class AssignMentTransferTab(object):
'''the tab in the GUI where assignments
can be transferred in bulk to the ccpn analysis
project. A difference is made between two types
of assignments:
1) spin systems to residues, which also
implies resonanceSets to atomSets.
2) resonances to peak dimensions.
The user is able to configure which assignments
should be transferred to the project.
Attributes:
guiParent: gui object this tab is part of.
frame: the frame in which this element lives.
dataModel(src.cython.malandro.DataModel): dataModel
object describing the assignment proposed by
the algorithm.
selectedSolution (int): The index of the solution/run
that is used asa the template to make the assignments.
resonanceToDimension (bool): True if resonances should
be assigned to peak dimensions. False if not.
spinSystemToResidue (bool): True if spin system to
residue assignment should be carried out.
minScore (float): The minimal score of a spin system
assignment to a residue to be allowed
to transfer this assignment to the project
intra (bool): True if intra-residual peaks should be
assigned.
sequential (bool): True if sequential peaks should be
assigned.
noDiagonal (bool): If True, purely diagonal peaks are
ignored during the transfer of assignments.
allSpectra (bool): If True, all spectra will be assigned.
If False, one specified spectrum will be assigned.
spectrum (src.cython.malandro.Spectrum): The spectrum
that should be assigned.
'''
def __init__(self, parent, frame):
'''Init. args: parent: the guiElement that this
tab is part of.
frame: the frame this part of the
GUI lives in.
'''
self.guiParent = parent
self.frame = frame
# Buttons and fields,
# will be set in body():
self.peaksCheckButton = None
self.residuesCheckButton = None
self.intraCheckButton = None
self.sequentialCheckButton = None
self.noDiagonalCheckButton = None
self.spinSystemTypeSelect = None
self.minScoreEntry = None
self.solutionNumberEntry = None
self.spectrumSelect = None
self.spectraPullDown = None
self.assignedResidueStrategySelect = None
self.transferButton = None
# Settings that determine how assignments
# are transferred to the analysis project:
self.minScore = 80.0
self.dataModel = None
self.spectrum = None
self.selectedSolution = 1
self.body()
self.resonanceToDimension = True
self.spinSystemToResidue = True
self.intra = True
self.sequential = True
self.noDiagonal = True
self.allSpectra = True
self.spinSystemType = 0
self.strategy = 0
def body(self):
'''Describes the body of this tab. It consists
out of a number of radio buttons, check buttons
and number entries that allow the user to
indicate which assignments should be transferred.
'''
# self.frame.expandColumn(0)
self.frame.expandGrid(8, 0)
self.frame.expandGrid(8, 1)
typeOfAssignmentFrame = LabelFrame(
self.frame, text='type of assignment')
typeOfAssignmentFrame.grid(row=0, column=0, sticky='nesw')
# typeOfAssignmentFrame.expandGrid(0,5)
peakSelectionFrame = LabelFrame(
self.frame, text='which peaks to assign')
peakSelectionFrame.grid(row=0, column=1, sticky='nesw', rowspan=2)
spinSystemSelectionFrame = LabelFrame(self.frame,
text='Which spin-systems to use')
spinSystemSelectionFrame.grid(row=2, column=0, sticky='nesw')
tipText = 'What to do when a residue has already a spin system assigned to it.'
assignedResidueFrame = LabelFrame(self.frame,
text='if residue already has spin-system',
tipText=tipText)
assignedResidueFrame.grid(row=2, column=1, sticky='nesw')
spectrumSelectionFrame = LabelFrame(self.frame, text='spectra')
spectrumSelectionFrame.grid(row=1, column=0, sticky='nesw')
row = 0
Label(typeOfAssignmentFrame,
text='Resonances to Peak Dimensions',
grid=(row, 0))
self.peaksCheckButton = CheckButton(typeOfAssignmentFrame,
selected=True,
grid=(row, 1))
row += 1
Label(typeOfAssignmentFrame,
text='SpinSystems to Residues',
grid=(row, 0))
self.residuesCheckButton = CheckButton(
typeOfAssignmentFrame, selected=True, grid=(row, 1))
row = 0
Label(peakSelectionFrame, text='Intra-Residual', grid=(row, 0))
self.intraCheckButton = CheckButton(
peakSelectionFrame, selected=True, grid=(row, 1))
row += 1
Label(peakSelectionFrame, text='Sequential', grid=(row, 0))
self.sequentialCheckButton = CheckButton(
peakSelectionFrame, selected=True, grid=(row, 1))
row += 1
Label(peakSelectionFrame,
text='Do not assign diagonal peaks',
grid=(row, 0))
self.noDiagonalCheckButton = CheckButton(
peakSelectionFrame, selected=True, grid=(row, 1))
entries = ['Only assigned spin systems',
'All that have a score of at least: ',
'User Defined',
'Solution number:']
tipTexts = ['Only assign resonances of spin systems that already have a sequential assignment for the assignment of peak dimensions. Spin system to residue assignment is not relevant in this case.',
'Assign all spin systems that have a score of at least a given percentage. 50% or lower is not possible, because than spin systems might have to be assigned to more than 1 residue, which is impossible.',
"As defined in the lower row of buttons in the 'results' tab.",
'One of the single solutions of the annealing.']
self.spinSystemTypeSelect = RadioButtons(spinSystemSelectionFrame,
entries=entries, grid=(0, 0),
select_callback=None,
direction=VERTICAL,
gridSpan=(4, 1),
tipTexts=tipTexts)
tipText = 'The minimal amount of colabelling the different nuclei should have in order to still give rise to a peak.'
self.minScoreEntry = FloatEntry(spinSystemSelectionFrame,
grid=(1, 1), width=7,
text=str(self.minScore),
returnCallback=self.changeMinScore,
tipText=tipText)
self.minScoreEntry.bind('<Leave>', self.changeMinScore, '+')
self.solutionNumberEntry = IntEntry(spinSystemSelectionFrame,
grid=(3, 1), width=7, text=1,
returnCallback=self.solutionUpdate,
tipText=tipText)
self.solutionNumberEntry.bind('<Leave>', self.solutionUpdate, '+')
#self.solutionPullDown = PulldownList(spinSystemSelectionFrame, None, grid=(3,1), sticky='w')
entries = ['all spectra', 'only:']
tipTexts = ['Assign peaks in all the spectra that where selected before the annealing ran.',
'Only assign peaks in one particular spectrum. You can of course repeat this multiple times for different spectra.']
self.spectrumSelect = RadioButtons(spectrumSelectionFrame,
entries=entries,
grid=(0, 0),
select_callback=None,
direction=VERTICAL,
gridSpan=(2, 1), tipTexts=tipTexts)
self.spectraPullDown = PulldownList(spectrumSelectionFrame,
self.changeSpectrum,
grid=(1, 1), sticky='w')
entries = ['skip this residue',
'de-assign old spin system from residue',
'assign, but never merge',
'warn to merge']
tipTexts = ["Don't assign the new spin system to the residue. The residue is not skipped when the old spin system does not contain any resonances",
"De-assign old spin system from residue, unless the old spin system is a spin system without any resonances.",
"Don't merge any spin systems, merging can be performed later if nescesary in the Resonance --> SpinSystems window.",
"Ask to merge individually for each spin system, this might result in clicking on a lot of popups."]
self.assignedResidueStrategySelect = RadioButtons(assignedResidueFrame,
entries=entries,
grid=(0, 0),
select_callback=None,
direction=VERTICAL,
gridSpan=(2, 1),
tipTexts=tipTexts)
texts = ['Transfer Assignments']
commands = [self.transferAssignments]
self.transferButton = ButtonList(
self.frame, commands=commands, texts=texts)
self.transferButton.grid(row=5, column=0, sticky='nsew', columnspan=2)
def update(self):
'''Update the nescesary elements in the
tab. Is called when the algorithm
has produced possible assignments.
