def plotPrediction(time, data, predictedTime,
predictedData, predictedVar, options):
"""
Function for ploting N-day ahead prediction
Args:
time: the data time
data: the original data
predictedTime: the predicted time period
predictedData: the predicted data
predictedVar: the predicted variance
options: options for the plot, for details please refer to @dlm
"""
# plot the original data
plotData(time, data, showDataPoint=options.showDataPoint,
color=options.dataColor,
label='time series')
# overlay the predicted data
plotData(predictedTime, predictedData,
showDataPoint=options.showFittedPoint,
color=options.predictedColor,
label='N-day prediction')
# overlay confidence interval
if options.showConfidenceInterval:
upper, lower = getInterval(predictedData,
predictedVar,
p=options.confidence)
plotInterval(time=predictedTime,
upper=upper, lower=lower,
intervalType=options.intervalType,
color=options.predictedColor)
def getInterval(self, p=0.95, filterType='forwardFilter', name='main'):
""" get the confidence interval for data or component.
If the filtered dates are not
(0, self.n - 1), then a warning will prompt stating the actual
filtered dates.
Args:
p: The confidence level.
filterType: the type of CI to be returned. Could be
'forwardFilter', 'backwardSmoother', and 'predict'.
Default to 'forwardFilter'.
name: the component to get CI. When name = 'main', then it
returns the confidence interval for the time series. When
name = some component's name, then it returns the confidence
interval for that component. Default to 'main'.
Returns:
A tuple with the first element being a list of upper bounds
and the second being a list of the lower bounds.
"""
# get the working date
start, end = self._checkAndGetWorkingDates(filterType=filterType)
end += 1
# get the mean and the variance for the time series data
if name == 'main':
# get out of the matrix form
if filterType == 'forwardFilter':
compMean = self._1DmatrixToArray(
self.result.filteredObs[start:end])
compVar = self._1DmatrixToArray(
self.result.filteredObsVar[start:end])
elif filterType == 'backwardSmoother':
compMean = self._1DmatrixToArray(
self.result.smoothedObs[start:end])
compVar = self._1DmatrixToArray(
self.result.smoothedObsVar[start:end])
elif filterType == 'predict':
compMean = self._1DmatrixToArray(
self.result.predictedObs[start:end])
compVar = self._1DmatrixToArray(
self.result.predictedObsVar[start:end])
else:
raise NameError('Incorrect filter type.')
# get the mean and variance for the component
else:
self._checkComponent(name)
compMean = self._getComponentMean(name=name,
filterType=filterType,
start=start, end=(end - 1))
compVar = self._getComponentVar(name=name,
filterType=filterType,
start=start, end=(end - 1))
# get the upper and lower bound
upper, lower = getInterval(compMean, compVar, p)
return (upper, lower)
def getInterval(self, p=0.95, filterType='forwardFilter', name='main'):
""" get the confidence interval for data or component.
If the filtered dates are not
(0, self.n - 1), then a warning will prompt stating the actual
filtered dates.
Args:
p: The confidence level.
filterType: the type of CI to be returned. Could be
'forwardFilter', 'backwardSmoother', and 'predict'.
Default to 'forwardFilter'.
name: the component to get CI. When name = 'main', then it
returns the confidence interval for the time series. When
name = some component's name, then it returns the confidence
interval for that component. Default to 'main'.
Returns:
A tuple with the first element being a list of upper bounds
and the second being a list of the lower bounds.
"""
# get the working date
start, end = self._checkAndGetWorkingDates(filterType=filterType)
# get the mean and the variance for the time series data
if name == 'main':
# get out of the matrix form
if filterType == 'forwardFilter':
compMean = self._1DmatrixToArray(
self.result.filteredObs[start:end])
compVar = self._1DmatrixToArray(
self.result.filteredObsVar[start:end])
elif filterType == 'backwardSmoother':
compMean = self._1DmatrixToArray(
self.result.smoothedObs[start:end])
compVar = self._1DmatrixToArray(
self.result.smoothedObsVar[start:end])
elif filterType == 'predict':
compMean = self._1DmatrixToArray(
self.result.predictedObs[start:end])
compVar = self._1DmatrixToArray(
self.result.predictedObsVar[start:end])
else:
raise NameError('Incorrect filter type.')
