def doParse(dataArray,lk,outputBaseFilename,setOfRequestedColumns,column_names,dataRanges,all_best_fit_data,nBins,alt_best_fit):
#Perform all numerical operations required for chain parsing
# Determine the minimum log-likelihood requested as an isocontour in 2D plots
min_contour = None
if doProfile.value and twoDplots.value and contours2D.value:
min_contour = deltaLnLike(1.0,0.01*max(contours2D.value))
# Standardise likelihood, prior and multiplicity labels, and rescale likelihood and columns if necessary
standardise(dataArray,lk)
# Sort array if required
doSort(dataArray,lk)
# Find best-fit point
[bestFit,worstFit,bestFitIndex] = getBestFit(dataArray,lk,outputBaseFilename,column_names,all_best_fit_data,alt_best_fit,min_contour)
# Find posterior mean
[totalMult, posteriorMean] = getPosteriorMean(dataArray,lk,outputBaseFilename)
# Get evidence for mcmc
[lnZMain,lnZMainError] = getEvidence(dataArray,lk,bestFit,totalMult,outputBaseFilename)
# Save data minima and maxima
saveExtrema(dataArray,lk,outputBaseFilename,setOfRequestedColumns,dataRanges)
# Save variables to plot in log scale
saveLogVars(lk,outputBaseFilename,logPlots)
# Save lookup keys for parameters
saveLookupKeys(lk,outputBaseFilename)
# Do binning for 1D plots
oneDsampler(dataArray,lk,bestFit,worstFit,outputBaseFilename,dataRanges,nBins)
# Do binning for 2D plots
twoDsampler(dataArray,lk,bestFit,worstFit,outputBaseFilename,dataRanges,nBins)
def doParse(dataArray,lk,outputBaseFilename,setOfRequestedColumns,column_names,dataRanges,all_best_fit_data,nBins,alt_best_fit):
#Perform all numerical operations required for chain parsing
# Determine the minimum log-likelihood requested as an isocontour in 2D plots
min_contour = None
if doProfile.value and twoDplots.value and contours2D.value:
min_contour = deltaLnLike(1.0,0.01*max(contours2D.value))
# Standardise likelihood, prior and multiplicity labels, and rescale likelihood and columns if necessary
standardise(dataArray,lk)
# Sort array if required
doSort(dataArray,lk)
# Find best-fit point
[bestFit,worstFit,bestFitIndex] = getBestFit(dataArray,lk,outputBaseFilename,column_names,all_best_fit_data,alt_best_fit,min_contour)
# Find posterior mean
[totalMult, posteriorMean] = getPosteriorMean(dataArray,lk,outputBaseFilename)
# Get evidence for mcmc
[lnZMain,lnZMainError] = getEvidence(dataArray,lk,bestFit,totalMult,outputBaseFilename)
# Save data minima and maxima
saveExtrema(dataArray,lk,outputBaseFilename,setOfRequestedColumns,dataRanges)
# Save lookup keys for parameters
saveLookupKeys(lk,outputBaseFilename)
# Do binning for 1D plots
oneDsampler(dataArray,lk,bestFit,worstFit,outputBaseFilename,dataRanges,nBins)
# Do binning for 2D plots
twoDsampler(dataArray,lk,bestFit,worstFit,outputBaseFilename,dataRanges,nBins)
def twoDsampler(dataArray, lk, bestFit, worstFit, outputBaseFilename, dataRanges, nAllBins):
# Do sample sorting for 2D plots
if twoDplots.value is None:
return
# Determine profile likelihood contour levels (same for all plots of a given dimensionality)
if contours.value is not None:
profContourLevels = [np.exp(-deltaLnLike(1.0, 0.01 * contour)) for contour in contours.value]
outName = outputBaseFilename + "_like2D.contours"
outfile = smart_open(outName, "w")
outfile.write(
"# This 2D profile likelihood ratio contours file created by pippi "
+ pippiVersion
+ " on "
+ datetime.datetime.now().strftime("%c")
+ "\n"
)
outfile.write(" ".join([str(x) for x in profContourLevels]))
outfile.close
for plot in twoDplots.value:
print " Parsing data for 2D plots of quantities ", plot
nBins = [nAllBins[plot[j]] for j in range(2)]
likeGrid = np.empty((nBins[0], nBins[1]), dtype=np.float64)
likeGrid[:, :] = worstFit + 100.0
postGrid = np.zeros((nBins[0], nBins[1]), dtype=np.float64)
# Work out maximum and minimum values of parameters/derived quantities
minVal = [dataRanges[plot[j]][0] for j in range(2)]
maxVal = [dataRanges[plot[j]][1] for j in range(2)]
rangeOfVals = [maxVal[j] - minVal[j] for j in range(2)]
# Pad edges of grid
binSep = [rangeOfVals[j] / (nBins[j] - 2) for j in range(2)]
minVal = [minVal[j] - binSep[j] for j in range(2)]
maxVal = [maxVal[j] + binSep[j] for j in range(2)]
rangeOfVals = [rangeOfVals[j] + 2.0 * binSep[j] for j in range(2)]
# Calculate bin centres
binCentresOrig = []
binCentresInterp = []
for j in range(2):
binCentresOrig.append(
np.array([minVal[j] + (x + 0.5) * rangeOfVals[j] / nBins[j] for x in range(nBins[j])])
)
for j in range(2):
binCentresInterp.append(
np.array(
[
binCentresOrig[j][0]
+ x * (binCentresOrig[j][-1] - binCentresOrig[j][0]) / (resolution.value - 1)
for x in range(resolution.value)
]
)
)
# Loop over points in chain
for i in range(dataArray.shape[0] - 1, -1, -1):
[in1, in2] = [
min(int((dataArray[i, lk[plot[j]]] - minVal[j]) / rangeOfVals[j] * nBins[j]), nBins[j] - 2)
for j in range(2)
]
# Profile over likelihoods
if doProfile.value:
likeGrid[in1, in2] = min(dataArray[i, lk[labels.value[refLike]]], likeGrid[in1, in2])
# Marginalise by addding to posterior sample count
if doPosterior.value:
postGrid[in1, in2] += dataArray[i, lk[labels.value[refMult]]]
# Convert -log(profile likelihoods) to profile likelihood ratio
likeGrid = np.exp(bestFit - likeGrid)
# Precompute co-ordinate grids for splines to avoid repetition
if intMethod.value == "spline":
oldCoords = np.array(
[[binCentresOrig[0][i], binCentresOrig[1][j]] for i in range(nBins[0]) for j in range(nBins[1])]
)
newCoords = [
[binCentresInterp[0][i], binCentresInterp[1][j]]
for i in range(resolution.value)
for j in range(resolution.value)
]
