def Project(self, projType):
import numpy as np
from PYME.DSView.image import ImageStack
from PYME.DSView import ViewIm3D
import os
if projType == 'mean':
filt_ims = [np.atleast_3d(self.image.data[:,:,:,chanNum].mean(2)) for chanNum in range(self.image.data.shape[3])]
elif projType == 'max':
filt_ims = [np.atleast_3d(self.image.data[:,:,:,chanNum].max(2)) for chanNum in range(self.image.data.shape[3])]
fns = os.path.split(self.image.filename)[1]
im = ImageStack(filt_ims, titleStub = '%s - %s' %(fns, projType))
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
im.mdh['Processing.Projection'] = projType
if self.dsviewer.mode == 'visGUI':
mode = 'visGUI'
else:
mode = 'lite'
dv = ViewIm3D(im, mode=mode, glCanvas=self.dsviewer.glCanvas)
#set scaling to (0,1)
for i in range(im.data.shape[3]):
dv.do.Gains[i] = 1.0
def OnExtractPupil(self, event):
import numpy as np
import pylab
from PYME.PSFGen import fourierHNA
from PYME.DSView.image import ImageStack
from PYME.DSView import ViewIm3D
z_ = np.arange(self.image.data.shape[2])*self.image.mdh['voxelsize.z']*1.e3
z_ -= z_.mean()
self.configure_traits(kind='modal')
pupil = fourierHNA.ExtractPupil(np.maximum(self.image.data[:,:,:] - .001, 0), z_, self.image.mdh['voxelsize.x']*1e3, self.wavelength, self.NA, nIters=self.iterations, size=self.pupilSize)
pylab.figure()
pylab.subplot(121)
pylab.imshow(np.abs(pupil), interpolation='nearest')
pylab.subplot(122)
pylab.imshow(np.angle(pupil)*(np.abs(pupil) > 0), interpolation='nearest')
pupil = pupil*(np.abs(pupil) > 0)
#im = ImageStack([np.abs(pupil), np.angle(pupil)*(np.abs(pupil) > 0)], titleStub = 'Extracted Pupil')
im = ImageStack(pupil, titleStub = 'Extracted Pupil')
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
#im.mdh['Processing.CropROI'] = roi
mode = 'pupil'
dv = ViewIm3D(im, mode=mode, glCanvas=self.dsviewer.glCanvas, parent=wx.GetTopLevelParent(self.dsviewer))
def OnSegmentAnneal(self, event):
newImages = [np.zeros(self.image.data.shape[:3], "b") for i in range(self.image.data.shape[3])]
im = ImageStack(newImages)
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh["Parent"] = self.image.filename
if self.dsviewer.mode == "visGUI":
mode = "visGUI"
else:
mode = "lite"
self.res = ViewIm3D(im, parent=wx.GetTopLevelParent(self.dsviewer), mode=mode, glCanvas=self.dsviewer.glCanvas)
self.panAnneal = SegmentationPanel(self.res, self.image, newImages)
self.pinfo1 = (
aui.AuiPaneInfo()
.Name("annealPanel")
.Left()
.Caption("Segmentation")
.DestroyOnClose(True)
.CloseButton(False)
.MinimizeButton(True)
.MinimizeMode(aui.AUI_MINIMIZE_CAPT_SMART | aui.AUI_MINIMIZE_POS_RIGHT)
) # .MinimizeButton(True).MinimizeMode(aui.AUI_MINIMIZE_CAPT_SMART|aui.AUI_MINIMIZE_POS_RIGHT)#.CaptionVisible(False)
self.res._mgr.AddPane(self.panAnneal, self.pinfo1)
self.res._mgr.Update()
def OnApplyThreshold(self, event):
import numpy as np
from PYME.DSView.image import ImageStack
from PYME.DSView import ViewIm3D
filt_ims = [np.atleast_3d(self.image.data[:,:,:,chanNum].squeeze() > self.dsviewer.do.thresholds[chanNum]) for chanNum in range(self.image.data.shape[3])]
