def main():
# Organism
# Dendraster
# Largest Organism diameter in mm
Dorg = 0.3
dataFolder = '/Volumes/GRAVMACH1 2/HopkinsEmbroyologyCourse_GoodData/2018_06_11/Dendraster_starved_11Days_nofood'
# Load the set of tracks we want to analyze
TrackNames = {0: 'Dendraster1', 1: 'Dendraster2', 2: 'Dendraster3'}
TrackFile = 'track.csv'
SquaredDisp_XY = []
SquaredDisp_Z = []
trackLen_array = np.zeros(len(TrackNames.keys()))
# SquaredDisp_subtracks =
for ii, currFolder in TrackNames.items():
Track_curr = Track.GravMachineTrack(
os.path.join(dataFolder, currFolder, TrackFile))
trackLen_array[ii] = Track_curr.trackLen
SquaredDisp_Z.append(
squared_displacement(Track_curr.Z, 0, Track_curr.trackLen))
SquaredDisp_XY.append(
squared_displacement(Track_curr.X, 0, Track_curr.trackLen) +
squared_displacement(Track_curr.Y, 0, Track_curr.trackLen))
maxTrackLen = np.max(trackLen_array)
stackedArray_Z = np.zeros((len(TrackNames.keys(), maxTrackLen)))
stackedArray_XY = np.zeros((len(TrackNames.keys(), maxTrackLen)))
for ii in TrackNames.keys():
stackedArray_Z[ii, :trackLen_array[ii]] = SquaredDisp_Z[ii]
stackedArray_XY[ii, :trackLen_array[ii]] = SquaredDisp_XY[ii]
stackedArray_Z = np.ma.array(stackedArray_Z, mask=stackedArray_Z == 0)
stackedArray_XY = np.ma.array(stackedArray_XY, mask=stackedArray_XY == 0)
RMSD_Z = (stackedArray_Z.mean(axis=0))
RMSD_XY = (stackedArray_XY.mean(axis=0))
# print(50*'*')
# print('Tail: {}'.format(TrackName))
# resultFolder = os.path.join(dataFolder,'TrackResults')
# fullPath = os.path.join(dataFolder,TrackName)
#
# saveFile = os.path.join(resultFolder,TrackName+'_Blink_Signals'+'.pkl')
# if(not os.path.exists(resultFolder)):
# os.makedirs(resultFolder)
#======================================================================
# Plots of Tracks
#======================================================================
Tmin = 0
Tmax = 0
OrgTrack1 = Track.GravMachineTrack(dataFolder, file, Tmin, Tmax)
# OrgTrack1.smoothTrack(20)
#OrgTrack1.computeVelocity()
#
#BlinkFile = os.path.join(BlinkDataFolder, BlinkFile)
#with open(BlinkFile, 'rb') as f:
#
# Time, peak_indicator, peak_indicator_neg, TimeBetweenBlinks, BlinkDurations = pickle.load(f)
#OrgTrack1.loadPeaks(peak_indicator, peak_indicator_neg)
OrgTrack1.plot3DComponents(signalZ=OrgTrack1.ZobjWheel, labels=1, save=1)
#df = OrgTrack1.plotAlignedBlinks(labels=1)
def main():
font = cv2.FONT_HERSHEY_SIMPLEX
# font = cv2.FONT_HERSHEY_COMPLEX_SMALL
#--------------------------------------------------------------------------
# Acorn Worm
#--------------------------------------------------------------------------
# path="F:/GravityMachineBackup/HopkinsEmbryologyCourse/2018_06_12/Starfish/StarFish6highfreq"
#--------------------------------------------------------------------------
# Sea Cucumber
#--------------------------------------------------------------------------
# path = '/Volumes/GRAVMACH1 2/HopkinsEmbroyologyCourse_GoodData/2018_06_07/seacucmber9_Auto'
# path = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_07/seacucmber4_auto_verylong_goodtrack'
#--------------------------------------------------------------------------
# Snail
#--------------------------------------------------------------------------
# path = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_13/Snail/snail2'
# path = '/Volumes/GRAVMACH1 2/HopkinsEmbroyologyCourse_GoodData/2018_06_13/Snail/snail4'
#--------------------------------------------------------------------------
# Starfish
#--------------------------------------------------------------------------
# path = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_12/Starfish/StarFish6'
# path = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_12/Starfish/StarFish6'
#--------------------------------------------------------------------------
# Dendraster
#--------------------------------------------------------------------------