# get the mean and variance for the component
else:
self._checkComponent(name)
compMean = self._getComponentMean(name=name,
filterType=filterType,
start=start, end=end)
compVar = self._getComponentVar(name=name,
filterType=filterType,
start=start, end=end)
# get the upper and lower bound
upper, lower = getInterval(compMean, compVar, p)
return (upper, lower)
def getPredictedInterval(self, p):
""" get the predicted confidence interval.
If the predicted dates are not
(0, self.n - 1), then a warning will prompt stating the actual predicted dates.
Args:
p: The confidence level.
Returns:
A tuple with the first element being a list of upper bounds
and the second being a list of the lower bounds.
"""
if self.result.filteredSteps != [0, self.n - 1]:
print('The predicted dates are from ' + str(self.result.filteredSteps[0]) + \
' to ' + str(self.result.filteredSteps[1]))
start = self.result.filteredSteps[0]
end = self.result.filteredSteps[1] + 1
upper, lower = getInterval(self.result.predictedObs[start : end], \
self.result.predictedObsVar[start : end], p)
return (self._1DmatrixToArray(upper), self._1DmatrixToArray(lower))
def plotPrediction(time, data, predictedTime, predictedData, predictedVar,
options):
"""
Function for ploting N-day ahead prediction
Args:
time: the data time
data: the original data
predictedTime: the predicted time period
predictedData: the predicted data
predictedVar: the predicted variance
options: options for the plot, for details please refer to @dlm
"""
# plot the original data
plotData(time,
data,
showDataPoint=options.showDataPoint,
color=options.dataColor,
label='time series')
# overlay the predicted data
plotData(predictedTime,
predictedData,
showDataPoint=options.showFittedPoint,
color=options.predictedColor,
label='N-day prediction')
# overlay confidence interval
if options.showConfidenceInterval:
upper, lower = getInterval(predictedData,
predictedVar,
p=options.confidence)
plotInterval(time=predictedTime,
upper=upper,
lower=lower,
intervalType=options.intervalType,
color=options.predictedColor)
def plotInMultipleFigure(time, data, result, options):
"""
Plot the dlm results in multiple figure, each with one result and the
original data
Args:
time: the time label
data: the original data
result: the fitted result from dlm class
options: options for the plot, for details please refer to @dlm
"""
# first compute how many plots are needed
numOfPlots = options.plotFilteredData + options.plotPredictedData + \
options.plotSmoothedData
size = (numOfPlots, 1)
location = 1
# plot all needed results
# plot fitered results if needed
if options.plotFilteredData:
# the location
subplot(size, location)
# plot original data
plotData(time=time,
data=data,
showDataPoint=options.showDataPoint,
color=options.dataColor,
label='time series')
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
if start < end:
plotData(time=time[start:end],
data=to1dArray(result.filteredObs[start:end]),
showDataPoint=options.showFittedPoint,
color=options.filteredColor,
label='filtered series')
if options.showConfidenceInterval:
upper, lower = getInterval(result.filteredObs[start:end],
result.filteredObsVar[start:end],
p=options.confidence)
plotInterval(time=time[start:end],
upper=to1dArray(upper),
lower=to1dArray(lower),
intervalType=options.intervalType,
color=options.filteredColor)
plt.legend(loc='best', shadow=True) # , fontsize = 'x-large')
location += 1
# plot predicted results if needed
if options.plotPredictedData:
# the location
subplot(size, location)
# plot original data
plotData(time=time,
data=data,
showDataPoint=options.showDataPoint,
color=options.dataColor,
label='time series')
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
if start < end:
plotData(time=time[start:end],
data=to1dArray(result.predictedObs),
showDataPoint=options.showFittedPoint,
color=options.predictedColor,
label='one-day prediction')
if options.showConfidenceInterval:
upper, lower = getInterval(result.predictedObs[start:end],
result.filteredObsVar[start:end],
p=options.confidence)
plotInterval(time=time[start:end],
upper=to1dArray(upper),
lower=to1dArray(lower),
intervalType=options.intervalType,
color=options.predictedColor)
plt.legend(loc='best', shadow=True)
location += 1
# plot smoothed results if needed
if options.plotSmoothedData:
# the location
subplot(size, location)
# plot original data
plotData(time=time,
data=data,
showDataPoint=options.showDataPoint,
color=options.dataColor,
label='time series')
start = result.smoothedSteps[0]
end = result.smoothedSteps[1] + 1
if start < end:
plotData(time=time[start:end],
data=to1dArray(result.smoothedObs),
showDataPoint=options.showFittedPoint,
color=options.smoothedColor,
label='smoothed series')
if options.showConfidenceInterval:
upper, lower = getInterval(result.smoothedObs[start:end],
result.smoothedObsVar[start:end],
p=options.confidence)
plotInterval(time=time[start:end],
upper=to1dArray(upper),
lower=to1dArray(lower),
intervalType=options.intervalType,
color=options.smoothedColor)
plt.legend(loc='best', shadow=True)
def plotComponent(time, data, result, options):
"""
Plot the results of a single component in one figure
Args:
time: the time label
data: a dictionary contains all information. The basic keys are
data['filteredMean'] = ...