if self.dsviewer.mode == 'visGUI':
mode = 'visGUI'
elif self.dsviewer.mode == 'graph':
mode = 'graph'
filt_ims = [fi.squeeze() for fi in filt_ims]
else:
mode = 'lite'
im = ImageStack(sum(filt_ims) > 0.5, titleStub = 'Thresholded Image')
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
dv = ViewIm3D(im, mode=mode, glCanvas=self.dsviewer.glCanvas, parent=wx.GetTopLevelParent(self.dsviewer))
#set scaling to (0,1)
for i in range(im.data.shape[3]):
dv.do.Gains[i] = 1.0
def OnLabel(self, event):
import numpy as np
from scipy import ndimage
from PYME.DSView.image import ImageStack
from PYME.DSView import ViewIm3D
filt_ims = [np.atleast_3d(self.image.data[:,:,:,chanNum].squeeze() > self.dsviewer.do.thresholds[chanNum]) for chanNum in range(self.image.data.shape[3])]
#print sum(filt_ims).shape
mask = sum(filt_ims) > 0.5
labs, nlabs = ndimage.label(mask)
im = ImageStack(labs, titleStub = 'Thresholded Image')
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
#im.mdh['Processing.CropROI'] = roi
if self.dsviewer.mode == 'visGUI':
mode = 'visGUI'
else:
mode = 'lite'
dv = ViewIm3D(im, mode=mode, glCanvas=self.dsviewer.glCanvas, parent=wx.GetTopLevelParent(self.dsviewer))
#set scaling to (0,1)
for i in range(im.data.shape[3]):
dv.do.Gains[i] = 1.0
def OnPSFFromPupil(self, event):
import numpy as np
#import pylab
from PYME.PSFGen import fourierHNA
from PYME.DSView.image import ImageStack
from PYME.DSView import ViewIm3D
self.configure_traits(kind='modal')
z_ = np.arange(self.sizeZ) * float(self.zSpacing)
z_ -= z_.mean()
ps = fourierHNA.PsfFromPupil(
self.image.data[:, :], z_, self.image.mdh['voxelsize.x'] * 1e3,
self.wavelength) #, shape = [self.sizeX, self.sizeX])
ps = ps / ps[:, :, self.sizeZ / 2].sum()
im = ImageStack(ps, titleStub='Generated PSF')
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
#im.mdh['Processing.CropROI'] = roi
mode = 'psf'
dv = ViewIm3D(im,
mode=mode,
glCanvas=self.dsviewer.glCanvas,
parent=wx.GetTopLevelParent(self.dsviewer))
def OnGaussianFilter(self, event):
import numpy as np
from scipy.ndimage import gaussian_filter
from PYME.DSView.image import ImageStack
from PYME.DSView import ViewIm3D
dlg = wx.TextEntryDialog(self.dsviewer, 'Blur size [pixels]:', 'Gaussian Blur', '[1,1,1]')
if dlg.ShowModal() == wx.ID_OK:
sigmas = eval(dlg.GetValue())
#print sigmas
#print self.images[0].img.shape
filt_ims = [np.atleast_3d(gaussian_filter(self.image.data[:,:,:,chanNum].squeeze(), sigmas)) for chanNum in range(self.image.data.shape[3])]
im = ImageStack(filt_ims, titleStub = 'Filtered Image')
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
im.mdh['Processing.GaussianFilter'] = sigmas
if self.dsviewer.mode == 'visGUI':
mode = 'visGUI'
else:
mode = 'lite'
dv = ViewIm3D(im, mode=mode, glCanvas=self.dsviewer.glCanvas)
#set scaling to (0,1)
for i in range(im.data.shape[3]):
dv.do.Gains[i] = 1.0
dlg.Destroy()
def OnSubtractBackground(self, event):
from PYME.DSView.image import ImageStack
from PYME.DSView import ViewIm3D
from PYME.PSFEst import extractImages
