class PSFSettings(HasTraits):
wavelength_nm = Float(700.)
NA = Float(1.47)
vectorial = Bool(False)
zernike_modes = Dict()
zernike_modes_lower = Dict()
phases = List([0, .5, 1, 1.5])
four_pi = Bool(False)
def default_traits_view(self):
from traitsui.api import View, Item
#from PYME.ui.custom_traits_editors import CBEditor
return View(Item(name='wavelength_nm'),
Item(name='NA'),
Item(name='vectorial'),
Item(name='four_pi', label='4Pi'),
Item(name='zernike_modes'),
Item(name='zernike_modes_lower',
visible_when='four_pi==True'),
Item(name='phases',
visible_when='four_pi==True',
label='phases/pi'),
resizable=True,
buttons=['OK'])
class PointFeatureBase(ModuleBase):
"""
common base class for feature extraction routines - implements normalisation and PCA routines
"""
outputColumnName = CStr('features')
columnForEachFeature = Bool(
False
) #if true, outputs a column for each feature - useful for visualising
normalise = Bool(True) #subtract mean and divide by std. deviation
PCA = Bool(
True
) # reduce feature dimensionality by performing PCA - TODO - should this be a separate module and be chained instead?
PCA_components = Int(3) # 0 = same dimensionality as features
def _process_features(self, data, features):
from PYME.IO import tabular
out = tabular.MappingFilter(data)
out.mdh = getattr(data, 'mdh', None)
if self.normalise:
features = features - features.mean(0)[None, :]
features = features / features.std(0)[None, :]
if self.PCA:
from sklearn.decomposition import PCA
pca = PCA(n_components=(
self.PCA_components if self.PCA_components > 0 else None
)).fit(features)
features = pca.transform(features)
out.pca = pca #save the pca object just in case we want to look at what the principle components are (this is hacky)
out.addColumn(self.outputColumnName, features)
if self.columnForEachFeature:
for i in range(features.shape[1]):
out.addColumn('feat_%d' % i, features[:, i])
return out
class ArithmaticFilter(ModuleBase):
"""
Module with two image inputs and one image output
Parameters
----------
inputName0: PYME.IO.image.ImageStack
inputName1: PYME.IO.image.ImageStack
outputName: PYME.IO.image.ImageStack
"""
inputName0 = Input('input')
inputName1 = Input('input')
outputName = Output('filtered_image')
processFramesIndividually = Bool(False)
def filter(self, image0, image1):
if self.processFramesIndividually:
filt_ims = []
for chanNum in range(image0.data.shape[3]):
out = []
for i in range(image0.data.shape[2]):
d0 = image0.data[:, :, i, chanNum].squeeze().astype('f')
d1 = image1.data[:, :, i, chanNum].squeeze().astype('f')
out.append(
np.atleast_3d(
self.applyFilter(d0, d1, chanNum, i, image0)))
filt_ims.append(np.concatenate(out, 2))
else:
filt_ims = []
for chanNum in range(image0.data.shape[3]):
d0 = image0.data[:, :, :, chanNum].squeeze().astype('f')
d1 = image1.data[:, :, :, chanNum].squeeze().astype('f')
filt_ims.append(
np.atleast_3d(self.applyFilter(d0, d1, chanNum, 0,
image0)))
im = ImageStack(filt_ims, titleStub=self.outputName)
im.mdh.copyEntriesFrom(image0.mdh)
im.mdh['Parents'] = '%s, %s' % (image0.filename, image1.filename)
self.completeMetadata(im)
return im
def execute(self, namespace):
namespace[self.outputName] = self.filter(namespace[self.inputName0],
namespace[self.inputName1])
def completeMetadata(self, im):
pass
class PointFeaturesPairwiseDist(PointFeatureBase):
"""
Create a feature vector for each point in a point-cloud using a histogram of it's distances to all other points
"""
inputLocalisations = Input('localisations')
outputName = Output('features')
binWidth = Float(100.) # width of the bins in nm
numBins = Int(20) #number of bins (starting at 0)
threeD = Bool(True)
normaliseRelativeDensity = Bool(
False
) # divide by the sum of all radial bins. If not performed, the first principle component will likely be average density
def execute(self, namespace):
from PYME.Analysis.points import DistHist
points = namespace[self.inputLocalisations]
if self.threeD:
x, y, z = points['x'], points['y'], points['z']
f = np.array([
DistHist.distanceHistogram3D(x[i], y[i], z[i], x, y, z,
self.numBins, self.binWidth)
for i in xrange(len(x))
])
else:
x, y = points['x'], points['y']
f = np.array([
DistHist.distanceHistogram(x[i], y[i], x, y, self.numBins,
self.binWidth)
for i in xrange(len(x))
])
namespace[self.outputName] = self._process_features(points, f)
class EngineLayer(BaseLayer):
"""
Base class for layers who delegate their rendering to an engine.
"""
engine = Instance(BaseEngine)
show_lut = Bool(True)
def render(self, gl_canvas):
if self.visible:
return self.engine.render(gl_canvas, self)
@abc.abstractmethod
def get_vertices(self):
"""
Provides the engine with a way of obtaining vertex data. Should be over-ridden in derived class
Returns
-------
a numpy array of vertices suitable for passing to glVertexPointerf()
"""
raise(NotImplementedError())
@abc.abstractmethod
def get_normals(self):
"""
Provides the engine with a way of obtaining vertex data. Should be over-ridden in derived class
Returns
-------
a numpy array of normals suitable for passing to glNormalPointerf()
"""
raise (NotImplementedError())
@abc.abstractmethod
def get_colors(self):
"""
Provides the engine with a way of obtaining vertex data. Should be over-ridden in derived class
Returns
-------
a numpy array of vertices suitable for passing to glColorPointerf()
"""
raise (NotImplementedError())
class ExtractChannelByName(ModuleBase):
"""Extract one channel from an image using regular expression matching to image channel names - by default this is case insensitive"""
inputName = Input('input')
outputName = Output('filtered_image')
channelNamePattern = CStr('channel0')
caseInsensitive = Bool(True)
def _matchChannels(self, channelNames):
# we put this into its own function so that we can call it externally for testing
import re
flags = 0
if self.caseInsensitive:
flags |= re.I
idxs = [
i for i, c in enumerate(channelNames)
if re.search(self.channelNamePattern, c, flags)
]
return idxs
def _pickChannel(self, image):
channelNames = image.mdh['ChannelNames']
idxs = self._matchChannels(channelNames)
if len(idxs) < 1:
raise RuntimeError(
"Expression '%s' did not match any channel names" %
self.channelNamePattern)
if len(idxs) > 1:
raise RuntimeError(
("Expression '%s' did match more than one channel name: " %
self.channelNamePattern) +
', '.join([channelNames[i] for i in idxs]))
idx = idxs[0]
chan = image.data[:, :, :, idx]
im = ImageStack(chan, titleStub='Filtered Image')
im.mdh.copyEntriesFrom(image.mdh)
im.mdh['ChannelNames'] = [channelNames[idx]]
im.mdh['Parent'] = image.filename
return im
def execute(self, namespace):
namespace[self.outputName] = self._pickChannel(
namespace[self.inputName])
class BaseLayer(HasTraits):
"""
This class represents a layer that should be rendered. It should represent a fairly high level concept of a layer -
e.g. a Point-cloud of data coming from XX, or a Surface representation of YY. If such a layer can be rendered multiple
different but similar ways (e.g. points/pointsprites or shaded/wireframe etc) which otherwise share common settings
e.g. point size, point colour, etc ... these representations should be coded as one layer with a selectable rendering
backend or 'engine' responsible for managing shaders and actually executing the opengl code. In this case use the
`EngineLayer` class as a base
.
In simpler cases, such as rendering an overlay it is acceptable for a layer to do it's own rendering and manage it's
own shader. In this case, use `SimpleLayer` as a base.
"""
visible = Bool(True)
def __init__(self, context=None, **kwargs):
self._context = context
#HasTraits.__init__(**kwargs)
@property
def bbox(self):
"""Bounding box in form [x0,y0,z0, x1,y1,z1] (or none if a bounding box does not make sense for this layer)
over-ride in derived classes
"""
return None
@abc.abstractmethod
def render(self, gl_canvas):
"""
Abstract render method to be over-ridden in derived classes. Should check self.visible before drawing anything.
Parameters
----------
gl_canvas : the canvas to draw to - an instance of PYME.LMVis.gl_render3D_shaders.LMGLShaderCanvas
"""
pass
class LabelByRegionProperty(Filter):
"""Asigns a region property to each contiguous region in the input mask.
Optionally throws away all regions for which property is outside a given range.
"""
regionProperty = Enum(['area', 'circularity', 'aspectratio'])
filterByProperty = Bool(False)
propertyMin = Float(0)
propertyMax = Float(1e6)
def applyFilter(self, data, chanNum, frNum, im):
mask = data > 0.5
labs, nlabs = ndimage.label(mask)
rp = skimage.measure.regionprops(labs, None, cache=True)
m2 = np.zeros_like(mask, dtype='float')
objs = ndimage.find_objects(labs)
for region in rp:
oslices = objs[region.label - 1]
r = labs[oslices] == region.label
#print r.shape
if self.regionProperty == 'area':
propValue = region.area
elif self.regionProperty == 'aspectratio':
propValue = region.major_axis_length / region.minor_axis_length
elif self.regionProperty == 'circularity':
propValue = 4 * math.pi * region.area / (region.perimeter *
region.perimeter)
if self.filterByProperty:
if (propValue >= self.propertyMin) and (propValue <=
self.propertyMax):
m2[oslices] += r * propValue
else:
m2[oslices] += r * propValue
return m2
def completeMetadata(self, im):
im.mdh['Labelling.Property'] = self.regionProperty
class DetectPSF(ModuleBase):
"""
Detect PSF based on diff of gaussian
Image dims in X, Y, Z, C where C are processed independently.
Returns list of (X, Y, Z) per C
"""
inputName = Input('input')
min_sigma = Float(1.0)
max_sigma = Float(3.0)
sigma_ratio = Float(1.6)
percent_threshold = Float(0.1)
overlap = Float(0.5)
exclude_border = Int(50)
ignore_z = Bool(True)
output_pos = Output('psf_pos')
# output_img = Output('output')
def execute(self, namespace):
ims = namespace[self.inputName]
pixel_size = ims.mdh['voxelsize.x']
pos = list()
counts = ims.data.shape[3]
for c in np.arange(counts):
mean_project = ims.data[:,:,:,c].mean(2).squeeze()
mean_project[mean_project==2**16-1] = 200
mean_project -= mean_project.min()
mean_project /= mean_project.max()
# if skimage is new enough to support exclude_border
#blobs = feature.blob_dog(mean_project, self.min_sigma / pixel_size, self.max_sigma / pixel_size, overlap=self.overlap, threshold=self.percent_threshold*mean_project.max(), exclude_border=self.exclude_border)
#otherwise:
blobs = feature.blob_dog(mean_project, self.min_sigma / pixel_size, self.max_sigma / pixel_size, overlap=self.overlap, threshold=self.percent_threshold*mean_project.max())
edge_mask = (blobs[:, 0] > self.exclude_border) & (blobs[:, 0] < mean_project.shape[0] - self.exclude_border)
edge_mask &= (blobs[:, 1] > self.exclude_border) & (blobs[:, 1] < mean_project.shape[1] - self.exclude_border)
blobs = blobs[edge_mask]
# is list of x, y, sig
if self.ignore_z:
blobs = np.insert(blobs, 2, ims.data.shape[2]//2, axis=1)
else:
raise Exception("z centering not yet implemented")
blobs = blobs.astype(np.int)
# print blobs
pos.append(blobs)
namespace[self.output_pos] = pos
if True:
try:
# from matplotlib import pyplot
fig, axes = pyplot.subplots(1, counts, figsize=(4*counts, 3), squeeze=False)
for c in np.arange(counts):
mean_project = ims.data[:,:,:,c].mean(2).squeeze()
mean_project[mean_project==2**16-1] = 200
axes[0, c].imshow(mean_project)
axes[0, c].set_axis_off()
for x, y, z, sig in pos[c]:
cir = pyplot.Circle((y, x), sig, color='red', linewidth=2, fill=False)
axes[0, c].add_patch(cir)
except Exception as e:
print e
class ModuleCollection(HasTraits):
modules = List()
execute_on_invalidation = Bool(False)
def __init__(self, *args, **kwargs):
HasTraits.__init__(self, *args, **kwargs)
self.namespace = {}
# we open hdf files and don't necessarily read their contents into memory - these need to be closed when we
# either delete the recipe, or clear the namespace
self._open_input_files = []
self.recipe_changed = dispatch.Signal()
self.recipe_executed = dispatch.Signal()
def invalidate_data(self):
if self.execute_on_invalidation:
self.execute()
def clear(self):
self.namespace.clear()
def new_output_name(self, stub):
count = len([k.startswith(stub) for k in self.namespace.keys()])
if count == 0:
return stub
else:
return '%s_%d' % (stub, count)
def dependancyGraph(self):
dg = {}
#only add items to dependancy graph if they are not already in the namespace
#calculated_objects = namespace.keys()
for mod in self.modules:
#print mod
s = mod.inputs
try:
s.update(dg[mod])
except KeyError:
pass
dg[mod] = s
for op in mod.outputs:
#if not op in calculated_objects:
dg[op] = {
mod,
}
return dg
def reverseDependancyGraph(self):
dg = self.dependancyGraph()
rdg = {}
for k, vs in dg.items():
for v in vs:
vdeps = set()
try:
vdeps = rdg[v]
except KeyError:
pass
vdeps.add(k)
rdg[v] = vdeps
return rdg
def _getAllDownstream(self, rdg, keys):
"""get all the downstream items which depend on the given key"""
downstream = set()
next_level = set()
for k in keys:
try:
next_level.update(rdg[k])
except KeyError:
pass
if len(list(next_level)) > 0:
downstream.update(next_level)
downstream.update(self._getAllDownstream(rdg, list(next_level)))
return downstream
def prune_dependencies_from_namespace(self,
keys_to_prune,
keep_passed_keys=False):
rdg = self.reverseDependancyGraph()
if keep_passed_keys:
downstream = list(self._getAllDownstream(rdg, list(keys_to_prune)))
else:
downstream = list(keys_to_prune) + list(
self._getAllDownstream(rdg, list(keys_to_prune)))
#print downstream
for dsi in downstream:
try:
self.namespace.pop(dsi)
except KeyError:
#the output is not in our namespace, no need to prune
pass
except AttributeError:
#we might not have our namespace defined yet
pass
def resolveDependencies(self):
import toposort
#build dependancy graph
dg = self.dependancyGraph()
#solve the dependency tree
return toposort.toposort_flatten(dg, sort=False)
def execute(self, **kwargs):
#remove anything which is downstream from changed inputs
#print self.namespace.keys()
for k, v in kwargs.items():
#print k, v
try:
if not (self.namespace[k] == v):
#input has changed
print('pruning: ', k)
self.prune_dependencies_from_namespace([k])
except KeyError:
#key wasn't in namespace previously
print('KeyError')
pass
self.namespace.update(kwargs)
exec_order = self.resolveDependencies()
for m in exec_order:
if isinstance(
m,
ModuleBase) and not m.outputs_in_namespace(self.namespace):
try:
m.execute(self.namespace)
except:
logger.exception("Error in recipe module: %s" % m)
raise
self.recipe_executed.send_robust(self)
if 'output' in self.namespace.keys():
return self.namespace['output']
@classmethod
def fromMD(cls, md):
c = cls()
moduleNames = set([s.split('.')[0] for s in md.keys()])
mc = []
for mn in moduleNames:
mod = all_modules[mn]()
mod.set(**md[mn])
mc.append(mod)
#return cls(modules=mc)
c.modules = mc
return c
def get_cleaned_module_list(self):
l = []
for mod in self.modules:
#l.append({mod.__class__.__name__: mod.get()})
ct = mod.class_traits()
mod_traits_cleaned = {}
for k, v in mod.get().items():
if not k.startswith(
'_'
): #don't save private data - this is usually used for caching etc ..,
try:
if (not (v == ct[k].default)) or (k.startswith(
'input')) or (k.startswith('output')):
#don't save defaults
if isinstance(v, dict) and not type(v) == dict:
v = dict(v)
elif isinstance(v, list) and not type(v) == list:
v = list(v)
elif isinstance(v, set) and not type(v) == set:
v = set(v)
mod_traits_cleaned[k] = v
except KeyError:
# for some reason we have a trait that shouldn't be here
pass
l.append({module_names[mod.__class__]: mod_traits_cleaned})
return l
def toYAML(self):
import yaml
class MyDumper(yaml.SafeDumper):
def represent_mapping(self, tag, value, flow_style=None):
return super(MyDumper,
self).represent_mapping(tag, value, False)
return yaml.dump(self.get_cleaned_module_list(), Dumper=MyDumper)
def toJSON(self):
import json
return json.dumps(self.get_cleaned_module_list())
def _update_from_module_list(self, l):
"""
Update from a parsed yaml or json list of modules
It probably makes no sense to call this directly as the format is pretty wack - a
list of dictionarys each with a single entry, but that is how the yaml parses
Parameters
----------
l: list
List of modules as obtained from parsing a yaml recipe,
Each module is a dictionary mapping with a single e.g.
