class ScanningExecutionThread(QThread):
ScanningResult = pyqtSignal(np.ndarray, np.ndarray, object, object) #The signal for the measurement, we can connect to this signal
def __init__(self, RoundQueueDict, RoundCoordsDict, GeneralSettingDict, *args, **kwargs):
super().__init__(*args, **kwargs)
self.RoundQueueDict = RoundQueueDict
self.RoundCoordsDict = RoundCoordsDict
self.GeneralSettingDict = GeneralSettingDict
self.Status_list = None
self.ludlStage = LudlStage("COM12")
self.watchdog_flag = True
# self.PMTimageDict = {}
# for i in range(int(len(self.RoundQueueDict)/2-1)): # initial the nested PMTimageDict dictionary. -2 because default keys for insight and filter.
# self.PMTimageDict['RoundPackage_{}'.format(i+1)] = {}
self.clock_source = 'Dev1 as clock source' # Should be set by GUI.
self.scansavedirectory = self.GeneralSettingDict['savedirectory']
self.meshgridnumber = int(self.GeneralSettingDict['Meshgrid'])
def Try_until_Success(func):
""" This is the decorator to try to execute the function until succeed.
"""
def wrapper(*args, **kwargs):
success = None
failnumber = 0
while success is None:
if failnumber <8:
try:
returnValue = func(*args, **kwargs)
success = True
except:
failnumber += 1
print('Laser action failed, failnumber: {}'.format(failnumber))
time.sleep(0.2)
else:
print('Fail for 8 times, give up - -')
success = False
return returnValue
return wrapper
def run(self):
"""
# ==========================================================================================================================================================
# Initialization
# ==========================================================================================================================================================
"""
#%%
"""
# =============================================================================
# connect the Objective motor
# =============================================================================
"""
print('----------------------Starting to connect the Objective motor-------------------------')
self.pi_device_instance = PIMotor()
print('Objective motor connected.')
self.errornum = 0
self.ObjCurrentPos = self.pi_device_instance.pidevice.qPOS(self.pi_device_instance.pidevice.axes)
"""
# =============================================================================
# connect the Hmamatsu camera
# =============================================================================
"""
self._use_camera = False
for key in self.RoundQueueDict:
if "RoundPackage_" in key:
for waveform_and_cam_key in self.RoundQueueDict[key][1]:
if "CameraPackage_" in waveform_and_cam_key:
if len(self.RoundQueueDict[key][1][waveform_and_cam_key]) != 0:
self._use_camera = True
if self._use_camera == True:
print('Connecting camera...')
self.HamamatsuCam = CamActuator()
self.HamamatsuCam.initializeCamera()
else:
print('No camera involved.')
"""
# =============================================================================
# connect the Insight X3
# =============================================================================
"""
if len(self.RoundQueueDict['InsightEvents']) != 0:
self.Laserinstance = InsightX3('COM11')
try:
querygap = 1.1
# self.Laserinstance.SetWatchdogTimer(0) # Disable the laser watchdog!
# Status_watchdog_thread = threading.Thread(target = self.Status_watchdog, args=[querygap], daemon=True)
# Status_watchdog_thread.start()
time.sleep(1)
#-------------Initialize laser--------------
self.watchdog_flag = False
time.sleep(0.5)
warmupstatus = 0
while int(warmupstatus) != 100:
try:
warmupstatus = self.Laserinstance.QueryWarmupTime()
time.sleep(0.6)
except:
time.sleep(0.6)
# if int(warmupstatus) == 100:
# self.warmupstatus = True
# print('Laser fully warmed up.')
# if 'Laser state:Ready' in self.Status_list:
# self.Laserinstance.Turn_On_PumpLaser()
# self.laserRun = True
# elif 'Laser state:RUN' in self.Status_list:
# self.laserRun = True
self.watchdog_flag = True
time.sleep(0.5)
except:
print('Laser not connected.')
# If turn on the laser shutter in the beginning
if 'Shutter_Open' in self.GeneralSettingDict['StartUpEvents']:
time.sleep(0.5)
while True:
try:
self.Laserinstance.Open_TunableBeamShutter()
break
except:
time.sleep(1)
time.sleep(0.5)
#%%
"""
# ==========================================================================================================================================================
# Execution
# ==========================================================================================================================================================
"""
GridSequence = 0
TotalGridNumber = self.meshgridnumber**2
ScanningMaxCoord = int(np.amax(self.RoundCoordsDict['CoordsPackage_1'])) # Get the largest coordinate
for EachGrid in range(TotalGridNumber):
"""
# =============================================================================
# For each small repeat unit in the scanning meshgrid
# =============================================================================
"""
ScanningGridOffset_Row = int(GridSequence % self.meshgridnumber) * (ScanningMaxCoord + self.GeneralSettingDict['Scanning step']) # Offset coordinate row value for each well.
ScanningGridOffset_Col = int(GridSequence/self.meshgridnumber) * (ScanningMaxCoord + self.GeneralSettingDict['Scanning step']) # Offset coordinate colunm value for each well.
GridSequence += 1
time.sleep(0.5)
for EachRound in range(int(len(self.RoundQueueDict)/2-1)): # EachRound is the round sequence number starting from 0, while the actual number used in dictionary is 1.
print ('----------------------------------------------------------------------------')
print('Below is Grid {}, Round {}.'.format(EachGrid, EachRound+1)) # EachRound+1 is the corresponding round number when setting the dictionary starting from round 1.
"""
# =============================================================================
# Unpack the settings for each round
# =============================================================================
"""
# Initialize variables
CoordOrder = 0 # Counter for n th coordinates, for appending cell properties array.
CellPropertiesDict = {}
ND_filter1_Pos = None
ND_filter2_Pos = None
EM_filter_Pos = None
cp_end_index = -1
self.IndexLookUpCellPropertiesDict = {} #look up dictionary for each cell properties
#-------------Unpack the focus stack information.
ZStackinfor = self.GeneralSettingDict['FocusStackInfoDict']['RoundPackage_{}'.format(EachRound+1)]
self.ZStackNum = int(ZStackinfor[ZStackinfor.index('Focus')+5])
self.ZStackStep = float(ZStackinfor[ZStackinfor.index('Being')+5:len(ZStackinfor)])
#-------------Unpack infor for stage move.
CoordsNum = int(len(self.RoundCoordsDict['CoordsPackage_{}'.format(EachRound+1)])/2) #Each pos has 2 coords
#-------------Unpack infor for filter event. In the list, the first one is for ND filter and the second one is for emission filter.
FilterEventIndexList = [i for i,x in enumerate(self.RoundQueueDict['FilterEvents']) if 'Round_{}'.format(EachRound+1) in x]
if len(FilterEventIndexList) > 0:
NDposText = self.RoundQueueDict['FilterEvents'][FilterEventIndexList[0]]
NDnumber = NDposText[NDposText.index('ToPos_')+6:len(NDposText)]
EMposText = self.RoundQueueDict['FilterEvents'][FilterEventIndexList[1]]
EMprotein = EMposText[EMposText.index('ToPos_')+6:len(EMposText)]
# "COM9" for filter 1 port, which has ND values from 0 to 3.
# "COM7" for filter 2 port, which has ND values from 0 to 0.5.
if NDnumber == '0':
ND_filter1_Pos = 0
ND_filter2_Pos = 0
elif NDnumber == '1':
ND_filter1_Pos = 1
ND_filter2_Pos = 0
elif NDnumber == '2':
ND_filter1_Pos = 2
ND_filter2_Pos = 0
elif NDnumber == '2.3':
ND_filter1_Pos = 2
ND_filter2_Pos = 2
elif NDnumber == '2.5':
ND_filter1_Pos = 2
ND_filter2_Pos = 3
elif NDnumber == '0.5':
ND_filter1_Pos = 0
ND_filter2_Pos = 3
elif NDnumber == '0.3':
ND_filter1_Pos = 0
ND_filter2_Pos = 2
if EMprotein == 'Arch':
EM_filter_Pos = 0
elif EMprotein == 'eGFP' or EMprotein == 'Citrine':
EM_filter_Pos = 1
#-------------Unpack infor for Insight X3. In the list, the first one is for shutter event and the second one is for wavelength event.