# Interpolate posterior pdf and profile likelihood to requested display resolution
if doProfile.value:
if intMethod.value == "spline":
likeGrid = np.array(likeGrid).reshape(nBins[0] * nBins[1])
interpolator = twoDspline(oldCoords, likeGrid)
likeGrid = np.array(interpolator(newCoords)).reshape(resolution.value, resolution.value)
else:
interpolator = twoDbilinear(binCentresOrig[0], binCentresOrig[1], likeGrid, ky=1, kx=1)
likeGrid = np.array(
[
interpolator(binCentresInterp[0][j], binCentresInterp[1][i])
for j in range(resolution.value)
for i in range(resolution.value)
]
).reshape(resolution.value, resolution.value)
# Fix any points sent NaN by scipy's crappy interpolators
likeGrid[np.isnan(likeGrid)] = 0.0
# Kill off any points that have been sent negative due to ringing
likeGrid[np.isneginf(likeGrid)] = 0.0
likeGrid[likeGrid < 0] = 0.0
# Fix any points that have been sent >1 due to ringing
likeGrid[np.isposinf(likeGrid)] = 1.0
likeGrid[likeGrid > 1] = 1.0
# Make sure we haven't erased the best-fit point by interpolating over it
likeGrid[np.unravel_index(likeGrid.argmax(), likeGrid.shape)] = 1.0
if doPosterior.value:
if intMethod.value == "spline":
postGrid = np.array(postGrid).reshape(nBins[0] * nBins[1])
interpolator = twoDspline(oldCoords, postGrid)
postGrid = np.array(interpolator(newCoords)).reshape(resolution.value, resolution.value)
else:
interpolator = twoDbilinear(binCentresOrig[0], binCentresOrig[1], postGrid, ky=1, kx=1)
postGrid = np.array(
[
interpolator(binCentresInterp[0][j], binCentresInterp[1][i])
for j in range(resolution.value)
for i in range(resolution.value)
]
).reshape(resolution.value, resolution.value)
# Kill off any points that have been sent negative due to ringing
postGrid[~np.isfinite(postGrid)] = 0.0
postGrid[postGrid < 0] = 0.0
# Rescale posterior pdf back into the range [0,1]
postGrid = postGrid / postGrid.max()
# Find posterior pdf contour levels
if contours.value is not None and doPosterior.value:
# Zero posterior contour levels
postContourLevels = [None for contour in contours.value]
# Zero posterior integral
integratedPosterior = 0.0
# Sort bins in order of posterior mass
sortedPostGrid = np.ma.sort(postGrid.flatten())
# Work out the new total multiplicity
totalMult = np.sum(sortedPostGrid)
# Work through bins backwards until total posterior mass adds up to the requested confidence levels
for i in range(sortedPostGrid.shape[0] - 1, -1, -1):
integratedPosterior += sortedPostGrid[i] / totalMult
for j, contour in enumerate(contours.value):
if 100 * integratedPosterior >= contour and postContourLevels[j] is None:
postContourLevels[j] = sortedPostGrid[i]
if all([x is not None for x in postContourLevels]):
break
# Write profile likelihood to file
if doProfile.value:
outName = outputBaseFilename + "_" + "_".join([str(x) for x in plot]) + "_like2D.ct2"
outfile = smart_open(outName, "w")
outfile.write(
"# This 2D binned profile likelihood ratio file created by pippi "
+ pippiVersion
+ " on "
+ datetime.datetime.now().strftime("%c")
+ "\n"
)
outfile.write(
"\n".join(
[
str(binCentresInterp[0][i]) + "\t" + str(binCentresInterp[1][j]) + "\t" + str(likeGrid[i, j])
for i in range(likeGrid.shape[0])
for j in range(likeGrid.shape[1])
]
)
)
outfile.close
# Write posterior pdf and contours to file
if doPosterior.value:
outName = outputBaseFilename + "_" + "_".join([str(x) for x in plot]) + "_post2D.ct2"
outfile = smart_open(outName, "w")
outfile.write(
"# This 2D binned posterior pdf file created by pippi "
+ pippiVersion
+ " on "
+ datetime.datetime.now().strftime("%c")
+ "\n"
)
outfile.write(
"\n".join(
[
str(binCentresInterp[0][i]) + "\t" + str(binCentresInterp[1][j]) + "\t" + str(postGrid[i, j])
for i in range(postGrid.shape[0])
for j in range(postGrid.shape[1])
]
)
)
outfile.close
if contours.value is not None:
outName = outputBaseFilename + "_" + "_".join([str(x) for x in plot]) + "_post2D.contours"
outfile = smart_open(outName, "w")
outfile.write(
"# This 2D posterior pdf contours file created by pippi "
+ pippiVersion
+ " on "
+ datetime.datetime.now().strftime("%c")
+ "\n"
)
outfile.write(" ".join([str(x) for x in postContourLevels]))
outfile.close
def oneDsampler(dataArray, lk, bestFit, worstFit, outputBaseFilename, dataRanges, nAllBins):
# Do sample sorting for 1D plots
if oneDplots.value is None:
return
if contours.value is not None:
# Determine profile likelihood contour levels (same for all plots of a given dimensionality)
profContourLevels = [np.exp(-deltaLnLike(0.5, 0.01 * contour)) for contour in contours.value]
outfile = smart_open(outputBaseFilename + "_like1D.contours", "w")
outfile.write(
"# This 1D profile likelihood ratio contours file created by pippi "
+ pippiVersion
+ " on "
+ datetime.datetime.now().strftime("%c")
+ "\n"
)
outfile.write(" ".join([str(x) for x in profContourLevels]) + "\n")
outfile.close
for plot in oneDplots.value:
print " Parsing data for 1D plots of quantity ", plot
nBins = nAllBins[plot]
likeGrid = np.empty((nBins), dtype=np.float64)
likeGrid[:] = worstFit + 100.0
postGrid = np.zeros((nBins), dtype=np.float64)
# Work out maximum and minimum values of parameter/derived quantity
minVal = dataRanges[plot][0]
maxVal = dataRanges[plot][1]
rangeOfVals = maxVal - minVal
binSep = rangeOfVals / nBins
# Throw a sensible error if the range of values is a delta function
if rangeOfVals <= 0:
sys.exit(
"Error: datastream "
+ str(plot)
+ " contains exactly the same "
+ "number for every point, so pippi cannot bin it further within its range. Does it make "
+ "sense to plot this quantity at all, if it is constant?"
