gDEAQjS/Q7dfUEA5gCo6jQWBzI5Ps72WHxjIL4+VoRjhVXd\nP4Es0k85ZahrasX04ewWQUREcnTXPq36cr+u8n3KAXR89+mG9k14SbrZYACIQvsaZDcAFZ2e53mV\n70NGYvHEIHg42mJFchYUhW2n6deS1Ro421tjQrCX7ChERGShDPV95DYAwbprT7QXxgAA3fKHnYqi\npPfkhXRrig8LIQ6Xlpb2flLqFc72Nnh8cgj25pXjx5wy2XHIyLS0abHzRDGmDPGFrTWXRRARkRwG\n+QRSFCUfwOZOa347d6OIUxRlpe66Cr+cOS5HF7qZ5BhFUWK8vb31lpmu3/xYfwz06IMVyVnQajkr\nTP+zL68c1RdbEM9uEUREJJFBCmFdARyjm/V1022agxAisaMI1m2e24z2tcLQ/Z5qiHykH3bWVlg2\nNQwnimrw1bELsuOQEUlWF8HR1go3DuayCCIiksdQM8LpACqEEAkA1gA/F74vCiHyhBCVnR7XcV9V\nT5dLkPG6fUR/DO3ngpd2nEJTa5vsOGQEWtu0+OZ4MSYP8YW9jZXsOEREZMG6O1mu13TMAne6nYr2\nThRdH5fUdYxMl0rVfvTyovcP4pMDBbh/QqDsSCTZwTMVKK9vRnwEu0UQEZFc3KVCenfjYC9MCPHE\nqm9zUdvYIjsOSZai1sDeRoWbw7jGn4iI5GIhTHonhMBz08NRUd+MtT/kd/8EMltarYIUtQY3h/rA\nwdZgX0gRERFdEgthMogRfm64bUQ/rP3xNEpqG2XHIUnSCypRUtuEeB6iQURERoCFMBnMsqlhaGnT\n4s1dV3VCN5mR7Zka2FqrMDncR3YUIiIiFsJkOAFejpgf64+NB88hv7ROdhwyMEVRkKIuwsTBXnC2\nt5Edh4iIiIUwGdbjkwfDzlqFV77Jlh2FDOxoYTUuVDciPoKHaBARkXFgIUwG5e1sh8U3BuHrzCIc\nOVclOw4ZUHJmEaxVAnFDfGVHISIiAsBCmCRYPDEIno62WJF8EorCo5ctgaIoSFZrMCHEC64OXBZB\nRETGgYUwGZyTnTWeuGUw9udX4PvsUtlxyACOX6hBQUUDD9EgIiKjwkKYpJg3xh/+Hg5YkZwFrZaz\nwuYuRa2BlUpg6jAWwkREZDxYCJMUttYqLJsWhixNLb44cl52HNIjRVGwXV2E2EAPeDjayo5DRET0\nMxbCJM1tw/thhJ8r/vn1SZTU8JANc5VTUof80nrED2e3CCIiMi4shEkalUrg1dkjUd/ciqe3HOES\nCTO1PbMIQgDThrFbBBERGRcWwiRViI8z/nr7MOzJLce73+fJjkN6kKLWYPQgD/g428uOQkRE9Ass\nhEm6OaMH4tYR/fDqzmykna2UHYd6UV5pHbI0tYgfzk1yRERkfFgIk3RCCLxw13D0c7XHExszUH2x\nRXYk6iUpag0AYDrbphERkRFiIUxGwcXeBm/Oi0RxTSN+/1kmD9owE8nqIkT6u6Gfax/ZUYiIiH6F\nhTAZjSh/dzwzNQxfZxZh06FzsuPQdSoob4D6fA0P0SAiIqPFQpiMypKJQbhxsBf++p/jyC6ulR2H\nrkPK8SIAQHwE26YREZFxYiFMRkWlEnhl9kg421vjsU/S0djSJjsSXaPtmRpEDHDBQA8H2VGIiIgu\niYUwGR0fZ3u8MnsUsovr8Pf/npAdh67BhaqLOHKuirPBRERk1FgIk1G6KdQbiRODsOFAAZIzi2TH\noavU0S2C64OJiMiYsRAmo7VsahhG+rniuU+PobCyQXYcugopag3C+zojyNtJdhQiIqLLYiFMRsvW\nWoVV86KgVYCnNh1Ba5tWdiTqgZLaRhw6W8HewUREZPRYCJNR8/d0wD9nRuDw2Uq8sStHdhzqgR3H\ni6EowIzhXB9MRETGjYUwGb07Rg3ArGg/vLU7F3tzy2THoW4kZxYh2NsRg324LIKIiIwbC2EyCX+7\nYxgCvRzx1OYjKK9rkh2HLqO8rgkHTlcgPqIfhBCy4xAREV0RC2EyCQ621lg1LxJVDS14dtsxHsFs\npHaeKEabVkH8cK4PJiIi42ewQlgIsVwIkSCESOw0liCEiBNCLL/SGBEADOvvit/PCMe3WSV4f88Z\n2XHoErarNfD3cMDQfi6yoxAREXXLIIWwECIOABRF2QYgWAgRJISI0o2lAqgSQkRdaswQ+ch03Ds+\nAHFDfLEi+STU56tlx6FOqhtasDe3DPHD+3JZBBERmQRDzQhPAZCvu84DEAdgDoAq3Vj+FcaIfiaE\nwEsJI+DlZIfHN2agrqlVdiTS2XmyGK1ahafJERGRyTBUIVwOwEN37QYgWPd7RafHeF5mjOgX3B1t\n8fqcUThbXo8/f6mWHYd0UtRF6O9qj5F+rrKjEBER9YihCuFtaC9+gfbitvxaX0gIkSiEOCyEOFxa\nWtor4cj0xAZ54vHJg/FZ+nl8ll4oO47Fq21swQ/ZZZjObhFERGRCDFIIK4qSD2BzpzW/+WhfAtF5\nlrj8MmNdXytJUZQYRVFivL299RucjNrjk0MwJsADf/xCjdNl9bLjWLRvs0rQ3KbFDHaLICIiE2Ko\nzXJRAGIURUkH4KbbNLcZQJDuIUEAUi8zRnRJ1lYqvD53FGysVHh8YzqaWttkR7JYyZka+DjbIcrf\nXXYUIiKiHjPUjHA6gAohRAKANZ3GOjpKVCmKkn6pMUPkI9PV360PXkoYAfX5GqxMOSU7jkVqaG7F\nd9klmB7RFyoVl0UQEZHpsDbUG+lmgbuOJfVkjOhKpg7ri3vHDcJ7P53GDSFemBTuIzuSRfnuVCka\nW7SYHsFlEUREZFp4shyZhd/NGIIh/VzwzNajKK5plB3HoiSrNfB0tMWYAI/uH0xERGREWAiTWbC3\nscKqeZG42NyGpzcfQZuWRzAbQmNLG749WYypw/rC2or/nRARkWnhJxeZjRAfJ/ztN8OwN68c736X\nKzuORfghuxT1zW2I57IIIiIyQSyEyazMivHD7SP747XUHKSdrej+CXRdUtQauPaxwbhgnn1DRESm\nh4UwmRUhBP45MwL93ezxxMYjqG5okR3JbDW3arHzZDGmDPWFDZdFEBGRCeKnF5kdF3sbrJoXheKa\nRjz/2TEoCtcL68OevDLUNrbyEA0iIjJZLITJLI0a6IZnp4UhWa3BJwcLZMcxS8mZRXC2s8aEEC/Z\nUYiIiK4JC2EyW4tvDMLEUG/831cncEpTKzuOWWlp0+KbE8W4ZYgP7KytZMchIiK6JiyEyWypVAKv\nzBoJZ3sbPL4xHRebeQRzbzmQX4GqhhZMj+gnOwoREdE1YyFMZs3b2Q6vzh6J7OI6/P3rE7LjmI1k\ndREcbK1wc5i37ChERETXjIUwmb2Jod5YclMQPjlQgK+PFcmOY/LatAp2HNdgUpgP7G24LIKIiEwX\nC2GyCMumhmHkQDc8/6UyRUIAABRdSURBVNkxnKtokB3HpB0+U4GyumbEs1sEERGZOBbCZBFsrFRY\nNTcSUIAnN2WgpU0rO5LJSlZrYGetwqQwH9lRiIiIrgsLYbIY/p4O+Nddw5FeUIXXU7NlxzFJWq2C\nFLUGN4V6w9HOWnYcIiKi68JCmCzK7SP7Y07MQLzzXR725pbJjmNyMs5VQVPTiBnD2S2CiIhMHwth\nsjh/+c1QBHk54qnNR1Be1yQ7jklJziyCjZXA5CFcFkFERKaPhTBZHAdba7w1PwpVF1vwzNaj0Gp5\nBHNPKIqCZLUGNw72hou9jew4RERE142FMFmkIf1c8Mdbh+C7U6V4f89p2XFMQub5apyvuojpEewW\nQURE5oGFMFmshWMHYepQX7yYkoXMwmrZcYxesloDa5XA1KG+sqMQERH1ChbCZLGEEFiZMALeTnZ4\nfGM66ppaZUcyWoqiIDmzCOOCPeHmYCs7DhERUa9gIUwWzc3BFq/PjURBRQP+9IVadhyjlaWpxZny\nBsRHsFsEERGZDxbCZPHGBHrgyVtC8XnGeXyaVig7jlFKziyCSgBTh3FZBBERmQ8WwkQAHpscgthA\nD/zpSzXyS+tkxzE6yWoNxgR6wMvJTnYUIiKiXsNCmAiAlUrg9bmjYGutwuMbM9DU2iY7ktHILalF\nTkkdl0UQEZHZYSFMpNPPtQ9eShiJ4xdqsCI5S3Yco5GcqQEAtk0jIiKzw0KYqJMpQ31x3/gAfLDn\nDHadLJYdxyhsV2sQM8gdvi72sqMQERH1KhbCRF08Hx+Oof1csGzrUWiqG2XHkepMWT1OFtVwNpiI\niMwSC2GiLuxtrLBqfiSaWrV4anMG2iz4COZkdfuyiPjhXB9MRETmh4Uw0SUEezvhb78Zhv35FXhn\nd67sONIkq4sw0s8VA9z6yI5CRETU6wxWCAshEoQQcUKIxB6OLTdUNqJLSYj2wx2j+uP1XTk4fKZC\ndhyDK6xswLHCas4GExGR2TJIISyEiAKQryhKKoB8IUTUFcagG6vquE0kgxAC/7gzAn7uffDkpiOo\namiWHcmgUjqWRXB9MBHR/7d378FV1vkdxz+/XCCEADEQLuUmJ4CIASHGdUdtrZpUwtrZuhIRp/90\nnQ07O7q6dgTb7thp7Y4Lu9Zd2ZkuOJ3pZUdFstu63SUg0dbRrjfuhMuCOYpYEgwJkYsQcvn1j/M7\ncgwJuZznnOec87xfM5kkT57zPN/8Jsn55Pd8z/NDhkpma8Qa9z5krd3Zz7blktrdtrCkiiTWB1xm\nTF6unrt/sU6cvqDVv9wra4PTL1zX0Kz5U8Zq5vjRfpcCAEBCJCUIu5AbNsY0Smrrb5ukwpiPJWl8\nMuoDruT66YVavWSetu4/oV+8+7Hf5SRF82cXtOPoKWaDAQAZLVmtEYWKzPSul/S8MSbU17ZBHqvG\nGLPdGLO9paUlcUUDMR68dZZum1usp35zQIeaT/tdTsJt3c/dIgAAmS9ZrRE1kp621q6VVC1pWT/b\n2iUVuccUSmrtfSBr7QZrbbm1try4uDgpxQNZWUbP3He9xo3K1UMv7NL5i5m9BPPmfU2aM7FAsycW\n+F0KAAAJk/Tbp0VfCNfPto2SojPDIUn1ya0O6N+EgpF69r5Famw5q7//zX6/y0mYljMdev+jNmaD\nAQAZLycZJ7HWrjXGrDLGhCUVWWs3SFI/28qNMRWS2mNeVAekhFvnTNC3byvRP/1Po26dXayvLcy8\nsPjqgWb1WGnpAvqDAQCZLSlBWIqE4UFu25CcioDheaxyrt4Jt+qJX+3VwmnjNL0o3++SPFW3r1mz\nJozWNZPG+F0KAAAJxcpywBDlZmfpufsXS1b67ku71Nnd43dJnjl17qLeDreqqnSyjDF+lwMAQEIR\nhIFhmF6Urx/eu1C7Pm7XP2477Hc5ntl24IS6e6yqSjOv5QMAgN4IwsAwfW3hFK34ynT9/I1GvXXk\npN/leKKuoUnTrhql0qlj/S4FAICEIwgDcXjy7us0u7hA33t5t06e7fC7nLh8dr5Tb31wkrYIAEBg\nEISBOIwaka11DyzW6fOd+suX96inJ32XYH790Al1dltumwYACAyCMBCneZPH6vt3z9cbh1v0z299\n6Hc5w7Z5X7Mmj83TommFfpcCAEBSEIQBD/z5TTN013WTtHbrIe39pH3gB6SYsx1deuNwi5aUTlZW\nFm0RAIBgIAgDHjDGaM29C1VcMFIPv7hLZy50+l3SkPz3oU91satHVaUsogEACA6CMOCRwvwRem7F\nYn1y6ry+/58NsjZ9+oW3NDRrQsFIlV9d5HcpAAAkDUEY8FD51UV69M45emX3cdXu+MTvcgbl/MVu\nvX7oU9113SRl0xYBAAgQgjDgse/cPltfDRXpyVf2q7HlrN/lDOiNwy0639mtpdwtAgAQMARhwGPZ\nWUY/Wb5YeblZeviFXero6va7pCuqa2jSVfm5umkWbREAgGAhCAMJMHlcnn5cfb0ONJ3W05sP+V1O\nvzq6uvXawU/1J/MnKyebPwcAgGDhmQ9IkDuvnaS/uOVq/cvvPlL9gRN+l9Ont46c1NmOLlUt4G4R\nAIDgIQgDCfRE1Txd9wdj9XjtHjV9dt7vci6zeV+zxuTl6OaSCX6XAgBA0hGEgQQamZOtdSsWq6Or\nR4++tFvdKbQE88WuHm070KzK+ZM0Ioc/BQCA4OHZD0iwUHGBnvp6qd79sE0/e/0Dv8v5wtvhVp2+\n0KWqUu4WAQAIJoIwkAT33jBN9yyeqp++dljvfdjmdzmSpC0NTRo9Ilt/OIe2CABAMBGEgSR56s9K\nNaMoX4++tEvtn1/0tZau7h5t3X9Cd1w7SXm52b7WAgCAXwjCQJIUjMzRuhVlajnboVW1e31dgvm9\nj9rUdu6ilpZytwgAQHARhIEkWjBtnFYvmadXD5zQL9456lsddfualZebpduuKfatBgAA/EYQBpLs\nm7fM0u3XFOup3x7UwabTST9/T4/Vlv3Nuv2aicofkZP08wMAkCoIwkCSZWUZ/bj6ehWOytVDL+zU\n5xe7knr+HR+fUsuZDlUt4G4RAIBgIwgDPhhfMFI/Wb5I4ZPn9He/PpDUc2/e16QROVm6Y97EpJ4X\nAIBUQxAGfHLz7An6zh+XaOP2Y/qvPceTcs6eHqstDc36oznFKhhJWwQAINgIwoCPHq2Yq7IZhfrr\nX+3TsbbPE36+PZ+0q+mzC6ribhEAABCEAT/lZmfpp/cvloz08Iu71Nndk9DzbWloVm62UcW1kxJ6\nHgAA0gFBGPDZ9KJ8rbl3oXYfa9czrx5O2Hmstdrc0KSbSyZoXH5uws4DAEC6SFoQNsYsM8ZUGGNq\nYraVue3L+thvVbJqA/y2dMEUPXDTDP38jUa9eaQlIefYf/y0jrWd19IFtEUAACAlKQgbY8okha21\n9ZLC7nNJWmmtrZUUcqG4TJLcfu0x+wEZ78m752vupAJ9b+MetZzp8Pz4dQ1Nys4yqpxPEAYAQEpu\na8Qa9z5krd3pZoEbJclau9Zau1PSckntbr+wpIok1gf4Ki83Wz97oExnLnTqsZd3q6fHuyWYrbWq\n29esr4aKVDR6hGfHBQAgnSUlCLuQGzbGNEpqc5tvlDTezQRH2yAKY74uSeOTUR+QKuZOGqMn/3S+\n3jxyUs+/GfbsuIdPnFX45DktKWURDQAAopLVGlGoyEzveknPG2NC7kutLiQrtk94gGPVGGO2G2O2\nt7QkppcS8NMDX5mhqtLJ+tHW32v3sfaBHzAIdQ1NMka66zruFgEAQFSyWiNqJD1trV0rqVpStC0i\nOuUVVmSGuF1SkdtWKKm194GstRusteXW2vLi4uKEFw4kmzFGP/zGQk0am6fvvrhLZy50xn3Mun3N\nuvHqIk0ck+dBhQAAZIak3z4t+kI4SfWSojPDIUnvS9rYa1t9susDUsG4/Fw9t2KR/q/9vP7mPxpk\n7fD7hRtbzur3J86wiAYAAL0kq0d4raQad2u0GjerG1bkzhDL3D61MW0SFZLao58DQXTDzCI9VjlX\nv95zXJt2fDLs42xpaJYkLSEIAwDwJTnJOpELw723bRjMNiCovn1bif73g5P621f2q2zGVZo9sWDI\nx9i8r0mLZxRqyrhRCagQAID0xcpyQArLzjJ6dvkijRqRrYde2KkLnd1DevzHrZ9r//HTWsrdIgAA\nuAxBGEhxk8bm6Znq63Wo+Yye3nxwSI+ta2iSRFsEAAB9IQgDaeD2eRP14K2z9K9vH9Wr+5sH/bi6\nhmYtmDpO04vyE1gdAADpiSAMpIlVS65R6dSxWvXLvWr67PyA+x9vP6/dx9qZDQYAoB8EYSBNjMzJ\n1roVZers6tEjL+1W9wBLMEfvFsFt0wAA6BtBGEgjsyaM1j/cU6r3PmzTutePXHHfuoYmzZs8RqHi\nod9pAgCAICAIA2nmnsXT9I2yqXrutSN6N3zZ4ouSpE9PX9D2o6dUxd0iAADoF0EYSENPfb1UM8eP\n1iMv7dapcxcv+/rW/c2yVqpaQFsEAAD9IQgDaWj0yBytW7FYrec69Hjt3suWYK5raFZJ8WjNGcYC\nHAAABAVBGEhTpVPH6Ymqa1V/8IT+7e2jX2xvPduhd8KtWrpgiowxPlYIAEBqIwgDaeybt1ytO+ZN\n1A82H9SB46clSa8eOKEeyyIaAAAMhCAMpDFjjH60bKGuys/VQy/u1OcXu1TX0KyZ4/M1f8pYv8sD\nACClEYSBNDe+YKSeXb5IH548p8c37dXvPjipJaWTaYsAAGAABGEgA9xcMkEP3T5bv93XpK4eq6Xc\nNg0AgAHl+F0AAG88cuccvRNuVcuZDi2cNs7vcgAASHkEYSBD5GRn6d8fvEkXOrtpiwAAYBAIwkAG\nycvNVl5utt9lAACQFugRBgAAQCARhAEAABBIBGEAAAAEEkEYAAAAgUQQBgAAQCARhAEAABBIBGEA\nAAAEEkEYAAAAgUQQBgAAQCARhAEAABBIBGEAAAAEEkEYAAAAgWSstX7XMGzGmBZJR3049QRJJ304\nb6ZiPL3FeHqL8fQW4+k9xtRbjKe3/BjPmdba4sHsmNZB2C/GmO3W2nK/68gUjKe3GE9vMZ7eYjy9\nx5h6i/H0VqqPJ60RAAAACCSCMAAAAAKJIDw8G/wuIMMwnt5iPL3FeHqL8fQeY+otxtNbKT2e9AgD\nAAAgkJgRBjKQMWaV3zUAAJDqCMJDYIypcW9r/K4lExhjKtwb4+khY0yFpBv9riMTRH82jTE1fteS\nCYwxZcaYZcaYZX7Xku7cWFpjTKN7W+93TenO/WxW8PvuDWPMKjemKT2eBOFBcuGi3lq7QVLIfY5h\nMsaUSaq01tZLKnOfA6mmxhjTKCnsdyEZYqW1tlaRv6H8zsenyFprrLUlkqolMaEQB/fzGHbPSWF+\nPuMTzUju973EGBPyuaR+EYQHLyQpGn7D7nMMk7V2p7V2tfs0ZK3d6WtBGcIYU+b+kMMb37LWljCm\n8XOzwI2SZK1dy+98fHr9TIastfyzFr/oPxM8J8WvUpcmEBp1KT+lHILwIFlrN7jZYEkqk7Tdz3oy\nhetlXel3HRmkyO8CMkzIXSql5zp+N0oa7y7pM54eiV6t9LuOdOeCb9hdAWrzu54M0KpLz0eFkkp8\nrOWKCMJD5C6XbOO/RW9Ya9dKWmmMKfS7lnTHbLD33MxlvSIBLmVnNNJIa/RvJ33Cnqm01rb7XUS6\nc89B7ZLWS3o+lS/lp4laXQq/4xUJximJIDx0FS68IQ5uVijagxWWlNLN9GkiFPNCJHow4+ReGBsN\na62iHSpesb3WYfGCTq/we+6NGklPu+f3akn8oxYH16qzsdfzfEoiCA+BMaYmGoKZHYpbhb582SRl\nf0nShbW21r0woUiRMUV8tuvSJecS0Q4Vr3pd+mciJOl9H2vJCG7Wktlgj7mrQIxrHFwALndXgArd\nc1NKYkGNQXLBd5MivUNFkqq5DD187jLUfYqMZ6W1lj5hpBx32582RV48w5WgODGe3nJBeDV/P73h\netfDityRI6VXQ0sHMVfUwqncTkoQBgAAQCDRGgEAAIBAIggDAAAgkAjCAAAACCSCMAAAAAKJIAwA\nAIBAIggDQC9uwRcbu7qUMWaVu/3XcI+5KpGrqRljtsVTX69jFUZrdYu0rOprmxfnAgA/EYQBoG9h\nRZZbTXnRJco9vPdpkaTl7pi17p6/fW0DgLRGEAaAvtVLCveeZe09G2qM2eHeV7hZ2U3GmEY3i7rN\nGLMjZpnR5THblsUcY73b9sW+7njr3bFiZ6Y3xRwjusLlGknlvY65yj2+r/Nti3lb1vt8kn4gqSK6\nZLf7flf3sa3PetyxNrm3HTHnCMWcd1M0wAOAX3L8LgAAUpW1dqULooNeRdJaW+2C30prbaX7eLmk\nVvf1SkkyxpySVBsN2tbaG1ww3KHIks5SZInS6MdfrHxlrV3da9/ViqzW1nsZ01Af5wtJWm+trXWh\ne42k6OPKrbUlbp8ct080QK9RZMWt2phg21890XPHfk+1iiytvtPtH11mnaVsAfiGGWEAuLKVGnyL\nRHQZ0faYj8OSojOf22L23e4C5w2KzOZukvS8vhwMewfwkugxrLWDCZB9na9NUqUxZr0i31usoS4b\nf6V66ntvj7ZuGGO2Sap2tQCAbwjCAHAF1tp6RcJsbGgcL0VaAIZ4uOqYj8uttWFFZkvrrbXV1tpq\nSRuv8PhGSdEZ3kJFZlSvpLKP8/2VpB3W2pWSNg2x/rjqcbPfG90sdaMkT17cBwDDRWsEAAzAtUic\nch/XGmNWulnNnQM8tLd297giSd9yx9sQ7bN1+/Q7+2ytXRuzb5G+HKz71Pt8igTtNcaYSkUCfiim\nhzmqTVJZr7tcXLZtGPVsl7TJGBNWZOZ79UD1A0AiGWut3zUAADwW07/bu28YAODQGgEAAIBAYkYY\nAAAAgcSMMAAAAAKJIAwAAIBAIggDAAAgkAjCAAAACCSCMAAAAAKJIAwAAIBA+n+j65NjohOY8AAA\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff88d4120f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.common import Transformations\n",
+    "diff = Transformations.Differential(1)\n",
+    "\n",
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, cheng.TrendWeightedFTS, \n",
+    "                              range(2,10), [1], transformation=diff, tam=[10, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the partitioning schemas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAALICAYAAABijlFfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nADlCgQUAAAAAOfJ/5DVKocyjUk0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff88b23bd30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.partitioners import Grid, Util as pUtil\n",
+    "\n",
+    "fuzzy_sets = Grid.GridPartitioner(enrollments, 18)\n",
+    "fuzzy_sets2 = Grid.GridPartitioner(enrollments, 4, transformation=diff)\n",
+    "\n",
+    "pUtil.plot_partitioners(enrollments, [fuzzy_sets,fuzzy_sets2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trend Weighted FTS:\n",
+      "A10 -> A10(0.167),A12(0.333),A9(0.5)\n",
+      "A12 -> A14(1.0)\n",
+      "A14 -> A13(0.167),A14(0.333),A14(0.5)\n",
+      "A2 -> A3(1.0)\n",
+      "A3 -> A4(1.0)\n",
+      "A4 -> A6(1.0)\n",
+      "A6 -> A6(0.167),A7(0.333),A8(0.5)\n",
+      "A7 -> A6(0.067),A7(0.133),A7(0.2),A7(0.267),A8(0.333)\n",
+      "A8 -> A10(0.333),A10(0.667)\n",
+      "A9 -> A7(1.0)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model1 = cheng.TrendWeightedFTS(\"FTS\", partitioner=fuzzy_sets)\n",
+    "model1.fit(enrollments)\n",