data['filteredVar'] = ...
data['predictedMean']= ...
data['predictedVar'] = ...
data['smoothedMean'] = ...
data['smoothedVar'] = ...
data['name'] = ...
not all are needed, depending on the setting in options.
result: the fitted result from dlm class
options: options for the plot, for details please refer to @dlm
"""
if options.separatePlot:
numOfPlots = options.plotFilteredData + options.plotPredictedData + \
options.plotSmoothedData
size = (numOfPlots, 1)
location = 1
# plot fitered results if needed
if options.plotFilteredData:
if options.separatePlot:
subplot(size, location)
location += 1
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
plotData(time=time[start:end],
data=data['filteredMean'],
showDataPoint=options.showFittedPoint,
color=options.filteredColor,
label='filtered ' + data['name'])
if options.showConfidenceInterval:
upper, lower = getInterval(data['filteredMean'],
map(abs, data['filteredVar']),
p=options.confidence)
plotInterval(time=time[start:end],
upper=upper,
lower=lower,
intervalType=options.intervalType,
color=options.filteredColor)
plt.legend(loc='best', shadow=True)
# plot predicted results if needed
if options.plotPredictedData:
if options.separatePlot:
subplot(size, location)
location += 1
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
plotData(time=time[start:end],
data=data['predictedMean'],
showDataPoint=options.showFittedPoint,
color=options.predictedColor,
label='one-step predict for ' + data['name'])
if options.showConfidenceInterval:
upper, lower = getInterval(data['predictedMean'],
map(abs, data['predictedVar']),
p=options.confidence)
plotInterval(time=time[start:end],
upper=upper,
lower=lower,
intervalType=options.intervalType,
color=options.predictedColor)
plt.legend(loc='best', shadow=True)
# plot smoothed results if needed
if options.plotSmoothedData:
if options.separatePlot:
subplot(size, location)
location += 1
start = result.smoothedSteps[0]
end = result.smoothedSteps[1] + 1
plotData(time=time[start:end],
data=data['smoothedMean'],
showDataPoint=options.showFittedPoint,
color=options.smoothedColor,
label='smoothed ' + data['name'])
if options.showConfidenceInterval:
upper, lower = getInterval(data['smoothedMean'],
map(abs, data['smoothedVar']),
p=options.confidence)
plotInterval(time=time[start:end],
upper=upper,
lower=lower,
intervalType=options.intervalType,
color=options.smoothedColor)
plt.legend(loc='best', shadow=True)
def plotSingleState(time, dimension, result, options):
"""
Plot a single coordinate in the latent state vector
Args:
time: the time label
dimension: the coordinate of the plot
result: the fitted result from dlm class
options: options for the plot, for details please refer to @dlm
"""
# plot fitered results if needed
if options.plotFilteredData:
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
data = [item[dimension, 0] for item in result.filteredState[start:end]]
var = [
abs(item[dimension, dimension])
for item in result.filteredCov[start:end]
]
plotData(time=time[start:end],
data=data,
showDataPoint=options.showFittedPoint,
color=options.filteredColor,
label='filtered state')
if options.showConfidenceInterval:
upper, lower = getInterval(data, var, p=options.confidence)
plotInterval(time=time[start:end],
upper=upper,
lower=lower,
intervalType=options.intervalType,
color=options.filteredColor)
# plot predicted results if needed
if options.plotPredictedData:
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
data = [
item[dimension, 0] for item in result.predictedState[start:end]
]
var = [
abs(item[dimension, dimension])
for item in result.predictedCov[start:end]
]
plotData(time=time[start:end],
data=data,
showDataPoint=options.showFittedPoint,
color=options.predictedColor,
label='predicted state')
if options.showConfidenceInterval:
upper, lower = getInterval(data, var, p=options.confidence)
plotInterval(time=time[start:end],
upper=upper,
lower=lower,
intervalType=options.intervalType,
color=options.predictedColor)
# plot smoothed results if needed
if options.plotSmoothedData:
start = result.smoothedSteps[0]
end = result.smoothedSteps[1] + 1
data = [item[dimension, 0] for item in result.smoothedState[start:end]]
var = [
abs(item[dimension, dimension])
