def process_data(conn,image,rectangles,coords):
    """
        Get the coordinates in each roi, write to a file
        and append to dataset
    """    
    message = ""
    for i,rect in enumerate(rectangles):
        locs_list = []
        for c in range(coords.shape[0]):
            locs = get_coords_in_roi(coords[c,:,:],rect)
            locs_list.append(locs)
        file_name = "coords_in_roi_%s.csv" % i
        try:
            f = open(file_name,'w')
            for r in range(locs.shape[0]):
                row = locs[r,:]
                f.write(','.join([str(c) for c in row])+'\n')
        finally:
            f.close()

        new_file_ann, faMessage = script_util.createLinkFileAnnotation(
            conn, file_name, image, output="wrote coords file",
            mimetype="text/csv", desc=None)
        message += faMessage
    return message
def saveImages(conn, scriptParams):
    dataType = scriptParams['Data_Type']
    formatType = scriptParams['Format']    
    dirpath='tmp' #Use Root tmp dir so gets cleaned up
    unique_folder = 'Exportfiles' + str(time.time()) #random
    imagepath = path.join(dirpath, unique_folder)
    imagepath = path.sep + imagepath + path.sep
    print "Imagepath=", imagepath
    # Get the images or datasets
    message = ""
    objects, logMessage = script_utils.getObjects(conn, scriptParams)
    message += logMessage
    parent = objects[0] #??
    if not objects:
        return None, message
    
    if dataType == 'Dataset':
        images = []
        for ds in objects:
            images.extend(list(ds.listChildren()))
        if not images:
            message += "No image found in dataset(s)"
            return None, message
    else:
        images = objects

    imageIds = [i.getId() for i in images]
    print "Selected %d images for processing" % len(imageIds)
    #Create download folder
    if len(imageIds) > 0:
        mkdir(imagepath)
        #chdir(imagepath)
    imagefilenames =[]
    for iId in imageIds:
        img = conn.getObject("Image", iId)
        
        if img is not None:
            z = img.getSizeZ() / 2
            t = 0 
            saveimage = img.renderImage(z,t) # returns PIL Image jpeg
            imageFilename = getImageName(img, formatType.lower(), imagepath)
            imagefilenames.append(imageFilename)            
            message += saveAs(saveimage,imageFilename,formatType)
        
    #zip output files
    zipfilename = path.join(dirpath, unique_folder)
    zipfilename = path.sep + zipfilename + ".zip"
    message += createZipFile(zipfilename, imagefilenames)

    mimetype = 'application/zip'
    outputDisplayName = "Image export zip"
    namespace = NSCREATED + "/QBI/Utils/ExportImage"
    
    fileAnnotation, annMessage = script_utils.createLinkFileAnnotation(
        conn, zipfilename, parent, output=outputDisplayName, ns=namespace,
        mimetype=mimetype)
    message += annMessage
    return fileAnnotation, message         
def process_data(conn, image, file_type, rectangles, localisations, scalex, scaley, cIndex=0):
    """
    Get the coordinates in the ROI and form histograms of x and y coordinates
    
    @param conn:       the BlitzGateWay connection
    @param image:      the image containing the ROIs to process. the image were the localisations file is attached.
    @param file_type:  what type of dataset are we dealing with (see FILE_TYPES global) 
    @param rectangles: the ROIs scaled to nm
    @param coords:     the xy coordinates of localisations being histogrammed
    @param scalex:     multiplier to pixel size to determine bin size in histograms
    @param scaley:     multiplier to pixel size to determine bin size in histograms
    """
    message = ""
    pixels = image.getPrimaryPixels()
    # note pixel sizes (if available) to set for the new images
    physX = pixels.getPhysicalSizeX() * 1000.0
    physY = pixels.getPhysicalSizeY() * 1000.0
    x = file_type["x_col"]
    y = file_type["y_col"]
    for i, rect in enumerate(rectangles):
        binsx = ceil((rect[2] / physX) / scalex)
        binsy = ceil((rect[3] / physY) / scaley)
        print "binsx,binsy:", binsx, binsy
        rangex = rect[2]
        rangey = rect[3]
        print "rangex,rangey:", rangex, rangey

        locs_df = get_coords_in_roi(localisations[cIndex], rect, file_type)
        histx, edgesx = np.histogram(locs_df.loc[:, [x]].values, bins=binsx)
        histy, edgesy = np.histogram(locs_df.loc[:, [y]].values, bins=binsy)
        hist_dataX = np.zeros((histx.shape[0], 1))
        hist_dataX[:, 0] = histx
        hist_dataY = np.zeros((histy.shape[0], 1))
        hist_dataY[:, 0] = histy

        centersx = 0.5 * (edgesx[1:] + edgesx[:-1])
        centersy = 0.5 * (edgesy[1:] + edgesy[:-1])
        centers_dataX = np.zeros((centersx.shape[0], 1))
        centers_dataX[:, 0] = centersx
        centers_dataY = np.zeros((centersy.shape[0], 1))
        centers_dataY[:, 0] = centersy

        file_name = "scatter_hist_in_roi_%s.csv" % i
        with file(file_name, "w") as outfile:
            outfile.write("# scatter histogram data in x-direction\n")
            datax = np.concatenate((centers_dataX, hist_dataX), axis=1)
            np.savetxt(outfile, datax, fmt="%-7.2f", delimiter=",", newline="\n")
            outfile.write("# scatter histogram data in y-direction\n")
            datay = np.concatenate((centers_dataY, hist_dataY), axis=1)
            np.savetxt(outfile, datay, fmt="%-7.2f", delimiter=",", newline="\n")

        new_file_ann, faMessage = script_util.createLinkFileAnnotation(
            conn, file_name, image, output="wrote coords file for ROI %s" % i, mimetype="text/csv", desc=None
        )
        message += faMessage
    return message
def process_data(conn, image, file_type, rectangles, coords):
    """
        Get the coordinates in each roi, write to a file
        and append to dataset
    """
    message = ""
    sizeT = image.getSizeT()
    frame = FILE_TYPES[file_type]['frame']
    if sizeT > 1:
        desc = image.getDescription()

        if desc:
            start = desc.index('Start')
            stop = desc.index('Stop')
            starts = desc[start + 7:stop - 3]
            starts = [int(s) for s in starts.split(',')]
            stops = desc[stop + 6:len(desc) - 1]
            stops = [int(s) for s in stops.split(',')]
    else:
        starts = [1]
        stops = [coords[0][frame].max]

    def coord_gen():
        for rect in rectangles:
            for c in range(len(coords)):
                locs_df = coords[c]
                for t in range(sizeT):
                    coords_in_frames = locs_df[(locs_df[frame] >= starts[t])
                                               & (locs_df[frame] <= stops[t])]
                    yield get_coords_in_roi(coords_in_frames, rect[:-1],
                                            file_type)

    coord_generator = coord_gen()
    for rect in rectangles:
        rid = rect[-1]
        for c in range(len(coords)):
            for t in range(sizeT):
                file_name = "Coords_ROI%s_Time%s_Channel%s.csv" % (rid, t, c)
                with open(file_name, 'w') as f:
                    locs_df = coord_generator.next()
                    locs_df.to_csv(f,
                                   sep=',',
                                   float_format='%8.2f',
                                   index=False,
                                   encoding='utf-8')

                new_file_ann, faMessage = script_util.createLinkFileAnnotation(
                    conn,
                    file_name,
                    image,
                    output="wrote coords file for ROI %s" % rid,
                    mimetype="text/csv",
                    desc=None)
                message += faMessage
    return message
def attach_results(conn,ann,image,data,sizeC,sizeR):
    
    file_name = "ripleyl_plot_" + ann.getFile().getName()[:-4] + '.csv' 
    with file(file_name, 'w') as outfile:
        outfile.write('# ripley data for %s channels and %s ROIs: \n' % (sizeC, sizeR ))
        data.to_csv(outfile,sep=',',float_format='%8.2f',index=False,encoding='utf-8')
    
    description = "Ripley L function data created from:\n  Image ID: %d Annotation_ID: %d"\
                    % (image.getId(),ann.getId())        
    new_file_ann, faMessage = script_util.createLinkFileAnnotation(
        conn, file_name, image, output="Ripley L Plot csv (Excel) file",
        mimetype="text/csv", desc=description)     
    return new_file_ann
def process_data(conn,image,file_type,rectangles,coords):
    """
        Get the coordinates in each roi, write to a file
        and append to dataset
    """    
    message = ""
    sizeT = image.getSizeT()
    frame = FILE_TYPES[file_type]['frame']
    if sizeT > 1:
        desc = image.getDescription()
        
        if desc:
            start = desc.index('Start')
            stop = desc.index('Stop')
            starts = desc[start+7:stop-3]
            starts = [int(s) for s in starts.split(',')] 
            stops = desc[stop+6:len(desc)-1]
            stops = [int(s) for s in stops.split(',')]
    else:
        starts = [1]
        stops = [coords[0][frame].max]
        
    def coord_gen():
        for rect in rectangles:       
            for c in range(len(coords)):
                locs_df = coords[c]
                for t in range(sizeT):
                    coords_in_frames = locs_df[(locs_df[frame]>= starts[t]) & (locs_df[frame]<= stops[t])]
                    yield get_coords_in_roi(coords_in_frames,rect[:-1],file_type)

    coord_generator = coord_gen()
    for rect in rectangles:
        rid = rect[-1]
        for c in range(len(coords)):
            for t in range(sizeT):    
                file_name = "Coords_ROI%s_Time%s_Channel%s.csv" % (rid,t,c)
                with open(file_name,'w') as f:
                    locs_df = coord_generator.next()
                    locs_df.to_csv(f,sep=',',float_format='%8.2f',index=False,encoding='utf-8')
            
                new_file_ann, faMessage = script_util.createLinkFileAnnotation(
                    conn, file_name, image, output="wrote coords file for ROI %s" %rid,
                    mimetype="text/csv", desc=None)
                message += faMessage
    return message
def attach_results(conn, ann, image, data, sizeC, sizeR):

    file_name = "ripleyl_plot_" + ann.getFile().getName()[:-4] + '.csv'
    with file(file_name, 'w') as outfile:
        outfile.write('# ripley data for %s channels and %s ROIs: \n' %
                      (sizeC, sizeR))
        data.to_csv(outfile,
                    sep=',',
                    float_format='%8.2f',
                    index=False,
                    encoding='utf-8')

    description = "Ripley L function data created from:\n  Image ID: %d Annotation_ID: %d"\
                    % (image.getId(),ann.getId())
    new_file_ann, faMessage = script_util.createLinkFileAnnotation(
        conn,
        file_name,
        image,
        output="Ripley L Plot csv (Excel) file",
        mimetype="text/csv",
        desc=description)
    return new_file_ann
def processImages(conn, scriptParams):

    lineWidth = scriptParams['Line_Width']
    fileAnns = []
    message = ""

    # Get the images
    images, logMessage = scriptUtil.getObjects(conn, scriptParams)
    message += logMessage
    if not images:
        return None, message

    # Check for line and polyline ROIs and filter images list
    images = [image for image in images if
              image.getROICount(["Polyline", "Line"]) > 0]
    if not images:
        message += "No ROI containing line or polyline was found."
        return None, message

    for image in images:

        cNames = []
        colors = []
        for ch in image.getChannels():
            cNames.append(ch.getLabel())
            colors.append(ch.getColor().getRGB())

        sizeC = image.getSizeC()

        if 'Channels' in scriptParams:
            scriptParams['Channels'] = [i-1 for i in scriptParams['Channels']]
            # Convert user input from 1-based to 0-based
            for i in scriptParams['Channels']:
                print i, type(i)
        else:
            scriptParams['Channels'] = range(sizeC)

        # channelMinMax = []
        # for c in image.getChannels():
        #     minC = c.getWindowMin()
        #     maxC = c.getWindowMax()
        #     channelMinMax.append((minC, maxC))

        roiService = conn.getRoiService()
        result = roiService.findByImage(image.getId(), None)

        lines = []
        polylines = []

        for roi in result.rois:
            roiId = roi.getId().getValue()
            for s in roi.copyShapes():
                theZ = s.getTheZ() and s.getTheZ().getValue() or 0
                theT = s.getTheT() and s.getTheT().getValue() or 0
                # TODO: Add some filter of shapes. E.g. text? / 'lines' only
                # etc.
                if type(s) == omero.model.LineI:
                    x1 = s.getX1().getValue()
                    x2 = s.getX2().getValue()
                    y1 = s.getY1().getValue()
                    y2 = s.getY2().getValue()
                    lines.append({'id': roiId, 'theT': theT, 'theZ': theZ,
                                  'x1': x1, 'y1': y1, 'x2': x2, 'y2': y2})

                elif type(s) == omero.model.PolylineI:
                    points = pointsStringToXYlist(s.getPoints().getValue())
                    polylines.append({'id': roiId, 'theT': theT, 'theZ': theZ,
                                      'points': points})

        if len(lines) == 0 and len(polylines) == 0:
            print "Image: %s had no lines or polylines" % image.getId()
            continue

        # prepare column headers, including line-id if we are going to output
        # raw data.
        lineId = scriptParams['Sum_or_Average'] == 'Average, with raw data' \
            and 'Line, ' or ""
        colHeader = 'Image_ID, ROI_ID, Z, T, C, %sLine data %s of Line" \
            " Width %s\n' % (lineId, scriptParams['Sum_or_Average'],
                             scriptParams['Line_Width'])
        print 'colHeader', colHeader

        # prepare a csv file to write our data to...
        fileName = "Plot_Profile_%s.csv" % image.getId()
        try:
            f = open(fileName, 'w')
            f.write(colHeader)
            if len(lines) > 0:
                processLines(conn, scriptParams, image, lines, lineWidth, f)
            if len(polylines) > 0:
                processPolyLines(
                    conn, scriptParams, image, polylines, lineWidth, f)
        finally:
            f.close()

        fileAnn, faMessage = scriptUtil.createLinkFileAnnotation(
            conn, fileName, image, output="Line Plot csv (Excel) file",
            mimetype="text/csv", desc=None)
        if fileAnn:
            fileAnns.append(fileAnn)

    if not fileAnns:
        faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
            " more details"
    elif len(fileAnns) > 1:
        faMessage = "Created %s csv (Excel) files" % len(fileAnns)
    message += faMessage

    return fileAnns, message
Exemplo n.º 9
0
def makeThumbnailFigure(conn, scriptParams):
    """
    Makes the figure using the parameters in @scriptParams, attaches the
    figure to the parent Project/Dataset, and returns the file-annotation ID

    @ returns       Returns the id of the originalFileLink child. (ID object,
                    not value)
    """

    log("Thumbnail figure created by OMERO")
    log("")

    message = ""

    # Get the objects (images or datasets)
    objects, logMessage = scriptUtil.getObjects(conn, scriptParams)
    message += logMessage
    if not objects:
        return None, message

    # Get parent
    parent = None
    if "Parent_ID" in scriptParams and len(scriptParams["IDs"]) > 1:
        if scriptParams["Data_Type"] == "Image":
            parent = conn.getObject("Dataset", scriptParams["Parent_ID"])
        else:
            parent = conn.getObject("Project", scriptParams["Parent_ID"])

    if parent is None:
        parent = objects[0]  # Attach figure to the first object

    parentClass = parent.OMERO_CLASS
    log("Figure will be linked to %s%s: %s"
        % (parentClass[0].lower(), parentClass[1:], parent.getName()))

    tagIds = []
    if "Tag_IDs" in scriptParams:
        tagIds = scriptParams['Tag_IDs']
    if len(tagIds) == 0:
        tagIds = None

    showUntagged = False
    if (tagIds):
        showUntagged = scriptParams["Show_Untagged_Images"]

    thumbSize = scriptParams["Thumbnail_Size"]
    maxColumns = scriptParams["Max_Columns"]

    figHeight = 0
    figWidth = 0
    dsCanvases = []

    if scriptParams["Data_Type"] == "Dataset":
        for dataset in objects:
            log("Dataset: %s     ID: %d"
                % (dataset.getName(), dataset.getId()))
            images = list(dataset.listChildren())
            dsCanvas = paintDatasetCanvas(
                conn, images, dataset.getName(), tagIds, showUntagged,
                length=thumbSize, colCount=maxColumns)
            if dsCanvas is None:
                continue
            dsCanvases.append(dsCanvas)
            figHeight += dsCanvas.size[1]
            figWidth = max(figWidth, dsCanvas.size[0])
    else:
        imageCanvas = paintDatasetCanvas(
            conn, objects, "", tagIds,
            showUntagged, length=thumbSize, colCount=maxColumns)
        dsCanvases.append(imageCanvas)
        figHeight += imageCanvas.size[1]
        figWidth = max(figWidth, imageCanvas.size[0])

    if len(dsCanvases) == 0:
        message += "No figure created"
        return None, message

    figure = Image.new("RGB", (figWidth, figHeight), WHITE)
    y = 0
    for ds in dsCanvases:
        imgUtil.pasteImage(ds, figure, 0, y)
        y += ds.size[1]

    log("")
    figLegend = "\n".join(logLines)

    format = scriptParams["Format"]
    figureName = scriptParams["Figure_Name"]
    figureName = os.path.basename(figureName)
    output = "localfile"

    if format == 'PNG':
        output = output + ".png"
        figureName = figureName + ".png"
        figure.save(output, "PNG")
        mimetype = "image/png"
    elif format == 'TIFF':
        output = output + ".tiff"
        figureName = figureName + ".tiff"
        figure.save(output, "TIFF")
        mimetype = "image/tiff"
    else:
        output = output + ".jpg"
        figureName = figureName + ".jpg"
        figure.save(output)
        mimetype = "image/jpeg"

    namespace = NSCREATED + "/omero/figure_scripts/Thumbnail_Figure"
    fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
        conn, output, parent, output="Thumbnail figure", mimetype=mimetype,
        ns=namespace, desc=figLegend, origFilePathAndName=figureName)
    message += faMessage

    return fileAnnotation, message
def run_processing(conn,script_params):
    file_anns = []
    message = ""
    imageIds = []
    image_id = script_params['ImageID']
    imageIds.append(image_id)
    image = conn.getObject("Image",image_id)
    if not image:
        message = 'Could not find specified image'
        return message
        
    file_id = script_params['AnnotationID']
    ann = conn.getObject("Annotation",file_id)
    if not ann:
        message = 'Could not find specified annotation'
        return message
    
    radius = script_params['Radius']
    
    #other parameters
    if script_params['Convert_coordinates_to_nm']:
        cam_pix_size = script_params['Parent_Image_Pixel_Size']
    else:
        cam_pix_size = 1
    file_type = FILE_TYPES[script_params['File_Type']]
     
    path_to_ann = ann.getFile().getPath() + '/' + ann.getFile().getName()
    name,ext = os.path.splitext(path_to_ann)
    if ('txt' in ext) or ('csv' in ext):
        #get the path to the downloaded data
        path_to_data = download_data(ann)
        
        #get all the xy coords in that data
        locs = parse_sr_data(path_to_data,file_type,cam_pix_size)
        sizeC = len(locs)
        
        #get the rois to be processed
        rectangles = get_rectangles(conn,image_id)
        print 'rectanges:',rectangles
        
        #calculate local density
        locs_density = process_data(conn,image,file_type,sizeC,rectangles,locs,radius)
        