+    "\n",
+    "print(model1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trend Weighted FTS:\n",
+      "A0 -> A1(1.0)\n",
+      "A1 -> A0(0.036),A1(0.071),A1(0.107),A2(0.143),A2(0.179),A2(0.214),A3(0.25)\n",
+      "A2 -> A1(0.022),A1(0.044),A1(0.067),A1(0.089),A2(0.111),A2(0.133),A3(0.156),A3(0.178),A3(0.2)\n",
+      "A3 -> A2(0.1),A2(0.2),A2(0.3),A3(0.4)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model2 = cheng.TrendWeightedFTS(\"FTS Diff\", partitioner=fuzzy_sets2)\n",
+    "model2.append_transformation(diff)\n",
+    "model2.fit(enrollments)\n",
+    "\n",
+    "print(model2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[13336.366666666669,\n",
+       " 13865.322222222225,\n",
+       " 14923.233333333337,\n",
+       " 15584.427777777782,\n",
+       " 15452.188888888893,\n",
+       " 15452.188888888893,\n",
+       " 15452.188888888893,\n",
+       " 17039.055555555562,\n",
+       " 17215.37407407408,\n",
+       " 17215.37407407408,\n",
+       " 15452.188888888893,\n",
+       " 15452.188888888893,\n",
+       " 15452.188888888893,\n",
+       " 15584.427777777782,\n",
+       " 15584.427777777782,\n",
+       " 17039.055555555562,\n",
+       " 17215.37407407408,\n",
+       " 19154.877777777787,\n",
+       " 19022.638888888898,\n",
+       " 19022.638888888898,\n",
+       " 19022.638888888898,\n",
+       " 18625.92222222223]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model1.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12740.168421052631,\n",
+       " 13245.846428571429,\n",
+       " 13552.168421052631,\n",
+       " 14378.846428571429,\n",
+       " 15142.846428571429,\n",
+       " 15214.435714285715,\n",
+       " 15288.168421052631,\n",
+       " 15546.168421052631,\n",
+       " 16489.84642857143,\n",
+       " 16604.168421052633,\n",
+       " 16291.435714285715,\n",
+       " 15336.435714285715,\n",
+       " 15182.168421052631,\n",
+       " 15048.435714285715,\n",
+       " 14848.168421052631,\n",
+       " 15666.846428571429,\n",
+       " 16541.84642857143,\n",
+       " 17347.55,\n",
+       " 18652.84642857143,\n",
+       " 19013.168421052633,\n",
+       " 19022.168421052633,\n",
+       " 18779.435714285715]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model2.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQoAAAE/CAYAAAAOpf5VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAEEEhAAAAABBBIQAAAACQ5P8A3IKTLybtXc8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff88b26ada0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bchmk.plot_compared_series(enrollments, [model1, model2], bchmk.colors, intervals=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model\t\t& Order     & RMSE\t\t& SMAPE      & Theil's U\t\t\\\\ \n",
+      "TWFTS FTS\t\t& 1\t\t& 361.36\t\t& 0.85\t\t& 0.59\t\\\\ \n",
+      "TWFTS FTS Diff\t\t& 1\t\t& 849.57\t\t& 2.18\t\t& 1.39\t\\\\ \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "bchmk.print_point_statistics(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Residual Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "cannot convert float NaN to integer",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2216\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1299\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2437\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1131\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1135\u001b[0m                                                                 renderer)\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m         \u001b[0minterval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 974\u001b[0;31m         \u001b[0mtick_tups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    975\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m             \u001b[0;31m# handle inverted limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m         \"\"\"\n\u001b[0;32m--> 917\u001b[0;31m         \u001b[0mmajorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1951\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1952\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1953\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1955\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1959\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[1;32m   1960\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[0;32m-> 1961\u001b[0;31m         \u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0mprune\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'auto'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1902\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1903\u001b[0;31m                 nbins = np.clip(self.axis.get_tick_space(),\n\u001b[0m\u001b[1;32m   1904\u001b[0m                                 max(1, self._min_n_ticks - 1), 9)\n\u001b[1;32m   1905\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2060\u001b[0m         \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m31\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f25e2d76be0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.benchmarks import ResidualAnalysis as ra\n",
+    "\n",
+    "ra.plot_residuals(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyFTS/notebooks/HighOrderFTS.ipynb b/pyFTS/notebooks/HighOrderFTS.ipynb
new file mode 100644
index 0000000..9dc5c67
--- /dev/null
+++ b/pyFTS/notebooks/HighOrderFTS.ipynb
@@ -0,0 +1,601 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# High Order Fuzzy Time Series \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Common Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
+      "  from pandas.core import datetools\n",
+      "/usr/lib/python3/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['plt']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n",
+      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pylab as plt\n",
+    "from pyFTS.benchmarks import benchmarks as bchmk\n",
+    "from pyFTS.models import hofts\n",
+    "\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyFTS.data import Enrollments\n",
+    "\n",
+    "enrollments = Enrollments.get_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp0AAAFZCAYAAADaRJQBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5a18f72940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, hofts.HighOrderFTS, range(1,20), [1, 2, 3], tam=[10, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp0AAAFZCAYAAADaRJQBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQef/AbppTUrOKY6qAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5a16ed86a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.common import Transformations\n",
+    "diff = Transformations.Differential(1)\n",
+    "\n",
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, hofts.HighOrderFTS, range(1,20), [1, 2, 3], \n",
+    "                              transformation=diff, tam=[10, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the partitioning schemas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAALICAYAAABijlFfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCOEm/w8iMhm/tFhCuQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5a16ed8278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.partitioners import Grid, Util as pUtil\n",
+    "\n",
+    "fuzzy_sets = Grid.GridPartitioner(enrollments, 12)\n",
+    "fuzzy_sets2 = Grid.GridPartitioner(enrollments, 5, transformation=diff)\n",
+    "\n",
+    "pUtil.plot_partitioners(enrollments, [fuzzy_sets,fuzzy_sets2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "High Order FTS:\n",
+      "A1, A2, A2 -> A3,A4\n",
+      "A1, A2, A3 -> A3,A4\n",
+      "A1, A3, A2 -> A3,A4\n",
+      "A1, A3, A3 -> A3,A4\n",
+      "A10, A10, A10 -> A8,A9\n",
+      "A10, A10, A9 -> A8,A9\n",
+      "A10, A9, A10 -> A8,A9\n",
+      "A10, A9, A9 -> A8,A9\n",
+      "A2, A2, A2 -> A3,A4\n",
+      "A2, A2, A3 -> A3,A4,A5\n",
+      "A2, A2, A4 -> A4,A5\n",
+      "A2, A3, A2 -> A3,A4\n",
+      "A2, A3, A3 -> A3,A4,A5\n",
+      "A2, A3, A4 -> A4,A5\n",
+      "A2, A3, A5 -> A4,A5\n",
+      "A2, A4, A4 -> A4,A5\n",
+      "A2, A4, A5 -> A4,A5\n",
+      "A3, A2, A3 -> A4,A5\n",
+      "A3, A2, A4 -> A4,A5\n",
+      "A3, A3, A3 -> A4,A5\n",
+      "A3, A3, A4 -> A4,A5\n",
+      "A3, A3, A5 -> A4,A5\n",
+      "A3, A4, A4 -> A4,A5\n",
+      "A3, A4, A5 -> A4,A5\n",
+      "A3, A5, A4 -> A4,A5\n",
+      "A3, A5, A5 -> A4,A5\n",
+      "A4, A4, A4 -> A4,A5,A6\n",
+      "A4, A4, A5 -> A4,A5,A6,A7\n",
+      "A4, A4, A6 -> A6,A7\n",
+      "A4, A5, A4 -> A4,A5,A6\n",
+      "A4, A5, A5 -> A4,A5,A6,A7\n",
+      "A4, A5, A6 -> A6,A7,A8,A9\n",
+      "A4, A5, A7 -> A6,A7,A8,A9\n",
+      "A4, A6, A6 -> A6,A7,A8,A9\n",
+      "A4, A6, A7 -> A6,A7,A8,A9\n",
+      "A5, A4, A4 -> A4,A5,A6\n",
+      "A5, A4, A5 -> A4,A5,A6,A7\n",
+      "A5, A4, A6 -> A6,A7\n",
+      "A5, A5, A4 -> A4,A5,A6\n",
+      "A5, A5, A5 -> A4,A5,A6,A7\n",
+      "A5, A5, A6 -> A6,A7,A8,A9\n",
+      "A5, A5, A7 -> A6,A7,A8,A9\n",
+      "A5, A6, A6 -> A5,A6,A7,A8,A9\n",
+      "A5, A6, A7 -> A5,A6,A7,A8,A9\n",
+      "A5, A6, A8 -> A10,A9\n",
+      "A5, A6, A9 -> A10,A9\n",
+      "A5, A7, A6 -> A5,A6\n",
+      "A5, A7, A7 -> A5,A6\n",
+      "A5, A7, A8 -> A10,A9\n",
+      "A5, A7, A9 -> A10,A9\n",
+      "A6, A4, A4 -> A4,A5\n",
+      "A6, A4, A5 -> A4,A5\n",
+      "A6, A5, A4 -> A4,A5\n",
+      "A6, A5, A5 -> A4,A5\n",
+      "A6, A6, A4 -> A4,A5\n",
+      "A6, A6, A5 -> A4,A5\n",
+      "A6, A6, A6 -> A4,A5,A6\n",
+      "A6, A6, A7 -> A5,A6\n",
+      "A6, A6, A8 -> A10,A9\n",
+      "A6, A6, A9 -> A10,A9\n",
+      "A6, A7, A5 -> A4,A5\n",
+      "A6, A7, A6 -> A4,A5,A6\n",
+      "A6, A7, A7 -> A5,A6\n",
+      "A6, A7, A8 -> A10,A9\n",
+      "A6, A7, A9 -> A10,A9\n",
+      "A6, A8, A10 -> A10,A9\n",
+      "A6, A8, A9 -> A10,A9\n",
+      "A6, A9, A10 -> A10,A9\n",
+      "A6, A9, A9 -> A10,A9\n",
+      "A7, A5, A4 -> A4,A5\n",
+      "A7, A5, A5 -> A4,A5\n",
+      "A7, A6, A4 -> A4,A5\n",
+      "A7, A6, A5 -> A4,A5\n",
+      "A7, A6, A6 -> A4,A5\n",
+      "A7, A7, A5 -> A4,A5\n",
+      "A7, A7, A6 -> A4,A5\n",
+      "A7, A8, A10 -> A10,A9\n",
+      "A7, A8, A9 -> A10,A9\n",
+      "A7, A9, A10 -> A10,A9\n",
+      "A7, A9, A9 -> A10,A9\n",
+      "A8, A10, A10 -> A10,A9\n",
+      "A8, A10, A9 -> A10,A9\n",
+      "A8, A9, A10 -> A10,A9\n",
+      "A8, A9, A9 -> A10,A9\n",
+      "A9, A10, A10 -> A10,A8,A9\n",
+      "A9, A10, A9 -> A10,A8,A9\n",
+      "A9, A9, A10 -> A10,A8,A9\n",
+      "A9, A9, A9 -> A10,A8,A9\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model1 = hofts.HighOrderFTS(\"FTS\", partitioner=fuzzy_sets)\n",
+    "model1.fit(enrollments, order=3)\n",
+    "\n",
+    "print(model1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "High Order FTS:\n",
+      "A0, A1, A2 -> A2,A3\n",
+      "A0, A1, A3 -> A2,A3\n",
+      "A0, A2, A2 -> A2,A3\n",
+      "A0, A2, A3 -> A2,A3,A4\n",
+      "A0, A2, A4 -> A4\n",
+      "A0, A3, A3 -> A4\n",
+      "A0, A3, A4 -> A4\n",
+      "A1, A0, A1 -> A2,A3\n",
+      "A1, A0, A2 -> A2,A3\n",
+      "A1, A1, A0 -> A1,A2\n",
+      "A1, A1, A1 -> A1,A2,A3\n",
+      "A1, A1, A2 -> A2,A3\n",
+      "A1, A1, A3 -> A2,A3\n",
+      "A1, A1, A4 -> A2,A3\n",
+      "A1, A2, A0 -> A1,A2\n",
+      "A1, A2, A1 -> A1,A2\n",
+      "A1, A2, A2 -> A1,A2,A3,A4\n",
+      "A1, A2, A3 -> A2,A3,A4\n",
+      "A1, A2, A4 -> A2,A3,A4\n",
+      "A1, A3, A1 -> A1,A2\n",
+      "A1, A3, A2 -> A1,A2,A3,A4\n",
+      "A1, A3, A3 -> A2,A3,A4\n",
+      "A1, A3, A4 -> A4\n",
+      "A1, A4, A2 -> A2,A3\n",
+      "A1, A4, A3 -> A2,A3\n",
+      "A2, A0, A1 -> A2,A3\n",
+      "A2, A0, A2 -> A2,A3,A4\n",
+      "A2, A0, A3 -> A3,A4\n",
+      "A2, A1, A0 -> A1,A2\n",
+      "A2, A1, A1 -> A0,A1,A2,A3,A4\n",
+      "A2, A1, A2 -> A0,A1,A2,A3,A4\n",
+      "A2, A1, A3 -> A1,A2,A3,A4\n",
+      "A2, A1, A4 -> A2,A3\n",
+      "A2, A2, A0 -> A1,A2,A3\n",
+      "A2, A2, A1 -> A0,A1,A2,A3,A4\n",
+      "A2, A2, A2 -> A0,A1,A2,A3,A4\n",
+      "A2, A2, A3 -> A1,A2,A3,A4\n",
+      "A2, A2, A4 -> A2,A3\n",
+      "A2, A3, A0 -> A2,A3\n",
+      "A2, A3, A1 -> A1,A2,A3\n",
+      "A2, A3, A2 -> A1,A2,A3,A4\n",
+      "A2, A3, A3 -> A1,A2,A3,A4\n",
+      "A2, A3, A4 -> A2,A3\n",