[{'Filtering.Filter': {'filters': {'probe': [-0.5, 0.5]}, 'input': 'localizations', 'output': 'filtered'}}]
Returns
-------
"""
mc = []
if l is None:
l = []
for mdd in l:
mn, md = list(mdd.items())[0]
try:
mod = all_modules[mn](self)
except KeyError:
# still support loading old recipes which do not use hierarchical names
# also try and support modules which might have moved
mod = _legacy_modules[mn.split('.')[-1]](self)
mod.set(**md)
mc.append(mod)
self.modules = mc
self.recipe_changed.send_robust(self)
self.invalidate_data()
@classmethod
def _from_module_list(cls, l):
""" A factory method which contains the common logic for loading/creating from either
yaml or json. Do not call directly"""
c = cls()
c._update_from_module_list(l)
return c
@classmethod
def fromYAML(cls, data):
import yaml
l = yaml.load(data)
return cls._from_module_list(l)
def update_from_yaml(self, data):
"""
Update from a yaml formatted recipe description
Parameters
----------
data: str
either yaml formatted text, or the path to a yaml file.
Returns
-------
None
"""
import os
import yaml
if os.path.isfile(data):
with open(data) as f:
data = f.read()
l = yaml.load(data)
return self._update_from_module_list(l)
@classmethod
def fromJSON(cls, data):
import json
return cls._from_module_list(json.loads(data))
def add_module(self, module):
self.modules.append(module)
self.recipe_changed.send_robust(self)
@property
def inputs(self):
ip = set()
for mod in self.modules:
ip.update({k for k in mod.inputs if k.startswith('in')})
return ip
@property
def outputs(self):
op = set()
for mod in self.modules:
op.update({k for k in mod.outputs if k.startswith('out')})
return op
@property
def module_outputs(self):
op = set()
for mod in self.modules:
op.update(set(mod.outputs))
return op
@property
def file_inputs(self):
out = []
for mod in self.modules:
out += mod.file_inputs
return out
def save(self, context={}):
"""
Find all OutputModule instances and call their save methods with the recipe context
Parameters
----------
context : dict
A context dictionary used to substitute and create variable names.
"""
for mod in self.modules:
if isinstance(mod, OutputModule):
mod.save(self.namespace, context)
def gather_outputs(self, context={}):
"""
Find all OutputModule instances and call their generate methods with the recipe context
Parameters
----------
context : dict
A context dictionary used to substitute and create variable names.
"""
outputs = []
for mod in self.modules:
if isinstance(mod, OutputModule):
out = mod.generate(self.namespace, context)
if not out is None:
outputs.append(out)
return outputs
def loadInput(self, filename, key='input'):
"""Load input data from a file and inject into namespace
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
from PYME.IO import unifiedIO
import os
extension = os.path.splitext(filename)[1]
if extension in ['.h5r', '.h5', '.hdf']:
import tables
from PYME.IO import MetaDataHandler
from PYME.IO import tabular
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
key_prefix = '' if key == 'input' else key + '_'
try:
mdh = MetaDataHandler.NestedClassMDHandler(
MetaDataHandler.HDFMDHandler(h5f))
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()
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 isinstance(t, tables.VLArray):
from PYME.IO.ragged import RaggedVLArray
rag = RaggedVLArray(
h5f, t.name, copy=True
) #force an in-memory copy so we can close the hdf file properly
rag.mdh = mdh
self.namespace[key_prefix + t.name] = rag
elif isinstance(t, tables.table.Table):
# pipe our table into h5r or hdf source depending on the extension
tab = tabular.H5RSource(
h5f, t.name
) if extension == '.h5r' else tabular.HDFSource(
h5f, t.name)
tab.mdh = mdh
self.namespace[key_prefix + t.name] = tab
elif isinstance(t, tables.EArray):
# load using ImageStack._loadh5, which finds metdata
im = ImageStack(filename=filename, haveGUI=False)
# assume image is the main table in the file and give it the named key
self.namespace[key] = im
elif extension == '.csv':
logger.error('loading .csv not supported yet')
raise NotImplementedError
elif extension in ['.xls', '.xlsx']:
logger.error('loading .xls not supported yet')
raise NotImplementedError
else:
self.namespace[key] = ImageStack(filename=filename, haveGUI=False)
@property
def pipeline_view(self):
import wx
if wx.GetApp() is None:
return None
else:
from traitsui.api import View, ListEditor, InstanceEditor, Item
#v = tu.View(tu.Item('modules', editor=tu.ListEditor(use_notebook=True, view='pipeline_view'), style='custom', show_label=False),
# buttons=['OK', 'Cancel'])
return View(Item('modules',
editor=ListEditor(
style='custom',
editor=InstanceEditor(view='pipeline_view'),
mutable=False),
style='custom',
show_label=False),
buttons=['OK', 'Cancel'])
def to_svg(self):
from . import recipeLayout
return recipeLayout.to_svg(self.dependancyGraph())
def _repr_svg_(self):
""" Make us look pretty in Jupyter"""
return self.to_svg()
class Filter(ModuleBase):
"""Module with one image input and one image output"""
inputName = Input('input')
outputName = Output('filtered_image')
processFramesIndividually = Bool(True)
def filter(self, image):
#from PYME.util.shmarray import shmarray
#import multiprocessing
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 execute(self, namespace):
namespace[self.outputName] = self.filter(namespace[self.inputName])
def completeMetadata(self, im):
pass
@classmethod
def dsviewer_plugin_callback(cls, dsviewer, showGUI=True, **kwargs):
"""Implements a callback which allows this module to be used as a plugin for dsviewer.
Parameters
----------
dsviewer : :class:`PYME.DSView.dsviewer.DSViewFrame` instance
This is the current :class:`~PYME.DSView.dsviewer.DSViewFrame` instance. The filter will be run with the
associated ``.image`` as input and display the output in a new window.
showGUI : bool
Should we show a GUI to set parameters (generated by calling configure_traits()), or just run with default
parameters.
**kwargs : dict
Optionally, provide default values for parameters. Makes most sense when used with showGUI = False
"""
from PYME.DSView import ViewIm3D
mod = cls(inputName='input', outputName='output', **kwargs)
if (not showGUI) or mod.configure_traits(kind='modal'):
namespace = {'input': dsviewer.image}
mod.execute(namespace)
ViewIm3D(mod['output'],
parent=dsviewer,
glCanvas=dsviewer.glCanvas)
class CalculateFRCBase(ModuleBase):
"""
Base class. Refer to derived classes for docstrings.
"""
pre_filter = Enum(['Tukey_1/8', None])
frc_smoothing_func = Enum(['Cubic Spline', 'Sigmoid', None])
multiprocessing = Bool(True)
# plot_graphs = Bool()
cubic_smoothing = Float(0.01)
save_path = File()
# output_fft_image_a = Output('FRC_fft_image_a')
# output_fft_image_b = Output('FRC_fft_image_b')
output_fft_images_cc = Output('FRC_fft_images_cc')
output_frc_dict = Output('FRC_dict')
output_frc_plot = Output('FRC_plot')
output_frc_raw = Output('FRC_raw')
def execute(self):
raise Exception("Base class not fully implemented")
def preprocess_images(self, image_pair):
# pad images to square shape
# image_pair = self.pad_images_to_equal_dims(image_pair)
dims_length = np.stack([im.shape for im in image_pair], 0)
assert np.all([np.all(dims_length[:, i] == dims_length[0, i])for i in range(dims_length.shape[1])]), "Images not the same dimension."
# apply filtering to zero near edge of images
if self.pre_filter == 'Tukey_1/8':
image_pair = self.filter_images_tukey(image_pair, 1./8)
elif self.pre_filter == None:
pass
else:
raise Exception()
return image_pair
# def pad_images_to_equal_dims(self, images):
# dims_length = np.stack([im.shape for im in images], 0)
# assert np.all([np.all(dims_length[:, i] == dims_length[0, i])for i in xrange(dims_length.shape[1])]), "Images not the same dimension."