        #write the data to a csv
        file_name = "localisation_density_" + ann.getFile().getName()[:-4] + '.csv'
        with file(file_name, 'a') as outfile:
            outfile.write('# localisation density data for %s channels and %s ROIs: \n' % (sizeC, len(locs_density)))
            for r in range(len(locs_density)):
                outfile.write('# ROI %s\n' % rectangles[r][-1])
                outfile.write('Channel,%s,%s,Density within %s [nm]\n' % (file_type['x_col'],file_type['y_col'],str(radius)))
                density = locs_density[r][1:,:]
                np.savetxt(outfile, density, fmt='%-7.2f', delimiter=',', newline='\n')
                        
        new_file_ann, faMessage = script_util.createLinkFileAnnotation(
            conn, file_name, image, output="Wrote localisation density csv (Excel) file",
            mimetype="text/csv", desc=None)
        if new_file_ann:
            file_anns.append(new_file_ann)

        if not file_anns:
            faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
                " more details"
        elif len(file_anns) > 1:
            faMessage = "Created %s csv (Excel) files" % len(file_anns)
    else:
        message = 'file annotation must be txt or csv'
        return message
    # clean up
    delete_downloaded_data(ann)
    
    message += faMessage
    return message
Exemplo n.º 11
0
def processImages(conn, scriptParams):

    lineWidth = scriptParams['Line_Width']
    fileAnns = []
    message = ""

    # Get the images
    images, logMessage = scriptUtil.getObjects(conn, scriptParams)
    message += logMessage
    if not images:
        return None, message

    # Check for line and polyline ROIs and filter images list
    images = [
        image for image in images
        if image.getROICount(["Polyline", "Line"]) > 0
    ]
    if not images:
        message += "No ROI containing line or polyline was found."
        return None, message

    for image in images:

        cNames = []
        colors = []
        for ch in image.getChannels():
            cNames.append(ch.getLabel())
            colors.append(ch.getColor().getRGB())

        sizeC = image.getSizeC()

        if 'Channels' in scriptParams:
            scriptParams['Channels'] = [
                i - 1 for i in scriptParams['Channels']
            ]
            # Convert user input from 1-based to 0-based
            for i in scriptParams['Channels']:
                print i, type(i)
        else:
            scriptParams['Channels'] = range(sizeC)

        # channelMinMax = []
        # for c in image.getChannels():
        #     minC = c.getWindowMin()
        #     maxC = c.getWindowMax()
        #     channelMinMax.append((minC, maxC))

        roiService = conn.getRoiService()
        result = roiService.findByImage(image.getId(), None)

        lines = []
        polylines = []

        for roi in result.rois:
            roiId = roi.getId().getValue()
            for s in roi.copyShapes():
                theZ = s.getTheZ() and s.getTheZ().getValue() or 0
                theT = s.getTheT() and s.getTheT().getValue() or 0
                # TODO: Add some filter of shapes. E.g. text? / 'lines' only
                # etc.
                if type(s) == omero.model.LineI:
                    x1 = s.getX1().getValue()
                    x2 = s.getX2().getValue()
                    y1 = s.getY1().getValue()
                    y2 = s.getY2().getValue()
                    lines.append({
                        'id': roiId,
                        'theT': theT,
                        'theZ': theZ,
                        'x1': x1,
                        'y1': y1,
                        'x2': x2,
                        'y2': y2
                    })

                elif type(s) == omero.model.PolylineI:
                    points = pointsStringToXYlist(s.getPoints().getValue())
                    polylines.append({
                        'id': roiId,
                        'theT': theT,
                        'theZ': theZ,
                        'points': points
                    })

        if len(lines) == 0 and len(polylines) == 0:
            print "Image: %s had no lines or polylines" % image.getId()
            continue

        # prepare column headers, including line-id if we are going to output
        # raw data.
        lineId = scriptParams['Sum_or_Average'] == 'Average, with raw data' \
            and 'Line, ' or ""
        colHeader = 'Image_ID, ROI_ID, Z, T, C, %sLine data %s of Line" \
            " Width %s\n' % (lineId, scriptParams['Sum_or_Average'],
                             scriptParams['Line_Width'])
        print 'colHeader', colHeader

        # prepare a csv file to write our data to...
        fileName = "Plot_Profile_%s.csv" % image.getId()
        try:
            f = open(fileName, 'w')
            f.write(colHeader)
            if len(lines) > 0:
                processLines(conn, scriptParams, image, lines, lineWidth, f)
            if len(polylines) > 0:
                processPolyLines(conn, scriptParams, image, polylines,
                                 lineWidth, f)
        finally:
            f.close()

        fileAnn, faMessage = scriptUtil.createLinkFileAnnotation(
            conn,
            fileName,
            image,
            output="Line Plot csv (Excel) file",
            mimetype="text/csv",
            desc=None)
        if fileAnn:
            fileAnns.append(fileAnn)

    if not fileAnns:
        faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
            " more details"
    elif len(fileAnns) > 1:
        faMessage = "Created %s csv (Excel) files" % len(fileAnns)
    message += faMessage

    return fileAnns, message
Exemplo n.º 12
0
def splitViewFigure(conn, scriptParams):
    """
    Processes the arguments, populating defaults if necessary. Prints the details to log (fig-legend).
    Even handles missing arguments that are not optional (from when this ran from commandline with everything optional)
    then calls makeSplitViewFigure() to make the figure, attaches it to the Image as an 'originalFile' annotation,
    with fig-legend as the description. 
    
    @return: the id of the originalFileLink child. (ID object, not value) 
    """

    log("Split-View figure created by OMERO on %s" % date.today())
    log("")

    message = ""  # message to be returned to the client
    imageIds = []
    pixelIds = []
    imageLabels = []

    # function for getting image labels.
    def getLabels(fullName, tagsList, pdList):
        name = fullName.split("/")[-1]
        return [name]

    # default function for getting labels is getName (or use datasets / tags)
    if scriptParams["Image_Labels"] == "Datasets":

        def getDatasets(name, tagsList, pdList):
            return [dataset for project, dataset in pdList]

        getLabels = getDatasets
    elif scriptParams["Image_Labels"] == "Tags":

        def getTags(name, tagsList, pdList):
            return tagsList

        getLabels = getTags

    # Get the images
    images, logMessage = scriptUtil.getObjects(conn, scriptParams)
    message += logMessage
    if not images:
        return None, message

    # Attach figure to the first image
    omeroImage = images[0]

    # process the list of images
    log("Image details:")
    for image in images:
        imageIds.append(image.getId())
        pixelIds.append(image.getPrimaryPixels().getId())

    pdMap = figUtil.getDatasetsProjectsFromImages(
        conn.getQueryService(),
        imageIds)  # a map of imageId : list of (project, dataset) names.
    tagMap = figUtil.getTagsFromImages(conn.getMetadataService(), imageIds)
    # Build a legend entry for each image
    for image in images:
        name = image.getName()
        imageDate = image.getAcquisitionDate()
        iId = image.getId()
        tagsList = tagMap[iId]
        pdList = pdMap[iId]

        tags = ", ".join(tagsList)
        pdString = ", ".join(["%s/%s" % pd for pd in pdList])
        log(" Image: %s  ID: %d" % (name, iId))
        log("  Date: %s" % date.fromtimestamp(imageDate / 1000))
        log("  Tags: %s" % tags)
        log("  Project/Datasets: %s" % pdString)

        imageLabels.append(getLabels(name, tagsList, pdList))

    # use the first image to define dimensions, channel colours etc.
    sizeX = omeroImage.getSizeX()
    sizeY = omeroImage.getSizeY()
    sizeZ = omeroImage.getSizeZ()
    sizeC = omeroImage.getSizeC()

    # set image dimensions
    zStart = -1
    zEnd = -1
    if "Z_Start" in scriptParams:
        zStart = scriptParams["Z_Start"]
    if "Z_End" in scriptParams:
        zEnd = scriptParams["Z_End"]

    width = "Width" in scriptParams and scriptParams["Width"] or sizeX
    height = "Height" in scriptParams and scriptParams["Height"] or sizeY

    log("Image dimensions for all panels (pixels): width: %d  height: %d" %
        (width, height))

    # Make split-indexes list. If argument wasn't specified, include them all.
    splitIndexes = []
    if "Split_Indexes" in scriptParams:
        splitIndexes = scriptParams["Split_Indexes"]
    else:
        splitIndexes = range(sizeC)

    # Make channel-names map. If argument wasn't specified, name by index
    channelNames = {}
    for c in range(sizeC):
        channelNames[c] = str(c)
    if "Channel_Names" in scriptParams:
        cNameMap = scriptParams["Channel_Names"]
        for c in cNameMap:
            index = int(c)
            channelNames[index] = cNameMap[c]

    mergedIndexes = []  # the channels in the combined image,
    mergedColours = {}
    if "Merged_Colours" in scriptParams:
        cColourMap = scriptParams["Merged_Colours"]
        for c in cColourMap:
            rgb = cColourMap[c]
            rgba = imgUtil.RGBIntToRGBA(rgb)
            mergedColours[int(c)] = rgba
            mergedIndexes.append(int(c))
        mergedIndexes.sort()
    else:
        mergedIndexes = range(sizeC)

    colourChannels = not scriptParams["Split_Panels_Grey"]

    algorithm = omero.constants.projection.ProjectionType.MAXIMUMINTENSITY
    if "Mean Intensity" == scriptParams["Algorithm"]:
        algorithm = omero.constants.projection.ProjectionType.MEANINTENSITY

    stepping = min(scriptParams["Stepping"], sizeZ)

    scalebar = None
    if "Scalebar" in scriptParams:
        scalebar = scriptParams["Scalebar"]
        log("Scalebar is %d microns" % scalebar)

    r, g, b, a = OVERLAY_COLOURS[scriptParams["Overlay_Colour"]]
    overlayColour = (r, g, b)

    mergedNames = scriptParams["Merged_Names"]

    print "splitIndexes", splitIndexes
    print "channelNames", channelNames
    print "colourChannels", colourChannels
    print "mergedIndexes", mergedIndexes
    print "mergedColours", mergedColours
    print "mergedNames", mergedNames
    fig = makeSplitViewFigure(conn, pixelIds, zStart, zEnd, splitIndexes,
                              channelNames, colourChannels, mergedIndexes,
                              mergedColours, mergedNames, width, height,
                              imageLabels, algorithm, stepping, scalebar,
                              overlayColour)

    figLegend = "\n".join(logStrings)
    format = JPEG
    if scriptParams["Format"] == "PNG":
        format = PNG
    output = scriptParams["Figure_Name"]

    if format == PNG:
        output = output + ".png"
        fig.save(output, "PNG")
        mimetype = "image/png"
    else:
        output = output + ".jpg"
        fig.save(output)
        mimetype = "image/jpeg"

    # Upload the figure 'output' to the server, creating a file annotation and attaching it to the omeroImage, adding the
    # figLegend as the fileAnnotation description.
    namespace = omero.constants.namespaces.NSCREATED + "/omero/figure_scripts/Split_View_Figure"
    fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
        conn,
        output,
        omeroImage,
        output="Split view figure",
        mimetype=mimetype,
        ns=namespace,
        desc=figLegend)
    message += faMessage

    return fileAnnotation, message
def analyseImage(conn, image, cIndex):

    print "\n---------------------"
    print "Analysing Image: ", image.getName()
    # Get dictionary of tIndex:ellipse
    ellipses = getEllipses(conn, image.getId())
    # Get dictionary of tIndex:averageIntensity
    intensityData = getEllipseData(image, ellipses, cIndex)

    # Get dictionary of tIndex:timeStamp (secs)
    timeValues = getTimes(conn, image)

    # We now have all the Data we need from OMERO

    # create lists of times (secs) and intensities...
    timeList = []
    valueList = []

    # ...Ordered by tIndex
    for t in range(image.getSizeT()):
        if t in intensityData:
            timeList.append(timeValues[t])
            valueList.append(intensityData[t])

    print "Analysing pixel values for %s time points" % len(timeList)

    # Find the bleach intensity & time
    bleachValue = min(valueList)
    bleachTindex = valueList.index(bleachValue)
    bleachTime = timeList[bleachTindex]
    preBleachValue = valueList[bleachTindex-1]

    print "Bleach at tIndex: %s, TimeStamp: %0.2f seconds" \
        % (bleachTindex, bleachTime)
    print "Before Bleach: %0.2f, After Bleach: %0.2f" \
        % (preBleachValue, bleachValue)

    # Use last timepoint for max recovery
    recoveryValue = valueList[-1]
    endTimepoint = timeList[-1]
    mobileFraction = (recoveryValue - bleachValue) / \
        (preBleachValue - bleachValue)

    print "Recovered to: %0.2f, after %0.2f seconds" \
        % (recoveryValue, endTimepoint)
    print "Mobile Fraction: %0.2f" % mobileFraction

    halfRecovery = (recoveryValue + bleachValue)/2

    # quick & dirty - pick the first timepoint where we exceed half recovery
    recoveryValues = valueList[bleachTindex:]
    # just the values & times after bleach time
    recoveryTimes = timeList[bleachTindex:]
    for t, v in zip(recoveryTimes, recoveryValues):
        if v >= halfRecovery:
            tHalf = t - bleachTime
            break

    print "tHalf: %0.2f seconds" % tHalf

    csvLines = [
        "Time (secs)," + ",".join([str(t) for t in timeList]),
        "\n",
        "Average pixel value," + ",".join([str(v) for v in valueList]),
        "\n",
        "tHalf (secs), %0.2f seconds" % tHalf,
        "mobileFraction, %0.2f" % mobileFraction
        ]

    f = open("FRAP.csv", "w")
    f.writelines(csvLines)
    f.close()

    namespace = "/omero-user-scripts/example/Simple_FRAP/"
    scriptUtil.createLinkFileAnnotation(conn, "FRAP.csv", image, ns=namespace)

    return tHalf
def batchImageExport(conn, scriptParams):

    # for params with default values, we can get the value directly
    splitCs = scriptParams["Export_Individual_Channels"]
    mergedCs = scriptParams["Export_Merged_Image"]
    greyscale = scriptParams["Individual_Channels_Grey"]
    dataType = scriptParams["Data_Type"]
    folder_name = scriptParams["Folder_Name"]
    folder_name = os.path.basename(folder_name)
    format = scriptParams["Format"]
    projectZ = "Choose_Z_Section" in scriptParams and \
        scriptParams["Choose_Z_Section"] == 'Max projection'

    if (not splitCs) and (not mergedCs):
        log("Not chosen to save Individual Channels OR Merged Image")
        return

    # check if we have these params
    channelNames = []
    if "Channel_Names" in scriptParams:
        channelNames = scriptParams["Channel_Names"]
    zoomPercent = None
    if "Zoom" in scriptParams and scriptParams["Zoom"] != "100%":
        zoomPercent = int(scriptParams["Zoom"][:-1])

    # functions used below for each imaage.
    def getZrange(sizeZ, scriptParams):
        zRange = None
        if "Choose_Z_Section" in scriptParams:
            zChoice = scriptParams["Choose_Z_Section"]
            # NB: all Z indices in this script are 1-based
            if zChoice == 'ALL Z planes':
                zRange = (1, sizeZ+1)
            elif "OR_specify_Z_index" in scriptParams:
                zIndex = scriptParams["OR_specify_Z_index"]
                zIndex = min(zIndex, sizeZ)
                zRange = (zIndex,)
            elif "OR_specify_Z_start_AND..." in scriptParams and \
                    "...specify_Z_end" in scriptParams:
                start = scriptParams["OR_specify_Z_start_AND..."]
                start = min(start, sizeZ)
                end = scriptParams["...specify_Z_end"]
                end = min(end, sizeZ)
                # in case user got zStart and zEnd mixed up
                zStart = min(start, end)
                zEnd = max(start, end)
                if zStart == zEnd:
                    zRange = (zStart,)
                else:
                    zRange = (zStart, zEnd+1)
        return zRange

    def getTrange(sizeT, scriptParams):
        tRange = None
        if "Choose_T_Section" in scriptParams:
            tChoice = scriptParams["Choose_T_Section"]
            # NB: all T indices in this script are 1-based
            if tChoice == 'ALL T planes':
                tRange = (1, sizeT+1)
            elif "OR_specify_T_index" in scriptParams:
                tIndex = scriptParams["OR_specify_T_index"]
                tIndex = min(tIndex, sizeT)
                tRange = (tIndex,)
            elif "OR_specify_T_start_AND..." in scriptParams and \
                    "...specify_T_end" in scriptParams:
                start = scriptParams["OR_specify_T_start_AND..."]
                start = min(start, sizeT)
                end = scriptParams["...specify_T_end"]
                end = min(end, sizeT)
                # in case user got zStart and zEnd mixed up
                tStart = min(start, end)
                tEnd = max(start, end)
                if tStart == tEnd:
                    tRange = (tStart,)
                else:
                    tRange = (tStart, tEnd+1)
        return tRange

    # Get the images or datasets
    message = ""
    objects, logMessage = script_utils.getObjects(conn, scriptParams)
    message += logMessage
    if not objects:
        return None, message

    # Attach figure to the first image
    parent = objects[0]

    if dataType == 'Dataset':
        images = []
        for ds in objects:
            images.extend(list(ds.listChildren()))
        if not images:
            message += "No image found in dataset(s)"
            return None, message
    else:
        images = objects

    log("Processing %s images" % len(images))

    # somewhere to put images
    curr_dir = os.getcwd()
    exp_dir = os.path.join(curr_dir, folder_name)
    try:
        os.mkdir(exp_dir)
    except:
        pass
    # max size (default 12kx12k)
    size = conn.getDownloadAsMaxSizeSetting()
    size = int(size)

    ids = []
    # do the saving to disk

    for img in images:
        pixels = img.getPrimaryPixels()
        if (pixels.getId() in ids):
            continue
        ids.append(pixels.getId())
        sizeX = pixels.getSizeX()
        sizeY = pixels.getSizeY()
        if sizeX*sizeY > size:
            log("  ** Can't export a 'Big' image to %s. **" % format)
            if len(images) == 1:
                return None, "Can't export a 'Big' image to %s." % format
            continue
        else:
            log("Exporting image as %s: %s" % (format, img.getName()))

        if format == 'OME-TIFF':
            saveAsOmeTiff(conn, img, folder_name)
        else:
            if img._prepareRE().requiresPixelsPyramid():
                log("  ** Can't export a 'Big' image to OME-TIFF. **")
            log("\n----------- Saving planes from image: '%s' ------------"
                % img.getName())
            sizeC = img.getSizeC()
            sizeZ = img.getSizeZ()
            sizeT = img.getSizeT()
            zRange = getZrange(sizeZ, scriptParams)
            tRange = getTrange(sizeT, scriptParams)
            log("Using:")
            if zRange is None:
                log("  Z-index: Last-viewed")
            elif len(zRange) == 1:
                log("  Z-index: %d" % zRange[0])
            else:
                log("  Z-range: %s-%s" % (zRange[0], zRange[1]-1))
            if projectZ:
                log("  Z-projection: ON")
            if tRange is None:
                log("  T-index: Last-viewed")
            elif len(tRange) == 1:
                log("  T-index: %d" % tRange[0])
            else:
                log("  T-range: %s-%s" % (tRange[0], tRange[1]-1))
            log("  Format: %s" % format)
            if zoomPercent is None:
                log("  Image Zoom: 100%")
            else:
                log("  Image Zoom: %s" % zoomPercent)
            log("  Greyscale: %s" % greyscale)
            log("Channel Rendering Settings:")
            for ch in img.getChannels():
                log("  %s: %d-%d"
                    % (ch.getLabel(), ch.getWindowStart(), ch.getWindowEnd()))

            try:
                savePlanesForImage(
                    conn, img, sizeC, splitCs, mergedCs, channelNames, zRange,
                    tRange, greyscale, zoomPercent, projectZ=projectZ,
                    format=format, folder_name=folder_name)
            finally:
                # Make sure we close Rendering Engine
                img._re.close()

