def execute(self, namespace):
self._namespace = namespace
import multiprocessing
# from PYME.util import mProfile
# mProfile.profileOn(["frc.py"])
if self.multiprocessing:
proccess_count = np.clip(2, 1, multiprocessing.cpu_count()-1)
self._pool = multiprocessing.Pool(processes=proccess_count)
pipeline = namespace[self.inputName]
mapped_pipeline = tabular.mappingFilter(pipeline)
self._pixel_size_in_nm = self.pixel_size_in_nm * np.ones(3, dtype=np.float)
image_pair = self.generate_image_pair(mapped_pipeline)
image_pair = self.preprocess_images(image_pair)
# Should use DensityMapping recipe eventually when it is ready.
mdh = MetaDataHandler.NestedClassMDHandler()
mdh['Rendering.Method'] = "np.histogramdd"
if 'imageID' in pipeline.mdh.getEntryNames():
mdh['Rendering.SourceImageID'] = pipeline.mdh['imageID']
try:
mdh['Rendering.SourceFilename'] = pipeline.resultsSource.h5f.filename
except:
pass
mdh.Source = MetaDataHandler.NestedClassMDHandler(pipeline.mdh)
mdh['Rendering.NEventsRendered'] = [image_pair[0].sum(), image_pair[1].sum()]
mdh['voxelsize.units'] = 'um'
mdh['voxelsize.x'] = self.pixel_size_in_nm * 1E-3
mdh['voxelsize.y'] = self.pixel_size_in_nm * 1E-3
ims = ImageStack(data=np.stack(image_pair, axis=-1), mdh=mdh)
namespace[self.output_images] = ims
# if self.plot_graphs:
# from PYME.DSView.dsviewer import ViewIm3D
# ViewIm3D(ims)
frc_res, rawdata = self.calculate_FRC_from_images(image_pair, pipeline.mdh)
# smoothed_frc = self.SmoothFRC(frc_freq, frc_corr)
#
# self.CalculateThreshold(frc_freq, frc_corr, smoothed_frc)
namespace[self.output_frc_dict] = frc_res
namespace[self.output_frc_raw] = rawdata
if self.multiprocessing:
self._pool.close()
self._pool.join()
# mProfile.profileOff()
# mProfile.report()
self.save_to_file(namespace)
def list_h5(filename):
import tables
from PYME.IO import MetaDataHandler
from PYME.IO import tabular
from PYME.IO import unifiedIO
import json
with unifiedIO.local_or_temp_filename(filename) as fn:
with tables.open_file(fn, mode='r') as h5f:
#make sure our hdf file gets closed
try:
mdh = MetaDataHandler.NestedClassMDHandler(
MetaDataHandler.HDFMDHandler(h5f))
print('Metadata:\n____________')
print(repr(mdh))
except tables.FileModeError: # Occurs if no metadata is found, since we opened the table in read-mode
logger.warning(
'No metadata found, proceeding with empty metadata')
mdh = MetaDataHandler.NestedClassMDHandler()
print('\n\n')
for t in h5f.list_nodes('/'):
# FIXME - The following isinstance tests are not very safe (and badly broken in some cases e.g.
# PZF formatted image data, Image data which is not in an EArray, etc ...)
# Note that EArray is only used for streaming data!
# They should ideally be replaced with more comprehensive tests (potentially based on array or dataset
# dimensionality and/or data type) - i.e. duck typing. Our strategy for images in HDF should probably
# also be improved / clarified - can we use hdf attributes to hint at the data intent? How do we support
# > 3D data?
if not isinstance(t, tables.Group):
print(t.name)
print('______________')
if isinstance(t, tables.VLArray):
data = h5f.get_node(h5f.root, t.name)
print('Ragged (VLArray) with %d rows' % len(data))
print('Row 0: %s' % data)
elif isinstance(t, tables.table.Table):
# pipe our table into h5r or hdf source depending on the extension
data = h5f.get_node(h5f.root, t.name)
print('Table with %d rows\n dtype = %s' %
(len(data), data[0].dtype))
elif isinstance(t, tables.EArray):
data = h5f.get_node(h5f.root, t.name)
print('Image, shape = %s' % data.shape)
print('\n\n')
def mdh(self):
if self._mdh is None:
try:
self._mdh = MetaDataHandler.HDFMDHandler(self._h5file)
if self.mode == 'r':
self._mdh = MetaDataHandler.NestedClassMDHandler(self._mdh)
except IOError:
# our file was opened in read mode and didn't have any metadata to start with
self._mdh = MetaDataHandler.NestedClassMDHandler()
return self._mdh
def Generate(self, settings):
mdh = MetaDataHandler.NestedClassMDHandler()
copy_sample_metadata(self.pipeline.mdh, mdh)
mdh['Rendering.Method'] = self.name
if 'imageID' in self.pipeline.mdh.getEntryNames():
mdh['Rendering.SourceImageID'] = self.pipeline.mdh['imageID']
mdh['Rendering.SourceFilename'] = getattr(self.pipeline, 'filename',
'')
mdh['Rendering.NEventsRendered'] = len(
self.pipeline[self.pipeline.keys(
)[0]]) # in future good to use colourfilter for per channel info?