The only thing that has to be updated
in practice in this tab is the pulldown
with spectra.
'''
self.dataModel = self.guiParent.connector.results
self.updateSpectra()
def setDataModel(self, dataModel):
'''Here the dataModel, which is the dataModel
containing the suggested assignments body
the algorithm, can be set.
'''
self.dataModel = dataModel
self.update()
def updateSpectra(self, *opt):
'''Updates the spectra shown in the spectra
pulldown. These are only the spectra that
were used by the algorithm. All other spectra
in the project are not relevant since for those
no simulated peaks have been matched to real
peaks.
'''
if not self.dataModel:
return
spectrum = self.spectrum
spectra = self.dataModel.getSpectra()
if spectra:
names = [spectrum.name for spectrum in spectra]
index = 0
if self.spectrum not in spectra:
self.spectrum = spectra[0]
else:
index = spectra.index(self.spectrum)
self.spectraPullDown.setup(names, spectra, index)
def changeSpectrum(self, spectrum):
'''Select a spectum to be assigned.'''
self.spectrum = spectrum
def solutionUpdate(self, event=None, value=None):
'''Select a solution. A solution is a
one to one mapping of spin systems
to residues produced by one run of
the algorithm.
args: event: event object, this is
one of the values the number
entry calls his callback
function with.
value: the index of the solution/run.
'''
if not self.dataModel:
return
Nsolutions = len(self.dataModel.chain.residues[0].solutions)
if value is None:
value = self.solutionNumberEntry.get()
if value == self.selectedSolution:
return
else:
self.selectedSolution = value
if value < 1:
self.solutionNumberEntry.set(1)
self.selectedSolution = 1
elif value > Nsolutions:
self.selectedSolution = Nsolutions
self.solutionNumberEntry.set(self.selectedSolution)
else:
self.solutionNumberEntry.set(self.selectedSolution)
def fetchOptions(self):
'''Fetches user set options from the gui in
one go and stores them in their corresponding
instance variables.
'''
self.resonanceToDimension = self.peaksCheckButton.get()
self.spinSystemToResidue = self.residuesCheckButton.get()
self.intra = self.intraCheckButton.get()
self.sequential = self.sequentialCheckButton.get()
self.noDiagonal = self.noDiagonalCheckButton.get()
self.spinSystemType = self.spinSystemTypeSelect.getIndex()
self.strategy = ['skip', 'remove', 'noMerge', None][
self.assignedResidueStrategySelect.getIndex()]
self.allSpectra = [True, False][self.spectrumSelect.getIndex()]
def changeMinScore(self, event=None):
'''Set the minimal score for which a spin system
to residue assignment gets transferred to the
ccpn analysis project.
'''
newMinScore = self.minScoreEntry.get()
if self.minScore != newMinScore:
if newMinScore <= 50.0:
self.minScore = 51.0
self.minScoreEntry.set(51.0)
elif newMinScore > 100.0:
self.minScore = 100.0
self.minScoreEntry.set(100.0)
else:
self.minScore = newMinScore
def transferAssignments(self):
'''Transfer assignments to project depending
on the settings from the GUI.
'''
self.fetchOptions()
if not self.dataModel or (not self.resonanceToDimension and not self.spinSystemToResidue):
return
strategy = self.strategy
lookupSpinSystem = [self.getAssignedSpinSystem,
self.getBestScoringSpinSystem,
self.getUserDefinedSpinSystem,
self.getSelectedSolutionSpinSystem][self.spinSystemType]
residues = self.dataModel.chain.residues
spinSystemSequence = [lookupSpinSystem(res) for res in residues]
ccpnSpinSystems = []
ccpnResidues = []
# if self.spinSystemType == 0 it means that it for sure already
# assigned like this
if self.spinSystemToResidue and not self.spinSystemType == 0:
for spinSys, res in zip(spinSystemSequence, residues):
if spinSys and res:
ccpnSpinSystems.append(spinSys.getCcpnResonanceGroup())
ccpnResidues.append(res.getCcpnResidue())
assignSpinSystemstoResidues(ccpnSpinSystems,
ccpnResidues,
strategy=strategy,
guiParent=self.guiParent)
if self.resonanceToDimension:
allSpectra = self.allSpectra
if self.intra:
for residue, spinSystem in zip(residues, spinSystemSequence):
if not spinSystem:
continue
intraLink = residue.getIntraLink(spinSystem)
for pl in intraLink.getPeakLinks():
peak = pl.getPeak()
if not allSpectra and peak.getSpectrum() is not self.spectrum:
continue
if not peak:
continue
resonances = pl.getResonances()
if self.noDiagonal and len(set(resonances)) < len(resonances):
continue
for resonance, dimension in zip(resonances, peak.getDimensions()):
ccpnResonance = resonance.getCcpnResonance()
ccpnDimension = dimension.getCcpnDimension()
assignResToDim(ccpnDimension, ccpnResonance)
if self.sequential:
for residue, spinSystemA, spinSystemB in zip(residues,
spinSystemSequence,
spinSystemSequence[1:]):
if not spinSystemA or not spinSystemB:
continue
link = residue.getLink(spinSystemA, spinSystemB)
for pl in link.getPeakLinks():
peak = pl.getPeak()
if not allSpectra and peak.getSpectrum() is not self.spectrum:
continue
if not peak:
continue
resonances = pl.getResonances()
if self.noDiagonal and len(set(resonances)) < len(resonances):
continue
for resonance, dimension in zip(resonances, peak.getDimensions()):
ccpnResonance = resonance.getCcpnResonance()
ccpnDimension = dimension.getCcpnDimension()
assignResToDim(ccpnDimension, ccpnResonance)
self.guiParent.resultsTab.update()
def getAssignedSpinSystem(self, residue):
'''Get the spinSystem that is assigned in the project
to a residue.
args: residue (src.cython.malandro.Residue)
return: spinSystem (src.cython.malandro.SpinSystem)
'''
ccpCode = residue.ccpCode
seqCode = residue.getSeqCode()
spinSystems = self.dataModel.getSpinSystems()[ccpCode]
ccpnResidue = residue.getCcpnResidue()
if ccpnResidue:
assignedResonanceGroups = ccpnResidue.getResonanceGroups()
if len(assignedResonanceGroups) > 1:
print 'There is more than one spin system assigned to residue %s, did not know which one to use to assign peaks. Therefor this residue is skipped.' % (seqCode)
return
assignedResonanceGroup = ccpnResidue.findFirstResonanceGroup()
if assignedResonanceGroup:
for spinSystem in spinSystems:
if spinSystem.getSerial() == assignedResonanceGroup.serial:
# Just checking to make sure, analysis project could
# have changed
if not self.skipResidue(residue, spinSystem):
return spinSystem
def getBestScoringSpinSystem(self, residue):
'''Get the spinSystem that scores the highest,
i.e. is assigned in most of the runs to the
given residue.
args: residue (src.cython.malandro.Residue)
return: spinSystem (src.cython.malandro.SpinSystem)
'''
solutions = residue.solutions
weigth = 1.0 / len(solutions)
score, bestSpinSystem = max([(solutions.count(solution) * weigth * 100.0, solution) for solution in solutions])
if score >= self.minScore and not bestSpinSystem.getIsJoker() and not self.skipResidue(residue, bestSpinSystem):
return bestSpinSystem
return None
def getUserDefinedSpinSystem(self, residue):
'''Get the spinSystem that is defined by the user
(probably in the resultsTab) as the correct
assignment of the given residue.
args: residue (src.cython.malandro.Residue)
return: spinSystem (src.cython.malandro.SpinSystem)
'''
userDefinedSpinSystem = residue.userDefinedSolution
if userDefinedSpinSystem and not userDefinedSpinSystem.getIsJoker() and not self.skipResidue(residue, userDefinedSpinSystem):
return userDefinedSpinSystem
return None
def getSelectedSolutionSpinSystem(self, residue):
'''I a solution corresponding to one specific run
of the algorithm is defined, return which spinSystem
in that run got assigned to the given residue.
args: residue (src.cython.malandro.Residue)
return: spinSystem (src.cython.malandro.SpinSystem)
'''
solutions = residue.solutions
spinSystem = solutions[self.selectedSolution - 1]
if not spinSystem.getIsJoker() and not self.skipResidue(residue, spinSystem):
return spinSystem
return None
def skipResidue(self, residue, spinSystem):
'''One strategy is to skip all residues that
already have a spin system assignment.
