def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument("estResNumber",
help="estimation result number",
type=int)
parser.add_argument(
"paramType",
help=
"Parameter type: indPointsPosteriorMean, indPointsPosteriorCov, indPointsLocs, kernel, embeddingC, embeddingD"
)
parser.add_argument(
"--intermediateDesc",
help="Descriptor of the intermediate model estimate to plot",
type=str,
default="None")
parser.add_argument("--trial",
help="Parameter trial number",
type=int,
default=0)
parser.add_argument("--latent",
help="Parameter latent number",
type=int,
default=0)
parser.add_argument("--neuron",
help="Parameter neuron number",
type=int,
default=0)
parser.add_argument("--kernelParamIndex",
help="Kernel parameter index",
type=int,
default=0)
parser.add_argument("--indPointIndex",
help="Parameter inducing point index",
type=int,
default=0)
parser.add_argument(
"--indPointIndex2",
help=
"Parameter inducing point index2 (used for inducing points covariance parameters)",
type=int,
default=0)
parser.add_argument("--paramValueStart",
help="Start parameter value",
type=float,
default=0.01)
parser.add_argument("--paramValueEnd",
help="End parameters value",
type=float,
default=2.0)
parser.add_argument("--paramValueStep",
help="Step for parameter values",
type=float,
default=0.01)
parser.add_argument("--yMin",
help="Minimum y value",
type=float,
default=-math.inf)
parser.add_argument("--yMax",
help="Minimum y value",
type=float,
default=+math.inf)
parser.add_argument("--nQuad",
help="Number of quadrature points",
type=int,
default=200)
args = parser.parse_args()
estResNumber = args.estResNumber
paramType = args.paramType
intermediateDesc = args.intermediateDesc
trial = args.trial
latent = args.latent
neuron = args.neuron
kernelParamIndex = args.kernelParamIndex
indPointIndex = args.indPointIndex
indPointIndex2 = args.indPointIndex2
paramValueStart = args.paramValueStart
paramValueEnd = args.paramValueEnd
paramValueStep = args.paramValueStep
yMin = args.yMin
yMax = args.yMax
nQuad = args.nQuad
if intermediateDesc == "None":
modelFilename = "results/{:08d}_estimatedModel.pickle".format(
estResNumber)
else:
modelFilename = "results/{:08d}_{:s}_estimatedModel.pickle".format(
estResNumber, intermediateDesc)
# create model
with open(modelFilename, "rb") as f:
modelRes = pickle.load(f)
model = modelRes["model"]
indPointsLocsKMSRegEpsilon = model._eLL._svEmbeddingAllTimes._svPosteriorOnLatents._indPointsLocsKMS._epsilon
paramUpdateFun = lowerBoundVsOneParamUtils.getParamUpdateFun(
paramType=paramType)
paramValues = np.arange(paramValueStart, paramValueEnd, paramValueStep)
conditionNumbers = np.empty(len(paramValues))
for i in range(len(paramValues)):
paramUpdateFun(model=model,
paramValue=paramValues[i],
trial=None,
latent=latent,
neuron=neuron,
kernelParamIndex=kernelParamIndex,
indPointIndex=indPointIndex,
indPointIndex2=indPointIndex2)
Kzz = model._eLL._svEmbeddingAllTimes._svPosteriorOnLatents._indPointsLocsKMS._Kzz
eValues, _ = torch.eig(Kzz[latent][trial, :, :])
conditionNumbers[i] = eValues[:, 0].max() / eValues[:, 0].min()
title = lowerBoundVsOneParamUtils.getParamTitle(
paramType=paramType,
trial=trial,
latent=latent,
neuron=neuron,
kernelParamIndex=kernelParamIndex,
indPointIndex=indPointIndex,
indPointIndex2=indPointIndex2,
indPointsLocsKMSRegEpsilon=indPointsLocsKMSRegEpsilon)
figFilenamePattern = condNumberVsOneParamUtils.getFigFilenamePattern(
prefixNumber=estResNumber,
descriptor="estimatedParam",
paramType=paramType,
trial=trial,
latent=latent,
neuron=neuron,
indPointsLocsKMSRegEpsilon=indPointsLocsKMSRegEpsilon,
kernelParamIndex=kernelParamIndex,
indPointIndex=indPointIndex,
