def plotFitAll(self,
isMultiple=False,
params: lmfit.Parameters = None,
numPoint: int = None,
**kwargs):
"""
Plots the fitted with observed data over time.
Parameters
----------
isMultiple: bool
plots all variables on a single plot
#@expand
"""
self._updateFit(params, numPoint)
bandLowTS = None
bandHighTS = None
if self.bootstrapResult is not None:
statistic = self.bootstrapResult.simulate(numPoint=100,
numSample=1000)
self._plotFittedTS = statistic.meanTS
bandLowTS = statistic.percentileDct[LOW_PERCENTILE]
bandHighTS = statistic.percentileDct[HIGH_PERCENTILE]
analyzer = ResidualsAnalyzer(self.observedTS,
self._plotFittedTS,
residualsTS=self.residualsTS,
bandLowTS=bandLowTS,
bandHighTS=bandHighTS,
isPlot=self._isPlot)
self._addKeyword(kwargs, po.NUM_ROW, 2)
analyzer.plotFittedObservedOverTime(**kwargs)
def plotFitAll(self,
params: lmfit.Parameters = None,
numPoint: int = None,
**kwargs):
"""
Plots the fitted with observed data over time.
Parameters
----------
#@expand
"""
if numPoint is None:
numPoint = len(self.observedTS)
fittedTS = self._getFittedTS(params, numPoint)
bandLowTS = None
bandHighTS = None
if self.bootstrapResult is not None:
statistic = self.bootstrapResult.simulate(numPoint=100,
numSample=1000)
fittedTS = statistic.meanTS
bandLowTS = statistic.percentileDct[LOW_PERCENTILE]
bandHighTS = statistic.percentileDct[HIGH_PERCENTILE]
analyzer = ResidualsAnalyzer(self.observedTS,
fittedTS,
residualsTS=self.residualsTS,
bandLowTS=bandLowTS,
bandHighTS=bandHighTS,
isPlot=self._isPlot)
self._addKeyword(kwargs, po.NUM_ROW, 2)
analyzer.plotFittedObservedOverTime(**kwargs)
def setUp(self):
self.observedTS = OBSERVED_TS
self.fittedTS = FITTED_TS
self.fitter = FITTER
self.statistic = self.fitter.bootstrapResult.simulate(numPoint=30,
numSample=300)
self.analyzer = ResidualsAnalyzer(self.observedTS,
self.fittedTS,
isPlot=IS_PLOT)
def testPlotFittedObservedOverTime1(self):
if IGNORE_TEST:
return
analyzer = ResidualsAnalyzer(
self.observedTS,
self.statistic.meanTS,
bandLowTS=self.statistic.percentileDct[LOW_PERCENTILE],
bandHighTS=self.statistic.percentileDct[HIGH_PERCENTILE],
isPlot=IS_PLOT)
analyzer.plotFittedObservedOverTime(numCol=3, numRow=2)
#
self.analyzer.plotFittedObservedOverTime(numCol=3, numRow=2)
def testPlotFittedObservedOverTime2(self):
if IGNORE_TEST:
return
meanTS = self.fittedTS.copy(isInitialize=True)
stdTS = self.fittedTS.copy(isInitialize=True)
for col in meanTS.colnames:
meanTS[col] = 2
stdTS[col] = 1
analyzer = ResidualsAnalyzer(self.observedTS,
self.fittedTS,
meanFittedTS=meanTS,
stdFittedTS=stdTS,
isPlot=IS_PLOT)
analyzer.plotFittedObservedOverTime(numCol=3, numRow=2)
class TestReidualAnalyzer(unittest.TestCase):
def setUp(self):
self.observedTS = OBSERVED_TS
self.fittedTS = FITTED_TS
self.fitter = FITTER
self.statistic = self.fitter.bootstrapResult.simulate(numPoint=30,
numSample=300)
self.analyzer = ResidualsAnalyzer(self.observedTS,
self.fittedTS,
isPlot=IS_PLOT)
def testPlotResidualsOverTime(self):
if IGNORE_TEST:
return
self.analyzer.plotResidualsOverTime(numCol=3,
numRow=2,
ylim=[-1.5, 1.5])
def testPlotFittedObservedOverTime1(self):
if IGNORE_TEST:
return
analyzer = ResidualsAnalyzer(
self.observedTS,
self.statistic.meanTS,
bandLowTS=self.statistic.percentileDct[LOW_PERCENTILE],
bandHighTS=self.statistic.percentileDct[HIGH_PERCENTILE],
isPlot=IS_PLOT)
analyzer.plotFittedObservedOverTime(numCol=3, numRow=2)
#
self.analyzer.plotFittedObservedOverTime(numCol=3, numRow=2)
def testPlotFittedObservedOverTime2(self):
if IGNORE_TEST:
return
meanTS = self.fittedTS.copy(isInitialize=True)
stdTS = self.fittedTS.copy(isInitialize=True)
for col in meanTS.colnames:
meanTS[col] = 2
stdTS[col] = 1
analyzer = ResidualsAnalyzer(self.observedTS,
self.fittedTS,
meanFittedTS=meanTS,
stdFittedTS=stdTS,
isPlot=IS_PLOT)
analyzer.plotFittedObservedOverTime(numCol=3, numRow=2)
def testPlotResidualsHistograms(self):
if IGNORE_TEST:
return
self.analyzer.plotResidualsHistograms(ylim=[0, 5],
xlim=[0, 6],
titlePosition=[.3, .9],
bins=10)
def testPlotAll(self):
if IGNORE_TEST:
return
self.analyzer.plotAll()
def plotResiduals(self,
params: lmfit.Parameters = None,
numPoint: int = None,
**kwargs):
"""
Plots residuals of a fit over time.