mdh.Source = MetaDataHandler.NestedClassMDHandler(self.pipeline.mdh)
for cb in renderMetadataProviders:
cb(mdh)
pixelSize = settings['pixelSize']
sliceThickness = settings['zSliceThickness']
status = statusLog.StatusLogger('Generating %s Image ...' % self.name)
# get image bounds at integer multiple of pixel size
imb = self._get_image_bounds(pixelSize, sliceThickness,
*settings.get('zBounds', [None, None]))
#record the pixel origin in nm from the corner of the camera for futrue overlays
ox, oy, oz = MetaDataHandler.origin_nm(mdh)
if not imb.z0 == 0:
# single plane in z stack
# FIXME - what is z for 3D fitting at a single focal plane? Check for pipeline['focus']==0 instead?
oz = 0
mdh['Origin.x'] = ox + imb.x0
mdh['Origin.y'] = oy + imb.y0
mdh['Origin.z'] = oz + imb.z0
colours = settings['colours']
oldC = self.colourFilter.currentColour
ims = []
for c in colours:
self.colourFilter.setColour(c)
ims.append(np.atleast_3d(self.genIm(settings, imb, mdh)))
self.colourFilter.setColour(oldC)
return GeneratedImage(ims,
imb,
pixelSize,
sliceThickness,
colours,
mdh=mdh)
def testFrames(detThresh = 0.9, offset = 0):
plt.close('all')
matplotlib.interactive(False)
plt.clf()
sq = min(mdh.getEntry('EstimatedLaserOnFrameNo') + 1000, dataSource.getNumSlices()/4)
zps = np.array(range(mdh.getEntry('EstimatedLaserOnFrameNo') + 20, mdh.getEntry('EstimatedLaserOnFrameNo') + 24) + range(sq, sq + 4) + range(dataSource.getNumSlices()/2,dataSource.getNumSlices() /2+4))
zps += offset
fitMod = cFitType.GetStringSelection()
#bgFrames = int(tBackgroundFrames.GetValue())
bgFrames = [int(v) for v in tBackgroundFrames.GetValue().split(':')]
for i in range(12):
#if 'Analysis.NumBGFrames' in md.getEntryNames():
#bgi = range(max(zps[i] - bgFrames,mdh.getEntry('EstimatedLaserOnFrameNo')), zps[i])
bgi = range(max(zps[i] + bgFrames[0],mdh.getEntry('EstimatedLaserOnFrameNo')), max(zps[i] + bgFrames[1],mdh.getEntry('EstimatedLaserOnFrameNo')))
#else:
# bgi = range(max(zps[i] - 10,md.EstimatedLaserOnFrameNo), zps[i])
if 'Splitter' in fitMod:
ft = remFitBuf.fitTask(seriesName, zps[i], detThresh, MetaDataHandler.NestedClassMDHandler(mdh), 'SplitterObjFindR', bgindices=bgi, SNThreshold=True)
else:
ft = remFitBuf.fitTask(seriesName, zps[i], detThresh, MetaDataHandler.NestedClassMDHandler(mdh), 'LatObjFindFR', bgindices=bgi, SNThreshold=True)
res = ft()
xp = np.floor(i/4)/3.
yp = (3 - i%4)/4.
#print xp, yp
plt.axes((xp,yp, 1./6,1./4.5))
#d = ds[zps[i], :,:].squeeze().T
d = dataSource.getSlice(zps[i]).T
plt.imshow(d, cmap=cm.hot, interpolation='nearest', hold=False, clim=(np.median(d.ravel()), d.max()))
plt.title('Frame %d' % zps[i])
plt.xlim(0, d.shape[1])
plt.ylim(0, d.shape[0])
plt.xticks([])
plt.yticks([])
#print 'i = %d, ft.index = %d' % (i, ft.index)
#subplot(4,6,2*i+13)
xp += 1./6
plt.axes((xp,yp, 1./6,1./4.5))
d = ft.ofd.filteredData.T
#d = ft.data.squeeze().T
plt.imshow(d, cmap=cm.hot, interpolation='nearest', hold=False, clim=(np.median(d.ravel()), d.max()))
plt.plot([p.x for p in ft.ofd], [p.y for p in ft.ofd], 'o', mew=2, mec='g', mfc='none', ms=9)
if ft.driftEst:
plt.plot([p.x for p in ft.ofdDr], [p.y for p in ft.ofdDr], 'o', mew=2, mec='b', mfc='none', ms=9)
if ft.fitModule in remFitBuf.splitterFitModules:
plt.plot([p.x for p in ft.ofd], [d.shape[0] - p.y for p in ft.ofd], 'o', mew=2, mec='g', mfc='none', ms=9)
#axis('tight')
plt.xlim(0, d.shape[1])
plt.ylim(0, d.shape[0])
plt.xticks([])
plt.yticks([])
plt.show()
matplotlib.interactive(True)
def misfallA(r2,
mdh,
zCoeffs,
ns=1.51,
axialShift=None,
colourRatio=None,
beadSize=0):
mdh = MetaDataHandler.NestedClassMDHandler(mdh)
if not axialShift == None:
mdh['Analysis.AxialShift'] = axialShift
if not colourRatio == None:
mdh['Analysis.ColourRatio'] = colourRatio
voxelsize = mdh.voxelsize_nm
voxelsize.z = 50.