InsightX3EventIndexList = [i for i,x in enumerate(self.RoundQueueDict['InsightEvents']) if 'Round_{}'.format(EachRound+1) in x]
"""
# =============================================================================
# Execute Insight event at the beginning of each round
# =============================================================================
"""
if len(InsightX3EventIndexList) == 1:
print(InsightX3EventIndexList)
InsightText = self.RoundQueueDict['InsightEvents'][InsightX3EventIndexList[0]]
if 'Shutter_Open' in InsightText:
self.watchdog_flag = False
time.sleep(0.5)
while True:
try:
self.Laserinstance.Open_TunableBeamShutter()
break
except:
time.sleep(1)
time.sleep(0.5)
print('Laser shutter open.')
self.watchdog_flag = True
time.sleep(0.5)
elif 'Shutter_Close' in InsightText:
self.watchdog_flag = False
time.sleep(0.5)
while True:
try:
self.Laserinstance.Close_TunableBeamShutter()
break
except:
time.sleep(1)
time.sleep(0.5)
print('Laser shutter closed.')
self.watchdog_flag = True
time.sleep(0.5)
elif 'WavelengthTo' in InsightText:
self.watchdog_flag = False
time.sleep(0.5)
TargetWavelen = int(InsightText[InsightText.index('To_')+3:len(InsightText)])
print(TargetWavelen)
while True:
try:
self.Laserinstance.SetWavelength(TargetWavelen)
break
except:
time.sleep(1)
time.sleep(5)
self.watchdog_flag = True
time.sleep(0.5)
elif len(InsightX3EventIndexList) == 2:
InsightText_wl = self.RoundQueueDict['InsightEvents'][InsightX3EventIndexList[1]]
InsightText_st = self.RoundQueueDict['InsightEvents'][InsightX3EventIndexList[0]]
if 'WavelengthTo' in InsightText_wl and 'Shutter_Open' in InsightText_st:
self.watchdog_flag = False
time.sleep(0.5)
TargetWavelen = int(InsightText_wl[InsightText_wl.index('To_')+3:len(InsightText_wl)])
while True:
try:
self.Laserinstance.SetWavelength(TargetWavelen)
break
except:
time.sleep(1)
time.sleep(5)
while True:
try:
self.Laserinstance.Open_TunableBeamShutter()
break
except:
time.sleep(1)
print('Laser shutter open.')
self.watchdog_flag = True
time.sleep(0.5)
elif 'WavelengthTo' in InsightText_wl and 'Shutter_Close' in InsightText_st:
self.watchdog_flag = False
time.sleep(0.5)
TargetWavelen = int(InsightText_wl[InsightText_wl.index('To_')+3:len(InsightText_wl)])
while True:
try:
self.Laserinstance.SetWavelength(TargetWavelen)
break
except:
time.sleep(1)
time.sleep(5)
while True:
try:
self.Laserinstance.Close_TunableBeamShutter()
break
except:
time.sleep(1)
time.sleep(1)
print('Laser shutter closed.')
self.watchdog_flag = True
time.sleep(0.5)
time.sleep(2)
"""
# =============================================================================
# Execute filter event at the beginning of each round
# =============================================================================
"""
if ND_filter1_Pos != None and ND_filter2_Pos != None:
#Move filter 1
self.filter1 = ELL9Filter("COM9")
self.filter1.moveToPosition(ND_filter1_Pos)
time.sleep(1)
#Move filter 2
self.filter2 = ELL9Filter("COM7")
self.filter2.moveToPosition(ND_filter2_Pos)
time.sleep(1)
if EM_filter_Pos != None:
self.filter3 = ELL9Filter("COM15")
self.filter3.moveToPosition(EM_filter_Pos)
time.sleep(1)
self.currentCoordsSeq = 0
#%%
for EachCoord in range(CoordsNum):
#-----------------------------------------------At each stage coordinate:--------------------------------------------
self.error_massage = None
self.currentCoordsSeq += 1
"""
# =============================================================================
# Stage movement
# =============================================================================
"""
RowIndex = int(self.RoundCoordsDict['CoordsPackage_{}'.format(EachRound+1)][EachCoord*2:EachCoord*2+2][0]) + ScanningGridOffset_Row
ColumnIndex = int(self.RoundCoordsDict['CoordsPackage_{}'.format(EachRound+1)][EachCoord*2:EachCoord*2+2][1]) + ScanningGridOffset_Col
try:
self.ludlStage.moveAbs(RowIndex,ColumnIndex) # Row/Column indexs of np.array are opposite of stage row-col indexs.
except:
self.error_massage = 'Fail_MoveStage'
self.errornum += 1
print('Stage move failed! Error number: {}'.format(int(self.errornum)))
print ('Round {}. Current index: {}.'.format(EachRound+1, [RowIndex,ColumnIndex]))
time.sleep(1)
"""
# =============================================================================
# Get the z stack objective positions ready
# =============================================================================
"""
#-------------------------------------------If focus correction applies----------------------------------------
if len(self.GeneralSettingDict['FocusCorrectionMatrixDict']) > 0:
FocusPosArray = self.GeneralSettingDict['FocusCorrectionMatrixDict']['RoundPackage_{}_Grid_{}'.format(EachRound+1, EachGrid)]
# print(FocusPosArray)
FocusPosArray = FocusPosArray.flatten('F')
FocusPos_fromCorrection = FocusPosArray[EachCoord]
print('Target focus pos: '.format(FocusPos_fromCorrection))
# Without focus correction
if len(self.GeneralSettingDict['FocusCorrectionMatrixDict']) == 0:
ZStacklinspaceStart = self.ObjCurrentPos['1'] - (math.floor(self.ZStackNum/2)-1)*self.ZStackStep
ZStacklinspaceEnd = self.ObjCurrentPos['1'] + (self.ZStackNum - math.floor(self.ZStackNum/2))*self.ZStackStep
# With focus correction
elif len(self.GeneralSettingDict['FocusCorrectionMatrixDict']) > 0:
ZStacklinspaceStart = FocusPos_fromCorrection - (math.floor(self.ZStackNum/2)-1)*self.ZStackStep
ZStacklinspaceEnd = FocusPos_fromCorrection + (self.ZStackNum - math.floor(self.ZStackNum/2))*self.ZStackStep
ZStackPosList = np.linspace(ZStacklinspaceStart, ZStacklinspaceEnd, num = self.ZStackNum)
print(ZStackPosList)
"""
# =============================================================================
# Execute waveform packages
# =============================================================================
"""
self.WaveforpackageNum = int(len(self.RoundQueueDict['RoundPackage_{}'.format(EachRound+1)][0]))
#Execute each individual waveform package
print('*******************************************Round {}. Current index: {}.**************************************************'.format(EachRound+1, [RowIndex,ColumnIndex]))
#------------------Move to Z stack focus-------------------
for EachZStackPos in range(self.ZStackNum):
print('--------------------------------------------Stack {}--------------------------------------------------'.format(EachZStackPos+1))
if self.ZStackNum > 1:
self.ZStackOrder = int(EachZStackPos +1) # Here the first one is 1, not starting from 0.
