for i in range(len(testDiffImages)):
aggregateDiffImages.append(
im.aggregateIntensityImage(testDiffImages[:i + 1]))
for i, img in enumerate(testDiffImages):
print('peak {}, image {}: max- {}, min {}'.format(
peak, i, im.getMaxIntensity(img), im.getMinIntensity(img)))
for i, img in enumerate(aggregateDiffImages):
print('peak {}, image {}: max- {}, min {}'.format(
peak, i, im.getMaxIntensity(img), im.getMinIntensity(img)))
testingOutDir = dm.makeOutDir(jellyOutDir, 'testAggPlot')
testingOutfile = testingOutDir / 'testPlot for {} - {}.jpeg'.format(
stack.name, peak - prePeakInflectionDiff)
im.saveDifferenceTestingAggregationImage(relaxedImg,
testDiffImages,
aggregateDiffImages,
testingOutfile)
jelly_max_change_frames = {
'20200605_JellyTest_825pm_1_xaa': (0, 31),
'20200622_Jlo_512pm_xai': (9, 37),
'20200624_Beyonce_930pm_xag': (0, 74),
'20200622_513pm_Britney_xad': (14, 47),
'20200624_Pink_930pm_xaf': (0, 99),
'20200624_Pink_930pm_xat': (0, 99)
}
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