zs = 50. * np.arange(-30., 31)
p1 = fourierHNA.GenZernikeDPSF(zs, 70, zCoeffs, ns=ns)
interpolator.setModel('foo', p1, voxelsize)
estimator.splines.clear()
estimator.calibrate(interpolator, mdh)
sp_all = [
startEstROI(r2[j], mdh, interpolator, estimator,
mdh['Analysis.ColourRatio'], mdh['Analysis.AxialShift'])
for j in range(len(r2))
]
mf = np.array([(fitROI(r2[j], mdh, interpolator, sp_all[j],
mdh['Analysis.ColourRatio'],
mdh['Analysis.AxialShift'])[1]).sum()
for j in range(len(r2))])
#return mf[mf < (median(mf)+ 2*std(mf))].mean()
return np.sqrt(mf).mean()
def execute(self, namespace):
from PYME.localization import traveling_salesperson
from scipy.spatial import distance_matrix
points = namespace[self.input]
try:
positions = np.stack([points['x_um'], points['y_um']], axis=1)
except KeyError:
# units don't matter for these calculations, but we want to preserve them on the other side
positions = np.stack([points['x'], points['y']], axis=1) / 1e3
distances = distance_matrix(positions, positions)
route, best_distance, og_distance = traveling_salesperson.two_opt(
distances, self.epsilon)
# plot_path(positions, route)
out = tabular.MappingFilter({
'x_um': positions[:, 0][route],
'y_um': positions[:, 1][route]
})
out.mdh = MetaDataHandler.NestedClassMDHandler()
try:
out.mdh.copyEntriesFrom(points.mdh)
except AttributeError:
pass
out.mdh['TravelingSalesperson.Distance'] = best_distance
out.mdh['TravelingSalesperson.OriginalDistance'] = og_distance
namespace[self.output] = out
def start(self):
self._gen_weights()
self.genCoords()
#metadata handling
self.mdh = MetaDataHandler.NestedClassMDHandler()
self.mdh.setEntry('StartTime', time.time())
self.mdh.setEntry('AcquisitionType', 'Tiled overview')
#loop over all providers of metadata
for mdgen in MetaDataHandler.provideStartMetadata:
mdgen(self.mdh)
self._x0 = self.xp[0]
self._y0 = self.yp[0]
self._pixel_size = self.mdh.getEntry('voxelsize.x')
self.background = self.mdh.getOrDefault('Camera.ADOffset',
self.background)
# make our x0, y0 independent of the camera ROI setting
x0_cam, y0_cam = MetaDataHandler.get_camera_physical_roi_origin(
self.mdh)
x0 = self._x0 + self._pixel_size * x0_cam
y0 = self._y0 + self._pixel_size * y0_cam
self.P = tile_pyramid.ImagePyramid(self._tiledir,
self._base_tile_size,
x0=x0,
y0=y0,
pixel_size=self._pixel_size)
pointScanner.PointScanner.start(self)
def __init__(self):
#list of tuples of form (class, chan, name) describing the instaled piezo channels
self.piezos = []
self.lasers = []
self.hardwareChecks = []
#entries should be of the form: "x" : (piezo, channel, multiplier)
# where multiplyier is what to multiply by to get the units to micrometers
self.positioning = {}
self.joystick = None
self.cam = None
self.cameras = {}
self.camControls = {}
self.stackNum = 0
#self.WantEventNotification = []
self.StatusCallbacks = [
] #list of functions which provide status information
self.CleanupFunctions = [] #list of functions to be called at exit
self.PACallbacks = [
] #list of functions to be called when a new aquisator is created
self.saturationThreshold = 16383 #14 bit
self.lastFrameSaturated = False
#self.cam.saturationIntervened = False
self.microscope_name = None
self.saturatedMessage = ''
protocol.scope = self
