def plot_distribution(ax, cmap, probabilitydist, fig, time_from, reference_data=None):
"""
Plot forecasted ProbabilityDistribution objects on a matplotlib axis
:param ax: matplotlib axis
:param cmap: matplotlib colormap name
:param probabilitydist: list of ProbabilityDistribution objects
:param fig: matplotlib figure
:param time_from: starting time (on x axis) to begin the plots
:param reference_data:
:return:
"""
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection
patches = []
colors = []
for ct, dt in enumerate(probabilitydist):
disp = 0.0
if reference_data is not None:
disp = reference_data[time_from+ct]
for y in dt.bins:
s = Rectangle((time_from+ct, y+disp), 1, dt.resolution, fill=True, lw = 0)
patches.append(s)
colors.append(dt.density(y))
scale = Transformations.Scale()
colors = scale.apply(colors)
pc = PatchCollection(patches=patches, match_original=True)
pc.set_clim([0, 1])
pc.set_cmap(cmap)
pc.set_array(np.array(colors))
ax.add_collection(pc)
cb = fig.colorbar(pc, ax=ax)
cb.set_label('Density')
def plot_distribution(ax,
cmap,
probabilitydist,
fig,
time_from,
reference_data=None):
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection
patches = []
colors = []
for ct, dt in enumerate(probabilitydist):
disp = 0.0
if reference_data is not None:
disp = reference_data[time_from + ct]
for y in dt.bins:
s = Rectangle((time_from + ct, y + disp),
1,
dt.resolution,
fill=True,
lw=0)
patches.append(s)
colors.append(dt.density(y))
scale = Transformations.Scale()
colors = scale.apply(colors)
pc = PatchCollection(patches=patches, match_original=True)
pc.set_clim([0, 1])
pc.set_cmap(cmap)
pc.set_array(np.array(colors))
ax.add_collection(pc)
cb = fig.colorbar(pc, ax=ax)
cb.set_label('Density')
def __init__(self, **kwargs):
self.h = kwargs.get('h', .5)
"""Width parameter"""
self.kernel = kwargs.get("kernel", "epanechnikov")
"""Kernel function"""
self.data = kwargs.get("data", None)
self.transf = Transformations.Scale(min=0, max=1)
if self.data is not None:
self.data = self.transf.apply(self.data)
def __init__(self,h, kernel="epanechnikov"):
self.h = h
"""Width parameter"""
self.kernel = kernel
"""Kernel function"""
self.transf = Transformations.Scale(min=0,max=1)
def __init__(self, h, kernel="epanechnikov"):
self.h = h
self.kernel = kernel
self.transf = Transformations.Scale(min=0, max=1)