#
# return images
#
# dims_length = dims_length[0, :]
# max_dim = dims_length.max()
#
# padding = np.empty((dims_length.shape[0], 2), dtype=np.int)
# for dim in xrange(dims_length.shape[0]):
# total_padding = max_dim - dims_length[dim]
# padding[dim] = [total_padding // 2, total_padding - total_padding //2]
#
# results = list()
# for im in images:
# results.append(np.pad(im, padding, mode='constant', constant_values=0))
#
# return results
def filter_images_tukey(self, images, alpha):
from scipy.signal import tukey
# window = tukey(images[0].shape[0], alpha=alpha)
# window_nd = np.prod(np.stack(np.meshgrid(*(window,)*images[0].ndim)), axis=0)
windows = [tukey(images[0].shape[i], alpha=alpha) for i in range(images[0].ndim)]
window_nd = np.prod(np.stack(np.meshgrid(*windows, indexing='ij')), axis=0)
# for im in images:
# im *= window_nd
return [images[0]*window_nd, images[1]*window_nd]
def calculate_FRC_from_images(self, image_pair, mdh):
ft_images = list()
if self.multiprocessing:
results = list()
for im in image_pair:
results.append(self._pool.apply_async(np.fft.fftn, (im,)))
for res in results:
ft_images.append(res.get())
del results
else:
for im in image_pair:
ft_images.append(np.fft.fftn(im))
# im_fft_freq = np.fft.fftfreq(image_pair[0].shape[0], self._pixel_size_in_nm)
# im_R = np.sqrt(im_fft_freq[:, None]**2 + im_fft_freq[None, :]**2)
im_fft_freqs = [np.fft.fftfreq(image_pair[0].shape[i], self._pixel_size_in_nm[i]) for i in range(image_pair[0].ndim)]
im_R = np.linalg.norm(np.stack(np.meshgrid(*im_fft_freqs, indexing='ij')), axis=0)
im1_fft_power = np.multiply(ft_images[0], np.conj(ft_images[0]))
im2_fft_power = np.multiply(ft_images[1], np.conj(ft_images[1]))
im12_fft_power = np.multiply(ft_images[0], np.conj(ft_images[1]))
## fft_ims = ImageStack(data=np.stack([np.fft.fftshift(im1_fft_power),
## np.fft.fftshift(im2_fft_power),
## np.fft.fftshift(im12_fft_power)], axis=-1), mdh=mdh)
## self._namespace[self.output_fft_images] = fft_ims
# self._namespace[self.output_fft_image_a] = ImageStack(data=np.fft.fftshift(im1_fft_power), titleStub="ImageA_FFT")
# self._namespace[self.output_fft_image_b] = ImageStack(data=np.fft.fftshift(im2_fft_power), titleStub="ImageB_FFT")
# self._namespace[self.output_fft_images_cc] = ImageStack(data=np.fft.fftshift(im12_fft_power), titleStub="ImageA_Image_B_FFT_CC")
try:
self._namespace[self.output_fft_images_cc] = ImageStack(data=np.stack([np.atleast_3d(np.fft.fftshift(im1_fft_power)),
np.atleast_3d(np.fft.fftshift(im2_fft_power)),
np.atleast_3d(np.fft.fftshift(im12_fft_power))], 3), titleStub="ImageA_Image_FFT_CC")
# if self.plot_graphs:
# from PYME.DSView.dsviewer import ViewIm3D, View3D
# # ViewIm3D(self._namespace[self.output_fft_image_a])
# # ViewIm3D(self._namespace[self.output_fft_image_b])
# ViewIm3D(self._namespace[self.output_fft_images_cc])
# # View3D(np.fft.fftshift(im_R))
except Exception as e:
print (e)
im1_fft_flat_res = CalculateFRCBase.BinData(im_R.flatten(), im1_fft_power.flatten(), statistic='mean', bins=201)
im2_fft_flat_res = CalculateFRCBase.BinData(im_R.flatten(), im2_fft_power.flatten(), statistic='mean', bins=201)
im12_fft_flat_res = CalculateFRCBase.BinData(im_R.flatten(), im12_fft_power.flatten(), statistic='mean', bins=201)
corr = np.real(im12_fft_flat_res.statistic) / np.sqrt(np.abs(im1_fft_flat_res.statistic*im2_fft_flat_res.statistic))
smoothed_frc = self.smooth_frc(im12_fft_flat_res.bin_edges[:-1], corr, self.cubic_smoothing)
res, rawdata = self.calculate_threshold(im12_fft_flat_res.bin_edges[:-1], corr, smoothed_frc, im12_fft_flat_res.counts)
return res, rawdata
def smooth_frc(self, freq, corr, cubic_smoothing):
if self.frc_smoothing_func is None:
interp_frc = interpolate.interp1d(freq, corr, kind='next', )
return interp_frc
elif self.frc_smoothing_func == "Sigmoid":
func = CalculateFRCBase.Sigmoid
fit_res = optimize.minimize(lambda a, x: np.sum(np.square(func(a[0], a[1], x)-corr)), [1, freq[len(freq)/2]], args=(freq), method='Nelder-Mead')
return partial(func, n=fit_res.x[0], c=fit_res.x[1])
elif self.frc_smoothing_func == "Cubic Spline":
# smoothed so that average deviation loss is less than 0.2% of original. Somewhat arbitrary but probably not totally unreasonable since FRC is bounded 0 to 1.
# interp_frc = interpolate.UnivariateSpline(freq, corr, k=3, s=len(freq)*(0.002*np.var(corr)))
# interp_frc = interpolate.UnivariateSpline(freq, corr, k=3, s=(0.05*np.std(corr)))
interp_frc = interpolate.UnivariateSpline(freq, corr, k=3, s=cubic_smoothing)
return interp_frc
def calculate_threshold(self, freq, corr, corr_func, counts):
res = dict()
fsc_0143 = optimize.minimize(lambda x: np.square(corr_func(x=x)-0.143), freq[np.argmax(corr_func(x=freq)-0.143 < 0)], method='Nelder-Mead')
res['frc 1/7'] = 1./fsc_0143.x[0]
sigma = 1.0 / np.sqrt(counts*0.5)
sigma_spl = interpolate.UnivariateSpline(freq, sigma, k=3, s=0)
fsc_3sigma = optimize.minimize(lambda x: np.square(corr_func(x=x)-3.*sigma_spl(x)), freq[np.argmax(corr_func(x=freq)-3.*sigma_spl(freq) < 0)], method='Nelder-Mead')
res['frc 3 sigma'] = 1./fsc_3sigma.x[0]
# van Heel and Schatz, 2005, Fourier shell correlation threshold criteria
half_bit = (0.2071 + 1.9102 / np.sqrt(counts)) / (1.2071 + 0.9102 / np.sqrt(counts))
half_bit_spl = interpolate.UnivariateSpline(freq, half_bit, k=3, s=0)
fsc_half_bit = optimize.minimize(lambda x: np.square(corr_func(x=x)-half_bit_spl(x)), freq[np.argmax(corr_func(x=freq)-half_bit_spl(freq) < 0)], method='Nelder-Mead')
res['frc half bit'] = 1./fsc_half_bit.x[0]
# fsc_max = np.max([fsc_0143.x[0], fsc_2sigma.x[0], fsc_3sigma.x[0], fsc_5sigma.x[0], fsc_half_bit.x[0]])
# axes[1].set_xlim(0, np.min([2*fsc_max, im12_fft_flat_res.bin_edges[-1]]))
# if not self.plot_graphs:
# ioff()
def plot():
frc_text = ""
fig, axes = subplots(1,2,figsize=(10,4))
axes[0].plot(freq, corr)
axes[0].plot(freq, corr_func(x=freq))
axes[0].axhline(0.143, ls='--', color='red')
axes[0].axvline(fsc_0143.x[0], ls='--', color='red', label='1/7')
frc_text += "\n1/7: {:.2f} nm".format(1./fsc_0143.x[0])
axes[0].plot(freq, 3*sigma_spl(freq), ls='--', color='pink')
axes[0].axvline(fsc_3sigma.x[0], ls='--', color='pink', label='3 sigma')
frc_text += "\n3 sigma: {:.2f} nm".format(1./fsc_3sigma.x[0])
axes[0].plot(freq, half_bit_spl(freq), ls='--', color='purple')
axes[0].axvline(fsc_half_bit.x[0], ls='--', color='purple', label='1/2 bit')
frc_text += "\n1/2 bit: {:.2f} nm".format(1./fsc_half_bit.x[0])
axes[0].legend()
# axes[0].set_ylim(None, 1.1)
x_ticklocs = axes[0].get_xticks()
axes[0].set_xticklabels(["{:.1f}".format(1./i) for i in x_ticklocs])
axes[0].set_ylabel("FSC/FRC")
axes[0].set_xlabel("Resol (nm)")
axes[1].text(0.5, 0.5, frc_text, horizontalalignment='center', verticalalignment='center', transform=axes[1].transAxes)
axes[1].set_axis_off()
# if self.plot_graphs:
# fig.show()
# else:
# ion()
plot()
self._namespace[self.output_frc_plot] = Plot(plot)
rawdata = {'freq':freq, 'corr':corr, 'smooth':corr_func(x=freq), '1/7':np.ones_like(freq)/7, '3 sigma':3*sigma_spl(freq), '1/2 bit':half_bit_spl(freq)}
return res, rawdata
def save_to_file(self, namespace):
if self.save_path is not "":
try:
np.savez_compressed(self.save_path, raw=namespace[self.output_frc_raw], results=namespace[self.output_frc_dict])
except Exception as e:
raise e
@staticmethod
def BinData(indexes, data, statistic='mean', bins=10):
# Calculates binned statistics. Supports complex number.
if statistic == 'mean':
func = np.mean
elif statistic == 'sum':
func = np.sum
class Result(object):
statistic = None
bin_edges = None
counts = None
bins = np.linspace(indexes.min(), indexes.max(), bins)
binned = np.zeros(len(bins)-1, dtype=data.dtype)
counts = np.zeros(len(bins)-1, dtype=np.int)
indexes_sort_arg = np.argsort(indexes.flatten())
indexes_sorted = indexes.flatten()[indexes_sort_arg]
data_sorted = data.flatten()[indexes_sort_arg]
edges_indexes = np.searchsorted(indexes_sorted, bins)
for i in range(bins.shape[0]-1):
values = data_sorted[edges_indexes[i]:edges_indexes[i+1]]
binned[i] = func(values)
counts[i] = len(values)
res = Result()
res.statistic = binned
res.bin_edges = bins
res.counts = counts
return res
@staticmethod
def Sigmoid(n, c, x):
res = 1 - 1 / (1 + np.exp(n*(-x+c)))
return res
class RGBImageUpload(ImageUpload):
"""
Create RGB (png) image and upload it to an OMERO server, optionally
attaching localization files.
Parameters
----------
input_image : str
name of image in the recipe namespace to upload
input_localization_attachments : dict
maps tabular types (keys) to attachment filenames (values). Tabular's
will be saved as '.hdf' files and attached to the image
filePattern : str
pattern to determine name of image on OMERO server
omero_dataset : str
name of OMERO dataset to add the image to. If the dataset does not
already exist it will be created.
omero_project : str
name of OMERO project to link the dataset to. If the project does not
already exist it will be created.
zoom : float
how large to zoom the image
scaling : str
how to scale the intensity - one of 'min-max' or 'percentile'
scaling_factor: float
`percentile` scaling only - which percentile to use
colorblind_friendly : bool
Use cyan, magenta, and yellow rather than RGB. True, by default.
Notes
-----
OMERO server address and user login information must be stored in the user
PYME config directory under plugins/config/pyme-omero, e.g.
/Users/Andrew/.PYME/plugins/config/pyme-omero. The file should be yaml
formatted with the following keys:
user
password
address
port [optional]
The project/image/dataset will be owned by the user set in the yaml file.
"""
filePattern = '{file_stub}.png'
scaling = Enum(['min-max', 'percentile'])
scaling_factor = Float(0.95)
zoom = Int(1)
colorblind_friendly = Bool(True)
def _save(self, image, path):
from PIL import Image
from PYME.IO.rgb_image import image_to_rgb, image_to_cmy
if (self.colorblind_friendly and (image.data.shape[3] != 1)):
im = image_to_cmy(image,
zoom=self.zoom,
scaling=self.scaling,
scaling_factor=self.scaling_factor)
else:
im = image_to_rgb(image,
zoom=self.zoom,
scaling=self.scaling,
scaling_factor=self.scaling_factor)
rgb = Image.fromarray(im, mode='RGB')
rgb.save(path)
class LabelRange(Filter):
"""Asigns a unique integer label to each contiguous region in the input mask.
Throws away all regions which are outside of given number of pixel range.
Also uses the number of sites from a second input channel to decide if region is retained,
retaining only those with the number sites in a given range.
"""
inputSitesLabeled = Input(
"sites") # sites and the main input must have the same shape!
minRegionPixels = Int(10)
maxRegionPixels = Int(100)
minSites = Int(4)
maxSites = Int(6)
sitesAsMaxima = Bool(False)
def filter(self, image, imagesites):
#from PYME.util.shmarray import shmarray
#import multiprocessing
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'),
imagesites.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'),
imagesites.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 execute(self, namespace):
namespace[self.outputName] = self.filter(
namespace[self.inputName], namespace[self.inputSitesLabeled])
def applyFilter(self, data, sites, chanNum, frNum, im):
# siteLabels = self.recipe.namespace[self.sitesLabeled]
mask = data > 0.5
labs, nlabs = ndimage.label(mask)
rSize = self.minRegionPixels
rMax = self.maxRegionPixels
minSites = self.minSites
maxSites = self.maxSites
m2 = 0 * mask
objs = ndimage.find_objects(labs)
for i, o in enumerate(objs):
r = labs[o] == i + 1
#print r.shape
area = r.sum()
if (area >= rSize) and (area <= rMax):
if self.sitesAsMaxima:
nsites = sites[o][r].sum()
else:
nsites = (np.unique(sites[o][r]) > 0).sum(
) # count the unique labels (excluding label 0 which is background)
if (nsites >= minSites) and (nsites <= maxSites):
m2[o] += r
labs, nlabs = ndimage.label(m2 > 0)
return labs
def completeMetadata(self, im):
im.mdh['Labelling.MinSize'] = self.minRegionPixels
im.mdh['Labelling.MaxSize'] = self.maxRegionPixels
im.mdh['Labelling.MinSites'] = self.minSites
im.mdh['Labelling.MaxSites'] = self.maxSites
class CalculateFRCFromImages(CalculateFRCBase):
"""
Take a pair of images and calculates the fourier shell/ring correlation (FSC / FRC).
Inputs
------
input_image_a : ImageStack
First of two images.
Outputs
-------
output_fft_images_cc : ImageStack
Fast Fourier transform original and cross-correlation images.
output_frc_dict : dict
FSC/FRC results.
output_frc_plot : Plot
Output plot of the FSC / FRC curve.
output_frc_raw : dict
Complete FSC/FRC results.
