def dynamicRangeImg(peaksOnBinaryImage, fileSubset, peak2InflectionDiff,
peak2TroughDiff):
dynamicRangeImagesMasked = []
# getting dynamic range images for testing
for i, peak in enumerate(peaksOnBinaryImage):
troughInfile = fileSubset[peak + peak2TroughDiff]
relaxedInfile = fileSubset[peak + peak2InflectionDiff]
peakInfile = fileSubset[peak]
troughImg = im.getJellyGrayImageFromFile(troughInfile)
relaxedImg = im.getJellyGrayImageFromFile(relaxedInfile)
peakImg = im.getJellyGrayImageFromFile(peakInfile)
# misnomer
peakDiff = im.getGrayscaleImageDiff_absolute(troughImg, peakImg)
binaryPeakDiff = im.getBinaryJelly(peakDiff, lower_bound=0.05)
dynamicRangeImg = im.dynamicRangeImg_AreaBased(relaxedImg,
binaryPeakDiff, 5)
dynamicRangeImagesMasked.append(dynamicRangeImg)
return np.average(dynamicRangeImagesMasked, axis=0)
def differenceAngleFinder(files):
global currentSegmentEndingFrame
i = 0
# movement parameters
firstStationaryAfterMovement = 0 # first stationary frame (i) after movement
isMoving = False
isQuestionablyStationary = False
centroidBefore = None
lastStationaryCentroid = None
counter = 0
peak = 0
pulseCountInQuestionablyStationary = 0
centroid = None
data = []
movingPeaks = []
# initializes lists with 'numConsecutiveDrops' of files
def reinitializeTestFramesAndAreas(j):
testFrames = [
] # this list should never be more than 5 entries long, ex. [51, 52, 53, 54, 55]
testAreas = [
] # this list should never be more than 5 entries long, ex. [253, 255, 256, 255, 255]
while len(testFrames) < numConsecutiveDrops and j < framesInChunk:
image = im.getJellyImageFromFile(files[j])
binary_image = im.getBinaryJelly(image, lowerThreshold)
area = im.findBinaryArea(binary_image)
testFrames.append(j)
testAreas.append(area)
j += 1
return testFrames, testAreas, j
# function to save out data
def saveOutData():
df = pd.DataFrame(data,
columns=[
'global frame', 'chunk frame', 'angle',
'centroid x', 'centroid y'
])
if DEBUG: print(df.head())
dataTitle = '{}_{:03}.csv'.format(chunkName, movementSegment)
df.to_csv(str(angleOutputDir / dataTitle), index=False)
testFrames, testAreas, i = reinitializeTestFramesAndAreas(i)
try:
while i < framesInChunk:
isDownturn = dm.is_downturn(0, testAreas, numConsecutiveDrops)
if isDownturn:
peak = i - numConsecutiveDrops
print("chunk: {}, i: {}, peak: {}".format(chunkName, i, peak))
# checks that peaks are within testing bounds
if peak + peak2InflectionDiff >= 0 and peak + peak2TroughDiff < framesInChunk:
troughInfile = files[peak + peak2TroughDiff]
relaxedInfile = files[peak + peak2InflectionDiff]
troughImg = im.getJellyGrayImageFromFile(troughInfile)
relaxedImg = im.getJellyGrayImageFromFile(relaxedInfile)
centroidDiff = im.getGrayscaleImageDiff_absolute(
troughImg, relaxedImg)
binaryCentroidDiff = im.getBinaryJelly(centroidDiff,
lower_bound=0.05,
upper_bound=1)
centroidRegion = im.findJellyRegion(binaryCentroidDiff)
centroid = im.findCentroid_boundingBox(centroidRegion)
if lastStationaryCentroid is None:
lastStationaryCentroid = centroid
if CONFIRMATIONIMAGES:
im.saveJellyPlot(
im.getCentroidVerificationImg(
centroidDiff, binaryCentroidDiff, centroid),
str(confirmationImagesPath /