FocusPos = ZStackPosList[EachZStackPos]
print('Target focus pos: {}'.format(FocusPos))
pos = PIMotor.move(self.pi_device_instance.pidevice, FocusPos)
self.ObjCurrentPosInStack = self.pi_device_instance.pidevice.qPOS(self.pi_device_instance.pidevice.axes)
print("Current position: {:.4f}".format(self.ObjCurrentPosInStack['1']))
time.sleep(0.3)
else:
self.ZStackOrder = 1
#------------For waveforms in each coordinate----------
for EachWaveform in range(self.WaveforpackageNum):
# Extract information
WaveformPackageToBeExecute = self.RoundQueueDict['RoundPackage_{}'.format(EachRound+1)][0]['WaveformPackage_{}'.format(EachWaveform+1)]
CameraPackageToBeExecute = self.RoundQueueDict['RoundPackage_{}'.format(EachRound+1)][1]["CameraPackage_{}".format(EachWaveform+1)]
WaveformPackageGalvoInfor = self.RoundQueueDict['GalvoInforPackage_{}'.format(EachRound+1)]['GalvoInfor_{}'.format(EachWaveform+1)]
self.readinchan = WaveformPackageToBeExecute[3]
self.RoundWaveformIndex = [EachRound+1, EachWaveform+1] # first is current round number, second is current waveform package number.
self.CurrentPosIndex = [RowIndex, ColumnIndex]
#----------------Camera operations-----------------
_camera_isUsed = False
if CameraPackageToBeExecute != {}: # if camera operations are configured
_camera_isUsed = True
CamSettigList = CameraPackageToBeExecute["Settings"]
self.HamamatsuCam.StartStreaming(BufferNumber = CameraPackageToBeExecute["Buffer_number"],
trigger_source = CamSettigList[CamSettigList.index("trigger_source")+1],
exposure_time = CamSettigList[CamSettigList.index("exposure_time")+1],
trigger_active = CamSettigList[CamSettigList.index("trigger_active")+1])
# Make sure that the camera is prepared before waveform execution.
# while self.HamamatsuCam.isStreaming == False:
# print('Waiting for camera...')
# time.sleep(0.5)
time.sleep(1)
print('Now start waveforms')
#----------------Waveforms operations--------------
if WaveformPackageGalvoInfor != 'NoGalvo': # Unpack the information of galvo scanning.
self.readinchan = WaveformPackageGalvoInfor[0]
self.repeatnum = WaveformPackageGalvoInfor[1]
self.PMT_data_index_array = WaveformPackageGalvoInfor[2]
self.averagenum = WaveformPackageGalvoInfor[3]
self.lenSample_1 = WaveformPackageGalvoInfor[4]
self.ScanArrayXnum = WaveformPackageGalvoInfor[5]
if self.clock_source == 'Dev1 as clock source':
self.adcollector = execute_analog_readin_optional_digital_thread()
self.adcollector.set_waves(WaveformPackageToBeExecute[0], WaveformPackageToBeExecute[1], WaveformPackageToBeExecute[2], WaveformPackageToBeExecute[3]) #[0] = sampling rate, [1] = analogcontainer_array, [2] = digitalcontainer_array, [3] = readinchan
self.adcollector.collected_data.connect(self.ProcessData)
self.adcollector.run()
time.sleep(0.2)
#self.ai_dev_scaling_coeff = self.adcollector.get_ai_dev_scaling_coeff()
elif self.clock_source == 'Cam as clock source' :
self.adcollector = execute_analog_and_readin_digital_optional_camtrig_thread()
self.adcollector.set_waves(WaveformPackageToBeExecute[0], WaveformPackageToBeExecute[1], WaveformPackageToBeExecute[2], WaveformPackageToBeExecute[3])
self.adcollector.collected_data.connect(self.ProcessData)
self.adcollector.run()
#------------------Camera saving-------------------
if _camera_isUsed == True:
self.HamamatsuCam.isSaving = True
tif_name = os.path.join(self.scansavedirectory, 'Round'+str(self.RoundWaveformIndex[0])+'_Coords'+str(self.currentCoordsSeq)+'_R'+str(self.CurrentPosIndex[0])+'C'+str(self.CurrentPosIndex[1])+'_Cam_'+'Zpos'+str(self.ZStackOrder)+'.tif')
self.HamamatsuCam.StopStreaming(saving_dir = tif_name)
# Make sure that the saving process is finished.
while self.HamamatsuCam.isSaving == True:
print('Camera saving...')
time.sleep(0.5)
time.sleep(1)
time.sleep(0.6) # Wait for receiving data to be done.
time.sleep(0.3)
print('*************************************************************************************************************************')
#%%
"""
# ==========================================================================================================================================================
# Finalizing
# ==========================================================================================================================================================
"""
# Switch off laser
if len(self.RoundQueueDict['InsightEvents']) != 0:
self.watchdog_flag = False
time.sleep(0.5)
while True:
try:
self.Laserinstance.Close_TunableBeamShutter()
break
except:
time.sleep(1)
time.sleep(0.5)
self.Laserinstance.SaveVariables()
while True:
try:
self.Laserinstance.Turn_Off_PumpLaser()
break
except:
time.sleep(1)
# Disconnect camera
if self._use_camera == True:
self.HamamatsuCam.Exit()
# Disconnect focus motor
try:
PIMotor.CloseMotorConnection(self.pi_device_instance.pidevice)
print('Objective motor disconnected.')
except:
pass
#%%
#--------------------------------------------------------------Reconstruct and save images from 1D recorded array.--------------------------------------------------------------------------------
def ProcessData(self, data_waveformreceived):
print('ZStackOrder is:'+str(self.ZStackOrder)+'numis_'+str(self.ZStackNum))
self.adcollector.save_as_binary(self.scansavedirectory)
self.channel_number = len(data_waveformreceived)
if self.channel_number == 1:
if 'Vp' in self.readinchan:
pass
elif 'Ip' in self.readinchan:
pass
elif 'PMT' in self.readinchan: # repeatnum, PMT_data_index_array, averagenum, ScanArrayXnum
self.data_collected_0 = data_waveformreceived[0]*-1
self.data_collected_0 = self.data_collected_0[0:len(self.data_collected_0)-1]
print(len(self.data_collected_0))
for imageSequence in range(self.repeatnum):
try:
self.PMT_image_reconstructed_array = self.data_collected_0[np.where(self.PMT_data_index_array == imageSequence+1)]
# if imageSequence == int(self.repeatnum)-1:
# self.PMT_image_reconstructed_array = self.PMT_image_reconstructed_array[0:len(self.PMT_image_reconstructed_array)-1] # Extra one sample at the end.
# print(self.PMT_image_reconstructed_array.shape)
Dataholder_average = np.mean(self.PMT_image_reconstructed_array.reshape(self.averagenum, -1), axis=0)
# print(Dataholder_average.shape)
Value_yPixels = int(self.lenSample_1/self.ScanArrayXnum)
self.PMT_image_reconstructed = np.reshape(Dataholder_average, (Value_yPixels, self.ScanArrayXnum))
self.PMT_image_reconstructed = self.PMT_image_reconstructed[:, 50:550] # Crop size based on: M:\tnw\ist\do\projects\Neurophotonics\Brinkslab\Data\Xin\2019-12-30 2p beads area test 4um
# self.PMT_image_reconstructed = self.PMT_image_reconstructed[:, 70:326] # for 256*256 images
#---------------------------------------------For multiple images in one z pos, Stack the arrays into a 3d array--------------------------------------------------------------------------
if imageSequence == 0:
self.PMT_image_reconstructed_stack = self.PMT_image_reconstructed[np.newaxis, :, :] # Turns into 3d array
else:
self.PMT_image_reconstructed_stack = np.concatenate((self.PMT_image_reconstructed_stack, self.PMT_image_reconstructed[np.newaxis, :, :]), axis=0)
print(self.PMT_image_reconstructed_stack.shape)
#---------------------------------------------Calculate the z max projection-----------------------------------------------------------------------
if self.repeatnum == 1: # Consider one repeat image situlation
if self.ZStackNum > 1:
if self.ZStackOrder == 1:
self.PMT_image_maxprojection_stack = self.PMT_image_reconstructed[np.newaxis, :, :]
else:
self.PMT_image_maxprojection_stack = np.concatenate((self.PMT_image_maxprojection_stack, self.PMT_image_reconstructed[np.newaxis, :, :]), axis=0)
else:
self.PMT_image_maxprojection_stack = self.PMT_image_reconstructed[np.newaxis, :, :]
# Save the max projection image
if self.ZStackOrder == self.ZStackNum:
self.PMT_image_maxprojection = np.max(self.PMT_image_maxprojection_stack, axis=0)
LocalimgZprojection = Image.fromarray(self.PMT_image_maxprojection) #generate an image object
LocalimgZprojection.save(os.path.join(self.scansavedirectory, 'Round'+str(self.RoundWaveformIndex[0])+'_Coords'+str(self.currentCoordsSeq)+'_R'+str(self.CurrentPosIndex[0])+'C'+str(self.CurrentPosIndex[1])+'_PMT_'+str(imageSequence)+'Zmax'+'.tif')) #save as tif
#
Localimg = Image.fromarray(self.PMT_image_reconstructed) #generate an image object
Localimg.save(os.path.join(self.scansavedirectory, 'Round'+str(self.RoundWaveformIndex[0])+'_Coords'+str(self.currentCoordsSeq)+'_R'+str(self.CurrentPosIndex[0])+'C'+str(self.CurrentPosIndex[1])+'_PMT_'+str(imageSequence)+'Zpos'+str(self.ZStackOrder)+'.tif')) #save as tif
plt.figure()
plt.imshow(self.PMT_image_reconstructed, cmap = plt.cm.gray) # For reconstructed image we pull out the first layer, getting 2d img.