        # write log for exported images (not needed for ome-tiff)
        logFile = open(os.path.join(exp_dir, 'Batch_Image_Export.txt'), 'w')
        try:
            for s in logStrings:
                logFile.write(s)
                logFile.write("\n")
        finally:
            logFile.close()

    if len(os.listdir(exp_dir)) == 0:
        return None, "No files exported. See 'info' for more details"
    # zip everything up (unless we've only got a single ome-tiff)
    if format == 'OME-TIFF' and len(os.listdir(exp_dir)) == 1:
        ometiffIds = [t.id for t in parent.listAnnotations(ns=NSOMETIFF)]
        print "Deleting OLD ome-tiffs: %s" % ometiffIds
        conn.deleteObjects("Annotation", ometiffIds)
        export_file = os.path.join(folder_name, os.listdir(exp_dir)[0])
        namespace = NSOMETIFF
        outputDisplayName = "OME-TIFF"
        mimetype = 'image/tiff'
    else:
        export_file = "%s.zip" % folder_name
        compress(export_file, folder_name)
        mimetype = 'application/zip'
        outputDisplayName = "Batch export zip"
        namespace = NSCREATED + "/omero/export_scripts/Batch_Image_Export"

    fileAnnotation, annMessage = script_utils.createLinkFileAnnotation(
        conn, export_file, parent, output=outputDisplayName, ns=namespace,
        mimetype=mimetype)
    message += annMessage
    return fileAnnotation, message
Exemplo n.º 15
0
def movieFigure(conn, commandArgs):
    """
    Makes the figure using the parameters in @commandArgs, attaches the figure
    to the parent Project/Dataset, and returns the file-annotation ID

    @param session      The OMERO session
    @param commandArgs  Map of parameters for the script
    @ returns           Returns the id of the originalFileLink child. (ID
                        object, not value)
    """

    log("Movie figure created by OMERO on %s" % date.today())
    log("")

    timeLabels = {
        "SECS_MILLIS": "seconds",
        "SECS": "seconds",
        "MINS": "minutes",
        "HOURS": "hours",
        "MINS_SECS": "mins:secs",
        "HOURS_MINS": "hours:mins"
    }
    timeUnits = "SECS"
    if "Time_Units" in commandArgs:
        timeUnits = commandArgs["Time_Units"]
        # convert from UI name to timeLabels key
        timeUnits = timeUnits.replace(" ", "_")
    if timeUnits not in timeLabels.keys():
        timeUnits = "SECS"
    log("Time units are in %s" % timeLabels[timeUnits])

    pixelIds = []
    imageIds = []
    imageLabels = []
    message = ""  # message to be returned to the client

    # function for getting image labels.
    def getImageNames(fullName, tagsList, pdList):
        name = fullName.split("/")[-1]
        return [name]

    # default function for getting labels is getName (or use datasets / tags)
    if "Image_Labels" in commandArgs:
        if commandArgs["Image_Labels"] == "Datasets":

            def getDatasets(name, tagsList, pdList):
                return [dataset for project, dataset in pdList]

            getLabels = getDatasets
        elif commandArgs["Image_Labels"] == "Tags":

            def getTags(name, tagsList, pdList):
                return tagsList

            getLabels = getTags
        else:
            getLabels = getImageNames
    else:
        getLabels = getImageNames

    # Get the images
    images, logMessage = scriptUtil.getObjects(conn, commandArgs)
    message += logMessage
    if not images:
        return None, message

    # Attach figure to the first image
    omeroImage = images[0]

    # process the list of images
    log("Image details:")
    for image in images:
        imageIds.append(image.getId())
        pixelIds.append(image.getPrimaryPixels().getId())

    # a map of imageId : list of (project, dataset) names.
    pdMap = figUtil.getDatasetsProjectsFromImages(conn.getQueryService(),
                                                  imageIds)
    tagMap = figUtil.getTagsFromImages(conn.getMetadataService(), imageIds)
    # Build a legend entry for each image
    for image in images:
        name = image.getName()
        iId = image.getId()
        imageDate = image.getAcquisitionDate()
        tagsList = tagMap[iId]
        pdList = pdMap[iId]

        tags = ", ".join(tagsList)
        pdString = ", ".join(["%s/%s" % pd for pd in pdList])
        log(" Image: %s  ID: %d" % (name, iId))
        log("  Date: %s" % date.fromtimestamp(imageDate / 1000))
        log("  Tags: %s" % tags)
        log("  Project/Datasets: %s" % pdString)

        imageLabels.append(getLabels(name, tagsList, pdList))

    # use the first image to define dimensions, channel colours etc.
    sizeX = omeroImage.getSizeX()
    sizeY = omeroImage.getSizeY()
    sizeZ = omeroImage.getSizeZ()
    sizeT = omeroImage.getSizeT()

    tIndexes = []
    if "T_Indexes" in commandArgs:
        for t in commandArgs["T_Indexes"]:
            tIndexes.append(t)
        print "T_Indexes", tIndexes
    if len(tIndexes) == 0:  # if no t-indexes given, use all t-indices
        tIndexes = range(sizeT)

    zStart = -1
    zEnd = -1
    if "Z_Start" in commandArgs:
        zStart = commandArgs["Z_Start"]
    if "Z_End" in commandArgs:
        zEnd = commandArgs["Z_End"]

    width = sizeX
    if "Width" in commandArgs:
        width = commandArgs["Width"]

    height = sizeY
    if "Height" in commandArgs:
        height = commandArgs["Height"]

    spacer = (width / 25) + 2

    algorithm = ProjectionType.MAXIMUMINTENSITY
    if "Algorithm" in commandArgs:
        a = commandArgs["Algorithm"]
        if (a == "Mean Intensity"):
            algorithm = ProjectionType.MEANINTENSITY

    stepping = 1
    if "Stepping" in commandArgs:
        s = commandArgs["Stepping"]
        if (0 < s < sizeZ):
            stepping = s

    scalebar = None
    if "Scalebar_Size" in commandArgs:
        sb = commandArgs["Scalebar_Size"]
        try:
            scalebar = int(sb)
            if scalebar <= 0:
                scalebar = None
            else:
                log("Scalebar is %d microns" % scalebar)
        except:
            log("Invalid value for scalebar: %s" % str(sb))
            scalebar = None

    overlayColour = (255, 255, 255)
    if "Scalebar_Colour" in commandArgs:
        r, g, b, a = OVERLAY_COLOURS[commandArgs["Scalebar_Colour"]]
        overlayColour = (r, g, b)

    maxColCount = 10
    if "Max_Columns" in commandArgs:
        maxColCount = commandArgs["Max_Columns"]

    figure = createMovieFigure(conn, pixelIds, tIndexes, zStart, zEnd, width,
                               height, spacer, algorithm, stepping, scalebar,
                               overlayColour, timeUnits, imageLabels,
                               maxColCount)

    log("")
    figLegend = "\n".join(logLines)

    #print figLegend    # bug fixing only
    format = commandArgs["Format"]

    figureName = "movieFigure"
    if "Figure_Name" in commandArgs:
        figureName = str(commandArgs["Figure_Name"])
        figureName = os.path.basename(figureName)
    output = "localfile"
    if format == 'PNG':
        output = output + ".png"
        figureName = figureName + ".png"
        figure.save(output, "PNG")
        mimetype = "image/png"
    elif format == 'TIFF':
        output = output + ".tiff"
        figureName = figureName + ".tiff"
        figure.save(output, "TIFF")
        mimetype = "image/tiff"
    else:
        output = output + ".jpg"
        figureName = figureName + ".jpg"
        figure.save(output)
        mimetype = "image/jpeg"
    namespace = NSCREATED + "/omero/figure_scripts/Movie_Figure"
    fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
        conn,
        output,
        omeroImage,
        output="Movie figure",
        mimetype=mimetype,
        ns=namespace,
        desc=figLegend,
        origFilePathAndName=figureName)
    message += faMessage

    return fileAnnotation, message
Exemplo n.º 16
0
def analyseImage(conn, image, cIndex):

    print "\n---------------------"
    print "Analysing Image: ", image.getName()
    # Get dictionary of tIndex:ellipse
    ellipses = getEllipses(conn, image.getId())
    # Get dictionary of tIndex:averageIntensity
    intensityData = getEllipseData(image, ellipses, cIndex)

    # Get dictionary of tIndex:timeStamp (secs)
    timeValues = getTimes(conn, image)

    # We now have all the Data we need from OMERO

    # create lists of times (secs) and intensities...
    timeList = []
    valueList = []

    # ...Ordered by tIndex
    for t in range(image.getSizeT()):
        if t in intensityData:
            timeList.append(timeValues[t])
            valueList.append(intensityData[t])

    print "Analysing pixel values for %s time points" % len(timeList)

    # Find the bleach intensity & time
    bleachValue = min(valueList)
    bleachTindex = valueList.index(bleachValue)
    bleachTime = timeList[bleachTindex]
    preBleachValue = valueList[bleachTindex - 1]

    print "Bleach at tIndex: %s, TimeStamp: %0.2f seconds" \
        % (bleachTindex, bleachTime)
    print "Before Bleach: %0.2f, After Bleach: %0.2f" \
        % (preBleachValue, bleachValue)

    # Use last timepoint for max recovery
    recoveryValue = valueList[-1]
    endTimepoint = timeList[-1]
    mobileFraction = (recoveryValue - bleachValue) / \
        (preBleachValue - bleachValue)

    print "Recovered to: %0.2f, after %0.2f seconds" \
        % (recoveryValue, endTimepoint)
    print "Mobile Fraction: %0.2f" % mobileFraction

    halfRecovery = (recoveryValue + bleachValue) / 2

    # quick & dirty - pick the first timepoint where we exceed half recovery
    recoveryValues = valueList[bleachTindex:]
    # just the values & times after bleach time
    recoveryTimes = timeList[bleachTindex:]
    for t, v in zip(recoveryTimes, recoveryValues):
        if v >= halfRecovery:
            tHalf = t - bleachTime
            break

    print "tHalf: %0.2f seconds" % tHalf

    csvLines = [
        "Time (secs)," + ",".join([str(t) for t in timeList]), "\n",
        "Average pixel value," + ",".join([str(v) for v in valueList]), "\n",
        "tHalf (secs), %0.2f seconds" % tHalf,
        "mobileFraction, %0.2f" % mobileFraction
    ]

    f = open("FRAP.csv", "w")
    f.writelines(csvLines)
    f.close()

    namespace = "/omero-user-scripts/example/Simple_FRAP/"
    scriptUtil.createLinkFileAnnotation(conn, "FRAP.csv", image, ns=namespace)

    return tHalf
Exemplo n.º 17
0
def writeMovie(commandArgs, conn):
    """
    Makes the movie.
    
    @ returns        Returns the file annotation
    """
    log("Movie created by OMERO")
    log("")

    message = ""

    conn.SERVICE_OPTS.setOmeroGroup('-1')
    session = conn.c.sf
    gateway = conn
    scriptService = session.getScriptService()
    queryService = session.getQueryService()
    updateService = session.getUpdateService()
    rawFileStore = session.createRawFileStore()

    # Get the images
    images, logMessage = scriptUtil.getObjects(conn, commandArgs)
    message += logMessage
    if not images:
        return None, message
    omeroImage = images[
        0]  # Get the first valid image (should be expanded to process the list)

    if commandArgs["RenderingDef_ID"] >= 0:
        omeroImage._prepareRenderingEngine(rdid=commandArgs["RenderingDef_ID"])
    pixels = omeroImage.getPrimaryPixels()
    pixelsId = pixels.getId()

    sizeX = pixels.getSizeX()
    sizeY = pixels.getSizeY()
    sizeZ = pixels.getSizeZ()
    sizeC = pixels.getSizeC()
    sizeT = pixels.getSizeT()

    if (sizeX == None or sizeY == None or sizeZ == None or sizeT == None
            or sizeC == None):
        return

    if (pixels.getPhysicalSizeX() == None):
        commandArgs["Scalebar"] = 0

    cRange = range(0, sizeC)
    if "Channels" in commandArgs and validChannels(commandArgs["Channels"],
                                                   sizeC):
        cRange = commandArgs["Channels"]

    tzList = calculateRanges(sizeZ, sizeT, commandArgs)

    timeMap = calculateAquisitionTime(conn, pixelsId, cRange, tzList)
    if (timeMap == None):
        commandArgs["Show_Time"] = False
    if (timeMap != None):
        if (len(timeMap) == 0):
            commandArgs["Show_Time"] = False

    pixelTypeString = pixels.getPixelsType().getValue()
    frameNo = 1
    omeroImage.setActiveChannels(map(lambda x: x + 1, cRange))
    renderingEngine = omeroImage._re

    overlayColour = (255, 255, 255)
    if "Overlay_Colour" in commandArgs:
        r, g, b, a = COLOURS[commandArgs["Overlay_Colour"]]
        overlayColour = (r, g, b)

    canvasColour = tuple(COLOURS[commandArgs["Canvas_Colour"]][:3])
    mw = commandArgs["Min_Width"]
    if mw < sizeX:
        mw = sizeX
    mh = commandArgs["Min_Height"]
    if mh < sizeY:
        mh = sizeY
    ovlpos = None
    canvas = None
    if sizeX < mw or sizeY < mh:
        ovlpos = ((mw - sizeX) / 2, (mh - sizeY) / 2)
        canvas = Image.new("RGBA", (mw, mh), canvasColour)

    format = commandArgs["Format"]
    fileNames = []

    # add intro...
    if "Intro_Slide" in commandArgs and commandArgs["Intro_Slide"].id:
        intro_duration = commandArgs["Intro_Duration"]
        intro_fileId = commandArgs["Intro_Slide"].id.val
        intro_filenames = write_intro_end_slides(conn, commandArgs,
                                                 intro_fileId, intro_duration,
                                                 mw, mh)
        fileNames.extend(intro_filenames)

    # prepare watermark
    if "Watermark" in commandArgs and commandArgs["Watermark"].id:
        watermark = prepareWatermark(conn, commandArgs, mw, mh)

    # add movie frames...
    for tz in tzList:
        t = tz[0]
        z = tz[1]
        plane = getPlane(renderingEngine, z, t)
        planeImage = numpy.array(plane, dtype='uint32')
        planeImage = planeImage.byteswap()
        planeImage = planeImage.reshape(sizeX, sizeY)
        image = Image.frombuffer('RGBA', (sizeX, sizeY), planeImage.data,
                                 'raw', 'ARGB', 0, 1)
        if ovlpos is not None:
            image2 = canvas.copy()
            image2.paste(image, ovlpos, image)
            image = image2

        if "Scalebar" in commandArgs and commandArgs["Scalebar"]:
            image = addScalebar(commandArgs["Scalebar"], image, pixels,
                                commandArgs)
        planeInfo = "z:" + str(z) + "t:" + str(t)
        if "Show_Time" in commandArgs and commandArgs["Show_Time"]:
            time = timeMap[planeInfo]
            image = addTimePoints(time, pixels, image, overlayColour)
        if "Show_Plane_Info" in commandArgs and commandArgs["Show_Plane_Info"]:
            image = addPlaneInfo(z, t, pixels, image, overlayColour)
        if "Watermark" in commandArgs and commandArgs["Watermark"].id:
            image = pasteWatermark(image, watermark)
        if format == QT:
            filename = str(frameNo) + '.png'
            image.save(filename, "PNG")
        else:
            filename = str(frameNo) + '.jpg'
            image.save(filename, "JPEG")
        fileNames.append(filename)
        frameNo += 1

    # add exit frames... "outro"
    # add intro...
    if "Ending_Slide" in commandArgs and commandArgs["Ending_Slide"].id:
        end_duration = commandArgs["Ending_Duration"]
        end_fileId = commandArgs["Ending_Slide"].id.val
        end_filenames = write_intro_end_slides(conn, commandArgs, end_fileId,
                                               end_duration, mw, mh)
        fileNames.extend(end_filenames)

    filelist = ",".join(fileNames)

    ext = formatMap[format]
    movieName = "Movie"
    if "Movie_Name" in commandArgs:
        movieName = commandArgs["Movie_Name"]
    if not movieName.endswith(".%s" % ext):
        movieName = "%s.%s" % (movieName, ext)

    movieName = re.sub("[$&\;|\(\)<>' ]", "",
                       movieName)  # spaces etc in file name cause problems
    framesPerSec = 2
    if "FPS" in commandArgs:
        framesPerSec = commandArgs["FPS"]
    buildAVI(mw, mh, filelist, framesPerSec, movieName, format)
    figLegend = "\n".join(logLines)
    mimetype = formatMimetypes[format]

    if not os.path.exists(movieName):
        print "mencoder Failed to create movie file: %s" % movieName
        return None, "Failed to create movie file: %s" % movieName
    if not commandArgs["Do_Link"]:
        originalFile = scriptUtil.createFile(updateService, movieName,
                                             mimetype, movieName)
        scriptUtil.uploadFile(rawFileStore, originalFile, movieName)
        return originalFile, message

    namespace = omero.constants.namespaces.NSCREATED + "/omero/export_scripts/Make_Movie"
    fileAnnotation, annMessage = scriptUtil.createLinkFileAnnotation(
        conn,
        movieName,
        omeroImage,
        output="Movie",
        ns=namespace,
        mimetype=mimetype)
    message += annMessage
    return fileAnnotation._obj, message
def run_processing(conn,script_params):
    file_anns = []
    message = ""
    imageIds = []
    image_id = script_params['ImageID']
    imageIds.append(image_id)
    image = conn.getObject("Image",image_id)
    if not image:
        message = 'Could not find specified image'
        return message
        
    file_id = script_params['AnnotationID']
    ann = conn.getObject("Annotation",file_id)
    if not ann:
        message = 'Could not find specified annotation'
        return message
    
    #other parameters
    sr_pix_size = script_params['SR_pixel_size']
    if script_params['Convert_coordinates_to_nm']:
        cam_pix_size = script_params['Parent_Image_Pixel_Size']
    else:
        cam_pix_size = 1
    file_type = script_params['File_Type']
     
    path_to_ann = ann.getFile().getPath() + '/' + ann.getFile().getName()
    name,ext = os.path.splitext(path_to_ann)
    if ('txt' in ext) or ('csv' in ext):
        #get the path to the downloaded data
        path_to_data = download_data(ann)
        
        #get all the xy coords in that data
        coords = parse_sr_data(path_to_data,file_type,cam_pix_size)
        
        #get the rois to be processed
        rectangles,rectIds = get_rectangles(conn,image_id,sr_pix_size)
        
        #calculate near neighbour distances
        nn_data,nn_hist,bins = process_data(conn,image,rectangles,coords)
        
        #put the data in an omero table
#         put_data_in_table(conn,imageIds,rectIds,nn_data,nn_hist,bins)
        
        #write the data to a csv
        file_name = "near_neighbours_" + ann.getFile().getName()[:-4] + '.csv'
        print file_name
        try:
            f = open(file_name,'w')
            for r in range(len(nn_data)):
                row = nn_data[r]
                for chan in range(row.shape[1]):
                    f.write(','.join([str(c) for c in row[:,chan]])+'\n')
        finally:
            f.close()

        new_file_ann, faMessage = script_util.createLinkFileAnnotation(
            conn, file_name, image, output="Wrote near neighbour csv (Excel) file",
            mimetype="text/csv", desc=None)
        if new_file_ann:
            file_anns.append(new_file_ann)

        if not file_anns:
            faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
                " more details"
        elif len(file_anns) > 1:
            faMessage = "Created %s csv (Excel) files" % len(file_anns)
        message += faMessage
    else:
        message = 'file annotation must be txt or csv'
        return message
    # clean up
    delete_downloaded_data(ann)
    
    message += faMessage
    return message
Exemplo n.º 19
0
def roiFigure(conn, commandArgs):
    """
    This processes the script parameters, adding defaults if needed.
    Then calls a method to make the figure, and finally uploads and attaches
    this to the primary image.