)
# Calculate bin centres
binCentresOrig = np.array([minVal + (x + 0.5) * rangeOfVals / nBins for x in range(nBins)])
binCentresInterp = np.array(
[
binCentresOrig[0] + x * (binCentresOrig[-1] - binCentresOrig[0]) / (resolution.value - 1)
for x in range(resolution.value)
]
)
# Loop over points in chain
for i in range(dataArray.shape[0] - 1, -1, -1):
index = min(int((dataArray[i, lk[plot]] - minVal) / rangeOfVals * nBins), nBins - 1)
# Profile over likelihoods
if doProfile.value:
likeGrid[index] = min(dataArray[i, lk[labels.value[refLike]]], likeGrid[index])
if doPosterior.value:
# Marginalise by addding to posterior sample count
postGrid[index] += dataArray[i, lk[labels.value[refMult]]]
# Convert -log(profile likelihoods) to profile likelihood ratio
if doProfile.value:
likeGrid = np.exp(bestFit - likeGrid)
# Rescale posterior pdf to maximum 1
if doPosterior.value:
postGrid = postGrid / postGrid.max()
# Save raw binned profile likelihood and posterior pdf for outputting in histogram files
if doProfile.value:
likeGridHistogram = likeGrid
if doPosterior.value:
postGridHistogram = postGrid
# Interpolate profile likelihoods and posterior pdfs to requested resolution
if doProfile.value:
interpolator = oneDspline(binCentresOrig, likeGrid)
likeGrid = interpolator(binCentresInterp)
# Fix any points sent NaN by scipy's crappy interpolators
likeGrid[np.isnan(likeGrid)] = 0.0
# Kill off any points that have been sent negative due to ringing
likeGrid[np.isneginf(likeGrid)] = 0.0
likeGrid[likeGrid < 0] = 0.0
# Fix any points that have been sent >1 due to ringing
likeGrid[np.isposinf(likeGrid)] = 1.0
likeGrid[likeGrid > 1] = 1.0
if doPosterior.value:
interpolator = oneDspline(binCentresOrig, postGrid)
postGrid = interpolator(binCentresInterp)
# Kill off any points that have been sent negative due to ringing
postGrid[~np.isfinite(postGrid)] = 0.0
postGrid[postGrid < 0] = 0.0
# Rescale posterior pdf so that it has maximum 1
postGrid = postGrid / postGrid.max()
# Find posterior pdf contour levels
if contours.value is not None and doPosterior.value:
# Zero posterior contour levels
postContourLevels = [None for contour in contours.value]
# Zero posterior integral
integratedPosterior = 0.0
# Sort bins in order of posterior mass
sortedPostGrid = np.ma.sort(postGrid)
# Work out the new total multiplicity
totalMult = np.sum(sortedPostGrid)
# Work through bins backwards until total posterior mass adds up to the requested confidence levels
for i in range(sortedPostGrid.shape[0] - 1, -1, -1):
integratedPosterior += sortedPostGrid[i] / totalMult
for j, contour in enumerate(contours.value):
if 100 * integratedPosterior >= contour and postContourLevels[j] is None:
postContourLevels[j] = sortedPostGrid[i]
if all([x is not None for x in postContourLevels]):
break
# Write profile likelihood to file
if doProfile.value:
outfile = smart_open(outputBaseFilename + "_" + str(plot) + "_like1D.ct2", "w")
outfile.write(
"# This 1D binned profile likelihood ratio file created by pippi "
+ pippiVersion
+ " on "
+ datetime.datetime.now().strftime("%c")
+ "\n"
)
outfile.write("\n".join([str(binCentresInterp[i]) + "\t" + str(x) for i, x in enumerate(likeGrid)]))
outfile.close
outfile = smart_open(outputBaseFilename + "_" + str(plot) + "_like1Dhist.ct2", "w")
outfile.write(
"# This 1D binned profile likelihood ratio file created by pippi "
+ pippiVersion
+ " on "
+ datetime.datetime.now().strftime("%c")
+ "\n"
)
outfile.write(
"\n".join(
[
str(binCentresOrig[i] - 0.5 * binSep)
+ "\t"
+ str(x)
+ "\n"
+ str(binCentresOrig[i] + 0.5 * binSep)
+ "\t"
+ str(x)
for i, x in enumerate(likeGridHistogram)
]
)
)
outfile.close
# Write posterior pdf and contours to file
if doPosterior.value:
outfile = smart_open(outputBaseFilename + "_" + str(plot) + "_post1D.ct2", "w")
outfile.write(
"# This 1D binned posterior pdf file created by pippi "
+ pippiVersion
+ " on "
+ datetime.datetime.now().strftime("%c")
+ "\n"
)
outfile.write("\n".join([str(binCentresInterp[i]) + "\t" + str(x) for i, x in enumerate(postGrid)]))
outfile.close
outfile = smart_open(outputBaseFilename + "_" + str(plot) + "_post1Dhist.ct2", "w")
outfile.write(
"# This 1D binned posterior pdf file created by pippi "
+ pippiVersion
+ " on "
+ datetime.datetime.now().strftime("%c")
+ "\n"
)
outfile.write(
"\n".join(
[
str(binCentresOrig[i] - 0.5 * binSep)
+ "\t"
+ str(x)
+ "\n"
+ str(binCentresOrig[i] + 0.5 * binSep)
+ "\t"
+ str(x)
for i, x in enumerate(postGridHistogram)
]
)
)
outfile.close
if contours.value is not None:
outfile = smart_open(outputBaseFilename + "_" + str(plot) + "_post1D.contours", "w")
outfile.write(
"# This 1D posterior pdf contours file created by pippi "
+ pippiVersion
+ " on "
+ datetime.datetime.now().strftime("%c")
+ "\n"
)
outfile.write(" ".join([str(x) for x in postContourLevels]) + "\n")
outfile.close
def twoDsampler(dataArray,lk,bestFit,worstFit,outputBaseFilename,dataRanges,nAllBins):
# Do sample sorting for 2D plots
if twoDplots.value is None: return
# Determine profile likelihood contour levels (same for all plots of a given dimensionality)
if contours2D.value is not None:
profContourLevels = [np.exp(-deltaLnLike(1.0,0.01*contour)) for contour in contours2D.value]
outName = outputBaseFilename+'_like2D.contours'
outfile = smart_open(outName,'w')
outfile.write('# This 2D profile likelihood ratio contours file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write(' '.join([str(x) for x in profContourLevels]))
outfile.close
for plot in twoDplots.value:
print(' Parsing data for 2D plots of quantities ',plot)
nBins = [nAllBins[plot[j]] for j in range(2)]
likeGrid = np.empty((nBins[0],nBins[1]), dtype=np.float64)
likeGrid[:,:] = worstFit + 100.0
postGrid = np.zeros((nBins[0],nBins[1]), dtype=np.float64)
num_obs = 0
if obsPlots.value is not None:
for column in obsPlots.value:
if column in lk:
num_obs = num_obs+1
obsGrid = np.zeros((num_obs,nBins[0],nBins[1]), dtype=np.float64)
k = -1
minimum_obs = np.zeros(num_obs)
obsMinVal = np.zeros(num_obs)
obsMaxVal = np.zeros(num_obs)
obsFloor = np.zeros(num_obs)
if obsPlots.value is not None:
for column in obsPlots.value:
if column in lk:
k = k+1
obsMinVal[k] = dataArray[:,lk[column]].min()
obsMaxVal[k] = dataArray[:,lk[column]].max()
# temporarily set the whole grid to a very low value
obsGrid[k,:,:] = obsMinVal[k] - 100