plt.show()
except:
print('No.{} image failed to generate.'.format(imageSequence))
elif self.channel_number == 2:
if 'PMT' not in self.readinchan:
pass
elif 'PMT' in self.readinchan:
self.data_collected_0 = data_waveformreceived[0]*-1
self.data_collected_0 = self.data_collected_0[0:len(self.data_collected_0)-1]
print(len(self.data_collected_0))
for imageSequence in range(self.repeatnum):
try:
self.PMT_image_reconstructed_array = self.data_collected_0[np.where(self.PMT_data_index_array == imageSequence+1)]
if imageSequence == int(self.repeatnum)-1:
self.PMT_image_reconstructed_array = self.PMT_image_reconstructed_array[0:len(self.PMT_image_reconstructed_array)-1] # Extra one sample at the end.
Dataholder_average = np.mean(self.PMT_image_reconstructed_array.reshape(self.averagenum, -1), axis=0)
Value_yPixels = int(self.lenSample_1/self.ScanArrayXnum)
self.PMT_image_reconstructed = np.reshape(Dataholder_average, (Value_yPixels, self.ScanArrayXnum))
self.PMT_image_reconstructed = self.PMT_image_reconstructed[:, 50:550]
# Stack the arrays into a 3d array
if imageSequence == 0:
self.PMT_image_reconstructed_stack = self.PMT_image_reconstructed[np.newaxis, :, :]
else:
self.PMT_image_reconstructed_stack = np.concatenate((self.PMT_image_reconstructed_stack, self.PMT_image_reconstructed[np.newaxis, :, :]), axis=0)
#---------------------------------------------Calculate the z max projection-----------------------------------------------------------------------
if self.repeatnum == 1: # Consider one repeat image situlation
if self.ZStackNum > 1:
if self.ZStackOrder == 1:
self.PMT_image_maxprojection_stack = self.PMT_image_reconstructed[np.newaxis, :, :]
else:
self.PMT_image_maxprojection_stack = np.concatenate((self.PMT_image_maxprojection_stack, self.PMT_image_reconstructed[np.newaxis, :, :]), axis=0)
else:
self.PMT_image_maxprojection_stack = self.PMT_image_reconstructed[np.newaxis, :, :]
# Save the max projection image
if self.ZStackOrder == self.ZStackNum:
self.PMT_image_maxprojection = np.max(self.PMT_image_maxprojection_stack, axis=0)
LocalimgZprojection = Image.fromarray(self.PMT_image_maxprojection) #generate an image object
LocalimgZprojection.save(os.path.join(self.scansavedirectory, 'Round'+str(self.RoundWaveformIndex[0])+'_Coords'+str(self.currentCoordsSeq)+'_R'+str(self.CurrentPosIndex[0])+'C'+str(self.CurrentPosIndex[1])+'_PMT_'+str(imageSequence)+'Zmax'+'.tif')) #save as tif
Localimg = Image.fromarray(self.PMT_image_reconstructed) #generate an image object
Localimg.save(os.path.join(self.scansavedirectory, 'Round'+str(self.RoundWaveformIndex[0])+'_Coords'+str(self.currentCoordsSeq)+'_R'+str(self.CurrentPosIndex[0])+'C'+str(self.CurrentPosIndex[1])+'_PMT_'+str(imageSequence)+'Zpos'+str(self.ZStackOrder)+'.tif')) #save as tif
plt.figure()
plt.imshow(self.PMT_image_reconstructed, cmap = plt.cm.gray)
plt.show()
except:
print('No.{} image failed to generate.'.format(imageSequence))
print('ProcessData executed.')
#----------------------------------------------------------------WatchDog for laser----------------------------------------------------------------------------------
def Status_watchdog(self, querygap):
while True:
if self.watchdog_flag == True:
self.Status_list = self.Laserinstance.QueryStatus()
time.sleep(querygap)
else:
print('Watchdog stopped')
time.sleep(querygap)
class ScanningExecutionThread(QThread):
ScanningResult = pyqtSignal(np.ndarray, np.ndarray, object, object) #The signal for the measurement, we can connect to this signal
#%%
def __init__(self, RoundQueueDict, RoundCoordsDict, GeneralSettingDict, *args, **kwargs):
super().__init__(*args, **kwargs)
self.RoundQueueDict = RoundQueueDict
self.RoundCoordsDict = RoundCoordsDict
self.GeneralSettingDict = GeneralSettingDict
self.Status_list = None
self.ludlStage = LudlStage("COM12")
self.watchdog_flag = True
self.clock_source = 'DAQ' # Should be set by GUI.
self.scansavedirectory = self.GeneralSettingDict['savedirectory']
self.meshgridnumber = int(self.GeneralSettingDict['Meshgrid'])
self.wavelength_offset = 0 # An offset of 0.002 mm is observed between 900 and 1280 nm.
#%%
def run(self):
"""~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Connect devices.
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"""
"""
# =============================================================================
# connect the Objective motor
# =============================================================================
"""
print('----------------------Starting to connect the Objective motor-------------------------')
self.pi_device_instance = PIMotor()
print('Objective motor connected.')
self.errornum = 0
self.init_focus_position = self.pi_device_instance.pidevice.qPOS(self.pi_device_instance.pidevice.axes)['1']
print("init_focus_position : {}".format(self.init_focus_position))
"""
# =============================================================================
# connect the Hmamatsu camera
# =============================================================================
"""
self._use_camera = False
# Check if camera is used.
for key in self.RoundQueueDict:
if "RoundPackage_" in key:
for waveform_and_cam_key in self.RoundQueueDict[key][1]:
if "CameraPackage_" in waveform_and_cam_key:
if len(self.RoundQueueDict[key][1][waveform_and_cam_key]) != 0:
self._use_camera = True
if self._use_camera:
print('Connecting camera...')
self.HamamatsuCam = CamActuator()
self.HamamatsuCam.initializeCamera()
else:
print('No camera involved.')
"""
# =============================================================================
# connect the Insight X3
# =============================================================================
"""
if len(self.RoundQueueDict['InsightEvents']) != 0:
self.Laserinstance = InsightX3('COM11')
try:
# querygap = 1.1
# self.Laserinstance.SetWatchdogTimer(0) # Disable the laser watchdog!
# Status_watchdog_thread = threading.Thread(target = self.Status_watchdog, args=[querygap], daemon=True)
# Status_watchdog_thread.start()
# time.sleep(1)
#-------------Initialize laser--------------
self.watchdog_flag = False
time.sleep(0.5)
warmupstatus = 0
while int(warmupstatus) != 100:
warmupstatus = self.Laserinstance.QueryWarmupTime()
time.sleep(0.6)
self.watchdog_flag = True
time.sleep(0.5)
except:
print('Laser not connected.')
# If turn on the laser shutter in the beginning
if 'Shutter_Open' in self.GeneralSettingDict['StartUpEvents']:
time.sleep(0.5)
self.Laserinstance.Open_TunableBeamShutter()
time.sleep(0.5)
"""
# =============================================================================
# Initialize ML
# =============================================================================
"""
self._use_ML = False
# Check if machine learning segmentation is used.
for key in self.RoundQueueDict:
if "RoundPackage_" in key:
for photocycle_key in self.RoundQueueDict[key][2]:
if "PhotocyclePackage_" in photocycle_key:
if len(self.RoundQueueDict[key][2][photocycle_key]) != 0:
self._use_ML = True
if self._use_ML:
from ImageAnalysis.ImageProcessing_MaskRCNN import ProcessImageML
self.Predictor = ProcessImageML()
print("ML loaded.")
