def fitGaussianX(img, m, b, bright, offset, starX, starY, starR, gX, gY, gR, hotX, hotY):
parDist,perpDist = GT.getDistancesHorizontal(m, b, img.shape[1], img.shape[0], offset)
parF,perpF,imgF = getTrailBand(parDist, perpDist, img, 6)
x,y = GT.getXY(parF,perpF,m,b,offset)
trailScaled = imgF/bright(parF)
fig,axes = plt.subplots(1,2,sharex = True, sharey = True)
axes[0].plot(perpF,trailScaled,"bo")
axes[0].set_title("Trail Gaussian With Stars")
mask = getValueMask(img, x, y, starX, starY, starR, gX, gY, gR, hotX, hotY, 1)
perpF = perpF[mask]
trailScaled = trailScaled[mask]
axes[1].plot(perpF,trailScaled,"bo")
axes[1].set_title("Trail Gaussian Without Stars")
plt.show()
sDev = cf(gauss, perpF, trailScaled)[0][0]
x = np.arange(-5,5,0.1)
plt.plot(perpF,trailScaled,"bo",label = "Pixel Brightnesses")
plt.plot(x,gauss(x,sDev), label = "Gaussian Fit")
plt.title("Gaussian Fit")
plt.show()
return sDev
def fitBrightnessSDev(img, m, b, offset, starX, starY, starR, gX, gY, gR, hotX, hotY, trailSize):
parDist,perpDist = GT.getDistancesHorizontal(m, b, img.shape[1], img.shape[0], offset)
parDistF,perpDistF,imgF = getTrailBand(parDist, perpDist, img, trailSize)
x,y = GT.getXY(parDistF,perpDistF,m,b,offset)
mask = getValueMask(img, x, y, starX, starY, starR, gX, gY, gR, hotX, hotY, 1)
par = parDistF[mask]
perp = perpDistF[mask]
band = imgF[mask]
b1, b2, b3, b4, b5, sDev = cf(GT.gaussian2, [par, perp], band, bounds = ([-math.inf,-math.inf,-math.inf,-math.inf,-math.inf,0],[math.inf,math.inf,math.inf,math.inf,math.inf,trailSize]))[0]
return [np.poly1d([b1, b2, b3, b4, b5]), sDev]
def estimateBackground(img, m, b, offset, starX, starY, starR, gX, gY, gR, hotX, hotY):
parDist,perpDist = GT.getDistancesHorizontal(m, b, img.shape[1], img.shape[0], offset)
par,perp,band = getNonTrailBand(parDist, perpDist, img, 10, 20)
x,y = GT.getXY(par,perp,m,b,offset)
mask = getValueMask(img, x, y, starX, starY, starR, gX, gY, gR, hotX, hotY, 1)
par = par[mask]
band = band[mask]
mean = np.mean(band)
plt.plot(par,band,"bo",label = "Background Pixel Brightnesses")
plt.plot([par[0],par[-1]],[mean, mean],label = "Average Background Brightness: "+str(np.round(mean,decimals = 3)))
plt.title("Background Estimate")
plt.legend()
plt.show()
print(mean)
return mean