Python ResultsTable.incrementCounter方法代码示例

# 需要导入模块: from ij.measure import ResultsTable [as 别名]
# 或者: from ij.measure.ResultsTable import incrementCounter [as 别名]
def writeCSV(filePath, results, header):
	""" Write a table as an csv file """
	rt = ResultsTable()
	for i in range(len(results[1])): 
		rt.incrementCounter()
		for j in range(len(results)):
			rt.addValue(str(header[j]), results[j][i])
	rt.show("Results")
	rt.saveAs(filePath); 

# 需要导入模块: from ij.measure import ResultsTable [as 别名]
# 或者: from ij.measure.ResultsTable import incrementCounter [as 别名]

#.........这里部分代码省略.........
			image = self.imp
			mode3D = self.checkbox3D.isSelected()
			if image is not None and cell is not None and mode3D:
				roi = cell.slices[selectedSlice].roi
				if (image.z - 1 != selectedSlice):
					image.z = selectedSlice + 1				
				image.setRoi(roi, True)
			if self.preview is not None and not mode3D:
				self.preview.setRoi(cell.roi, True)

	def updateSlice(self, event):
		if self.checkbox3D.isSelected():
			self.updateSlice3D(self.imp)
		else:
			self.updateSlice2D(self.preview)

	def updateSlice3D(self, imp):
		selectedCell = self.cellList.selectedIndex
		selectedSlice = self.sliceList.selectedIndex
		if selectedCell >= 0 and selectedSlice >= 0 and imp is not None:
			cell = self.cells[selectedCell]
			impRoi = imp.getRoi()
			if cell is not None and impRoi is not None:
				index = selectedSlice + 1
				roi = ShapeRoi(impRoi, position = index)
				cell.mode3D = True
				cell.name = "Cell %i (3D)" % cell.n
				cell.slices[selectedSlice].roi = roi
				if (index + 1 <= len(cell.slices)):
					imp.z = index + 1			
			self.cellList.repaint(self.cellList.getCellBounds(selectedCell, selectedCell))
			self.sliceList.repaint(self.sliceList.getCellBounds(selectedSlice, selectedSlice))

	def updateSlice2D(self, imp):
		selectedCell = self.cellList.selectedIndex
		if selectedCell >= 0 and imp is not None:
			cell = self.cells[selectedCell]
			impRoi = imp.getRoi()
			if cell is not None and impRoi is not None:
				roi = ShapeRoi(impRoi, position = 1)
				cell.mode3D = False
				cell.name = "Cell %i (2D)" % cell.n
				cell.roi = roi	
			self.cellList.repaint(self.cellList.getCellBounds(selectedCell, selectedCell))
	
	def imageOpened(self, imp):
		pass

	def imageClosed(self, imp):
		pass

	def imageUpdated(self, imp):
		if self.checkbox3D.isSelected():
			if imp is not None:
				selectedCell = self.cellList.selectedIndex
				selectedSlice = imp.z - 1
			if imp == self.imp and selectedSlice != self.sliceList.selectedIndex:
				self.sliceList.selectedIndex = selectedSlice

	def doneSelecting(self, event):
		oluts = self.imp.luts
		luts = []
		channels = []
		for c, method in enumerate(self.methods):
			if method != "None":
				luts.append(oluts[c])
				channels.append(c)
		for cell in self.cells:
			manders = self.getManders(self.imp, cell)
			if manders is not None:
				chimps, thrimps, thrs, raws, thrds = manders
				index = self.cells.index(cell) + 1
				title = "Cell_%i-" % index + self.imp.title
				self.saveMultichannelImage(title, chimps, oluts)
				title = "Cell_%i_thrd-" % index + self.imp.title
				self.saveMultichannelImage(title, thrimps, luts)
				self.results.incrementCounter()
				row = self.results.getCounter() - 1
				for i, thr in enumerate(thrs):
					if thr is not None:
						self.results.setValue("Threshold %i" % (i + 1), row, int(thr))
				for i, pair in enumerate(self.pairs):
					self.results.setValue("%i-%i M1 raw" % pair, row, float(raws[i].m1))
					self.results.setValue("%i-%i M2 raw" % pair, row, float(raws[i].m2))
					self.results.setValue("%i-%i M1 thrd" % pair, row, float(thrds[i].m1))
					self.results.setValue("%i-%i M2 thrd" % pair, row, float(thrds[i].m2))
		self.closeImage()
		if not self.processNextFile():
			print "All done - happy analysis!"
			self.results.show("Manders collocalization results")
			self.exit()