ccdCalibrator.setScope(self)
self.initDone = False
self._OpenSettingsDB()
self.spoolController = SpoolController(self) #, defDir, **kwargs)
self.state = StateManager(self)
self.state.registerHandler('ActiveCamera', self.GetActiveCameraName,
self._SetCamera, True)
self.state.registerHandler('Camera.IntegrationTime',
self._GetActiveCameraIntegrationTime,
self._SetActiveCameraIntegrationTime, True)
self.state.registerHandler('Camera.ROI', self._GetActiveCameraROI,
self._SetActiveCameraROI, True)
self.state.registerHandler('Camera.Binning',
self._GetActiveCameraBinning,
self._SetActiveCameraBinning, True)
self.actions = ActionManager(self)
MetaDataHandler.provideStartMetadata.append(self.GenStartMetadata)
#provision to set global metadata values in startup script
self.mdh = MetaDataHandler.NestedClassMDHandler()
def execute(self, namespace):
from PYME.Analysis.points.astigmatism import astigTools
from PYME.IO import unifiedIO
import json
inp = namespace[self.input_name]
if 'mdh' not in dir(inp):
raise RuntimeError('MapAstigZ needs metadata')
if self.astigmatism_calibration_location == '': # grab calibration from the metadata
calibration_location = inp.mdh['Analysis.AstigmatismMapID']
else:
calibration_location = self.astigmatism_calibration_location
s = unifiedIO.read(calibration_location)
astig_calibrations = json.loads(s)
mapped = tabular.MappingFilter(inp)
z, zerr = astigTools.lookup_astig_z(mapped, astig_calibrations, self.rough_knot_spacing, plot=False)
mapped.addColumn('astigmatic_z', z)
mapped.addColumn('astigmatic_z_lookup_error', zerr)
mapped.setMapping('z', 'astigmatic_z + z')
mapped.mdh = MetaDataHandler.NestedClassMDHandler(inp.mdh)
mapped.mdh['Analysis.astigmatism_calibration_used'] = calibration_location
namespace[self.output_name] = mapped
def test_GaussMultifitSR():
"""
simple test to see if the multifit algorithm is working. We should detect roughly the same number of molecules
as we simulated. This is only a loose test, and should pick up any critical reverse compatible breaks rather than
actual fit performance.
"""
from PYME.localization.FitFactories import GaussMultifitSR
from PYME.IO import MetaDataHandler
x, y, im = gen_image()
mdh = MetaDataHandler.NestedClassMDHandler()
mdh['Analysis.PSFSigma'] = 140.
mdh['Analysis.ResidualMax'] = .5
#mdh['Analysis.subtractBackground'] = False
mdh['Camera.ReadNoise'] = 1.0
mdh['Camera.NoiseFactor'] = 1.0
mdh['Camera.ElectronsPerCount'] = 1.0
mdh['Camera.TrueEMGain'] = 1.0
mdh['voxelsize.x'] = .07
mdh['voxelsize.y'] = .07
ff = GaussMultifitSR.FitFactory(np.atleast_3d(im) - 2.0, mdh)
res = ff.FindAndFit(1.8)
nSim = len(x)
nFound = len(res)
print('nFound: %d, nSim: %d' % (nFound, nSim))
assert (nFound > 0.5 * nSim and nFound < 2.0 * nSim)
def __init__(self,
data=None,
mdh=None,
filename=None,
queueURI=None,
events=[],
titleStub='Untitled Image',
haveGUI=True,
load_prompt=None):
global nUntitled
self.data = data #image data
self.mdh = mdh #metadata (a MetaDataHandler class)
self.events = events #events
self.queueURI = queueURI
self.filename = filename
self.haveGUI = haveGUI
#default 'mode' / image type - see PYME/DSView/modules/__init__.py
self.mode = 'default'
self.saved = False
self.volatile = False #is the data likely to change and need refreshing?