d_bg = extractImages.backgroundCorrectPSFWF(self.image.data[:,:,:])
im = ImageStack(d_bg, titleStub = 'Filtered Image')
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
dv = ViewIm3D(im, mode='psf', glCanvas=self.dsviewer.glCanvas)
def filter(self, image):
if self.processFramesIndividually:
filt_ims = []
for chanNum in range(image.data.shape[3]):
filt_ims.append(np.concatenate([np.atleast_3d(self.applyFilter(image.data[:,:,i,chanNum].squeeze().astype('f'), chanNum, i, image)) for i in range(image.data.shape[2])], 2))
else:
filt_ims = [np.atleast_3d(self.applyFilter(image.data[:,:,:,chanNum].squeeze().astype('f'), chanNum, 0, image)) for chanNum in range(image.data.shape[3])]
im = ImageStack(filt_ims, titleStub = self.outputName)
im.mdh.copyEntriesFrom(image.mdh)
im.mdh['Parent'] = image.filename
self.completeMetadata(im)
return im
def OnDiagSplit(self, event):
import numpy as np
#from scipy.ndimage import gaussian_filter
from PYME.DSView.image import ImageStack
from PYME.DSView import ViewIm3D
#dlg = wx.TextEntryDialog(self.dsviewer, 'Blur size [pixels]:', 'Gaussian Blur', '[1,1,1]')
#if dlg.ShowModal() == wx.ID_OK:
#sigmas = eval(dlg.GetValue())
#print sigmas
#print self.images[0].img.shape
x0, x1, y0, y1 = [self.do.selection_begin_x, self.do.selection_end_x, self.do.selection_begin_y, self.do.selection_end_y]
dx = x1 - x0
dy = y1 - y0
m = dy/dx
c = y0 - m*x0
d = self.image.data
X, Y = np.ogrid[:d.shape[0], :d.shape[1]]
msk = Y > (m*X + c)
#filt_ims = [np.atleast_3d(self.image.data[roi[0][0]:roi[0][1],roi[1][0]:roi[1][1],:,chanNum].squeeze()) for chanNum in range(self.image.data.shape[3])]
imn = (d[:,:,:, 0] - self.do.Offs[0])*self.do.Gains[0]*msk[:,:, None] + (d[:,:,:,1]- self.do.Offs[1])*self.do.Gains[1]*(1-msk)[:,:,None]
im = ImageStack(imn, titleStub = 'Cropped Image')
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
#im.mdh['Processing.CropROI'] = roi
if self.dsviewer.mode == 'visGUI':
mode = 'visGUI'
else:
mode = 'lite'
dv = ViewIm3D(im, mode=mode, glCanvas=self.dsviewer.glCanvas, parent=wx.GetTopLevelParent(self.dsviewer))
#set scaling to (0,1)
for i in range(im.data.shape[3]):
dv.do.Gains[i] = 1.0
def loadInput(filename):
'''Load input data from a file
Currently only handles images (anything you can open in dh5view). TODO -
extend to other types.
'''
#modify this to allow for different file types - currently only supports images
return ImageStack(filename=filename, haveGUI=False)
def View3D(data, titleStub='Untitled Image', mdh = None, mode='lite',
parent=None, glCanvas=None):
im = ImageStack(data = data, mdh = mdh, titleStub=titleStub)
dvf = DSViewFrame(im, mode=mode, size=(500, 500),
parent=parent, glCanvas=glCanvas)
dvf.SetSize((500,500))
dvf.Show()
return dvf
def OnGaussianFilter(self, event):
import numpy as np
from scipy.ndimage import gaussian_filter1d, convolve1d
from PYME.DSView.image import ImageStack
from PYME.DSView import ViewIm3D
#dlg = wx.TextEntryDialog(self.dsviewer, 'Blur size [pixels]:', 'Gaussian Blur', '[1,1,1]')
if True: #dlg.ShowModal() == wx.ID_OK:
#sigmas = eval(dlg.GetValue())
#print sigmas
#print self.images[0].img.shape
#filt_ims = [np.atleast_3d(gaussian_filter(self.image.data[:,:,:,chanNum].squeeze(), sigmas)) for chanNum in range(self.image.data.shape[3])]
ims = self.image.data[:,:,0,0].astype('f')
ims = ims - np.median(ims, 0)
ims = gaussian_filter1d(ims, 2, 1)
ims = convolve1d(ims, np.ones(10), 0)
ims = np.rollaxis(ims, 0, 3)
#ims = (ims - ims.min()).astype('uint16')
im = ImageStack(ims, titleStub = 'Filtered Image')
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
#im.mdh['Processing.'] = sigmas
if self.dsviewer.mode == 'visGUI':
mode = 'visGUI'
else:
mode = 'lite'
dv = ViewIm3D(im, mode=mode, glCanvas=self.dsviewer.glCanvas)
#set scaling to (0,1)
for i in range(im.data.shape[3]):
dv.do.Gains[i] = 1.0
#imfc = MultiChannelImageViewFrame(self.parent, self.parent.glCanvas, filt_ims, self.image.names, title='Filtered Image - %3.1fnm bins' % self.image.pixelSize)
#self.parent.generatedImages.append(imfc)
#imfc.Show()
dlg.Destroy()
def OnSegmentAnneal(self, event):
newImages = [np.zeros(self.image.data.shape[:3], 'b') for i in range(self.image.data.shape[3])]
im = ImageStack(newImages)
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
if self.dsviewer.mode == 'visGUI':
mode = 'visGUI'
else:
mode = 'lite'
self.res = ViewIm3D(im, parent=wx.GetTopLevelParent(self.dsviewer), mode = mode, glCanvas = self.dsviewer.glCanvas)
self.panAnneal = SegmentationPanel(self.res, self.image, newImages)
self.pinfo1 = aui.AuiPaneInfo().Name("annealPanel").Left().Caption('Segmentation').DestroyOnClose(True).CloseButton(False).MinimizeButton(True).MinimizeMode(aui.AUI_MINIMIZE_CAPT_SMART|aui.AUI_MINIMIZE_POS_RIGHT)#.MinimizeButton(True).MinimizeMode(aui.AUI_MINIMIZE_CAPT_SMART|aui.AUI_MINIMIZE_POS_RIGHT)#.CaptionVisible(False)
self.res._mgr.AddPane(self.panAnneal, self.pinfo1)
self.res._mgr.Update()
def _fetchMap(self, md, mapName):
'''retrive a map, with a given name. First try and get it from the Queue,
then try finding it locally'''
try:
varmap = md.taskQueue.getQueueData(md.dataSourceID, 'MAP', mapName)
except:
fn = getFullExistingFilename(mapName)
varmap = ImageStack(filename=fn).data[:,:,0].squeeze() #this should handle .tif, .h5, and a few others
return varmap
def OnLabelWatershed(self, event):
import numpy as np
from PYME.cpmath import watershed
from PYME.DSView.image import ImageStack
from PYME.DSView import ViewIm3D
nChans = self.image.data.shape[3]
filt_ims = [np.atleast_3d(self.image.data[:,:,:,chanNum].squeeze()) for chanNum in range(nChans)]
img = (-sum([im/im.max() for im in filt_ims])*(2**15)/nChans).astype('int16')
mask = (sum([filt_ims[chanNum] > self.do.thresholds[chanNum] for chanNum in range(nChans)]) > .5).astype('int16')
#self.image.labelThresholds = [(self.dsviewer.do.Offs[chanNum] + 0.5/self.dsviewer.do.Gains[chanNum]) for chanNum in range(self.image.data.shape[3])]
#print sum(filt_ims).shape
labs = watershed.fast_watershed(img, self.image.labels.astype('int16'), mask=mask)
#store a copy in the image for measurements etc ...