Parameters
----------
#@expand
"""
self._updateFit(params, numPoint)
analyzer = ResidualsAnalyzer(self.observedTS,
self._plotFittedTS,
residualsTS=self.residualsTS,
isPlot=self._isPlot)
analyzer.plotResidualsOverTime(**kwargs)
def plotResidualsAll(self,
params: lmfit.Parameters = None,
numPoint: int = None,
**kwargs):
"""
Plots a set of residual plots
Parameters
----------
#@expand
"""
self._updateFit(params, numPoint)
analyzer = ResidualsAnalyzer(self.observedTS,
self._plotFittedTS,
residualsTS=self.residualsTS,
isPlot=self._isPlot)
analyzer.plotAll(**kwargs)
def plot(self, kind: str, params=None, numPoint=None, **kwargs):
"""
High level plot routine.
A figure may contain 1 or more plot, and each plot may contain
1 or more curve. Figure (f), plot (p), and curve (c) are the possible
scope of an option. If the option is singled valued, then it applies
to all instances of its scope. If it has multiple values, then the
index of the value corresponds to the instance within the scope.
A figure scope is always single valued.
Parameters
----------
kind: name of plot type
FitAll: plots of fitted, observed data over time
ParameterEstimatePairs: Pairwise plots of bootstrap estimates
ParameterHistograms: histographs of bootstrap parameter values
Residuals: plot residuals over time
ResidualsAll: do all residuals plots
ResidualsHistograms: histogram of residual values
ResidualAutoCorrelations: Autocorrelations of residuals
ResidualCrossCorrelations: Cross correlations of residuals
alpha: (c) transparency; in [0, 1] (float)
bins: (f) number of bins in a histogram plot (int)
color: (c) color of the line (str, default="blue")
columns: (f) list of columns to plot (list, default=[])
figsize: (f) (horizontal width, vertical height) (list, default=[8, 6])
legend: (p) tuple of str for legend (list)
linestyle: (c) line style (str)
marker: (c) marker for line (str)
markersize: (c) size of marker for the line; >0 (float)
num_row: (f) rows of plots (int)
num_col: (f) columns of plots (int)
subplot_width_space: (f) horizontal space between plots (float)
timeseries2: (f) second timeseries
title: (p) plot title (str)
title_fontsize: (p) point size for title (float)
title_position: (p) (x, y) relative position; x,y in [0, 1]
suptitle: (f) figure title (str)
xlabel: (f) x axis title (str)
xlim: (f) order pair of lower and upper (list)
xticklabels: (f) list of labels for x ticks (list)
ylabel: (f) label for x axis (str)
ylim: (f) order pair of lower and upper (str)
yticklabels: (f) list of labels for y ticks (list)
"""
MODULE_KINDS = [
'ResidualsAll',
'Residuals',
'FitAll',
'ParameterEstimatePairs',
'ParameterHistograms',
]
RESIDUAL_MODULE_KINDS = [
'ResidualsHistograms',
'ResidualAutoCorrelations',
'ResidualCrossCorrelations',
]
kinds = list(MODULE_KINDS)
kinds.extend(RESIDUAL_MODULE_KINDS)
if not kind in kinds:
raise ValueError("Invalid plot kind. Options are: %s" % str(kinds))
if kind in MODULE_KINDS:
statement = "self.plot%s(**kwargs)" % kind
exec(statement)
else:
self._updateFit(params, numPoint)
analyzer = ResidualsAnalyzer(
self.observedTS,
self._plotFittedTS,
meanFittedTS=self.bootstrapResult.fittedStatistic.meanTS,
stdFittedTS=self.bootstrapResult.fittedStatistic.stdTS,
residualsTS=self.residualsTS,
isPlot=self._isPlot)
statement = "analyzer.plot%s(**kwargs)" % kind
exec(statement)