indPointIndex2=indPointIndex2)
layout = {
"title": title,
"xaxis": {
"title": "Parameter Value"
},
"yaxis": {
"title": "Condition Number",
"range": [yMin, yMax]
},
}
data = []
data.append(
{
"type": "scatter",
"mode": "lines+markers",
"x": paramValues,
"y": conditionNumbers,
"name": "cNum trial 0",
}, )
fig = go.Figure(
data=data,
layout=layout,
)
fig.write_image(figFilenamePattern.format("png"))
fig.write_html(figFilenamePattern.format("html"))
pio.renderers.default = "browser"
fig.show()
pdb.set_trace()
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument("estResNumber", help="estimation result number", type=int)
parser.add_argument("paramType", help="Parameter type: indPointsPosteriorMean, indPointsPosteriorCov, indPointsLocs, kernel, embeddingC, embeddingD")
parser.add_argument("--partialDesc", help="Descriptor of the partial result to plot", type=str, default="None")
parser.add_argument("--trial", help="Parameter trial number", type=int, default=0)
parser.add_argument("--latent", help="Parameter latent number", type=int, default=0)
parser.add_argument("--neuron", help="Parameter neuron number", type=int, default=0)
parser.add_argument("--kernelParamIndex", help="Kernel parameter index", type=int, default=0)
parser.add_argument("--indPointIndex", help="Parameter inducing point index", type=int, default=0)
parser.add_argument("--indPointIndex2", help="Parameter inducing point index2 (used for inducing points covariance parameters)", type=int, default=0)
parser.add_argument("--scaledParamValueStart", help="Start scaled parameter value", type=float, default=1.0)
parser.add_argument("--scaledParamValueEnd", help="End scaled parameters value", type=float, default=20.0)
parser.add_argument("--scaledParamValueStep", help="Step for scaled parameter values", type=float, default=0.01)
parser.add_argument("--yMin", help="Minimum y value", type=float, default=-math.inf)
parser.add_argument("--yMax", help="Minimum y value", type=float, default=+math.inf)
parser.add_argument("--percMargin", help="Percentage value for margin=perecMargin*max(abs(yMin), abs(yMax))", type=float, default=0.1)
parser.add_argument("--nQuad", help="Number of quadrature points", type=int, default=200)
args = parser.parse_args()
estResNumber = args.estResNumber
paramType = args.paramType
partialDesc = args.partialDesc
trial = args.trial
latent = args.latent
neuron = args.neuron
kernelParamIndex = args.kernelParamIndex
indPointIndex = args.indPointIndex
indPointIndex2 = args.indPointIndex2
scaledParamValueStart = args.scaledParamValueStart
scaledParamValueEnd = args.scaledParamValueEnd
scaledParamValueStep = args.scaledParamValueStep
yMin = args.yMin
yMax = args.yMax
percMargin = args.percMargin
nQuad = args.nQuad
if paramType=="kernel":
estMetaDataFilename = "results/{:08d}_estimation_metaData.ini".format(estResNumber)
estMetaDataConfig = configparser.ConfigParser()
estMetaDataConfig.read(estMetaDataFilename)
estInitNumber = int(estMetaDataConfig["estimation_params"]["estInitNumber"])
estInitConfig = configparser.ConfigParser()
estInitFilename = "data/{:08d}_estimation_metaData.ini".format(estInitNumber)
estInitConfig.read(estInitFilename)
if kernelParamIndex==0:
refParam0 = float(estInitConfig["kernel_params"]["kLengthscaleScaledValueLatent{:d}".format(latent)])
refParam0Scale = float(estInitConfig["kernel_params"]["kLengthscaleScaleLatent{:d}".format(latent)])
elif kernelParamIndex==1:
refParam0 = float(estInitConfig["kernel_params"]["kPeriodScaledValueLatent{:d}".format(latent)])
refParam0Scale = float(estInitConfig["kernel_params"]["kPeriodScaleLatent{:d}".format(latent)])
else:
raise NotImplementedError("Currently only kernelParamIndex=0 or 1 are supported")
else:
raise NotImplementedError("Currently only paramType=kernel is supported")
if partialDesc=="None":
modelFilename = "results/{:08d}_estimatedModel.pickle".format(estResNumber)
else:
modelFilename = "results/{:08d}_{:s}_estimatedModel.pickle".format(estResNumber, partialDesc)
# create model
with open(modelFilename, "rb") as f: modelRes = pickle.load(f)
model = modelRes["model"]
refParams = [refParam0]
indPointsLocsKMSRegEpsilon = model._eLL._svEmbeddingAllTimes._svPosteriorOnLatents._indPointsLocsKMS._epsilon
refParams.append(lowerBoundVsOneParamUtils.getReferenceParam(paramType=paramType, model=model, trial=trial, latent=latent, neuron=neuron, kernelParamIndex=kernelParamIndex, indPointIndex=indPointIndex, indPointIndex2=indPointIndex2)/refParam0Scale)
paramUpdateFun = lowerBoundVsOneParamUtils.getParamUpdateFun(paramType=paramType)
scaledParamValues = np.arange(scaledParamValueStart, scaledParamValueEnd, scaledParamValueStep)
lowerBoundValues = np.empty(scaledParamValues.shape)
for i in range(len(scaledParamValues)):
paramUpdateFun(model=model, paramValue=scaledParamValues[i]*refParam0Scale, trial=trial, latent=latent, neuron=neuron, kernelParamIndex=kernelParamIndex, indPointIndex=indPointIndex, indPointIndex2=indPointIndex2)
# if scaledParamValues[i]>=6.62:
# pdb.set_trace()
lowerBoundValues[i] = model.eval()
title = lowerBoundVsOneParamUtils.getParamTitle(paramType=paramType, trial=trial, latent=latent, neuron=neuron, kernelParamIndex=kernelParamIndex, indPointIndex=indPointIndex, indPointIndex2=indPointIndex2, indPointsLocsKMSRegEpsilon=indPointsLocsKMSRegEpsilon)
if partialDesc=="None":
figDesc = "lowerBoundVs1DParam_estimatedParams"
else:
figDesc = "lowerBoundVs1DParam_{:s}_estimatedParams".format(partialDesc)
figFilenamePattern = lowerBoundVsOneParamUtils.getFigFilenamePattern(prefixNumber=estResNumber, descriptor=figDesc, paramType=paramType, trial=trial, latent=latent, neuron=neuron, indPointsLocsKMSRegEpsilon=indPointsLocsKMSRegEpsilon, kernelParamIndex=kernelParamIndex, indPointIndex=indPointIndex, indPointIndex2=indPointIndex2)
fig = plot.svGPFA.plotUtilsPlotly.getPlotLowerBoundVsOneParam(paramValues=scaledParamValues, lowerBoundValues=lowerBoundValues, refParams=refParams, title=title, yMin=yMin, yMax=yMax, lowerBoundLineColor="red", refParamsLineColors=["red", "magenta"], percMargin=percMargin)
fig.write_image(figFilenamePattern.format("png"))
fig.write_html(figFilenamePattern.format("html"))
pio.renderers.default = "browser"
fig.show()
pdb.set_trace()
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument("estResNumber",
help="estimation result number",
type=int)
parser.add_argument(
"paramType",
help=
"Parameter type: indPointsPosteriorMean, indPointsPosteriorCov, indPointsLocs, kernel, embeddingC, embeddingD"
)
parser.add_argument("--trial",
help="Parameter trial number",
type=int,
default=0)
parser.add_argument("--latent",
help="Parameter latent number",
type=int,
default=0)
parser.add_argument("--neuron",
help="Parameter neuron number",
type=int,
default=0)
parser.add_argument("--kernelParamIndex",
help="Kernel parameter index",
type=int,
default=0)
parser.add_argument("--indPointIndex",
help="Parameter inducing point index",
type=int,
default=0)
parser.add_argument(
"--indPointIndex2",
help=
"Parameter inducing point index2 (used for inducing points covariance parameters)",
type=int,
default=0)
parser.add_argument("--paramValueStart",
help="Start parameter value",
type=float,
default=1.0)
parser.add_argument("--paramValueEnd",
help="End parameters value",
type=float,
default=20.0)