# path = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_11/Dendraster_starved_11Days_nofood/Dendraster3'
#--------------------------------------------------------------------------
# Noctiluca
#--------------------------------------------------------------------------
# path = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_14/Noctiluca/Noctilica7'
# path = '/Volumes/DEEPAK-SSD/GravityMachine/PuertoRico_2018/GravityMachineData/2018_11_06/Tow_1/Centric_diatom_3_Good'
path = '/Volumes/DEEPAK-SSD/Pyro_Division_Tracks/1130div'
#------------------------------------------------------------------------------
# Folder in which images are stored
#------------------------------------------------------------------------------
# If analyzing original images
#------------------------------------------------------------------------------
imageFolder = '/Volumes/DEEPAK-SSD/Pyro_Division_Tracks/1130div/Cropped'
# imageFolder = '/Volumes/DEEPAK-SSD/Pyro_Division_Tracks/1130div/RegisteredProcessed'
# imageFolder = '/Volumes/DEEPAK-SSD/GravityMachine/MovieRawFiles/Centric_Diatom_small_40_240/Images_GS'
# imageFolder = os.path.join(path,'images')
# imageFolder = '/Volumes/GRAVMACH1 2/GravityMachine/Results/PIV_Results/seacucmber9_Auto_IMG_33743_33971_Registered'
#------------------------------------------------------------------------------
# If analyzing preprocessed images then replace above folder with the folder containing those images
#------------------------------------------------------------------------------
# imageFolder = '/Volumes/GRAVMACH1/GravityMachine/Results/flowtrace_python/Seacucumber4_T_744_Registered_GS'
# imageFolder = '/Volumes/GRAVMACH1/GravityMachine/Results/flowtrace_python/Starfish6_SideView_FlowTrace_FINAL'
# imageFolder = '/Volumes/GRAVMACH1/GravityMachine/Results/flowtrace_python/Starfish6_FrontView_FlowTrace_FINAL'
# Acorn worm
# path ='/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_06/AcornWorm_Experiment2_nolight/AcornWorm7'
# imageFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_06/AcornWorm_Experiment2_nolight/AcornWorm7/images'
#Dendraster
# path = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_11/Dendraster_starved_11Days_nofood/Dendraster3'
# Brittle Star
# path = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/BrittleStar/BrittleStar9_Long_Good_Ytracking'
# Snail
# path = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_13/Snail/snail1'
# Polychaete 1
# path = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_12/Polychaete_4D/Polychaete6'
# Polychaete 2
# path = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_13/Polychaete/Poly4'
#
# imageFolder = os.path.join(path,'images')
# path = '/Volumes/GRAVMACH1/Hopkins_2018_08_31/MarSno2'
# imageFolder = '/Volumes/GRAVMACH1/GravityMachine/Results/flowtrace_python/FinalVideos/MarineSnowFlowTrace'
# Pyro Data
# path = '/Volumes/GRAVMACH1/PyroTracks_2018_12_21/nnn1'
# imageFolder = '/Volumes/GRAVMACH1/PyroTracks_2018_12_21/Pyro_Division_Processed_1'
# imageFolder = '/Volumes/GRAVMACH1/PyroTracks_2018_12_21/Pyro_Ballooning_Cropped'
dataFolder, fileName = os.path.split(path)
imageType = 'Processed'
rootImageFolder, folderName = os.path.split(imageFolder)
# Choose the track to analyze
TrackName = 'track_division.csv'
savePath = os.path.join(rootImageFolder, folderName + '_TimeStamped')
if (not os.path.exists(savePath)):
os.makedirs(savePath)
Tmin = 0
Tmax = 0
Track1 = Track.GravMachineTrack(path, TrackName, Tmin, Tmax)
T_start = Track1.Time[0]
if (imageType == 'Raw'):
for ii in range(Track1.trackLen):
if (Track1.ImageName[ii]):
Time = Track1.Time[ii] - T_start
ImgName = Track1.ImageName[ii]
print(ImgName)
frame_color = cv2.imread(os.path.join(imageFolder, ImgName))
# cv2.imshow('raw',frame_color)
frame_gs = cv2.cvtColor(frame_color, cv2.COLOR_BGR2GRAY)
clahe = cv2.createCLAHE(clipLimit=1.5, tileGridSize=(12, 12))
frame_clahe = clahe.apply(frame_gs)
cv2.putText(frame_clahe,
str(np.round(Time, decimals=2)) + ' s', (30, 30),
font, 5, (255, 255, 255), 3, cv2.LINE_AA)