+      "A2, A4, A2 -> A1,A2,A3\n",
+      "A2, A4, A3 -> A1,A2,A3\n",
+      "A2, A4, A4 -> A2,A3\n",
+      "A3, A0, A2 -> A3,A4\n",
+      "A3, A0, A3 -> A3,A4\n",
+      "A3, A1, A1 -> A0,A1,A3,A4\n",
+      "A3, A1, A2 -> A0,A1,A2,A3,A4\n",
+      "A3, A1, A3 -> A1,A2,A3,A4\n",
+      "A3, A2, A0 -> A2,A3\n",
+      "A3, A2, A1 -> A0,A1,A2,A3,A4\n",
+      "A3, A2, A2 -> A0,A1,A2,A3,A4\n",
+      "A3, A2, A3 -> A0,A1,A2\n",
+      "A3, A3, A0 -> A2,A3\n",
+      "A3, A3, A1 -> A1,A2,A3\n",
+      "A3, A3, A2 -> A0,A1,A2\n",
+      "A3, A3, A3 -> A0,A1,A2\n",
+      "A3, A3, A4 -> A2,A3\n",
+      "A3, A4, A2 -> A1,A2,A3,A4\n",
+      "A3, A4, A3 -> A1,A2,A3,A4\n",
+      "A3, A4, A4 -> A2,A3\n",
+      "A4, A2, A1 -> A1,A2\n",
+      "A4, A2, A2 -> A0,A1,A2\n",
+      "A4, A2, A3 -> A0,A1,A2,A3\n",
+      "A4, A2, A4 -> A2,A3\n",
+      "A4, A3, A1 -> A1,A2\n",
+      "A4, A3, A2 -> A0,A1,A2\n",
+      "A4, A3, A3 -> A0,A1,A2,A3\n",
+      "A4, A3, A4 -> A2,A3\n",
+      "A4, A4, A2 -> A3,A4\n",
+      "A4, A4, A3 -> A3,A4\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model2 = hofts.HighOrderFTS(\"FTS Diff\", partitioner=fuzzy_sets2)\n",
+    "model2.append_transformation(diff)\n",
+    "model2.fit(enrollments, order=3)\n",
+    "\n",
+    "print(model2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[14923.233333333337,\n",
+       " 15319.950000000004,\n",
+       " 15319.950000000004,\n",
+       " 15716.666666666672,\n",
+       " 16113.383333333339,\n",
+       " 16113.383333333339,\n",
+       " 17700.250000000007,\n",
+       " 17303.53333333334,\n",
+       " 15716.666666666672,\n",
+       " 15319.950000000004,\n",
+       " 15319.950000000004,\n",
+       " 15716.666666666672,\n",
+       " 15716.666666666672,\n",
+       " 16113.383333333339,\n",
+       " 17700.250000000007,\n",
+       " 19287.116666666676,\n",
+       " 19287.116666666676,\n",
+       " 19287.116666666676,\n",
+       " 18890.40000000001,\n",
+       " 18493.68333333334]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model1.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[13435.14,\n",
+       " 14264.14,\n",
+       " 15028.14,\n",
+       " 15126.2,\n",
+       " 15418.2,\n",
+       " 15429.14,\n",
+       " 16375.14,\n",
+       " 16734.2,\n",
+       " 16944.38,\n",
+       " 15248.2,\n",
+       " 15806.32,\n",
+       " 14960.2,\n",
+       " 14978.2,\n",
+       " 15305.08,\n",
+       " 16427.14,\n",
+       " 17471.08,\n",
+       " 18785.2,\n",
+       " 19143.2,\n",
+       " 19399.260000000002,\n",
+       " 18691.2]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model2.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQwAAAE/CAYAAAADu44SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngSEAAAAA0Pb/rEqd2aPu9QEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5a1550b0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bchmk.plot_compared_series(enrollments, [model1, model2], bchmk.colors, intervals=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model\t\t& Order     & RMSE\t\t& SMAPE      & Theil's U\t\t\\\\ \n",
+      "HOFTSFTS\t\t& 3\t\t& 426.81\t\t& 0.99\t\t& 0.7\t\\\\ \n",
+      "HOFTSFTS Diff\t\t& 3\t\t& 970.87\t\t& 2.53\t\t& 1.58\t\\\\ \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "bchmk.print_point_statistics(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Residual Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "cannot convert float NaN to integer",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2216\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1299\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2437\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1131\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1135\u001b[0m                                                                 renderer)\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m         \u001b[0minterval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 974\u001b[0;31m         \u001b[0mtick_tups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    975\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m             \u001b[0;31m# handle inverted limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m         \"\"\"\n\u001b[0;32m--> 917\u001b[0;31m         \u001b[0mmajorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1951\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1952\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1953\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1955\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1959\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[1;32m   1960\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[0;32m-> 1961\u001b[0;31m         \u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0mprune\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'auto'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1902\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1903\u001b[0;31m                 nbins = np.clip(self.axis.get_tick_space(),\n\u001b[0m\u001b[1;32m   1904\u001b[0m                                 max(1, self._min_n_ticks - 1), 9)\n\u001b[1;32m   1905\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2060\u001b[0m         \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m31\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5a151d6f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.benchmarks import ResidualAnalysis as ra\n",
+    "\n",
+    "ra.plot_residuals(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyFTS/notebooks/Hwang - HighOrderFTS.ipynb b/pyFTS/notebooks/Hwang - HighOrderFTS.ipynb
new file mode 100644
index 0000000..419d170
--- /dev/null
+++ b/pyFTS/notebooks/Hwang - HighOrderFTS.ipynb	
@@ -0,0 +1,447 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# High Order Fuzzy Time Series by Hwang, Chen and Lee (1998)\n",
+    "\n",
+    "Jeng-Ren Hwang, Shyi-Ming Chen, and Chia-Hoang Lee, “Handling forecasting problems using fuzzy time series,” \n",
+    "Fuzzy Sets Syst., no. 100, pp. 217–228, 1998."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Common Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
+      "  from pandas.core import datetools\n",
+      "/usr/lib/python3/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['plt']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n",
+      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pylab as plt\n",
+    "from pyFTS.benchmarks import benchmarks as bchmk\n",
+    "from pyFTS.models import hwang\n",
+    "\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyFTS.data import Enrollments\n",
+    "\n",
+    "enrollments = Enrollments.get_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp0AAAFZCAYAAADaRJQBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6da206fcf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, hwang.HighOrderFTS, range(1,20), [1, 2, 3], tam=[10, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp0AAAFZCAYAAADaRJQBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6da07115c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.common import Transformations\n",
+    "diff = Transformations.Differential(1)\n",
+    "\n",
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, hwang.HighOrderFTS, range(1,20), [1, 2, 3], \n",
+    "                              transformation=diff, tam=[10, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the partitioning schemas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAALICAYAAABijlFfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6da1fb8c18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.partitioners import Grid, Util as pUtil\n",
+    "\n",
+    "fuzzy_sets = Grid.GridPartitioner(enrollments, 12)\n",
+    "fuzzy_sets2 = Grid.GridPartitioner(enrollments, 10, transformation=diff)\n",
+    "\n",
+    "pUtil.plot_partitioners(enrollments, [fuzzy_sets,fuzzy_sets2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hwang High Order FTS:\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model1 = hwang.HighOrderFTS(\"FTS\", partitioner=fuzzy_sets)\n",
+    "model1.fit(enrollments, order=2)\n",
+    "\n",
+    "print(model1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hwang High Order FTS:\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model2 = hwang.HighOrderFTS(\"FTS Diff\", partitioner=fuzzy_sets2)\n",
+    "model2.append_transformation(diff)\n",
+    "model2.fit(enrollments, order=3)\n",
+    "\n",
+    "print(model2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334,\n",
+       " 16113.38333333334]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model1.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12746.67,\n",
+       " 13254.67,\n",
+       " 13558.67,\n",
+       " 14387.67,\n",
+       " 15151.67,\n",
+       " 15002.67,\n",
+       " 15294.67,\n",
+       " 15552.67,\n",
+       " 16498.67,\n",
+       " 16610.67,\n",
+       " 16079.67,\n",
+       " 15124.67,\n",
+       " 15188.67,\n",
+       " 14836.67,\n",
+       " 14854.67,\n",
+       " 15675.67,\n",
+       " 16550.67,\n",
+       " 17841.67,\n",
+       " 18661.67,\n",
+       " 19019.67,\n",
+       " 19028.67,\n",
+       " 18567.67]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model2.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQkAAAE/CAYAAADlkkVWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAwOEICQEAAAAAAACH+/8BRTf1mHGQpNwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6da0633d68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bchmk.plot_compared_series(enrollments, [model1, model2], bchmk.colors, intervals=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model\t\t& Order     & RMSE\t\t& SMAPE      & Theil's U\t\t\\\\ \n",
+      "HwangFTS\t\t& 1\t\t& 1691.47\t\t& 4.1\t\t& 2.58\t\\\\ \n",
+      "HwangFTS Diff\t\t& 1\t\t& 808.61\t\t& 2.17\t\t& 1.32\t\\\\ \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "bchmk.print_point_statistics(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Residual Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "cannot convert float NaN to integer",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2216\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1299\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2437\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1131\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1135\u001b[0m                                                                 renderer)\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m         \u001b[0minterval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 974\u001b[0;31m         \u001b[0mtick_tups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    975\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m             \u001b[0;31m# handle inverted limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m         \"\"\"\n\u001b[0;32m--> 917\u001b[0;31m         \u001b[0mmajorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1951\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1952\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1953\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1955\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1959\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[1;32m   1960\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[0;32m-> 1961\u001b[0;31m         \u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0mprune\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'auto'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1902\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1903\u001b[0;31m                 nbins = np.clip(self.axis.get_tick_space(),\n\u001b[0m\u001b[1;32m   1904\u001b[0m                                 max(1, self._min_n_ticks - 1), 9)\n\u001b[1;32m   1905\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2060\u001b[0m         \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m31\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8023d90160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.benchmarks import ResidualAnalysis as ra\n",
+    "\n",