#support for specifying metadata as filename
if isinstance(mdh, string_types): #os.path.exists(mdh):
self.mdh = None
self._findAndParseMetadata(mdh)
if (data is None):
#if we've supplied data, use that, otherwise load from file
self.Load(filename, prompt=load_prompt)
#do the necessary munging to get the data in the format we want it
self.SetData(self.data)
#generate a placeholder filename / window title
if self.filename is None:
self.filename = '%s %d' % (titleStub, nUntitled[titleStub])
nUntitled[titleStub] += 1
self.seriesName = self.filename
#generate some empty metadata if we don't have any
if self.mdh is None:
self.mdh = MetaDataHandler.NestedClassMDHandler()
#hack to make spectral data behave right - doesn't really belong here
if 'Spectrum.Wavelengths' in self.mdh.getEntryNames():
self.xvals = self.mdh['Spectrum.Wavelengths']
self.xlabel = 'Wavelength [nm]'
#if we have 1D data, plot as graph rather than image
if self.data.shape[1] == 1:
self.mode = 'graph'
#add ourselves to the list of open images
openImages[self.filename] = self
def execute(self, namespace):
from PYME.localization.FitFactories import GaussMultifitSR
import pandas as pd
img = namespace[self.inputImage]
img.mdh['Analysis.PSFSigma'] = self.blobSigma
res = []
for i in range(img.data.shape[2]):
md = MetaDataHandler.NestedClassMDHandler(img.mdh)
md['tIndex'] = i
ff = GaussMultifitSR.FitFactory(self.scale * img.data[:, :, i],
img.mdh,
noiseSigma=np.ones_like(
img.data[:, :, i].squeeze()))
res.append(tabular.FitResultsSource(ff.FindAndFit(self.threshold)))
#FIXME - this shouldn't be a DataFrame
res = pd.DataFrame(np.vstack(res))
res.mdh = img.mdh
namespace[self.outputName] = res
def execute(self, namespace):
import PYME.Analysis.points.spherical_harmonics as spharm
from PYME.IO import MetaDataHandler
inp = namespace[self.input_name]
modes, coefficients, centre = spharm.sphere_expansion_clean(
inp['x'],
inp['y'],
inp['z'] * self.z_scale,
mmax=self.max_m_mode,
centre_points=True,
nIters=self.n_iterations,
tol_init=self.init_tolerance)
mdh = MetaDataHandler.NestedClassMDHandler()
try:
mdh.copyEntriesFrom(namespace[self.input_name].mdh)
except AttributeError:
pass
mdh['Processing.SphericalHarmonicShell.ZScale'] = self.z_scale
mdh['Processing.SphericalHarmonicShell.MaxMMode'] = self.max_m_mode
mdh['Processing.SphericalHarmonicShell.NIterations'] = self.n_iterations
mdh['Processing.SphericalHarmonicShell.InitTolerance'] = self.init_tolerance
mdh['Processing.SphericalHarmonicShell.Centre'] = centre
output_dtype = [('modes', '<2i4'), ('coefficients', '<f4')]
out = np.zeros(len(coefficients), dtype=output_dtype)
out['modes'] = modes
out['coefficients'] = coefficients
out = tabular.RecArraySource(out)
out.mdh = mdh
namespace[self.output_name] = out
def _loadBioformats(self, filename):
#from PYME.IO.FileUtils import readTiff
from PYME.IO.DataSources import BioformatsDataSource
try:
import bioformats
except ImportError:
logger.exception(
'Error importing bioformats - is the python-bioformats module installed?'
)
raise
#mdfn = self.FindAndParseMetadata(filename)
print("Bioformats:loading data")
self.dataSource = BioformatsDataSource.DataSource(filename, None)
self.mdh = MetaDataHandler.NestedClassMDHandler(MetaData.BareBones)
print("Bioformats:loading metadata")
OMEXML = bioformats.get_omexml_metadata(filename).encode('utf8')
print("Bioformats:parsing metadata")
OMEmd = MetaDataHandler.OMEXMLMDHandler(OMEXML)
self.mdh.copyEntriesFrom(OMEmd)
print("Bioformats:done")
print(self.dataSource.shape)
self.dataSource = BufferedDataSource.DataSource(
self.dataSource, min(self.dataSource.getNumSlices(), 50))
self.data = self.dataSource #this will get replaced with a wrapped version
print(self.data.shape)
#from PYME.ParallelTasks.relativeFiles import getRelFilename
self.seriesName = getRelFilename(filename)
self.mode = 'default'
def __init__(self, scope, **kwargs):
"""
Create a stack settings object.
NB - extra args map 1:1 to stack metadata entries. Start and end pos are ignored if ScanMode = 'Middle and Number'
Parameters
----------
scope
ScanMode
StartPos
EndPos
StepSize
NumSlices
ScanPiezo
"""
#PreviewAquisator.__init__(self, chans, cam, shutters, None)
self.scope = scope
#self.log = _log