    @param: session         The OMERO session
    @param: commandArgs     Map of String:Object parameters for the script.
                            Objects are not rtypes, since getValue() was
                            called when the map was processed below.
                            But, list and map objects may contain rtypes (need
                            to call getValue())

    @return:                the id of the originalFileLink child. (ID object,
                            not value)
    """

    log("ROI figure created by OMERO on %s" % date.today())
    log("")

    message = ""  # message to be returned to the client
    pixelIds = []
    imageIds = []
    imageLabels = []

    # function for getting image labels.
    def getImageNames(fullName, tagsList, pdList):
        name = fullName.split("/")[-1]
        return [name]

    # default function for getting labels is getName (or use datasets / tags)
    if "Image_Labels" in commandArgs:
        if commandArgs["Image_Labels"] == "Datasets":

            def getDatasets(name, tagsList, pdList):
                return [dataset for project, dataset in pdList]

            getLabels = getDatasets
        elif commandArgs["Image_Labels"] == "Tags":

            def getTags(name, tagsList, pdList):
                return tagsList

            getLabels = getTags
        else:
            getLabels = getImageNames
    else:
        getLabels = getImageNames

    # Get the images
    images, logMessage = scriptUtil.getObjects(conn, commandArgs)
    message += logMessage
    if not images:
        return None, message

    # Check for rectangular ROIs and filter images list
    images = [image for image in images if image.getROICount("Rectangle") > 0]
    if not images:
        message += "No rectangle ROI found."
        return None, message

    # Attach figure to the first image
    omeroImage = images[0]

    # process the list of images
    log("Image details:")
    for image in images:
        imageIds.append(image.getId())
        pixelIds.append(image.getPrimaryPixels().getId())

    # a map of imageId : list of (project, dataset) names.
    pdMap = figUtil.getDatasetsProjectsFromImages(conn.getQueryService(),
                                                  imageIds)
    tagMap = figUtil.getTagsFromImages(conn.getMetadataService(), imageIds)
    # Build a legend entry for each image
    for image in images:
        name = image.getName()
        iId = image.getId()
        imageDate = image.getAcquisitionDate()
        tagsList = tagMap[iId]
        pdList = pdMap[iId]

        tags = ", ".join(tagsList)
        pdString = ", ".join(["%s/%s" % pd for pd in pdList])
        log(" Image: %s  ID: %d" % (name, iId))
        if imageDate:
            log("  Date: %s" % imageDate)
        else:
            log("  Date: not set")
        log("  Tags: %s" % tags)
        log("  Project/Datasets: %s" % pdString)

        imageLabels.append(getLabels(name, tagsList, pdList))

    # use the first image to define dimensions, channel colours etc.
    sizeX = omeroImage.getSizeX()
    sizeY = omeroImage.getSizeY()
    sizeZ = omeroImage.getSizeZ()
    sizeC = omeroImage.getSizeC()

    width = sizeX
    if "Width" in commandArgs:
        w = commandArgs["Width"]
        try:
            width = int(w)
        except:
            log("Invalid width: %s Using default value: %d" % (str(w), sizeX))

    height = sizeY
    if "Height" in commandArgs:
        h = commandArgs["Height"]
        try:
            height = int(h)
        except:
            log("Invalid height: %s Using default value" % (str(h), sizeY))

    log("Image dimensions for all panels (pixels): width: %d  height: %d" %
        (width, height))

    # the channels in the combined image,
    if "Merged_Channels" in commandArgs:
        # convert to 0-based
        mergedIndexes = [c - 1 for c in commandArgs["Merged_Channels"]]
    else:
        mergedIndexes = range(sizeC)  # show all
    mergedIndexes.reverse()

    #  if no colours added, use existing rendering settings.
    mergedColours = {}
    # Actually, nicer to always use existing rendering settings.
    # if "Merged_Colours" in commandArgs:
    #     for i, c in enumerate(commandArgs["Merged_Colours"]):
    #         if c in COLOURS:
    #             mergedColours[i] = COLOURS[c]

    algorithm = ProjectionType.MAXIMUMINTENSITY
    if "Algorithm" in commandArgs:
        a = commandArgs["Algorithm"]
        if (a == "Mean Intensity"):
            algorithm = ProjectionType.MEANINTENSITY

    stepping = 1
    if "Stepping" in commandArgs:
        s = commandArgs["Stepping"]
        if (0 < s < sizeZ):
            stepping = s

    scalebar = None
    if "Scalebar" in commandArgs:
        sb = commandArgs["Scalebar"]
        try:
            scalebar = int(sb)
            if scalebar <= 0:
                scalebar = None
            else:
                log("Scalebar is %d microns" % scalebar)
        except:
            log("Invalid value for scalebar: %s" % str(sb))
            scalebar = None

    overlayColour = (255, 255, 255)
    if "Scalebar_Colour" in commandArgs:
        if commandArgs["Scalebar_Colour"] in OVERLAY_COLOURS:
            r, g, b, a = OVERLAY_COLOURS[commandArgs["Scalebar_Colour"]]
            overlayColour = (r, g, b)

    roiZoom = None
    if "Roi_Zoom" in commandArgs:
        roiZoom = float(commandArgs["Roi_Zoom"])
        if roiZoom == 0:
            roiZoom = None

    maxColumns = None
    if "Max_Columns" in commandArgs:
        maxColumns = commandArgs["Max_Columns"]

    showRoiDuration = False
    if "Show_ROI_Duration" in commandArgs:
        showRoiDuration = commandArgs["Show_ROI_Duration"]

    roiLabel = "FigureROI"
    if "Roi_Selection_Label" in commandArgs:
        roiLabel = commandArgs["Roi_Selection_Label"]

    spacer = (width / 50) + 2

    print "showRoiDuration", showRoiDuration
    fig = getSplitView(conn, imageIds, pixelIds, mergedIndexes, mergedColours,
                       width, height, imageLabels, spacer, algorithm, stepping,
                       scalebar, overlayColour, roiZoom, maxColumns,
                       showRoiDuration, roiLabel)

    # fig.show()        # bug-fixing only

    if fig is None:
        logMessage = "No figure produced"
        log("\n" + logMessage)
        message += logMessage
        return None, message
    figLegend = "\n".join(logStrings)

    # print figLegend    # bug fixing only
    format = commandArgs["Format"]

    figureName = "movieROIFigure"
    if "Figure_Name" in commandArgs:
        figureName = commandArgs["Figure_Name"]
        figureName = os.path.basename(figureName)
    output = "localfile"
    if format == 'PNG':
        output = output + ".png"
        figureName = figureName + ".png"
        fig.save(output, "PNG")
        mimetype = "image/png"
    elif format == 'TIFF':
        output = output + ".tiff"
        figureName = figureName + ".tiff"
        fig.save(output, "TIFF")
        mimetype = "image/tiff"
    else:
        output = output + ".jpg"
        figureName = figureName + ".jpg"
        fig.save(output)
        mimetype = "image/jpeg"

    # Use util method to upload the figure 'output' to the server, attaching
    # it to the omeroImage, adding the
    # figLegend as the fileAnnotation description.
    # Returns the id of the originalFileLink child. (ID object, not value)
    namespace = NSCREATED + "/omero/figure_scripts/Movie_ROI_Figure"
    fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
        conn,
        output,
        omeroImage,
        output="Movie ROI figure",
        mimetype=mimetype,
        ns=namespace,
        desc=figLegend,
        origFilePathAndName=figureName)
    message += faMessage

    return fileAnnotation, message
Exemplo n.º 20
0
def batchImageExport(conn, scriptParams):
    
    # for params with default values, we can get the value directly
    splitCs = scriptParams["Export_Individual_Channels"]
    mergedCs = scriptParams["Export_Merged_Image"] 
    greyscale = scriptParams["Individual_Channels_Grey"]
    dataType = scriptParams["Data_Type"]
    ids = scriptParams["IDs"]
    folder_name = scriptParams["Folder_Name"]
    format = scriptParams["Format"]
    projectZ = "Choose_Z_Section" in scriptParams and scriptParams["Choose_Z_Section"] == 'Max projection'
    
    if (not splitCs) and (not mergedCs):
        log("Not chosen to save Individual Channels OR Merged Image")
        return
        
    # check if we have these params
    channelNames = []
    if "Channel_Names" in scriptParams:
        channelNames = scriptParams["Channel_Names"]
    imgWidth = None
    if "Image_Width" in scriptParams:
        imgWidth = scriptParams["Image_Width"]
    
    
    # functions used below for each imaage.
    def getZrange(sizeZ, scriptParams):
        zRange = None
        if "Choose_Z_Section" in scriptParams:
            zChoice = scriptParams["Choose_Z_Section"]
            # NB: all Z indices in this script are 1-based
            if zChoice == 'ALL Z planes':
                zRange = (1, sizeZ+1)
            elif "OR_specify_Z_index" in scriptParams:
                zIndex = scriptParams["OR_specify_Z_index"]
                zIndex = min(zIndex, sizeZ)
                zRange = (zIndex,)
            elif "OR_specify_Z_start_AND..." in scriptParams and "...specify_Z_end" in scriptParams:
                start = scriptParams["OR_specify_Z_start_AND..."]
                start = min(start, sizeZ)
                end = scriptParams["...specify_Z_end"]
                end = min(end, sizeZ)
                zStart = min(start, end)  # in case user got zStart and zEnd mixed up
                zEnd = max(start, end)
                if zStart == zEnd:
                    zRange = (zStart,)
                else:
                    zRange = (zStart, zEnd+1)
        return zRange
    
    def getTrange(sizeT, scriptParams):
        tRange = None
        if "Choose_T_Section" in scriptParams:
            tChoice = scriptParams["Choose_T_Section"]
            # NB: all T indices in this script are 1-based
            if tChoice == 'ALL T planes':
                tRange = (1, sizeT+1)
            elif "OR_specify_T_index" in scriptParams:
                tIndex = scriptParams["OR_specify_T_index"]
                tIndex = min(tIndex, sizeT)
                tRange = (tIndex,)
            elif "OR_specify_T_start_AND..." in scriptParams and "...specify_T_end" in scriptParams:
                start = scriptParams["OR_specify_T_start_AND..."]
                start = min(start, sizeT)
                end = scriptParams["...specify_T_end"]
                end = min(end, sizeT)
                tStart = min(start, end)  # in case user got zStart and zEnd mixed up
                tEnd = max(start, end)
                if tStart == tEnd:
                    tRange = (tStart,)
                else:
                    tRange = (tStart, tEnd+1)
        return tRange

    # Get the images or datasets
    message = ""
    objects, logMessage = script_utils.getObjects(conn, scriptParams)
    message += logMessage
    if not objects:
        return None, message
    
    # Attach figure to the first image
    parent = objects[0]
    
    if dataType == 'Dataset':
        images = []
        for ds in objects:
            images.extend( list(ds.listChildren()) )
        if not images:
            message += "No image found in dataset(s)"
            return None, message
    else:
        images = objects
        
    log("Processing %s images" % len(images))
    
    # somewhere to put images
    curr_dir = os.getcwd()
    exp_dir = os.path.join(curr_dir, folder_name)
    try:
        os.mkdir(exp_dir)
    except:
        pass
    
    # do the saving to disk
    if format == 'OME-TIFF':
        for img in images:
            log("Exporting image as OME-TIFF: %s" % img.getName())
            saveAsOmeTiff(conn, img, folder_name)

    else:
        for img in images:
            log("\n----------- Saving planes from image: '%s' ------------" % img.getName())
            sizeC = img.getSizeC()
            sizeZ = img.getSizeZ()
            sizeT = img.getSizeT()
            zRange = getZrange(sizeZ, scriptParams)
            tRange = getTrange(sizeT, scriptParams)
            log("Using:")
            if zRange is None:      log("  Z-index: Last-viewed")
            elif len(zRange) == 1:  log("  Z-index: %d" % zRange[0])
            else:                   log("  Z-range: %s-%s" % ( zRange[0],zRange[1]-1) )
            if projectZ:            log("  Z-projection: ON")
            if tRange is None:      log("  T-index: Last-viewed")
            elif len(tRange) == 1:  log("  T-index: %d" % tRange[0])
            else:                   log("  T-range: %s-%s" % ( tRange[0],tRange[1]-1) )
            log("  Format: %s" % format)
            if imgWidth is None:    log("  Image Width: no resize")
            else:                   log("  Image Width: %s" % imgWidth)
            log("  Greyscale: %s" % greyscale)
            log("Channel Rendering Settings:")
            for ch in img.getChannels():
                log("  %s: %d-%d" % (ch.getLabel(), ch.getWindowStart(), ch.getWindowEnd()) )
        
            savePlanesForImage(conn, img, sizeC, splitCs, mergedCs, channelNames,
                zRange, tRange, greyscale, imgWidth, projectZ=projectZ, format=format, folder_name=folder_name)

        # write log for exported images (not needed for ome-tiff)
        logFile = open(os.path.join(exp_dir, 'Batch_Image_Export.txt'), 'w')
        try:
            for s in logStrings:
                logFile.write(s)
                logFile.write("\n")
        finally:
            logFile.close()

    # zip everything up (unless we've only got a single ome-tiff)
    if format == 'OME-TIFF' and len(os.listdir(exp_dir)) == 1:
        export_file = os.path.join(folder_name, os.listdir(exp_dir)[0])
        mimetype = 'image/tiff'
    else:
        export_file = "%s.zip" % folder_name
        compress(export_file, folder_name)
        mimetype='application/zip'

    namespace = omero.constants.namespaces.NSCREATED+"/omero/export_scripts/Batch_Image_Export"
    fileAnnotation, annMessage = script_utils.createLinkFileAnnotation(conn, export_file, parent, 
        output="Batch export zip", ns=namespace, mimetype=mimetype)
    message += annMessage
    return fileAnnotation, message
Exemplo n.º 21
0
def batchImageExport(conn, scriptParams):

    # for params with default values, we can get the value directly
    splitCs = scriptParams["Export_Individual_Channels"]
    mergedCs = scriptParams["Export_Merged_Image"]
    greyscale = scriptParams["Individual_Channels_Grey"]
    dataType = scriptParams["Data_Type"]
    folder_name = scriptParams["Folder_Name"]
    folder_name = os.path.basename(folder_name)
    format = scriptParams["Format"]
    projectZ = "Choose_Z_Section" in scriptParams and scriptParams["Choose_Z_Section"] == "Max projection"

    if (not splitCs) and (not mergedCs):
        log("Not chosen to save Individual Channels OR Merged Image")
        return

    # check if we have these params
    channelNames = []
    if "Channel_Names" in scriptParams:
        channelNames = scriptParams["Channel_Names"]
    zoomPercent = None
    if "Zoom" in scriptParams and scriptParams["Zoom"] != "100%":
        zoomPercent = int(scriptParams["Zoom"][:-1])

    # functions used below for each imaage.
    def getZrange(sizeZ, scriptParams):
        zRange = None
        if "Choose_Z_Section" in scriptParams:
            zChoice = scriptParams["Choose_Z_Section"]
            # NB: all Z indices in this script are 1-based
            if zChoice == "ALL Z planes":
                zRange = (1, sizeZ + 1)
            elif "OR_specify_Z_index" in scriptParams:
                zIndex = scriptParams["OR_specify_Z_index"]
                zIndex = min(zIndex, sizeZ)
                zRange = (zIndex,)
            elif "OR_specify_Z_start_AND..." in scriptParams and "...specify_Z_end" in scriptParams:
                start = scriptParams["OR_specify_Z_start_AND..."]
                start = min(start, sizeZ)
                end = scriptParams["...specify_Z_end"]
                end = min(end, sizeZ)
                # in case user got zStart and zEnd mixed up
                zStart = min(start, end)
                zEnd = max(start, end)
                if zStart == zEnd:
                    zRange = (zStart,)
                else:
                    zRange = (zStart, zEnd + 1)
        return zRange

    def getTrange(sizeT, scriptParams):
        tRange = None
        if "Choose_T_Section" in scriptParams:
            tChoice = scriptParams["Choose_T_Section"]
            # NB: all T indices in this script are 1-based
            if tChoice == "ALL T planes":
                tRange = (1, sizeT + 1)
            elif "OR_specify_T_index" in scriptParams:
                tIndex = scriptParams["OR_specify_T_index"]
                tIndex = min(tIndex, sizeT)
                tRange = (tIndex,)
            elif "OR_specify_T_start_AND..." in scriptParams and "...specify_T_end" in scriptParams:
                start = scriptParams["OR_specify_T_start_AND..."]
                start = min(start, sizeT)
                end = scriptParams["...specify_T_end"]
                end = min(end, sizeT)
                # in case user got zStart and zEnd mixed up
                tStart = min(start, end)
                tEnd = max(start, end)
                if tStart == tEnd:
                    tRange = (tStart,)
                else:
                    tRange = (tStart, tEnd + 1)
        return tRange

    # Get the images or datasets
    message = ""
    objects, logMessage = script_utils.getObjects(conn, scriptParams)
    message += logMessage
    if not objects:
        return None, message

    # Attach figure to the first image
    parent = objects[0]

    if dataType == "Dataset":
        images = []
        for ds in objects:
            images.extend(list(ds.listChildren()))
        if not images:
            message += "No image found in dataset(s)"
            return None, message
    else:
        images = objects

    log("Processing %s images" % len(images))