+    "ra.plot_residuals(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyFTS/notebooks/Ismail & Efendi - ImprovedWeightedFTS.ipynb b/pyFTS/notebooks/Ismail & Efendi - ImprovedWeightedFTS.ipynb
new file mode 100644
index 0000000..30a2390
--- /dev/null
+++ b/pyFTS/notebooks/Ismail & Efendi - ImprovedWeightedFTS.ipynb	
@@ -0,0 +1,460 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# First Order Improved Weighted Fuzzy Time Series by Efendi, Ismail and Deris (2013)\n",
+    "\n",
+    "R. Efendi, Z. Ismail, and M. M. Deris, “Improved weight Fuzzy Time Series as used in the exchange rates forecasting of \n",
+    "US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1, p. 1350005, 2013."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Common Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
+      "  from pandas.core import datetools\n",
+      "/usr/lib/python3/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['plt']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n",
+      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pylab as plt\n",
+    "from pyFTS.benchmarks import benchmarks as bchmk\n",
+    "from pyFTS.models import ismailefendi\n",
+    "\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyFTS.data import Enrollments\n",
+    "\n",
+    "enrollments = Enrollments.get_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAF+CAYAAACPsKJfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nId6R86AAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f25e54d69b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, ismailefendi.ImprovedWeightedFTS, \n",
+    "                              range(4,12), [1], tam=[10, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAF+CAYAAACI8nxKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfwaiZEYuH1vRAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f25e3464710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.common import Transformations\n",
+    "diff = Transformations.Differential(1)\n",
+    "\n",
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, ismailefendi.ImprovedWeightedFTS, \n",
+    "                              range(2,10), [1], transformation=diff, tam=[10, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the partitioning schemas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAALICAYAAABijlFfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f25e1517e80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.partitioners import Grid, Util as pUtil\n",
+    "\n",
+    "fuzzy_sets = Grid.GridPartitioner(enrollments, 11)\n",
+    "fuzzy_sets2 = Grid.GridPartitioner(enrollments, 4, transformation=diff)\n",
+    "\n",
+    "pUtil.plot_partitioners(enrollments, [fuzzy_sets,fuzzy_sets2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Improved Weighted FTS:\n",
+      "A2 -> A2(0.667),A3(0.333)\n",
+      "A3 -> A4(1.0)\n",
+      "A4 -> A4(0.714),A5(0.286)\n",
+      "A5 -> A4(0.333),A6(0.667)\n",
+      "A6 -> A5(0.333),A6(0.333),A7(0.333)\n",
+      "A7 -> A8(1.0)\n",
+      "A8 -> A9(1.0)\n",
+      "A9 -> A8(0.5),A9(0.5)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model1 = ismailefendi.ImprovedWeightedFTS(\"FTS\", partitioner=fuzzy_sets)\n",
+    "model1.fit(enrollments)\n",
+    "\n",
+    "print(model1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Improved Weighted FTS:\n",
+      "A0 -> A1(1.0)\n",
+      "A1 -> A0(0.143),A1(0.286),A2(0.429),A3(0.143)\n",
+      "A2 -> A1(0.444),A2(0.222),A3(0.333)\n",
+      "A3 -> A2(0.75),A3(0.25)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model2 = ismailefendi.ImprovedWeightedFTS(\"FTS Diff\", partitioner=fuzzy_sets2)\n",
+    "model2.append_transformation(diff)\n",
+    "model2.fit(enrollments)\n",
+    "\n",
+    "print(model2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[13769.148484848485,\n",
+       " 13769.148484848485,\n",
+       " 13769.148484848485,\n",
+       " 15211.754545454547,\n",
+       " 15459.058441558444,\n",
+       " 15459.058441558444,\n",
+       " 15459.058441558444,\n",
+       " 16365.839393939395,\n",
+       " 16942.88181818182,\n",
+       " 16942.88181818182,\n",
+       " 16365.839393939395,\n",
+       " 15459.058441558444,\n",
+       " 15459.058441558444,\n",
+       " 15459.058441558444,\n",
+       " 15459.058441558444,\n",
+       " 16365.839393939395,\n",
+       " 16942.88181818182,\n",
+       " 18674.009090909094,\n",
+       " 19539.57272727273,\n",
+       " 19106.790909090912,\n",
+       " 19106.790909090912,\n",
+       " 19539.57272727273]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model1.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12801.572222222223,\n",
+       " 13113.492857142857,\n",
+       " 13613.572222222223,\n",
+       " 14246.492857142857,\n",
+       " 15010.492857142857,\n",
+       " 15280.6125,\n",
+       " 15349.572222222223,\n",
+       " 15607.572222222223,\n",
+       " 16357.492857142857,\n",
+       " 16665.57222222222,\n",
+       " 16357.6125,\n",
+       " 15402.6125,\n",
+       " 15243.572222222223,\n",
+       " 15114.6125,\n",
+       " 14909.572222222223,\n",
+       " 15534.492857142857,\n",
+       " 16409.492857142857,\n",
+       " 17347.55,\n",
+       " 18520.492857142857,\n",
+       " 19074.57222222222,\n",
+       " 19083.57222222222,\n",
+       " 18845.6125]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model2.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQcAAAE/CAYAAAD7W3XlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNElen1Fw/gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f25e3464630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bchmk.plot_compared_series(enrollments, [model1, model2], bchmk.colors, intervals=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model\t\t& Order     & RMSE\t\t& SMAPE      & Theil's U\t\t\\\\ \n",
+      "IWFTS FTS\t\t& 1\t\t& 485.63\t\t& 1.17\t\t& 0.79\t\\\\ \n",
+      "IWFTS FTS Diff\t\t& 1\t\t& 880.72\t\t& 2.27\t\t& 1.44\t\\\\ \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "bchmk.print_point_statistics(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Residual Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "cannot convert float NaN to integer",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2216\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1299\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2437\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1131\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1135\u001b[0m                                                                 renderer)\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m         \u001b[0minterval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 974\u001b[0;31m         \u001b[0mtick_tups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    975\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m             \u001b[0;31m# handle inverted limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m         \"\"\"\n\u001b[0;32m--> 917\u001b[0;31m         \u001b[0mmajorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1951\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1952\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1953\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1955\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1959\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[1;32m   1960\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[0;32m-> 1961\u001b[0;31m         \u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0mprune\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'auto'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1902\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1903\u001b[0;31m                 nbins = np.clip(self.axis.get_tick_space(),\n\u001b[0m\u001b[1;32m   1904\u001b[0m                                 max(1, self._min_n_ticks - 1), 9)\n\u001b[1;32m   1905\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2060\u001b[0m         \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m31\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f25e2d76be0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.benchmarks import ResidualAnalysis as ra\n",
+    "\n",
+    "ra.plot_residuals(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyFTS/notebooks/Partitioners.ipynb b/pyFTS/notebooks/Partitioners.ipynb
new file mode 100644
index 0000000..53e5d35
--- /dev/null
+++ b/pyFTS/notebooks/Partitioners.ipynb
@@ -0,0 +1,315 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
+      "  from pandas.core import datetools\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from pyFTS.partitioners import CMeans, Grid, FCM, Huarng, Entropy, Util as pUtil\n",
+    "from pyFTS.common import Membership as mf\n",
+    "from pyFTS.benchmarks import benchmarks as bchmk\n",
+    "from pyFTS.data import Enrollments\n",
+    "\n",
+    "enrollments = Enrollments.get_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAALICAYAAABijlFfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f79682bc1d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "part = pUtil.explore_partitioners(enrollments, 10, methods=[Grid.GridPartitioner], \n",
+    "                                  mf=[mf.trimf, mf.trapmf, mf.gaussmf])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Grid - trimf:\n",
+      "A0: trimf([10797.38, 11749.5, 12701.62])\n",
+      "A1: trimf([11749.5, 12701.62, 13653.740000000002])\n",
+      "A2: trimf([12701.62, 13653.740000000002, 14605.860000000002])\n",
+      "A3: trimf([13653.740000000002, 14605.860000000002, 15557.980000000003])\n",
+      "A4: trimf([14605.860000000002, 15557.980000000003, 16510.100000000002])\n",
+      "A5: trimf([15557.980000000005, 16510.100000000006, 17462.220000000005])\n",
+      "A6: trimf([16510.100000000006, 17462.220000000005, 18414.340000000004])\n",
+      "A7: trimf([17462.220000000005, 18414.340000000004, 19366.460000000003])\n",
+      "A8: trimf([18414.340000000007, 19366.460000000006, 20318.580000000005])\n",
+      "A9: trimf([19366.46000000001, 20318.58000000001, 21270.700000000008])\n",
+      "\n",
+      "Grid - trapmf:\n",
+      "A0: trapmf([10797.38, 11273.44, 12225.56, 12701.62])\n",
+      "A1: trapmf([11749.5, 12225.560000000001, 13177.68, 13653.740000000002])\n",
+      "A2: trapmf([12701.62, 13177.680000000002, 14129.800000000001, 14605.860000000002])\n",
+      "A3: trapmf([13653.740000000002, 14129.800000000003, 15081.920000000002, 15557.980000000003])\n",
+      "A4: trapmf([14605.860000000002, 15081.920000000004, 16034.040000000003, 16510.100000000002])\n",
+      "A5: trapmf([15557.980000000005, 16034.040000000006, 16986.160000000007, 17462.220000000005])\n",
+      "A6: trapmf([16510.100000000006, 16986.160000000003, 17938.280000000006, 18414.340000000004])\n",
+      "A7: trapmf([17462.220000000005, 17938.280000000002, 18890.400000000005, 19366.460000000003])\n",
+      "A8: trapmf([18414.340000000007, 18890.400000000005, 19842.520000000008, 20318.580000000005])\n",
+      "A9: trapmf([19366.46000000001, 19842.520000000008, 20794.64000000001, 21270.700000000008])\n",
+      "\n",
+      "Grid - gaussmf:\n",
+      "A0: gaussmf([11749.5, 317.3733333333334])\n",
+      "A1: gaussmf([12701.62, 317.3733333333334])\n",
+      "A2: gaussmf([13653.740000000002, 317.3733333333334])\n",
+      "A3: gaussmf([14605.860000000002, 317.3733333333334])\n",
+      "A4: gaussmf([15557.980000000003, 317.3733333333334])\n",
+      "A5: gaussmf([16510.100000000006, 317.3733333333334])\n",
+      "A6: gaussmf([17462.220000000005, 317.3733333333334])\n",
+      "A7: gaussmf([18414.340000000004, 317.3733333333334])\n",
+      "A8: gaussmf([19366.460000000006, 317.3733333333334])\n",
+      "A9: gaussmf([20318.58000000001, 317.3733333333334])\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for p in part:\n",
+    "    print(p)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAALICAYAAABijlFfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1ZIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f792f8e0f60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "part = pUtil.explore_partitioners(enrollments, 10, methods=[Grid.GridPartitioner, CMeans.CMeansPartitioner, \n",
+    "                                                           FCM.FCMPartitioner, Entropy.EntropyPartitioner,\n",