# set a value just below the actual minimum
if obsMinVal[k] < 0:
obsFloor[k] = obsMinVal[k]*1.01
if obsMinVal[k] > 0:
obsFloor[k] = obsMinVal[k]*0.99
if obsMinVal[k] == 0:
obsFloor[k] = -0.01
# Work out maximum and minimum values of parameters/derived quantities
minVal = [dataRanges[plot[j]][0] for j in range(2)]
maxVal = [dataRanges[plot[j]][1] for j in range(2)]
rangeOfVals = [maxVal[j] - minVal[j] for j in range(2)]
# Pad edges of grid
binSep = [rangeOfVals[j]/(nBins[j]-2) for j in range(2)]
minVal = [minVal[j] - binSep[j] for j in range(2)]
maxVal = [maxVal[j] + binSep[j] for j in range(2)]
rangeOfVals = [rangeOfVals[j] + 2.0 * binSep[j] for j in range(2)]
# Calculate bin centres
binCentresOrig = []
binCentresInterp = []
for j in range(2): binCentresOrig.append(np.array([minVal[j] + (x+0.5)*rangeOfVals[j]/nBins[j] for x in range(nBins[j])]))
for j in range(2): binCentresInterp.append(np.array([binCentresOrig[j][0] + x*(binCentresOrig[j][-1]-binCentresOrig[j][0])\
/(resolution.value-1) for x in range(resolution.value)]))
# Loop over points in chain
for i in range(dataArray.shape[0]-1,-1,-1):
[in1,in2] = [min(int((dataArray[i,lk[plot[j]]]-minVal[j])/rangeOfVals[j]*nBins[j]),nBins[j]-2) for j in range(2)]
# Take observable at maximum likelihood for this bin
if dataArray[i,lk[labels.value[refLike]]] < likeGrid[in1,in2]:
if obsPlots.value is not None:
k = -1
for column in obsPlots.value:
if column in lk:
k = k + 1
obsGrid[k,in1,in2] = dataArray[i,lk[column]]
# Profile over likelihoods
if doProfile.value: likeGrid[in1,in2] = min(dataArray[i,lk[labels.value[refLike]]],likeGrid[in1,in2])
# Marginalise by addding to posterior sample count
if doPosterior.value: postGrid[in1,in2] += dataArray[i,lk[labels.value[refMult]]]
# Convert -log(profile likelihoods) to profile likelihood ratio
likeGrid = np.exp(bestFit - likeGrid)
#obsFloor = np.zeros(k+1)
k = -1
if obsPlots.value is not None:
for column in obsPlots.value:
if column in lk:
k = k + 1
obsContourLevels = [obsFloor[k],obsMinVal[k],obsMaxVal[k]]
outName = outputBaseFilename+'_'+'_'.join([str(x) for x in plot])+'_obs2D_' + str(column) + '.contours'
outfile = smart_open(outName,'w')
outfile.write('# This 2D observable color-map levels file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write(' '.join([str(x) for x in obsContourLevels]))
outfile.close
# Precompute co-ordinate grids for splines to avoid repetition
if intMethod.value == 'spline':
oldCoords = np.array([[binCentresOrig[0][i], binCentresOrig[1][j]] for i in range(nBins[0]) for j in range(nBins[1])])
newCoords = [[binCentresInterp[0][i], binCentresInterp[1][j]] for i in range(resolution.value) for j in range(resolution.value)]
# Interpolate posterior pdf, profile likelihood and observable to requested display resolution
if doProfile.value:
if intMethod.value == 'spline':
likeGrid = np.array(likeGrid).reshape(nBins[0]*nBins[1])
interpolator = twoDspline(oldCoords,likeGrid)
likeGrid = np.array(interpolator(newCoords)).reshape(resolution.value,resolution.value)
else:
interpolator = twoDbilinear(binCentresOrig[0], binCentresOrig[1], likeGrid, ky = 1, kx = 1)
likeGrid = np.array([interpolator(binCentresInterp[0][j], binCentresInterp[1][i])
for j in range(resolution.value) for i in range(resolution.value)]).reshape(resolution.value,resolution.value)
# Fix any points sent NaN by scipy's crappy interpolators
likeGrid[np.isnan(likeGrid)] = 0.0
# Kill off any points that have been sent negative due to ringing
likeGrid[np.isneginf(likeGrid)] = 0.0
likeGrid[likeGrid<0] = 0.0
# Fix any points that have been sent >1 due to ringing
likeGrid[np.isposinf(likeGrid)] = 1.0
likeGrid[likeGrid>1] = 1.0
# Make sure we haven't erased the best-fit point by interpolating over it
likeGrid[np.unravel_index(likeGrid.argmax(),likeGrid.shape)] = 1.0
if doPosterior.value:
if intMethod.value == 'spline':
postGrid = np.array(postGrid).reshape(nBins[0]*nBins[1])
interpolator = twoDspline(oldCoords,postGrid)
postGrid = np.array(interpolator(newCoords)).reshape(resolution.value,resolution.value)
else:
interpolator = twoDbilinear(binCentresOrig[0], binCentresOrig[1], postGrid, ky = 1, kx = 1)
postGrid = np.array([interpolator(binCentresInterp[0][j], binCentresInterp[1][i])
for j in range(resolution.value) for i in range(resolution.value)]).reshape(resolution.value,resolution.value)
# Kill off any points that have been sent negative due to ringing
postGrid[~np.isfinite(postGrid)] = 0.0
postGrid[postGrid<0] = 0.0
# Rescale posterior pdf back into the range [0,1]
postGrid = postGrid / postGrid.max()
if obsPlots.value is not None:
k = -1
for column in obsPlots.value:
if column in lk:
k = k + 1
obsGrid_temp = obsGrid[k,:,:]
obsGrid_temp[obsGrid_temp == (obsMinVal[k]-100) ] = obsMinVal[k]
if intMethod.value == 'spline':
obsGrid_temp = np.array(obsGrid_temp).reshape(nBins[0]*nBins[1])
interpolator = twoDspline(oldCoords,obsGrid_temp)
obsGrid_temp = np.array(interpolator(newCoords)).reshape(resolution.value,resolution.value)
else:
interpolator = twoDbilinear(binCentresOrig[0], binCentresOrig[1], obsGrid_temp, ky = 1, kx = 1)
obsGrid_temp = np.array([interpolator(binCentresInterp[0][j], binCentresInterp[1][i])
for j in range(resolution.value) for i in range(resolution.value)]).reshape(resolution.value,resolution.value)
obsGrid_temp[obsGrid_temp < obsMinVal[k] ] = obsMinVal[k]
# set points outside the contours to the floor value (effectively "no data")
obsGrid_temp[likeGrid<min(profContourLevels)] = obsFloor[k]
# Write observable to file
outName = outputBaseFilename+'_'+'_'.join([str(x) for x in plot])+'_obs2D_'+str(column)+'.ct2'
outfile = smart_open(outName,'w')
outfile.write('# This 2D binned observable file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('\n'.join([str(binCentresInterp[0][i])+'\t'+str(binCentresInterp[1][j])+'\t'+str(obsGrid_temp[i,j]) \
for i in range(obsGrid_temp.shape[0]) for j in range(obsGrid_temp.shape[1])]))
outfile.close
# make a dummy grid with maximum and minimum values (of the reduced grid) to use for colorbar
obsGrid_temp_list = obsGrid_temp.reshape(obsGrid_temp.shape[1] * obsGrid_temp.shape[0] )
obsGrid_temp_list = obsGrid_temp_list[obsGrid_temp_list != obsFloor[k]]
obsMinValReduced = obsGrid_temp_list.min()
obsMaxValReduced = obsGrid_temp_list.max()
dummyGrid = np.zeros([2, 3])
dummyGrid[0,0] = binCentresInterp[0][0]