	def windowClosing(self, e):
		print "Closing plugin - BYE!!!"
		self.exit()

	def exit(self):
		ImagePlus.removeImageListener(self)
		self.closeImage()
		self.closeMainWindow()

# 需要导入模块: from ij.measure import ResultsTable [as 别名]
# 或者: from ij.measure.ResultsTable import incrementCounter [as 别名]
		ipch3 = impch3.getImageStack().getProcessor(int(zA[i]) + 1)
		dotRoi = OvalRoi(int(yA[i] - xyoffset), int(xA[i] - xyoffset), thdist, thdist)	
		ipch2.setRoi(dotRoi)
  		#stats = IS.getStatistics(ip, options, imp.getCalibration())
  		stats = IS.getStatistics(ipch2, options, cal)
		ipch3.setRoi(dotRoi)
  		statsch3 = IS.getStatistics(ipch3, options, cal)
  		print "dot", i
  		print "...ch2 TotalInt ", stats.area * stats.mean
  		print "...ch2 Area     ", stats.area
  		print "...ch2 mean     ", stats.mean
  		print ".."  		
  		print "...ch3 TotalInt ", statsch3.area * statsch3.mean
  		print "...ch3 Area     ", statsch3.area
  		print "...ch3 mean     ", statsch3.mean
	 	rt.incrementCounter()
	 	rt.setValue("DotID", ct, i)
	 	rt.setValue("DotX", ct, yA[i])
	 	rt.setValue("DotY", ct, xA[i])
	 	rt.setValue("DotZ", ct, zA[i])	 	
		rt.setValue("Ch2_TotalIntensity", ct, stats.area * stats.mean)
		rt.setValue("Ch2_MeanIntensity", ct, stats.mean)
		rt.setValue("Ch3_TotalIntensity", ct, statsch3.area * statsch3.mean)
		rt.setValue("Ch3_meanIntensity", ct, statsch3.mean)
		ct += 1
rt.show("Dot Intensity")


#AREA, AREA_FRACTION, CENTER_OF_MASS, CENTROID, CIRCULARITY, ELLIPSE, FERET, 
#INTEGRATED_DENSITY, INVERT_Y, KURTOSIS, LABELS, LIMIT, MAX_STANDARDS, MEAN, 
#MEDIAN, MIN_MAX, MODE, PERIMETER, RECT, SCIENTIFIC_NOTATION, SHAPE_DESCRIPTORS, 

# 需要导入模块: from ij.measure import ResultsTable [as 别名]
# 或者: from ij.measure.ResultsTable import incrementCounter [as 别名]

#.........这里部分代码省略.........
		if targetfile:
			filenums.append( int(targetfile.group(1)) )

	# sort the file numbers
	sorted_filenums = sorted(filenums)

	# make a file stats string
	file_stats_str = file_stem + '\n' + str(fi.width) +'x' + str(fi.height) + 'x' + \
		str(len(sorted_filenums)) +' ('+str(bit_depth)+'-bit)\n' + file_timestamp


	# now open a dialog to let the user set options
	dlg = GenericDialog("Load Octopus Stream (v"+__version__+")")
	dlg.addMessage(file_stats_str)
	dlg.addStringField("Title: ", file_stem)
	dlg.addNumericField("Start: ", 1, 0);
	dlg.addNumericField("End: ", len(sorted_filenums), 0)
	dlg.addCheckbox("Open headers", True)
	dlg.addCheckbox("Contiguous stream?", False)
	dlg.addCheckbox("8-bit unsigned", bit_depth==8)
	dlg.showDialog()