+    "                                                           Huarng.HuarngPartitioner], \n",
+    "                                  mf=[mf.trimf])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Grid - trimf:\n",
+      "A0: trimf([10797.38, 11749.5, 12701.62])\n",
+      "A1: trimf([11749.5, 12701.62, 13653.740000000002])\n",
+      "A2: trimf([12701.62, 13653.740000000002, 14605.860000000002])\n",
+      "A3: trimf([13653.740000000002, 14605.860000000002, 15557.980000000003])\n",
+      "A4: trimf([14605.860000000002, 15557.980000000003, 16510.100000000002])\n",
+      "A5: trimf([15557.980000000005, 16510.100000000006, 17462.220000000005])\n",
+      "A6: trimf([16510.100000000006, 17462.220000000005, 18414.340000000004])\n",
+      "A7: trimf([17462.220000000005, 18414.340000000004, 19366.460000000003])\n",
+      "A8: trimf([18414.340000000007, 19366.460000000006, 20318.580000000005])\n",
+      "A9: trimf([19366.46000000001, 20318.58000000001, 21270.700000000008])\n",
+      "\n",
+      "CMeans - trimf:\n",
+      "A1: trimf([11749.5, 13495.0, 14696.0])\n",
+      "A2: trimf([13495.0, 14696.0, 15154.0])\n",
+      "A3: trimf([14696.0, 15154.0, 15460.8])\n",
+      "A4: trimf([15154.0, 15460.8, 15922.5])\n",
+      "A5: trimf([15460.8, 15922.5, 16743.25])\n",
+      "A6: trimf([15922.5, 16743.25, 18150.0])\n",
+      "A7: trimf([16743.25, 18150.0, 18923.0])\n",
+      "A8: trimf([18150.0, 18923.0, 19328.0])\n",
+      "A9: trimf([18923.0, 19328.0, 19337.0])\n",
+      "A10: trimf([19328.0, 19337.0, 21270.7])\n",
+      "\n",
+      "FCM - trimf:\n",
+      "A1: trimf([11749.5, 13462.456, 15276.077])\n",
+      "A2: trimf([13462.456, 15276.077, 15327.725])\n",
+      "A3: trimf([15276.077, 15327.725, 15497.013])\n",
+      "A4: trimf([15327.725, 15497.013, 16616.283])\n",
+      "A5: trimf([15497.013, 16616.283, 16696.83])\n",
+      "A6: trimf([16616.283, 16696.83, 18049.081])\n",
+      "A7: trimf([16696.83, 18049.081, 18945.715])\n",
+      "A8: trimf([18049.081, 18945.715, 19067.026])\n",
+      "A9: trimf([18945.715, 19067.026, 21270.7])\n",
+      "\n",
+      "Entropy - trimf:\n",
+      "A1: trimf([11749.5, 13867, 15163])\n",
+      "A2: trimf([13867, 15163, 15460])\n",
+      "A3: trimf([15163, 15460, 15603])\n",
+      "A4: trimf([15460, 15603, 16388])\n",
+      "A5: trimf([15603, 16388, 16919])\n",
+      "A6: trimf([16388, 16919, 18970])\n",
+      "A7: trimf([16919, 18970, 21270.7])\n",
+      "\n",
+      "Huarng - trimf:\n",
+      "A0: trimf([11650, 11750, 11850])\n",
+      "A1: trimf([11750, 11850, 11950])\n",
+      "A2: trimf([11850, 11950, 12050])\n",
+      "A3: trimf([11950, 12050, 12150])\n",
+      "A4: trimf([12050, 12150, 12250])\n",
+      "A5: trimf([12150, 12250, 12350])\n",
+      "A6: trimf([12250, 12350, 12450])\n",
+      "A7: trimf([12350, 12450, 12550])\n",
+      "A8: trimf([12450, 12550, 12650])\n",
+      "A9: trimf([12550, 12650, 12750])\n",
+      "A10: trimf([12650, 12750, 12850])\n",
+      "A11: trimf([12750, 12850, 12950])\n",
+      "A12: trimf([12850, 12950, 13050])\n",
+      "A13: trimf([12950, 13050, 13150])\n",
+      "A14: trimf([13050, 13150, 13250])\n",
+      "A15: trimf([13150, 13250, 13350])\n",
+      "A16: trimf([13250, 13350, 13450])\n",
+      "A17: trimf([13350, 13450, 13550])\n",
+      "A18: trimf([13450, 13550, 13650])\n",
+      "A19: trimf([13550, 13650, 13750])\n",
+      "A20: trimf([13650, 13750, 13850])\n",
+      "A21: trimf([13750, 13850, 13950])\n",
+      "A22: trimf([13850, 13950, 14050])\n",
+      "A23: trimf([13950, 14050, 14150])\n",
+      "A24: trimf([14050, 14150, 14250])\n",
+      "A25: trimf([14150, 14250, 14350])\n",
+      "A26: trimf([14250, 14350, 14450])\n",
+      "A27: trimf([14350, 14450, 14550])\n",
+      "A28: trimf([14450, 14550, 14650])\n",
+      "A29: trimf([14550, 14650, 14750])\n",
+      "A30: trimf([14650, 14750, 14850])\n",
+      "A31: trimf([14750, 14850, 14950])\n",
+      "A32: trimf([14850, 14950, 15050])\n",
+      "A33: trimf([14950, 15050, 15150])\n",
+      "A34: trimf([15050, 15150, 15250])\n",
+      "A35: trimf([15150, 15250, 15350])\n",
+      "A36: trimf([15250, 15350, 15450])\n",
+      "A37: trimf([15350, 15450, 15550])\n",
+      "A38: trimf([15450, 15550, 15650])\n",
+      "A39: trimf([15550, 15650, 15750])\n",
+      "A40: trimf([15650, 15750, 15850])\n",
+      "A41: trimf([15750, 15850, 15950])\n",
+      "A42: trimf([15850, 15950, 16050])\n",
+      "A43: trimf([15950, 16050, 16150])\n",
+      "A44: trimf([16050, 16150, 16250])\n",
+      "A45: trimf([16150, 16250, 16350])\n",
+      "A46: trimf([16250, 16350, 16450])\n",
+      "A47: trimf([16350, 16450, 16550])\n",
+      "A48: trimf([16450, 16550, 16650])\n",
+      "A49: trimf([16550, 16650, 16750])\n",
+      "A50: trimf([16650, 16750, 16850])\n",
+      "A51: trimf([16750, 16850, 16950])\n",
+      "A52: trimf([16850, 16950, 17050])\n",
+      "A53: trimf([16950, 17050, 17150])\n",
+      "A54: trimf([17050, 17150, 17250])\n",
+      "A55: trimf([17150, 17250, 17350])\n",
+      "A56: trimf([17250, 17350, 17450])\n",
+      "A57: trimf([17350, 17450, 17550])\n",
+      "A58: trimf([17450, 17550, 17650])\n",
+      "A59: trimf([17550, 17650, 17750])\n",
+      "A60: trimf([17650, 17750, 17850])\n",
+      "A61: trimf([17750, 17850, 17950])\n",
+      "A62: trimf([17850, 17950, 18050])\n",
+      "A63: trimf([17950, 18050, 18150])\n",
+      "A64: trimf([18050, 18150, 18250])\n",
+      "A65: trimf([18150, 18250, 18350])\n",
+      "A66: trimf([18250, 18350, 18450])\n",
+      "A67: trimf([18350, 18450, 18550])\n",
+      "A68: trimf([18450, 18550, 18650])\n",
+      "A69: trimf([18550, 18650, 18750])\n",
+      "A70: trimf([18650, 18750, 18850])\n",
+      "A71: trimf([18750, 18850, 18950])\n",
+      "A72: trimf([18850, 18950, 19050])\n",
+      "A73: trimf([18950, 19050, 19150])\n",
+      "A74: trimf([19050, 19150, 19250])\n",
+      "A75: trimf([19150, 19250, 19350])\n",
+      "A76: trimf([19250, 19350, 19450])\n",
+      "A77: trimf([19350, 19450, 19550])\n",
+      "A78: trimf([19450, 19550, 19650])\n",
+      "A79: trimf([19550, 19650, 19750])\n",
+      "A80: trimf([19650, 19750, 19850])\n",
+      "A81: trimf([19750, 19850, 19950])\n",
+      "A82: trimf([19850, 19950, 20050])\n",
+      "A83: trimf([19950, 20050, 20150])\n",
+      "A84: trimf([20050, 20150, 20250])\n",
+      "A85: trimf([20150, 20250, 20350])\n",
+      "A86: trimf([20250, 20350, 20450])\n",
+      "A87: trimf([20350, 20450, 20550])\n",
+      "A88: trimf([20450, 20550, 20650])\n",
+      "A89: trimf([20550, 20650, 20750])\n",
+      "A90: trimf([20650, 20750, 20850])\n",
+      "A91: trimf([20750, 20850, 20950])\n",
+      "A92: trimf([20850, 20950, 21050])\n",
+      "A93: trimf([20950, 21050, 21150])\n",
+      "A94: trimf([21050, 21150, 21250])\n",
+      "A95: trimf([21150, 21250, 21350])\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for p in part:\n",
+    "    print(p)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyFTS/notebooks/Sadaei et Al - ExponentialyWeightedFTS.ipynb b/pyFTS/notebooks/Sadaei et Al - ExponentialyWeightedFTS.ipynb
new file mode 100644
index 0000000..be6fe3a
--- /dev/null
+++ b/pyFTS/notebooks/Sadaei et Al - ExponentialyWeightedFTS.ipynb	
@@ -0,0 +1,508 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# First Order Exponentialy Weighted Fuzzy Time Series by Sadaei et al. (2013)\n",
+    "\n",
+    "H. J. Sadaei, R. Enayatifar, A. H. Abdullah, and A. Gani, “Short-term load forecasting using a hybrid model with a \n",
+    "refined exponentially weighted fuzzy time series and an improved harmony search,” Int. J. Electr. Power Energy Syst., vol. 62, no. from 2005, pp. 118–129, 2014.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Common Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
+      "  from pandas.core import datetools\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/lib/python3/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['plt']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n",
+      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pylab as plt\n",
+    "from pyFTS.benchmarks import benchmarks as bchmk\n",
+    "from pyFTS.models import sadaei\n",
+    "\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyFTS.data import Enrollments\n",
+    "\n",
+    "enrollments = Enrollments.get_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAF+CAYAAACPsKJfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0AsAAICY9/9fNv6AXxpAwAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6554424cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Parameter c = 1.1\n",
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, sadaei.ExponentialyWeightedFTS, \n",
+    "                              range(4,20), [1], tam=[10, 5], parameters=1.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAF+CAYAAACI8nxKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6554424b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Parameter c = 2\n",
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, sadaei.ExponentialyWeightedFTS, \n",
+    "                              range(4,20), [1], tam=[10, 5], parameters=2.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAF+CAYAAACI8nxKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f65563a1a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.common import Transformations\n",
+    "diff = Transformations.Differential(1)\n",
+    "\n",
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, sadaei.ExponentialyWeightedFTS, \n",
+    "                              range(2,10), [1], transformation=diff, tam=[10, 5], parameters=1.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAF+CAYAAACI8nxKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noVTE2DwiYAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f655645d128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, sadaei.ExponentialyWeightedFTS, \n",
+    "                              range(2,10), [1], transformation=diff, tam=[10, 5], parameters=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the partitioning schemas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAALICAYAAABijlFfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6556504b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.partitioners import Grid, Util as pUtil\n",
+    "\n",
+    "fuzzy_sets = Grid.GridPartitioner(enrollments, 8)\n",
+    "fuzzy_sets2 = Grid.GridPartitioner(enrollments, 4, transformation=diff)\n",
+    "\n",
+    "pUtil.plot_partitioners(enrollments, [fuzzy_sets,fuzzy_sets2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exponentialy Weighted FTS:\n",
+      "A1 -> A2(1.0)\n",
+      "A2 -> A2(0.30211480362537757),A2(0.33232628398791536),A3(0.36555891238670696)\n",
+      "A3 -> A3(0.08744401757481343),A3(0.09618841933229477),A3(0.10580726126552426),A3(0.1163879873920767),A3(0.12802678613128438),A3(0.14082946474441282),A4(0.15491241121885413),A4(0.17040365234073956)\n",
+      "A4 -> A3(0.16379748079474535),A4(0.18017722887421989),A4(0.1981949517616419),A4(0.2180144469378061),A5(0.23981589163158673)\n",
+      "A5 -> A6(1.0)\n",
+      "A6 -> A6(0.30211480362537757),A6(0.33232628398791536),A6(0.36555891238670696)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model1 = sadaei.ExponentialyWeightedFTS(\"FTS\", partitioner=fuzzy_sets, c=1.1)\n",
+    "model1.fit(enrollments)\n",
+    "\n",
+    "print(model1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exponentialy Weighted FTS:\n",
+      "A0 -> A1(1.0)\n",
+      "A1 -> A0(0.10540549970059566),A1(0.11594604967065524),A1(0.12754065463772077),A2(0.14029472010149285),A2(0.15432419211164214),A2(0.16975661132280637),A3(0.18673227245508703)\n",
+      "A2 -> A1(0.07364053907434345),A1(0.08100459298177781),A1(0.08910505227995559),A1(0.09801555750795117),A2(0.10781711325874628),A2(0.11859882458462091),A3(0.13045870704308302),A3(0.14350457774739134),A3(0.1578550355221305)\n",
+      "A3 -> A2(0.21547080370609778),A2(0.23701788407670757),A2(0.26071967248437833),A3(0.28679163973281624)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model2 = sadaei.ExponentialyWeightedFTS(\"FTS Diff\", partitioner=fuzzy_sets2, c=1.1)\n",