If that is the case determine whether to
skip the given residue.
args: residue (src.cython.malandro.Residue)
spinSystem (src.cython.malandro.SpinSystem)
return: boolean, True if residue should be skipped.
'''
if self.strategy == 0:
assignedGroups = residue.getCcpnResidue().getResonanceGroups()
assignedSerials = set([spinSys.serial for spinSys in assignedGroups])
if assignedSerials and spinSystem.getSerial() not in assignedSerials:
return True
return False
class EditPeakFindParamsPopup(BasePopup):
"""
** Peak Settings and Non-Interactive Peak Finding **
The purpose of this dialog is to allow the user to select settings for
finding and integrating peaks, and also to be able to find peaks in an
arbitrary region that is specified in a table rather than via a spectrum
window.
** Find Parameters tab **
This can be used to specify how peak finding works.
First of all, you can search for just positive peaks, just negative
peaks or both, and the default is that it is just positive peaks.
However, this is further filtered by what the contour levels are.
If there are no positive contour levels for a given spectrum then
positive peaks are not found even if this dialog says they can be,
and similarly if there are no negative contour levels for a given
spectrum then negative peaks are not found even if this dialog says
they can be.
The peak finding algorithm looks for local extrema (maximum for
positive peaks and minima for negative peaks). But on a grid there
are various ways to define what you mean by an extremum. Suppose
you are trying to determine if point p is a maximum (similar
considerations apply for minimum). You would want the intensity
at all nearby points to be less than or equal to the intensity at p.
You can just check points that are just +- one point from p in each
dimension, or you can also check "diagonal" points. For
example, if you are looking at point p = (x, y) in 2D, then the
former would mean checking the four points (x-1, y), (x+1, y)
(x, y-1) and (x, y+1), whereas for the latter you would also have
to check (x-1, y-1), (x-1, y+1), (x+1, y-1) and (x+1, y+1). In
N dimensions the "diagonal" method involves checking 3^N-1 points
whereas the "non-diagonal" method involves checking only 2N points.
In general the "non-diagonal" method is probably the one to use,
and it is the default.
Peaks are only found above (for positive peaks) or below (for negative
peaks) some threshold. By default this is determined by the contour level
for the spectrum. For positive peaks the threshold is the minimum
positive contour level, and for negative peaks the threshold is the
maximum negative contour level. However these levels can be scaled up
(or down) using the "Scale relative to contour levels" option (default
value 1). For example, if you have drawn the contour levels low to
show a bit of noise, but do not want the noise picked as peaks, then
you could select a scale of 2 (or whatever) to increase the threshold.
The "Exclusion buffer around peaks" is so that in crowded regions you
do not get too many peaks near one location. By default the exclusion
buffer is 1 point in each dimension, but this can be increased to make
the algorithm find fewer peaks.
By default the peak finding only looks at the orthogonal region that
is displayed in the given window where peak finding is taking place.
Sometimes it looks like a peak should be found because in x, y you
can see an extremum, but unless it is also an extremum in the orthogonal
dimensions it is not picked. You can widen out the points being
examined in the orthogonal dimensions by using the "Extra thickness in
orthogonal dims" option, which is specified in points.
The "Minimum drop factor" is by what factor the intensity needs to drop
from its extreme value for there to be considered to be a peak. This
could help remove sinc wiggle peaks, for example. The default is that
the drop factor is 0, which in effect means that there is no condition.
The "Volume method" is what is used to estimate the volume of peaks that
are found. The default is "box sum", which just looks at a fixed size
box around the peak centre and sums the intensities in that. The size
of the box is set in the table in the Spectrum Widths tab. The
"truncated box sum" is the same as "box sum" except that the summing
stops in a given direction when (if) the intensities start increasing.
The "parabolic" fit fits a quadratic equation in each dimension to the
intensity at the peak centre and ad +- 1 points and then uses the
equivalent Gaussian fit to estimate the volume.
** Spectrum Widths **
This can be used to specify minimum linewidths (in Hz) for there to be
considered a peak to exist in the peak finding algorithm. It is also
where the Boxwidth for each dimension in each spectrum is specified.
** Diagonal Exclusions **
This can be used to exclude peaks from being found in regions near
the diagonal (so in homonuclear experiments). The exclusion region
is specified in ppm and is independent of spectrum.
** Region Peak Find **
This can be used to find peaks non-interactively (so not having to
control shift drag inside a spectrum window). The region being
analysed is specified in the table. There are two types of conditions
that can be specified, "include" for regions that should be included
and "exclude" for regions that should be excluded. The regions are
specified in ppm.
The "Whole Region" button will set the selected row in the table to be
the entire fundamental region of the spectrum.
The "Add Region" button adds an extra row to the table, and the "Delete
Region" button removes the selected row.
The "Adjust Params" button goes to the Find Parameters tab.
The "Find Peaks!" button does the peak finding.