'{}_{}_centroid.png'.format(peak, chunkName)))
if isMoving:
data.append([
peak + lastFrameOfPreviousChunk, peak, np.nan,
centroid[0], centroid[1]
])
movedBefore = isMoving
isMoving = im.distance(centroid, lastStationaryCentroid
) > movementThreshold2KeepMoving
lastStationaryCentroid = centroid
if movedBefore and not isMoving:
firstStationaryAfterMovement = i
pulseCountInQuestionablyStationary = 0
isQuestionablyStationary = True
elif isQuestionablyStationary:
data.append([
peak + lastFrameOfPreviousChunk, peak, np.nan,
centroid[0], centroid[1]
])
isMoving = im.distance(
centroid, lastStationaryCentroid
) > movementThreshold4reinitialization
if isMoving:
movingPeaks.append(peak)
isQuestionablyStationary = False
pulseCountInQuestionablyStationary += 1
if i - firstStationaryAfterMovement > numFrames2ConfirmStationary:
# now there is confirmed time after initial stationary point
if firstStationaryAfterMovement == 0:
data = []
else:
# must mutate data to take out
data = data[:
-pulseCountInQuestionablyStationary]
currentSegmentEndingFrame = i
saveSegmentVariableParams()
saveOutData()
data = []
i = firstStationaryAfterMovement
# peak2InflectionDiff, peak2TroughDiff, postPeakRefractoryPeriod, infflectionTestDiff,
# inflectionTestBinaryThreshold, and chosen SD are all static.
initialize_params(files, i)
isQuestionablyStationary = False
pulseCountInQuestionablyStationary = 0
# until count from current i to last stationary i reaches this point,
# the program is in a holding pattern of sorts.
else:
testInfile = files[peak + peak2InflectionDiff +
inflectionTestDiff]
testImg = im.getJellyGrayImageFromFile(testInfile)
if CONFIRMATIONIMAGES:
plt.imsave(
str(confirmationImagesPath /
'{}_{}_interestFrames.png'.format(
peak, chunkName)),
im.juxtaposeImages(
np.array([[
relaxedImg, testImg, peakImg, troughImg
]])))
if centroidBefore is not None:
reinitializeAreaPlot = im.distance(
centroid, centroidBefore
) > movementThreshold4newNormalizationImg
if reinitializeAreaPlot:
peakInfile = files[peak]
peakImg = im.getJellyGrayImageFromFile(
peakInfile)
peakDiff = im.getGrayscaleImageDiff_absolute(
troughImg, peakImg)
binaryPeakDiff = im.getBinaryJelly(
peakDiff, lower_bound=0.05, upper_bound=1)
averagedDynamicRangeMaskedImg = im.dynamicRangeImg_AreaBased(
relaxedImg, binaryPeakDiff, 5)
else:
peakInfile = files[peak]
peakImg = im.getJellyGrayImageFromFile(peakInfile)
peakDiff = im.getGrayscaleImageDiff_absolute(
troughImg, peakImg)
binaryPeakDiff = im.getBinaryJelly(
peakDiff, lower_bound=0.05, upper_bound=1)
averagedDynamicRangeMaskedImg = im.dynamicRangeImg_AreaBased(
relaxedImg, binaryPeakDiff, 5)
centroidBefore = centroid
if CONFIRMATIONIMAGES:
im.saveJellyPlot(
averagedDynamicRangeMaskedImg,
str(confirmationImagesPath /
'{}_{}_dynRng.png'.format(peak,
chunkName)))
testDiff = im.getGrayscaleImageDiff_absolute(
testImg, relaxedImg)
normalizedTestDiff = testDiff / averagedDynamicRangeMaskedImg
binaryDiffImg = im.getBinaryJelly(
normalizedTestDiff,
lower_bound=inflectionTestBinaryThreshold)
biggestRegion = im.findJellyRegion(binaryDiffImg)
if biggestRegion is not None:
local_com = im.findCentroid_regionProp(