	# if we cancel the dialog, exit here
	if dlg.wasCanceled():
		return

	# set some params
	file_title = dlg.getNextString()
	file_start = dlg.getNextNumber()
	file_end = dlg.getNextNumber()
	DISPLAY_HEADER = bool( dlg.getNextBoolean() )

	# check the ranges
	if file_start > file_end: 
		file_start, file_end = file_end, file_start
	if file_start < 1: 
		file_start = 1
	if file_end > len(sorted_filenums): 
		file_end = len(sorted_filenums) 

	# now set these to the actual file numbers in the stream
	file_start = sorted_filenums[int(file_start)-1]
	file_end = sorted_filenums[int(file_end)-1]

	files_to_open = [n for n in sorted_filenums if n>=file_start and n<=file_end]

	# if we've got too many, truncate the list
	if (len(files_to_open) * fi.nImages * fi.width * fi.height) > (MAX_FRAMES_TO_IMPORT*512*512):
		dlg = GenericDialog("Warning")
		dlg.addMessage("This may use a lot of memory. Continue?")
		dlg.showDialog()
		if dlg.wasCanceled(): return False

	IJ.log( "Opening file: " + op.getDirectory() + op.getFileName() )
	IJ.log( file_stats_str + "\nFile range: " + str(files_to_open[0]) + \
		"-" + str(files_to_open[-1]) +"\n" )

	# make a results table for the metadata
	# NOTE: horrible looping at the moment, but works
	if DISPLAY_HEADER:
		rt = ResultsTable()

	# ok now we can put the files together into the stack
	for i in files_to_open:

		# open the original .dat file and get the stack
		fi.fileName = get_Octopus_filename( op.getDirectory(), file_stem, i)
		
		if os.path.isfile( fi.fileName ):
			fo = FileOpener(fi)
			imp = fo.open(False).getStack() 
	
			# put the slices into the stack
			for im_slice in xrange( imp.getSize() ):
				ip = imp.getProcessor( im_slice+1 )
				if bit_depth == 8:
					bi = ip.getBufferedImage()
				else:
					bi = ip.get16BitBufferedImage() 
				stack.addSlice( file_title,  ip )


			if DISPLAY_HEADER:
				header = get_Octopus_header(op.getDirectory(), file_stem, i)
				for n in xrange(len(header['N'])):
					rt.incrementCounter()
					for k in header.keys():
						rt.addValue(k, parse_header( header[k][n] ) )

		else:
			break

	# done!
	output = ImagePlus('Octopus ('+file_stem+')', stack)
	output.show()

	if DISPLAY_HEADER:
		rt.show("Octopus header metadata")

	return True

# 需要导入模块: from ij.measure import ResultsTable [as 别名]
# 或者: from ij.measure.ResultsTable import incrementCounter [as 别名]
		
		thr1, thrimp1 = calculateThreshold(imp1, roi, methods[0])
		thr2, thrimp2 = calculateThreshold(imp2, roi, methods[1])
		
		cursor = TwinCursor(img1.randomAccess(), img2.randomAccess(), Views.iterable(mask).localizingCursor())
		rtype = img1.randomAccess().get().createVariable()
		raw = manders.calculateMandersCorrelation(cursor, rtype)
		rthr1 = rtype.copy()
		rthr2 = rtype.copy()
		rthr1.set(thr1)
		rthr2.set(thr2)
		cursor.reset()
		thrd = manders.calculateMandersCorrelation(cursor, rthr1, rthr2, ThresholdMode.Above)
		print "Results are: %f %f %f %f" % (raw.m1, raw.m2, thrd.m1, thrd.m2)

		results.incrementCounter()
		rowno = results.getCounter() - 1
		results.setValue("Cell", rowno, int(rowno))
		results.setValue("Threshold 1", rowno, int(thr1))
		results.setValue("Threshold 2", rowno, int(thr2))
		results.setValue("M1 raw", rowno, float(raw.m1))
		results.setValue("M2 raw", rowno, float(raw.m2))
		results.setValue("M1 thrd", rowno, float(thrd.m1))
		results.setValue("M2 thrd", rowno, float(thrd.m2))
		
		thrimp = RGBStackMerge.mergeChannels([thrimp1, thrimp2], False)
		saver = FileSaver(thrimp)
		saver.saveAsTiffStack(outputDir + "Cell_%i-" % results.getCounter() + title + ".tif")
		thrimp.close()

results.show("Colocalization results")