    # somewhere to put images
    curr_dir = os.getcwd()
    exp_dir = os.path.join(curr_dir, folder_name)
    try:
        os.mkdir(exp_dir)
    except:
        pass
    # max size (default 12kx12k)
    size = conn.getDownloadAsMaxSizeSetting()
    size = int(size)

    ids = []
    # do the saving to disk

    for img in images:
        pixels = img.getPrimaryPixels()
        if pixels.getId() in ids:
            continue
        ids.append(pixels.getId())
        sizeX = pixels.getSizeX()
        sizeY = pixels.getSizeY()
        if sizeX * sizeY > size:
            log("  ** Can't export a 'Big' image to %s. **" % format)
            if len(images) == 1:
                return None, "Can't export a 'Big' image to %s." % format
            continue
        else:
            log("Exporting image as %s: %s" % (format, img.getName()))

        if format == "OME-TIFF":
            saveAsOmeTiff(conn, img, folder_name)
        else:
            if img._prepareRE().requiresPixelsPyramid():
                log("  ** Can't export a 'Big' image to OME-TIFF. **")
            log("\n----------- Saving planes from image: '%s' ------------" % img.getName())
            sizeC = img.getSizeC()
            sizeZ = img.getSizeZ()
            sizeT = img.getSizeT()
            zRange = getZrange(sizeZ, scriptParams)
            tRange = getTrange(sizeT, scriptParams)
            log("Using:")
            if zRange is None:
                log("  Z-index: Last-viewed")
            elif len(zRange) == 1:
                log("  Z-index: %d" % zRange[0])
            else:
                log("  Z-range: %s-%s" % (zRange[0], zRange[1] - 1))
            if projectZ:
                log("  Z-projection: ON")
            if tRange is None:
                log("  T-index: Last-viewed")
            elif len(tRange) == 1:
                log("  T-index: %d" % tRange[0])
            else:
                log("  T-range: %s-%s" % (tRange[0], tRange[1] - 1))
            log("  Format: %s" % format)
            if zoomPercent is None:
                log("  Image Zoom: 100%")
            else:
                log("  Image Zoom: %s" % zoomPercent)
            log("  Greyscale: %s" % greyscale)
            log("Channel Rendering Settings:")
            for ch in img.getChannels():
                log("  %s: %d-%d" % (ch.getLabel(), ch.getWindowStart(), ch.getWindowEnd()))

            try:
                savePlanesForImage(
                    conn,
                    img,
                    sizeC,
                    splitCs,
                    mergedCs,
                    channelNames,
                    zRange,
                    tRange,
                    greyscale,
                    zoomPercent,
                    projectZ=projectZ,
                    format=format,
                    folder_name=folder_name,
                )
            finally:
                # Make sure we close Rendering Engine
                img._re.close()

        # write log for exported images (not needed for ome-tiff)
        logFile = open(os.path.join(exp_dir, "Batch_Image_Export.txt"), "w")
        try:
            for s in logStrings:
                logFile.write(s)
                logFile.write("\n")
        finally:
            logFile.close()

    if len(os.listdir(exp_dir)) == 0:
        return None, "No files exported. See 'info' for more details"
    # zip everything up (unless we've only got a single ome-tiff)
    if format == "OME-TIFF" and len(os.listdir(exp_dir)) == 1:
        ometiffIds = [t.id for t in parent.listAnnotations(ns=NSOMETIFF)]
        print "Deleting OLD ome-tiffs: %s" % ometiffIds
        conn.deleteObjects("Annotation", ometiffIds)
        export_file = os.path.join(folder_name, os.listdir(exp_dir)[0])
        namespace = NSOMETIFF
        outputDisplayName = "OME-TIFF"
        mimetype = "image/tiff"
    else:
        export_file = "%s.zip" % folder_name
        compress(export_file, folder_name)
        mimetype = "application/zip"
        outputDisplayName = "Batch export zip"
        namespace = NSCREATED + "/omero/export_scripts/Batch_Image_Export"

    fileAnnotation, annMessage = script_utils.createLinkFileAnnotation(
        conn, export_file, parent, output=outputDisplayName, ns=namespace, mimetype=mimetype
    )
    message += annMessage
    return fileAnnotation, message
Exemplo n.º 22
0
def processImages(conn, scriptParams):

    fileAnns = []
    message =""
    # Get the images
    images, logMessage = scriptUtil.getObjects(conn, scriptParams)
    message += logMessage
    if not images:
        return None, message
    # Check for line and polyline ROIs and filter images list
    images = [image for image in images if image.getROICount(["Polyline","Line"])>0]
    if not images:
        message += "No ROI containing line or polyline was found."
        return None, message

    for image in images:
        print "\nAnalysing Image: %s ID: %s" % (image.getName(), image.getId())
        
        if image.getSizeT() > 1:
            message += "%s ID: %s appears to be a time-lapse Image, not a kymograph." % (image.getName(), image.getId())
            continue
        
        roiService = conn.getRoiService()
        result = roiService.findByImage(image.getId(), None)
        
        secsPerPixelY = image.getPixelSizeY()
        micronsPerPixelX = image.getPixelSizeX()
        if secsPerPixelY and micronsPerPixelX:
            micronsPerSec = micronsPerPixelX / secsPerPixelY
        else: micronsPerSec = None
        
        # for each line or polyline, create a row in csv table: y(t), x, dy(dt), dx, x/t (line), x/t (average)
        colNames = "\nt_start (pixels), x_start (pixels), t_end (pixels), x_end (pixels), dt (pixels), dx (pixels), x/t, average x/t, speed(um/sec)"
        tableData = ""
        for roi in result.rois:
            for s in roi.copyShapes():
                if type(s) == omero.model.LineI:
                    tableData += "\nLine"
                    x1 = s.getX1().getValue()
                    x2 = s.getX2().getValue()
                    y1 = s.getY1().getValue()
                    y2 = s.getY2().getValue()
                    dx = abs(x1-x2)
                    dy = abs(y1-y2)
                    dxPerY = float(dx)/dy
                    tableData += "\n"
                    tableData += ",".join([str(x) for x in (y1, x1, y2, x2, dy, dx, dxPerY, dxPerY, "")])
                    if micronsPerSec:
                        speed = dxPerY * micronsPerSec
                        tableData += "%s" % speed
            
                elif type(s) == omero.model.PolylineI:
                    tableData += "\nPolyline"
                    points = pointsStringToXYlist(s.getPoints().getValue())
                    xStart, yStart = points[0]
                    for i in range(1, len(points)):
                        x1, y1 = points[i-1]
                        x2, y2 = points[i]
                        dx = abs(x1-x2)
                        dy = abs(y1-y2)
                        dxPerY = float(dx)/dy
                        avXperY = abs(float(x2-xStart)/(y2-yStart))
                        tableData += "\n"
                        tableData += ",".join([str(x) for x in (y1, x1, y2, x2, dy, dx, dxPerY, avXperY, "")])
                        if micronsPerSec:
                            speed = dxPerY * micronsPerSec
                            tableData += "%s" % speed

        # write table data to csv...
        if len(tableData) > 0:
            tableString = "secsPerPixelY: %s" % secsPerPixelY
            tableString += '\nmicronsPerPixelX: %s' % micronsPerPixelX
            tableString += "\nmicronsPerSec: %s" % micronsPerSec
            tableString += "\n"
            tableString += colNames 
            tableString += tableData
            print tableString
            csvFileName = 'kymograph_velocities_%s.csv' % image.getId()
            csvFile = open(csvFileName, 'w')
            try:
                csvFile.write(tableString)
            finally:
                csvFile.close()

            fileAnn, faMessage = scriptUtil.createLinkFileAnnotation(conn, csvFileName, image, 
                output="Line Plot csv (Excel) file", mimetype="text/csv", desc=None)
            print fileAnn, faMessage
            if fileAnn:
                fileAnns.append(fileAnn)
        else:
            print "Found NO lines or polylines to analyse for Image"

    if not fileAnns:
        faMessage = "No Analysis files created. See 'Info' or 'Error' for more details"
    elif len(fileAnns) > 1:
        faMessage = "Created %s csv (Excel) files" % len(fileAnns)
    message += faMessage
    return fileAnns, message
Exemplo n.º 23
0
def processImages(conn, scriptParams):

    fileAnns = []
    message = ""
    # Get the images
    images, logMessage = scriptUtil.getObjects(conn, scriptParams)
    message += logMessage
    if not images:
        return None, message
    # Check for line and polyline ROIs and filter images list
    images = [
        image for image in images
        if image.getROICount(["Polyline", "Line"]) > 0
    ]
    if not images:
        message += "No ROI containing line or polyline was found."
        return None, message

    for image in images:
        print "\nAnalysing Image: %s ID: %s" \
            % (image.getName(), image.getId())

        if image.getSizeT() > 1:
            message += "%s ID: %s appears to be a time-lapse Image," \
                " not a kymograph." % (image.getName(), image.getId())
            continue

        roiService = conn.getRoiService()
        result = roiService.findByImage(image.getId(), None)

        secsPerPixelY = image.getPixelSizeY()
        micronsPerPixelX = image.getPixelSizeX()
        if secsPerPixelY and micronsPerPixelX:
            micronsPerSec = micronsPerPixelX / secsPerPixelY
        else:
            micronsPerSec = None

        # for each line or polyline, create a row in csv table: y(t), x,
        # dy(dt), dx, x/t (line), x/t (average)
        colNames = "\nt_start (pixels), x_start (pixels), t_end (pixels)," \
            " x_end (pixels), dt (pixels), dx (pixels), x/t, average x/t," \
            " speed(um/sec)"
        tableData = ""
        for roi in result.rois:
            for s in roi.copyShapes():
                if type(s) == omero.model.LineI:
                    tableData += "\nLine"
                    x1 = s.getX1().getValue()
                    x2 = s.getX2().getValue()
                    y1 = s.getY1().getValue()
                    y2 = s.getY2().getValue()
                    dx = abs(x1 - x2)
                    dy = abs(y1 - y2)
                    dxPerY = float(dx) / dy
                    tableData += "\n"
                    tableData += ",".join([
                        str(x)
                        for x in (y1, x1, y2, x2, dy, dx, dxPerY, dxPerY, "")
                    ])
                    if micronsPerSec:
                        speed = dxPerY * micronsPerSec
                        tableData += "%s" % speed

                elif type(s) == omero.model.PolylineI:
                    tableData += "\nPolyline"
                    points = pointsStringToXYlist(s.getPoints().getValue())
                    xStart, yStart = points[0]
                    for i in range(1, len(points)):
                        x1, y1 = points[i - 1]
                        x2, y2 = points[i]
                        dx = abs(x1 - x2)
                        dy = abs(y1 - y2)
                        dxPerY = float(dx) / dy
                        avXperY = abs(float(x2 - xStart) / (y2 - yStart))
                        tableData += "\n"
                        tableData += ",".join([
                            str(x) for x in (y1, x1, y2, x2, dy, dx, dxPerY,
                                             avXperY, "")
                        ])
                        if micronsPerSec:
                            speed = dxPerY * micronsPerSec
                            tableData += "%s" % speed

        # write table data to csv...
        if len(tableData) > 0:
            tableString = "secsPerPixelY: %s" % secsPerPixelY
            tableString += '\nmicronsPerPixelX: %s' % micronsPerPixelX
            tableString += "\nmicronsPerSec: %s" % micronsPerSec
            tableString += "\n"
            tableString += colNames
            tableString += tableData
            print tableString
            csvFileName = 'kymograph_velocities_%s.csv' % image.getId()
            csvFile = open(csvFileName, 'w')
            try:
                csvFile.write(tableString)
            finally:
                csvFile.close()

            fileAnn, faMessage = scriptUtil.createLinkFileAnnotation(
                conn,
                csvFileName,
                image,
                output="Line Plot csv (Excel) file",
                mimetype="text/csv",
                desc=None)
            print fileAnn, faMessage
            if fileAnn:
                fileAnns.append(fileAnn)
        else:
            print "Found NO lines or polylines to analyse for Image"

    if not fileAnns:
        faMessage = "No Analysis files created. See 'Info' or 'Error'" \
            " for more details"
    elif len(fileAnns) > 1:
        faMessage = "Created %s csv (Excel) files" % len(fileAnns)
    message += faMessage
    return fileAnns, message
Exemplo n.º 24
0
def roiFigure(conn, commandArgs):
    """
    This processes the script parameters, adding defaults if needed.
    Then calls a method to make the figure, and finally uploads and attaches
    this to the primary image.

    @param: session         The OMERO session
    @param: commandArgs     Map of String:Object parameters for the script.
                            Objects are not rtypes, since getValue() was
                            called when the map was processed below.
                            But, list and map objects may contain rtypes (need
                            to call getValue())

    @return:                the id of the originalFileLink child. (ID object,
                            not value)
    """

    log("ROI figure created by OMERO on %s" % date.today())
    log("")

    message = ""  # message to be returned to the client
    pixelIds = []
    imageIds = []
    imageLabels = []

    # function for getting image labels.
    def getImageNames(fullName, tagsList, pdList):
        name = fullName.split("/")[-1]
        return [name]

    # default function for getting labels is getName (or use datasets / tags)
    if "Image_Labels" in commandArgs:
        if commandArgs["Image_Labels"] == "Datasets":
            def getDatasets(name, tagsList, pdList):
                return [dataset for project, dataset in pdList]
            getLabels = getDatasets
        elif commandArgs["Image_Labels"] == "Tags":
            def getTags(name, tagsList, pdList):
                return tagsList
            getLabels = getTags
        else:
            getLabels = getImageNames
    else:
        getLabels = getImageNames

    # Get the images
    images, logMessage = scriptUtil.getObjects(conn, commandArgs)
    message += logMessage
    if not images:
        return None, message

    # Check for rectangular ROIs and filter images list
    images = [image for image in images if image.getROICount("Rectangle") > 0]
    if not images:
        message += "No rectangle ROI found."
        return None, message

    # Attach figure to the first image
    omeroImage = images[0]

    # process the list of images. If imageIds is not set, script can't run.
    log("Image details:")
    for image in images:
        imageIds.append(image.getId())
        pixelIds.append(image.getPrimaryPixels().getId())

    # a map of imageId : list of (project, dataset) names.
    pdMap = figUtil.getDatasetsProjectsFromImages(conn.getQueryService(),
                                                  imageIds)
    tagMap = figUtil.getTagsFromImages(conn.getMetadataService(), imageIds)
    # Build a legend entry for each image
    for image in images:
        name = image.getName()
        imageDate = image.getAcquisitionDate()
        iId = image.getId()
        tagsList = tagMap[iId]
        pdList = pdMap[iId]

        tags = ", ".join(tagsList)
        pdString = ", ".join(["%s/%s" % pd for pd in pdList])
        log(" Image: %s  ID: %d" % (name, iId))
        if imageDate:
            log("  Date: %s" % imageDate)
        else:
            log("  Date: not set")
        log("  Tags: %s" % tags)
        log("  Project/Datasets: %s" % pdString)

        imageLabels.append(getLabels(name, tagsList, pdList))

    # use the first image to define dimensions, channel colours etc.
    sizeX = omeroImage.getSizeX()
    sizeY = omeroImage.getSizeY()
    sizeZ = omeroImage.getSizeZ()
    sizeC = omeroImage.getSizeC()

    width = sizeX
    if "Width" in commandArgs:
        w = commandArgs["Width"]
        try:
            width = int(w)
        except:
            log("Invalid width: %s Using default value: %d" % (str(w), sizeX))

    height = sizeY
    if "Height" in commandArgs:
        h = commandArgs["Height"]
        try:
            height = int(h)
        except:
            log("Invalid height: %s Using default value" % (str(h), sizeY))

    log("Image dimensions for all panels (pixels): width: %d  height: %d"
        % (width, height))

    mergedIndexes = []    # the channels in the combined image,
    mergedColours = {}
    if "Merged_Colours" in commandArgs:
        cColourMap = commandArgs["Merged_Colours"]
        for c in cColourMap:
            rgb = cColourMap[c]
            try:
                rgb = int(rgb)
                cIndex = int(c)
            except ValueError:
                print "Merged_Colours map should be index:rgbInt. Not %s:%s" \
                    % (c, rgb)
                continue
            rgba = imgUtil.RGBIntToRGBA(rgb)
            mergedColours[cIndex] = rgba
            mergedIndexes.append(cIndex)
        mergedIndexes.sort()
    # make sure we have some merged channels
    if len(mergedIndexes) == 0:
        mergedIndexes = range(sizeC)
    mergedIndexes.reverse()

    mergedNames = False
    if "Merged_Names" in commandArgs:
        mergedNames = commandArgs["Merged_Names"]

    # Make channel-names map. If argument wasn't specified, name by index
    channelNames = {}
    if "Channel_Names" in commandArgs:
        cNameMap = commandArgs["Channel_Names"]
        for c in range(sizeC):
            if str(c) in cNameMap:
                channelNames[c] = cNameMap[str(c)]
            else:
                channelNames[c] = str(c)
    else:
        for c in range(sizeC):
            channelNames[c] = str(c)

    # Make split-indexes list. If no "Split_Indexes", show none:
    # http://www.openmicroscopy.org/community/viewtopic.php?f=4&t=940
    splitIndexes = []
    if "Split_Indexes" in commandArgs:
        for index in commandArgs["Split_Indexes"]:
            splitIndexes.append(index)

    colourChannels = True
    if "Split_Panels_Grey" in commandArgs and commandArgs["Split_Panels_Grey"]:
        colourChannels = False

    algorithm = ProjectionType.MAXIMUMINTENSITY
    if "Algorithm" in commandArgs:
        a = commandArgs["Algorithm"]
        if (a == "Mean Intensity"):
            algorithm = ProjectionType.MEANINTENSITY

    stepping = 1
    if "Stepping" in commandArgs:
        s = commandArgs["Stepping"]
        if (0 < s < sizeZ):
            stepping = s

    scalebar = None
    if "Scalebar" in commandArgs:
        sb = commandArgs["Scalebar"]
        try:
            scalebar = int(sb)
            if scalebar <= 0:
                scalebar = None
            else:
                log("Scalebar is %d microns" % scalebar)
        except:
            log("Invalid value for scalebar: %s" % str(sb))
            scalebar = None

    overlayColour = (255, 255, 255)
    if "Overlay_Colour" in commandArgs:
        r, g, b, a = OVERLAY_COLOURS[commandArgs["Overlay_Colour"]]
        overlayColour = (r, g, b)

    roiZoom = None
    if "ROI_Zoom" in commandArgs:
        roiZoom = float(commandArgs["ROI_Zoom"])
        if roiZoom == 0:
            roiZoom = None

    roiLabel = "FigureROI"
    if "ROI_Label" in commandArgs:
        roiLabel = commandArgs["ROI_Label"]

    spacer = (width/50) + 2

    fig = getSplitView(
        conn, imageIds, pixelIds, splitIndexes, channelNames, mergedNames,
        colourChannels, mergedIndexes, mergedColours, width, height,
        imageLabels, spacer, algorithm, stepping, scalebar, overlayColour,
        roiZoom, roiLabel)

    if fig is None:
        logMessage = "No figure produced"
        log("\n"+logMessage)
        message += logMessage
        return None, message
    # fig.show()        # bug-fixing only

    log("")
    figLegend = "\n".join(logStrings)

    # print figLegend    # bug fixing only
    format = commandArgs["Format"]

    figureName = "roiFigure"
    if "Figure_Name" in commandArgs:
        figureName = commandArgs["Figure_Name"]
        figureName = os.path.basename(figureName)
    output = "localfile"
    if format == 'PNG':
        output = output + ".png"
        figureName = figureName + ".png"
        fig.save(output, "PNG")
        mimetype = "image/png"
    elif format == 'TIFF':
        output = output + ".tiff"
        figureName = figureName + ".tiff"
        fig.save(output, "TIFF")
        mimetype = "image/tiff"
    else:
        output = output + ".jpg"
        figureName = figureName + ".jpg"
        fig.save(output)
        mimetype = "image/jpeg"

    # Use util method to upload the figure 'output' to the server, attaching
    # it to the omeroImage, adding the
    # figLegend as the fileAnnotation description.
    # Returns the id of the originalFileLink child. (ID object, not value)
    namespace = NSCREATED + "/omero/figure_scripts/ROI_Split_Figure"
    fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
        conn, output, omeroImage, output="ROI Split figure",
        mimetype=mimetype, ns=namespace, desc=figLegend,
        origFilePathAndName=figureName)
    message += faMessage

    return fileAnnotation, message
Exemplo n.º 25
0
def makeThumbnailFigure(conn, scriptParams):
    """
    Makes the figure using the parameters in @scriptParams, attaches the
    figure to the parent Project/Dataset, and returns the file-annotation ID