Parameters
----------
image_b_path : File
File path of the second of the two images.
c_channel : int
Color channel of the images to use.
image_a_z : int
Ignored unless flatten_z is True. In which case either select the z plane to use (>=0) or performs a maximum project (<0) for the first image.
image_b_z : int
Ignored unless flatten_z is True. In which case either select the z plane to use (>=0) or performs a maximum project (<0) for the second image.
flatten_z : Bool
If enabled ignores z information and only performs a FRC.
pre_filter : string
Methods to filter the images prior to Fourier transform.
frc_smoothing_func : string
Methods to smooth the FSC / FRC curve.
cubic_smoothing : float
Smoothing factor for cubic spline.
multiprocessing : Bool
Enables multiprocessing.
save_path : File
(Optional) File path to save output
"""
input_image_a = Input('input')
# image_a_dim = Int(2)
# image_a_index = Int(0)
# image_b_dim = Int(2)
# image_b_index = Int(1)
image_b_path = File(info_text="Filepath of image to compare against. Leave blank to compare against currently opened image.")
c_channel = Int(0)
flatten_z = Bool(True)
image_a_z = Int(-1)
image_b_z = Int(-1)
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)
# image_pair = self.generate_image_pair(mapped_pipeline)
# ims = namespace[self.input_images]
image_a = namespace[self.input_image_a]
if len(self.image_b_path.strip()) == 0:
image_b = image_a
else:
image_b = ImageStack(filename=self.image_b_path)
self._pixel_size_in_nm = np.zeros(3, dtype=np.float)
self._pixel_size_in_nm[0] = image_a.mdh.voxelsize.x
self._pixel_size_in_nm[1] = image_a.mdh.voxelsize.y
try:
self._pixel_size_in_nm[2] = image_a.mdh.voxelsize.z
except:
pass
if image_a.mdh.voxelsize.units == 'um':
self._pixel_size_in_nm *= 1.E3
# print(self._pixel_size_in_nm)
# image_indices = [[self.image_a_dim, self.image_a_index], [self.image_b_dim, self.image_b_index]]
# image_slices = list()
# for i in xrange(2):
# slices = [slice(None, None), slice(None, None)]
# for j in xrange(2, image_indices[i][0]+1):
# if j == image_indices[i][0]:
# slices.append(slice(image_indices[i][1], image_indices[i][1]+1))
# else:
# slices.append(slice(None, None))
# image_slices.append(slices)
#
# image_pair = [ims.data[image_slices[0]].squeeze(), ims.data[image_slices[1]].squeeze()]
image_a_data = image_a.data[:,:,:,self.c_channel].squeeze()
image_b_data = image_b.data[:,:,:,self.c_channel].squeeze()
if self.flatten_z:
print("2D mode. Slice if z index >= 0 otherwise max projection")
if self.image_a_z >= 0:
image_a_data = image_a_data[:,:,self.image_a_z]
else:
image_a_data = image_a_data.max(2)
if self.image_b_z >= 0:
image_b_data = image_b_data[:,:,self.image_b_z]
else:
image_b_data = image_b_data.max(2)
# print(np.allclose(image_a_data, image_b_data))
image_pair = [image_a_data, image_b_data]
# print(image_pair[0].shape)
image_pair = self.preprocess_images(image_pair)
frc_res, rawdata = self.calculate_FRC_from_images(image_pair, None)
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)
class RCCDriftCorrection(RCCDriftCorrectionBase):
"""
For localization data.
Performs drift correction using cross-correlation, including redundant RCC from
Wang et al. Optics Express 2014 22:13 (Bo Huang's RCC algorithm).
Runtime will vary hugely depending on size of dataset and settings.
``cache_fft`` is necessary for large datasets.
Inputs
------
input_for_correction : Tabular
Dataset used to calculate drift.
input_for_mapping : Tabular
*Deprecated.* Dataset to correct.
Outputs
-------
output_drift : Tuple of arrays
Drift results.
output_drift_plot : Plot
*Deprecated.* Plot of drift results.
output_cross_cor : ImageStack
Cross correlation images if ``debug_cor_file`` is not blank.
outputName : Tabular
*Deprecated.* Drift-corrected dataset.
Parameters
----------
step : Int
Setting for image construction. Step size between images
window : Int
Setting for image construction. Number of frames used per image. Should be equal or larger than step size.
binsize : Float
Setting for image construction. Pixel size.
flatten_z : Bool
Setting for image construction. Ignore z information if enabled.
tukey_size : Float
Setting for image construction. Shape parameter for Tukey filter (``scipy.signal.tukey``).
cache_fft : File
Use file as disk cache if provided.
method : String
Redundant, mean, or direct cross-correlation.
shift_max : Float
Rejection threshold for RCC.
corr_window : Float
Size of correlation window. Frames are only compared if within this frame range. N/A for DCC.
multiprocessing : Float
Enables multiprocessing.
debug_cor_file : File
Enables debugging. Use file as disk cache if provided.
"""
input_for_correction = Input('Localizations')
input_for_mapping = Input('Localizations')
# redundant cross-corelation, mean cross-correlation, direction cross-correlation
step = Int(2500)
window = Int(2500)
binsize = Float(30)
flatten_z = Bool()
tukey_size = Float(0.25)
outputName = Output('corrected_localizations')
def calc_corr_drift_from_locs(self, x, y, z, t):
# bin edges for histogram
bx = np.arange(x.min(), x.max() + self.binsize + 1, self.binsize)
by = np.arange(y.min(), y.max() + self.binsize + 1, self.binsize)
bz = np.arange(z.min(), z.max() + self.binsize + 1, self.binsize)
# pad bin length to odd number so image size is even
if bx.shape[0] % 2 == 0:
bx = np.concatenate([bx, [bx[-1] + bx[1] - bx[0]]])
if by.shape[0] % 2 == 0:
by = np.concatenate([by, [by[-1] + by[1] - by[0]]])
if bz.shape[0] > 2 and bz.shape[0] % 2 == 0:
bz = np.concatenate([bz, [bz[-1] + bz[1] - bz[0]]])
assert (bx.shape[0] % 2 == 1) and (by.shape[0] % 2 == 1), "Ops. Image not correctly padded to even size."
# start time of all windows, allow partial window near end of pipeline
time_values = np.arange(t.min(), t.max() + 1, self.step)
# 2d array, start and end time of windows
time_values = np.stack([time_values, np.clip(time_values + self.window, None, t.max())], axis=1)
n_steps = time_values.shape[0]
# center time of center for returning. last window may have different spacing
time_values_mid = time_values.mean(axis=1)
if (np.any(np.diff(t) < 0)): # in case pipeline is not sorted for whatever reason
t_sort_arg = np.argsort(t)
t = t[t_sort_arg]
x = x[t_sort_arg]
y = y[t_sort_arg]
z = z[t_sort_arg]
time_indexes = np.zeros_like(time_values, dtype=int)
time_indexes[:, 0] = np.searchsorted(t, time_values[:, 0], side='left')
time_indexes[:, 1] = np.searchsorted(t, time_values[:, 1]-1, side='right')
# print('time indexes')
# print(time_values)
# print(time_values_mid)
# print(time_indexes)
# Fourier transformed (and binned) set of images to correlate against
# one another
# Crude way of swaping longest axis to the last for optimizing rfft performance.
# Code changed for this is limited to this method.
xyz = np.asarray([x, y, z])
bxyz = np.asarray([bx, by, bz])
dims_order = np.arange(len(xyz))
dims_length = np.asarray([len(b) for b in bxyz])
dims_largest_index = np.argmax(dims_length)
dims_order[-1], dims_order[dims_largest_index] = dims_order[dims_largest_index], dims_order[-1]
xyz = xyz[dims_order]
bxyz = bxyz[dims_order]
dims_length = dims_length[dims_order]
# use memmap for caching if ft_cache is defined
if self.cache_fft == "":
ft_images = np.zeros((n_steps, dims_length[0]-1, dims_length[1]-1, (dims_length[2]-1)//2 + 1, ), dtype=np.complex)
else:
ft_images = np.memmap(self.cache_fft, dtype=np.complex, mode='w+', shape=(n_steps, dims_length[0]-1, dims_length[1]-1, (dims_length[2]-1)//2 + 1, ))
print(ft_images.shape)
print("{:,} bytes".format(ft_images.nbytes))
print("{:.2f} s. About to start heavy lifting.".format(time.time() - self._start_time))
# fill ft_images
# if multiprocessing, can either use or not caching
# if not multiprocessing, don't pass filenames for caching, just the memmap array is fine
if self.multiprocessing:
dt = ft_images.dtype
sh = ft_images.shape
args = [(i, xyz[:,slice(*ti)].T, bxyz, (self.cache_fft, dt, sh, i), self.tukey_size) for i, ti in enumerate(time_indexes)]
for i, (j, res) in enumerate(self._pool.imap_unordered(calc_fft_from_locs_helper, args)):
if self.cache_fft == "":
ft_images[j] = res
if ((i+1) % (n_steps//5) == 0):
print("{:.2f} s. Completed calculating {} of {} total ft images.".format(time.time() - self._start_time, i+1, n_steps))
else:
# For each window we wish to correlate...
for i, ti in enumerate(time_indexes):
# .. we generate an image and store ft of image
t_slice = slice(*ti)
ft_images[i] = calc_fft_from_locs(xyz[:,t_slice].T, bxyz, filter_size=self.tukey_size)
if ((i+1) % (n_steps//5) == 0):
print("{:.2f} s. Completed calculating {} of {} total ft images.".format(time.time() - self._start_time, i+1, n_steps))
print("{:.2f} s. Finished generating ft array.".format(time.time() - self._start_time))
print("{:,} bytes".format(ft_images.nbytes))
shifts, coefs = self.calc_corr_drift_from_ft_images(ft_images)
# clean up of ft_images, potentially really large array
if isinstance(ft_images, np.memmap):
ft_images.flush()
del ft_images
# print(shifts)
# print(coefs)
return time_values_mid, self.binsize * shifts[:, dims_order], coefs
def _execute(self, namespace):
# from PYME.util import mProfile
# self._start_time = time.time()
self.trait_setq(**{"_start_time": time.time()})
print("Starting drift correction module.")
if self.multiprocessing:
proccess_count = np.clip(multiprocessing.cpu_count()-1, 1, None)
self.trait_setq(**{"_pool": multiprocessing.Pool(processes=proccess_count)})
locs = namespace[self.input_for_correction]
# mProfile.profileOn(['localisations.py', 'processing.py'])
drift_res = self.calc_corr_drift_from_locs(locs['x'], locs['y'], locs['z'] * (0 if self.flatten_z else 1), locs['t'])
t_shift, shifts = self.rcc(self.shift_max, *drift_res)
# mProfile.profileOff()
# mProfile.report()
if self.multiprocessing:
self._pool.close()
self._pool.join()
# convert frame-to-frame drift to drift from origin
shifts = np.cumsum(shifts, 0)
out = tabular.mappingFilter(namespace[self.input_for_mapping])
t_out = out['t']
# cubic interpolate with no smoothing
dx = interpolate.CubicSpline(t_shift, shifts[:, 0])(t_out)
dy = interpolate.CubicSpline(t_shift, shifts[:, 1])(t_out)
dz = interpolate.CubicSpline(t_shift, shifts[:, 2])(t_out)
if 'dx' in out.keys():
# getting around oddity with mappingFilter
# addColumn adds a new column but also keeps the old column
# __getitem__ returns the new column
# but mappings usues the old column
# Wrap with another level of mappingFilter so the new column becomes the 'old column'
out.addColumn('dx', dx)
out.addColumn('dy', dy)
out.addColumn('dz', dz)
out = tabular.mappingFilter(out)
# out.mdh = namespace[self.input_localizations].mdh
out.setMapping('x', 'x + dx')
out.setMapping('y', 'y + dy')
out.setMapping('z', 'z + dz')
else:
out.addColumn('dx', dx)
out.addColumn('dy', dy)
out.addColumn('dz', dz)
out.setMapping('x', 'x + dx')
out.setMapping('y', 'y + dy')
out.setMapping('z', 'z + dz')
# propagate metadata, if present
try:
out.mdh = locs.mdh
except AttributeError:
pass
namespace[self.outputName] = out
namespace[self.output_drift] = t_shift, shifts
# non essential, only for plotting out drift data
namespace[self.output_drift_plot] = Plot(partial(generate_drift_plot, t_shift, shifts))
namespace[self.output_cross_cor] = self._cc_image
class TriangleRenderLayer(EngineLayer):
"""
Layer for viewing triangle meshes.
"""
# properties to show in the GUI. Note that we also inherit 'visible' from BaseLayer
vertexColour = CStr('constant',
desc='Name of variable used to colour our points')
cmap = Enum(*cm.cmapnames,
default='gist_rainbow',
desc='Name of colourmap used to colour faces')
clim = ListFloat(
[0, 1],
desc='How our variable should be scaled prior to colour mapping')
alpha = Float(1.0, desc='Face tranparency')
method = Enum(*ENGINES.keys(), desc='Method used to display faces')
normal_mode = Enum(['Per vertex', 'Per face'])
display_normals = Bool(False)
normal_scaling = Float(10.0)
dsname = CStr(
'output',
desc=
'Name of the datasource within the pipeline to use as a source of triangles (should be a TriangularMesh object)'
)
_datasource_choices = List()
_datasource_keys = List()
def __init__(self,
pipeline,
method='wireframe',
dsname='',
context=None,
**kwargs):
EngineLayer.__init__(self, context=context, **kwargs)
self._pipeline = pipeline
self.engine = None
self.cmap = 'gist_rainbow'
self.x_key = 'x' # TODO - make these traits?
self.y_key = 'y'
self.z_key = 'z'
self.xn_key = 'xn'
self.yn_key = 'yn'
self.zn_key = 'zn'
self._bbox = None
# define a signal so that people can be notified when we are updated (currently used to force a redraw when
# parameters change)
self.on_update = dispatch.Signal()
# define responses to changes in various traits
self.on_trait_change(self._update, 'vertexColour')
self.on_trait_change(lambda: self.on_update.send(self), 'visible')
self.on_trait_change(
self.update,
'cmap, clim, alpha, dsname, normal_mode, display_normals, normal_scaling'
)
self.on_trait_change(self._set_method, 'method')
# update any of our traits which were passed as command line arguments
self.set(**kwargs)
# update datasource and method
self.dsname = dsname
if self.method == method:
#make sure we still call _set_method even if we start with the default method
self._set_method()
else:
self.method = method
# if we were given a pipeline, connect ourselves to the onRebuild signal so that we can automatically update
# ourselves
if not self._pipeline is None:
try:
self._pipeline.onRebuild.connect(self.update)
except AttributeError:
pass
@property
def datasource(self):
"""
Return the datasource we are connected to (does not go through the pipeline for triangles_mesh).