parser.add_argument("--paramValueStep",
help="Step for parameter values",
type=float,
default=0.01)
parser.add_argument("--yMin",
help="Minimum y value",
type=float,
default=-math.inf)
parser.add_argument("--yMax",
help="Minimum y value",
type=float,
default=+math.inf)
parser.add_argument("--nQuad",
help="Number of quadrature points",
type=int,
default=200)
args = parser.parse_args()
estResNumber = args.estResNumber
paramType = args.paramType
trial = args.trial
latent = args.latent
neuron = args.neuron
kernelParamIndex = args.kernelParamIndex
indPointIndex = args.indPointIndex
indPointIndex2 = args.indPointIndex2
paramValueStart = args.paramValueStart
paramValueEnd = args.paramValueEnd
paramValueStep = args.paramValueStep
yMin = args.yMin
yMax = args.yMax
nQuad = args.nQuad
modelFilename = "results/{:08d}_estimatedModel.pickle".format(estResNumber)
# create model
with open(modelFilename, "rb") as f:
modelRes = pickle.load(f)
model = modelRes["model"]
indPointsLocsKMSRegEpsilon = model._eLL._svEmbeddingAllTimes._svPosteriorOnLatents._indPointsLocsKMS._epsilon
refParam = lowerBoundVsOneParamUtils.getReferenceParam(
paramType=paramType,
model=model,
trial=trial,
latent=latent,
neuron=neuron,
kernelParamIndex=kernelParamIndex,
indPointIndex=indPointIndex,
indPointIndex2=indPointIndex2)
paramUpdateFun = lowerBoundVsOneParamUtils.getParamUpdateFun(
paramType=paramType)
paramValues = np.arange(paramValueStart, paramValueEnd, paramValueStep)
klTraceTerms = np.empty(paramValues.shape)
for i in range(len(paramValues)):
paramUpdateFun(model=model,
paramValue=paramValues[i],
trial=trial,
latent=latent,
neuron=neuron,
kernelParamIndex=kernelParamIndex,
indPointIndex=indPointIndex,
indPointIndex2=indPointIndex2)
klTraceTerms[i] = -computeKLtraceTerm(model=model, k=0, trial=0)
title = lowerBoundVsOneParamUtils.getParamTitle(
paramType=paramType,
trial=trial,
latent=latent,
neuron=neuron,
kernelParamIndex=kernelParamIndex,
indPointIndex=indPointIndex,
indPointIndex2=indPointIndex2,
indPointsLocsKMSRegEpsilon=indPointsLocsKMSRegEpsilon)
figFilenamePattern = lowerBoundVsOneParamUtils.getFigFilenamePattern(
prefixNumber=estResNumber,
descriptor="klTraceTerm_estimatedParam",
paramType=paramType,
trial=trial,
latent=latent,
neuron=neuron,
indPointsLocsKMSRegEpsilon=indPointsLocsKMSRegEpsilon,
kernelParamIndex=kernelParamIndex,
indPointIndex=indPointIndex,
indPointIndex2=indPointIndex2)
fig = plot.svGPFA.plotUtilsPlotly.getPlotLowerBoundVsOneParam(
paramValues=paramValues,
lowerBoundValues=klTraceTerms,
refParam=refParam,
ylab="-KL Trace Term",
title=title,
yMin=yMin,
yMax=yMax,
lowerBoundLineColor="red",
refParamLineColor="magenta")
fig.write_image(figFilenamePattern.format("png"))
fig.write_html(figFilenamePattern.format("html"))
pio.renderers.default = "browser"
fig.show()
pdb.set_trace()
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument("simResNumber",
help="simulation result number",
type=int)
parser.add_argument(
"paramType",
help=
"Parameter type: indPointsPosteriorMean, indPointsPosteriorCov, indPointsLocs, kernel, embeddingC, embeddingD"
)
parser.add_argument(
"indPointsLocsKMSRegEpsilon",
help=
"regularization epsilon for the inducing points locations covariance",
type=float)
parser.add_argument("--trial",
help="Parameter trial number",
type=int,
default=0)
parser.add_argument("--latent",
help="Parameter latent number",
type=int,
default=0)
parser.add_argument("--neuron",
help="Parameter neuron number",
type=int,
default=0)
parser.add_argument("--kernelParamIndex",
help="Kernel parameter index",
type=int,
default=0)
parser.add_argument("--indPointIndex",