"""
def __init__(self, parent, *args, **kw):
self.spectrum = None
BasePopup.__init__(self,
parent=parent,
title='Peak : Peak Finding',
**kw)
def body(self, guiFrame):
self.geometry('600x350')
guiFrame.expandGrid(0, 0)
tipTexts = ['', '', '', '']
options = [
'Find Parameters', 'Spectrum Widths', 'Diagonal Exclusions',
'Region Peak Find'
]
tabbedFrame = TabbedFrame(guiFrame, options=options, grid=(0, 0))
frameA, frameB, frameC, frameD = tabbedFrame.frames
self.tabbedFrame = tabbedFrame
# Find Params
frameA.expandGrid(2, 0)
row = 0
label = LabelFrame(frameA,
text='Extrema to search for:',
grid=(row, 0),
gridSpan=(1, 2))
label.expandGrid(0, 1)
entries = ['positive and negative', 'positive only', 'negative only']
tipTexts = [
'Sets whether peak picking within spectra find intensity maxima, minima or both maxima and minima',
]
self.extrema_buttons = RadioButtons(label,
entries=entries,
select_callback=self.apply,
direction='horizontal',
grid=(0, 0),
tipTexts=tipTexts)
row += 1
label = LabelFrame(frameA,
text='Nearby points to check:',
grid=(row, 0),
gridSpan=(1, 2))
label.expandGrid(None, 1)
entries = ['+-1 in at most one dim', '+-1 allowed in any dim']
tipTexts = [
'Sets how permissive the peak picking in when searching for intensity extrema; by adding extra points to the selected search region',
]
self.adjacent_buttons = RadioButtons(label,
entries=entries,
select_callback=self.apply,
direction='horizontal',
grid=(0, 0),
tipTexts=tipTexts)
row += 1
labelFrame = LabelFrame(frameA,
text='Other parameters:',
grid=(row, 0),
gridSpan=(1, 2))
labelFrame.expandGrid(5, 2)
frow = 0
label = Label(labelFrame,
text='Scale relative to contour levels:',
grid=(frow, 0),
sticky='e')
tipText = 'Threshold above which peaks are picked, relative to the lowest displayed contour; 1.0 means picking exactly what is visible'
self.scale_entry = FloatEntry(labelFrame,
grid=(frow, 1),
tipText=tipText,
returnCallback=self.apply,
width=10)
self.scale_entry.bind('<Leave>', self.apply, '+')
frow += 1
label = Label(labelFrame,
text='Exclusion buffer around peaks (in points):',
grid=(frow, 0),
sticky='e')
tipText = 'The size of the no-pick region, in data points, around existing picked peaks; eliminates duplicate picking'
self.buffer_entry = IntEntry(labelFrame,
returnCallback=self.apply,
grid=(frow, 1),
width=10,
tipText=tipText)
self.buffer_entry.bind('<Leave>', self.apply, '+')
frow += 1
label = Label(labelFrame,
text='Extra thickness in orthogonal dims (in points):',
grid=(frow, 0),
sticky='e')
tipText = 'Sets whether to consider any additional planes (Z dimension) when calculating peak volume integrals'
self.thickness_entry = IntEntry(labelFrame,
returnCallback=self.apply,
width=10,
grid=(frow, 1),
tipText=tipText)
self.thickness_entry.bind('<Leave>', self.apply, '+')
frow += 1
label = Label(labelFrame,
text='Minimum drop factor (0.0-1.0):',
grid=(frow, 0),
sticky='e')
tipText = ''
self.drop_entry = FloatEntry(labelFrame,
returnCallback=self.apply,
width=10,
grid=(frow, 1),
tipText=tipText)
self.drop_entry.bind('<Leave>', self.apply, '+')
frow += 1
label = Label(labelFrame,
text='Volume method:',
grid=(frow, 0),
sticky='e')
tipText = 'Selects which method to use to calculate peak volume integrals when peaks are picked; box sizes are specified in "Spectrum Widths"'
self.method_menu = PulldownList(labelFrame,
texts=PeakBasic.PEAK_VOLUME_METHODS,
grid=(frow, 1),
callback=self.apply,
tipText=tipText)
# Spectrum widths
frameB.expandGrid(1, 1)
label = Label(frameB, text='Spectrum: ')
label.grid(row=0, column=0, sticky='e')
tipText = 'The spectrum which determines the widths being shown'
self.expt_spectrum = PulldownList(frameB,
tipText=tipText,
callback=self.setSpectrumProperties)
self.expt_spectrum.grid(row=0, column=1, sticky='w')
self.editLinewidthEntry = FloatEntry(self,
text='',
returnCallback=self.setLinewidth,
width=10)
self.editBoxwidthEntry = FloatEntry(self,
text='',
returnCallback=self.setBoxwidth,
width=10)
tipTexts = [
'The number of the spectrum dimension to which the settings apply',
'The nuclear isotope measures in the spectrum dimension',
'The smallest value for the linewidth of a peak for it to be picked',
'The size of the spectrum region to perform the volume integral over'
]
headingList = [
'Dimension', 'Isotope', 'Minimum Linewidth (Hz)', 'Boxwidth'
]
editSetCallbacks = [None, None, self.setLinewidth, self.setBoxwidth]
editGetCallbacks = [None, None, self.getLinewidth, self.getBoxwidth]
editWidgets = [
None, None, self.editLinewidthEntry, self.editBoxwidthEntry
]
self.spectrumMatrix = ScrolledMatrix(frameB,
initialRows=6,
editSetCallbacks=editSetCallbacks,
editGetCallbacks=editGetCallbacks,
editWidgets=editWidgets,
headingList=headingList,
callback=self.selectCell,
tipTexts=tipTexts)
self.spectrumMatrix.grid(row=1, column=0, columnspan=2, sticky='nsew')
# Diagonal Exclusions
frameC.expandGrid(0, 0)
tipTexts = [
'The isotope as measures on the axis of a spectrum window',
'The distance from the homonuclear diagonal line within which no peak picking can occur'
]
self.exclusionEntry = FloatEntry(self,
text='',
returnCallback=self.setExclusion,
width=10)
headingList = ['Isotope', 'Diagonal Exclusion (ppm)']
editSetCallbacks = [None, self.setExclusion]
editGetCallbacks = [None, self.getExclusion]
editWidgets = [None, self.exclusionEntry]
self.isotopeMatrix = ScrolledMatrix(frameC,
editSetCallbacks=editSetCallbacks,
editGetCallbacks=editGetCallbacks,
editWidgets=editWidgets,
headingList=headingList,
grid=(0, 0),
tipTexts=tipTexts)
# Region peak find
self.regionFindPeakList = None
self.regionCondition = None
self.regionConditions = []
self.regionCol = 1
row = 0
label = Label(frameD, text='Peak List: ', grid=(0, 0))
tipText = 'Selects which peak list to perform region-wide peak picking for'
self.regionPeakListPulldown = PulldownList(
frameD,
callback=self.changeRegionPeakList,
grid=(0, 1),
tipText=tipText)
row += 1
frameD.expandGrid(row, 1)
self.regionEntry = FloatEntry(self,
text='',
returnCallback=self.setRegion,
width=10)
self.conditionMenu = PulldownList(self,
texts=('include', 'exclude'),
callback=self.setCondition)
tipTexts = [
'Whether to include or exclude the states region from region-wide peak picking',
]
headingList = ['Condition']
editSetCallbacks = [None]
editGetCallbacks = [None]
editWidgets = [self.conditionMenu]
self.regionFindMatrix = ScrolledMatrix(
frameD,
headingList=headingList,
callback=self.selectRegionCell,
editWidgets=editWidgets,
editGetCallbacks=editGetCallbacks,
editSetCallbacks=editSetCallbacks,
grid=(row, 0),
gridSpan=(1, 2))
row += 1
tipTexts = [
'Sets the currently selected region row to cover the whole spectrum',
'Add a new region row, which may them be set for exclusion or inclusion when peak picking large areas',
'Remove the selected region specification',
'Go to the panel for setting the parameters that control how peaks extrema are picked',
'Using the stated regions and parameters, perform region-wide peak picking'
]
texts = [
'Whole Region', 'Add Region', 'Delete Region', 'Adjust Params',
'Find Peaks!'