self.image.labels = labs
im = ImageStack(labs, titleStub = 'Labelled Image')
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
im.mdh['Labelling.WatershedThresholds'] = self.do.thresholds
#im.mdh['Labelling.MinSize'] = rSize
#im.mdh['Labelling.Thresholds'] = self.image.labelThresholds
#im.mdh['Processing.CropROI'] = roi
if self.dsviewer.mode == 'visGUI':
mode = 'visGUI'
else:
mode = 'lite'
dv = ViewIm3D(im, mode=mode, glCanvas=self.dsviewer.glCanvas, parent=wx.GetTopLevelParent(self.dsviewer))
#set scaling to (0,1)
for i in range(im.data.shape[3]):
dv.do.Gains[i] = 1.0
def OnCrop(self, event):
import numpy as np
#from scipy.ndimage import gaussian_filter
from PYME.DSView.image import ImageStack
from PYME.DSView import ViewIm3D
#dlg = wx.TextEntryDialog(self.dsviewer, 'Blur size [pixels]:', 'Gaussian Blur', '[1,1,1]')
#if dlg.ShowModal() == wx.ID_OK:
#sigmas = eval(dlg.GetValue())
#print sigmas
#print self.images[0].img.shape
roi = [[self.do.selection_begin_x, self.do.selection_end_x + 1],[self.do.selection_begin_y, self.do.selection_end_y +1], [0, self.image.data.shape[2]]]
filt_ims = [np.atleast_3d(self.image.data[roi[0][0]:roi[0][1],roi[1][0]:roi[1][1],:,chanNum].squeeze()) for chanNum in range(self.image.data.shape[3])]
im = ImageStack(filt_ims, titleStub = 'Cropped Image')
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
im.mdh['Processing.CropROI'] = roi
vx, vy, vz = self.image.voxelsize
ox, oy, oz = self.image.origin
im.mdh['Origin.x'] = ox + roi[0][0]*vx
im.mdh['Origin.y'] = oy + roi[1][0]*vy
im.mdh['Origin.z'] = oz
if self.dsviewer.mode == 'visGUI':
mode = 'visGUI'
else:
mode = 'lite'
dv = ViewIm3D(im, mode=mode, glCanvas=self.dsviewer.glCanvas, parent=wx.GetTopLevelParent(self.dsviewer))
#set scaling to (0,1)
for i in range(im.data.shape[3]):
dv.do.Gains[i] = 1.0
def OnInit(self):
import sys
from optparse import OptionParser
op = OptionParser(usage = 'usage: %s [options] [filename]' % sys.argv[0])
op.add_option('-m', '--mode', dest='mode', help="mode (or personality), as defined in PYME/DSView/modules/__init__.py")
op.add_option('-q', '--queueURI', dest='queueURI', help="the Pyro URI of the task queue - to avoid having to use the nameserver lookup")
op.add_option('-t', '--test', dest='test', help="Show a test image", action="store_true", default=False)
options, args = op.parse_args()
if options.test:
import pylab
im = ImageStack(pylab.randn(100,100))
elif len (args) > 0:
im = ImageStack(filename=args[0], queueURI=options.queueURI)
else:
im = ImageStack(queueURI=options.queueURI)
if options.mode == None:
mode = im.mode
else:
mode = options.mode
vframe = DSViewFrame(im, None, im.filename, mode = mode)
self.SetTopWindow(vframe)
vframe.Show(1)
if len(args) > 1:
for fn in args[1:]:
im = ImageStack(filename=fn)
ViewIm3D(im)
return 1
def OnPSFFromPupil(self, event):
import numpy as np
#import pylab
from PYME.PSFGen import fourierHNA
from PYME.DSView.image import ImageStack
from PYME.DSView import ViewIm3D
self.configure_traits(kind='modal')
z_ = np.arange(self.sizeZ)*float(self.zSpacing)
z_ -= z_.mean()
ps = fourierHNA.PsfFromPupil(self.image.data[:,:], z_, self.image.mdh['voxelsize.x']*1e3, self.wavelength)#, shape = [self.sizeX, self.sizeX])
ps = ps/ps[:,:,self.sizeZ/2].sum()
im = ImageStack(ps, titleStub = 'Generated PSF')
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
#im.mdh['Processing.CropROI'] = roi