self.mdh = MetaDataHandler.NestedClassMDHandler()
#register as a provider of metadata
MetaDataHandler.provideStartMetadata.append(self.ProvideStackMetadata)
self._settings_changed = threading.Condition()
d1 = dict(self.DEFAULTS)
d1.update(kwargs)
self.update(**d1)
self.direction = self.FORWARDS
from PYME.Acquire import webui
# add webui endpoints (if running under webui)
webui.add_endpoints(self, '/stack_settings')
def execute(self, namespace):
from PYME.Analysis.points.traveling_salesperson import sort
points = namespace[self.input]
try:
positions = np.stack([points['x_um'], points['y_um']], axis=1)
except KeyError:
# units don't matter for these calculations, but we want to preserve them on the other side
positions = np.stack([points['x'], points['y']], axis=1) / 1e3
start_index = 0 if not self.start_from_corner else np.argmin(positions.sum(axis=1))
positions, ogd, final_distance = sort.tsp_sort(positions, start_index, self.epsilon, return_path_length=True)
out = tabular.DictSource({'x_um': positions[:, 0],
'y_um': positions[:, 1]})
out.mdh = MetaDataHandler.NestedClassMDHandler()
try:
out.mdh.copyEntriesFrom(points.mdh)
except AttributeError:
pass
out.mdh['TravelingSalesperson.Distance'] = final_distance
out.mdh['TravelingSalesperson.OriginalDistance'] = ogd
namespace[self.output] = out
def OnDeconvWiener(self, event):
#from PYME.Deconv import weiner
decMDH = MetaDataHandler.NestedClassMDHandler(self.image.mdh)
decMDH['Deconvolution.OriginalFile'] = self.image.filename
decMDH['Deconvolution.Method'] = 'Wiener'
im = numpy.zeros(self.image.data.shape[:3], 'f4')
decView = View3D(im,
'Deconvolution Result',
mdh=decMDH,
parent=self.dsviewer)
decView.wienerPanel = WienerDeconvolver(decView, self.image,
decView.image)
self.pinfo1 = aui.AuiPaneInfo().Name("wienerPanel").Left(
).Caption('Wiener Filter').DestroyOnClose(True).CloseButton(
False
) #.MinimizeButton(True).MinimizeMode(aui.AUI_MINIMIZE_CAPT_SMART|aui.AUI_MINIMIZE_POS_RIGHT)#.CaptionVisible(False)
decView._mgr.AddPane(decView.wienerPanel, self.pinfo1)
decView._mgr.Update()
self.dsviewer.decView = decView
def execute(self, namespace):
#from PYME.localization.FitFactories import DumbellFitR
from PYME.IO import MetaDataHandler
img = namespace[self.inputImage]
md = MetaDataHandler.NestedClassMDHandler()
#set metadata entries needed for fitting to suitable defaults
md['Camera.ADOffset'] = img.data[:, :, 0].min()
md['Camera.TrueEMGain'] = 1.0
md['Camera.ElectronsPerCount'] = 1.0
md['Camera.ReadNoise'] = 1.0
md['Camera.NoiseFactor'] = 1.0
#copy across the entries from the real image, replacing the defaults
#if necessary
md.copyEntriesFrom(img.mdh)
inp = namespace[self.inputPositions]
res = np.zeros(len(inp['x']), dtype=[('r%d' % r, 'f4') for r in self.radii])
ff_t = -1
aggFunc = getattr(self, '_get_%s' % self.mode)
ps = img.pixelSize
print('pixel size: %s' % ps)
for x, y, t, i in zip(inp['x'], inp['y'], inp['t'], range(len(inp['x']))):
for r in self.radii:
res[i]['r%d' % r] = aggFunc(img.data, np.round(x / ps), np.round(y / ps), t, r)
res = tabular.RecArraySource(res)
res.mdh = md
namespace[self.outputName] = res
def execute(self, namespace):
from PYME.IO.FileUtils import readSpeckle
from PYME.IO import MetaDataHandler
import os
fileInfo = {'SEP': os.sep}
seriesLength = 100000
mdh = MetaDataHandler.NestedClassMDHandler()
mdh['voxelsize.x'] = .001 # default pixel size - FIXME
mdh['voxelsize.y'] = .001
#use a default sensor size of 512
#this gets over-ridden below if we supply an image
clip_region = [
self.edgeRejectionPixels, self.edgeRejectionPixels,
512 - self.edgeRejectionPixels, 512 - self.edgeRejectionPixels
]
if not self.inputImage == '':
inp = namespace[self.inputImage]
mdh.update(inp.mdh)
seriesLength = inp.data.shape[2]
clip_region = [
self.edgeRejectionPixels, self.edgeRejectionPixels,
inp.data.shape[0] - self.edgeRejectionPixels,
inp.data.shape[1] - self.edgeRejectionPixels
]
try:
fileInfo['DIRNAME'], fileInfo['IMAGENAME'] = os.path.split(
inp.filename)
fileInfo['IMAGESTUB'] = fileInfo['IMAGENAME'].split('MM')[0]
except:
pass
speckleFN = self.speckleFilename.format(**fileInfo)
specks = readSpeckle.readSpeckles(speckleFN)
traces = readSpeckle.gen_traces_from_speckles(
specks,
leadFrames=self.leadFrames,