help="Parameter inducing point index",
type=int,
default=0)
parser.add_argument(
"--indPointIndex2",
help=
"Parameter inducing point index2 (used for inducing points covariance parameters)",
type=int,
default=0)
parser.add_argument("--paramValueStart",
help="Start parameter value",
type=float,
default=1.0)
parser.add_argument("--paramValueEnd",
help="End parameters value",
type=float,
default=20.0)
parser.add_argument("--paramValueStep",
help="Step for parameter values",
type=float,
default=0.01)
parser.add_argument("--yMin",
help="Minimum y value",
type=float,
default=-math.inf)
parser.add_argument("--yMax",
help="Minimum y value",
type=float,
default=+math.inf)
parser.add_argument("--nQuad",
help="Number of quadrature points",
type=int,
default=200)
args = parser.parse_args()
simResNumber = args.simResNumber
paramType = args.paramType
indPointsLocsKMSRegEpsilon = args.indPointsLocsKMSRegEpsilon
trial = args.trial
latent = args.latent
neuron = args.neuron
kernelParamIndex = args.kernelParamIndex
indPointIndex = args.indPointIndex
indPointIndex2 = args.indPointIndex2
paramValueStart = args.paramValueStart
paramValueEnd = args.paramValueEnd
paramValueStep = args.paramValueStep
yMin = args.yMin
yMax = args.yMax
nQuad = args.nQuad
# load data and initial values
simResConfigFilename = "results/{:08d}_simulation_metaData.ini".format(
simResNumber)
simResConfig = configparser.ConfigParser()
simResConfig.read(simResConfigFilename)
simInitConfigFilename = simResConfig["simulation_params"][
"simInitConfigFilename"]
simResFilename = simResConfig["simulation_results"]["simResFilename"]
simInitConfig = configparser.ConfigParser()
simInitConfig.read(simInitConfigFilename)
nLatents = int(simInitConfig["control_variables"]["nLatents"])
nNeurons = int(simInitConfig["control_variables"]["nNeurons"])
trialsLengths = [
float(str) for str in simInitConfig["control_variables"]
["trialsLengths"][1:-1].split(",")
]
nTrials = len(trialsLengths)
# firstIndPointLoc = float(simInitConfig["control_variables"]["firstIndPointLoc"])
# indPointsLocsKMSRegEpsilon = float(simInitConfig["control_variables"]["indPointsLocsKMSRegEpsilon"])
with open(simResFilename, "rb") as f:
simRes = pickle.load(f)
spikesTimes = simRes["spikes"]
KzzChol = simRes["KzzChol"]
indPointsMeans = simRes["indPointsMeans"]
C, d = utils.svGPFA.configUtils.getLinearEmbeddingParams(
CFilename=simInitConfig["embedding_params"]["C_filename"],
dFilename=simInitConfig["embedding_params"]["d_filename"])
legQuadPoints, legQuadWeights = utils.svGPFA.miscUtils.getLegQuadPointsAndWeights(
nQuad=nQuad, trialsLengths=trialsLengths)
kernels = utils.svGPFA.configUtils.getKernels(nLatents=nLatents,
config=simInitConfig,
forceUnitScale=True)
kernelsParams0 = utils.svGPFA.initUtils.getKernelsParams0(kernels=kernels,
noiseSTD=0.0)
# Z0 = utils.svGPFA.initUtils.getIndPointLocs0(nIndPointsPerLatent=nIndPointsPerLatent, trialsLengths=trialsLengths, firstIndPointLoc=firstIndPointLoc)
Z0 = utils.svGPFA.configUtils.getIndPointsLocs0(nLatents=nLatents,
nTrials=nTrials,
config=simInitConfig)
nIndPointsPerLatent = [Z0[k].shape[1] for k in range(nLatents)]
# patch to acommodate Lea's equal number of inducing points across trials
qMu0 = [[] for k in range(nLatents)]
for k in range(nLatents):
qMu0[k] = torch.empty((nTrials, nIndPointsPerLatent[k], 1),
dtype=torch.double)
for r in range(nTrials):
qMu0[k][r, :, :] = indPointsMeans[r][k]
# end patch
srQSigma0Vecs = utils.svGPFA.initUtils.getSRQSigmaVecsFromSRMatrices(
srMatrices=KzzChol)
qUParams0 = {"qMu0": qMu0, "srQSigma0Vecs": srQSigma0Vecs}
kmsParams0 = {"kernelsParams0": kernelsParams0, "inducingPointsLocs0": Z0}
qKParams0 = {