dummyGrid[0,1] = binCentresInterp[0][0]
dummyGrid[0,2] = obsMinValReduced
dummyGrid[1,0] = binCentresInterp[0][1]
dummyGrid[1,1] = binCentresInterp[0][1]
dummyGrid[1,2] = obsMaxValReduced
outName = outputBaseFilename+'_'+'_'.join([str(x) for x in plot])+'_obs2D_'+str(column)+'_colorbar.ct2'
outfile = smart_open(outName,'w')
outfile.write('# This 2D binned observable file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('\n'.join([str(dummyGrid[i,0])+'\t'+str(dummyGrid[i,1])+'\t'+str(dummyGrid[i,2]) \
for i in range(2) ]))
outfile.close
# Find posterior pdf contour levels
if contours2D.value is not None and doPosterior.value:
# Zero posterior contour levels
postContourLevels = [None for contour in contours2D.value]
# Zero posterior integral
integratedPosterior = 0.0
# Sort bins in order of posterior mass
sortedPostGrid = np.ma.sort(postGrid.flatten())
# Work out the new total multiplicity
totalMult = np.sum(sortedPostGrid)
# Work through bins backwards until total posterior mass adds up to the requested confidence levels
for i in range(sortedPostGrid.shape[0]-1,-1,-1):
integratedPosterior += sortedPostGrid[i]/totalMult
for j,contour in enumerate(contours2D.value):
if 100*integratedPosterior >= contour and postContourLevels[j] is None:
postContourLevels[j] = sortedPostGrid[i]
if all([x is not None for x in postContourLevels]): break
# Write profile likelihood to file
if doProfile.value:
outName = outputBaseFilename+'_'+'_'.join([str(x) for x in plot])+'_like2D.ct2'
outfile = smart_open(outName,'w')
outfile.write('# This 2D binned profile likelihood ratio file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('\n'.join([str(binCentresInterp[0][i])+'\t'+str(binCentresInterp[1][j])+'\t'+str(likeGrid[i,j]) \
for i in range(likeGrid.shape[0]) for j in range(likeGrid.shape[1])]))
outfile.close
# Write posterior pdf and contours to file
if doPosterior.value:
outName = outputBaseFilename+'_'+'_'.join([str(x) for x in plot])+'_post2D.ct2'
outfile = smart_open(outName,'w')
outfile.write('# This 2D binned posterior pdf file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('\n'.join([str(binCentresInterp[0][i])+'\t'+str(binCentresInterp[1][j])+'\t'+str(postGrid[i,j]) \
for i in range(postGrid.shape[0]) for j in range(postGrid.shape[1])]))
outfile.close
if contours2D.value is not None:
outName = outputBaseFilename+'_'+'_'.join([str(x) for x in plot])+'_post2D.contours'
outfile = smart_open(outName,'w')
outfile.write('# This 2D posterior pdf contours file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write(' '.join([str(x) for x in postContourLevels]))
outfile.close
def oneDsampler(dataArray,lk,bestFit,worstFit,outputBaseFilename,dataRanges,nAllBins):
# Do sample sorting for 1D plots
if oneDplots.value is None: return
if contours1D.value is not None:
# Determine profile likelihood contour levels (same for all plots of a given dimensionality)
profContourLevels = [np.exp(-deltaLnLike(0.5,0.01*contour)) for contour in contours1D.value]
outfile = smart_open(outputBaseFilename+'_like1D.contours','w')
outfile.write('# This 1D profile likelihood ratio contours file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write(' '.join([str(x) for x in profContourLevels])+'\n')
outfile.close
for plot in oneDplots.value:
print(' Parsing data for 1D plots of quantity ',plot)
nBins = nAllBins[plot]
likeGrid = np.empty((nBins), dtype=np.float64)
likeGrid[:] = worstFit + 100.0
postGrid = np.zeros((nBins), dtype=np.float64)
# Work out maximum and minimum values of parameter/derived quantity
minVal = dataRanges[plot][0]
maxVal = dataRanges[plot][1]
rangeOfVals = maxVal - minVal
# Fix things if the range of values is a delta function
if (rangeOfVals <= 0): rangeOfVals = maxVal * 1e-3
binSep = rangeOfVals / nBins
# Calculate bin centres
binCentresOrig = np.array([minVal + (x+0.5)*rangeOfVals/nBins for x in range(nBins)])
binCentresInterp = np.array([binCentresOrig[0] + x*(binCentresOrig[-1]-binCentresOrig[0])\
/(resolution.value-1) for x in range(resolution.value)])
# Loop over points in chain
for i in range(dataArray.shape[0]-1,-1,-1):
index = min(int((dataArray[i,lk[plot]]-minVal)/rangeOfVals*nBins),nBins-1)
# Profile over likelihoods
if doProfile.value: likeGrid[index] = min(dataArray[i,lk[labels.value[refLike]]],likeGrid[index])
if doPosterior.value:
# Marginalise by addding to posterior sample count
postGrid[index] += dataArray[i,lk[labels.value[refMult]]]
# Convert -log(profile likelihoods) to profile likelihood ratio
if doProfile.value: likeGrid = np.exp(bestFit - likeGrid)
# Rescale posterior pdf to maximum 1
if doPosterior.value: postGrid = postGrid / postGrid.max()
# Save raw binned profile likelihood and posterior pdf for outputting in histogram files
if doProfile.value: likeGridHistogram = likeGrid
if doPosterior.value: postGridHistogram = postGrid
# Interpolate profile likelihoods and posterior pdfs to requested resolution
if doProfile.value:
interpolator = oneDspline(binCentresOrig, likeGrid)
likeGrid = interpolator(binCentresInterp)
# Fix any points sent NaN by scipy's crappy interpolators
likeGrid[np.isnan(likeGrid)] = 0.0
# Kill off any points that have been sent negative due to ringing
likeGrid[np.isneginf(likeGrid)] = 0.0
likeGrid[likeGrid<0] = 0.0
# Fix any points that have been sent >1 due to ringing
likeGrid[np.isposinf(likeGrid)] = 1.0
likeGrid[likeGrid>1] = 1.0
if doPosterior.value:
interpolator = oneDspline(binCentresOrig, postGrid)
postGrid = interpolator(binCentresInterp)
# Kill off any points that have been sent negative due to ringing
postGrid[~np.isfinite(postGrid)] = 0.0
postGrid[postGrid<0] = 0.0
# Rescale posterior pdf so that it has maximum 1
postGrid = postGrid / postGrid.max()
# Find posterior pdf contour levels
if contours1D.value is not None and doPosterior.value:
# Zero posterior contour levels
postContourLevels = [None for contour in contours1D.value]
# Zero posterior integral
integratedPosterior = 0.0
# Sort bins in order of posterior mass
sortedPostGrid = np.ma.sort(postGrid)
# Work out the new total multiplicity
totalMult = np.sum(sortedPostGrid)
# Work through bins backwards until total posterior mass adds up to the requested confidence levels
for i in range(sortedPostGrid.shape[0]-1,-1,-1):
integratedPosterior += sortedPostGrid[i]/totalMult
for j,contour in enumerate(contours1D.value):