#%%
"""~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Execution
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"""
GridSequence = 0
TotalGridNumber = self.meshgridnumber**2
for EachGrid in range(TotalGridNumber):
"""
#------------------------------------------------------------------------------
#:::::::::::::::::::::::::::::::: AT EACH GRID ::::::::::::::::::::::::::::::::
#------------------------------------------------------------------------------
"""
self.Grid_index = EachGrid
"""
# =============================================================================
# For each small repeat unit in the scanning meshgrid
# =============================================================================
"""
time.sleep(0.5)
for EachRound in range(int(len(self.RoundQueueDict)/2-1)): # EachRound is the round sequence number starting from 0, while the actual number used in dictionary is 1.
"""
#-------------------------------------------------------------------------------
#:::::::::::::::::::::::::::::::: AT EACH ROUND ::::::::::::::::::::::::::::::::
#-------------------------------------------------------------------------------
"""
print ('----------------------------------------------------------------------------')
print('Below is Grid {}, Round {}.'.format(EachGrid, EachRound+1)) # EachRound+1 is the corresponding round number when setting the dictionary starting from round 1.
"""
# =============================================================================
# Execute Insight event at the beginning of each round
# =============================================================================
"""
self.laser_init(EachRound)
"""
# =============================================================================
# Execute filter event at the beginning of each round
# =============================================================================
"""
self.filters_init(EachRound)
"""
# =============================================================================
# Generate focus position list at the beginning of each round
# =============================================================================
"""
if EachRound == 0:
ZStackinfor = self.GeneralSettingDict['FocusStackInfoDict']['RoundPackage_{}'.format(EachRound+1)]
ZStackNum = int(ZStackinfor[ZStackinfor.index('Focus')+5])
ZStackStep = float(ZStackinfor[ZStackinfor.index('Being')+5:len(ZStackinfor)])
# Generate position list.
ZStacklinspaceStart = self.init_focus_position - (math.floor(ZStackNum/2)) * ZStackStep
ZStacklinspaceEnd = self.init_focus_position + (ZStackNum - math.floor(ZStackNum/2)-1) * ZStackStep
self.ZStackPosList = np.linspace(ZStacklinspaceStart, ZStacklinspaceEnd, num = ZStackNum)
self.currentCoordsSeq = 0
#%%
#-------------Unpack infor for stage move.
CoordsNum = len(self.RoundCoordsDict['CoordsPackage_{}'.format(EachRound+1)])
for EachCoord in range(CoordsNum):
"""
#------------------------------------------------------------------------------------
#:::::::::::::::::::::::::::::::: AT EACH COORDINATE ::::::::::::::::::::::::::::::::
#------------------------------------------------------------------------------------
"""
self.error_massage = None
self.currentCoordsSeq += 1
"""
# =============================================================================
# Stage movement
# =============================================================================
"""
self.coord_array = self.RoundCoordsDict['CoordsPackage_{}'.format(EachRound+1)][EachCoord]
# Offset coordinate row value for each well.
ScanningGridOffset_Row = int(EachGrid % self.meshgridnumber) * (self.GeneralSettingDict['StageGridOffset'])
# Offset coordinate colunm value for each well.
ScanningGridOffset_Col = int(EachGrid/self.meshgridnumber) * (self.GeneralSettingDict['StageGridOffset'])
RowIndex = self.coord_array['row'] + ScanningGridOffset_Row
ColumnIndex = self.coord_array['col'] + ScanningGridOffset_Col
try:
# for _ in range(2): # Repeat twice
# self.ludlStage.moveAbs(RowIndex,ColumnIndex) # Row/Column indexs of np.array are opposite of stage row-col indexs.
# time.sleep(1)
self.ludlStage.moveAbs(RowIndex,ColumnIndex) # Row/Column indexs of np.array are opposite of stage row-col indexs.
time.sleep(1.2)
except:
self.error_massage = 'Fail_MoveStage'
self.errornum += 1
print('Stage move failed! Error number: {}'.format(int(self.errornum)))
time.sleep(0.2)
"""
# =============================================================================
# Focus position
# Unpack the focus stack information, conduct auto-focusing if set.
# =============================================================================
"""
# Here also generate the ZStackPosList.
self.ZStackNum = self.unpack_focus_stack(EachGrid, EachRound, EachCoord)
self.stack_focus_degree_list = []
print('*******************************************Round {}. Current index: {}.**************************************************'.format\
(EachRound+1, [RowIndex,ColumnIndex]))
#------------------Move to Z stack focus-------------------
for EachZStackPos in range(self.ZStackNum):
"""
#------------------------------------------------------------------------------------
#:::::::::::::::::::::::::::::::: AT EACH ZSTACK ::::::::::::::::::::::::::::::::::::
#------------------------------------------------------------------------------------
"""
print('--------------------------------------------Stack {}--------------------------------------------------'.format(EachZStackPos+1))
if self.ZStackNum > 1:
self.ZStackOrder = int(EachZStackPos +1) # Here the first one is 1, not starting from 0.
self.FocusPos = self.ZStackPosList[EachZStackPos]# + self.wavelength_offset
# Add the focus degree of previous image to the list.
# For stack of 3 only.
if EachZStackPos > 0:
try:
self.stack_focus_degree_list.append(self.FocusDegree_img_reconstructed)
except:
# FocusDegree_img_reconstructed is not generated with camera imaging.
pass
print('stack_focus_degree_list is {}'.format(self.stack_focus_degree_list))
#-----Suppose now it's the 3rd stack position-----
if len(self.stack_focus_degree_list) == 2:
if self.stack_focus_degree_list[-1] < self.stack_focus_degree_list[-2]:
self.FocusPos = self.ZStackPosList[0] - (self.ZStackPosList[1] - self.ZStackPosList[0])/2
print('Focus degree decreasing, run the other direction.')
#-------------------------------------------------
print('Target focus pos: {}'.format(self.FocusPos))
self.pi_device_instance.move(self.FocusPos)
# self.auto_focus_positionInStack = self.pi_device_instance.pidevice.qPOS(self.pi_device_instance.pidevice.axes)
# print("Current position: {:.4f}".format(self.auto_focus_positionInStack['1']))
time.sleep(0.3)
else:
self.ZStackOrder = 1
self.FocusPos = self.init_focus_position
"""
# =============================================================================
# Execute waveform packages
# =============================================================================
"""
self.Waveform_sequence_Num = int(len(self.RoundQueueDict['RoundPackage_{}'.format(EachRound+1)][0]))
#------------For waveforms in each coordinate----------
for EachWaveform in range(self.Waveform_sequence_Num):
"""
# =============================================================================
# For photo-cycle
# =============================================================================
"""
# Get the photo cycle information
PhotocyclePackageToBeExecute = self.RoundQueueDict['RoundPackage_{}'.format(EachRound+1)][2] \
["PhotocyclePackage_{}".format(EachWaveform+1)]
# See if in this waveform sequence photo cycle is involved.
# PhotocyclePackageToBeExecute is {} if not configured.
if len(PhotocyclePackageToBeExecute) > 0:
# Load the previous acquired camera image
self.cam_tif_name = r"M:\tnw\ist\do\projects\Neurophotonics\Brinkslab\Data\Octoscope\2020-8-13 Screening Archon1 library V5 and 6\V6\Round2_Coords181_R19800C0_PMT_0Zmax.tif"
previous_cam_img = imread(self.cam_tif_name)
img_width = previous_cam_img.shape[1]
img_height = previous_cam_img.shape[0]
# Get the segmentation of full image.
fig, ax = plt.subplots()
MLresults = self.Predictor.DetectionOnImage(previous_cam_img, axis = ax)
ROI_number = len(MLresults["scores"])
print('roi number: {}'.format(ROI_number))
for each_ROI in range(ROI_number):
# if MLresults['class_ids'][each_ROI] == 3:
ROIlist = MLresults['rois'][each_ROI]
print(ROIlist)