"""
try:
return self._pipeline.get_layer_data(self.dsname)
except AttributeError:
try:
return self._pipeline[self.dsname]
except AttributeError:
return None
#return self.datasource
@property
def _ds_class(self):
# from PYME.experimental import triangle_mesh
from PYME.experimental import _triangle_mesh as triangle_mesh
return triangle_mesh.TrianglesBase
def _set_method(self):
self.engine = ENGINES[self.method](self._context)
self.update()
def _get_cdata(self):
try:
cdata = self.datasource[self.vertexColour]
except (KeyError, TypeError):
cdata = np.array([0, 1])
return cdata
def _update(self, *args, **kwargs):
#pass
cdata = self._get_cdata()
self.clim = [float(np.nanmin(cdata)), float(np.nanmax(cdata))]
self.update(*args, **kwargs)
def update(self, *args, **kwargs):
try:
self._datasource_choices = [
k for k, v in self._pipeline.dataSources.items()
if isinstance(v, self._ds_class)
]
except AttributeError:
pass
if not self.datasource is None:
dks = [
'constant',
]
if hasattr(self.datasource, 'keys'):
dks = dks + sorted(self.datasource.keys())
self._datasource_keys = dks
if not (self.engine is None or self.datasource is None):
print('lw update')
self.update_from_datasource(self.datasource)
self.on_update.send(self)
@property
def bbox(self):
return self._bbox
def update_from_datasource(self, ds):
"""
Pulls vertices/normals from a binary STL file. See PYME.IO.FileUtils.stl for more info. Calls update_data on the input.
Parameters
----------
ds :
PYME.experimental.triangular_mesh.TriangularMesh object
Returns
-------
None
"""
#t = ds.vertices[ds.faces]
#n = ds.vertex_normals[ds.faces]
x, y, z = ds.vertices[ds.faces].reshape(-1, 3).T
if self.normal_mode == 'Per vertex':
xn, yn, zn = ds.vertex_normals[ds.faces].reshape(-1, 3).T
else:
xn, yn, zn = np.repeat(ds.face_normals.T, 3, axis=1)
if self.vertexColour in ['', 'constant']:
c = np.ones(len(x))
clim = [0, 1]
#elif self.vertexColour == 'vertex_index':
# c = np.arange(0, len(x))
else:
c = ds[self.vertexColour][ds.faces].ravel()
clim = self.clim
cmap = getattr(cm, self.cmap)
alpha = float(self.alpha)
# Do we have coordinates? Concatenate into vertices.
if x is not None and y is not None and z is not None:
vertices = np.vstack((x.ravel(), y.ravel(), z.ravel()))
self._vertices = vertices.T.ravel().reshape(len(x.ravel()), 3)
if not xn is None:
self._normals = np.vstack(
(xn.ravel(), yn.ravel(),
zn.ravel())).T.ravel().reshape(len(x.ravel()), 3)
else:
self._normals = -0.69 * np.ones(self._vertices.shape)
self._bbox = np.array(
[x.min(), y.min(),
z.min(), x.max(),
y.max(), z.max()])
else:
self._bbox = None
# TODO: This temporarily sets all triangles to the color red. User should be able to select color.
if c is None:
c = np.ones(self._vertices.shape[0]) * 255 # vector of pink
if clim is not None and c is not None and cmap is not None:
cs_ = ((c - clim[0]) / (clim[1] - clim[0]))
cs = cmap(cs_)
if self.method in ['flat', 'tessel']:
alpha = cs_ * alpha
cs[:, 3] = alpha
if self.method == 'tessel':
cs = np.power(cs, 0.333)
self._colors = cs.ravel().reshape(len(c), 4)
else:
# cs = None
if not self._vertices is None:
self._colors = np.ones((self._vertices.shape[0], 4), 'f')
self._alpha = alpha
self._color_map = cmap
self._color_limit = clim
def get_vertices(self):
return self._vertices
def get_normals(self):
return self._normals
def get_colors(self):
return self._colors
def get_color_map(self):
return self._color_map
@property
def colour_map(self):
return self._color_map
def get_color_limit(self):
return self._color_limit
@property
def default_view(self):
from traitsui.api import View, Item, Group, InstanceEditor, EnumEditor
from PYME.ui.custom_traits_editors import HistLimitsEditor, CBEditor
return View([
Group([
Item('dsname',
label='Data',
editor=EnumEditor(name='_datasource_choices'),
visible_when='_datasource_choices')
]),
Item('method'),
Item('normal_mode', visible_when='method=="shaded"'),
Item('vertexColour',
editor=EnumEditor(name='_datasource_keys'),
label='Colour'),
Group([
Item('clim',
editor=HistLimitsEditor(data=self._get_cdata),
show_label=False),
],
visible_when='vertexColour != "constant"'),
Group([
Item('cmap', label='LUT'),
Item('alpha',
visible_when='method in ["flat", "tessel", "shaded"]')
])
], )
# buttons=['OK', 'Cancel'])
def default_traits_view(self):
return self.default_view
class CalculateFRCFromLocs(CalculateFRCBase):
"""
Generates a pair of images from localization data and calculates the fourier shell/ring correlation (FSC / FRC).
Inputs
------
inputName : TabularBase
Localization data.
Outputs
-------
outputName : TabularBase
Localization data labeled with how it was divided (FRC_group).
output_images : ImageStack
Pair of 2D or 3D histogram rendered images.
output_fft_images_cc : ImageStack
Fast Fourier transform original and cross-correlation images.
output_frc_dict : dict
FSC/FRC results.
output_frc_plot : Plot
Output plot of the FSC / FRC curve.
output_frc_raw : dict
Complete FSC/FRC results.
Parameters
----------
split_method : string
Different methods of dividing data into halves.
pixel_size_in_nm : float
Pixel size used for rendering the images.
flatten_z : Bool
If enabled ignores z information and only performs a FRC.
pre_filter : string
Methods to filter the images prior to Fourier transform.
frc_smoothing_func : string
Methods to smooth the FSC / FRC curve.
cubic_smoothing : float
Smoothing factor for cubic spline.
multiprocessing : Bool
Enables multiprocessing.
save_path : File
(Optional) File path to save output
"""
inputName = Input('Localizations')
split_method = Enum(['halves_random', 'halves_time', 'halves_100_time_chunk', 'halves_10_time_chunk', 'fixed_time', 'fixed_10_time_chunk'])
pixel_size_in_nm = Float(5)
# pre_filter = Enum(['Tukey_1/8', None])
# frc_smoothing_func = Enum(['Cubic Spline', 'Sigmoid', None])
# plot_graphs = Bool()
# multiprocessing = Bool(True)
flatten_z = Bool(True)
outputName = Output('FRC_ds')
output_images = Output('FRC_images')
# output_fft_images = Output('FRC_fft_images')
# output_frc_dict = Output('FRC_dict')
# output_frc_plot = Output('FRC_plot')
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 generate_image_pair(self, mapped_pipeline):
# Split localizations into 2 sets
mask = np.zeros(mapped_pipeline['t'].shape, dtype=np.bool)
if self.split_method == 'halves_time':
sort_arg = np.argsort(mapped_pipeline['t'])
mask[sort_arg[:len(sort_arg)//2]] = 1
elif self.split_method == 'halves_random':
mask[:len(mask)//2] = 1
np.random.shuffle(mask)
elif self.split_method == 'halves_100_time_chunk':
sort_arg = np.argsort(mapped_pipeline['t'])
chunksize = mask.shape[0] / 100.0
for i in range(50):
mask[sort_arg[int(np.round(i*2*chunksize)):int(np.round((i*2+1)*chunksize))]] = 1
elif self.split_method == 'halves_10_time_chunk':
sort_arg = np.argsort(mapped_pipeline['t'])
chunksize = mask.shape[0] * 0.1
for i in range(5):
mask[sort_arg[int(np.round(i*2*chunksize)):int(np.round((i*2+1)*chunksize))]] = 1
elif self.split_method == 'fixed_time':
time_cutoff = (mapped_pipeline['t'].ptp() + 1) // 2 + mapped_pipeline['t'].min()
mask[mapped_pipeline['t'] < time_cutoff] = 1
elif self.split_method == 'fixed_10_time_chunk':
time_cutoffs = np.linspace(mapped_pipeline['t'].min(), mapped_pipeline['t'].max(), 11, dtype=np.float)
for i in range((time_cutoffs.shape[0]-1)//2):
mask[(mapped_pipeline['t'] > time_cutoffs[i*2]) & (mapped_pipeline['t'] < time_cutoffs[i*2+1])] = 1
if self.split_method.startswith('halves_'):
assert np.abs(mask.sum() - (~mask).sum()) <= 1, "datasets uneven, {} vs {}".format(mask.sum(), (~mask).sum())
else:
print("variable counts between images, {} vs {}".format(mask.sum(), (~mask).sum()))
mapped_pipeline.addColumn("FRC_group", mask)
self._namespace[self.outputName] = mapped_pipeline
dims = ['x', 'y', 'z']
if self.flatten_z:
dims.remove('z')
# Simple hist2d binning
bins = list()
data = list()
for i, dim in enumerate(dims):
bins.append(np.arange(np.floor(mapped_pipeline[dim].min()).astype(int), np.ceil(mapped_pipeline[dim].max()).astype(int) + self.pixel_size_in_nm, self.pixel_size_in_nm))
data.append(mapped_pipeline[dim])
data = np.stack(data, axis=1)
# print(bins)
if self.multiprocessing:
results = list()
results.append(self._pool.apply_async(np.histogramdd, (data[mask==0],), kwds= {"bins":bins}))
results.append(self._pool.apply_async(np.histogramdd, (data[mask==1],), kwds= {"bins":bins}))
image_a = results[0].get()[0]
image_b = results[1].get()[0]
else:
image_a = np.histogramdd(data[mask==0], bins=bins)[0]
image_b = np.histogramdd(data[mask==1], bins=bins)[0]
return image_a, image_b
class EnsembleFitROIs(
ModuleBase): # Note that this should probably be moved somewhere else
inputName = Input('ROIs')
fit_type = CStr('LorentzianConvolvedSolidSphere_ensemblePSF')
ensemble_parameter_guess = Float(50.)
hold_ensemble_parameter_constant = Bool(False)
outputName = Output('fit_results')
def execute(self, namespace):
inp = namespace[self.inputName]
# generate RegionHandler from tables
handler = RegionHandler()
handler._load_from_list(inp)
fit_class = region_fitters.fitters[self.fit_type]
fitter = fit_class(handler)
if self.hold_ensemble_parameter_constant:
fitter.fit_profiles(self.ensemble_parameter_guess)
else:
fitter.ensemble_fit(self.ensemble_parameter_guess)
res = tabular.RecArraySource(fitter.results)
# propagate metadata, if present
res.mdh = MetaDataHandler.NestedClassMDHandler(
getattr(inp, 'mdh', None))
res.mdh['EnsembleFitROIs.FitType'] = self.fit_type
res.mdh[
'EnsembleFitROIs.EnsembleParameterGuess'] = self.ensemble_parameter_guess
res.mdh[
'EnsembleFitROIs.HoldEnsembleParamConstant'] = self.hold_ensemble_parameter_constant
namespace[self.outputName] = res
@property
def _fitter_choices(self):
return list(region_fitters.ensemble_fitters.keys()) #FIXME???
@property
def default_view(self):
from traitsui.api import View, Item
from PYME.ui.custom_traits_editors import CBEditor
return View(Item('inputName',
editor=CBEditor(choices=self._namespace_keys)),
Item('_'),
Item('fit_type',
editor=CBEditor(choices=self._fitter_choices)),
Item('_'),
Item('ensemble_parameter_guess'),
Item('_'),
Item('hold_ensemble_parameter_constant'),
Item('_'),
Item('outputName'),
buttons=['OK'])
@property
def pipeline_view(self):
from traitsui.api import View, Item
from PYME.ui.custom_traits_editors import CBEditor
return View(
Item('fit_type', editor=CBEditor(choices=self._fitter_choices)),
Item('_'),
Item('ensemble_parameter_guess'),
Item('_'),
Item('hold_ensemble_parameter_constant'),
)
@property
def dsview_view(self):
from traitsui.api import View, Item
from PYME.ui.custom_traits_editors import CBEditor
return View(Item('fit_type',
editor=CBEditor(choices=self._fitter_choices)),
Item('_'),
Item('ensemble_parameter_guess'),
Item('_'),
Item('hold_ensemble_parameter_constant'),
buttons=['OK'])
class PointCloudRenderLayer(EngineLayer):
"""
A layer for viewing point-cloud data, using one of 3 engines (indicated above)
"""
# properties to show in the GUI. Note that we also inherit 'visible' from BaseLayer
vertexColour = CStr('', desc='Name of variable used to colour our points')
point_size = Float(30.0, desc='Rendered size of the points in nm')
cmap = Enum(*cm.cmapnames, default='gist_rainbow', desc='Name of colourmap used to colour points')
clim = ListFloat([0, 1], desc='How our variable should be scaled prior to colour mapping')
alpha = Float(1.0, desc='Point tranparency')
method = Enum(*ENGINES.keys(), desc='Method used to display points')
display_normals = Bool(False)
normal_scaling = Float(10.0)
dsname = CStr('output', desc='Name of the datasource within the pipeline to use as a source of points')
_datasource_keys = List()
_datasource_choices = List()
def __init__(self, pipeline, method='points', dsname='', context=None, **kwargs):
EngineLayer.__init__(self, context=context, **kwargs)
self._pipeline = pipeline
self.engine = None
self.cmap = 'gist_rainbow'
self.x_key = 'x' #TODO - make these traits?
self.y_key = 'y'
self.z_key = 'z'
self.xn_key = 'xn'
self.yn_key = 'yn'
self.zn_key = 'zn'
self._bbox = None
# define a signal so that people can be notified when we are updated (currently used to force a redraw when
# parameters change)
self.on_update = dispatch.Signal()
# define responses to changes in various traits
self.on_trait_change(self._update, 'vertexColour')
self.on_trait_change(lambda: self.on_update.send(self), 'visible')
self.on_trait_change(self.update, 'cmap, clim, alpha, dsname, point_size')
self.on_trait_change(self._set_method, 'method')
# update any of our traits which were passed as command line arguments
self.set(**kwargs)
# update datasource name and method
#logger.debug('Setting dsname and method')
self.dsname = dsname
self.method = method
self._set_method()
# if we were given a pipeline, connect ourselves to the onRebuild signal so that we can automatically update
# ourselves
if not self._pipeline is None:
self._pipeline.onRebuild.connect(self.update)
@property
def datasource(self):
"""
Return the datasource we are connected to (through our dsname property).