+    "model2.append_transformation(diff)\n",
+    "model2.fit(enrollments)\n",
+    "\n",
+    "print(model2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[14129.8,\n",
+       " 14564.869939577038,\n",
+       " 14564.869939577038,\n",
+       " 14564.869939577038,\n",
+       " 15661.27507002801,\n",
+       " 15661.27507002801,\n",
+       " 15661.27507002801,\n",
+       " 15661.27507002801,\n",
+       " 16559.635161586215,\n",
+       " 16559.635161586215,\n",
+       " 16559.635161586215,\n",
+       " 15661.27507002801,\n",
+       " 15661.27507002801,\n",
+       " 15661.27507002801,\n",
+       " 15661.27507002801,\n",
+       " 16559.635161586215,\n",
+       " 16559.635161586215,\n",
+       " 18890.399999999998,\n",
+       " 18890.399999999994,\n",
+       " 18890.399999999994,\n",
+       " 18890.399999999994,\n",
+       " 18890.399999999994]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model1.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12870.91127138479,\n",
+       " 13080.342728533089,\n",
+       " 13682.91127138479,\n",
+       " 14213.342728533089,\n",
+       " 14977.342728533089,\n",
+       " 15272.594096099978,\n",
+       " 15418.91127138479,\n",
+       " 15676.91127138479,\n",
+       " 16324.342728533089,\n",
+       " 16734.91127138479,\n",
+       " 16349.594096099978,\n",
+       " 15394.594096099978,\n",
+       " 15312.91127138479,\n",
+       " 15106.594096099978,\n",
+       " 14978.91127138479,\n",
+       " 15501.342728533089,\n",
+       " 16376.342728533089,\n",
+       " 17347.55,\n",
+       " 18487.34272853309,\n",
+       " 19143.91127138479,\n",
+       " 19152.91127138479,\n",
+       " 18837.59409609998]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model2.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABOMAAAE/CAYAAAAE3AcuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f655639aa58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bchmk.plot_compared_series(enrollments, [model1, model2], bchmk.colors, intervals=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model\t\t& Order     & RMSE\t\t& SMAPE      & Theil's U\t\t\\\\ \n",
+      "EWFTS\t\t& 1\t\t& 624.71\t\t& 1.5\t\t& 1.02\t\\\\ \n",
+      "EWFTS\t\t& 1\t\t& 877.76\t\t& 2.27\t\t& 1.43\t\\\\ \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "bchmk.print_point_statistics(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Residual Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "cannot convert float NaN to integer",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2216\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1299\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2437\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1131\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1135\u001b[0m                                                                 renderer)\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m         \u001b[0minterval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 974\u001b[0;31m         \u001b[0mtick_tups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    975\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m             \u001b[0;31m# handle inverted limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m         \"\"\"\n\u001b[0;32m--> 917\u001b[0;31m         \u001b[0mmajorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1951\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1952\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1953\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1955\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1959\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[1;32m   1960\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[0;32m-> 1961\u001b[0;31m         \u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0mprune\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'auto'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1902\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1903\u001b[0;31m                 nbins = np.clip(self.axis.get_tick_space(),\n\u001b[0m\u001b[1;32m   1904\u001b[0m                                 max(1, self._min_n_ticks - 1), 9)\n\u001b[1;32m   1905\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2060\u001b[0m         \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m31\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f65562d7ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.benchmarks import ResidualAnalysis as ra\n",
+    "\n",
+    "ra.plot_residuals(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyFTS/notebooks/Song - ConventionalFTS.ipynb b/pyFTS/notebooks/Song - ConventionalFTS.ipynb
new file mode 100644
index 0000000..c778005
--- /dev/null
+++ b/pyFTS/notebooks/Song - ConventionalFTS.ipynb	
@@ -0,0 +1,477 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# First Order Traditional Fuzzy Time Series method by Song & Chissom (1993)\n",
+    "\n",
+    "Q. Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets Syst., vol. 54, no. 3, pp. 269–277, 1993."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Common Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
+      "  from pandas.core import datetools\n",
+      "/usr/lib/python3/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['plt']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n",
+      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pylab as plt\n",
+    "from pyFTS.benchmarks import benchmarks as bchmk\n",
+    "from pyFTS.models import song\n",
+    "\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyFTS.data import Enrollments\n",
+    "\n",
+    "enrollments = Enrollments.get_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAF+CAYAAACI8nxKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nABCV/j9w8mtuZk6aEgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6c0dd239b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, song.ConventionalFTS, range(1,20), [1], tam=[10, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAF+CAYAAACI8nxKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npEQQBgAAQFIiCAMAACApEYQBAACQlP5/qZXv1eqAD40AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6c0bca7dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.common import Transformations\n",
+    "diff = Transformations.Differential(1)\n",
+    "\n",
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, song.ConventionalFTS, \n",
+    "                              range(1,20), [1], transformation=diff, tam=[10, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the partitioning schemas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAALICAYAAABijlFfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6c0bbecc18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.partitioners import Grid, Util as pUtil\n",
+    "\n",
+    "fuzzy_sets = Grid.GridPartitioner(enrollments, 10)\n",
+    "fuzzy_sets2 = Grid.GridPartitioner(enrollments, 3, transformation=diff)\n",
+    "\n",
+    "pUtil.plot_partitioners(enrollments, [fuzzy_sets,fuzzy_sets2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Traditional FTS:\n",
+      "[[0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]\n",
+      " [0.00000000e+00 0.00000000e+00 1.00000000e+00 1.91046234e-15\n",
+      "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]\n",
+      " [0.00000000e+00 0.00000000e+00 1.00000000e+00 1.00000000e+00\n",
+      "  3.82092468e-15 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]\n",
+      " [0.00000000e+00 0.00000000e+00 1.91046234e-15 1.91046234e-15\n",
+      "  1.00000000e+00 3.82092468e-15 0.00000000e+00 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]\n",
+      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
+      "  1.00000000e+00 1.00000000e+00 3.82092468e-15 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]\n",
+      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
+      "  1.00000000e+00 1.00000000e+00 3.82092468e-15 1.00000000e+00\n",
+      "  3.82092468e-15 0.00000000e+00]\n",
+      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
+      "  3.82092468e-15 3.82092468e-15 3.82092468e-15 3.82092468e-15\n",
+      "  3.82092468e-15 0.00000000e+00]\n",
+      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
+      "  1.00000000e+00 3.82092468e-15]\n",
+      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00\n",
+      "  1.00000000e+00 3.82092468e-15]\n",
+      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00 0.00000000e+00 3.82092468e-15\n",
+      "  3.82092468e-15 3.82092468e-15]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "model1 = song.ConventionalFTS(\"FTS\", partitioner=fuzzy_sets)\n",
+    "model1.fit(enrollments)\n",
+    "\n",
+    "print(model1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Traditional FTS:\n",
+      "[[0.        1.        0.       ]\n",
+      " [1.        1.        0.9999996]\n",
+      " [0.        0.9999996 0.9999996]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "model2 = song.ConventionalFTS(\"FTS Diff\", partitioner=fuzzy_sets2)\n",
+    "model2.append_transformation(diff)\n",
+    "model2.fit(enrollments)\n",
+    "\n",
+    "print(model2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[13177.68,\n",
+       " 13177.68,\n",
+       " 13177.68,\n",
+       " 13653.740000000002,\n",
+       " 15557.980000000003,\n",
+       " 15557.980000000003,\n",
+       " 15557.980000000003,\n",
+       " 15557.980000000003,\n",
+       " 16034.040000000005,\n",
+       " 16034.040000000005,\n",
+       " 16034.040000000005,\n",
+       " 15557.980000000003,\n",
+       " 15557.980000000003,\n",
+       " 15557.980000000003,\n",
+       " 15557.980000000003,\n",
+       " 15557.980000000003,\n",
+       " 16034.040000000005,\n",
+       " 17938.280000000006,\n",
+       " 18890.400000000005,\n",
+       " 18890.400000000005,\n",
+       " 18890.400000000005,\n",
+       " 17938.280000000006]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model1.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12870.2,\n",
+       " 12966.433333333332,\n",
+       " 13270.433333333332,\n",
+       " 14099.433333333332,\n",
+       " 14863.433333333332,\n",
+       " 15126.2,\n",
+       " 15006.433333333332,\n",
+       " 15264.433333333332,\n",
+       " 16210.433333333332,\n",
+       " 16734.2,\n",
+       " 16203.2,\n",
+       " 15248.2,\n",
+       " 15312.2,\n",
+       " 14960.2,\n",
+       " 14978.2,\n",
+       " 15387.433333333332,\n",
+       " 16262.433333333332,\n",
+       " 17553.433333333334,\n",
+       " 18373.433333333334,\n",
+       " 18731.433333333334,\n",
+       " 19152.2,\n",
+       " 18691.2]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model2.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABPoAAAE/CAYAAADfZ60MAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAKAAgj4AAAAAKICgDwAAAAAK8H9er4PLriCS6gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6c0bb5b160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bchmk.plot_compared_series(enrollments, [model1, model2], bchmk.colors, intervals=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model\t\t& Order     & RMSE\t\t& SMAPE      & Theil's U\t\t\\\\ \n",
+      "FTS FTS\t\t& 1\t\t& 907.99\t\t& 2.22\t\t& 1.48\t\\\\ \n",