for item in result.smoothedCov[start:end]
]
plotData(time=time[start:end],
data=data,
showDataPoint=options.showFittedPoint,
color=options.smoothedColor,
label='smoothed state')
if options.showConfidenceInterval:
upper, lower = getInterval(data, var, p=options.confidence)
plotInterval(time=time[start:end],
upper=upper,
lower=lower,
intervalType=options.intervalType,
color=options.smoothedColor)
plt.legend(loc='best', shadow=True)
def plotInOneFigure(time, data, result, options):
"""
Plot the dlm results in one figure
Args:
time: the time label
data: the original data
result: the fitted result from dlm class
options: options for the plot, for details please refer to @dlm
"""
# plot the original data
plotData(time=time,
data=data,
showDataPoint=options.showDataPoint,
color=options.dataColor,
label='time series')
# plot fitered results if needed
if options.plotFilteredData:
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
plotData(time=time[start:end],
data=to1dArray(result.filteredObs[start:end]),
showDataPoint=options.showFittedPoint,
color=options.filteredColor,
label='filtered series')
if options.showConfidenceInterval:
upper, lower = getInterval(result.filteredObs[start:end],
result.filteredObsVar[start:end],
p=options.confidence)
plotInterval(time=time[start:end],
upper=to1dArray(upper),
lower=to1dArray(lower),
intervalType=options.intervalType,
color=options.filteredColor)
# plot predicted results if needed
if options.plotPredictedData:
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
plotData(time=time[start:end],
data=to1dArray(result.predictedObs),
showDataPoint=options.showFittedPoint,
color=options.predictedColor,
label='one-day prediction')
if options.showConfidenceInterval:
upper, lower = getInterval(result.predictedObs[start:end],
result.filteredObsVar[start:end],
p=options.confidence)
plotInterval(time=time[start:end],
upper=to1dArray(upper),
lower=to1dArray(lower),
intervalType=options.intervalType,
color=options.predictedColor)
# plot smoothed results if needed
if options.plotSmoothedData:
start = result.smoothedSteps[0]
end = result.smoothedSteps[1] + 1
plotData(time=time[start:end],
data=to1dArray(result.smoothedObs),
showDataPoint=options.showFittedPoint,
color=options.smoothedColor,
label='smoothed series')
if options.showConfidenceInterval:
upper, lower = getInterval(result.smoothedObs[start:end],
result.smoothedObsVar[start:end],
p=options.confidence)
plotInterval(time=time[start:end],
upper=to1dArray(upper),
lower=to1dArray(lower),
intervalType=options.intervalType,
color=options.smoothedColor)
plt.legend(loc='best', shadow=True) # , fontsize = 'x-large')
def plotInMultipleFigure(time, data, result, options):
"""
Plot the dlm results in multiple figure, each with one result and the
original data
Args:
time: the time label
data: the original data
result: the fitted result from dlm class
options: options for the plot, for details please refer to @dlm
"""
# first compute how many plots are needed
numOfPlots = options.plotFilteredData + options.plotPredictedData + \
options.plotSmoothedData
size = (numOfPlots, 1)
location = 1
# plot all needed results
# plot fitered results if needed
if options.plotFilteredData:
# the location
subplot(size, location)
# plot original data
plotData(time=time, data=data,
showDataPoint=options.showDataPoint, color=options.dataColor,
label='time series')
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
if start < end:
plotData(time=time[start:end],
data=to1dArray(result.filteredObs[start:end]),
showDataPoint=options.showFittedPoint,
color=options.filteredColor,
label='filtered series')
if options.showConfidenceInterval:
upper, lower = getInterval(result.filteredObs[start:end],
result.filteredObsVar[start:end],
p=options.confidence)
plotInterval(time=time[start:end],
upper=to1dArray(upper), lower=to1dArray(lower),
intervalType=options.intervalType,
color=options.filteredColor)
plt.legend(loc='best', shadow=True) # , fontsize = 'x-large')
location += 1
# plot predicted results if needed
if options.plotPredictedData:
# the location
subplot(size, location)
# plot original data
plotData(time=time, data=data,
showDataPoint=options.showDataPoint,
color=options.dataColor, label='time series')