if 100*integratedPosterior >= contour and postContourLevels[j] is None:
postContourLevels[j] = sortedPostGrid[i]
if all([x is not None for x in postContourLevels]): break
# Write profile likelihood to file
if doProfile.value:
outfile = smart_open(outputBaseFilename+'_'+str(plot)+'_like1D.ct2','w')
outfile.write('# This 1D binned profile likelihood ratio file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('\n'.join([str(binCentresInterp[i])+'\t'+str(x) for i,x in enumerate(likeGrid)]))
outfile.close
outfile = smart_open(outputBaseFilename+'_'+str(plot)+'_like1Dhist.ct2','w')
outfile.write('# This 1D binned profile likelihood ratio file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('\n'.join([str(binCentresOrig[i]-0.5*binSep)+'\t'+str(x)+'\n'+
str(binCentresOrig[i]+0.5*binSep)+'\t'+str(x) for i,x in enumerate(likeGridHistogram)]))
outfile.close
# Write posterior pdf and contours to file
if doPosterior.value:
outfile = smart_open(outputBaseFilename+'_'+str(plot)+'_post1D.ct2','w')
outfile.write('# This 1D binned posterior pdf file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('\n'.join([str(binCentresInterp[i])+'\t'+str(x) for i,x in enumerate(postGrid)]))
outfile.close
outfile = smart_open(outputBaseFilename+'_'+str(plot)+'_post1Dhist.ct2','w')
outfile.write('# This 1D binned posterior pdf file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('\n'.join([str(binCentresOrig[i]-0.5*binSep)+'\t'+str(x)+'\n'+
str(binCentresOrig[i]+0.5*binSep)+'\t'+str(x) for i,x in enumerate(postGridHistogram)]))
outfile.close
if contours1D.value is not None:
outfile = smart_open(outputBaseFilename+'_'+str(plot)+'_post1D.contours','w')
outfile.write('# This 1D posterior pdf contours file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write(' '.join([str(x) for x in postContourLevels])+'\n')
outfile.close
def twoDsampler(dataArray,lk,bestFit,worstFit,outputBaseFilename,dataRanges,nAllBins):
# Do sample sorting for 2D plots
if twoDplots.value is None: return
# Determine profile likelihood contour levels (same for all plots of a given dimensionality)
if contours2D.value is not None:
profContourLevels = [np.exp(-deltaLnLike(1.0,0.01*contour)) for contour in contours2D.value]
outName = outputBaseFilename+'_like2D.contours'
outfile = smart_open(outName,'w')
outfile.write('# This 2D profile likelihood ratio contours file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write(' '.join([str(x) for x in profContourLevels]))
outfile.close
for plot in twoDplots.value:
print ' Parsing data for 2D plots of quantities ',plot
nBins = [nAllBins[plot[j]] for j in range(2)]
likeGrid = np.empty((nBins[0],nBins[1]), dtype=np.float64)
likeGrid[:,:] = worstFit + 100.0
postGrid = np.zeros((nBins[0],nBins[1]), dtype=np.float64)
num_obs = 0
if obsPlots.value is not None:
for column in obsPlots.value:
if column in lk:
num_obs = num_obs+1
obsGrid = np.zeros((num_obs,nBins[0],nBins[1]), dtype=np.float64)
k = -1
minimum_obs = np.zeros(num_obs)
obsMinVal = np.zeros(num_obs)
obsMaxVal = np.zeros(num_obs)
obsFloor = np.zeros(num_obs)
if obsPlots.value is not None:
for column in obsPlots.value:
if column in lk:
k = k+1
obsMinVal[k] = dataArray[:,lk[column]].min()
obsMaxVal[k] = dataArray[:,lk[column]].max()
# temporarily set the whole grid to a very low value
obsGrid[k,:,:] = obsMinVal[k] - 100
# set a value just below the actual minimum
if obsMinVal[k] < 0:
obsFloor[k] = obsMinVal[k]*1.01
if obsMinVal[k] > 0:
obsFloor[k] = obsMinVal[k]*0.99
if obsMinVal[k] == 0:
obsFloor[k] = -0.01
# Work out maximum and minimum values of parameters/derived quantities
minVal = [dataRanges[plot[j]][0] for j in range(2)]
maxVal = [dataRanges[plot[j]][1] for j in range(2)]
rangeOfVals = [maxVal[j] - minVal[j] for j in range(2)]
# Pad edges of grid
binSep = [rangeOfVals[j]/(nBins[j]-2) for j in range(2)]
minVal = [minVal[j] - binSep[j] for j in range(2)]
maxVal = [maxVal[j] + binSep[j] for j in range(2)]
rangeOfVals = [rangeOfVals[j] + 2.0 * binSep[j] for j in range(2)]
# Calculate bin centres
binCentresOrig = []
binCentresInterp = []
for j in range(2): binCentresOrig.append(np.array([minVal[j] + (x+0.5)*rangeOfVals[j]/nBins[j] for x in range(nBins[j])]))
for j in range(2): binCentresInterp.append(np.array([binCentresOrig[j][0] + x*(binCentresOrig[j][-1]-binCentresOrig[j][0])\
/(resolution.value-1) for x in range(resolution.value)]))
# Loop over points in chain
for i in range(dataArray.shape[0]-1,-1,-1):
[in1,in2] = [min(int((dataArray[i,lk[plot[j]]]-minVal[j])/rangeOfVals[j]*nBins[j]),nBins[j]-2) for j in range(2)]
# Take observable at maximum likelihood for this bin
if dataArray[i,lk[labels.value[refLike]]] < likeGrid[in1,in2]:
if obsPlots.value is not None:
k = -1
for column in obsPlots.value:
if column in lk:
k = k + 1
obsGrid[k,in1,in2] = dataArray[i,lk[column]]
# Profile over likelihoods
if doProfile.value: likeGrid[in1,in2] = min(dataArray[i,lk[labels.value[refLike]]],likeGrid[in1,in2])
# Marginalise by addding to posterior sample count
if doPosterior.value: postGrid[in1,in2] += dataArray[i,lk[labels.value[refMult]]]
# Convert -log(profile likelihoods) to profile likelihood ratio
likeGrid = np.exp(bestFit - likeGrid)
#obsFloor = np.zeros(k+1)
k = -1
if obsPlots.value is not None:
for column in obsPlots.value:
if column in lk:
k = k + 1
obsContourLevels = [obsFloor[k],obsMinVal[k],obsMaxVal[k]]
outName = outputBaseFilename+'_'+'_'.join([str(x) for x in plot])+'_obs2D_' + str(column) + '.contours'
outfile = smart_open(outName,'w')
outfile.write('# This 2D observable color-map levels file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write(' '.join([str(x) for x in obsContourLevels]))
outfile.close
# Precompute co-ordinate grids for splines to avoid repetition
if intMethod.value == 'spline':
oldCoords = np.array([[binCentresOrig[0][i], binCentresOrig[1][j]] for i in range(nBins[0]) for j in range(nBins[1])])
newCoords = [[binCentresInterp[0][i], binCentresInterp[1][j]] for i in range(resolution.value) for j in range(resolution.value)]
# Interpolate posterior pdf, profile likelihood and observable to requested display resolution
if doProfile.value:
if intMethod.value == 'spline':
likeGrid = np.array(likeGrid).reshape(nBins[0]*nBins[1])
interpolator = twoDspline(oldCoords,likeGrid)
likeGrid = np.array(interpolator(newCoords)).reshape(resolution.value,resolution.value)