+      "FTS FTS Diff\t\t& 1\t\t& 979.77\t\t& 2.67\t\t& 1.6\t\\\\ \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "bchmk.print_point_statistics(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Residual Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "cannot convert float NaN to integer",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2216\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1299\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2437\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1131\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1135\u001b[0m                                                                 renderer)\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m         \u001b[0minterval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 974\u001b[0;31m         \u001b[0mtick_tups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    975\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m             \u001b[0;31m# handle inverted limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m         \"\"\"\n\u001b[0;32m--> 917\u001b[0;31m         \u001b[0mmajorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1951\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1952\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1953\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1955\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1959\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[1;32m   1960\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[0;32m-> 1961\u001b[0;31m         \u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0mprune\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'auto'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1902\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1903\u001b[0;31m                 nbins = np.clip(self.axis.get_tick_space(),\n\u001b[0m\u001b[1;32m   1904\u001b[0m                                 max(1, self._min_n_ticks - 1), 9)\n\u001b[1;32m   1905\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2060\u001b[0m         \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m31\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6c0baa1278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.benchmarks import ResidualAnalysis as ra\n",
+    "\n",
+    "ra.plot_residuals(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyFTS/notebooks/Yu - WeightedFTS.ipynb b/pyFTS/notebooks/Yu - WeightedFTS.ipynb
new file mode 100644
index 0000000..2d73fca
--- /dev/null
+++ b/pyFTS/notebooks/Yu - WeightedFTS.ipynb	
@@ -0,0 +1,461 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# First Order Weighted Fuzzy Time Series by Yu(2005)\n",
+    "\n",
+    "H.-K. Yu, “Weighted fuzzy time series models for TAIEX forecasting,” \n",
+    "Phys. A Stat. Mech. its Appl., vol. 349, no. 3, pp. 609–624, 2005."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Common Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
+      "  from pandas.core import datetools\n",
+      "/usr/lib/python3/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['plt']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n",
+      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pylab as plt\n",
+    "from pyFTS.benchmarks import benchmarks as bchmk\n",
+    "from pyFTS.models import yu\n",
+    "\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyFTS.data import Enrollments\n",
+    "\n",
+    "enrollments = Enrollments.get_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAF+CAYAAACPsKJfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6c6337b908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, yu.WeightedFTS, range(4,20), [1], tam=[10, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the partitioning effects on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAF+CAYAAACI8nxKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noCn9P7lEZk5jHItWAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6c635b8e80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.common import Transformations\n",
+    "diff = Transformations.Differential(1)\n",
+    "\n",
+    "tmp = bchmk.simpleSearch_RMSE(enrollments, enrollments, yu.WeightedFTS, \n",
+    "                              range(2,10), [1], transformation=diff, tam=[10, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the partitioning schemas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAALICAYAAABijlFfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nADlCgQUAAAAAOfJ/5DVKocyjUk0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6c614e72b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.partitioners import Grid, Util as pUtil\n",
+    "\n",
+    "fuzzy_sets = Grid.GridPartitioner(enrollments, 18)\n",
+    "fuzzy_sets2 = Grid.GridPartitioner(enrollments, 4, transformation=diff)\n",
+    "\n",
+    "pUtil.plot_partitioners(enrollments, [fuzzy_sets,fuzzy_sets2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on original data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Weighted FTS:\n",
+      "A10 -> A10(0.167),A12(0.333),A9(0.5)\n",
+      "A12 -> A14(1.0)\n",
+      "A14 -> A13(0.167),A14(0.333),A14(0.5)\n",
+      "A2 -> A3(1.0)\n",
+      "A3 -> A4(1.0)\n",
+      "A4 -> A6(1.0)\n",
+      "A6 -> A6(0.167),A7(0.333),A8(0.5)\n",
+      "A7 -> A6(0.067),A7(0.133),A7(0.2),A7(0.267),A8(0.333)\n",
+      "A8 -> A10(0.333),A10(0.667)\n",
+      "A9 -> A7(1.0)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model1 = yu.WeightedFTS(\"FTS\", partitioner=fuzzy_sets)\n",
+    "model1.fit(enrollments)\n",
+    "\n",
+    "print(model1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a model on transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Weighted FTS:\n",
+      "A0 -> A1(1.0)\n",
+      "A1 -> A0(0.036),A1(0.071),A1(0.107),A2(0.143),A2(0.179),A2(0.214),A3(0.25)\n",
+      "A2 -> A1(0.022),A1(0.044),A1(0.067),A1(0.089),A2(0.111),A2(0.133),A3(0.156),A3(0.178),A3(0.2)\n",
+      "A3 -> A2(0.1),A2(0.2),A2(0.3),A3(0.4)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model2 = yu.WeightedFTS(\"FTS Diff\", partitioner=fuzzy_sets2)\n",
+    "model2.append_transformation(diff)\n",
+    "model2.fit(enrollments)\n",
+    "\n",
+    "print(model2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[13336.366666666669,\n",
+       " 13865.322222222225,\n",
+       " 14923.233333333337,\n",
+       " 15540.348148148152,\n",
+       " 15381.661481481486,\n",
+       " 15381.661481481486,\n",
+       " 15381.661481481486,\n",
+       " 17039.055555555562,\n",
+       " 17391.692592592597,\n",
+       " 17391.692592592597,\n",
+       " 15452.188888888893,\n",
+       " 15381.661481481486,\n",
+       " 15381.661481481486,\n",
+       " 15540.348148148152,\n",
+       " 15540.348148148152,\n",
+       " 17039.055555555562,\n",
+       " 17391.692592592597,\n",
+       " 19154.877777777787,\n",
+       " 18890.40000000001,\n",
+       " 18890.40000000001,\n",
+       " 18890.40000000001,\n",
+       " 18625.92222222223]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model1.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12952.553333333333,\n",
+       " 13025.257142857143,\n",
+       " 13764.553333333333,\n",
+       " 14158.257142857143,\n",
+       " 14922.257142857143,\n",
+       " 15249.73,\n",
+       " 15500.553333333333,\n",
+       " 15758.553333333333,\n",
+       " 16269.257142857143,\n",
+       " 16816.553333333333,\n",
+       " 16326.73,\n",
+       " 15371.73,\n",
+       " 15394.553333333333,\n",
+       " 15083.73,\n",
+       " 15060.553333333333,\n",
+       " 15446.257142857143,\n",
+       " 16321.257142857143,\n",
+       " 17347.55,\n",
+       " 18432.257142857143,\n",
+       " 19225.553333333333,\n",
+       " 19234.553333333333,\n",
+       " 18814.73]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model2.predict(enrollments)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQQAAAE/CAYAAAAQbM7mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhM64qzcZ+QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6c634d4eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bchmk.plot_compared_series(enrollments, [model1, model2], bchmk.colors, intervals=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model\t\t& Order     & RMSE\t\t& SMAPE      & Theil's U\t\t\\\\ \n",
+      "WFTS FTS\t\t& 1\t\t& 385.42\t\t& 0.89\t\t& 0.63\t\\\\ \n",
+      "WFTS FTS Diff\t\t& 1\t\t& 883.87\t\t& 2.29\t\t& 1.44\t\\\\ \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "bchmk.print_point_statistics(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Residual Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "cannot convert float NaN to integer",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2216\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1299\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2437\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1131\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1135\u001b[0m                                                                 renderer)\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m         \u001b[0minterval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 974\u001b[0;31m         \u001b[0mtick_tups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    975\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m             \u001b[0;31m# handle inverted limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m         \"\"\"\n\u001b[0;32m--> 917\u001b[0;31m         \u001b[0mmajorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1951\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1952\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1953\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1955\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1959\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[1;32m   1960\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[0;32m-> 1961\u001b[0;31m         \u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0mprune\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'auto'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1902\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1903\u001b[0;31m                 nbins = np.clip(self.axis.get_tick_space(),\n\u001b[0m\u001b[1;32m   1904\u001b[0m                                 max(1, self._min_n_ticks - 1), 9)\n\u001b[1;32m   1905\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2060\u001b[0m         \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m31\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6c633850f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyFTS.benchmarks import ResidualAnalysis as ra\n",
+    "\n",
+    "ra.plot_residuals(enrollments, [model1, model2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyFTS/partitioners/Grid.py b/pyFTS/partitioners/Grid.py
index 6890aa2..156145b 100644
--- a/pyFTS/partitioners/Grid.py
+++ b/pyFTS/partitioners/Grid.py
@@ -8,7 +8,16 @@ from pyFTS.partitioners import partitioner
 
 class GridPartitioner(partitioner.Partitioner):
     """Even Length Grid Partitioner"""
+
     def __init__(self, data, npart, func = Membership.trimf, transformation=None, indexer=None):
+        """
+        Even Length Grid Partitioner
+        :param data: Training data of which the universe of discourse will be extracted. The universe of discourse is the open interval between the minimum and maximum values of the training data.
+        :param npart: The number of universe of discourse partitions, i.e., the number of fuzzy sets that will be created
+        :param func: Fuzzy membership function (pyFTS.common.Membership)
+        :param transformation: data transformation to be applied on data
+        :param indexer:
+        """
         super(GridPartitioner, self).__init__("Grid", data, npart, func=func, transformation=transformation, indexer=indexer)
 
     def build(self, data):
@@ -18,7 +27,7 @@ class GridPartitioner(partitioner.Partitioner):
         partlen = dlen / self.partitions
 
         count = 0
-        for c in np.linspace(self.min, self.max, self.partitions):
+        for c in np.arange(self.min, self.max, partlen):
             if self.membership_function == Membership.trimf:
                 sets.append(
                     FuzzySet.FuzzySet(self.prefix + str(count), Membership.trimf, [c - partlen, c, c + partlen],c))
diff --git a/pyFTS/partitioners/partitioner.py b/pyFTS/partitioners/partitioner.py
index f45c89d..caff6e5 100644
--- a/pyFTS/partitioners/partitioner.py
+++ b/pyFTS/partitioners/partitioner.py
@@ -12,9 +12,9 @@ class Partitioner(object):
         """
         Universe of Discourse partitioner scheme. Split data on several fuzzy sets
         :param name: partitioner name
-        :param data: original data to be partitioned
-        :param npart: number of partitions 
-        :param func: membership function
+        :param data: Training data of which the universe of discourse will be extracted. The universe of discourse is the open interval between the minimum and maximum values of the training data.