# 需要导入模块: from ij.measure import ResultsTable [as 别名]
# 或者: from ij.measure.ResultsTable import incrementCounter [as 别名]

#.........这里部分代码省略.........
    branch_lengths = []
    summed_lengths = []
    graphs = skel_result.getGraph()

    for graph in graphs:
        summed_length = 0.0
        edges = graph.getEdges()
        for edge in edges:
            length = edge.getLength()
            branch_lengths.append(length)
            summed_length += length
        summed_lengths.append(summed_length)

    output_parameters["branch length mean"] = eztables.statistical.average(branch_lengths)
    output_parameters["branch length median"] = eztables.statistical.median(branch_lengths)
    output_parameters["branch length stdevp"] = eztables.statistical.stdevp(branch_lengths)

    output_parameters["summed branch lengths mean"] = eztables.statistical.average(summed_lengths)
    output_parameters["summed branch lengths median"] = eztables.statistical.median(summed_lengths)
    output_parameters["summed branch lengths stdevp"] = eztables.statistical.stdevp(summed_lengths)

    branches = list(skel_result.getBranches())
    output_parameters["network branches mean"] = eztables.statistical.average(branches)
    output_parameters["network branches median"] = eztables.statistical.median(branches)
    output_parameters["network branches stdevp"] = eztables.statistical.stdevp(branches)

    # Create/append results to a ResultsTable...
    status.showStatus("Display results...")
    if "Mito Morphology" in list(WindowManager.getNonImageTitles()):
        rt = WindowManager.getWindow("Mito Morphology").getTextPanel().getOrCreateResultsTable()
    else:
        rt = ResultsTable()

    rt.incrementCounter()
    for key in output_order:
        rt.addValue(key, str(output_parameters[key]))

    # Add user comments intelligently
    if user_comment != None and user_comment != "":
        if "=" in user_comment:
            comments = user_comment.split(",")
            for comment in comments:
                rt.addValue(comment.split("=")[0], comment.split("=")[1])
        else:
            rt.addValue("Comment", user_comment)

    rt.show("Mito Morphology")

	# Create overlays on the original ImagePlus and display them if 2D...
    if imp.getNSlices() == 1:
        status.showStatus("Generate overlays...")
        IJ.run(skeleton, "Green", "")
        IJ.run(binary, "Magenta", "")

        skeleton_ROI = ImageRoi(0,0,skeleton.getProcessor())
        skeleton_ROI.setZeroTransparent(True)
        skeleton_ROI.setOpacity(1.0)
        binary_ROI = ImageRoi(0,0,binary.getProcessor())
        binary_ROI.setZeroTransparent(True)
        binary_ROI.setOpacity(0.25)

        overlay = Overlay()
        overlay.add(binary_ROI)
        overlay.add(skeleton_ROI)

        imp.setOverlay(overlay)

# 需要导入模块: from ij.measure import ResultsTable [as 别名]
# 或者: from ij.measure.ResultsTable import incrementCounter [as 别名]
	resultsTable = ResultsTable()
	resultsTable.showRowNumbers(False)

	for i in range(0, len(results)):
		if options['oneShot']:
			localBackground = options['localBackground']
			seedRadius = options['seedRadius']
			gaussXY = options['gaussXY']
			gaussZ = options['gaussZ']
		else:
			localBackground = parameters[i]['localBackground']
			seedRadius = parameters[i]['seedRadius']
			gaussXY = parameters[i]['gaussXY']
			gaussZ = parameters[i]['gaussZ']
		
		resultsTable.incrementCounter()
		resultsTable.addValue("Threshold", localBackground)
		resultsTable.addValue("Seed radius", seedRadius)
		resultsTable.addValue("GXY", gaussXY)
		resultsTable.addValue("GZ", gaussZ)
		resultsTable.addValue("TOTAL", results[i]['all'])
		resultsTable.addValue("0-250", results[i]['0'])
		resultsTable.addValue("251-500", results[i]['250'])
		resultsTable.addValue("501-750", results[i]['500'])
		resultsTable.addValue("751-1000", results[i]['750'])
		resultsTable.addValue("1001-1500", results[i]['1000'])
		resultsTable.addValue(">1501", results[i]['1500'])
		resultsTable.addValue("Skipped", results[i]['edge'])

	resultsTable.save(options['outputDir'] + options['outputFile'])