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
if start < end:
plotData(time=time[start:end],
data=to1dArray(result.predictedObs),
showDataPoint=options.showFittedPoint,
color=options.predictedColor,
label='one-day prediction')
if options.showConfidenceInterval:
upper, lower = getInterval(result.predictedObs[start:end],
result.filteredObsVar[start:end],
p=options.confidence)
plotInterval(time=time[start:end],
upper=to1dArray(upper), lower=to1dArray(lower),
intervalType=options.intervalType,
color=options.predictedColor)
plt.legend(loc='best', shadow=True)
location += 1
# plot smoothed results if needed
if options.plotSmoothedData:
# the location
subplot(size, location)
# plot original data
plotData(time=time, data=data,
showDataPoint=options.showDataPoint,
color=options.dataColor, label='time series')
start = result.smoothedSteps[0]
end = result.smoothedSteps[1] + 1
if start < end:
plotData(time=time[start:end],
data=to1dArray(result.smoothedObs),
showDataPoint=options.showFittedPoint,
color=options.smoothedColor,
label='smoothed series')
if options.showConfidenceInterval:
upper, lower = getInterval(result.smoothedObs[start:end],
result.smoothedObsVar[start:end],
p=options.confidence)
plotInterval(time=time[start:end],
upper=to1dArray(upper), lower=to1dArray(lower),
intervalType=options.intervalType,
color=options.smoothedColor)
plt.legend(loc='best', shadow=True)
def plotComponent(time, data, result, options):
"""
Plot the results of a single component in one figure
Args:
time: the time label
data: a dictionary contains all information. The basic keys are
data['filteredMean'] = ...
data['filteredVar'] = ...
data['predictedMean']= ...
data['predictedVar'] = ...
data['smoothedMean'] = ...
data['smoothedVar'] = ...
data['name'] = ...
not all are needed, depending on the setting in options.
result: the fitted result from dlm class
options: options for the plot, for details please refer to @dlm
"""
if options.separatePlot:
numOfPlots = options.plotFilteredData + options.plotPredictedData + \
options.plotSmoothedData
size = (numOfPlots, 1)
location = 1
# plot fitered results if needed
if options.plotFilteredData:
if options.separatePlot:
subplot(size, location)
location += 1
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
plotData(time=time[start:end],
data=data['filteredMean'],
showDataPoint=options.showFittedPoint,
color=options.filteredColor,
label='filtered ' + data['name'])
if options.showConfidenceInterval:
upper, lower = getInterval(data['filteredMean'],
list(map(abs, data['filteredVar'])),
p=options.confidence)
plotInterval(time=time[start:end],
upper=upper, lower=lower,
intervalType=options.intervalType,
color=options.filteredColor)
plt.legend(loc='best', shadow=True)
# plot predicted results if needed
if options.plotPredictedData:
if options.separatePlot:
subplot(size, location)
location += 1
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
plotData(time=time[start:end],
data=data['predictedMean'],
showDataPoint=options.showFittedPoint,
color=options.predictedColor,
label='one-step predict for ' + data['name'])
if options.showConfidenceInterval:
upper, lower = getInterval(data['predictedMean'],
list(map(abs, data['predictedVar'])),
p=options.confidence)
plotInterval(time=time[start:end],
upper=upper, lower=lower,
intervalType=options.intervalType,
color=options.predictedColor)
plt.legend(loc='best', shadow=True)
# plot smoothed results if needed
if options.plotSmoothedData:
if options.separatePlot:
subplot(size, location)
location += 1
start = result.smoothedSteps[0]
end = result.smoothedSteps[1] + 1
plotData(time=time[start:end],
data=data['smoothedMean'],
showDataPoint=options.showFittedPoint,
color=options.smoothedColor,
label='smoothed ' + data['name'])
if options.showConfidenceInterval:
upper, lower = getInterval(data['smoothedMean'],
list(map(abs, data['smoothedVar'])),
p=options.confidence)
plotInterval(time=time[start:end],
upper=upper, lower=lower,
intervalType=options.intervalType,
color=options.smoothedColor)
plt.legend(loc='best', shadow=True)
def plotSingleState(time, dimension, result, options):
"""
Plot a single coordinate in the latent state vector
Args:
time: the time label
dimension: the coordinate of the plot
result: the fitted result from dlm class