"svPosteriorOnIndPoints": qUParams0,
"kernelsMatricesStore": kmsParams0
}
qHParams0 = {"C0": C, "d0": d}
initialParams = {
"svPosteriorOnLatents": qKParams0,
"svEmbedding": qHParams0
}
quadParams = {
"legQuadPoints": legQuadPoints,
"legQuadWeights": legQuadWeights
}
# create model
model = stats.svGPFA.svGPFAModelFactory.SVGPFAModelFactory.buildModel(
conditionalDist=stats.svGPFA.svGPFAModelFactory.PointProcess,
linkFunction=stats.svGPFA.svGPFAModelFactory.ExponentialLink,
embeddingType=stats.svGPFA.svGPFAModelFactory.LinearEmbedding,
kernels=kernels)
model.setMeasurements(measurements=spikesTimes)
model.setInitialParams(initialParams=initialParams)
model.setQuadParams(quadParams=quadParams)
model.setIndPointsLocsKMSRegEpsilon(
indPointsLocsKMSRegEpsilon=indPointsLocsKMSRegEpsilon)
model.buildKernelsMatrices()
refParam = lowerBoundVsOneParamUtils.getReferenceParam(
paramType=paramType,
model=model,
trial=trial,
latent=latent,
neuron=neuron,
kernelParamIndex=kernelParamIndex,
indPointIndex=indPointIndex,
indPointIndex2=indPointIndex2)
paramUpdateFun = lowerBoundVsOneParamUtils.getParamUpdateFun(
paramType=paramType)
paramValues = np.arange(paramValueStart, paramValueEnd, paramValueStep)
lowerBoundValues = np.empty(paramValues.shape)
for i in range(len(paramValues)):
paramUpdateFun(model=model,
paramValue=paramValues[i],
trial=trial,
latent=latent,
neuron=neuron,
kernelParamIndex=kernelParamIndex,
indPointIndex=indPointIndex,
indPointIndex2=indPointIndex2)
Kzz = model._eLL._svEmbeddingAllTimes._svPosteriorOnLatents._indPointsLocsKMS._Kzz
# begin debug code
# for k in range(nLatents):
# nTrial = Kzz[k].shape[0]
# for r in range(nTrial):
# # torch.sum(torch.abs(torch.eig(Kzz[k][0,:,:]).eigenvalues[:,1]))==0
# imEigenval = torch.eig(Kzz[k][0,:,:]).eigenvalues[:,1]
# if torch.any(imEigenval!=0.0):
# print("{:f}".format(paramValues[i]))
# if paramValues[i]>=16.48:
# pdb.set_trace()
# end debug code
# if paramValues[i]>=4.0:
# pdb.set_trace()
lowerBoundValues[i] = model.eval()
# begin fix plotly problem with nInf values
# nInfIndices = np.where(lowerBoundValues==-np.inf)[0]
# minNoNInfLowerBound = lowerBoundValues[nInfIndices.max()+1]
# lowerBoundNoNInfValues = lowerBoundValues
# lowerBoundNoNInfValues[nInfIndices] = minNoNInfLowerBound
# end fix plotly problem with nInf values
# begin fix plotly problem with nInf values
boundedLowerBound = lowerBoundValues
smallIndices = np.where(lowerBoundValues < yMin)[0]
boundedLowerBound[smallIndices] = None
largeIndices = np.where(lowerBoundValues > yMax)[0]
boundedLowerBound[largeIndices] = None
# end fix plotly problem with nInf values
title = lowerBoundVsOneParamUtils.getParamTitle(
paramType=paramType,
trial=trial,
latent=latent,
neuron=neuron,
kernelParamIndex=kernelParamIndex,
indPointIndex=indPointIndex,
indPointIndex2=indPointIndex2,
indPointsLocsKMSRegEpsilon=indPointsLocsKMSRegEpsilon)
figFilenamePattern = lowerBoundVsOneParamUtils.getFigFilenamePattern(
prefixNumber=simResNumber,
descriptor="lowerBoundVs1DParam_generativeParams",
paramType=paramType,
trial=trial,
latent=latent,
neuron=neuron,
indPointsLocsKMSRegEpsilon=indPointsLocsKMSRegEpsilon,
kernelParamIndex=kernelParamIndex,
indPointIndex=indPointIndex,
indPointIndex2=indPointIndex2)
fig = plot.svGPFA.plotUtilsPlotly.getPlotLowerBoundVsOneParam(
paramValues=paramValues,
lowerBoundValues=lowerBoundValues,
refParams=[refParam],
title=title,
yMin=yMin,
yMax=yMax,
lowerBoundLineColor="blue",
refParamsLineColors=["blue"])
fig.write_image(figFilenamePattern.format("png"))
fig.write_html(figFilenamePattern.format("html"))
pio.renderers.default = "browser"
fig.show()
pdb.set_trace()