# plt.clf()
# cv2.imshow('frame',frame_clahe)
# k = cv2.waitKey(1) & 0xff
# if k == 27 : break
# plt.show()
cv2.imwrite(os.path.join(savePath, ImgName), frame_clahe)
elif (imageType == 'Processed'):
# List all images from the folder:
FilesList = os.listdir(imageFolder)
FilesList.sort()
print(FilesList)
FilesList = FilesList[1:]
print(Track1.Time[0])
# imgName_start = FilesList[0][:-25] + '.tif'
# imgName_end = FilesList[-1][:-25] + '.tif'
imgName_start = FilesList[0]
imgName_end = FilesList[-1]
print(50 * '-')
print(imgName_start)
print(imgName_end)
print(50 * '-')
startIndex, *rest = np.where(Track1.ImageName == imgName_start)
stopIndex, *rest = np.where(Track1.ImageName == imgName_end)
startIndex = int(startIndex)
stopIndex = int(stopIndex)
indexArray = []
for ii in range(startIndex, stopIndex):
if (Track1.ImageName[ii]):
indexArray.append(ii)
indexArray = np.array(indexArray)
print(indexArray)
imageCounter = 0
trackCounter = 0
while imageCounter < len(FilesList) - 1:
file = FilesList[imageCounter]
currIndex = indexArray[trackCounter]
# imgName = file[:-25] + '.tif'
imgName = file
if (Track1.ImageName[currIndex] == imgName):
print('Correct Image found in Track')
trackCounter += 1
imageCounter += 1
else:
print('Missing image: {}'.format(Track1.ImageName[currIndex]))
while (Track1.ImageName[currIndex] != imgName):
print('Searching for next available image...')
trackCounter += 1
currIndex = indexArray[trackCounter]
print(Track1.ImageName[currIndex])
print('Next sequential image found!')
#
Time = Track1.Time[currIndex]
frame_a_color = cv2.imread(os.path.join(imageFolder, file))
frame_gs = cv2.cvtColor(frame_a_color, cv2.COLOR_BGR2GRAY)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(12, 12))
frame_clahe = clahe.apply(frame_gs)
cv2.putText(img=frame_clahe,
text=str(np.round(Time, decimals=2)) + ' s',
org=(10, 20),
fontFace=font,
fontScale=0.75,
color=(255, 255, 255),
thickness=2,
lineType=cv2.LINE_AA)
# plt.clf()
cv2.imshow('frame', frame_clahe)
k = cv2.waitKey(1) & 0xff
if k == 27: break
# ax = plt.gca()
# plt.show()
cv2.imwrite(os.path.join(savePath, imgName), frame_clahe)
#------------------------------------------------------------------------------
# If analyzing preprocessed images then replace above folder with the folder containing those images
#------------------------------------------------------------------------------
# imageFolder = '/Volumes/GRAVMACH1/GravityMachine/Results/flowtrace_python/Seacucumber4_T_744_Registered_GS'
# Choose the track to analyze
TrackName = 'track000.csv'
#--------------------------------------------------------------------------
# Load data from the track into numpy arrays
#--------------------------------------------------------------------------
# Time, Xobj, Yobj, Zobj, ThetaWheel, ZobjWheel, ManualTracking,ImageName,focusMeasure, focusPhase, MaxfocusMeasure = readCSV(os.path.join(path,TrackName))
Tmin = 0
Tmax = 0
Track1 = Track.GravMachineTrack(path, TrackName, Tmin, Tmax)
Track_full = Track.GravMachineTrack(path, TrackName)
nData = Track1.trackLen
# print("{:~^50} {}".format('No:of Data points:',nData))
#--------------------------------------------------------------------------
# Specify the time interval OR the starting image of the track on which do PIV
#--------------------------------------------------------------------------
AnalysisDict = {0: 'ImageBasedSeq', 1: 'ImageBasedNonSeq', 2: 'TimeBased'}
AnalysisType = 0
#--------------------------------------------------------------------------
# Global index of the dataset
#--------------------------------------------------------------------------
indexArray = np.array(range(0, Track1.trackLen))
GainsList = [5, 10, 20, 50] # Columns
df = pd.DataFrame(data=[], dtype='float')
print(df)
rootFolder = '/Volumes/GRAVMACH1/GravityMachine/YTrackingControls_2018_10_24/Speed_250um_s'
# rootFolder = 'Y:\Projects\GravityMachine\YTrackingControls_2018_10_24\Speed_250um_s's
trackFile = 'track.csv'