followFrames=self.followFrames,
seriesLength=seriesLength,
clipRegion=clip_region)
#turn this into an inputFilter object
inp = tabular.RecArraySource(traces)
#create a mapping to covert the co-ordinates in pixels to co-ordinates in nm
vs = mdh.voxelsize_nm
map = tabular.MappingFilter(inp,
x='x_pixels*%3.2f' % vs.x,
y='y_pixels*%3.2f' % vs.y)
map.mdh = mdh
namespace[self.outputName] = map
def execute(self, namespace):
from PYME.Analysis.points.traveling_salesperson import sectioned_two_opt
points = namespace[self.input]
try:
positions = np.stack([points['x_um'], points['y_um']], axis=1)
except KeyError:
positions = np.stack([points['x'], points['y']], axis=1) / 1e3
final_route = sectioned_two_opt.tsp_chunk_two_opt_multiproc(positions, self.epsilon, self.points_per_chunk,
self.n_processes)
# note that we sorted the positions / sections once before, need to propagate that through before sorting
out = tabular.DictSource({k: points[k][final_route] for k in points.keys()})
out.mdh = MetaDataHandler.NestedClassMDHandler()
try:
out.mdh.copyEntriesFrom(points.mdh)
except AttributeError:
pass
# use the already sorted output to get the final distance
try:
og_distance = np.sqrt((points['x_um'][1:] - points['x_um'][:-1]) ** 2 + (points['y_um'][1:] - points['y_um'][:-1]) ** 2).sum()
final_distance = np.sqrt((out['x_um'][1:] - out['x_um'][:-1]) ** 2 + (out['y_um'][1:] - out['y_um'][:-1]) ** 2).sum()
except KeyError:
og_distance = np.sqrt((points['x'][1:] - points['x'][:-1]) ** 2 + (points['y'][1:] - points['y'][:-1]) ** 2).sum() / 1e3
final_distance = np.sqrt((out['x'][1:] - out['x'][:-1]) ** 2 + (out['y'][1:] - out['y'][:-1]) ** 2).sum() / 1e3
out.mdh['TravelingSalesperson.OriginalDistance'] = og_distance
out.mdh['TravelingSalesperson.Distance'] = final_distance
namespace[self.output] = out
def to_hdf(self, filename, tablename='Data', keys=None, metadata=None):
from PYME.IO import h5rFile, MetaDataHandler
# NOTE that we ignore metadata input
metadata = MetaDataHandler.NestedClassMDHandler()
metadata[
'spherical_harmonic_shell.standard_deviations'] = self.standard_deviations.tolist(
)
metadata[
'spherical_harmonic_shell.scaling_factors'] = self.scaling_factors.tolist(
)
metadata[
'spherical_harmonic_shell.principal_axes'] = self.principal_axes.tolist(
)
metadata[
'spherical_harmonic_shell.summed_residuals'] = self._summed_residuals
metadata['spherical_harmonic_shell.n_points_used_in_fitting'] = len(
self.x)
metadata['spherical_harmonic_shell.x0'] = self.x0
metadata['spherical_harmonic_shell.y0'] = self.y0
metadata['spherical_harmonic_shell.z0'] = self.z0
metadata[
'spherical_harmonic_shell.sampling_fraction'] = self.sampling_fraction
with h5rFile.H5RFile(filename, 'a') as f:
f.appendToTable(tablename, self.to_recarray(keys))
f.updateMetadata(metadata)
def fitallA(r2, mdh, zCoeffs, ns=1.51, axialShift=200.):
mdh = MetaDataHandler.NestedClassMDHandler(mdh)
mdh['Analysis.AxialShift'] = axialShift
voxelsize = mdh.voxelsize_nm
voxelsize.z = 50.
zs = 50. * np.arange(-30., 31)
p1 = fourierHNA.GenZernikeDPSF(zs, 70, zCoeffs, ns=ns)
interpolator.setModel('foo', p1, voxelsize)
estimator.splines.clear()
estimator.calibrate(interpolator, mdh)
sp_all = [
startEstROI(r2[j], mdh, interpolator, estimator,
mdh['Analysis.ColourRatio'], mdh['Analysis.AxialShift'])
for j in range(len(r2))
]
fr = np.array([(fitROI(r2[j], mdh, interpolator, sp_all[j],
mdh['Analysis.ColourRatio'],
mdh['Analysis.AxialShift'])[0])
for j in range(len(r2))])
#return mf[mf < (median(mf)+ 2*std(mf))].mean()
return fr
def OnMAquireOnePic(self, event):
import numpy as np
self.scope.frameWrangler.stop()
ds2 = np.atleast_3d(self.scope.frameWrangler.currentFrame.reshape(self.scope.cam.GetPicWidth(),self.scope.cam.GetPicHeight()).copy())
#metadata handling
mdh = MetaDataHandler.NestedClassMDHandler()
mdh.setEntry('StartTime', time.time())
mdh.setEntry('AcquisitionType', 'SingleImage')
#loop over all providers of metadata
for mdgen in MetaDataHandler.provideStartMetadata:
mdgen(mdh)
im = dsviewer.ImageStack(data = ds2, mdh = mdh, titleStub='Unsaved Image')
if not im.mode == 'graph':
im.mode = 'lite'
#print im.mode
dvf = dsviewer.DSViewFrame(im, mode= im.mode, size=(500, 500))
dvf.SetSize((500,500))
dvf.Show()