biggestRegion)
zeroDegreePoint = (centroid[0], 0)
angle = dm.getAngle(zeroDegreePoint, centroid,
local_com)
if CONFIRMATIONIMAGES:
im.saveJellyPlot(
binaryDiffImg,
str(confirmationImagesPath /
'{}_{}_angle.png'.format(
peak, chunkName)),
[centroid, local_com, zeroDegreePoint])
else:
angle = np.nan
if CONFIRMATIONIMAGES:
im.saveJellyPlot(
binaryDiffImg,
str(confirmationImagesPath /
'{}_{}_angle.png'.format(
peak, chunkName)), [centroid])
data.append([
peak + lastFrameOfPreviousChunk, peak, angle,
centroid[0], centroid[1]
])
movedBefore = isMoving
isMoving = im.distance(
centroid, lastStationaryCentroid
) > movementThreshold4reinitialization
if isMoving and not movedBefore:
isQuestionablyStationary = False
lastStationaryCentroid = centroid
i += postPeakRefractoryPeriod
counter += 1
testFrames, testAreas, i = reinitializeTestFramesAndAreas(i)
else:
testFrames.pop(0)
testAreas.pop(0)
image = im.getJellyImageFromFile(files[i])
binary_image = im.getBinaryJelly(image, lowerThreshold)
area = im.findBinaryArea(binary_image)
testFrames.append(i)
testAreas.append(area)
i += 1
counter += 1
except Exception as error:
print('{} error occured.'.format(error))
print(
"chunkName: {}, index: {}, isMoving: {}, isQStat: {}, centroid: {}"
.format(chunkName, i, isMoving, isQuestionablyStationary,
str(centroid)))
raise
finally:
currentSegmentEndingFrame = i
saveSegmentVariableParams()
saveOutData()
def initialize_params(files, startingFrameNum):
global lowerThreshold
global pathOfCurrentParamDF
global segmentVerificationDir
global movementSegment
global currentSegmentStartingFrame
global currentSegmentEndingFrame
global isChunkAnalysisFinished
currentSegmentStartingFrame = startingFrameNum
currentSegmentEndingFrame = startingFrameNum
reinitializeElapsedTime()
saveSegmentVariableParams()
if startingFrameNum != 0:
movementSegment += 1
if startingFrameNum + numFramesForParamInitialization < framesInChunk:
fileSubset = files[startingFrameNum:startingFrameNum +
numFramesForParamInitialization]
else:
fileSubset = files[startingFrameNum:]
# create segment directory named with global movement segment
segmentName = '{}_{}_{:03}'.format(recordingName, chunkName,
movementSegment)
segmentVerificationDir = dm.makeOutDir(segmentDir, segmentName)
pathOfCurrentParamDF = segmentVerificationDir / '{}_params.csv'.format(
segmentName)
saveSegmentVariableParams()
thresholdingDir = dm.makeOutDir(segmentVerificationDir,
'{}_ThresholdingPlots'.format(segmentName))
plotDir = dm.makeOutDir(segmentVerificationDir,
'{}_AngleVerificationPlots'.format(segmentName))
centroidDir = dm.makeOutDir(
segmentVerificationDir,
'{}_CentroidVerificationPlots'.format(segmentName))
segmentOrientationDir = dm.makeOutDir(
segmentVerificationDir,
'{}_RelaxedFramesForOrientation'.format(segmentName))
dynamicRangeDir = dm.makeOutDir(
segmentVerificationDir,
'{}_dynamicRangeNormalizationImages'.format(segmentName))
if DEBUG: print('loading initialization stack\n')
init_movie = init.get_init_movie(fileSubset)
if DEBUG: print('calculating lowerThreshold\n')
lowerThreshold = init.autoLowerThreshold(
init_movie, roughness_saveOut_dir=thresholdingDir)
saveSegmentVariableParams()