options: options for the plot, for details please refer to @dlm
"""
# plot fitered results if needed
if options.plotFilteredData:
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
data = [item[dimension, 0] for item in result.filteredState[start:end]]
var = [abs(item[dimension, dimension])
for item in result.filteredCov[start:end]]
plotData(time=time[start:end],
data=data,
showDataPoint=options.showFittedPoint,
color=options.filteredColor,
label='filtered state')
if options.showConfidenceInterval:
upper, lower = getInterval(data, var, p=options.confidence)
plotInterval(time=time[start:end],
upper=upper, lower=lower,
intervalType=options.intervalType,
color=options.filteredColor)
# plot predicted results if needed
if options.plotPredictedData:
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
data = [item[dimension, 0]
for item in result.predictedState[start:end]]
var = [abs(item[dimension, dimension])
for item in result.predictedCov[start:end]]
plotData(time=time[start:end],
data=data,
showDataPoint=options.showFittedPoint,
color=options.predictedColor,
label='predicted state')
if options.showConfidenceInterval:
upper, lower = getInterval(data, var, p=options.confidence)
plotInterval(time=time[start:end],
upper=upper, lower=lower,
intervalType=options.intervalType,
color=options.predictedColor)
# plot smoothed results if needed
if options.plotSmoothedData:
start = result.smoothedSteps[0]
end = result.smoothedSteps[1] + 1
data = [item[dimension, 0] for item in result.smoothedState[start:end]]
var = [abs(item[dimension, dimension])
for item in result.smoothedCov[start:end]]
plotData(time=time[start:end],
data=data,
showDataPoint=options.showFittedPoint,
color=options.smoothedColor,
label='smoothed state')
if options.showConfidenceInterval:
upper, lower = getInterval(data, var, p=options.confidence)
plotInterval(time=time[start:end],
upper=upper, lower=lower,
intervalType=options.intervalType,
color=options.smoothedColor)
plt.legend(loc='best', shadow=True)
def plotInOneFigure(time, data, result, options):
"""
Plot the dlm results in one figure
Args:
time: the time label
data: the original data
result: the fitted result from dlm class
options: options for the plot, for details please refer to @dlm
"""
# plot the original data
plotData(time=time, data=data,
showDataPoint=options.showDataPoint, color=options.dataColor,
label='time series')
# plot fitered results if needed
if options.plotFilteredData:
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
plotData(time=time[start:end],
data=to1dArray(result.filteredObs[start:end]),
showDataPoint=options.showFittedPoint,
color=options.filteredColor,
label='filtered series')
if options.showConfidenceInterval:
upper, lower = getInterval(result.filteredObs[start:end],
result.filteredObsVar[start:end],
p=options.confidence)
plotInterval(time=time[start:end],
upper=to1dArray(upper), lower=to1dArray(lower),
intervalType=options.intervalType,
color=options.filteredColor)
# plot predicted results if needed
if options.plotPredictedData:
start = result.filteredSteps[0]
end = result.filteredSteps[1] + 1
plotData(time=time[start:end],
data=to1dArray(result.predictedObs),
showDataPoint=options.showFittedPoint,
color=options.predictedColor,
label='one-day prediction')
if options.showConfidenceInterval:
upper, lower = getInterval(result.predictedObs[start:end],
result.filteredObsVar[start:end],
p=options.confidence)
plotInterval(time=time[start:end],
upper=to1dArray(upper), lower=to1dArray(lower),
intervalType=options.intervalType,
color=options.predictedColor)
# plot smoothed results if needed
if options.plotSmoothedData:
start = result.smoothedSteps[0]
end = result.smoothedSteps[1] + 1
plotData(time=time[start:end],
data=to1dArray(result.smoothedObs),
showDataPoint=options.showFittedPoint,
color=options.smoothedColor,
label='smoothed series')
if options.showConfidenceInterval:
upper, lower = getInterval(result.smoothedObs[start:end],
result.smoothedObsVar[start:end],
p=options.confidence)
plotInterval(time=time[start:end],
upper=to1dArray(upper), lower=to1dArray(lower),
intervalType=options.intervalType,
color=options.smoothedColor)
plt.legend(loc='best', shadow=True) # , fontsize = 'x-large')