def plot_embedding(self,
title='',
edges=False,
colors=None,
labels=None,
axis=True,
plot=True,
ax=None,
**kwargs):
assert self.dim >= 2
if ax is None:
fig, ax = plt.subplots()
else:
plot = False
if edges is True:
edges = self.D['edge_list']
elif edges is False:
edges = None
if colors is True:
colors = self.sample_colors
plots.plot2D(self.embedding,
edges=edges,
colors=colors,
labels=labels,
axis=axis,
ax=ax,
title=title,
**kwargs)
if plot is True:
plt.draw()
plt.pause(1)
def plot_images(self,title=None,edges=False,
colors=True,plot=True,
ax=None,**kwargs):
if ax is None:
fig, ax = plt.subplots(1,self.n_perspectives,
figsize=(3*self.n_perspectives,3))
else:
plot = False
if edges is False:
edges = [None]*self.n_perspectives
elif edges is True:
edges = []
for k in range(self.n_perspectives):
edges.append(self.distances[k]['edge_list'])
else:
edges = edges
for k in range(self.n_perspectives):
if colors is True:
colors_k = [0]+list(self.distances[k][0:self.n_samples-1]) ####
elif colors is False:
colors_k = None
else:
colors_k = colors
plots.plot2D(self.images[k],edges=edges[k],colors=colors_k,ax=ax[k],
**kwargs)
plt.suptitle(title)
if plot is True:
plt.draw()
plt.pause(1.0)
def figureY(self,
title='perspectives',
edges=False,
colors=True,
plot=True,
ax=None,
**kwargs):
if ax is None:
fig, ax = plt.subplots(1, self.K)
else:
plot = False
for k in range(self.K):
if edges is True:
edges_k = self.D[k]['edges']
elif edges is False:
edges_k = None
else:
edges_k = edges[k]
if colors is True:
colors_k = self.D[k]['colors']
else:
colors_k = None
plots.plot2D(self.Y[k],
edges=edges_k,
colors=colors_k,
ax=ax[k],
**kwargs)
plt.suptitle(title)
if plot is True:
plt.draw()
plt.pause(1.0)
def plot(self):
plot2D(self)
date = self.date_combo_box.currentText()
begin = datetime.strptime(date, "%Y-%m-%d %H:%M:%S")
city_by_hour_prediction = [
(city.city_id,
city.forecast.get_forecast(begin - timedelta(minutes=1),
begin + timedelta(minutes=1)))
for city in self.cities
]
self.map_widget.render_3d_map(
city_by_hour_prediction, {
'temp': self.temperature_check_box.isChecked(),
'pres': self.pressure_check_box.isChecked(),
'clouds': self.clouds_check_box.isChecked(),
'pre': self.precipitation_check_box.isChecked(),
'wind': self.wind_check_box.isChecked()
})
def plot_image(self,index,title='embedding',edges=False,colors='default',
labels=None,
axis=True,plot=True,
ax=None,**kwargs):
assert self.image_dimension >= 2
if edges is True:
edges = self.distances['edge_list']
elif edges is False:
edges = None
if colors == 'default':
colors = self.sample_colors
if self.image_dimension == 2:
plots.plot2D(self.images[index],edges=edges,colors=colors,
labels=labels,
axis=axis,ax=ax,title=title,**kwargs)
else:
plots.plot3D(self.X,edges=edges,colors=colors,title=title,
ax=ax,**kwargs)
if plot is True:
plt.draw()
plt.pause(1)
def plot_images(self,title=None,edges=None,
colors=True,plot=True,
ax=None,**kwargs):
if ax is None:
fig, ax = plt.subplots(1,self.n_perspectives,
figsize=(3*self.n_perspectives,3))
else:
plot = False
if edges is None:
edges = [None]*self.n_perspectives
else:
edges = edges
#setup colors
if colors is True:
colors = self.image_colors
if colors is None:
colors = self.sample_colors
for k in range(self.n_perspectives):
if isinstance(colors,list) and len(colors)==self.n_perspectives:
colors_k = colors[k]
else:
colors_k = colors
if isinstance(colors_k, int):
assert colors_k in range(self.n_samples)
colors_k = scipy.spatial.distance.squareform(self.distances[k])[colors_k]
plots.plot2D(self.images[k],edges=edges[k],colors=colors_k,ax=ax[k],
weight=self.weights[k], **kwargs)
#ax[k].set_xlabel('individual cost:'+ f'{self.individual_cost[k]}')
plt.suptitle(title)
if plot is True:
plt.draw()
return fig