# get areas of jellies both the region and the whole value true in binary image.
binaryImageAreas = init.getBinaryAreas(init_movie, lowerThreshold)
peaksOnBinaryImage = init.downturnFinder(
init_movie, postPeakRefractoryPeriod, lowerThreshold,
numConsecutiveDrops, peak2InflectionDiff, peak2TroughDiff)
plotOutpath = segmentVerificationDir / '{}_areaPlot.png'.format(
segmentName)
init.saveAreasPlot(binaryImageAreas, peaksOnBinaryImage, plotOutpath, [
peak2InflectionDiff, peak2InflectionDiff + inflectionTestDiff,
peak2TroughDiff
], postPeakRefractoryPeriod)
saveSegmentVariableParams()
i = 0
while i < len(peaksOnBinaryImage):
if peaksOnBinaryImage[
i] + peak2InflectionDiff < 0 or peaksOnBinaryImage[
i] + peak2TroughDiff >= numFramesForParamInitialization:
peaksOnBinaryImage.pop(i)
else:
i += 1
for i, peak in enumerate(peaksOnBinaryImage):
relaxedInfile = fileSubset[peak + peak2InflectionDiff]
testInfile = fileSubset[peak + peak2InflectionDiff +
inflectionTestDiff]
peakInfile = fileSubset[peak]
troughInfile = fileSubset[peak + peak2TroughDiff]
relaxedImg = im.getJellyGrayImageFromFile(relaxedInfile)
testImg = im.getJellyGrayImageFromFile(testInfile)
peakImg = im.getJellyGrayImageFromFile(peakInfile)
troughImg = im.getJellyGrayImageFromFile(troughInfile)
centroidDiff = im.getGrayscaleImageDiff_absolute(troughImg, relaxedImg)
binaryCentroidDiff = im.getBinaryJelly(centroidDiff, lower_bound=0.05)
centroidRegion = im.findJellyRegion(binaryCentroidDiff)
centroid = im.findCentroid_boundingBox(centroidRegion)
centroidVerOutFile = centroidDir / 'centroid for {}_{:03}.png'.format(
segmentName, peak + peak2InflectionDiff)
im.saveJellyPlot(
im.getCentroidVerificationImg(centroidDiff, binaryCentroidDiff,
centroid), centroidVerOutFile)
if i == 0:
orientationFrameStamp = dm.getFrameNumFromFile(peakInfile)
orientationTimeStamp = int(orientationFrameStamp / framerate)
orientationOutFile = orientationDir / '{}_ts_{:02}h_{:02}m_{:02}s_{:03}.png'.format(
chunkName, int(orientationTimeStamp / 3600),
int(orientationTimeStamp / 60) % 60, orientationTimeStamp % 60,
movementSegment)
im.saveJellyPlot(relaxedImg, orientationOutFile,
[centroid, (centroid[0], 15)])
orientationOutFile = segmentOrientationDir / 'relaxedFrame_{:03}.png'.format(
peak + peak2InflectionDiff)
im.saveJellyPlot(relaxedImg, orientationOutFile,
[centroid, (centroid[0], 15)])
peakDiff = im.getGrayscaleImageDiff_absolute(troughImg, peakImg)
binaryPeakDiff = im.getBinaryJelly(peakDiff,
lower_bound=0.05,
upper_bound=1)
averagedDynamicRangeMaskedImg = im.dynamicRangeImg_AreaBased(
relaxedImg, binaryPeakDiff, 5)
dynamicRangeImgOutfile = dynamicRangeDir / 'dynamicRangeImg_{:03}.png'.format(
peak + peak2InflectionDiff)
im.saveJellyPlot(averagedDynamicRangeMaskedImg, dynamicRangeImgOutfile)
testDiff = im.getGrayscaleImageDiff_absolute(testImg, relaxedImg)
normalizedTestDiff = testDiff / averagedDynamicRangeMaskedImg
binaryDiffImg = im.getBinaryJelly(
normalizedTestDiff, lower_bound=inflectionTestBinaryThreshold)
biggestRegion = im.findJellyRegion(binaryDiffImg)
if biggestRegion is not None:
local_com = im.findCentroid_regionProp(biggestRegion)
zeroDegreePoint = (centroid[0], 0)
testingOutfile = plotDir / 'testPlot for {} - {:03}.png'.format(
segmentName, peak + peak2InflectionDiff)
im.saveJellyPlot(binaryDiffImg, testingOutfile,
[centroid, zeroDegreePoint, local_com])
else:
testingOutfile = plotDir / 'testPlot for {} - {:03}.png'.format(
segmentName, peak + peak2InflectionDiff)
im.saveJellyPlot(binaryDiffImg, testingOutfile, [centroid])