# np array's column([1]) is the width of image, and is the row in stage coordinates.
ROI_center_width = int(ROIlist[1] + (ROIlist[3] - ROIlist[1])/2)
print('ROI_center_width '.format(ROIlist))
ROI_center_height = int(ROIlist[0] + (ROIlist[2] + ROIlist[0])/2)
print('ROI_center_height '.format(ROIlist))
cam_stage_transform_factor = 1.135
stage_move_col = (int((img_width)/2) - ROI_center_width) * cam_stage_transform_factor
print(stage_move_col)
stage_move_row = (int((img_height)/2) - ROI_center_height) * cam_stage_transform_factor
print(stage_move_row)
# Move cell of interest to the center of field of view
self.ludlStage.moveRel(xRel = stage_move_row, yRel= stage_move_col)
time.sleep(1)
# Move the cell back
self.ludlStage.moveRel(xRel = -1*stage_move_row, yRel= -1*stage_move_col)
time.sleep(1)
#--------------------------------------------------
"""
# =============================================================================
# Execute pre-set operations at EACH COORDINATE.
# =============================================================================
"""
self.inidividual_coordinate_operation(EachRound, EachWaveform, RowIndex, ColumnIndex)
time.sleep(0.6) # Wait for receiving data to be done.
time.sleep(0.5)
print('*************************************************************************************************************************')
#%%
"""~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Disconnect devices.
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"""
# Switch off laser
if len(self.RoundQueueDict['InsightEvents']) != 0:
self.watchdog_flag = False
time.sleep(0.5)
self.Laserinstance.Close_TunableBeamShutter()
time.sleep(0.5)
self.Laserinstance.SaveVariables()
self.Laserinstance.Turn_Off_PumpLaser()
# Disconnect camera
if self._use_camera == True:
self.HamamatsuCam.Exit()
# Disconnect focus motor
try:
self.pi_device_instance.CloseMotorConnection()
print('Objective motor disconnected.')
except:
pass
#%%
def laser_init(self, EachRound):
"""
Execute Insight event at the beginning of each round
Parameters
----------
EachRound : int
Round index.
Returns
-------
None.
"""
#-Unpack infor for Insight X3. In the list, the first one is for shutter event and the second one is for wavelength event.
InsightX3EventIndexList = [i for i,x in enumerate(self.RoundQueueDict['InsightEvents']) if 'Round_{}'.format(EachRound+1) in x]
if len(InsightX3EventIndexList) == 1:
print(InsightX3EventIndexList)
InsightText = self.RoundQueueDict['InsightEvents'][InsightX3EventIndexList[0]]
if 'Shutter_Open' in InsightText:
self.watchdog_flag = False
time.sleep(0.5)
self.Laserinstance.Open_TunableBeamShutter()
time.sleep(0.5)
print('Laser shutter open.')
self.watchdog_flag = True
time.sleep(0.5)
elif 'Shutter_Close' in InsightText:
self.watchdog_flag = False
time.sleep(0.5)
self.Laserinstance.Close_TunableBeamShutter()
time.sleep(0.5)
print('Laser shutter closed.')
self.watchdog_flag = True
time.sleep(0.5)
elif 'WavelengthTo' in InsightText:
self.watchdog_flag = False
time.sleep(0.5)
TargetWavelen = int(InsightText[InsightText.index('To_')+3:len(InsightText)])
if TargetWavelen == 1280:
self.wavelength_offset = -0.002 # give an offset if wavelength goes to 1280.
elif TargetWavelen == 900:
self.wavelength_offset = 0
self.Laserinstance.SetWavelength(TargetWavelen)
time.sleep(5)
self.watchdog_flag = True
time.sleep(0.5)
elif len(InsightX3EventIndexList) == 2:
InsightText_wl = self.RoundQueueDict['InsightEvents'][InsightX3EventIndexList[1]]
InsightText_st = self.RoundQueueDict['InsightEvents'][InsightX3EventIndexList[0]]
if 'WavelengthTo' in InsightText_wl and 'Shutter_Open' in InsightText_st:
self.watchdog_flag = False
time.sleep(0.5)
TargetWavelen = int(InsightText_wl[InsightText_wl.index('To_')+3:len(InsightText_wl)])
self.Laserinstance.SetWavelength(TargetWavelen)
if TargetWavelen == 1280:
self.wavelength_offset = -0.002 # give an offset if wavelength goes to 1280.
elif TargetWavelen == 900:
self.wavelength_offset = 0
time.sleep(5)
self.Laserinstance.Open_TunableBeamShutter()
print('Laser shutter open.')
self.watchdog_flag = True
time.sleep(0.5)
elif 'WavelengthTo' in InsightText_wl and 'Shutter_Close' in InsightText_st:
self.watchdog_flag = False
time.sleep(0.5)
TargetWavelen = int(InsightText_wl[InsightText_wl.index('To_')+3:len(InsightText_wl)])
self.Laserinstance.SetWavelength(TargetWavelen)
if TargetWavelen == 1280:
self.wavelength_offset = -0.002 # give an offset if wavelength goes to 1280.
elif TargetWavelen == 900:
self.wavelength_offset = 0
time.sleep(5)
self.Laserinstance.Close_TunableBeamShutter()
time.sleep(1)
print('Laser shutter closed.')
self.watchdog_flag = True
time.sleep(0.5)
time.sleep(2)
def filters_init(self, EachRound):
"""
Execute filter event at the beginning of each round.
Parameters
----------
EachRound : int
Round index.
Returns
-------
None.
"""
#-Unpack infor for filter event. In the list, the first one is for ND filter and the second one is for emission filter.
FilterEventIndexList = [i for i,x in enumerate(self.RoundQueueDict['FilterEvents']) if 'Round_{}'.format(EachRound+1) in x]
if len(FilterEventIndexList) > 0:
NDposText = self.RoundQueueDict['FilterEvents'][FilterEventIndexList[0]]
NDnumber = NDposText[NDposText.index('ToPos_')+6:len(NDposText)]
EMposText = self.RoundQueueDict['FilterEvents'][FilterEventIndexList[1]]
EMprotein = EMposText[EMposText.index('ToPos_')+6:len(EMposText)]
# "COM9" for filter 1 port, which has ND values from 0 to 3.
# "COM7" for filter 2 port, which has ND values from 0 to 0.5.
if NDnumber == '0':
ND_filter1_Pos = 0
ND_filter2_Pos = 0
elif NDnumber == '1':
ND_filter1_Pos = 1
ND_filter2_Pos = 0
elif NDnumber == '2':
ND_filter1_Pos = 2
ND_filter2_Pos = 0
elif NDnumber == '2.3':
ND_filter1_Pos = 2
ND_filter2_Pos = 2
elif NDnumber == '2.5':
ND_filter1_Pos = 2
ND_filter2_Pos = 3
elif NDnumber == '0.5':
ND_filter1_Pos = 0
ND_filter2_Pos = 3
elif NDnumber == '0.3':
ND_filter1_Pos = 0
ND_filter2_Pos = 2
if EMprotein == 'Arch':
EM_filter_Pos = 0
elif EMprotein == 'eGFP' or EMprotein == 'Citrine':
EM_filter_Pos = 1
# Execution
if ND_filter1_Pos != None and ND_filter2_Pos != None:
#Move filter 1
self.filter1 = ELL9Filter("COM9")
self.filter1.moveToPosition(ND_filter1_Pos)
time.sleep(1)
#Move filter 2
self.filter2 = ELL9Filter("COM7")
self.filter2.moveToPosition(ND_filter2_Pos)
time.sleep(1)
if EM_filter_Pos != None:
self.filter3 = ELL9Filter("COM15")
self.filter3.moveToPosition(EM_filter_Pos)
time.sleep(1)
def unpack_focus_stack(self, EachGrid, EachRound, EachCoord):
"""
Unpack the focus stack information.
Determine focus position either from pre-set numbers of by auto-focusing.