    @ returns       Returns the id of the originalFileLink child. (ID object,
                    not value)
    """

    log("Thumbnail figure created by OMERO")
    log("")

    message = ""

    # Get the objects (images or datasets)
    objects, logMessage = scriptUtil.getObjects(conn, scriptParams)
    message += logMessage
    if not objects:
        return None, message

    # Get parent
    parent = None
    if "Parent_ID" in scriptParams and len(scriptParams["IDs"]) > 1:
        if scriptParams["Data_Type"] == "Image":
            parent = conn.getObject("Dataset", scriptParams["Parent_ID"])
        else:
            parent = conn.getObject("Project", scriptParams["Parent_ID"])

    if parent is None:
        parent = objects[0]  # Attach figure to the first object

    parentClass = parent.OMERO_CLASS
    log("Figure will be linked to %s%s: %s" %
        (parentClass[0].lower(), parentClass[1:], parent.getName()))

    tagIds = []
    if "Tag_IDs" in scriptParams:
        tagIds = scriptParams['Tag_IDs']
    if len(tagIds) == 0:
        tagIds = None

    showUntagged = False
    if (tagIds):
        showUntagged = scriptParams["Show_Untagged_Images"]

    thumbSize = scriptParams["Thumbnail_Size"]
    maxColumns = scriptParams["Max_Columns"]

    figHeight = 0
    figWidth = 0
    dsCanvases = []

    if scriptParams["Data_Type"] == "Dataset":
        for dataset in objects:
            log("Dataset: %s     ID: %d" %
                (dataset.getName(), dataset.getId()))
            images = list(dataset.listChildren())
            title = dataset.getName().decode('utf8')
            dsCanvas = paintDatasetCanvas(conn,
                                          images,
                                          title,
                                          tagIds,
                                          showUntagged,
                                          length=thumbSize,
                                          colCount=maxColumns)
            if dsCanvas is None:
                continue
            dsCanvases.append(dsCanvas)
            figHeight += dsCanvas.size[1]
            figWidth = max(figWidth, dsCanvas.size[0])
    else:
        imageCanvas = paintDatasetCanvas(conn,
                                         objects,
                                         "",
                                         tagIds,
                                         showUntagged,
                                         length=thumbSize,
                                         colCount=maxColumns)
        dsCanvases.append(imageCanvas)
        figHeight += imageCanvas.size[1]
        figWidth = max(figWidth, imageCanvas.size[0])

    if len(dsCanvases) == 0:
        message += "No figure created"
        return None, message

    figure = Image.new("RGB", (figWidth, figHeight), WHITE)
    y = 0
    for ds in dsCanvases:
        imgUtil.pasteImage(ds, figure, 0, y)
        y += ds.size[1]

    log("")
    figLegend = "\n".join(logLines)

    format = scriptParams["Format"]
    figureName = scriptParams["Figure_Name"]
    figureName = os.path.basename(figureName)
    output = "localfile"

    if format == 'PNG':
        output = output + ".png"
        figureName = figureName + ".png"
        figure.save(output, "PNG")
        mimetype = "image/png"
    elif format == 'TIFF':
        output = output + ".tiff"
        figureName = figureName + ".tiff"
        figure.save(output, "TIFF")
        mimetype = "image/tiff"
    else:
        output = output + ".jpg"
        figureName = figureName + ".jpg"
        figure.save(output)
        mimetype = "image/jpeg"

    namespace = NSCREATED + "/omero/figure_scripts/Thumbnail_Figure"
    fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
        conn,
        output,
        parent,
        output="Thumbnail figure",
        mimetype=mimetype,
        ns=namespace,
        desc=figLegend,
        origFilePathAndName=figureName)
    message += faMessage

    return fileAnnotation, message
Exemplo n.º 26
0
def writeMovie(commandArgs, conn):
    """
    Makes the movie.

    @ returns        Returns the file annotation
    """
    log("Movie created by OMERO")
    log("")

    message = ""

    session = conn.c.sf
    updateService = session.getUpdateService()
    rawFileStore = session.createRawFileStore()

    # Get the images
    images, logMessage = scriptUtil.getObjects(conn, commandArgs)
    message += logMessage
    if not images:
        return None, message
    # Get the first valid image (should be expanded to process the list)
    omeroImage = images[0]

    if commandArgs["RenderingDef_ID"] >= 0:
        omeroImage._prepareRenderingEngine(rdid=commandArgs["RenderingDef_ID"])
    pixels = omeroImage.getPrimaryPixels()
    pixelsId = pixels.getId()

    sizeX = pixels.getSizeX()
    sizeY = pixels.getSizeY()
    sizeZ = pixels.getSizeZ()
    sizeC = pixels.getSizeC()
    sizeT = pixels.getSizeT()

    if (sizeX is None or sizeY is None or sizeZ is None or sizeT is None or
            sizeC is None):
        return

    if (pixels.getPhysicalSizeX() is None):
        commandArgs["Scalebar"] = 0

    cRange = range(0, sizeC)
    cWindows = None
    cColours = None
    if "ChannelsExtended" in commandArgs and \
            validChannels(commandArgs["ChannelsExtended"], sizeC):
        cRange = []
        cWindows = []
        cColours = []
        for c in commandArgs["ChannelsExtended"]:
            m = re.match('^(?P<i>\d+)(\|(?P<ws>\d+)' +
                         '\:(?P<we>\d+))?(\$(?P<c>.+))?$', c)
            if m is not None:
                cRange.append(int(m.group('i'))-1)
                cWindows.append([float(m.group('ws')), float(m.group('we'))])
                cColours.append(m.group('c'))
    elif "Channels" in commandArgs and \
            validChannels(commandArgs["Channels"], sizeC):
        cRange = commandArgs["Channels"]

    tzList = calculateRanges(sizeZ, sizeT, commandArgs)

    timeMap = calculateAquisitionTime(conn, pixelsId, cRange, tzList)
    if (timeMap is None):
        commandArgs["Show_Time"] = False
    if (timeMap is not None):
        if (len(timeMap) == 0):
            commandArgs["Show_Time"] = False

    frameNo = 1
    omeroImage.setActiveChannels(map(lambda x: x+1, cRange),
                                 cWindows,
                                 cColours)
    renderingEngine = omeroImage._re

    overlayColour = (255, 255, 255)
    if "Overlay_Colour" in commandArgs:
        r, g, b, a = COLOURS[commandArgs["Overlay_Colour"]]
        overlayColour = (r, g, b)

    canvasColour = tuple(COLOURS[commandArgs["Canvas_Colour"]][:3])
    mw = commandArgs["Min_Width"]
    if mw < sizeX:
        mw = sizeX
    mh = commandArgs["Min_Height"]
    if mh < sizeY:
        mh = sizeY
    ovlpos = None
    canvas = None
    if sizeX < mw or sizeY < mh:
        ovlpos = ((mw-sizeX) / 2, (mh-sizeY) / 2)
        canvas = Image.new("RGBA", (mw, mh), canvasColour)

    format = commandArgs["Format"]
    fileNames = []

    # add intro...
    if "Intro_Slide" in commandArgs and commandArgs["Intro_Slide"].id:
        intro_duration = commandArgs["Intro_Duration"]
        intro_fileId = commandArgs["Intro_Slide"].id.val
        intro_filenames = write_intro_end_slides(
            conn, commandArgs, intro_fileId, intro_duration, mw, mh)
        fileNames.extend(intro_filenames)

    # prepare watermark
    if "Watermark" in commandArgs and commandArgs["Watermark"].id:
        watermark = prepareWatermark(conn, commandArgs, mw, mh)

    # add movie frames...
    for tz in tzList:
        t = tz[0]
        z = tz[1]
        plane = getPlane(renderingEngine, z, t)
        planeImage = numpy.array(plane, dtype='uint32')
        planeImage = planeImage.byteswap()
        planeImage = planeImage.reshape(sizeX, sizeY)
        image = Image.frombuffer('RGBA', (sizeX, sizeY), planeImage.data,
                                 'raw', 'ARGB', 0, 1)
        if ovlpos is not None:
            image2 = canvas.copy()
            image2.paste(image, ovlpos, image)
            image = image2

        if "Scalebar" in commandArgs and commandArgs["Scalebar"]:
            image = addScalebar(
                commandArgs["Scalebar"], image, pixels, commandArgs)
        planeInfo = "z:"+str(z)+"t:"+str(t)
        if "Show_Time" in commandArgs and commandArgs["Show_Time"]:
            time = timeMap[planeInfo]
            image = addTimePoints(time, pixels, image, overlayColour)
        if "Show_Plane_Info" in commandArgs and \
                commandArgs["Show_Plane_Info"]:
            image = addPlaneInfo(z, t, pixels, image, overlayColour)
        if "Watermark" in commandArgs and commandArgs["Watermark"].id:
            image = pasteWatermark(image, watermark)
        if format == QT:
            filename = str(frameNo) + '.png'
            image.save(filename, "PNG")
        else:
            filename = str(frameNo) + '.jpg'
            image.save(filename, "JPEG")
        fileNames.append(filename)
        frameNo += 1

    # add exit frames... "outro"
    # add intro...
    if "Ending_Slide" in commandArgs and commandArgs["Ending_Slide"].id:
        end_duration = commandArgs["Ending_Duration"]
        end_fileId = commandArgs["Ending_Slide"].id.val
        end_filenames = write_intro_end_slides(
            conn, commandArgs, end_fileId, end_duration, mw, mh)
        fileNames.extend(end_filenames)

    filelist = ",".join(fileNames)

    ext = formatMap[format]
    movieName = "Movie"
    if "Movie_Name" in commandArgs:
        movieName = commandArgs["Movie_Name"]
        movieName = os.path.basename(movieName)
    if not movieName.endswith(".%s" % ext):
        movieName = "%s.%s" % (movieName, ext)

    # spaces etc in file name cause problems
    movieName = re.sub("[$&\;|\(\)<>' ]", "", movieName)
    framesPerSec = 2
    if "FPS" in commandArgs:
        framesPerSec = commandArgs["FPS"]
    output = "localfile.%s" % ext
    buildAVI(mw, mh, filelist, framesPerSec, output, format)
    mimetype = formatMimetypes[format]

    if not os.path.exists(output):
        print "mencoder Failed to create movie file: %s" % output
        return None, "Failed to create movie file: %s" % output
    if not commandArgs["Do_Link"]:
        originalFile = scriptUtil.createFile(
            updateService, output, mimetype, movieName)
        scriptUtil.uploadFile(rawFileStore, originalFile, movieName)
        return originalFile, message

    namespace = NSCREATED + "/omero/export_scripts/Make_Movie"
    fileAnnotation, annMessage = scriptUtil.createLinkFileAnnotation(
        conn, output, omeroImage, ns=namespace,
        mimetype=mimetype, origFilePathAndName=movieName)
    message += annMessage
    return fileAnnotation._obj, message
Exemplo n.º 27
0
def splitViewFigure(conn, scriptParams):    
    """
    Processes the arguments, populating defaults if necessary. Prints the details to log (fig-legend).
    Even handles missing arguments that are not optional (from when this ran from commandline with everything optional)
    then calls makeSplitViewFigure() to make the figure, attaches it to the Image as an 'originalFile' annotation,
    with fig-legend as the description. 
    
    @return: the id of the originalFileLink child. (ID object, not value) 
    """

    log("Split-View figure created by OMERO on %s" % date.today())
    log("")
    
    message="" # message to be returned to the client
    imageIds = []
    pixelIds = []
    imageLabels = []

    # function for getting image labels.
    def getLabels(fullName, tagsList, pdList):
        name = fullName.split("/")[-1]
        return [name]
        
    # default function for getting labels is getName (or use datasets / tags)
    if scriptParams["Image_Labels"] == "Datasets":
        def getDatasets(name, tagsList, pdList):
            return [dataset for project, dataset in pdList]
        getLabels = getDatasets
    elif scriptParams["Image_Labels"] == "Tags":
        def getTags(name, tagsList, pdList):
            return tagsList
        getLabels = getTags
   
    # Get the images
    images, logMessage = scriptUtil.getObjects(conn, scriptParams)
    message += logMessage
    if not images:
        return None, message

    # Attach figure to the first image
    omeroImage = images[0] 
            
    # process the list of images
    log("Image details:")
    for image in images:
        imageIds.append(image.getId())
        pixelIds.append(image.getPrimaryPixels().getId())
    
    pdMap = figUtil.getDatasetsProjectsFromImages(conn.getQueryService(), imageIds)    # a map of imageId : list of (project, dataset) names. 
    tagMap = figUtil.getTagsFromImages(conn.getMetadataService(), imageIds)
    # Build a legend entry for each image
    for image in images:
        name = image.getName()
        imageDate = image.getAcquisitionDate()
        iId = image.getId()
        tagsList = tagMap[iId]
        pdList = pdMap[iId]
        
        tags = ", ".join(tagsList)
        pdString = ", ".join(["%s/%s" % pd for pd in pdList])
        log(" Image: %s  ID: %d" % (name, iId))
        log("  Date: %s" % date.fromtimestamp(imageDate/1000))
        log("  Tags: %s" % tags)
        log("  Project/Datasets: %s" % pdString)
        
        imageLabels.append(getLabels(name, tagsList, pdList))
    
    
    # use the first image to define dimensions, channel colours etc. 
    sizeX = omeroImage.getSizeX()
    sizeY = omeroImage.getSizeY()
    sizeZ = omeroImage.getSizeZ()
    sizeC = omeroImage.getSizeC()
        
    
    # set image dimensions
    zStart = -1
    zEnd = -1
    if "Z_Start" in scriptParams:
        zStart = scriptParams["Z_Start"]
    if "Z_End" in scriptParams:
        zEnd = scriptParams["Z_End"]
    
    width = "Width" in scriptParams and scriptParams["Width"] or sizeX
    height = "Height" in scriptParams and scriptParams["Height"] or sizeY

    log("Image dimensions for all panels (pixels): width: %d  height: %d" % (width, height))
    
    # Make split-indexes list. If argument wasn't specified, include them all. 
    splitIndexes = []
    if "Split_Indexes" in scriptParams:
        splitIndexes = scriptParams["Split_Indexes"]
    else:
        splitIndexes = range(sizeC)
    
    # Make channel-names map. If argument wasn't specified, name by index
    channelNames = {}
    for c in range(sizeC):
        channelNames[c] = str(c)
    if "Channel_Names" in scriptParams:
        cNameMap = scriptParams["Channel_Names"]
        for c in cNameMap:
            index = int(c)
            channelNames[index] = cNameMap[c]      
                        
    mergedIndexes = []    # the channels in the combined image, 
    mergedColours = {}    
    if "Merged_Colours" in scriptParams:
        cColourMap = scriptParams["Merged_Colours"]
        for c in cColourMap:
            rgb = cColourMap[c]
            rgba = imgUtil.RGBIntToRGBA(rgb)
            mergedColours[int(c)] = rgba
            mergedIndexes.append(int(c))
        mergedIndexes.sort()
    else:
        mergedIndexes = range(sizeC)

    colourChannels = not scriptParams["Split_Panels_Grey"]

    algorithm = omero.constants.projection.ProjectionType.MAXIMUMINTENSITY
    if "Mean Intensity" == scriptParams["Algorithm"]:
        algorithm = omero.constants.projection.ProjectionType.MEANINTENSITY

    stepping = min(scriptParams["Stepping"], sizeZ)

    scalebar = None
    if "Scalebar" in scriptParams:
        scalebar = scriptParams["Scalebar"]
        log("Scalebar is %d microns" % scalebar)

    r,g,b,a = OVERLAY_COLOURS[scriptParams["Overlay_Colour"]]
    overlayColour = (r,g,b)
        
    mergedNames = scriptParams["Merged_Names"]
    
    print "splitIndexes", splitIndexes
    print "channelNames", channelNames
    print "colourChannels", colourChannels
    print "mergedIndexes", mergedIndexes
    print "mergedColours", mergedColours
    print "mergedNames", mergedNames
    fig = makeSplitViewFigure(conn, pixelIds, zStart, zEnd, splitIndexes, channelNames, colourChannels, 
                        mergedIndexes, mergedColours, mergedNames, width, height, imageLabels, algorithm, stepping, scalebar, overlayColour)

    figLegend = "\n".join(logStrings)
    format = JPEG
    if scriptParams["Format"] == "PNG":
        format = PNG
    output = scriptParams["Figure_Name"]

    if format == PNG:
        output = output + ".png"
        fig.save(output, "PNG")
        mimetype = "image/png"
    else:
        output = output + ".jpg"
        fig.save(output)
        mimetype = "image/jpeg"

    # Upload the figure 'output' to the server, creating a file annotation and attaching it to the omeroImage, adding the 
    # figLegend as the fileAnnotation description.
    namespace = omero.constants.namespaces.NSCREATED+"/omero/figure_scripts/Split_View_Figure"
    fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(conn, output, omeroImage,
        output="Split view figure", mimetype=mimetype, ns=namespace, desc=figLegend)
    message += faMessage
    
    return fileAnnotation, message
def run_processing(conn,script_params):
    file_anns = []
    message = ""
    imageIds = []
    image_id = script_params['ImageID']
    imageIds.append(image_id)
    image = conn.getObject("Image",image_id)
    if not image:
        message = 'Could not find specified image'
        return message
        
    file_id = script_params['AnnotationID']
    ann = conn.getObject("Annotation",file_id)
    if not ann:
        message = 'Could not find specified annotation'
        return message
    
    #other parameters
    if script_params['Convert_coordinates_to_nm']:
        cam_pix_size = script_params['Parent_Image_Pixel_Size']
    else:
        cam_pix_size = 1
    file_type = FILE_TYPES[script_params['File_Type']]
     
    path_to_ann = ann.getFile().getPath() + '/' + ann.getFile().getName()
    name,ext = os.path.splitext(path_to_ann)
    if ('txt' in ext) or ('csv' in ext):
        #get the path to the downloaded data
        path_to_data = download_data(ann)
        
        #get all the xy coords in that data
        locs = parse_sr_data(path_to_data,file_type,cam_pix_size)
        sizeC = len(locs)
        
        #get the rois to be processed
        rectangles = get_rectangles(conn,image_id)
        
        #calculate near neighbour distances
        nn_hist,bins = process_data(conn,image,file_type,sizeC,rectangles,locs)
        print 'nn_hist:',len(nn_hist),nn_hist[0].shape
        print 'bins:',bins.shape
        #put the data in an omero table
#         put_data_in_table(conn,imageIds,rectIds,nn_data,nn_hist,bins)
        
        #write the data to a csv
        file_name = "near_neighbours_" + ann.getFile().getName()[:-4] + '.csv'
        with file(file_name, 'w') as outfile:
            outfile.write('# nearest neighbour data for %s channels and %s ROIs: \n' % (len(nn_hist), nn_hist[0].shape[1] ))
            for c, channel in enumerate(nn_hist):
                data = np.concatenate((bins,channel),axis=1)
                np.savetxt(outfile, data, fmt='%-7.2f', delimiter=',', newline='\n')
                if c > 0:
                    outfile.write('# New channel\n')
                    
        new_file_ann, faMessage = script_util.createLinkFileAnnotation(
            conn, file_name, image, output="Wrote near neighbour csv (Excel) file",
            mimetype="text/csv", desc=None)
        if new_file_ann:
            file_anns.append(new_file_ann)

        if not file_anns:
            faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
                " more details"
        elif len(file_anns) > 1:
            faMessage = "Created %s csv (Excel) files" % len(file_anns)
    else:
        message = 'file annotation must be txt or csv'
        return message
    # clean up
    delete_downloaded_data(ann)
    
    message += faMessage
    return message
Exemplo n.º 29
0
def batchImageExport(conn, scriptParams):