else:
interpolator = twoDbilinear(binCentresOrig[0], binCentresOrig[1], likeGrid, ky = 1, kx = 1)
likeGrid = np.array([interpolator(binCentresInterp[0][j], binCentresInterp[1][i])
for j in range(resolution.value) for i in range(resolution.value)]).reshape(resolution.value,resolution.value)
# Fix any points sent NaN by scipy's crappy interpolators
likeGrid[np.isnan(likeGrid)] = 0.0
# Kill off any points that have been sent negative due to ringing
likeGrid[np.isneginf(likeGrid)] = 0.0
likeGrid[likeGrid<0] = 0.0
# Fix any points that have been sent >1 due to ringing
likeGrid[np.isposinf(likeGrid)] = 1.0
likeGrid[likeGrid>1] = 1.0
# Make sure we haven't erased the best-fit point by interpolating over it
likeGrid[np.unravel_index(likeGrid.argmax(),likeGrid.shape)] = 1.0
if doPosterior.value:
if intMethod.value == 'spline':
postGrid = np.array(postGrid).reshape(nBins[0]*nBins[1])
interpolator = twoDspline(oldCoords,postGrid)
postGrid = np.array(interpolator(newCoords)).reshape(resolution.value,resolution.value)
else:
interpolator = twoDbilinear(binCentresOrig[0], binCentresOrig[1], postGrid, ky = 1, kx = 1)
postGrid = np.array([interpolator(binCentresInterp[0][j], binCentresInterp[1][i])
for j in range(resolution.value) for i in range(resolution.value)]).reshape(resolution.value,resolution.value)
# Kill off any points that have been sent negative due to ringing
postGrid[~np.isfinite(postGrid)] = 0.0
postGrid[postGrid<0] = 0.0
# Rescale posterior pdf back into the range [0,1]
postGrid = postGrid / postGrid.max()
if obsPlots.value is not None:
k = -1
for column in obsPlots.value:
if column in lk:
k = k + 1
obsGrid_temp = obsGrid[k,:,:]
obsGrid_temp[obsGrid_temp == (obsMinVal[k]-100) ] = obsMinVal[k]
if intMethod.value == 'spline':
obsGrid_temp = np.array(obsGrid_temp).reshape(nBins[0]*nBins[1])
interpolator = twoDspline(oldCoords,obsGrid_temp)
obsGrid_temp = np.array(interpolator(newCoords)).reshape(resolution.value,resolution.value)
else:
interpolator = twoDbilinear(binCentresOrig[0], binCentresOrig[1], obsGrid_temp, ky = 1, kx = 1)
obsGrid_temp = np.array([interpolator(binCentresInterp[0][j], binCentresInterp[1][i])
for j in range(resolution.value) for i in range(resolution.value)]).reshape(resolution.value,resolution.value)
obsGrid_temp[obsGrid_temp < obsMinVal[k] ] = obsMinVal[k]
# set points outside the contours to the floor value (effectively "no data")
obsGrid_temp[likeGrid<min(profContourLevels)] = obsFloor[k]
# Write observable to file
outName = outputBaseFilename+'_'+'_'.join([str(x) for x in plot])+'_obs2D_'+str(column)+'.ct2'
outfile = smart_open(outName,'w')
outfile.write('# This 2D binned observable file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('\n'.join([str(binCentresInterp[0][i])+'\t'+str(binCentresInterp[1][j])+'\t'+str(obsGrid_temp[i,j]) \
for i in range(obsGrid_temp.shape[0]) for j in range(obsGrid_temp.shape[1])]))
outfile.close
# make a dummy grid with maximum and minimum values (of the reduced grid) to use for colorbar
obsGrid_temp_list = obsGrid_temp.reshape(obsGrid_temp.shape[1] * obsGrid_temp.shape[0] )
obsGrid_temp_list = obsGrid_temp_list[obsGrid_temp_list != obsFloor[k]]
obsMinValReduced = obsGrid_temp_list.min()
obsMaxValReduced = obsGrid_temp_list.max()
dummyGrid = np.zeros([2, 3])
dummyGrid[0,0] = binCentresInterp[0][0]
dummyGrid[0,1] = binCentresInterp[0][0]
dummyGrid[0,2] = obsMinValReduced
dummyGrid[1,0] = binCentresInterp[0][1]
dummyGrid[1,1] = binCentresInterp[0][1]
dummyGrid[1,2] = obsMaxValReduced
outName = outputBaseFilename+'_'+'_'.join([str(x) for x in plot])+'_obs2D_'+str(column)+'_colorbar.ct2'
outfile = smart_open(outName,'w')
outfile.write('# This 2D binned observable file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('\n'.join([str(dummyGrid[i,0])+'\t'+str(dummyGrid[i,1])+'\t'+str(dummyGrid[i,2]) \
for i in range(2) ]))
outfile.close
# Find posterior pdf contour levels
if contours2D.value is not None and doPosterior.value:
# Zero posterior contour levels
postContourLevels = [None for contour in contours2D.value]
# Zero posterior integral
integratedPosterior = 0.0
# Sort bins in order of posterior mass
sortedPostGrid = np.ma.sort(postGrid.flatten())
# Work out the new total multiplicity
totalMult = np.sum(sortedPostGrid)
# Work through bins backwards until total posterior mass adds up to the requested confidence levels
for i in range(sortedPostGrid.shape[0]-1,-1,-1):
integratedPosterior += sortedPostGrid[i]/totalMult
for j,contour in enumerate(contours2D.value):
if 100*integratedPosterior >= contour and postContourLevels[j] is None:
postContourLevels[j] = sortedPostGrid[i]
if all([x is not None for x in postContourLevels]): break
# Write profile likelihood to file
if doProfile.value:
outName = outputBaseFilename+'_'+'_'.join([str(x) for x in plot])+'_like2D.ct2'
outfile = smart_open(outName,'w')
outfile.write('# This 2D binned profile likelihood ratio file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('\n'.join([str(binCentresInterp[0][i])+'\t'+str(binCentresInterp[1][j])+'\t'+str(likeGrid[i,j]) \
for i in range(likeGrid.shape[0]) for j in range(likeGrid.shape[1])]))
outfile.close
# Write posterior pdf and contours to file
if doPosterior.value:
outName = outputBaseFilename+'_'+'_'.join([str(x) for x in plot])+'_post2D.ct2'
outfile = smart_open(outName,'w')
outfile.write('# This 2D binned posterior pdf file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('\n'.join([str(binCentresInterp[0][i])+'\t'+str(binCentresInterp[1][j])+'\t'+str(postGrid[i,j]) \
for i in range(postGrid.shape[0]) for j in range(postGrid.shape[1])]))
outfile.close
if contours2D.value is not None:
outName = outputBaseFilename+'_'+'_'.join([str(x) for x in plot])+'_post2D.contours'
outfile = smart_open(outName,'w')
outfile.write('# This 2D posterior pdf contours file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write(' '.join([str(x) for x in postContourLevels]))
outfile.close
def twoDsampler(dataArray, lk, bestFit, worstFit, outputBaseFilename,
dataRanges, nAllBins):
# Do sample sorting for 2D plots
if twoDplots.value is None: return
# Determine profile likelihood contour levels (same for all plots of a given dimensionality)
if contours2D.value is not None:
profContourLevels = [
np.exp(-deltaLnLike(1.0, 0.01 * contour))
for contour in contours2D.value