Folders = os.listdir(os.path.join(rootFolder))
for currFolder in Folders:
currTrack = Track.GravMachineTrack(path=os.path.join(
rootFolder, currFolder),
file=trackFile)
liqLensAmp = 2 * currTrack.liqLensAmp[0]
liqLensFreq = currTrack.liqLensFreq[0]
liqLensGain = currTrack.liqLensGain[0]
relError = computeRelativeError(currTrack)
# Print the liquid lens parameters
print('Liquid Lens Amplitude: {}'.format(liqLensAmp))
print('Liquid Lens Frequency: {}'.format(liqLensFreq))
print('Liquid Lens Max Gain: {}'.format(liqLensGain))
print('Relative Tracking Error (%): {}'.format(100 * relError))
path = 'F:/GravityMachineBackup/HopkinsEmbryologyCourse/2018_06_12/Starfish/StarFish6highfreq'
dataFolder, fileName = os.path.split(path)
savFile = os.path.join(dataFolder, fileName + '_Video.avi')
imageFolder = os.path.join(path, 'images')
saveFolder = os.path.join(path, 'images_proc')
if (not os.path.exists(saveFolder)):
os.makedirs(saveFolder)
TrackName = 'track.csv'
Track1 = Track.GravMachineTrack(path, TrackName)
nData = Track1.trackLen
pixelsize = 1000 / 314
fig = plt.figure(1)
plt.ion()
scalebar = ScaleBar(pixelsize, 'um')
for ii in range(nData):
if (Track1.ImageName[ii]):
Time_curr = Track1.T[ii]
# Load the image
def main():
#--------------------------------------------------------------------------
# Sea Cucumber
#--------------------------------------------------------------------------
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_07/SeaCucumber'
# TrackNames = {0:'seacucmber4_auto_verylong_goodtrack'}
# TimePoint = 200
#--------------------------------------------------------------------------
# Dendraster
#--------------------------------------------------------------------------
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_11/Dendraster_starved_11Days_nofood'
# TrackNames = {0:'Dendraster3'}
# TimePoint = 85
# --------------------------------------------------------------------------
# Brittle Star
#--------------------------------------------------------------------------
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/BrittleStar'
#
# TrackNames = {0:'BrittleStar9_Long_Good_Ytracking'}
#
# TimePoint = 53
#--------------------------------------------------------------------------
# Sea Urchin
#--------------------------------------------------------------------------
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_06/SeaUrchin'
#
# TrackNames = {0: 'SeaUrchin7'}
# TimePoint = 66
#--------------------------------------------------------------------------
# Starfish
#--------------------------------------------------------------------------
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_12/Starfish'
# TrackNames = {0:'StarFish7'}
# TimePoint = 159
#--------------------------------------------------------------------------
# Acorn Worm
#--------------------------------------------------------------------------
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_06/AcornWorm_Experiment2_nolight'
# TrackNames = {0: 'AcornWorm3'}
# TimePoint = 119
#--------------------------------------------------------------------------
# Snail
#--------------------------------------------------------------------------
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_13/Snail'
##
# TrackNames = {0:'snail1'}
# TimePoint = 119
# --------------------------------------------------------------------------
# Polychaetes
dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_12/Polychaete_4D'
TrackNames = {0:'Polychaete6'}
TimePoint = 128
*rest,orgName = os.path.split(dataFolder)
saveFolder = '/Users/deepak/Dropbox/GravityMachine/GravityMachineManuscript/EnsembleTrackStatistics'
TimeDuration = 4
TrackFile = 'track_mod.csv'
for ii, currFolder in TrackNames.items():
path = os.path.join(dataFolder, TrackNames[ii])
Track_curr = Track.GravMachineTrack(path, TrackFile)
Track_curr.plot_XZ_fixedWindow(TimePoint = TimePoint, TimeDuration= TimeDuration)
def main():
# rootFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/'
# subFolders = {0:'Dendraster_starved_11Days_nofood',1:'Dendraster_starved_11Days_withfood',2:'Dendraster_well_fed_11Days_nofood'}
#--------------------------------------------------------------------------
# Starfish
#--------------------------------------------------------------------------
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_12/Starfish'
# TrackNames = {0:['StarFish1', 0,162],1:['StarFish6', 0,600],2:['StarFish7', 60,0],3:['StarFish9', 0,290]}
#--------------------------------------------------------------------------
# Dendraster
#--------------------------------------------------------------------------
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_11/Dendraster_starved_11Days_nofood'
# TrackNames = {0:['Dendraster1', 87,417], 1:['Dendraster2', 0,0], 2:['Dendraster3', 0,0]}
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_11/Dendraster_starved_11Days_withfood'
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_11/Dendraster_well_fed_11Days_nofood'
#--------------------------------------------------------------------------
# Sea cucumber
#--------------------------------------------------------------------------
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_07/SeaCucumber'
# TrackNames = {0:['seacucmber4_auto_verylong_goodtrack', 0,0], 1:['seacucmber9_Auto', 0,0]}
# TrackNames = {0:['seacucmber4_auto_verylong_goodtrack', 0,0]}
#--------------------------------------------------------------------------
# Sea Urchin
#--------------------------------------------------------------------------
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_06/SeaUrchin'
# TrackNames = {0: ['SeaUrchin5',0,0],1: ['SeaUrchin7',0,500],2: ['SeaUrchin8',0,0]}
#--------------------------------------------------------------------------
# Acorn Worm
#--------------------------------------------------------------------------
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_06/AcornWorm_Experiment2_nolight'
# TrackNames = {0: ['AcornWorm2',0,0],1: ['AcornWorm3',0,0],2: ['AcornWorm4',0,0],3: ['AcornWorm7',50,140]}
# --------------------------------------------------------------------------
# Brittle Star
#--------------------------------------------------------------------------
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/BrittleStar'
# TrackNames = {0:['BrittleStar1',0,0],1:['BrittleStar9_Long_Good_Ytracking',0,0],2:['BrittleStar10_Ytracking_Good',0,0],3:['BrittleStar12_Ytracking_Good',0,0]}
#--------------------------------------------------------------------------
# Snail
#--------------------------------------------------------------------------
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_13/Snail'
# TrackNames = {0:['snail1',0,575],1:['snail2',0,0],2:['snail4',0,0],3:['snail6',0,0],4:['snail8',0,0],5:['snail10',0,0],6:['snail13',0,0]}
# Noctilica
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_14/Noctiluca'
# TrackNames = {0:['Noctilica6',50,0],1:['Noctilica7',0,1000]}
# Polychaetes
dataFolder = [
'/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_12/Polychaete_4D',
'/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_13/Polychaete'
]
# TrackNames = {0:['Polychaete1',0,112],1:['Polychaete2',0,113], 2:['Polychaete3',0,37], 3:['Polychaete4',0,55], 4:['Polychaete6',0,113]}
TrackNames = [{
0: ['Polychaete1', 0, 112],
1: ['Polychaete2', 0, 113],
2: ['Polychaete3', 0, 37],
3: ['Polychaete4', 0, 55],
4: ['Polychaete6', 0, 113]
}, {
0: ['Poly1', 20, 80],
1: ['Poly2', 0, 116],
2: ['Poly3', 0, 0],
3: ['Poly4', 0, 93],
4: ['Poly5', 0, 34]
}]
# dataFolder = '/Volumes/GRAVMACH1/HopkinsEmbroyologyCourse_GoodData/2018_06_13/Polychaete'
# TrackNames = {0:['Poly1',20,80],1:['Poly2',0,116], 2:['Poly3',0,0], 3:['Poly4',0,93], 4:['Poly5',0,34]}
# *rest,orgName = os.path.split(dataFolder)
# saveFolder = '/Users/deepak/Dropbox/GravityMachine/GravityMachineManuscript/EnsembleTrackStatistics'
# Load the set of tracks we want to analyze
# TrackNames = {0:['Dendraster1', 87,417],1:['Dendraster2', 0,0],2:['Dendraster3',0,0]}
# TrackNames = {0:['Dendraster1', 0,600],1:['Dendraster2', 0,200],2:['Dendraster3',0,0]}
# TrackNames = {0:['StarFish1', 0,162],1:['StarFish6', 0,600],2:['StarFish7', 60,0],3:['StarFish9', 0,290]}
TrackFile = 'track_mod.csv'