    # for params with default values, we can get the value directly
    splitCs = scriptParams["Export_Individual_Channels"]
    mergedCs = scriptParams["Export_Merged_Image"]
    greyscale = scriptParams["Individual_Channels_Grey"]
    dataType = scriptParams["Data_Type"]
    ids = scriptParams["IDs"]
    folder_name = scriptParams["Folder_Name"]
    format = scriptParams["Format"]
    projectZ = "Choose_Z_Section" in scriptParams and scriptParams[
        "Choose_Z_Section"] == 'Max projection'

    if (not splitCs) and (not mergedCs):
        log("Not chosen to save Individual Channels OR Merged Image")
        return

    # check if we have these params
    channelNames = []
    if "Channel_Names" in scriptParams:
        channelNames = scriptParams["Channel_Names"]
    imgWidth = None
    if "Image_Width" in scriptParams:
        imgWidth = scriptParams["Image_Width"]

    # functions used below for each imaage.
    def getZrange(sizeZ, scriptParams):
        zRange = None
        if "Choose_Z_Section" in scriptParams:
            zChoice = scriptParams["Choose_Z_Section"]
            # NB: all Z indices in this script are 1-based
            if zChoice == 'ALL Z planes':
                zRange = (1, sizeZ + 1)
            elif "OR_specify_Z_index" in scriptParams:
                zIndex = scriptParams["OR_specify_Z_index"]
                zIndex = min(zIndex, sizeZ)
                zRange = (zIndex, )
            elif "OR_specify_Z_start_AND..." in scriptParams and "...specify_Z_end" in scriptParams:
                start = scriptParams["OR_specify_Z_start_AND..."]
                start = min(start, sizeZ)
                end = scriptParams["...specify_Z_end"]
                end = min(end, sizeZ)
                zStart = min(start,
                             end)  # in case user got zStart and zEnd mixed up
                zEnd = max(start, end)
                if zStart == zEnd:
                    zRange = (zStart, )
                else:
                    zRange = (zStart, zEnd + 1)
        return zRange

    def getTrange(sizeT, scriptParams):
        tRange = None
        if "Choose_T_Section" in scriptParams:
            tChoice = scriptParams["Choose_T_Section"]
            # NB: all T indices in this script are 1-based
            if tChoice == 'ALL T planes':
                tRange = (1, sizeT + 1)
            elif "OR_specify_T_index" in scriptParams:
                tIndex = scriptParams["OR_specify_T_index"]
                tIndex = min(tIndex, sizeT)
                tRange = (tIndex, )
            elif "OR_specify_T_start_AND..." in scriptParams and "...specify_T_end" in scriptParams:
                start = scriptParams["OR_specify_T_start_AND..."]
                start = min(start, sizeT)
                end = scriptParams["...specify_T_end"]
                end = min(end, sizeT)
                tStart = min(start,
                             end)  # in case user got zStart and zEnd mixed up
                tEnd = max(start, end)
                if tStart == tEnd:
                    tRange = (tStart, )
                else:
                    tRange = (tStart, tEnd + 1)
        return tRange

    # Get the images or datasets
    message = ""
    objects, logMessage = script_utils.getObjects(conn, scriptParams)
    message += logMessage
    if not objects:
        return None, message

    # Attach figure to the first image
    parent = objects[0]

    if dataType == 'Dataset':
        images = []
        for ds in objects:
            images.extend(list(ds.listChildren()))
        if not images:
            message += "No image found in dataset(s)"
            return None, message
    else:
        images = objects

    log("Processing %s images" % len(images))

    # somewhere to put images
    curr_dir = os.getcwd()
    exp_dir = os.path.join(curr_dir, folder_name)
    try:
        os.mkdir(exp_dir)
    except:
        pass

    # do the saving to disk
    if format == 'OME-TIFF':
        for img in images:
            log("Exporting image as OME-TIFF: %s" % img.getName())
            saveAsOmeTiff(conn, img, folder_name)

    else:
        for img in images:
            log("\n----------- Saving planes from image: '%s' ------------" %
                img.getName())
            sizeC = img.getSizeC()
            sizeZ = img.getSizeZ()
            sizeT = img.getSizeT()
            zRange = getZrange(sizeZ, scriptParams)
            tRange = getTrange(sizeT, scriptParams)
            log("Using:")
            if zRange is None: log("  Z-index: Last-viewed")
            elif len(zRange) == 1: log("  Z-index: %d" % zRange[0])
            else: log("  Z-range: %s-%s" % (zRange[0], zRange[1] - 1))
            if projectZ: log("  Z-projection: ON")
            if tRange is None: log("  T-index: Last-viewed")
            elif len(tRange) == 1: log("  T-index: %d" % tRange[0])
            else: log("  T-range: %s-%s" % (tRange[0], tRange[1] - 1))
            log("  Format: %s" % format)
            if imgWidth is None: log("  Image Width: no resize")
            else: log("  Image Width: %s" % imgWidth)
            log("  Greyscale: %s" % greyscale)
            log("Channel Rendering Settings:")
            for ch in img.getChannels():
                log("  %s: %d-%d" %
                    (ch.getLabel(), ch.getWindowStart(), ch.getWindowEnd()))

            savePlanesForImage(conn,
                               img,
                               sizeC,
                               splitCs,
                               mergedCs,
                               channelNames,
                               zRange,
                               tRange,
                               greyscale,
                               imgWidth,
                               projectZ=projectZ,
                               format=format,
                               folder_name=folder_name)

        # write log for exported images (not needed for ome-tiff)
        logFile = open(os.path.join(exp_dir, 'Batch_Image_Export.txt'), 'w')
        try:
            for s in logStrings:
                logFile.write(s)
                logFile.write("\n")
        finally:
            logFile.close()

    # zip everything up (unless we've only got a single ome-tiff)
    if format == 'OME-TIFF' and len(os.listdir(exp_dir)) == 1:
        export_file = os.path.join(folder_name, os.listdir(exp_dir)[0])
        mimetype = 'image/tiff'
    else:
        export_file = "%s.zip" % folder_name
        compress(export_file, folder_name)
        mimetype = 'application/zip'

    namespace = omero.constants.namespaces.NSCREATED + "/omero/export_scripts/Batch_Image_Export"
    fileAnnotation, annMessage = script_utils.createLinkFileAnnotation(
        conn,
        export_file,
        parent,
        output="Batch export zip",
        ns=namespace,
        mimetype=mimetype)
    message += annMessage
    return fileAnnotation, message
def process_data(conn,script_params,image,file_type,sizeC,rectangles,coords,rmax):
    """
    Calculates the ripley l function for coordinates in user-defined 
    rectangular region of interest
    
    @param conn:          the BlitzGateWay connection
    @param image:         the image being processed
    @param sizeC:         number of channels in the image
    @param rectangles:    the regions of interest
    @param coords:        the localisation coordinates
    @param rmax:          maximum distance scale for Ripley calculation
    
    """    
    updateService = conn.getUpdateService()
    parentDataset = image.getParent()
    parentProject = parentDataset.getParent()
    x = FILE_TYPES[file_type]['x_col']
    y = FILE_TYPES[file_type]['y_col']
    frame = FILE_TYPES[file_type]['frame']
    sizeZ = 1
    sizeT = image.getSizeT()
    if sizeT > 1:
        desc = image.getDescription()
        
        if desc:
            start = desc.index('Start')
            stop = desc.index('Stop')
            starts = desc[start+7:stop-3]
            starts = [int(s) for s in starts.split(',')] 
            stops = desc[stop+6:len(desc)-1]
            stops = [int(s) for s in stops.split(',')]
    else:
        starts = [1]
        stops = [coords[0][frame].max]
    
    new_images = []  
    new_ids = []
    for r,rect in enumerate(rectangles):
        planes = []
        ldf = []
        for c in range(sizeC):
            locs_df = coords[c]
            tt = 0
            for t in range(sizeT):
                conn.keepAlive()
                coords_in_frames = locs_df[(locs_df[frame]>= starts[t]) & (locs_df[frame]<= stops[t])]
                locs = get_coords_in_roi(coords_in_frames,rect,file_type)       
                box = [rect[0],rect[0]+rect[2],rect[1],rect[1]+rect[3]]
                pixelsX = math.ceil(float(rect[2] / 50.0))
                pixelsY = math.ceil(float(rect[3] / 50.0))
                print 'pixelsX,pixelsY:',pixelsX,pixelsY
                
                if len(locs.index)>0:
                    l = ripleykperpoint(locs.loc[:,[x,y]].values,rmax,box,0)
                    tx = np.linspace(box[0], box[1], pixelsX)
                    ty = np.linspace(box[2], box[3], pixelsY)
                    XI, YI = np.meshgrid(tx, ty)
                    print 'num x vals,num y vals:',locs.loc[:,[x]].values.shape,locs.loc[:,[y]].values.shape
                    rbf = Rbf(locs.loc[:,[x]].values, locs.loc[:,[y]].values, l, epsilon=2)
                    ZI = rbf(XI, YI)
                    ZI[ZI<0.0] = 0.0
                else:
                    ZI = np.zeros((pixelsY,pixelsX))
                    l = np.zeros(100)
                print 'ZI shape:',ZI.shape
                planes.append(ZI)
                ldf.append(pd.DataFrame(l))
        
        ripley_df = pd.concat(ldf,join='outer',axis=1)
                        
        def plane_gen():
            for p in planes:
                yield p  
                        
        imageName = 'Clusters_ROI_ID%s.ome.tif'%rect[-1]                
        description = "Cluster map for ROI%s of ImageID%s" % (rect[-1],image.getId())
        newImg = conn.createImageFromNumpySeq(
            plane_gen(), imageName,
            sizeZ=sizeZ, sizeC=sizeC, sizeT=sizeT,
            description=description)        
        
        new_images.append(newImg)
        new_ids.append(newImg.getId())
         
        file_name = "RLPP_ROI%s.csv" % (r)
        with open(file_name,'w') as f:
            f.write('# ripley per point data for %s channels and %s timepoints for ROI%s: \n' % (sizeC, sizeT, rect[-1]))
            ripley_df.to_csv(f,sep=',',float_format='%8.2f',index=False,encoding='utf-8')
    
        new_file_ann, faMessage = script_util.createLinkFileAnnotation(
            conn, file_name, newImg, output="wrote ripley per point data",
            mimetype="text/csv", desc=None)

    if script_params['New_Dataset'] and \
       len(script_params['Container_Name'].strip()) > 0:
        # create a new dataset for new images
        datasetName = script_params['Container_Name']
        print "\nMaking Dataset '%s' of Images from ROIs of Image: %s" \
            % (datasetName, image.getId())
        dataset = omero.model.DatasetI()
        dataset.name = rstring(datasetName)
        desc = "Images in this Dataset are from ROIs of parent Image:\n"\
            "  Name: %s\n  Image ID: %d" % (image.getName(), image.getId())
        dataset.description = rstring(desc)
        dataset = updateService.saveAndReturnObject(dataset)
        parentDataset = dataset
    else:
        # put new images in existing dataset
        dataset = None
        if parentDataset is not None and parentDataset.canLink():
            parentDataset = parentDataset._obj
        else:
            parentDataset = None
        parentProject = None    # don't add Dataset to parent.
    
    if parentDataset is None:
        link = None
        print "No dataset created or found for new images."\
            " Images will be orphans."
    else:
        link = []
        for cid in new_ids:
            dsLink = omero.model.DatasetImageLinkI()
            dsLink.parent = omero.model.DatasetI(
                parentDataset.id.val, False)
            dsLink.child = omero.model.ImageI(cid, False)
            updateService.saveObject(dsLink)
            link.append(dsLink)
        if parentProject and parentProject.canLink():
            # and put it in the   current project
            projectLink = omero.model.ProjectDatasetLinkI()
            projectLink.parent = omero.model.ProjectI(
                parentProject.getId(), False)
            projectLink.child = omero.model.DatasetI(
                dataset.id.val, False)
            updateService.saveAndReturnObject(projectLink)
    return new_images, dataset, link, new_ids
Exemplo n.º 31
0
def roiFigure(conn, commandArgs):
    """
    This processes the script parameters, adding defaults if needed.
    Then calls a method to make the figure, and finally uploads and attaches
    this to the primary image.

    @param: session         The OMERO session
    @param: commandArgs     Map of String:Object parameters for the script.
                            Objects are not rtypes, since getValue() was
                            called when the map was processed below.
                            But, list and map objects may contain rtypes (need
                            to call getValue())

    @return:                the id of the originalFileLink child. (ID object,
                            not value)
    """

    log("ROI figure created by OMERO on %s" % date.today())
    log("")

    message = ""  # message to be returned to the client
    pixelIds = []
    imageIds = []
    imageLabels = []

    # function for getting image labels.
    def getImageNames(fullName, tagsList, pdList):
        name = fullName.split("/")[-1]
        return [name]

    # default function for getting labels is getName (or use datasets / tags)
    if "Image_Labels" in commandArgs:
        if commandArgs["Image_Labels"] == "Datasets":
            def getDatasets(name, tagsList, pdList):
                return [dataset for project, dataset in pdList]
            getLabels = getDatasets
        elif commandArgs["Image_Labels"] == "Tags":
            def getTags(name, tagsList, pdList):
                return tagsList
            getLabels = getTags
        else:
            getLabels = getImageNames
    else:
        getLabels = getImageNames

    # Get the images
    images, logMessage = scriptUtil.getObjects(conn, commandArgs)
    message += logMessage
    if not images:
        return None, message

    # Check for rectangular ROIs and filter images list
    images = [image for image in images if image.getROICount("Rect") > 0]
    if not images:
        message += "No rectangle ROI found."
        return None, message

    # Attach figure to the first image
    omeroImage = images[0]

    # process the list of images
    log("Image details:")
    for image in images:
        imageIds.append(image.getId())
        pixelIds.append(image.getPrimaryPixels().getId())

    # a map of imageId : list of (project, dataset) names.
    pdMap = figUtil.getDatasetsProjectsFromImages(conn.getQueryService(),
                                                  imageIds)
    tagMap = figUtil.getTagsFromImages(conn.getMetadataService(), imageIds)
    # Build a legend entry for each image
    for image in images:
        name = image.getName()
        iId = image.getId()
        imageDate = image.getAcquisitionDate()
        tagsList = tagMap[iId]
        pdList = pdMap[iId]

        tags = ", ".join(tagsList)
        pdString = ", ".join(["%s/%s" % pd for pd in pdList])
        log(" Image: %s  ID: %d" % (name, iId))
        log("  Date: %s" % date.fromtimestamp(imageDate/1000))
        log("  Tags: %s" % tags)
        log("  Project/Datasets: %s" % pdString)

        imageLabels.append(getLabels(name, tagsList, pdList))

    # use the first image to define dimensions, channel colours etc.
    sizeX = omeroImage.getSizeX()
    sizeY = omeroImage.getSizeY()
    sizeZ = omeroImage.getSizeZ()
    sizeC = omeroImage.getSizeC()

    width = sizeX
    if "Width" in commandArgs:
        w = commandArgs["Width"]
        try:
            width = int(w)
        except:
            log("Invalid width: %s Using default value: %d" % (str(w), sizeX))

    height = sizeY
    if "Height" in commandArgs:
        h = commandArgs["Height"]
        try:
            height = int(h)
        except:
            log("Invalid height: %s Using default value" % (str(h), sizeY))

    log("Image dimensions for all panels (pixels): width: %d  height: %d"
        % (width, height))

    # the channels in the combined image,
    if "Merged_Channels" in commandArgs:
        # convert to 0-based
        mergedIndexes = [c-1 for c in commandArgs["Merged_Channels"]]
    else:
        mergedIndexes = range(sizeC)  # show all
    mergedIndexes.reverse()

    #  if no colours added, use existing rendering settings.
    mergedColours = {}
    # Actually, nicer to always use existing rendering settings.
    # if "Merged_Colours" in commandArgs:
    #     for i, c in enumerate(commandArgs["Merged_Colours"]):
    #         if c in COLOURS:
    #             mergedColours[i] = COLOURS[c]

    algorithm = ProjectionType.MAXIMUMINTENSITY
    if "Algorithm" in commandArgs:
        a = commandArgs["Algorithm"]
        if (a == "Mean Intensity"):
            algorithm = ProjectionType.MEANINTENSITY

    stepping = 1
    if "Stepping" in commandArgs:
        s = commandArgs["Stepping"]
        if (0 < s < sizeZ):
            stepping = s

    scalebar = None
    if "Scalebar" in commandArgs:
        sb = commandArgs["Scalebar"]
        try:
            scalebar = int(sb)
            if scalebar <= 0:
                scalebar = None
            else:
                log("Scalebar is %d microns" % scalebar)
        except:
            log("Invalid value for scalebar: %s" % str(sb))
            scalebar = None

    overlayColour = (255, 255, 255)
    if "Scalebar_Colour" in commandArgs:
        if commandArgs["Scalebar_Colour"] in OVERLAY_COLOURS:
            r, g, b, a = OVERLAY_COLOURS[commandArgs["Scalebar_Colour"]]
            overlayColour = (r, g, b)

    roiZoom = None
    if "Roi_Zoom" in commandArgs:
        roiZoom = float(commandArgs["Roi_Zoom"])
        if roiZoom == 0:
            roiZoom = None

    maxColumns = None
    if "Max_Columns" in commandArgs:
        maxColumns = commandArgs["Max_Columns"]

    showRoiDuration = False
    if "Show_ROI_Duration" in commandArgs:
        showRoiDuration = commandArgs["Show_ROI_Duration"]

    roiLabel = "FigureROI"
    if "Roi_Selection_Label" in commandArgs:
        roiLabel = commandArgs["Roi_Selection_Label"]

    spacer = (width/50) + 2

    print "showRoiDuration", showRoiDuration
    fig = getSplitView(
        conn, imageIds, pixelIds, mergedIndexes, mergedColours, width, height,
        imageLabels, spacer, algorithm, stepping, scalebar, overlayColour,
        roiZoom, maxColumns, showRoiDuration, roiLabel)

    # fig.show()        # bug-fixing only

    if fig is None:
        logMessage = "No figure produced"
        log("\n"+logMessage)
        message += logMessage
        return None, message
    figLegend = "\n".join(logStrings)

    # print figLegend    # bug fixing only
    format = commandArgs["Format"]

    figureName = "movieROIFigure"
    if "Figure_Name" in commandArgs:
        figureName = commandArgs["Figure_Name"]
        figureName = os.path.basename(figureName)
    output = "localfile"
    if format == 'PNG':
        output = output + ".png"
        figureName = figureName + ".png"
        fig.save(output, "PNG")
        mimetype = "image/png"
    elif format == 'TIFF':
        output = output + ".tiff"
        figureName = figureName + ".tiff"
        fig.save(output, "TIFF")
        mimetype = "image/tiff"
    else:
        output = output + ".jpg"
        figureName = figureName + ".jpg"
        fig.save(output)
        mimetype = "image/jpeg"