"""
return self._pipeline.get_layer_data(self.dsname)
def _set_method(self):
#logger.debug('Setting layer method to %s' % self.method)
self.engine = ENGINES[self.method](self._context)
self.update()
def _get_cdata(self):
try:
cdata = self.datasource[self.vertexColour]
except KeyError:
cdata = np.array([0, 1])
return cdata
def _update(self, *args, **kwargs):
cdata = self._get_cdata()
self.clim = [float(np.nanmin(cdata)), float(np.nanmax(cdata))+1e-9]
#self.update(*args, **kwargs)
def update(self, *args, **kwargs):
print('lw update')
self._datasource_choices = self._pipeline.layer_data_source_names
if not self.datasource is None:
self._datasource_keys = sorted(self.datasource.keys())
if not (self.engine is None or self.datasource is None):
self.update_from_datasource(self.datasource)
self.on_update.send(self)
@property
def bbox(self):
return self._bbox
def update_from_datasource(self, ds):
x, y = ds[self.x_key], ds[self.y_key]
if not self.z_key is None:
try:
z = ds[self.z_key]
except KeyError:
z = 0*x
else:
z = 0 * x
if not self.vertexColour == '':
c = ds[self.vertexColour]
else:
c = 0*x
if self.xn_key in ds.keys():
xn, yn, zn = ds[self.xn_key], ds[self.yn_key], ds[self.zn_key]
self.update_data(x, y, z, c, cmap=getattr(cm, self.cmap), clim=self.clim, alpha=self.alpha, xn=xn, yn=yn, zn=zn)
else:
self.update_data(x, y, z, c, cmap=getattr(cm, self.cmap), clim=self.clim, alpha=self.alpha)
def update_data(self, x=None, y=None, z=None, colors=None, cmap=None, clim=None, alpha=1.0, xn=None, yn=None, zn=None):
self._vertices = None
self._normals = None
self._colors = None
self._color_map = None
self._color_limit = 0
self._alpha = 0
if x is not None and y is not None and z is not None and len(x) > 0:
vertices = np.vstack((x.ravel(), y.ravel(), z.ravel()))
vertices = vertices.T.ravel().reshape(len(x.ravel()), 3)
if not xn is None:
normals = np.vstack((xn.ravel(), yn.ravel(), zn.ravel())).T.ravel().reshape(len(x.ravel()), 3)
else:
normals = -0.69 * np.ones(vertices.shape)
self._bbox = np.array([x.min(), y.min(), z.min(), x.max(), y.max(), z.max()])
else:
vertices = None
normals = None
self._bbox = None
if clim is not None and colors is not None and clim is not None:
cs_ = ((colors - clim[0]) / (clim[1] - clim[0]))
cs = cmap(cs_)
cs[:, 3] = alpha
cs = cs.ravel().reshape(len(colors), 4)
else:
if not vertices is None:
cs = np.ones((vertices.shape[0], 4), 'f')
else:
cs = None
self.set_values(vertices, normals, cs, cmap, clim, alpha)
def set_values(self, vertices=None, normals=None, colors=None, color_map=None, color_limit=None, alpha=None):
if vertices is not None:
self._vertices = vertices
if normals is not None:
self._normals = normals
if color_map is not None:
self._color_map = color_map
if colors is not None:
self._colors = colors
if color_limit is not None:
self._color_limit = color_limit
if alpha is not None:
self._alpha = alpha
def get_vertices(self):
return self._vertices
def get_normals(self):
return self._normals
def get_colors(self):
return self._colors
def get_color_map(self):
return self._color_map
@property
def colour_map(self):
return self._color_map
def get_color_limit(self):
return self._color_limit
@property
def default_view(self):
from traitsui.api import View, Item, Group, InstanceEditor, EnumEditor, TextEditor
from PYME.ui.custom_traits_editors import HistLimitsEditor, CBEditor
return View([Group([Item('dsname', label='Data', editor=EnumEditor(name='_datasource_choices')), ]),
Item('method'),
Item('vertexColour', editor=EnumEditor(name='_datasource_keys'), label='Colour', visible_when='cmap not in ["R", "G", "B", "C", "M","Y", "K"]'),
Group([Item('clim', editor=HistLimitsEditor(data=self._get_cdata, update_signal=self.on_update), show_label=False), ], visible_when='cmap not in ["R", "G", "B", "C", "M","Y", "K"]'),
Group(Item('cmap', label='LUT'),
Item('alpha', visible_when="method in ['pointsprites', 'transparent_points']", editor=TextEditor(auto_set=False, enter_set=True, evaluate=float)),
Item('point_size', label=u'Point\u00A0size', editor=TextEditor(auto_set=False, enter_set=True, evaluate=float)))])
#buttons=['OK', 'Cancel'])
def default_traits_view(self):
return self.default_view
class LUTOverlayLayer(OverlayLayer):
"""
This OverlayLayer produces a bar that indicates the given color map.
"""
show_bounds = Bool(False)
def __init__(self, offset=None, **kwargs):
"""
Parameters
----------
offset offset of the canvas origin where it should be drawn.
Currently only offset[0] is used
"""
if not offset:
offset = [10, 10]
OverlayLayer.__init__(self, offset, **kwargs)
self.set_offset(offset)
self._lut_width_px = 10.0
self._border_colour = [.5, .5, 0]
self.set_shader_program(DefaultShaderProgram)
self._labels = {}
def _get_label(self, layer):
from . import text
try:
return self._labels[layer]
except KeyError:
self._labels[layer] = [text.Text(), text.Text()]
return self._labels[layer]
def render(self, gl_canvas):
if not self.visible:
return
self._clear_shader_clipping()
labels = []
with self.shader_program:
glDisable(GL_DEPTH_TEST)
glDisable(GL_LIGHTING)
glDisable(GL_BLEND)
#view_size_x = gl_canvas.xmax - gl_canvas.xmin
#view_size_y = gl_canvas.ymax - gl_canvas.ymin
view_size_x, view_size_y = gl_canvas.Size
# upper right y
lb_ur_y = .1 * view_size_y
# lower right y
lb_lr_y = 0.9 * view_size_y
lb_len = lb_lr_y - lb_ur_y
lb_width = self._lut_width_px #* view_size_x / gl_canvas.Size[0]
visible_layers = [
l for l in gl_canvas.layers if
(getattr(l, 'visible', True) and getattr(l, 'show_lut', True))
]
for j, l in enumerate(visible_layers):
cmap = l.colour_map
# upper right x
lb_ur_x = view_size_x - self.get_offset(
)[0] - j * 1.5 * lb_width
# upper left x
lb_ul_x = lb_ur_x - lb_width
#print(lb_ur_x, lb_ur_y, lb_lr_y, lb_ul_x, lb_width, lb_len, view_size_x, view_size_y)
glBegin(GL_QUAD_STRIP)
for i in numpy.arange(0, 1.01, .01):
glColor3fv(cmap(i)[:3])
glVertex2f(lb_ul_x, lb_ur_y + (1. - i) * lb_len)
glVertex2f(lb_ur_x, lb_ur_y + (1. - i) * lb_len)
glEnd()
glBegin(GL_LINE_LOOP)
glColor3fv(self._border_colour)
glVertex2f(lb_ul_x, lb_lr_y)
glVertex2f(lb_ur_x, lb_lr_y)
glVertex2f(lb_ur_x, lb_ur_y)
glVertex2f(lb_ul_x, lb_ur_y)
glEnd()
if hasattr(l, 'clim') and self.show_bounds:
tl, tu = self._get_label(l)
cl, cu = l.clim
tu.text = '%.3G' % cu
tl.text = '%.3G' % cl
xc = lb_ur_x - 0.5 * lb_width
tu.pos = (xc - tu._w / 2, lb_ur_y - tu._h)
tl.pos = (xc - tl._w / 2, lb_lr_y)
labels.extend([tl, tu])
for l in labels:
l.render(gl_canvas)
class FiducialTrack(ModuleBase):
"""
Extract average fiducial track from input pipeline
Parameters
----------
radiusMultiplier: this number is multiplied with error_x to obtain search radius for clustering
timeWindow: the window along the time dimension used for clustering
filterScale: the size of the filter kernel used to smooth the resulting average fiducial track
filterMethod: enumrated choice of filter methods for smoothing operation (Gaussian, Median or Uniform kernel)
Notes
-----
Output is a new pipeline with added fiducial_x, fiducial_y columns
"""
import PYMEcs.Analysis.trackFiducials as tfs
inputName = Input('filtered')
radiusMultiplier = Float(5.0)
timeWindow = Int(25)
filterScale = Float(11)
filterMethod = Enum(tfs.FILTER_FUNCS.keys())
clumpMinSize = Int(50)
singleFiducial = Bool(True)
outputName = Output('fiducialAdded')
def execute(self, namespace):
import PYMEcs.Analysis.trackFiducials as tfs
inp = namespace[self.inputName]
mapped = tabular.mappingFilter(inp)
if self.singleFiducial:
# if all data is from a single fiducial we do not need to align
# we then avoid problems with incomplete tracks giving rise to offsets between
# fiducial track fragments
align = False
else:
align = True
t, x, y, z, isFiducial = tfs.extractTrajectoriesClump(
inp,
clumpRadiusVar='error_x',
clumpRadiusMultiplier=self.radiusMultiplier,
timeWindow=self.timeWindow,
clumpMinSize=self.clumpMinSize,
align=align)
rawtracks = (t, x, y, z)
tracks = tfs.AverageTrack(inp,
rawtracks,
filter=self.filterMethod,
filterScale=self.filterScale,
align=align)
# add tracks for all calculated dims to output
for dim in tracks.keys():
mapped.addColumn('fiducial_%s' % dim, tracks[dim])
mapped.addColumn('isFiducial', isFiducial)
# propogate metadata, if present
try:
mapped.mdh = inp.mdh
except AttributeError:
pass
namespace[self.outputName] = mapped
@property
def hide_in_overview(self):
return ['columns']
class EnsembleFitProfiles(ModuleBase):
inputName = Input('line_profiles')
fit_type = CStr(list(profile_fitters.ensemble_fitters.keys())[0])
ensemble_parameter_guess = Float(50.)
hold_ensemble_parameter_constant = Bool(False)
outputName = Output('fit_results')
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
@property
def _fitter_choices(self):
return list(profile_fitters.ensemble_fitters.keys())
@property
def default_view(self):
from traitsui.api import View, Item
from PYME.ui.custom_traits_editors import CBEditor
return View(Item('inputName',
editor=CBEditor(choices=self._namespace_keys)),
Item('_'),
Item('fit_type',
editor=CBEditor(choices=self._fitter_choices)),
Item('_'),
Item('ensemble_parameter_guess'),
Item('_'),
Item('hold_ensemble_parameter_constant'),
Item('_'),
Item('outputName'),
buttons=['OK'])
@property
def pipeline_view(self):
from traitsui.api import View, Item
from PYME.ui.custom_traits_editors import CBEditor
return View(
Item('fit_type', editor=CBEditor(choices=self._fitter_choices)),
Item('_'),
Item('ensemble_parameter_guess'),
Item('_'),
Item('hold_ensemble_parameter_constant'),
)
@property
def dsview_view(self):
from traitsui.api import View, Item
from PYME.ui.custom_traits_editors import CBEditor
return View(Item('fit_type',
editor=CBEditor(choices=self._fitter_choices)),
Item('_'),
Item('ensemble_parameter_guess'),
Item('_'),
Item('hold_ensemble_parameter_constant'),
buttons=['OK'])
class AlignPSF(ModuleBase):
"""
Align PSF stacks by redundant cross correlation.
