def imageToDict(filepath):
"""
Function to get the output data from an image
"""
header, data = readFits.read(filepath)
reverseHistogram = (1, 0)["Avg" in filepath]
image = DamicImage.DamicImage(data[:, :, -1], reverse=reverseHistogram)
dictImage = {}
### Define name, skips, size
dictImage['imgName'] = os.path.basename(filepath)
dictImage['NDCMS'] = header['NDCMS']
dictImage['NAXIS1'] = header['NAXIS1']
dictImage['NAXIS2'] = header['NAXIS2']
### Define output variables
#find image noise
dictImage['imgNoise'] = pd.computeImageNoise(data[:, :, :-1])
#find individual peak noise
nSmoothing = 4 if int(
header['NDCMS']
) > 1 else 12 # need less agressive moving average on skipper images
skImageNoise, skImageNoiseErr = pd.computeSkImageNoise(
image, nMovingAverage=nSmoothing)
dictImage['skNoise'] = float(skImageNoise)
dictImage['skNoiseError'] = float(skImageNoiseErr)
#find entropy of the average image
dictImage['aveImgS'] = pd.imageEntropy(data[:, :, -1])
#find rate of entropy change as a function of skips
entropySlope, entropySlopeErr, _ = pd.imageEntropySlope(data[:, :, :-1])
dictImage['dSdskip'] = entropySlope
dictImage['dSdskipError'] = entropySlopeErr
#find dark current
darkCurrent, darkCurrentErr = pd.computeDarkCurrent(
image, nMovingAverage=nSmoothing)
dictImage['DC'] = float(darkCurrent)
dictImage['DCError'] = float(darkCurrentErr)
# Compute pixel noise metrics
ntrials = 10000
singlePixelVariance, _ = ps.singlePixelVariance(data[:, :, :-1],
ntrials=ntrials)
imageNoiseVariance, _ = ps.imageNoiseVariance(data[:, :, :-1],
header['NDCMS'] -
c.SKIPPER_OFFSET,
ntrials=ntrials)
#find variance of random pixels
dictImage['pixVar'] = singlePixelVariance
#find variance of random clusters of pixels
dictImage['clustVar'] = imageNoiseVariance
#find tail ratio
dictImage['tailRatio'] = pd.computeImageTailRatio(image)
### Define input variables
headervars = [
'EXP', 'AMPL', 'HCKDIRN', 'VCKDIRN', 'ITGTIME', 'VIDGAIN', 'PRETIME',
'POSTIME', 'DGWIDTH', 'RGWIDTH', 'OGWIDTH', 'SWWIDTH', 'HWIDTH',
'HOWIDTH', 'VWIDTH', 'VOWIDTH', 'ONEVCKHI', 'ONEVCKLO', 'TWOVCKHI',
'TWOVCKLO', 'TGHI', 'TGLO', 'HUHI', 'HULO', 'HLHI', 'HLLO', 'RGHI',
'RGLO', 'SWLO', 'DGHI', 'DGLO', 'OGHI', 'OGLO', 'BATTR', 'VDD1',
'VDD2', 'DRAIN1', 'DRAIN2', 'VREF1', 'VREF2', 'OPG1', 'OPG2'
]
for var in headervars:
try:
dictImage[var] = header[var]
except:
dictImage[var] = -1
return dictImage
def paramsToList(params):
"""
Converts lmfit.params to a list. Only for poiss + gauss function
"""
par = [ params["sigma"].value, params["lamb"].value, params["offset"].value, params["ADU"].value, params["N"].value, params["npoisson"].value]
return par
if __name__ == "__main__":
filename = "../FS_Avg_Img_27.fits"
# filename = "../Img_00.fits"
header, data = readFits.read(filename)
# Test datark current
damicimage = DamicImage.DamicImage(data[:, :, -1], reverse=False, minRange=500)
plt.hist(damicimage.centers, bins=damicimage.edges, weights=damicimage.hpix) # Plot histogram of data
# Perform poisson gaus fit to data
minres = computeGausPoissDist(damicimage, )
params = minres.params
print(lmfit.fit_report(minres))
print(parseFitMinimum(minres))
# Plot fit results
par = paramsToList(params)
x = np.linspace(damicimage.centers[0], damicimage.centers[-1], 2000)
self.reverseHistogram()
def reverseHistogram(self):
# Shifts the histogram axis (centers and edges) to be centered around zero and flips the values
self.hpix = np.flip(self.hpix)
# self.centers = (self.centers - self.med)
# self.edges = (self.edges - self.med)
if __name__ == "__main__":
# Test to see if reversing image works
imgname = "../Img_11.fits"
header, data = readFits.read(imgname)
normalImage = DamicImage(data[:, :, -1], False, "normalImage test")
reverseImage = DamicImage(data[:, :, -1], True, "forward test")
normalImage.histogramImage(minRange=80)
reverseImage.histogramImage(minRange=80)
reverseImage.reverseHistogram()