mode = 'psf'
dv = ViewIm3D(im, mode=mode, glCanvas=self.dsviewer.glCanvas, parent=wx.GetTopLevelParent(self.dsviewer))
def getQueueData(self, fieldName, *args):
'''Get data, defined by fieldName and potntially additional arguments, ascociated with queue'''
if fieldName == 'FitResults':
startingAt, = args
with self.fileResultsLock.rlock:
if self.h5ResultsFile.__contains__('/FitResults'):
res = self.h5ResultsFile.root.FitResults[startingAt:]
else:
res = []
return res
elif fieldName == 'MAP':
mapName, = args
from PYME.ParallelTasks.relativeFiles import getFullExistingFilename
from PYME.DSView.image import ImageStack
fn = getFullExistingFilename(mapName)
varmap = ImageStack(filename=fn).data[:, :, 0].squeeze(
) #this should handle .tif, .h5, and a few others
return varmap
else:
return None
def OnDropFiles(self, x, y, filenames):
print(filenames)
for filename in filenames:
im = ImageStack(filename=filename)
ViewIm3D(im)
def OnOpen(self, event=None):
ViewIm3D(ImageStack())
def _pickChannel(self, image):
chan = image.data[:,:,:,self.channelToExtract]
im = ImageStack(chan, titleStub = 'Filtered Image')
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
def OnLabelSizeThreshold(self, event):
import numpy as np
from scipy import ndimage
from PYME.DSView.image import ImageStack
from PYME.DSView import ViewIm3D
#dlg = wx.TextEntryDialog(self.dsviewer, 'Blur size [pixels]:', 'Gaussian Blur', '[1,1,1]')
#if dlg.ShowModal() == wx.ID_OK:
#sigmas = eval(dlg.GetValue())
#print sigmas
#print self.images[0].img.shape
#roi = [[self.do.selection_begin_x, self.do.selection_end_x + 1],[self.do.selection_begin_y, self.do.selection_end_y +1], [0, self.image.data.shape[2]]]
#filt_ims = [np.atleast_3d(self.image.data[:,:,:,chanNum].squeeze() > self.dsviewer.do.Offs[chanNum]) for chanNum in range(self.image.data.shape[3])]
dlg = wx.TextEntryDialog(self.dsviewer, 'Minimum region size [pixels]:', 'Labelling', '1')
if dlg.ShowModal() == wx.ID_OK:
rSize = int(dlg.GetValue())
self.image.labelThresholds = self.do.thresholds
#filt_ims = [np.atleast_3d(self.image.data[:,:,:,chanNum].squeeze() > (self.dsviewer.do.Offs[chanNum] + 0.5/self.dsviewer.do.Gains[chanNum])) for chanNum in range(self.image.data.shape[3])]
filt_ims = [np.atleast_3d(self.image.data[:,:,:,chanNum].squeeze() > self.image.labelThresholds[chanNum]) for chanNum in range(self.image.data.shape[3])]
#self.image.labelThresholds = [(self.dsviewer.do.Offs[chanNum] + 0.5/self.dsviewer.do.Gains[chanNum]) for chanNum in range(self.image.data.shape[3])]
#print sum(filt_ims).shape
mask = sum(filt_ims) > 0.5
labs, nlabs = ndimage.label(mask)
if rSize > 1:
m2 = 0*mask
objs = ndimage.find_objects(labs)
for i, o in enumerate(objs):
r = labs[o] == i+1
#print r.shape
if r.sum() > rSize:
m2[o] = r
labs, nlabs = ndimage.label(m2)
#store a copy in the image for measurements etc ...
self.image.labels = labs
im = ImageStack(labs, titleStub = 'Labelled Image')
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
im.mdh['Labelling.MinSize'] = rSize
im.mdh['Labelling.Thresholds'] = self.image.labelThresholds
#im.mdh['Processing.CropROI'] = roi
if self.dsviewer.mode == 'visGUI':
mode = 'visGUI'
else:
mode = 'lite'
dv = ViewIm3D(im, mode=mode, glCanvas=self.dsviewer.glCanvas, parent=wx.GetTopLevelParent(self.dsviewer))
#set scaling to (0,1)
for i in range(im.data.shape[3]):
dv.do.Gains[i] = 1.0