"""
inputName = Input('psf_cropped')
normalize_z = Bool(True)
tukey = Float(0.50)
rcc_tolerance = Float(5.0)
z_crop_half_roi = Int(15)
peak_detect = Enum(['Gaussian', 'RBF'])
debug = Bool(False)
output_cross_corr_images = Output('cross_cor_img')
output_cross_corr_images_fitted = Output('cross_cor_img_fitted')
output_images = Output('psf_aligned')
def execute(self, namespace):
self._namespace = namespace
ims = namespace[self.inputName]
# X, Y, Z, 'C'
psf_stack = ims.data[:,:,:,:]
z_slice = slice(psf_stack.shape[2]//2-self.z_crop_half_roi, psf_stack.shape[2]//2+self.z_crop_half_roi+1)
cleaned_psf_stack = self.normalize_images(psf_stack[:,:,z_slice,:])
if self.tukey > 0:
masks = [signal.tukey(dim_len, self.tukey) for dim_len in cleaned_psf_stack.shape[:3]]
masks = np.product(np.meshgrid(*masks, indexing='ij'), axis=0)
cleaned_psf_stack *= masks[:,:,:,None]
drifts = self.calculate_shifts(cleaned_psf_stack, self.rcc_tolerance * 1E3 / ims.mdh['voxelsize.x'])
# print drifts
namespace[self.output_images] = ImageStack(self.shift_images(cleaned_psf_stack if self.debug else psf_stack, drifts), mdh=ims.mdh)
def normalize_images(self, psf_stack):
# in case it is already bg subtracted
cleaned_psf_stack = np.clip(psf_stack, 0, None)
# substact bg per stack
cleaned_psf_stack -= cleaned_psf_stack.min(axis=(0,1,2), keepdims=True)
if self.normalize_z:
# normalize intensity per plane
cleaned_psf_stack /= cleaned_psf_stack.max(axis=(0,1), keepdims=True) / 1.05
else:
# normalize intensity per psf stack
cleaned_psf_stack /= cleaned_psf_stack.max(axis=(0,1,2), keepdims=True) / 1.05
cleaned_psf_stack -= 0.05
np.clip(cleaned_psf_stack, 0, None, cleaned_psf_stack)
return cleaned_psf_stack
def calculate_shifts(self, psf_stack, drift_tolerance):
n_steps = psf_stack.shape[3]
coefs_size = n_steps * (n_steps-1) / 2
coefs = np.zeros((coefs_size, n_steps-1))
shifts = np.zeros((coefs_size, 3))
output_cross_corr_images = np.zeros((psf_stack.shape[0], psf_stack.shape[1], psf_stack.shape[2], coefs_size), dtype=np.float)
output_cross_corr_images_fitted = np.zeros((psf_stack.shape[0], psf_stack.shape[1], psf_stack.shape[2], coefs_size), dtype=np.float)
counter = 0
for i in np.arange(0, n_steps - 1):
for j in np.arange(i+1, n_steps):
coefs[counter, i:j] = 1
print "compare {} to {}".format(i, j)
correlate_result = signal.correlate(psf_stack[:,:,:,i], psf_stack[:,:,:,j], mode="same")
correlate_result -= correlate_result.min()
correlate_result /= correlate_result.max()
threshold = 0.50
correlate_result[correlate_result<threshold] = np.nan
labeled_image, labeled_counts = ndimage.label(~np.isnan(correlate_result))
# print(labeled_counts)
# protects against > 1 peak in the cross correlation results
# shouldn't happen anyway, but at least avoid fitting a single to multi-modal data
if labeled_counts > 1:
max_order = np.argsort(ndimage.maximum(correlate_result, labeled_image, np.arange(labeled_counts)+1))+1
correlate_result[labeled_image!=max_order[0]] = np.nan
output_cross_corr_images[:,:,:,counter] = np.nan_to_num(correlate_result)
dims = list()
for _, dim in enumerate(correlate_result.shape):
dims.append(np.arange(dim))
dims[-1] = dims[-1] - dims[-1].mean()
# peaks = np.nonzero(correlate_result==np.nanmax(correlate_result))
if self.peak_detect == "Gaussian":
res = optimize.least_squares(guassian_nd_error,
[1, 0, 0, 5., 0, 5., 0, 30.],
args=(dims, correlate_result))
output_cross_corr_images_fitted[:,:,:,counter] = gaussian_nd(res.x, dims)
# print("Gaussian")
# print("chi2: {}".format(np.sum(np.square(res.fun))/(res.fun.shape[0]-8)))
# print("fitted parameters: {}".format(res.x))
# res = optimize.least_squares(guassian_sq_nd_error,
# [1, 0, 0, 3., 0, 3., 0, 20.],
# args=(dims, correlate_result))
# output_cross_corr_images_fitted[:,:,:,counter] = gaussian_sq_nd(res.x, dims)
# print("Gaussian 2")
# print("chi2: {}".format(np.sum(np.square(res.fun))/(res.fun.shape[0]-8)))
# print("fitted parameters: {}".format(res.x))
#
# res = optimize.least_squares(lorentzian_nd_error,
# [1, 0, 0, 2., 0, 2., 0, 10.],
# args=(dims, correlate_result))
# output_cross_corr_images_fitted[:,:,:,counter] = lorentzian_nd(res.x, dims)
# print("lorentzian")
# print("chi2: {}".format(np.sum(np.square(res.fun))/(res.fun.shape[0]-8)))
# print("fitted parameters: {}".format(res.x))
shifts[counter, 0] = res.x[2]
shifts[counter, 1] = res.x[4]
shifts[counter, 2] = res.x[6]
elif self.peak_detect == "RBF":
rbf_interpolator = build_rbf(dims, correlate_result)
res = optimize.minimize(rbf_nd_error, [correlate_result.shape[0]*0.5, correlate_result.shape[1]*0.5, correlate_result.shape[2]*0.5], args=rbf_interpolator)
output_cross_corr_images_fitted[:,:,:,counter] = rbf_nd(rbf_interpolator, dims)
# print(res.x)
shifts[counter, :] = res.x
else:
raise Exception("peak founding method not recognised")
# print("fitted parameters: {}".format(res.x))
counter += 1
self._namespace[self.output_cross_corr_images] = ImageStack(output_cross_corr_images)
self._namespace[self.output_cross_corr_images_fitted] = ImageStack(output_cross_corr_images_fitted)
drifts = np.matmul(np.linalg.pinv(coefs), shifts)
residuals = np.matmul(coefs, drifts) - shifts
residuals_dist = np.linalg.norm(residuals, axis=1)
# shift_max = self.rcc_tolerance * 1E3 / mdh['voxelsize.x']
shift_max = drift_tolerance
# Sort and mask residual errors
residuals_arg = np.argsort(-residuals_dist)
residuals_arg = residuals_arg[residuals_dist[residuals_arg] > shift_max]
# Remove coefs rows
# Descending from largest residuals to small
# Only if matrix remains full rank
coefs_temp = np.empty_like(coefs)
counter = 0
for i, index in enumerate(residuals_arg):
coefs_temp[:] = coefs
coefs_temp[index, :] = 0
if np.linalg.matrix_rank(coefs_temp) == coefs.shape[1]:
coefs[:] = coefs_temp
# print("index {} with residual of {} removed".format(index, residuals_dist[index]))
counter += 1
else:
print("Could not remove all residuals over shift_max threshold.")
break
print("removed {} in total".format(counter))
drifts = np.matmul(np.linalg.pinv(coefs), shifts)
drifts = np.pad(drifts, [[1,0],[0,0]], 'constant', constant_values=0)
np.cumsum(drifts, axis=0, out=drifts)
psf_stack_mean = psf_stack / psf_stack.mean(axis=(0,1,2), keepdims=True)
psf_stack_mean = psf_stack_mean.mean(axis=3)
psf_stack_mean *= psf_stack_mean > psf_stack_mean.max() * 0.5
center_offset = ndimage.center_of_mass(psf_stack_mean) - np.asarray(psf_stack_mean.shape)*0.5
# print(center_offset)
# print drifts.shape
# print stats.trim_mean(drifts, 0.25, axis=0)
# drifts = drifts - stats.trim_mean(drifts, 0.25, axis=0)
drifts = drifts - center_offset
if True:
try:
# from matplotlib import pyplot
fig, axes = pyplot.subplots(1, 2, figsize=(6,3))
# new_residuals = np.matmul(coefs, drifts) - shifts
# new_residuals_dist = np.linalg.norm(new_residuals, axis=1)
# # print new_residuals_dist
# pyplot.hist(new_residuals_dist[coefs.any(axis=1)], 100)
# print drifts
# limits = np.max(np.abs(drifts), axis=0)
axes[0].scatter(drifts[:,0], drifts[:,1], s=50)
# axes[0].set_xlim(-limits[0], limits[0])
# axes[0].set_ylim(-limits[1], limits[1])
axes[0].set_xlabel('x')
axes[0].set_ylabel('y')
axes[1].scatter(drifts[:,0], drifts[:,2], s=50)
axes[1].set_xlabel('x')
axes[1].set_ylabel('z')
for ax in axes:
# ax.set_xlim(-1, 1)
# ax.set_ylim(-1, 1)
ax.axvline(0, color='red', ls='--')
ax.axhline(0, color='red', ls='--')
fig.tight_layout()
except Exception as e:
print e
return drifts
def shift_images(self, psf_stack, shifts):
kx = (np.fft.fftfreq(psf_stack.shape[0]))
ky = (np.fft.fftfreq(psf_stack.shape[1]))
kz = (np.fft.fftfreq(psf_stack.shape[2]))
kx, ky, kz = np.meshgrid(kx, ky, kz, indexing='ij')
shifted_images = np.zeros_like(psf_stack)
for i in np.arange(psf_stack.shape[3]):
psf = psf_stack[:,:,:,i]
ft_image = np.fft.fftn(psf)
shift = shifts[i]
shifted_images[:,:,:,i] = np.abs(np.fft.ifftn(ft_image*np.exp(-2j*np.pi*(kx*shift[0] + ky*shift[1] + kz*shift[2]))))
# shifted_images.append(shifted_image)
return shifted_images
class RCCDriftCorrectionBase(CacheCleanupModule):
"""
Performs drift correction using redundant cross-correlation from
Wang et al. Optics Express 2014 22:13 (Bo Huang's RCC algorithm).
Base class for other RCC recipes.
Can take cached fft input (as filename, not an 'input').
Only output drift as tuple of time points, and drift amount.
Currently not registered by itself since not very usefule.
"""
cache_fft = File("rcc_cache.bin")
method = Enum(['RCC', 'MCC', 'DCC'])
# redundant cross-corelation, mean cross-correlation, direct cross-correlation
shift_max = Float(5) # nm
corr_window = Int(5)
multiprocessing = Bool()
debug_cor_file = File()
output_drift = Output('drift')
output_drift_plot = Output('drift_plot')
# if debug_cor_file not blank, filled with imagestack of cross correlation
output_cross_cor = Output('cross_cor')
def calc_corr_drift_from_ft_images(self, ft_images):
n_steps = ft_images.shape[0]
# Matrix equation coefficient matrix
# Shape can be predetermined based on method
if self.method == "DCC":
coefs_size = n_steps - 1
elif self.corr_window > 0:
coefs_size = n_steps * self.corr_window - self.corr_window * (
self.corr_window + 1) // 2
else:
coefs_size = n_steps * (n_steps - 1) // 2
coefs = np.zeros((coefs_size, n_steps - 1))
shifts = np.zeros((coefs_size, 3))
counter = 0
ft_1_cache = list()
ft_2_cache = list()
autocor_shift_cache = list()
# print self.debug_cor_file
if not self.debug_cor_file == "":
cc_file_shape = [
shifts.shape[0], ft_images.shape[1], ft_images.shape[2],
(ft_images.shape[3] - 1) * 2
]
# flatten shortest dimension to reduce cross correlation to 2d images for easier debugging
min_arg = min(enumerate(cc_file_shape[1:]),
key=lambda x: x[1])[0] + 1
cc_file_shape.pop(min_arg)
cc_file_args = (self.debug_cor_file, np.float,
tuple(cc_file_shape))
cc_file = np.memmap(cc_file_args[0],
dtype=cc_file_args[1],
mode="w+",
shape=cc_file_args[2])
# del cc_file
cc_args = zip(range(shifts.shape[0]),
(cc_file_args, ) * shifts.shape[0])
else:
cc_args = (None, ) * shifts.shape[0]
# For each ft image, calculate correlation
for i in np.arange(0, n_steps - 1):
if self.method == "DCC" and i > 0:
break
ft_1 = ft_images[i, :, :]
autocor_shift = calc_shift(ft_1, ft_1)
for j in np.arange(i + 1, n_steps):
if (self.method != "DCC") and (self.corr_window > 0) and (
j - i > self.corr_window):
break
ft_2 = ft_images[j, :, :]
coefs[counter, i:j] = 1
# if multiprocessing, use cache when defined
if self.multiprocessing:
# if reading ft_images from cache, replace ft_1 and ft_2 with their indices
if not self.cache_fft == "":
ft_1 = i
ft_2 = j
ft_1_cache.append(ft_1)
ft_2_cache.append(ft_2)
autocor_shift_cache.append(autocor_shift)
else:
shifts[counter, :] = calc_shift(ft_1, ft_2, autocor_shift,
None, cc_args[counter])
if ((counter + 1) % max(coefs_size // 5, 1) == 0):
print(
"{:.2f} s. Completed calculating {} of {} total shifts."
.format(time.time() - self._start_time,
counter + 1, coefs_size))
counter += 1
if self.multiprocessing:
args = zip(
range(len(autocor_shift_cache)), ft_1_cache, ft_2_cache,
autocor_shift_cache,
len(ft_1_cache) *
((self.cache_fft, ft_images.dtype, ft_images.shape), ),
cc_args)
for i, (j, res) in enumerate(
self._pool.imap_unordered(calc_shift_helper, args)):
shifts[j, ] = res
if ((i + 1) % max(coefs_size // 5, 1) == 0):
print(
"{:.2f} s. Completed calculating {} of {} total shifts."
.format(time.time() - self._start_time, i + 1,
coefs_size))
print("{:.2f} s. Finished calculating all shifts.".format(
time.time() - self._start_time))
print("{:,} bytes".format(coefs.nbytes))
print("{:,} bytes".format(shifts.nbytes))
if not self.debug_cor_file == "":
# move time axis for ImageStack
cc_file = np.moveaxis(cc_file, 0, 2)
self.trait_setq(**{"_cc_image": ImageStack(data=cc_file.copy())})
del cc_file
else:
self.trait_setq(**{"_cc_image": None})
assert (np.all(np.any(
coefs, axis=1))), "Coefficient matrix filled less than expected."
mask = np.where(~np.isnan(shifts).any(axis=1))[0]
if len(mask) < shifts.shape[0]:
print("Removed {} cross correlations due to bad/missing data?".
format(shifts.shape[0] - len(mask)))
coefs = coefs[mask, :]
shifts = shifts[mask, :]
assert (coefs.shape[0] > 0) and (
np.linalg.matrix_rank(coefs) == n_steps -
1), "Something went wrong with coefficient matrix. Not full rank."
return shifts, coefs # shifts.shape[0] is n_steps - 1
def rcc(
self,
shift_max,
t_shift,
shifts,
coefs,
):
"""
Should probably rename function.
Takes cross correlation results and calculates shifts.
"""
print("{:.2f} s. About to start solving shifts array.".format(
time.time() - self._start_time))
# Estimate drift
drifts = np.matmul(np.linalg.pinv(coefs), shifts)
# print(t_shift)
# print(drifts)
print("{:.2f} s. Done solving shifts array.".format(time.time() -
self._start_time))
if self.method == "RCC":
# Calculate residual errors
residuals = np.matmul(coefs, drifts) - shifts
residuals_dist = np.linalg.norm(residuals, axis=1)
# Sort and mask residual errors
residuals_arg = np.argsort(-residuals_dist)
residuals_arg = residuals_arg[
residuals_dist[residuals_arg] > shift_max]
# Remove coefs rows
# Descending from largest residuals to small
# Only if matrix remains full rank
coefs_temp = np.empty_like(coefs)
counter = 0
for i, index in enumerate(residuals_arg):
coefs_temp[:] = coefs
coefs_temp[index, :] = 0
if np.linalg.matrix_rank(coefs_temp) == coefs.shape[1]:
coefs[:] = coefs_temp
# print("index {} with residual of {} removed".format(index, residuals_dist[index]))
counter += 1
else:
print(
"Could not remove all residuals over shift_max threshold."
