def plotSpotDist(mLabels,spots,outPath,subtractMean):
colors = ['r', 'g', 'b', 'y','k']
nColors = len(colors)
# go to nm
mLabelsNm = mLabels * 1e9
mSetSpots = sorted(set(spots))
labelsBySpot = []
rawBySpot = []
flattenedFromMean = []
# first, get the spot-wise labelling
for i,spot in enumerate(mSetSpots):
# get the indices of the spots
spotIdx = np.where(abs((spots - spot)) < 1e-9)[0]
thisSpotLabels = mLabelsNm[spotIdx]
meanV = np.mean(thisSpotLabels)
if (subtractMean):
thisSpotLabels -= meanV
labelsBySpot.append(thisSpotLabels)
flattenedFromMean.extend(thisSpotLabels)
if (subtractMean):
rawBySpot.append(thisSpotLabels + meanV)
else:
rawBySpot.append(thisSpotLabels)
# get the min and max from the labelsBySpot array
bins = np.linspace(min(flattenedFromMean),max(flattenedFromMean),10)
fig = pPlotUtil.figure(xSize=12,ySize=12)
ax = fig.add_subplot(111, projection='3d',)
for i,thisSpotLabels in enumerate(labelsBySpot):
mColor = colors[i % nColors]
height,left = np.histogram(thisSpotLabels,bins=bins)
ax.bar(left[:-1], height, zs=i,zdir='y', color=mColor, alpha=0.7,
edgecolor="none",linewidth=0)
xStr = r'$\Delta$ from Expected Surface Loc. [nm]'
pPlotUtil.lazyLabel(xStr,
"Surface Position (arb)",
"Dependence of Surface Location Distribution on Position",
zlab="Count")
pPlotUtil.savefig(fig,outPath + "AllSpots.png")
# get a figure showing the mean surface location, assuming
# we reshape into an Nx(whatever) array
N = 5
# -1: infer dimension
meanVals = [np.mean(mList) for mList in rawBySpot]
meanSurf = np.reshape(meanVals,(-1,N))
meanSurf -= np.min(meanSurf)
fig = pPlotUtil.figure(ySize=14,xSize=10)
ax = fig.add_subplot(111, projection='3d')
# convert to nm (XXX assuming grid is 1micron for each)
Nx = N
Ny = meanSurf.shape[0]
x = np.linspace(0, Nx, Nx) * 1e3
y = np.linspace(0, Ny, Ny) * 1e3
xv, yv = np.meshgrid(x, y)
ax.plot_wireframe(xv,yv,meanSurf)
pPlotUtil.lazyLabel("X Location [nm]","Y Location [nm]",
"Surface Position Varies with height")
pPlotUtil.zlabel("Surface height (relative to min)")
pPlotUtil.savefig(fig,outPath + "Surface.png")
fig = pPlotUtil.figure(ySize=14,xSize=10)
plt.subplot(2,1,1)
nPoints = len(flattenedFromMean)
vals,edges,_=plt.hist(flattenedFromMean,bins=bins)
# add a 'fudge' factor to make plotting better,
fudgeX = (max(edges)-min(edges))*0.05
xlim = [min(edges)-fudgeX,max(edges)+fudgeX]
yLim = [0,max(vals)]
pPlotUtil.lazyLabel(xStr,
"Number of counts",
"Algorithm finds surface within 10nm, >98%, N={:d}".\
format(nPoints))
normed = [0,max(vals)/sum(vals)]
plt.xlim(xlim)
propAx = pPlotUtil.secondAxis(plt.gca(),"Proportion",normed,yColor="Red")
propAx.axhline("0.05",color='r',
label="5% of Curves",linestyle='--',linewidth=4.0)
pPlotUtil.legend()
# plot the CDF
plt.subplot(2,1,2)
# add a zero at the start, so the plot matches the PDF
cdf = np.zeros(edges.size)
cdf[1:] = np.cumsum(vals/sum(vals))
mX = edges
plt.plot(mX,cdf,linestyle='--',linewidth=4,color='k')
plt.xlim(xlim)
pPlotUtil.lazyLabel(xStr,
"Cummulative Proportion",
("Cummulative Density Function," +
"Surface Detection Performance"))
plt.gca().fill_between(mX, 0, cdf,alpha=0.3)
pPlotUtil.savefig(fig,outPath + "FlatSpots.png")