Parameters
----------
EachGrid : int
Current grid index.
EachRound : int
Current round index.
EachCoord : int
Current coordinate index.
Returns
-------
ZStackNum : int
Number of focus positions in stack.
ZStackPosList : list
List of focus stack positions for objective to go to.
"""
ZStackinfor = self.GeneralSettingDict['FocusStackInfoDict']['RoundPackage_{}'.format(EachRound+1)]
ZStackNum = int(ZStackinfor[ZStackinfor.index('Focus')+5])
ZStackStep = float(ZStackinfor[ZStackinfor.index('Being')+5:len(ZStackinfor)])
# If manual focus correction applies, unpact the target focus infor.
if len(self.GeneralSettingDict['FocusCorrectionMatrixDict']) > 0:
FocusPosArray = self.GeneralSettingDict['FocusCorrectionMatrixDict']['RoundPackage_{}_Grid_{}'.format(EachRound+1, EachGrid)]
FocusPosArray = FocusPosArray.flatten('F')
FocusPos_fromCorrection = FocusPosArray[EachCoord]
ZStacklinspaceStart = FocusPos_fromCorrection - (math.floor(ZStackNum/2))* ZStackStep
ZStacklinspaceEnd = FocusPos_fromCorrection + (ZStackNum - math.floor(ZStackNum/2)-1) * ZStackStep
# With auto-focus correction
else:
# If go for auto-focus at this coordinate
auto_focus_flag = self.coord_array['auto_focus_flag']
# auto_focus_flag = False
print('focus_position {}'.format(self.coord_array['focus_position']))
#-----------------------Auto focus---------------------------------
if auto_focus_flag == "yes":
if self.coord_array['focus_position'] == -1.:
instance_FocusFinder = FocusFinder(motor_handle = self.pi_device_instance)
print("--------------Start auto-focusing-----------------")
# instance_FocusFinder.total_step_number = 7
# instance_FocusFinder.init_step_size = 0.013
# self.auto_focus_position = instance_FocusFinder.gaussian_fit()
self.auto_focus_position = instance_FocusFinder.bisection()
relative_move_coords = [[1550,0],[0,1550],[1550,1550]]
trial_num = 0
while self.auto_focus_position == False: # If there's no cell in FOV
if trial_num <= 2:
print('No cells found. move to next pos.')
# Move to next position in real scanning coordinates.
self.ludlStage.moveRel(relative_move_coords[trial_num][0],relative_move_coords[trial_num][1])
time.sleep(1)
print('Now stage pos is {}'.format(self.ludlStage.getPos()))
self.auto_focus_position = instance_FocusFinder.bisection()
# Move back
self.ludlStage.moveRel(-1*relative_move_coords[trial_num][0],-1*relative_move_coords[trial_num][1])
trial_num += 1
else:
print('No cells in neighbouring area.')
self.auto_focus_position = self.ZStackPosList[int(len(self.ZStackPosList)/2)]
break
print("--------------End of auto-focusing----------------")
time.sleep(1)
# Record the position, try to write it in the NEXT round dict.
try:
self.RoundCoordsDict['CoordsPackage_{}'.format(EachRound + 2)][EachCoord]['focus_position'] = self.auto_focus_position
except:# If it's already the last round, skip.
pass
# Generate position list.
ZStacklinspaceStart = self.auto_focus_position - (math.floor(ZStackNum/2)) * ZStackStep
ZStacklinspaceEnd = self.auto_focus_position + (ZStackNum - math.floor(ZStackNum/2)-1) * ZStackStep
else: # If there's already position from last round, move to it.
self.previous_auto_focus_position = self.RoundCoordsDict['CoordsPackage_{}'.format(EachRound+1)][EachCoord]['focus_position']
try:
# Record the position, try to write it in the NEXT round dict.
self.RoundCoordsDict['CoordsPackage_{}'.format(EachRound + 2)][EachCoord]['focus_position'] = self.previous_auto_focus_position
except:
pass
# Generate position list.
ZStacklinspaceStart = self.previous_auto_focus_position - (math.floor(ZStackNum/2)) * ZStackStep
ZStacklinspaceEnd = self.previous_auto_focus_position + (ZStackNum - math.floor(ZStackNum/2)-1) * ZStackStep
# self.previous_auto_focus_position = self.auto_focus_position
# print("=====================Move to last recorded position=================")
# self.pi_device_instance.move(self.previous_auto_focus_position)
# time.sleep(0.2)
# instance_FocusFinder = FocusFinder(motor_handle = self.pi_device_instance)
# print("--------------Start auto-focusing-----------------")
# self.auto_focus_position = instance_FocusFinder.bisection()
# try:
# if self.auto_focus_position[0] == False: # If there's no cell in FOV
# # Skip? mid_position = [False, self.current_pos]
# self.auto_focus_position = self.auto_focus_position[1]
# except:
# pass
# print("--------------End of auto-focusing----------------")
# time.sleep(1)
# # Record the position, try to write it in the NEXT round dict.
# try:
# self.RoundCoordsDict['CoordsPackage_{}'.format(EachRound + 2)][EachCoord]['focus_position'] = self.auto_focus_position
# except:# If it's already the last round, skip.
# pass
# Generate the position list, for none-auto-focus coordinates they will use the same list variable.
self.ZStackPosList = np.linspace(ZStacklinspaceStart, ZStacklinspaceEnd, num = ZStackNum)
print('ZStackPos is : {}'.format(self.ZStackPosList))
#------------------------------------------------------------------
# If not auto focus, use the same list variable self.ZStackPosList.
else:
pass
return ZStackNum
def inidividual_coordinate_operation(self, EachRound, EachWaveform, RowIndex, ColumnIndex):
"""
Execute pre-set operations at each coordinate.
Parameters
----------
EachRound : int
Current round index.
EachWaveform : int
Current waveform package index.
RowIndex : int
Current sample stage row index.
ColumnIndex : int
Current sample stage row index.
Returns
-------
None.
"""
# Extract information
WaveformPackageToBeExecute = self.RoundQueueDict['RoundPackage_{}'.format(EachRound+1)][0]['WaveformPackage_{}'.format(EachWaveform+1)]
CameraPackageToBeExecute = self.RoundQueueDict['RoundPackage_{}'.format(EachRound+1)][1]["CameraPackage_{}".format(EachWaveform+1)]
WaveformPackageGalvoInfor = self.RoundQueueDict['GalvoInforPackage_{}'.format(EachRound+1)]['GalvoInfor_{}'.format(EachWaveform+1)]
self.readinchan = WaveformPackageToBeExecute[3]
self.RoundWaveformIndex = [EachRound+1, EachWaveform+1] # first is current round number, second is current waveform package number.
self.CurrentPosIndex = [RowIndex, ColumnIndex]
#----------------Camera operations-----------------
_camera_isUsed = False
if CameraPackageToBeExecute != {}: # if camera operations are configured
_camera_isUsed = True
CamSettigList = CameraPackageToBeExecute["Settings"]
self.HamamatsuCam.StartStreaming(BufferNumber = CameraPackageToBeExecute["Buffer_number"],
trigger_source = CamSettigList[CamSettigList.index("trigger_source")+1],
exposure_time = CamSettigList[CamSettigList.index("exposure_time")+1],
trigger_active = CamSettigList[CamSettigList.index("trigger_active")+1],
subarray_hsize = CamSettigList[CamSettigList.index("subarray_hsize")+1],
subarray_vsize = CamSettigList[CamSettigList.index("subarray_vsize")+1],
subarray_hpos = CamSettigList[CamSettigList.index("subarray_hpos")+1],
subarray_vpos = CamSettigList[CamSettigList.index("subarray_vpos")+1])
# HamamatsuCam starts another thread to pull out frames from buffer.
# Make sure that the camera is prepared before waveform execution.
# while self.HamamatsuCam.isStreaming == False:
# print('Waiting for camera...')
# time.sleep(0.5)
time.sleep(1)
print('Now start waveforms')
#----------------Waveforms operations--------------
if WaveformPackageGalvoInfor != 'NoGalvo': # Unpack the information of galvo scanning.