]
outName = outputBaseFilename + '_like2D.contours'
outfile = smart_open(outName, 'w')
outfile.write('# This 2D profile likelihood ratio contours file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write(' '.join([str(x) for x in profContourLevels]))
outfile.close
for plot in twoDplots.value:
print ' Parsing data for 2D plots of quantities ', plot
nBins = [nAllBins[plot[j]] for j in range(2)]
likeGrid = np.empty((nBins[0], nBins[1]), dtype=np.float64)
likeGrid[:, :] = worstFit + 100.0
postGrid = np.zeros((nBins[0], nBins[1]), dtype=np.float64)
# Work out maximum and minimum values of parameters/derived quantities
minVal = [dataRanges[plot[j]][0] for j in range(2)]
maxVal = [dataRanges[plot[j]][1] for j in range(2)]
rangeOfVals = [maxVal[j] - minVal[j] for j in range(2)]
# Pad edges of grid
binSep = [rangeOfVals[j] / (nBins[j] - 2) for j in range(2)]
minVal = [minVal[j] - binSep[j] for j in range(2)]
maxVal = [maxVal[j] + binSep[j] for j in range(2)]
rangeOfVals = [rangeOfVals[j] + 2.0 * binSep[j] for j in range(2)]
# Calculate bin centres
binCentresOrig = []
binCentresInterp = []
for j in range(2):
binCentresOrig.append(
np.array([
minVal[j] + (x + 0.5) * rangeOfVals[j] / nBins[j]
for x in range(nBins[j])
]))
for j in range(2): binCentresInterp.append(np.array([binCentresOrig[j][0] + x*(binCentresOrig[j][-1]-binCentresOrig[j][0])\
/(resolution.value-1) for x in range(resolution.value)]))
# Loop over points in chain
for i in range(dataArray.shape[0] - 1, -1, -1):
[in1, in2] = [
min(
int((dataArray[i, lk[plot[j]]] - minVal[j]) /
rangeOfVals[j] * nBins[j]), nBins[j] - 2)
for j in range(2)
]
# Profile over likelihoods
if doProfile.value:
likeGrid[in1,
in2] = min(dataArray[i, lk[labels.value[refLike]]],
likeGrid[in1, in2])
# Marginalise by addding to posterior sample count
if doPosterior.value:
postGrid[in1, in2] += dataArray[i, lk[labels.value[refMult]]]
# Convert -log(profile likelihoods) to profile likelihood ratio
likeGrid = np.exp(bestFit - likeGrid)
# Precompute co-ordinate grids for splines to avoid repetition
if intMethod.value == 'spline':
oldCoords = np.array([[binCentresOrig[0][i], binCentresOrig[1][j]]
for i in range(nBins[0])
for j in range(nBins[1])])
newCoords = [[binCentresInterp[0][i], binCentresInterp[1][j]]
for i in range(resolution.value)
for j in range(resolution.value)]
# Interpolate posterior pdf and profile likelihood to requested display resolution
if doProfile.value:
if intMethod.value == 'spline':
likeGrid = np.array(likeGrid).reshape(nBins[0] * nBins[1])
interpolator = twoDspline(oldCoords, likeGrid)
likeGrid = np.array(interpolator(newCoords)).reshape(
resolution.value, resolution.value)
else:
interpolator = twoDbilinear(binCentresOrig[0],
binCentresOrig[1],
likeGrid,
ky=1,
kx=1)
likeGrid = np.array([
interpolator(binCentresInterp[0][j],
binCentresInterp[1][i])
for j in range(resolution.value)
for i in range(resolution.value)
]).reshape(resolution.value, resolution.value)
# Fix any points sent NaN by scipy's crappy interpolators
likeGrid[np.isnan(likeGrid)] = 0.0
# Kill off any points that have been sent negative due to ringing
likeGrid[np.isneginf(likeGrid)] = 0.0
likeGrid[likeGrid < 0] = 0.0
# Fix any points that have been sent >1 due to ringing
likeGrid[np.isposinf(likeGrid)] = 1.0
likeGrid[likeGrid > 1] = 1.0
# Make sure we haven't erased the best-fit point by interpolating over it
likeGrid[np.unravel_index(likeGrid.argmax(), likeGrid.shape)] = 1.0
if doPosterior.value:
if intMethod.value == 'spline':
postGrid = np.array(postGrid).reshape(nBins[0] * nBins[1])
interpolator = twoDspline(oldCoords, postGrid)
postGrid = np.array(interpolator(newCoords)).reshape(
resolution.value, resolution.value)
else:
interpolator = twoDbilinear(binCentresOrig[0],
binCentresOrig[1],
postGrid,
ky=1,
kx=1)
postGrid = np.array([
interpolator(binCentresInterp[0][j],
binCentresInterp[1][i])
for j in range(resolution.value)
for i in range(resolution.value)
]).reshape(resolution.value, resolution.value)
# Kill off any points that have been sent negative due to ringing
postGrid[~np.isfinite(postGrid)] = 0.0
postGrid[postGrid < 0] = 0.0
# Rescale posterior pdf back into the range [0,1]
postGrid = postGrid / postGrid.max()
# Find posterior pdf contour levels
if contours2D.value is not None and doPosterior.value:
# Zero posterior contour levels
postContourLevels = [None for contour in contours2D.value]
# Zero posterior integral
integratedPosterior = 0.0
# Sort bins in order of posterior mass
sortedPostGrid = np.ma.sort(postGrid.flatten())
# Work out the new total multiplicity
totalMult = np.sum(sortedPostGrid)
# Work through bins backwards until total posterior mass adds up to the requested confidence levels
for i in range(sortedPostGrid.shape[0] - 1, -1, -1):
integratedPosterior += sortedPostGrid[i] / totalMult
for j, contour in enumerate(contours2D.value):
if 100 * integratedPosterior >= contour and postContourLevels[
j] is None:
postContourLevels[j] = sortedPostGrid[i]
if all([x is not None for x in postContourLevels]): break
# Write profile likelihood to file
if doProfile.value:
outName = outputBaseFilename + '_' + '_'.join(
[str(x) for x in plot]) + '_like2D.ct2'
outfile = smart_open(outName, 'w')
outfile.write('# This 2D binned profile likelihood ratio file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('\n'.join([str(binCentresInterp[0][i])+'\t'+str(binCentresInterp[1][j])+'\t'+str(likeGrid[i,j]) \
for i in range(likeGrid.shape[0]) for j in range(likeGrid.shape[1])]))
outfile.close
# Write posterior pdf and contours to file
if doPosterior.value:
outName = outputBaseFilename + '_' + '_'.join(
[str(x) for x in plot]) + '_post2D.ct2'
outfile = smart_open(outName, 'w')
outfile.write('# This 2D binned posterior pdf file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('\n'.join([str(binCentresInterp[0][i])+'\t'+str(binCentresInterp[1][j])+'\t'+str(postGrid[i,j]) \
for i in range(postGrid.shape[0]) for j in range(postGrid.shape[1])]))
outfile.close
if contours2D.value is not None:
outName = outputBaseFilename + '_' + '_'.join(
[str(x) for x in plot]) + '_post2D.contours'
outfile = smart_open(outName, 'w')
outfile.write('# This 2D posterior pdf contours file created by pippi '\
+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write(' '.join([str(x) for x in postContourLevels]))
outfile.close