fig, axs = plt.subplots(1, 2, figsize=(14, 8))
axs[0].hist(normalImage.centers, weights=normalImage.hpix, bins=normalImage.edges)
axs[1].hist(
reverseImage.centers, weights=reverseImage.hpix, bins=reverseImage.edges
)
plt.show()
def processImage(filename, headerString):
"""
Function to do enclose the image processing function. Returns an analysisOutput object
"""
# Read image
header, data = readFits.read(filename)
try:
nskips = header["NDCMS"]
except KeyError:
nskips = 1
# Create skipper
reverseHistogram = (1, 0)["Avg" in filename]
image = DamicImage.DamicImage(data[:, :, -1], reverse=reverseHistogram)
processedImage = AnalysisOutput(filename,
nskips=nskips,
header=headerString)
# Compute average image entropy
processedImage.aveImgS = pd.imageEntropy(data[:, :, -1])
# Compute Entropy slope
entropySlope, entropySlopeErr, _ = pd.imageEntropySlope(data[:, :, :-1])
processedImage.dSdskip = pd.convertValErrToString(
(entropySlope, entropySlopeErr))
# Try to perform fit of poisson + gauss
minresult = PoissonGausFit.computeGausPoissDist(image)
# If sucessful parse results, otherwise fall back on individual fits
if minresult.success:
poisGausFitOut = PoissonGausFit.parseFitMinimum(minresult)
# Parse fit values
processedImage.skNoise = pd.convertValErrToString(
poisGausFitOut["sigma"])
processedImage.darkCurrent = pd.convertValErrToString(
poisGausFitOut["lambda"])
processedImage.aduConversion = pd.convertValErrToString(
poisGausFitOut["ADU"])
# Compute dark current (which implicitly requires pois + gaus fit to converge)
processedImage.tailRatio = pd.computeImageTailRatio(image)
else:
nSmoothing = (4 if nskips > 1000 else 8
) # need less agressive moving average on skipper images
skImageNoise, skImageNoiseErr = pd.computeSkImageNoise(
image, nMovingAverage=nSmoothing)
processedImage.skNoise = pd.convertValErrToString(
(skImageNoise, skImageNoiseErr))
# Compute Dark current
darkCurrent, darkCurrentErr = pd.computeDarkCurrent(
image, nMovingAverage=nSmoothing)
processedImage.darkCurrent = pd.convertValErrToString(
(darkCurrent, darkCurrentErr))
# Compute Overall image noise (fit to entire image) and skipper noise
processedImage.imgNoise = pd.computeImageNoise(data[:, :, :-1])
return processedImage
# files = [ f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f)) ] # filepattern = re.compile(filename) # filesmatched = list(filter(filepattern.match, files)) print(filename) if parameterScan == None: # datafile = os.path.join(directory, filesmatched[0]) datafile = filename[0] # read data header, data = readFits.read(datafile) data = data[:,:3000,:] # plot overall spectrum fullImage = DamicImage.DamicImage(np.mean(data[:, :, skipOffset:-1], axis=-1), bw=imagebw, reverse=reverse) fig, ax, fit = plotPixelSpectrum(fullImage, reverse=reverse) # row dark current axrow = plotDarkCurrentRows(fullImage, plotall=True, mask=mask) else: legend = [] darkcurrent = [] paramscan = []
rowIndex[:, 0] = np.random.uniform(0, nrows - pixelArrayMaxEdgeRow,
ntrials).astype(int)
colIndex[:, 0] = np.random.uniform(0, ncolumns - pixelArrayMaxEdgeCol,
ntrials).astype(int)
# Create pixel array to be as much of a square as possible
for i in range(npixels):
rowIndex[:, i] = rowIndex[:, 0] + i % pixelArrayMaxEdgeRow
colIndex[:, i] = colIndex[:, 0] + i // pixelArrayMaxEdgeRow
return rowIndex, colIndex
if __name__ == "__main__":
header, data = readFits.read("../Img_20.fits")
median, dist = singlePixelVariance(data[:, :, :-1], ntrials=100000)
print("Individual Pixel Variance Median: %0.2f" % median)
fig, ax = plt.subplots(1, 1, figsize=(12, 8))
med = np.median(dist)
mad = scipy.stats.median_absolute_deviation(dist)
bins = np.linspace(med - 3 * mad, med + 5 * mad, 200)
ax.hist(dist, bins=bins)
ax.set_title("Single Pixel Noise", fontsize=18)
ax.set_xlabel("Variance of Single Pixels", fontsize=16)
# plt.show()
pixMed, pixDist = imageNoiseVariance(data[:, :, :-1],
header["NDCMS"] - c.SKIPPER_OFFSET,