self.readinchan = WaveformPackageGalvoInfor[0]
self.repeatnum = WaveformPackageGalvoInfor[1]
self.PMT_data_index_array = WaveformPackageGalvoInfor[2]
self.averagenum = WaveformPackageGalvoInfor[3]
self.lenSample_1 = WaveformPackageGalvoInfor[4]
self.ScanArrayXnum = WaveformPackageGalvoInfor[5]
self.adcollector = DAQmission()
# self.adcollector.collected_data.connect(self.ProcessData)
self.adcollector.runWaveforms(clock_source = self.clock_source, sampling_rate = WaveformPackageToBeExecute[0],
analog_signals = WaveformPackageToBeExecute[1], digital_signals = WaveformPackageToBeExecute[2],
readin_channels = WaveformPackageToBeExecute[3])
self.adcollector.save_as_binary(self.scansavedirectory)
self.recorded_raw_data = self.adcollector.get_raw_data()
self.Process_raw_data()
#------------------Camera saving-------------------
if _camera_isUsed == True:
self.HamamatsuCam.isSaving = True
img_text = "_Cam_"+str(self.RoundWaveformIndex[1])+"_Zpos" + str(self.ZStackOrder)
self.cam_tif_name = self.generate_tif_name(extra_text = img_text)
self.HamamatsuCam.StopStreaming(saving_dir = self.cam_tif_name)
# Make sure that the saving process is finished.
while self.HamamatsuCam.isSaving == True:
print('Camera saving...')
time.sleep(0.5)
time.sleep(1)
time.sleep(0.5)
def Process_raw_data(self):
self.channel_number = len(self.recorded_raw_data)
self.data_collected_0 = self.recorded_raw_data[0][0:len(self.recorded_raw_data[0])-1]*-1
print(len(self.data_collected_0))
if self.channel_number == 1:
if 'Vp' in self.readinchan:
pass
elif 'Ip' in self.readinchan:
pass
elif 'PMT' in self.readinchan: # repeatnum, PMT_data_index_array, averagenum, ScanArrayXnum
# Reconstruct the image from np array and save it.
self.PMT_image_processing()
elif self.channel_number == 2:
if 'PMT' not in self.readinchan:
pass
elif 'PMT' in self.readinchan:
self.PMT_image_processing()
print('ProcessData executed.')
# def ProcessData(self, data_waveformreceived):
# print('ZStackOrder is:'+str(self.ZStackOrder)+'numis_'+str(self.ZStackNum))
# self.adcollector.save_as_binary(self.scansavedirectory)
# self.channel_number = len(data_waveformreceived)
# self.data_collected_0 = data_waveformreceived[0]*-1
# self.data_collected_0 = self.data_collected_0[0:len(self.data_collected_0)-1]
# print(len(self.data_collected_0))
# if self.channel_number == 1:
# if 'Vp' in self.readinchan:
# pass
# elif 'Ip' in self.readinchan:
# pass
# elif 'PMT' in self.readinchan: # repeatnum, PMT_data_index_array, averagenum, ScanArrayXnum
# # Reconstruct the image from np array and save it.
# self.PMT_image_processing()
# elif self.channel_number == 2:
# if 'PMT' not in self.readinchan:
# pass
# elif 'PMT' in self.readinchan:
# self.PMT_image_processing()
# print('ProcessData executed.')
def PMT_image_processing(self):
"""
Reconstruct the image from np array and save it.
Returns
-------
None.
"""
for imageSequence in range(self.repeatnum):
try:
self.PMT_image_reconstructed_array = self.data_collected_0[np.where(self.PMT_data_index_array == imageSequence+1)]
Dataholder_average = np.mean(self.PMT_image_reconstructed_array.reshape(self.averagenum, -1), axis=0)
Value_yPixels = int(self.lenSample_1/self.ScanArrayXnum)
self.PMT_image_reconstructed = np.reshape(Dataholder_average, (Value_yPixels, self.ScanArrayXnum))
self.PMT_image_reconstructed = self.PMT_image_reconstructed[:, 50:550] # Crop size based on: M:\tnw\ist\do\projects\Neurophotonics\Brinkslab\Data\Xin\2019-12-30 2p beads area test 4um
# self.PMT_image_reconstructed = self.PMT_image_reconstructed[:, 70:326] # for 256*256 images
# Evaluate the focus degree of re-constructed image.
self.FocusDegree_img_reconstructed = ProcessImage.local_entropy(self.PMT_image_reconstructed.astype('float32'))
print('FocusDegree_img_reconstructed is {}'.format(self.FocusDegree_img_reconstructed))
# Save the individual file.
with skimtiff.TiffWriter(os.path.join(self.scansavedirectory, 'Round'+str(self.RoundWaveformIndex[0]) + '_Grid' + str(self.Grid_index) +'_Coords'+str(self.currentCoordsSeq)+'_R'+str(self.CurrentPosIndex[0])+'C'+str(self.CurrentPosIndex[1])+'_PMT_'+str(imageSequence)+'Zpos'+str(self.ZStackOrder)+'.tif'), imagej = True) as tif:
tif.save(self.PMT_image_reconstructed.astype('float32'), compress=0, metadata = {"FocusPos: " : str(self.FocusPos)})
plt.figure()
plt.imshow(self.PMT_image_reconstructed, cmap = plt.cm.gray) # For reconstructed image we pull out the first layer, getting 2d img.
plt.show()
#---------------------------------------------For multiple images in one z pos, Stack the arrays into a 3d array--------------------------------------------------------------------------
# if imageSequence == 0:
# self.PMT_image_reconstructed_stack = self.PMT_image_reconstructed[np.newaxis, :, :] # Turns into 3d array
# else:
# self.PMT_image_reconstructed_stack = np.concatenate((self.PMT_image_reconstructed_stack, self.PMT_image_reconstructed[np.newaxis, :, :]), axis=0)
# print(self.PMT_image_reconstructed_stack.shape)
#---------------------------------------------Calculate the z max projection-----------------------------------------------------------------------
if self.repeatnum == 1: # Consider one repeat image situlation
if self.ZStackNum > 1:
if self.ZStackOrder == 1:
self.PMT_image_maxprojection_stack = self.PMT_image_reconstructed[np.newaxis, :, :]
else:
self.PMT_image_maxprojection_stack = np.concatenate((self.PMT_image_maxprojection_stack, self.PMT_image_reconstructed[np.newaxis, :, :]), axis=0)
else:
self.PMT_image_maxprojection_stack = self.PMT_image_reconstructed[np.newaxis, :, :]
# Save the max projection image
if self.ZStackOrder == self.ZStackNum:
self.PMT_image_maxprojection = np.max(self.PMT_image_maxprojection_stack, axis=0)
# Save the zmax file.
with skimtiff.TiffWriter(os.path.join(self.scansavedirectory, 'Round'+str(self.RoundWaveformIndex[0])+ '_Grid' + str(self.Grid_index) + '_Coords'+str(self.currentCoordsSeq)+'_R'+str(self.CurrentPosIndex[0])+'C'+str(self.CurrentPosIndex[1])+'_PMT_'+str(imageSequence)+'Zmax'+'.tif'), imagej = True) as tif:
tif.save(self.PMT_image_maxprojection.astype('float32'), compress=0, metadata = {"FocusPos: " : str(self.FocusPos)})
except:
print('No.{} image failed to generate.'.format(imageSequence))
def generate_tif_name(self, extra_text = "_"):
tif_name = os.path.join(self.scansavedirectory, 'Round'+str(self.RoundWaveformIndex[0])+'_Coords'+str(self.currentCoordsSeq)+ \
'_R'+str(self.CurrentPosIndex[0])+'C'+str(self.CurrentPosIndex[1])+ extra_text +'.tif')
return tif_name
#----------------------------------------------------------------WatchDog for laser----------------------------------------------------------------------------------
def Status_watchdog(self, querygap):
while True:
if self.watchdog_flag == True:
self.Status_list = self.Laserinstance.QueryStatus()
time.sleep(querygap)
else:
print('Watchdog stopped')
time.sleep(querygap)