# colors = colors[128:]
Velocities_X = np.array([])
Velocities_Y = np.array([])
Velocities_Z = np.array([])
Velocities_X_notWall = np.array([])
Velocities_Y_notWall = np.array([])
Velocities_Z_notWall = np.array([])
totalTrackLen = 0
TrackArray = []
plt.figure(1)
print(dataFolder[1])
for kk in range(len(TrackNames)):
if (kk == 0):
cmap = plt.cm.YlOrRd
colors = cmap(np.arange(256))
colors = colors[128:]
linestyle = '--'
else:
cmap = plt.cm.Purples
colors = cmap(np.arange(256))
colors = colors[128:]
linestyle = '-'
totalTracks = len(TrackNames[kk])
print(totalTracks)
print(len(colors))
indices = np.array(len(colors) * np.linspace(0, 0.9, totalTracks),
dtype='int')
plt_colors = colors[indices]
for ii, currFolder in TrackNames[kk].items():
path = os.path.join(dataFolder[kk], TrackNames[kk][ii][0])
Track_curr = Track.GravMachineTrack(path, TrackFile,
TrackNames[kk][ii][1],
TrackNames[kk][ii][2])
# TrackArray.append(Track.GravMachineTrack(path, TrackFile, TrackNames[kk][ii][1],TrackNames[kk][ii][2]))
# Track_curr.plotTrackWalls(walls=1)
# Track_curr.plotVelocity(Track_curr.Vx,Track_curr.Vy,Track_curr.Vz)
# Track_curr.plot3DComponents(Track_curr.X_smooth,Track_curr.Y_smooth,Track_curr.Z_smooth, walls = 1)
Track_curr.T = Track_curr.T - Track_curr.T[0]
Track_curr.Z = Track_curr.Z - Track_curr.Z[0]
plt.plot(Track_curr.T,
Track_curr.Z,
linewidth=2,
linestyle=linestyle,
color=plt_colors[ii])
plt.fill_between(Track_curr.T,
Track_curr.Z,
color=plt_colors[ii],
alpha=0.2)
AutoTracking = np.array(~Track_curr.ManualTracking[:-1],
dtype='bool')
#--------------------------------------------------------------------------
# Velocity distributions
#--------------------------------------------------------------------------
# Velocities_X = np.hstack((Velocities_X,np.array(Track_curr.Vx[AutoTracking])))
# Velocities_Y = np.hstack((Velocities_Y,np.array(Track_curr.Vy[AutoTracking])))
# Velocities_Z = np.hstack((Velocities_Z,np.array(Track_curr.Vz[AutoTracking])))
# Velocities_X_notWall = np.hstack((Velocities_X_notWall,np.array(Track_curr.Vx[~Track_curr.atWall[:-1]&AutoTracking])))
# Velocities_Y_notWall = np.hstack((Velocities_Y_notWall,np.array(Track_curr.Vy[~Track_curr.atWall[:-1]&AutoTracking])))
# Velocities_Z_notWall = np.hstack((Velocities_Z_notWall,np.array(Track_curr.Vz[~Track_curr.atWall[:-1]&AutoTracking])))
#--------------------------------------------------------------------------
totalTrackLen += (max(Track_curr.Time) - min(Track_curr.Time))
plt.xlabel('Time (s)')
plt.ylabel('Z displacement (mm)')
save_path_full = os.path.join(save_root, saveFile_full)
overwrite = False
if (not os.path.exists(save_path_noWalls) or not os.path.exists(save_path_full)
or overwrite):
for Organism, Tracks in TrackNames.items():
print('Current organism : {}'.format(Organism))
for ii, currTrack in Tracks.items():
print(currTrack)
path = os.path.join(dataFolder[Organism], currTrack[0])
# Load the track into memory
Track_curr = Track.GravMachineTrack(path, TrackFile, currTrack[1],
currTrack[2])
# plt.figure()
# plt.plot(Track_curr.Time,Track_curr.ZobjWheel,'bo-')
# plt.plot(Track_curr.T,Track_curr.Z,'r-')
# plt.show()
# Track_curr.plotTrackWalls(walls=1)
# Track_curr.plotVelocity(Track_curr.Vx,Track_curr.Vy,Track_curr.Vz)
# Track_curr.plot3DComponents(Track_curr.X_smooth,Track_curr.Y_smooth,Track_curr.Z_smooth, walls = 1)
Track_curr.T = Track_curr.T - Track_curr.T[0]
Track_curr.Z = Track_curr.Z - Track_curr.Z[0]
# plt.plot(Track_curr.T, Track_curr.Z, linewidth = 2, linestyle = linestyle, color = plt_colors[ii])
# plt.fill_between(Track_curr.T,Track_curr.Z,color = plt_colors[ii],alpha=0.2)