    # Use util method to upload the figure 'output' to the server, attaching
    # it to the omeroImage, adding the
    # figLegend as the fileAnnotation description.
    # Returns the id of the originalFileLink child. (ID object, not value)
    namespace = NSCREATED + "/omero/figure_scripts/Movie_ROI_Figure"
    fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
        conn, output, omeroImage, output="Movie ROI figure",
        mimetype=mimetype, ns=namespace, desc=figLegend,
        origFilePathAndName=figureName)
    message += faMessage

    return fileAnnotation, message
def run_processing(conn,script_params):
    """
    Collects params and starts the processing
    
    @param conn:          the BlitzGateWay connection
    @param script_params: the parameters collected from the script input
    """      
    file_anns = []
    message = ""
    
    rmax = script_params['Max_radius']
    expo_fit = script_params['Fit_exponential_model']
    expo_params = [script_params['Exponential_baseline'],\
                   script_params['Exponential_amplitude'],\
                   script_params['Exponential_decay']]
    expogauss_fit = script_params['Fit_exponential+gaussian_model']
    expogauss_params = [script_params['Density'],script_params['PSF'],\
                        script_params['Amplitude'],script_params['Decay'],\
                        script_params['Baseline']] 
         
    image_ids = script_params['IDs']
    for image in conn.getObjects("Image",image_ids):
        if not image:
            message = 'Could not find specified image'
            return message
        image_id = image.getId()
        sizeC = image.getSizeC()
        corr_func = script_params['Pair_correlation']
        if ('cross' in corr_func) and (sizeC != 2):
            return 'image should have two channels to cross-correlate' 
          
        message,output = process_data(conn,image,corr_func,rmax,expo_fit,expo_params,\
                                      expogauss_fit,expogauss_params)
        if output:
            file_name = "image%s_%s_correlation.csv" % (image_id,corr_func)
            with file(file_name, 'w') as outfile:
                outfile.write('# auto correlation data for %s ROIs and %s channels: \n' %\
                            (len(output), output[0]['correlation'].shape[1] ))
                
                for r, pair_corr in enumerate(output):
                    header = 'radius,'
                    data = np.concatenate((pair_corr['radius'],pair_corr['correlation']),axis=1)
                    for i in range(pair_corr['correlation'].shape[1]):
                        header += 'correlation,'
                    outfile.write('# Region of interest %s\n' % r)
                                
                    if pair_corr['exponential_fit'] is not None:
                        outfile.write('exponential fit params for ROI %s: \n' % r)
                        outfile.write('Baseline: %s \n' % pair_corr['exponential_params'][:,0])
                        outfile.write('Amplitude: %s \n' % pair_corr['exponential_params'][:,1])
                        outfile.write('Decay: %s \n' % pair_corr['exponential_params'][:,2])
                        for i in range(pair_corr['exponential_fit'].shape[1]):
                            header += 'fit,'
                        data = np.concatenate((data,pair_corr['exponential_fit']),axis=1)
                        
                    if pair_corr['exponential+gaussian_fit'] is not None:
                        outfile.write('exponential+gaussian fit params for ROI %s: \n' % r)
                        outfile.write('Density: %s \n' % pair_corr['exponential+gaussiam_params'][:,0])
                        outfile.write('PSF: %s \n' % pair_corr['exponential+gaussiam_params'][:,1])
                        outfile.write('Amplitude: %s \n' % pair_corr['exponential+gaussiam_params'][:,2])
                        outfile.write('Decay: %s \n' % pair_corr['exponential+gaussiam_params'][:,3])
                        outfile.write('Baseline: %s \n' % pair_corr['exponential+gaussiam_params'][:,4])
                        for i in range(pair_corr['exponential+gaussian_fit'].shape[1]):
                            header += 'fit,'
                        data = np.concatenate((data,pair_corr['exponential+gaussian_fit']),axis=1)                
                        
                    outfile.write(header[:-1] + '\n')    
                    np.savetxt(outfile, data, fmt='%-7.2f', delimiter=',', newline='\n')
                    
            new_file_ann, faMessage = script_util.createLinkFileAnnotation(
                conn, file_name, image, output="Pair correlation csv file",
                mimetype="text/csv", desc=None)
            if new_file_ann:
                file_anns.append(new_file_ann)
        
            if not file_anns:
                faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
                    " more details"
            elif len(file_anns) > 1:
                faMessage = "Created %s csv (Excel) files" % len(file_anns)
            
            message += faMessage
            
    if script_params['Email_Results'] and file_anns:
        email_results(conn,script_params,image_ids,file_anns)
    
    return message
def movieFigure(conn, commandArgs):
    """
    Makes the figure using the parameters in @commandArgs, attaches the figure
    to the parent Project/Dataset, and returns the file-annotation ID

    @param session      The OMERO session
    @param commandArgs  Map of parameters for the script
    @ returns           Returns the id of the originalFileLink child. (ID
                        object, not value)
    """

    log("Movie figure created by OMERO on %s" % date.today())
    log("")

    timeLabels = {"SECS_MILLIS": "seconds",
                  "SECS": "seconds",
                  "MINS": "minutes",
                  "HOURS": "hours",
                  "MINS_SECS": "mins:secs",
                  "HOURS_MINS": "hours:mins"}
    timeUnits = "SECS"
    if "Time_Units" in commandArgs:
        timeUnits = commandArgs["Time_Units"]
        # convert from UI name to timeLabels key
        timeUnits = timeUnits.replace(" ", "_")
    if timeUnits not in timeLabels.keys():
        timeUnits = "SECS"
    log("Time units are in %s" % timeLabels[timeUnits])

    pixelIds = []
    imageIds = []
    imageLabels = []
    message = ""  # message to be returned to the client

    # function for getting image labels.
    def getImageNames(fullName, tagsList, pdList):
        name = fullName.split("/")[-1]
        return [name]

    # default function for getting labels is getName (or use datasets / tags)
    if "Image_Labels" in commandArgs:
        if commandArgs["Image_Labels"] == "Datasets":
            def getDatasets(name, tagsList, pdList):
                return [dataset for project, dataset in pdList]
            getLabels = getDatasets
        elif commandArgs["Image_Labels"] == "Tags":
            def getTags(name, tagsList, pdList):
                return tagsList
            getLabels = getTags
        else:
            getLabels = getImageNames
    else:
        getLabels = getImageNames

    # Get the images
    images, logMessage = scriptUtil.getObjects(conn, commandArgs)
    message += logMessage
    if not images:
        return None, message

    # Attach figure to the first image
    omeroImage = images[0]

    # process the list of images
    log("Image details:")
    for image in images:
        imageIds.append(image.getId())
        pixelIds.append(image.getPrimaryPixels().getId())

    # a map of imageId : list of (project, dataset) names.
    pdMap = figUtil.getDatasetsProjectsFromImages(
        conn.getQueryService(), imageIds)
    tagMap = figUtil.getTagsFromImages(conn.getMetadataService(), imageIds)
    # Build a legend entry for each image
    for image in images:
        name = image.getName()
        iId = image.getId()
        imageDate = image.getAcquisitionDate()
        tagsList = tagMap[iId]
        pdList = pdMap[iId]

        tags = ", ".join(tagsList)
        pdString = ", ".join(["%s/%s" % pd for pd in pdList])
        log(" Image: %s  ID: %d" % (name, iId))
        if imageDate:
            log("  Date: %s" % imageDate)
        else:
            log("  Date: not set")
        log("  Tags: %s" % tags)
        log("  Project/Datasets: %s" % pdString)

        imageLabels.append(getLabels(name, tagsList, pdList))

    # use the first image to define dimensions, channel colours etc.
    sizeX = omeroImage.getSizeX()
    sizeY = omeroImage.getSizeY()
    sizeZ = omeroImage.getSizeZ()
    sizeT = omeroImage.getSizeT()

    tIndexes = []
    if "T_Indexes" in commandArgs:
        for t in commandArgs["T_Indexes"]:
            tIndexes.append(t)
        print "T_Indexes", tIndexes
    if len(tIndexes) == 0:      # if no t-indexes given, use all t-indices
        tIndexes = range(sizeT)

    zStart = -1
    zEnd = -1
    if "Z_Start" in commandArgs:
        zStart = commandArgs["Z_Start"]
    if "Z_End" in commandArgs:
        zEnd = commandArgs["Z_End"]

    width = sizeX
    if "Width" in commandArgs:
        width = commandArgs["Width"]

    height = sizeY
    if "Height" in commandArgs:
        height = commandArgs["Height"]

    spacer = (width/25) + 2

    algorithm = ProjectionType.MAXIMUMINTENSITY
    if "Algorithm" in commandArgs:
        a = commandArgs["Algorithm"]
        if (a == "Mean Intensity"):
            algorithm = ProjectionType.MEANINTENSITY

    stepping = 1
    if "Stepping" in commandArgs:
        s = commandArgs["Stepping"]
        if (0 < s < sizeZ):
            stepping = s

    scalebar = None
    if "Scalebar" in commandArgs:
        sb = commandArgs["Scalebar"]
        try:
            scalebar = int(sb)
            if scalebar <= 0:
                scalebar = None
            else:
                log("Scalebar is %d microns" % scalebar)
        except:
            log("Invalid value for scalebar: %s" % str(sb))
            scalebar = None

    overlayColour = (255, 255, 255)
    if "Scalebar_Colour" in commandArgs:
        r, g, b, a = OVERLAY_COLOURS[commandArgs["Scalebar_Colour"]]
        overlayColour = (r, g, b)

    maxColCount = 10
    if "Max_Columns" in commandArgs:
        maxColCount = commandArgs["Max_Columns"]

    figure = createMovieFigure(
        conn, pixelIds, tIndexes, zStart, zEnd, width, height, spacer,
        algorithm, stepping, scalebar, overlayColour, timeUnits, imageLabels,
        maxColCount)

    log("")
    figLegend = "\n".join(logLines)

    # print figLegend    # bug fixing only
    format = commandArgs["Format"]

    figureName = "movieFigure"
    if "Figure_Name" in commandArgs:
        figureName = str(commandArgs["Figure_Name"])
        figureName = os.path.basename(figureName)
    output = "localfile"
    if format == 'PNG':
        output = output + ".png"
        figureName = figureName + ".png"
        figure.save(output, "PNG")
        mimetype = "image/png"
    elif format == 'TIFF':
        output = output + ".tiff"
        figureName = figureName + ".tiff"
        figure.save(output, "TIFF")
        mimetype = "image/tiff"
    else:
        output = output + ".jpg"
        figureName = figureName + ".jpg"
        figure.save(output)
        mimetype = "image/jpeg"

    namespace = NSCREATED + "/omero/figure_scripts/Movie_Figure"
    fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
        conn, output, omeroImage, output="Movie figure", mimetype=mimetype,
        ns=namespace, desc=figLegend, origFilePathAndName=figureName)
    message += faMessage

    return fileAnnotation, message
def run_processing(conn, script_params):
    file_anns = []
    message = ""
    imageIds = []
    image_id = script_params['ImageID']
    imageIds.append(image_id)
    image = conn.getObject("Image", image_id)
    if not image:
        message = 'Could not find specified image'
        return message

    file_id = script_params['AnnotationID']
    ann = conn.getObject("Annotation", file_id)
    if not ann:
        message = 'Could not find specified annotation'
        return message

    radius = script_params['Radius']

    #other parameters
    if script_params['Convert_coordinates_to_nm']:
        cam_pix_size = script_params['Parent_Image_Pixel_Size']
    else:
        cam_pix_size = 1
    file_type = FILE_TYPES[script_params['File_Type']]

    path_to_ann = ann.getFile().getPath() + '/' + ann.getFile().getName()
    name, ext = os.path.splitext(path_to_ann)
    if ('txt' in ext) or ('csv' in ext):
        #get the path to the downloaded data
        path_to_data = download_data(ann)

        #get all the xy coords in that data
        locs = parse_sr_data(path_to_data, file_type, cam_pix_size)
        sizeC = len(locs)

        #get the rois to be processed
        rectangles = get_rectangles(conn, image_id)
        print 'rectanges:', rectangles

        #calculate local density
        locs_density = process_data(conn, image, file_type, sizeC, rectangles,
                                    locs, radius)

        #write the data to a csv
        file_name = "localisation_density_" + ann.getFile().getName(
        )[:-4] + '.csv'
        with file(file_name, 'a') as outfile:
            outfile.write(
                '# localisation density data for %s channels and %s ROIs: \n' %
                (sizeC, len(locs_density)))
            for r in range(len(locs_density)):
                outfile.write('# ROI %s\n' % rectangles[r][-1])
                outfile.write(
                    'Channel,%s,%s,Density within %s [nm]\n' %
                    (file_type['x_col'], file_type['y_col'], str(radius)))
                density = locs_density[r][1:, :]
                np.savetxt(outfile,
                           density,
                           fmt='%-7.2f',
                           delimiter=',',
                           newline='\n')

        new_file_ann, faMessage = script_util.createLinkFileAnnotation(
            conn,
            file_name,
            image,
            output="Wrote localisation density csv (Excel) file",
            mimetype="text/csv",
            desc=None)
        if new_file_ann:
            file_anns.append(new_file_ann)

        if not file_anns:
            faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
                " more details"
        elif len(file_anns) > 1:
            faMessage = "Created %s csv (Excel) files" % len(file_anns)
    else:
        message = 'file annotation must be txt or csv'
        return message
    # clean up
    delete_downloaded_data(ann)

    message += faMessage
    return message
Exemplo n.º 35
0
def run_processing(conn, script_params):
    """
    Collects params and starts the processing
    
    @param conn:          the BlitzGateWay connection
    @param script_params: the parameters collected from the script input
    """
    file_anns = []
    message = ""

    rmax = script_params['Max_radius']
    expo_fit = script_params['Fit_exponential_model']
    expo_params = [script_params['Exponential_baseline'],\
                   script_params['Exponential_amplitude'],\
                   script_params['Exponential_decay']]
    expogauss_fit = script_params['Fit_exponential+gaussian_model']
    expogauss_params = [script_params['Density'],script_params['PSF'],\
                        script_params['Amplitude'],script_params['Decay'],\
                        script_params['Baseline']]

    image_ids = script_params['IDs']
    for image in conn.getObjects("Image", image_ids):
        if not image:
            message = 'Could not find specified image'
            return message
        image_id = image.getId()
        sizeC = image.getSizeC()
        corr_func = script_params['Pair_correlation']
        if ('cross' in corr_func) and (sizeC != 2):
            return 'image should have two channels to cross-correlate'

        message,output = process_data(conn,image,corr_func,rmax,expo_fit,expo_params,\
                                      expogauss_fit,expogauss_params)
        if output:
            file_name = "image%s_%s_correlation.csv" % (image_id, corr_func)
            with file(file_name, 'w') as outfile:
                outfile.write('# auto correlation data for %s ROIs and %s channels: \n' %\
                            (len(output), output[0]['correlation'].shape[1] ))

                for r, pair_corr in enumerate(output):
                    header = 'radius,'
                    data = np.concatenate(
                        (pair_corr['radius'], pair_corr['correlation']),
                        axis=1)
                    for i in range(pair_corr['correlation'].shape[1]):
                        header += 'correlation,'
                    outfile.write('# Region of interest %s\n' % r)

                    if pair_corr['exponential_fit'] is not None:
                        outfile.write('exponential fit params for ROI %s: \n' %
                                      r)
                        outfile.write('Baseline: %s \n' %
                                      pair_corr['exponential_params'][:, 0])
                        outfile.write('Amplitude: %s \n' %
                                      pair_corr['exponential_params'][:, 1])
                        outfile.write('Decay: %s \n' %
                                      pair_corr['exponential_params'][:, 2])
                        for i in range(pair_corr['exponential_fit'].shape[1]):
                            header += 'fit,'
                        data = np.concatenate(
                            (data, pair_corr['exponential_fit']), axis=1)

                    if pair_corr['exponential+gaussian_fit'] is not None:
                        outfile.write(
                            'exponential+gaussian fit params for ROI %s: \n' %
                            r)
                        outfile.write(
                            'Density: %s \n' %
                            pair_corr['exponential+gaussiam_params'][:, 0])
                        outfile.write(
                            'PSF: %s \n' %
                            pair_corr['exponential+gaussiam_params'][:, 1])
                        outfile.write(
                            'Amplitude: %s \n' %
                            pair_corr['exponential+gaussiam_params'][:, 2])
                        outfile.write(
                            'Decay: %s \n' %
                            pair_corr['exponential+gaussiam_params'][:, 3])
                        outfile.write(
                            'Baseline: %s \n' %
                            pair_corr['exponential+gaussiam_params'][:, 4])
                        for i in range(pair_corr['exponential+gaussian_fit'].
                                       shape[1]):
                            header += 'fit,'
                        data = np.concatenate(
                            (data, pair_corr['exponential+gaussian_fit']),
                            axis=1)

                    outfile.write(header[:-1] + '\n')
                    np.savetxt(outfile,
                               data,
                               fmt='%-7.2f',
                               delimiter=',',
                               newline='\n')

            new_file_ann, faMessage = script_util.createLinkFileAnnotation(
                conn,
                file_name,
                image,
                output="Pair correlation csv file",
                mimetype="text/csv",
                desc=None)
            if new_file_ann:
                file_anns.append(new_file_ann)

            if not file_anns:
                faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
                    " more details"
            elif len(file_anns) > 1:
                faMessage = "Created %s csv (Excel) files" % len(file_anns)

            message += faMessage

    if script_params['Email_Results'] and file_anns:
        email_results(conn, script_params, image_ids, file_anns)

    return message
Exemplo n.º 36
0
def run_processing(conn,script_params):
    file_anns = []
    message = ""
    
    image_id = script_params['ImageID']
    image = conn.getObject("Image",image_id)
    if not image:
        message = 'Could not find specified image'
        return message
        
    file_id = script_params['AnnotationID']
    ann = conn.getObject("Annotation",file_id)
    if not ann:
        message = 'Could not find specified annotation'
        return message
    
    #other parameters
    sr_pix_size = script_params['SR_pixel_size']
    if script_params['Convert_coordinates_to_nm']:
        cam_pix_size = script_params['Parent_Image_Pixel_Size']
    else:
        cam_pix_size = 1
    file_type = script_params['File_Type']
    rmax = script_params['Max_radius']
     
    path_to_ann = ann.getFile().getPath() + '/' + ann.getFile().getName()
    name,ext = os.path.splitext(path_to_ann)
    if ('txt' in ext) or ('csv' in ext):
        path_to_data = download_data(ann)
        coords = parse_sr_data(path_to_data,file_type,cam_pix_size)
        rectangles = get_rectangles(conn,image_id,sr_pix_size)
        roi_data = process_data(conn,image,rectangles,coords,rmax)
        
        file_name = "ripleyl_plot_" + ann.getFile().getName()[:-4] + '.csv'
        print file_name
        try:
            f = open(file_name,'w')
            for chan in range(len(roi_data)):
                for r in range(roi_data.shape[0]):
                    row = roi_data[chan][r,:]
                    f.write(','.join([str(c) for c in row])+'\n')
        finally:
            f.close()

        new_file_ann, faMessage = script_util.createLinkFileAnnotation(
            conn, file_name, image, output="Ripley L Plot csv (Excel) file",
            mimetype="text/csv", desc=None)
        if new_file_ann:
            file_anns.append(new_file_ann)

        if not file_anns:
            faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
                " more details"
        elif len(file_anns) > 1:
            faMessage = "Created %s csv (Excel) files" % len(file_anns)
        message += faMessage
    else:
        message = 'file annotation must be txt or csv'
        return message
    # clean up
    delete_downloaded_data(ann)
    
    message += faMessage
    return message