# saves important parameters used in analysis to csv
saveSegmentVariableParams()
if DEBUG: print('saved parameter data')
if DEBUG:
print('finished saving outplots and angle verification at: {}'.format(
segmentVerificationDir))
return True
def selectInflectionThresholdandDiff(peaksOnBinaryImage, init_movie,
recordingName, peak2InflectionDiff,
peak2TroughDiff, use_conserved_trough,
initializationOutputDir, angleArrImageDir,
centroidDir, dynamicRangeDir):
if use_conserved_trough:
peaksOnBinaryImage = [x - peak2TroughDiff for x in peaksOnBinaryImage]
# make directory to store verification jelly plots
postInflectionDiffCases = list(range(2, 12))
thresholdCases = [0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5]
# out data: pulse angles x testDiffs
angleData = np.empty(
(len(peaksOnBinaryImage), len(postInflectionDiffCases),
len(thresholdCases))) # 3D array
angleData[:] = np.nan
otherDataCols = np.array(['relaxed', 'peak', 'trough', 'by eye', ''])
otherData = np.empty((len(peaksOnBinaryImage), len(otherDataCols)))
otherData[:] = np.nan
# read in by eye angle measurements
byEyeAngleDF = pd.DataFrame(peaksOnBinaryImage, columns=['peaks'])
byEyeAngleDF['by eye measurement (0 to 360)'] = np.nan
byEyeAngleDFioPath = initializationOutputDir / '{}_byEyeAngles.csv'.format(
recordingName)
byEyeAngleDF.to_csv(str(byEyeAngleDFioPath), index=False)
for i, peak in enumerate(peaksOnBinaryImage):
if DEBUG: print("{}: {}".format(i, peak))
troughImg = init_movie[peak + peak2TroughDiff]
relaxedImg = init_movie[peak + peak2InflectionDiff]
centroidDiff = im.getGrayscaleImageDiff_absolute(troughImg, relaxedImg)
binaryCentroidDiff = im.getBinaryJelly(centroidDiff, lower_bound=0.05)
centroidRegion = im.findJellyRegion(binaryCentroidDiff)[0]
centroid = im.findCentroid_boundingBox(centroidRegion)
centroidVerOutFile = centroidDir / 'centroid for {} - {:03}.png'.format(
recordingName, peak + peak2InflectionDiff)
im.saveJellyPlot(
im.getCentroidVerificationImg(centroidDiff, binaryCentroidDiff,
centroid), centroidVerOutFile)
peakImg = init_movie[peak]
peakDiff = im.getGrayscaleImageDiff_absolute(troughImg, peakImg)
binaryPeakDiff = im.getBinaryJelly(peakDiff, lower_bound=0.05)
if np.sum(binaryCentroidDiff) > np.sum(binaryPeakDiff):
binaryPeakDiff = binaryCentroidDiff
averagedDynamicRangeMaskedImg = im.dynamicRangeImg_AreaBased(
relaxedImg, binaryPeakDiff, 5)
dynamicRangeImgOutfile = dynamicRangeDir / 'dynamicRangeImg_{:03}.png'.format(
peak + peak2InflectionDiff)
im.saveJellyPlot(averagedDynamicRangeMaskedImg, dynamicRangeImgOutfile)
# dealing with inflection thresholding
testDiffImages = []
for j in postInflectionDiffCases:
testImg = init_movie[peak + peak2InflectionDiff + j]
testDiff = im.getGrayscaleImageDiff_absolute(testImg, relaxedImg)
normalizedTestDiff = testDiff / averagedDynamicRangeMaskedImg
testDiffImages.append(normalizedTestDiff)
testingOutfile = angleArrImageDir / 'testPlot for {} - {:03}.png'.format(
recordingName, peak + peak2InflectionDiff)
pulseAngleData = im.saveDifferenceTestingAggregationImage(
relaxedImg,
testDiffImages,
thresholdCases,
testingOutfile,
discludeVerificationArrayImg=False,
centroid=centroid)
for n, row in enumerate(pulseAngleData):
for m, angle in enumerate(row):
angleData[i][m][n] = angle
otherData[i][0] = peak + peak2InflectionDiff
otherData[i][1] = peak
otherData[i][2] = peak + peak2TroughDiff
angleDataAsRows = [x.ravel() for x in angleData]
pulseAngleOutput = np.concatenate(
(np.tile([postInflectionDiffCases],
len(thresholdCases)), angleDataAsRows))
otherDataOut = np.concatenate(([otherDataCols], otherData))