# 需要导入模块: from ij.measure import ResultsTable [as 别名]
# 或者: from ij.measure.ResultsTable import incrementCounter [as 别名]

#.........这里部分代码省略.........
    t_rows = rt.getColumnAsDoubles(t_col)

    # Assess n of data points and extract unique path ids
    n_rows = len(track_id_rows)
    row_indices = range(n_rows)
    track_ids = set(track_id_rows)
    n_tracks = len(track_ids)
    log("Table has %g rows" % n_rows)
    log("Table has %g tracks" % n_tracks)

    log("Parsing tracks...")
    for track_id in track_ids:


        for row, next_row in zip(row_indices, row_indices[1:]):

            if track_id_rows[row] != track_id:
                continue

            if not isNumber(angle_rows[row]):
                 rt.setValue("FLAG", row, "NA")
                 continue

            lower_bound = max(0, row - BOUT_WINDOW + 1)
            upper_bound = min(n_rows-1, row + BOUT_WINDOW)
            win_d2p = []
            for _ in range(lower_bound, upper_bound):
                win_d2p.append(d2p_rows[row])

            if sum(win_d2p) <= MIN_D2P * len(win_d2p):
                rt.setValue("FLAG", row, 0)

            else:
                current_angle = angle_rows[row]
                next_angle = angle_rows[next_row]
                current_delta = delta_rows[row]

                flag = -1 if current_angle < 0 else 1
                delta_change = (abs(current_delta) > 90)
                same_sign = ((current_angle<0) == (next_angle<0))
                if delta_change and not same_sign:
                    flag *= -1

                rt.setValue("FLAG", row, flag)
                if next_row == n_rows - 1:
                    rt.setValue("FLAG", next_row, flag)

    if rt.save(table_file.getAbsolutePath()):
        log("Processed table successfully saved (file overwritten)")
    else:
        log("Could not override input file. Displaying it...")
        rt.show(table_file.name)


    log("Creating onset table...")
    onset_rt = RT()
    onset_rt.showRowNumbers(False)

    frame_int = DEF_FRAME_INTERVAL
    if "table" in frame_rate_detection:
        frame_int = getFrameIntervalFromTable(row_indices, track_id_rows, t_rows)
    elif "image" in frame_rate_detection:
        frame_int = getFrameIntervalFromImage(image_file.getAbsolutePath())
    else:
        log("Using default frame rate")

    for track_id in track_ids:

        for prev_row, row in zip(row_indices, row_indices[1:]):

            if not track_id in (track_id_rows[prev_row], track_id_rows[row]):
                continue

            flag = rt.getValue("FLAG", row)
            if not isNumber(flag):
                continue

            flag = int(flag)
            if flag == 0:
                continue

            if flag == 1 or flag == -1:
                srow = onset_rt.getCounter()
                onset_rt.incrementCounter()
                onset_rt.setValue("TID", srow, track_id)
                from_frame = int(t_rows[prev_row]/frame_int) + 1
                to_frame = int(t_rows[row]/frame_int) + 1
                onset_rt.setValue("First disp. [t]", srow,
                    "%s to %s" % (t_rows[prev_row], t_rows[row]))
                onset_rt.setValue("First disp. [frames]", srow,
                    "%s to %s" % (from_frame, to_frame))
                onset_rt.setValue("ManualTag", srow, "")
                break

    out_path = suffixed_path(table_file.getAbsolutePath(), "ManualTagging")
    if onset_rt.save(out_path):
        log("Summary table successfully saved: %s" % out_path)
    else:
        log("File not saved... Displaying onset table")
        onset_rt.show("Onsets %s" % table_file.name)