]
commands = [
self.wholeRegion, self.addCondition, self.deleteCondition,
self.adjustParams, self.regionFindPeaks
]
buttons = ButtonList(frameD,
texts=texts,
commands=commands,
grid=(row, 0),
gridSpan=(1, 2),
tipTexts=tipTexts)
buttons.buttons[4].config(bg='#B0FFB0')
utilButtons = UtilityButtonList(tabbedFrame.sideFrame,
grid=(0, 0),
helpUrl=self.help_url,
sticky='e')
self.dataDim = None
self.setParamsEntries()
self.updateSpectrum()
self.setIsotopeProperties()
self.updateRegionPeakLists()
self.administerNotifiers(self.registerNotify)
def administerNotifiers(self, notifyFunc):
# Many more needed here, esp on the AnalysisProject prams
for func in ('__init__', 'delete', 'setName'):
notifyFunc(self.updateRegionPeakLists, 'ccp.nmr.Nmr.DataSource',
func)
notifyFunc(self.updateRegionPeakLists, 'ccp.nmr.Nmr.Experiment',
func)
for func in ('__init__', 'delete'):
notifyFunc(self.updateRegionPeakLists, 'ccp.nmr.Nmr.PeakList',
func)
for clazz in ('Experiment', 'DataSource'):
for func in ('__init__', 'delete', 'setName'):
notifyFunc(self.updateSpectrumTable, 'ccp.nmr.Nmr.%s' % clazz,
func)
for func in ('setPeakFindBoxWidth', 'setPeakFindMinLineWidth'):
notifyFunc(self.updateSpectrumTable,
'ccpnmr.Analysis.AnalysisDataDim', func)
def destroy(self):
self.administerNotifiers(self.unregisterNotify)
BasePopup.destroy(self)
def updateSpectrum(self, spectrum=None):
if not spectrum:
spectrum = self.spectrum
spectra = self.parent.getSpectra()
if spectra:
if spectrum not in spectra:
spectrum = spectra[0]
index = spectra.index(spectrum)
names = ['%s:%s' % (x.experiment.name, x.name) for x in spectra]
else:
index = 0
names = []
self.expt_spectrum.setup(names, spectra, index)
self.setSpectrumProperties(spectrum)
def updateNotifier(self, *extra):
self.updateSpectrum()
def setLinewidth(self, *event):
value = self.editLinewidthEntry.get()
if value is not None:
PeakFindParams.setPeakFindMinLinewidth(self.dataDim, value)
self.setSpectrumProperties(self.dataDim.dataSource)
def getLinewidth(self, dataDim):
if dataDim:
self.editLinewidthEntry.set(
PeakFindParams.getPeakFindMinLinewidth(self.dataDim))
def setBoxwidth(self, *event):
value = self.editBoxwidthEntry.get()
if value is not None:
PeakFindParams.setPeakFindBoxwidth(self.dataDim, value)
self.setSpectrumProperties(self.dataDim.dataSource)
def getBoxwidth(self, dataDim):
if dataDim:
self.editBoxwidthEntry.set(
PeakFindParams.getPeakFindBoxwidth(self.dataDim))
def selectCell(self, object, row, col):
self.dataDim = object
def setExclusion(self, *extra):
isotope = self.isotopeMatrix.currentObject
if not isotope:
return
value = self.exclusionEntry.get()
if value is not None:
setIsotopeExclusion(isotope, value)
self.setIsotopeProperties()
def getExclusion(self, isotope):
value = getIsotopeExclusion(isotope)
self.exclusionEntry.set(value)
def setParamsEntries(self):
project = self.project
params = PeakFindParams.getPeakFindParams(project)
self.scale_entry.set(params['scale'])
self.buffer_entry.set(params['buffer'])
self.thickness_entry.set(params['thickness'])
self.drop_entry.set(params['drop'])
volumeMethod = params['volumeMethod']
if volumeMethod == 'parabolic fit':
volumeMethod = PeakBasic.PEAK_VOLUME_METHODS[0]
self.method_menu.set(params['volumeMethod'])
if (params['nonadjacent']):
n = 1
else:
n = 0
self.adjacent_buttons.setIndex(n)
have_high = params['haveHigh']
have_low = params['haveLow']
if (have_high and have_low):
n = 0
elif (have_high):
n = 1
else:
n = 2
self.extrema_buttons.setIndex(n)
def apply(self, *extra):
params = {}
params['scale'] = self.scale_entry.get()
params['buffer'] = self.buffer_entry.get()
params['thickness'] = self.thickness_entry.get()
params['drop'] = self.drop_entry.get()
params['volumeMethod'] = self.method_menu.getText()
n = self.adjacent_buttons.getIndex()
if (n == 0):
nonadjacent = False
else:
nonadjacent = True
params['nonadjacent'] = nonadjacent
n = self.extrema_buttons.getIndex()
if (n == 0):
have_high = True
have_low = True
elif (n == 1):
have_high = True
have_low = False
elif (n == 2):
have_high = False
have_low = True
params['haveHigh'] = have_high
params['haveLow'] = have_low
project = self.project
try:
PeakFindParams.setPeakFindParams(project, params)
except Implementation.ApiError, e:
showError('Parameter error', e.error_msg, parent=self)
class PeakSeparatorGui(BasePopup):
"""
**Separate Merged Peaks Using Peak Models**
The Peak Separator code uses a Markov Chain Monte Carlo search which, using
idealised peak shapes, attempts to deconvolve overlapped peak regions into
their separate constituent peaks.
This routine is also suitable for accurately fitting model shapes to single
peaks in order to calculate precise intensities.
**Options Peak Separator Parameters**
*Min. Number of peaks* is by default set to one, it is not possible to set
this to a value less than one.
*Max. Number of peaks* is by default set to one, increasing this value allows
the search routine to fit more models. The best fit may be found with fewer than
the maximum number models. Higher numbers slow the routine, and setting this
value to 0 allows the routine to (effectively) fit unlimited peaks.
*Only pick positive peaks*. If you are not interested in negative peaks, removing
the possibility of fitting negative peaks can reduce search time.
*Peak Model* fits the spectra with either a Gaussian peak model or a Lorentzian
peak model.
**Options Region**
*Peak List* choose which peak list newly picked peaks should be added to. Peaks
picked using this method will have their details appended with 'PeakSepartor'
so you know where they came from.
*Region Table* shows which area of the current spectrum is about to be searched.
*Add Region*. Once an area of spectra has been highlighted clicking this button
will pass it's details on to the Peak Separator.
*Reset All* will reset all search parameters.
*Separate Peaks* will run the Peak Separator code with your current settings. This
may take a few minutes to run, depending on the size of the spectral region being
searched, the number of peaks being fitted and the speed of your machine. Please
wait while this completes.
After a successful Peak Separation run, the found peaks will be added to the
selected peak list. These peaks intensties (volume) have been found using the
peak model selected.
**Advanced Settings Tab**
*Rate* affects the speed of the Markov Chain Monte Carlo routine. A smaller value
results in longer execution, but possibly higher quality results. The default
setting is deemed sensible for the majority of runs.
*Line Width* offers a finer degree of control over maximum and minimum peak widths
for each dimension. The default values are *very* stupid and could do with
re-checking for each experiment.
*Re-Pick Entire Peak List* if you would like to use the Peak Separator to repick
*every* peak in your peak list, try this option - but note that this may take
a very long time!
"""
def __init__(self, parent, programName='Peak Separator', **kw):
self.parent = parent
self.programName = programName
self.versionInfo = 'Version 0.2'
self.help_url = 'http://www.ccpn.ac.uk/'
self.window = None
self.waiting = False
self.rootWindow = None
# just used for display - PeakSeparator will not see this
self._minSigmaHz = None
self._maxSigmaHz = None
self.customSigma = False
self.rePickPeakList = False
self._sampleStartPpm = None
self._sampleEndPpm = None
try:
self.project = parent.project
except:
pass
self.params = PeakSeparatorParams()
BasePopup.__init__(self,
parent=parent,
title=programName,
location='+100+100',
**kw)
if not self.analysisProject:
print '&&& init: No analysis project found ...'
try:
if parent.argumentServer:
self.argServer = parent.argumentServer
else:
print '&&& init: No argument server found...'