# warning: this results in mixed data. This cannot be saved by numpy csv methods. Pandas is easiest way to save.
outframe = np.concatenate((otherDataOut, pulseAngleOutput), axis=1)
# saves data into verification frame
dfOut = pd.DataFrame(outframe)
dataTitle = '{}_testDifferenceVerification.csv'.format(recordingName)
verificationCSVOutFile = initializationOutputDir / dataTitle
dfOut.to_csv(str(verificationCSVOutFile), header=False, index=False)
# setting test difference and threshold
def runSDanalysis():
if CHIME: dm.chime(MAC, 'input time')
print('time to enter by eye angles for each pulse')
print('entries must be from 0 to 360')
print('Enter \'1\' to continue.')
dm.getSelection([1])
byEyeAngleDF = pd.read_csv(str(byEyeAngleDFioPath))
byEyeAngles = byEyeAngleDF['by eye measurement (0 to 360)'].tolist()
i = 0
while i < len(byEyeAngles):
if byEyeAngles[i] == np.nan:
np.delete(angleData, i, 0)
byEyeAngles.pop(i)
else:
i += 1
angleDataShape = angleData.shape
diff2byeye = np.empty(
(angleDataShape[2], angleDataShape[1], angleDataShape[0]))
diff2byeye[:] = np.nan
for i in range(angleDataShape[0]):
for j in range(angleDataShape[1]):
for k in range(angleDataShape[2]):
diff2byeye[k][j][i] = dm.angleDifferenceCalc(
angleData[i][j][k], byEyeAngles[i])
squaredDiffs = np.square(diff2byeye)
summedDiffs = np.sum(squaredDiffs, axis=2)
varianceTable = summedDiffs / diff2byeye.shape[2]
sdTable = np.sqrt(varianceTable)
sdTableMinIndex = list([
np.where(sdTable == np.nanmin(sdTable))[0][0],
np.where(sdTable == np.nanmin(sdTable))[1][0]
])
lowSDthresholds = []
lowSDtestFrames = []
for x in np.sort(sdTable.ravel())[0:5]:
loc = np.where(sdTable == x)
lowSDthresholds.append(loc[0][0])
lowSDtestFrames.append(loc[1][0])
inflectionTestBinaryThreshold = thresholdCases[int(
np.median(lowSDthresholds))]
inflectionTestDiff = postInflectionDiffCases[int(
np.median(lowSDtestFrames))]
return inflectionTestBinaryThreshold, inflectionTestDiff, sdTable, sdTableMinIndex
inflectionTestBinaryThreshold, inflectionTestDiff, sdTable, sdTableMinIndex = runSDanalysis(
)
if CHIME: dm.chime(MAC, 'input time')
while True:
print('thresholding options: {}'.format(thresholdCases))
print('test frame options: {}'.format(postInflectionDiffCases))
np.set_printoptions(threshold=np.inf)
print(np.asarray(sdTable))
print('index of min sd: {}'.format(sdTableMinIndex))
print('selected sd: {}'.format(
sdTable[thresholdCases.index(inflectionTestBinaryThreshold)][
postInflectionDiffCases.index(inflectionTestDiff)]))
print('Params to change: ')
print('select \'1\' to change {} which is {}'.format(
'inflectionTestBinaryThreshold', inflectionTestBinaryThreshold))
print('select \'2\' to change {} which is {}'.format(
'inflectionTestDiff', inflectionTestDiff))
print('select \'3\' to update by eye measurements')
print('or \'4\' to continue.')
selectionVar = dm.getSelection([1, 2, 3, 4])
if selectionVar == '1':
inflectionTestBinaryThreshold = float(
dm.getSelection(thresholdCases))
elif selectionVar == '2':
inflectionTestDiff = int(dm.getSelection(postInflectionDiffCases))
elif selectionVar == '3':
inflectionTestBinaryThreshold, inflectionTestDiff, sdTable, sdTableMinIndex = runSDanalysis(
)
else:
break
chosenSD = sdTable[thresholdCases.index(inflectionTestBinaryThreshold)][
postInflectionDiffCases.index(inflectionTestDiff)]
return inflectionTestDiff, inflectionTestBinaryThreshold, chosenSD