self.snapNum += 1
self.scope.frameWrangler.Prepare(True)
self.scope.frameWrangler.start()
def Start(self):
self.image = np.zeros((self.shape_x, self.shape_y, 2), 'uint16')
mdh = MetaDataHandler.NestedClassMDHandler()
mdh.setEntry('StartTime', time.time())
mdh.setEntry('AcquisitionType', 'Stack')
#loop over all providers of metadata
for mdgen in MetaDataHandler.provideStartMetadata:
mdgen(mdh)
self.img = image.ImageStack(data=self.image, mdh=mdh)
#self.view = View3D(self.image, 'Z Stack', mdh = mdh)
#self.view = ViewIm3D(self.img, 'Z Stack')
self.running = True
self.zPoss = np.arange(self.stackSettings.GetStartPos(), self.stackSettings.GetEndPos()+.95*self.stackSettings.GetStepSize(),self.stackSettings.GetStepSize()*self.stackSettings.GetDirection())
#piezo = self.scope.positioning[self.stackSettings.GetScanChannel()]
#self.piezo = piezo[0]
#self.piezoChan = piezo[1]
self.posChan = self.stackSettings.GetScanChannel()
#self.startPos = self.piezo.GetPos(self.piezoChan)
self.startPos = self.scope.GetPos()[self.posChan]
self.scope.frameWrangler.stop()
self.scope.frameWrangler.onFrame.connect(self.OnCameraFrame)
self.OnAqStart()
self.scope.frameWrangler.start()
def pushImagesDS(self, image):
resultsFilename = _verifyResultsFilename(
genResultFileName(image.seriesName))
self.queueName = resultsFilename
debugPrint('Results file = %s' % resultsFilename)
self.resultsMdh = MetaDataHandler.NestedClassMDHandler(
self.analysisMDH)
self.resultsMdh['DataFileID'] = fileID.genDataSourceID(
image.dataSource)
mn = image.dataSource.moduleName
#dsID = self.image.seriesName
#if it's a buffered source, go back to underlying source
if mn == 'BufferedDataSource':
mn = image.dataSource.dataSource.moduleName
self.tq.createQueue('DSTaskQueue',
self.queueName,
self.resultsMdh,
mn,
image.seriesName,
resultsFilename,
startAt=self.analysisMDH['Analysis.StartAt'])
evts = image.dataSource.getEvents()
if len(evts) > 0:
self.tq.addQueueEvents(self.queueName, evts)
debugPrint('Queue created')
self.onImagesPushed.send(self)
def execute(self, namespace):
inp = namespace[self.inputName]
# generate LineProfileHandler from tables
handler = LineProfileHandler()
handler._load_profiles_from_list(inp)
fit_class = profile_fitters.ensemble_fitters[self.fit_type]
self.fitter = fit_class(handler)
if self.hold_ensemble_parameter_constant:
self.fitter.fit_profiles(self.ensemble_parameter_guess)
else:
self.fitter.ensemble_fit(self.ensemble_parameter_guess)
res = tabular.RecArraySource(self.fitter.results)
# propagate metadata, if present
res.mdh = MetaDataHandler.NestedClassMDHandler(
getattr(inp, 'mdh', None))
res.mdh['EnsembleFitProfiles.FitType'] = self.fit_type
res.mdh[
'EnsembleFitProfiles.EnsembleParameterGuess'] = self.ensemble_parameter_guess
res.mdh[
'EnsembleFitProfiles.HoldEnsembleParamConstant'] = self.hold_ensemble_parameter_constant
namespace[self.outputName] = res
def doStartLog(self):
"""Record pertinant information to metadata at start of acquisition.
Loops through all registered sources of start metadata and adds their entries.
See Also
--------
PYME.IO.MetaDataHandler
"""
dt = datetime.datetime.now()
self.dtStart = dt
self.tStart = time.time()
# create an in-memory metadata handler and populate this prior to copying data over to the spooler
# metadata handler. This significantly improves performance if the spooler metadata handler has high latency
# (as is the case for both the HDFMetaDataHandler and, especially, the QueueMetaDataHandler).
mdt = MetaDataHandler.NestedClassMDHandler()
mdt.setEntry('StartTime', self.tStart)
#loop over all providers of metadata
for mdgen in MetaDataHandler.provideStartMetadata:
mdgen(mdt)
self.md.copyEntriesFrom(mdt)
def __init__(self, testFrameSize=TEST_FRAME_SIZE, serverfilter=''):
self.testData = (100 * np.random.rand(*testFrameSize)).astype('uint16')
self.onFrame = dispatch.Signal(['frameData'])
self.spoolProgress = dispatch.Signal(['percent'])
self.mdh = MetaDataHandler.NestedClassMDHandler()
self.serverfilter = serverfilter
def __init__(self):
self.analysisMDH = MetaDataHandler.NestedClassMDHandler()
self.onMetadataChanged = dispatch.Signal()
self.propagateToAcquisisitonMetadata = False
MetaDataHandler.provideStartMetadata.append(self.genStartMetadata)