except:
print '&&& init: Test'
###########################################################################
def body(self, guiFrame):
self.geometry('450x500')
guiFrame.grid_rowconfigure(0, weight=1)
guiFrame.grid_columnconfigure(0, weight=1)
options = ['Peak Separator', 'Advanced Settings']
tabbedFrame = TabbedFrame(guiFrame, options=options)
tabbedFrame.grid(row=0, column=0, sticky='nsew')
buttons = UtilityButtonList(tabbedFrame.sideFrame,
helpUrl=self.help_url)
buttons.grid(row=0, column=0, sticky='e')
self.tabbedFrame = tabbedFrame
frameA, frameB = tabbedFrame.frames
#
# FrameA : Main Settings
#
frameA.grid_columnconfigure(1, weight=1)
row = 0 # Label row
row += 1
div = LabelDivider(frameA, text='Peak Separator Parameters')
div.grid(row=row, column=0, columnspan=2, sticky='ew')
row += 1
label = Label(frameA, text='Min. number of peaks:')
label.grid(row=row, column=0, sticky='w')
self.minPeaksEntry = IntEntry(frameA, returnCallback=self.applyChange, width=10, \
tipText='Minimum number of peaks to find (must be > 0)')
self.minPeaksEntry.grid(row=row, column=1, sticky='n')
self.minPeaksEntry.bind('<Leave>', self.applyChange, '+')
row += 1
label = Label(frameA, text='Max. number of peaks:')
label.grid(row=row, column=0, sticky='w')
self.maxPeaksEntry = IntEntry(frameA, returnCallback=self.applyChange, width=10, \
tipText='Maximum number of peaks to find (0 is unlimited - not recommended)')
self.maxPeaksEntry.grid(row=row, column=1, sticky='n')
self.maxPeaksEntry.bind('<Leave>', self.applyChange, '+')
row += 1
label = Label(frameA, text='Only pick positive peaks:')
label.grid(row=row, column=0, sticky='w')
entries = ['False', 'True']
self.posPeaksButtons = RadioButtons(
frameA,
entries=entries,
select_callback=self.applyChange,
direction='horizontal',
tipTexts=[
'Search for both positive and negative intensity peaks',
'Limit search to only positive peaks'
])
self.posPeaksButtons.grid(row=row, column=1, sticky='n')
row += 1
label = Label(frameA, text='Peak Model:')
label.grid(row=row, column=0, sticky='w')
### G/L Mixture works, but volume calculation involves Gamma function
# entries = ['Gaussian', 'Lorentzian', 'G/L Mixture']
entries = ['Gaussian', 'Lorentzian']
self.shapeButtons = RadioButtons(
frameA,
entries=entries,
select_callback=self.applyChange,
direction='horizontal',
tipTexts=[
'Choose a Gaussian model peak shape to fit to peaks',
'Choose a Lorentzian model peak shape to fit to peaks'
])
self.shapeButtons.grid(row=row, column=1, sticky='n')
row += 1
div = LabelDivider(frameA,
text='Region',
tipText='Region that search will limit itself to')
div.grid(row=row, column=0, columnspan=2, sticky='ew')
row += 1
label = Label(frameA, text='Peak List:')
label.grid(row=row, column=0, sticky='nw')
self.peakListPulldown = PulldownList(
frameA,
callback=self.setManuallyPickPeakList,
tipText='Select which peak list new peaks are to be added to')
self.peakListPulldown.grid(row=row, column=1, sticky='nw')
# tricky scrolled matrix
row += 1
self.regionTable = None
frameA.grid_rowconfigure(row, weight=1)
headings = ('dim.', 'start (ppm)', 'end (ppm)', 'actual size')
self.editDimEntry = IntEntry(self,
returnCallback=self.applyChange,
width=5,
tipText='Dimension number')
self.editStartEntry = FloatEntry(self,
returnCallback=self.applyChange,
width=5,
tipText='Search area lower bound')
self.editEndEntry = FloatEntry(self,
returnCallback=self.applyChange,
width=5,
tipText='Search area upper bound')
editWidgets = [
self.editDimEntry, self.editStartEntry, self.editEndEntry, None
]
editGetCallbacks = [None, None, None, None]
editSetCallbacks = [None, None, None, None]
self.regionTable = ScrolledMatrix(frameA,
headingList=headings,
multiSelect=False,
editWidgets=editWidgets,
editGetCallbacks=editGetCallbacks,
editSetCallbacks=editSetCallbacks,
initialRows=5)
self.regionTable.grid(row=row, column=0, columnspan=2, sticky='nsew')
# Run Button
row += 1
texts = ['Add Region']
commands = [self.updateFromRegion]
self.addResetButtons = ButtonList(
frameA,
texts=texts,
commands=commands,
tipTexts=['Add selected specrtral region'])
self.addResetButtons.grid(row=row, column=0, columnspan=2, sticky='ew')
row += 1
texts = ['Separate Peaks']
commands = [self.runPeakSeparator]
self.runButton = ButtonList(frameA,
texts=texts,
commands=commands,
expands=True,
tipTexts=['Run peak search now'])
self.runButton.grid(row=row, column=0, columnspan=2, sticky='nsew')
#
# FrameB : Further Settings
#
frameB.grid_columnconfigure(0, weight=1)
row = 0
div = LabelDivider(frameB, text='Rate:')
div.grid(row=row, column=0, columnspan=2, sticky='ew')
row += 1
label = Label(frameB, text='Rate of MCMC step size change')
label.grid(row=row, column=0, columnspan=1, sticky='w')
self.rateEntry = FloatEntry(frameB, returnCallback=self.applyChange, width=10, \
tipText='Rate effects speed of run, smaller values take longer but may produce better results')
self.rateEntry.grid(row=row, column=1, sticky='n')
self.rateEntry.bind('<Leave>', self.applyChange, '+')
self.rateEntry.set(self.params.rate)
# tricky scrolled matrix for line width
row += 2
div = LabelDivider(frameB, text='Line Width (Hz):')
div.grid(row=row, column=0, columnspan=2, sticky='ew')
row += 1
label = Label(frameB, text="Descr.")
label.grid(row=row, rowspan=2, column=0, sticky='w')
row += 1
self.lineWidthTable = None
frameB.grid_rowconfigure(row, weight=1)
lineWidthHeadings = ('dim.', 'min. σ (Hz)', 'max. σ (Hz)')
self.editMinSigmaEntry = FloatEntry(self,
returnCallback=self.applyChange,
width=5,
tipText='Minimum line width (Hz)')
self.editMaxSigmaEntry = FloatEntry(self,
returnCallback=self.applyChange,
width=5,
tipText='Maximum line width (Hz)')
# self.editDimEntry is also from regionTable
initialWidthRows = 4
editLineWidthWidgets = [
None, self.editMinSigmaEntry, self.editMaxSigmaEntry
]
editLineWidthGetCallbacks = [None, self.getSigmaMin, self.getSigmaMax]
editLineWidthSetCallbacks = [None, self.setSigmaMin, self.setSigmaMax]
self.lineWidthTable = ScrolledMatrix(
frameB,
headingList=lineWidthHeadings,
multiSelect=False,
editWidgets=editLineWidthWidgets,
editGetCallbacks=editLineWidthGetCallbacks,
editSetCallbacks=editLineWidthSetCallbacks,
initialRows=initialWidthRows)
self.lineWidthTable.grid(row=row,
column=0,
columnspan=2,
sticky='nsew')
# option to 'repick' exisiting peak list
row += initialWidthRows
div = LabelDivider(frameB, text='(optional - repick entire peak list)')
div.grid(row=row, column=0, columnspan=2, sticky='ew')
row += 1
self.repickListPulldown = PulldownList(
frameB,
callback=self.setRePickPeakList,
tipText=
'Select which peak list to repick (new peaks will be put into a new peak list)'
)
self.repickListPulldown.grid(row=row, column=0, sticky='nw')
texts = ['Repick Peak List']
commands = [self.runRepickPeaks]
self.runButton = ButtonList(
frameB,
texts=texts,
commands=commands,
expands=True,
tipTexts=['Repick selected peak list into a new peak list.'])