+        :param npart: The number of universe of discourse partitions, i.e., the number of fuzzy sets that will be created
+        :param func: Fuzzy membership function (pyFTS.common.Membership)
         :param names: list of partitions names. If None is given the partitions will be auto named with prefix
         :param prefix: prefix of auto generated partition names
         :param transformation: data transformation to be applied on data
diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py
index e323adc..326580b 100644
--- a/pyFTS/tests/general.py
+++ b/pyFTS/tests/general.py
@@ -9,9 +9,7 @@ import numpy as np
 import pandas as pd
 from pyFTS.common import Transformations
 
-#from pyFTS.benchmarks import benchmarks as bchmk
-
-os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
+from pyFTS.benchmarks import benchmarks as bchmk
 
 bc = Transformations.BoxCox(0)
 diff = Transformations.Differential(1)
@@ -21,358 +19,21 @@ diff = Transformations.Differential(1)
 DATASETS
 """
 
-#enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";")
-#enrollments = np.array(enrollments["Enrollments"])
+from pyFTS.data import Enrollments
 
-passengers = pd.read_csv("DataSets/AirPassengers.csv", sep=",")
-passengers = np.array(passengers["Passengers"])
-
-#sunspots = pd.read_csv("DataSets/sunspots.csv", sep=",")
-#sunspots = np.array(sunspots["SUNACTIVITY"])
-
-#gauss = random.normal(0,1.0,5000)
-#gauss_teste = random.normal(0,1.0,400)
-
-#taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
-#taiex = np.array(taiexpd["avg"][:5000])
-#del(taiexpd)
-
-#nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
-#nasdaq = np.array(nasdaqpd["avg"][0:5000])
-#del(nasdaqpd)
-
-#sp500pd = pd.read_csv("DataSets/S&P500.csv", sep=",")
-#sp500 = np.array(sp500pd["Avg"][11000:])
-#del(sp500pd)
-
-#sondapd = pd.read_csv("DataSets/SONDA_BSB_HOURLY_AVG.csv", sep=";")
-#sondapd = sondapd.dropna(axis=0, how='any')
-#sonda = np.array(sondapd["glo_avg"])
-#del(sondapd)
-
-#bestpd = pd.read_csv("DataSets/BEST_TAVG.csv", sep=";")
-#best = np.array(bestpd["Anomaly"])
-#del(bestpd)
-
-#print(lag)
-#print(a)
-#'''
-'''
-sonda = pd.read_csv("DataSets/SONDA_BSB_15MIN_AVG.csv", sep=";")
-
-sonda['data'] = pd.to_datetime(sonda['data'])
-
-sonda = sonda[:][527041:].dropna()
-
-sonda.index = np.arange(0,len(sonda.index))
-
-sonda_treino = sonda[:105313].dropna()
-sonda_teste = sonda[105314:].dropna()
-'''
+data = Enrollments.get_data()
 
 from pyFTS.partitioners import Grid
-from pyFTS import song, chen, yu, sadaei, ismailefendi, cheng
+from pyFTS.models import song, chen, yu, sadaei, ismailefendi, cheng, hofts
 
-train = passengers[:100]
-test = passengers[100:]
+train = data
+test = data
 
-fs = Grid.GridPartitioner(train, 10, transformation=bc)
+fs = Grid.GridPartitioner(train, 10) #, transformation=bc)
 
-methods = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, sadaei.ExponentialyWeightedFTS,
-           ismailefendi.ImprovedWeightedFTS, cheng.TrendWeightedFTS]
+#tmp = bchmk.simpleSearch_RMSE(train, test, hofts.HighOrderFTS, range(4,12), [2], tam=[10, 5])
 
-#fig, axes = plt.subplots(nrows=1, ncols=1, figsize=[15, 5])
+model = hofts.HighOrderFTS("", partitioner=fs)
+model.fit(train, order=3)
 
-#axes.plot(test, label="Original")
-
-for method in methods:
-    model = method("")
-    model.append_transformation(bc)
-    model.train(train, sets=fs.sets)
-
-    forecasts = model.forecast(test)
-
-    print(forecasts)
-
-#ix_m15 = SeasonalIndexer.DateTimeSeasonalIndexer('data',[SeasonalIndexer.DateTime.minute],[15],'glo_avg', name='m15')
-
-#fs1 = Grid.GridPartitioner(sonda_treino, 50, transformation=diff, indexer=ix_m15)
-
-#ix = cUtil.load_obj("models/sonda_ix_Mhm15.pkl")
-
-#fs = cUtil.load_obj("models/sonda_fs_Entropy40_diff.pkl")
-
-#from pyFTS.models import msfts
-
-#obj = msfts.MultiSeasonalFTS("sonda_msfts_Entropy40_Mhm15", indexer=ix)
-
-#obj.append_transformation(diff)
-
-#obj.train(sonda_treino, fs.sets)
-
-#cUtil.persist_obj(obj, "models/sonda_msfts_Entropy40_Mhm15.pkl")
-
-#ftse = cUtil.load_obj("models/sonda_ensemble_msfts.pkl")
-
-#tmp = ftse.forecast_distribution(sonda_teste[850:860],  h=0.5, method="gaussian")
-
-#print(tmp[0])
-
-#'''
-
-'''
-from pyFTS.models.seasonal import SeasonalIndexer
-
-indexers = []
-
-for i in ["models/sonda_ix_Mhm15.pkl"]: #, "models/sonda_ix_m15.pkl", "models/sonda_ix_Mh.pkl", ]:
-    obj = cUtil.load_obj(i)
-    indexers.append( obj )
-    print(obj)
-
-partitioners = []
-
-transformations = [""] #, "_diff"]
-for max_part in [30, 40, 50, 60, 70, 80, 90]:
-    for t in transformations:
-        obj = cUtil.load_obj("models/sonda_fs_grid_" + str(max_part) + t + ".pkl")
-        partitioners.append( obj )
-        print(obj)
-
-
-from pyFTS.ensemble import ensemble, multiseasonal
-
-fts = multiseasonal.SeasonalEnsembleFTS("sonda_msfts_Mhm15")
-
-fts.indexers = indexers
-fts.partitioners = partitioners
-
-fts.indexer = indexers[0]
-
-fts.train(sonda_treino, sets=None)
-'''
-#'''
-
-#ix = cUtil.load_obj("models/sonda_ix_m15.pkl")
-
-#ftse = cUtil.load_obj("models/msfts_Grid40_diff_Mhm15.pkl")
-
-#ftse.indexer = ix
-
-#ftse.update_uod(sonda_treino)
-
-#tmp = ftse.forecast_distribution(sonda_teste,h=1)
-
-#tmp = ftse.forecast(sonda_teste,h=1)
-
-#tmp[5].plot()
-#'''
-
-'''
-from pyFTS.benchmarks import benchmarks as bchmk
-#from pyFTS.benchmarks import distributed_benchmarks as bchmk
-#from pyFTS.benchmarks import parallel_benchmarks as bchmk
-from pyFTS.benchmarks import Util
-from pyFTS.benchmarks import arima, quantreg, Measures
-
-#Util.cast_dataframe_to_synthetic_point("experiments/taiex_point_analitic.csv","experiments/taiex_point_sintetic.csv",11)
-
-#Util.plot_dataframe_point("experiments/taiex_point_sintetic.csv","experiments/taiex_point_analitic.csv",11)
-"""
-arima100 = arima.ARIMA("", alpha=0.25)
-#tmp.append_transformation(diff)
-arima100.train(passengers, None, order=(1,0,0))
-
-arima101 = arima.ARIMA("", alpha=0.25)
-#tmp.append_transformation(diff)
-arima101.train(passengers, None, order=(1,0,1))
-
-arima200 = arima.ARIMA("", alpha=0.25)
-#tmp.append_transformation(diff)
-arima200.train(passengers, None, order=(2,0,0))
-
-arima201 = arima.ARIMA("", alpha=0.25)
-#tmp.append_transformation(diff)
-arima201.train(passengers, None, order=(2,0,1))
-
-
-#tmp = quantreg.QuantileRegression("", alpha=0.25, dist=True)
-#tmp.append_transformation(diff)
-#tmp.train(sunspots[:150], None, order=1)
-#teste = tmp.forecast_ahead_interval(sunspots[150:155], 5)
-#teste = tmp.forecast_ahead_distribution(nasdaq[1600:1604], steps=5, resolution=50)
-
-bchmk.plot_compared_series(enrollments,[tmp], ['blue','red'], points=False, intervals=True)
-
-#print(sunspots[150:155])
-#print(teste)
-
-#kk = Measures.get_interval_statistics(nasdaq[1600:1605], tmp)
-
-#print(kk)
-"""
-
-
-"""
-bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4,#models=[yu.WeightedFTS], # #
-                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
-                     partitions= np.arange(10,200,step=10), #transformation=diff,
-                     dump=True, save=True, file="experiments/sondaws_point_analytic.csv",
-                     nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
-
-
-
-bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4, #models=[yu.WeightedFTS], # #
-                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
-                     partitions= np.arange(3,20,step=2), #transformation=diff,
-                     dump=True, save=True, file="experiments/sondaws_point_analytic_diff.csv",
-                     nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
-
-
-
-
-
-bchmk.interval_sliding_window(best, 5000, train=0.8, inc=0.8,#models=[yu.WeightedFTS], # #
-                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
-                     partitions= np.arange(10,200,step=10),
-                     dump=True, save=True, file="experiments/best"
-                                                "_interval_analytic.csv",
-                     nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
-
-
-
-bchmk.interval_sliding_window(taiex, 2000, train=0.8, inc=0.1, #models=[yu.WeightedFTS], # #
-                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
-                     partitions= np.arange(3,20,step=2), transformation=diff,
-                     dump=True, save=True, file="experiments/taiex_interval_analytic_diff.csv",
-                     nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
-
-
-
-
-
-bchmk.ahead_sliding_window(sonda, 10000, steps=10, resolution=10, train=0.2, inc=0.2,
-                     partitioners=[Grid.GridPartitioner],
-                     partitions= np.arange(10,200,step=10), indexer=ix,
-                     dump=True, save=True, file="experiments/sondawind_ahead_analytic.csv",
-                     nodes=['192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
-
-
-bchmk.ahead_sliding_window(sonda, 10000, steps=10, resolution=10, train=0.2, inc=0.2,
-                     partitioners=[Grid.GridPartitioner],
-                     partitions= np.arange(3,20,step=2), transformation=diff, indexer=ix,
-                     dump=True, save=True, file="experiments/sondawind_ahead_analytic_diff.csv",
-                     nodes=['192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
-
-
-
-from pyFTS import pwfts
-from pyFTS.common import Transformations
-from pyFTS.partitioners import Grid
-
-#diff = Transformations.Differential(1)
-#fs = Grid.GridPartitioner(best, 190) #, transformation=diff)
-
-
-#model = pwfts.ProbabilisticWeightedFTS("FTS 1")
-#model.append_transformation(diff)
-#model.train(best[0:1600],fs.sets, order=3)
-
-#bchmk.plot_compared_intervals_ahead(best[1600:1700],[model], ['blue','red'],
-#                                    distributions=[True], save=True, file="pictures/best_ahead_forecasts",
-#                                    time_from=40, time_to=60, resolution=100)
-
-experiments = [
-    ["experiments/taiex_point_synthetic_diff.csv","experiments/taiex_point_analytic_diff.csv",16],
-    ["experiments/nasdaq_point_synthetic_diff.csv","experiments/nasdaq_point_analytic_diff.csv", 11],
-    ["experiments/sp500_point_synthetic_diff.csv","experiments/sp500_point_analytic_diff.csv", 21],
-    ["experiments/best_point_synthetic_diff.csv","experiments/best_point_analytic_diff.csv", 13],
-    ["experiments/sondasun_point_synthetic_diff.csv","experiments/sondasun_point_analytic_diff.csv", 15],
-    ["experiments/sondawind_point_synthetic_diff.csv","experiments/sondawind_point_analytic_diff.csv", 8],
-    ["experiments/gauss_point_synthetic_diff.csv","experiments/gauss_point_analytic_diff.csv", 16]
-]
-
-Util.unified_scaled_point(experiments,tam=[15,8],save=True,file="pictures/unified_experiments_point.png",
-                         ignore=['ARIMA(1,0,0)','ARIMA(2,0,0)','ARIMA(2,0,1)','ARIMA(2,0,2)','QAR(2)'],
-                         replace=[['ARIMA','ARIMA'],['QAR','QAR']])
-'''
-
-'''
-experiments = [
-    ["experiments/taiex_interval_synthetic.csv","experiments/taiex_interval_analytic.csv",16],
-    ["experiments/nasdaq_interval_synthetic_diff.csv","experiments/nasdaq_interval_analytic_diff.csv",11],
-    ["experiments/sp500_interval_synthetic_diff.csv","experiments/sp500_interval_analytic_diff.csv", 11],
-    ["experiments/best_interval_synthetic_diff.csv","experiments/best_interval_analytic_diff.csv",13],
-    ["experiments/sondasun_interval_synthetic_diff.csv","experiments/sondasun_interval_analytic_diff.csv",8],
-    ["experiments/sondawind_interval_synthetic_diff.csv","experiments/sondawind_interval_analytic_diff.csv",8],
-    ["experiments/gauss_interval_synthetic_diff.csv","experiments/gauss_interval_analytic_diff.csv", 8]
-]
-
-Util.unified_scaled_interval(experiments,tam=[15,8],save=True,file="pictures/unified_experiments_interval.png",
-                             ignore=['ARIMA(1,0,0)', 'ARIMA(2,0,0)', 'ARIMA(2,0,1)', 'ARIMA(2,0,2)', 'QAR(2)'],
-                             replace=[['ARIMA(1,0,1) - 0.05', 'ARIMA 0.05'], ['ARIMA(1,0,1) - 0.25', 'ARIMA 0.25'],
-                                      ['QAR(1) - 0.05', 'QAR 0.05'], ['QAR(1) - 0.25', 'QAR 0.25']])
-
-Util.unified_scaled_interval_pinball(experiments,tam=[15,8],save=True,file="pictures/unified_experiments_interval_pinball.png",
-                             ignore=['ARIMA(1,0,0)', 'ARIMA(2,0,0)', 'ARIMA(2,0,1)', 'ARIMA(2,0,2)', 'QAR(2)'],
-                             replace=[['ARIMA(1,0,1) - 0.05', 'ARIMA 0.05'], ['ARIMA(1,0,1) - 0.25', 'ARIMA 0.25'],
-                                      ['QAR(1) - 0.05', 'QAR 0.05'], ['QAR(1) - 0.25', 'QAR 0.25']])
-
-'''
-
-'''
-experiments = [
-    ["experiments/taiex_ahead_synthetic_diff.csv","experiments/taiex_ahead_analytic_diff.csv",16],
-    ["experiments/nasdaq_ahead_synthetic_diff.csv","experiments/nasdaq_ahead_analytic_diff.csv",11],
-    ["experiments/sp500_ahead_synthetic_diff.csv","experiments/sp500_ahead_analytic_diff.csv", 21],
-    ["experiments/best_ahead_synthetic_diff.csv","experiments/best_ahead_analytic_diff.csv", 24],
-    ["experiments/sondasun_ahead_synthetic_diff.csv","experiments/sondasun_ahead_analytic_diff.csv",13],
-    ["experiments/sondawind_ahead_synthetic_diff.csv","experiments/sondawind_ahead_analytic_diff.csv", 13],
-    ["experiments/gauss_ahead_synthetic_diff.csv","experiments/gauss_ahead_analytic_diff.csv",16]
-]
-
-Util.unified_scaled_ahead(experiments,tam=[15,8],save=True,file="pictures/unified_experiments_ahead.png",
-                          ignore=['ARIMA(1,0,0)', 'ARIMA(0,0,1)', 'ARIMA(2,0,0)', 'ARIMA(2,0,1)',
-                                  'ARIMA(2,0,2)', 'QAR(2)', 'ARIMA0.05'],
-                          replace=[['ARIMA(1,0,1) - 0.05', 'ARIMA 0.05'], ['ARIMA(1,0,1) - 0.25', 'ARIMA 0.25'],
-                                   ['QAR(1) - 0.05', 'QAR 0.05'], ['QAR(1) - 0.25', 'QAR 0.25']])
-
-
-
-'''
-
-'''
-from pyFTS.partitioners import Grid
-
-from pyFTS import sfts
-
-
-
-#print(ix.get_season_of_data(best[:2000]))
-
-#print(ix.get_season_by_index(45))
-
-#ix = SeasonalIndexer.LinearSeasonalIndexer([720,24],[False,True,False])
-
-#print(ix.get_season_of_data(sonda[6500:9000])[-20:])
-
-diff = Transformations.Differential(1)
-
-fs = Grid.GridPartitioner(sonda[:9000], 10, transformation=diff)
-
-
-tmp = sfts.SeasonalFTS("")
-tmp.indexer = ix
-tmp.append_transformation(diff)
-
-#tmp = pwfts.ProbabilisticWeightedFTS("")
-
-#tmp.append_transformation(diff)
-
-tmp.train(sonda[:9000], fs.sets, order=1)
-
-x = tmp.forecast(sonda[:1610])
-
-#print(taiex[1600:1610])
-print(x)
-'''
\ No newline at end of file
+print(model)
\ No newline at end of file