)
break
print("removed {} in total".format(counter))
# Estimate drift again
drifts = np.matmul(np.linalg.pinv(coefs), shifts)
print("{:.2f} s. RCC completed. Repeated solving shifts array.".
format(time.time() - self._start_time))
# pad with 0 drift for first time point
drifts = np.pad(drifts, [[1, 0], [0, 0]],
'constant',
constant_values=0)
return t_shift, drifts
def _execute(self, namespace):
# dervied versions of RCC need to override this method
# 'execute' of this RCC base class is not throughly tested as its use is probably quite limited.
# from PYME.util import mProfile
self._start_time = time.time()
print("Starting drift correction module.")
if self.multiprocessing:
proccess_count = np.clip(multiprocessing.cpu_count() - 1, 1, None)
self._pool = multiprocessing.Pool(processes=proccess_count)
# mProfile.profileOn(['localisations.py'])
drift_res = self.calc_corr_drift_from_ft_images(self.cache_fft)
t_shift, shifts = self.rcc(self.shift_max, *drift_res)
# mProfile.profileOff()
# mProfile.report()
if self.multiprocessing:
self._pool.close()
self._pool.join()
# convert frame-to-frame drift to drift from origin
shifts = np.cumsum(shifts, 0)
namespace[self.output_drift] = t_shift, shifts
class AveragePSF(ModuleBase):
"""
Input stacks of PSF and return the (normalized) average PSF.
Additional filter based on max error/residual between image and averaged image.
"""
inputName = Input('psf_aligned')
normalize_intensity = Bool(False)
# normalize_z = Bool(False)
output_var_image = Output('psf_var')
# smoothing_method = Enum(['RBF', 'Gaussian'])
# output_var_image_norm = Output('psf_var_norm')
gaussian_filter = List(Float, [0, 0, 0], 3, 3)
residual_threshold = Float(0.1)
output_images = Output('psf_combined')
def execute(self, namespace):
ims = namespace[self.inputName]
psf_raw = ims.data[:,:,:,:]
# always normalize first, since needed for statistics
psf_raw_norm = psf_raw.copy()
# if self.normalize_intensity == True:
psf_raw_norm /= psf_raw_norm.max(axis=(0,1,2), keepdims=True)
psf_raw_norm /= psf_raw_norm.sum(axis=(0,1), keepdims=True)
psf_raw_norm -= psf_raw_norm.min()
psf_raw_norm /= psf_raw_norm.max()
residual_max = np.abs(psf_raw_norm - psf_raw_norm.mean(axis=3, keepdims=True)).max(axis=(0,1,2))
print(residual_max)
mask = residual_max < self.residual_threshold
print "images ignore: {}".format(np.argwhere(~mask)[:,0])
print mask
psf_raw_norm = psf_raw_norm[:,:,:,mask]
print(psf_raw_norm.shape)
psf_raw_norm -= psf_raw_norm.min()
psf_raw_norm /= psf_raw_norm.max()
psf_var = psf_raw_norm.var(axis=3)
# psf_var_norm = psf_var / psf_combined.mean(axis=3)
namespace[self.output_var_image] = ImageStack(psf_var, mdh=ims.mdh)
# namespace[self.output_var_image_norm] = ImageStack(np.nan_to_num(psf_var_norm), mdh=ims.mdh)
# if requested not to normalize, revert back to original data
if not self.normalize_intensity:
psf_raw_norm = psf_raw.copy()[:,:,:,mask]
psf_combined = psf_raw_norm.mean(axis=3)
psf_combined -= psf_combined.min()
psf_combined /= psf_combined.max()
# if self.smoothing_method == 'RBF':
# dims = [np.arange(i) for i in psf_combined.shape]
# elif self.smoothing_method == 'Gaussian' and
if np.any(np.asarray(self.gaussian_filter)!=0):
psf_processed = ndimage.gaussian_filter(psf_combined, self.gaussian_filter)
else:
psf_processed = psf_combined
psf_processed -= psf_processed.min()
psf_processed /= psf_processed.max()
new_mdh = None
try:
new_mdh = MetaDataHandler.NestedClassMDHandler(ims.mdh)
new_mdh["PSFExtraction.GaussianFilter"] = self.gaussian_filter
new_mdh["PSFExtraction.NormalizeIntensity"] = self.normalize_intensity
except Exception as e:
print(e)
namespace[self.output_images] = ImageStack(psf_processed, mdh=new_mdh)
if True:
fig, axes = pyplot.subplots(2, 3, figsize=(9,6))
axes[0,0].set_title('X')
axes[0,0].plot(psf_raw_norm[:, psf_raw_norm.shape[1]//2, psf_raw_norm.shape[2]//2, :])
axes[1,0].plot(psf_combined[:, psf_combined.shape[1]//2, psf_combined.shape[2]//2], lw=1, color='red')
axes[1,0].plot(psf_processed[:, psf_processed.shape[1]//2, psf_processed.shape[2]//2], lw=1, ls='--', color='black')
axes[0,1].set_title('Y')
axes[0,1].plot(psf_raw_norm[psf_raw_norm.shape[0]//2, :, psf_raw_norm.shape[2]//2, :])
axes[1,1].plot(psf_combined[psf_combined.shape[0]//2, :, psf_combined.shape[2]//2], lw=1, color='red')
axes[1,1].plot(psf_processed[psf_processed.shape[0]//2, :, psf_processed.shape[2]//2], lw=1, ls='--', color='black')
axes[0,2].set_title('Z')
axes[0,2].plot(psf_raw_norm[psf_raw_norm.shape[0]//2, psf_raw_norm.shape[1]//2, :, :])
axes[1,2].plot(psf_combined[psf_combined.shape[0]//2, psf_combined.shape[1]//2, :], lw=1, color='red')
axes[1,2].plot(psf_processed[psf_processed.shape[0]//2, psf_processed.shape[1]//2, :], lw=1, ls='--', color='black')
fig.tight_layout()
fig, ax = pyplot.subplots(1, 1, figsize=(4,3))
ax.hist(residual_max, bins=20)
ax.axvline(self.residual_threshold, color='red', ls='--')
class LayerWrapper(HasTraits):
cmap = Enum(*cm.cmapnames, default='gist_rainbow')
clim = ListFloat([0, 1])
alpha = Float(1.0)
visible = Bool(True)
method = Enum(*ENGINES.keys())
engine = Instance(layers.BaseLayer)
dsname = CStr('output')
def __init__(self,
pipeline,
method='points',
ds_name='',
cmap='gist_rainbow',
clim=[0, 1],
alpha=1.0,
visible=True,
method_args={}):
self._pipeline = pipeline
#self._namespace=getattr(pipeline, 'namespace', {})
#self.dsname = None
self.engine = None
self.cmap = cmap
self.clim = clim
self.alpha = alpha
self.visible = visible
self.on_update = dispatch.Signal()
self.on_trait_change(lambda: self.on_update.send(self), 'visible')
self.on_trait_change(self.update, 'cmap, clim, alpha, dsname')
self.on_trait_change(self._set_method, 'method')
#self.set_datasource(ds_name)
self.dsname = ds_name
self._eng_params = dict(method_args)
self.method = method
self._pipeline.onRebuild.connect(self.update)
@property
def _namespace(self):
return self._pipeline.layer_datasources
@property
def bbox(self):
return self.engine.bbox
@property
def colour_map(self):
return self.engine.colour_map
@property
def data_source_names(self):
names = [] #'']
for k, v in self._namespace.items():
names.append(k)
if isinstance(v, tabular.ColourFilter):
for c in v.getColourChans():
names.append('.'.join([k, c]))
return names
@property
def datasource(self):
if self.dsname == '':
return self._pipeline
parts = self.dsname.split('.')
if len(parts) == 2:
# special case - permit access to channels using dot notation
# NB: only works if our underlying datasource is a ColourFilter
ds, channel = parts
return self._namespace.get(ds, None).get_channel_ds(channel)
else:
return self._namespace.get(self.dsname, None)
def _set_method(self):
if self.engine:
self._eng_params = self.engine.get('point_size', 'vertexColour')
#print(eng_params)
self.engine = ENGINES[self.method](self._context)
self.engine.set(**self._eng_params)
self.engine.on_trait_change(self._update, 'vertexColour')
self.engine.on_trait_change(self.update)
self.update()
# def set_datasource(self, ds_name):
# self._dsname = ds_name
#
# self.update()
def _update(self, *args, **kwargs):
cdata = self._get_cdata()
self.clim = [float(cdata.min()), float(cdata.max())]
#self.update(*args, **kwargs)
def update(self, *args, **kwargs):
print('lw update')
if not (self.engine is None or self.datasource is None):
self.engine.update_from_datasource(self.datasource,
getattr(cm, self.cmap),
self.clim, self.alpha)
self.on_update.send(self)
def render(self, gl_canvas):
if self.visible:
self.engine.render(gl_canvas)
def _get_cdata(self):
try:
cdata = self.datasource[self.engine.vertexColour]
except KeyError:
cdata = np.array([0, 1])
return cdata
@property
def default_view(self):
from traitsui.api import View, Item, Group, InstanceEditor, EnumEditor
from PYME.ui.custom_traits_editors import HistLimitsEditor, CBEditor
return View(
[
Group([
Item('dsname',
label='Data',
editor=EnumEditor(values=self.data_source_names)),
]),
Item('method'),
#Item('_'),
Group([
Item('engine',
style='custom',
show_label=False,
editor=InstanceEditor(
view=self.engine.view(self.datasource.keys()))),
]),
#Item('engine.color_key', editor=CBEditor(choices=self.datasource.keys())),
Group([
Item('clim',
editor=HistLimitsEditor(data=self._get_cdata),
show_label=False),
]),
Group([
Item('cmap', label='LUT'),
Item('alpha'),
Item('visible')
],
orientation='horizontal',
layout='flow')
], )
#buttons=['OK', 'Cancel'])
def default_traits_view(self):
return self.default_view
class TetrahedraRenderLayer(VertexRenderLayer):
"""
This program draws a WareFrame of the given points. They are interpreted as triangles.
"""
z_rescale = Float(1.0)
size_cutoff = Float(1000.)
internal_cull = Bool(True)
wireframe = Bool(False)
DRAW_MODE = GL_TRIANGLES
def __init__(self,
x=None,
y=None,
z=None,
colors=None,
color_map=None,
size_cutoff=None,
internal_cull=None,
z_rescale=None,
alpha=None,
is_wire_frame=False):
super(TetrahedraRenderLayer, self).__init__(colors=colors,
color_map=color_map,
alpha=alpha)
if size_cutoff:
self.size_cutoff = size_cutoff
if internal_cull:
self.internal_cull = internal_cull
if z_rescale:
self.z_rescale = z_rescale
if x:
p, a, n = gen3DTriangs(x,
y,
z / self.z_rescale,
self.size_cutoff,
internalCull=self.internal_cull)
if colors == 'z':
colors = p[:, 2]
else:
colors = 1. / a
color_limit = [colors.min(), colors.max()]
self.update_data(x,
y,
z,
colors,
cmap=cmap,
clim=clim,
alpha=alpha)
else:
pass
#self.set_values(p, n)
if is_wire_frame:
self.set_shader_program(WireFrameShaderProgram)
else:
self.set_shader_program(GouraudShaderProgram)
def update_from_datasource(self, ds, cmap=None, clim=None, alpha=1.0):
x, y = ds[self.x_key], ds[self.y_key]
if not self.z_key is None:
z = ds[self.z_key]
else:
z = 0 * x
p, a, n = gen3DTriangs(x,
y,
z / self.z_rescale,
self.size_cutoff,
internalCull=self.internal_cull)
if False: #not self.vertexColour == '':
#todo - set up for interpolated triangles
c = ds[self.vertexColour]
else:
c = 1. / a
self.update_data(p[:, 0],
p[:, 1],
p[:, 2],
c,
cmap=cmap,
clim=clim,
alpha=alpha)
def update_data(self,
x=None,
y=None,
z=None,
colors=None,
cmap=None,
clim=None,
alpha=1.0):
self._vertices = None
self._normals = None
self._colors = None
self._color_map = None
self._color_limit = 0
self._alpha = 0
if x is not None and y is not None and z is not None:
vertices = np.vstack((x.ravel(), y.ravel(), z.ravel()))
vertices = vertices.T.ravel().reshape(len(x.ravel()), 3)
normals = -0.69 * np.ones(vertices.shape)
else:
vertices = None
normals = None
if clim is not None and colors is not None and clim is not None:
cs_ = ((colors - clim[0]) / (clim[1] - clim[0]))
cs = cmap(cs_)
cs[:, 3] = alpha
cs = cs.ravel().reshape(len(colors), 4)
else:
#cs = None
if not vertices is None:
cs = np.ones((vertices.shape[0], 4), 'f')
else:
cs = None
color_map = None
color_limit = None
self.set_values(vertices, normals, cs, cmap, clim, alpha)
def render(self, gl_canvas):
"""
Parameters
----------
gl_canvas
nothing of the canvas is used. That's how it should be.
Returns
-------
"""
with self.shader_program:
n_vertices = self.get_vertices().shape[0]
glVertexPointerf(self.get_vertices())
glNormalPointerf(self.get_normals())
glColorPointerf(self.get_colors())
glPushMatrix()
glDrawArrays(self.DRAW_MODE, 0, n_vertices)
glPopMatrix()