self.runButton.grid(row=row, column=1, columnspan=1, sticky='nsew')
row += 1
div = LabelDivider(frameB)
row += 1
texts = ['Separate Peaks']
commands = [self.runPeakSeparator]
self.runButton = ButtonList(frameB,
texts=texts,
commands=commands,
expands=True,
tipTexts=['Run peak search now'])
self.runButton.grid(row=row, column=0, columnspan=2, sticky='nsew')
self.setWidgetEntries()
self.administerNotifiers(self.registerNotify)
def administerNotifiers(self, notifyFunc):
for func in ('__init__', 'delete'):
notifyFunc(self.updateAfter, 'ccp.nmr.Nmr.PeakList', func)
notifyFunc(self.updateAfter, 'ccp.nmr.Nmr.Experiment', 'setName')
notifyFunc(self.updateAfter, 'ccp.nmr.Nmr.DataSource', 'setName')
def destroy(self):
self.administerNotifiers(self.unregisterNotify)
BasePopup.destroy(self)
###########################################################################
# update parameters from PS Region
def updateFromRegion(self):
if not self.params.peakList:
print '&&& update from region: Need a peak list'
return
if (self.argServer.parent.currentRegion) == None:
showError('No Region',
'Please select a peak region to be separated')
return
self.rePickPeakList = False
getRegionParams(self.params, argServer=self.argServer)
if not self.customSigma: self.initSigmaParams()
self.setWidgetEntries()
###########################################################################
# update parameters from PS PeakList
def updateFromPeakList(self):
if not self.params.peakList:
print '&&& update from peakList: Need a peak list'
return
getPeakListParams(self.params)
if not self.customSigma: self.initSigmaParams()
self.setWidgetEntries()
###########################################################################
# Run the C library!
def runPeakSeparator(self):
""" run the peak separator """
# hack for Macs - focus isn't always lost on mouse move
# so bind event not always called. Shouldn't affect other OS.
self.applyChange()
if not self.params.peakList:
print '&&& Peak list not yet set'
else:
# SeparatePeakRoutine(self.params, self.params.peakList, routine='pymc' )
SeparatePeakRoutine(self.params,
self.params.peakList,
routine='bayesys')
def runRepickPeaks(self):
""" Run the Peak Separator on entire chosen peak list """
# hack for Macs - focus isn't always lost on mouse move
# so bind event not always called. Shouldn't affect other OS.
self.applyChange()
if not self.params.peakList:
print '&&& Peak list not yet set'
else:
SeparatePeaksInPeakList(self.params)
###########################################################################
def setWidgetEntries(self):
### Page One widgets
self.minPeaksEntry.set(self.params.minAtoms)
self.maxPeaksEntry.set(self.params.maxAtoms)
if self.params.positivePeaks == 1:
self.posPeaksButtons.set('True') # only pick pos peaks
else:
self.posPeaksButtons.set('False')
# do something fancy if different shapes for each dim!
n = self.params.peakShape - 3 # shape is only 3, 4, (5)
self.shapeButtons.setIndex(n)
if self.project is not None:
self.updatePeakListList()
self.updateSpectrumWindow()
if self.params.sampleStart and self.params.peakList:
if not self.rePickPeakList:
objectList = []
textMatrix = []
if len(self.params.samplePpmStart) != self.params.Ndim: return
for i in range(self.params.Ndim):
dim_entry = []
dim_entry.append('%2d' % (i + 1))
dim_entry.append('%7.3f' % self.params.samplePpmStart[i])
dim_entry.append('%7.3f' % self.params.samplePpmEnd[i])
dim_entry.append('%3d' % self.params.sampleSize[i])
textMatrix.append(dim_entry)
self.regionTable.update(textMatrix=textMatrix,
objectList=objectList)
### Page Two widgets
self.rateEntry.set(self.params.rate)
if self.params.peakList and self.params.Ndim:
textMatrix = []
objectList = []
for i in range(self.params.Ndim):
if self.params.isFreqDim[i]:
dim_entry = []
objectList.append(i)
dim_entry.append('%2d' % (i + 1))
dim_entry.append('%7.3f' % self._minSigmaHz[i])
dim_entry.append('%7.3f' % self._maxSigmaHz[i])
textMatrix.append(dim_entry)
self.lineWidthTable.update(textMatrix=textMatrix,
objectList=objectList)
def applyChange(self, *event):
""" Upon change, add settings to params """
# Page One apply changes
self.params.minAtoms = self.minPeaksEntry.get()
self.params.maxAtoms = self.maxPeaksEntry.get()
if self.posPeaksButtons.get() == 'True': # asked only pick pos peaks
self.params.positivePeaks = 1
else:
self.params.positivePeaks = 0
# do something fancy if different shapes for each dim!
n = self.shapeButtons.getIndex() # shape is only 3, 4, (5)
self.params.peakShape = n + 3
# Page Two apply changes
self.params.rate = float(self.rateEntry.get())
self.updateSigmaParams()
###########################################################################
# Peak list functions provide PeakSeparator some inherited params
def getPeakListList(self):
""" given a spectrum, get list of peak lists """
project = self.project
peakLists = []
for experiment in self.nmrProject.experiments:
for spectrum in experiment.dataSources:
for peakList in spectrum.peakLists:
peakLists.append([
'%s:%s:%d' %
(experiment.name, spectrum.name, peakList.serial),
peakList
])
peakLists.sort()
return peakLists
def updatePeakListList(self):
""" set the peaklist list in the pulldown menu """
peakListData = self.getPeakListList()
index = -1
names = []
peakList = self.params.peakList
if peakListData:
names = [x[0] for x in peakListData]
peakLists = [x[1] for x in peakListData]
if peakList not in peakLists:
peakList = peakLists[0]
index = peakLists.index(peakList)
else:
peakList = None
peakLists = []
if peakList is not self.params.peakList:
self.params.peakList = peakList
self.peakListPulldown.setup(names, peakLists, index)
self.repickListPulldown.setup(names, peakLists, index)
def setRePickPeakList(self, peakList):
""" Set the peak list to be repicked (and hit a Flag) """
self.rePickPeakList = True
self.setPeakList(peakList)
def setManuallyPickPeakList(self, peakList):
""" Set the peak list to add new peaks to (and hit a Flag) """
self.rePickPeakList = False
self.setPeakList(peakList)
def setPeakList(self, peakList):
""" Sets the Peak List """
if peakList is not self.params.peakList:
self.params.peakList = peakList
# # interrogate the peak list and get all the usefull parameters out
self.updateFromPeakList()
self.updateSpectrumWindow()
self.setWidgetEntries()
###########################################################################
# TBD I suspect this is for matching region with peak list, but may be obsolete now
def getSpectrumWindowList(self):
""" get list of windows which spectrum could be in """
windows = {}
if self.params.peakList:
views = getSpectrumViews(self.params.peakList.dataSource)
for view in views:
windows[view.spectrumWindowPane.spectrumWindow] = None
return [[w.name, w] for w in windows.keys()]
def updateSpectrumWindow(self):
""" update the spectrum window """
windowData = self.getSpectrumWindowList()
index = -1
names = []
window = self.rootWindow
if windowData:
names = [x[0] for x in windowData]
windows = [x[1] for x in windowData]
if window not in windows:
window = windows[0]
index = windows.index(window)
else:
window = None
windows = []
if window is not self.rootWindow:
self.rootWindow = window
###########################################################################
# get and set sigma stuff
def setSigmaMin(self, dim):
value = self.editMinSigmaEntry.get()
self._minSigmaHz[dim] = value
# dont go and re-write users settings
self.customSigma = True
# make sure changes are in params object
self.updateSigmaParams(dim)
self.setWidgetEntries()
def getSigmaMin(self, dim):
if dim is not None:
self.editMinSigmaEntry.set(self._minSigmaHz[dim])
def setSigmaMax(self, dim):
value = self.editMaxSigmaEntry.get()
self._maxSigmaHz[dim] = value
# dont go and re-write users settings
self.customSigma = True
# make sure changes are in params object
self.updateSigmaParams(dim)
self.setWidgetEntries()
def getSigmaMax(self, dim):
if dim is not None:
self.editMaxSigmaEntry.set(self._maxSigmaHz[dim])
def updateSigmaParams(self, dim=None):
""" updateSigmaParams Just updates the parameters (params obj) for sigma values.
