New probability plot in common.Util

This commit is contained in:
Petrônio Cândido 2019-05-29 12:49:10 -03:00
parent fc3e51a217
commit 6cbe608d05
2 changed files with 58 additions and 15 deletions

View File

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

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