If dim is None, do this for each dim
"""
dataDimRefs = self.params.dataDimRefs
if not dataDimRefs: return
if not self.params.minSigma or len(
self.params.minSigma) != self.params.Ndim:
self.params.minSigma = [0.] * self.params.Ndim
if not self.params.maxSigma or len(
self.params.maxSigma) != self.params.Ndim:
self.params.maxSigma = [0.] * self.params.Ndim
def updateSigmaParam(dim, dataDimRefs):
""" Convert and update sigma for dim """
if self.params.isFreqDim[dim]:
# note factor of two!
self.params.minSigma[dim] = self.rHz2pnt(
self._minSigmaHz[dim], dataDimRefs[dim]) / 2.
self.params.maxSigma[dim] = self.rHz2pnt(
self._maxSigmaHz[dim], dataDimRefs[dim]) / 2.
else:
self.params.minSigma[dim] = 1.0
self.params.maxSigma[dim] = 1.0
if dim:
updateSigmaParam(dim, dataDimRefs)
else:
for dim in range(self.params.Ndim):
updateSigmaParam(dim, dataDimRefs)
# utility functions for sigma values
def pnt2rHz(self, point, dataDimRef):
""" Point to relative Hz frequency relative to frequency at Zeroeth point
Necessary when (for example) looking for width of peak in Hz
"""
assert point, dataDimRef
sigmaBase = pnt2hz(0, dataDimRef)
sigmaHz = pnt2hz(point, dataDimRef)
return abs(sigmaHz - sigmaBase)
def rHz2pnt(self, freq, dataDimRef):
""" Relative Hz to point frequency relative to frequency at Zeroeth point
Necessary when (for example) looking for width of peak in Hz
"""
assert freq, dataDimRef
sigmaBase = hz2pnt(0, dataDimRef)
sigmaPoint = hz2pnt(freq, dataDimRef)
return abs(sigmaPoint - sigmaBase)
def initSigmaParams(self):
""" Set some initial default values for sigma """
self._minSigmaHz = []
self._maxSigmaHz = []
if self.params.Ndim:
for dim in range(self.params.Ndim):
self._minSigmaHz.append(6.)
self._maxSigmaHz.append(28.)
###########################################################################
def updateAll(self):
self.updateSpectrumWindow()
self.updatePeakListList()
self.waiting = False
def updateAfter(self, obj=None):
if self.waiting:
return
else:
self.waiting = True
self.after_idle(self.updateAll)
class AssignMentTransferTab(object):
'''the tab in the GUI where assignments
can be transferred in bulk to the ccpn analysis
project. A difference is made between two types
of assignments:
1) spin systems to residues, which also
implies resonanceSets to atomSets.
2) resonances to peak dimensions.
The user is able to configure which assignments
should be transferred to the project.
Attributes:
guiParent: gui object this tab is part of.
frame: the frame in which this element lives.
dataModel(src.cython.malandro.DataModel): dataModel
object describing the assignment proposed by
the algorithm.
selectedSolution (int): The index of the solution/run
that is used asa the template to make the assignments.
resonanceToDimension (bool): True if resonances should
be assigned to peak dimensions. False if not.
spinSystemToResidue (bool): True if spin system to
residue assignment should be carried out.
minScore (float): The minimal score of a spin system
assignment to a residue to be allowed
to transfer this assignment to the project
intra (bool): True if intra-residual peaks should be
assigned.
sequential (bool): True if sequential peaks should be
assigned.
noDiagonal (bool): If True, purely diagonal peaks are
ignored during the transfer of assignments.
allSpectra (bool): If True, all spectra will be assigned.
If False, one specified spectrum will be assigned.
spectrum (src.cython.malandro.Spectrum): The spectrum
that should be assigned.
'''
def __init__(self, parent, frame):
'''Init. args: parent: the guiElement that this
tab is part of.
frame: the frame this part of the
GUI lives in.
'''
self.guiParent = parent
self.frame = frame
# Buttons and fields,
# will be set in body():
self.peaksCheckButton = None
self.residuesCheckButton = None
self.intraCheckButton = None
self.sequentialCheckButton = None
self.noDiagonalCheckButton = None
self.spinSystemTypeSelect = None
self.minScoreEntry = None
self.solutionNumberEntry = None
self.spectrumSelect = None
self.spectraPullDown = None
self.assignedResidueStrategySelect = None
self.transferButton = None
# Settings that determine how assignments
# are transferred to the analysis project:
self.minScore = 80.0
self.dataModel = None
self.spectrum = None
self.selectedSolution = 1
self.body()
self.resonanceToDimension = True
self.spinSystemToResidue = True
self.intra = True
self.sequential = True
self.noDiagonal = True
self.allSpectra = True
self.spinSystemType = 0
self.strategy = 0
def body(self):
'''Describes the body of this tab. It consists
out of a number of radio buttons, check buttons
and number entries that allow the user to
indicate which assignments should be transferred.
'''
# self.frame.expandColumn(0)
self.frame.expandGrid(8, 0)
self.frame.expandGrid(8, 1)
typeOfAssignmentFrame = LabelFrame(self.frame,
text='type of assignment')
typeOfAssignmentFrame.grid(row=0, column=0, sticky='nesw')
# typeOfAssignmentFrame.expandGrid(0,5)
peakSelectionFrame = LabelFrame(self.frame,
text='which peaks to assign')
peakSelectionFrame.grid(row=0, column=1, sticky='nesw', rowspan=2)
spinSystemSelectionFrame = LabelFrame(self.frame,
text='Which spin-systems to use')
spinSystemSelectionFrame.grid(row=2, column=0, sticky='nesw')
tipText = 'What to do when a residue has already a spin system assigned to it.'
assignedResidueFrame = LabelFrame(
self.frame,
text='if residue already has spin-system',
tipText=tipText)
assignedResidueFrame.grid(row=2, column=1, sticky='nesw')
spectrumSelectionFrame = LabelFrame(self.frame, text='spectra')
spectrumSelectionFrame.grid(row=1, column=0, sticky='nesw')
row = 0
Label(typeOfAssignmentFrame,
text='Resonances to Peak Dimensions',
grid=(row, 0))
self.peaksCheckButton = CheckButton(typeOfAssignmentFrame,
selected=True,
grid=(row, 1))
row += 1
Label(typeOfAssignmentFrame,
text='SpinSystems to Residues',
grid=(row, 0))
self.residuesCheckButton = CheckButton(typeOfAssignmentFrame,
selected=True,
grid=(row, 1))
row = 0
Label(peakSelectionFrame, text='Intra-Residual', grid=(row, 0))
self.intraCheckButton = CheckButton(peakSelectionFrame,
selected=True,
grid=(row, 1))
row += 1
Label(peakSelectionFrame, text='Sequential', grid=(row, 0))
self.sequentialCheckButton = CheckButton(peakSelectionFrame,
selected=True,
grid=(row, 1))
row += 1
Label(peakSelectionFrame,
text='Do not assign diagonal peaks',
grid=(row, 0))
self.noDiagonalCheckButton = CheckButton(peakSelectionFrame,
selected=True,
grid=(row, 1))
entries = [
'Only assigned spin systems',
'All that have a score of at least: ', 'User Defined',
'Solution number:'
]
tipTexts = [
'Only assign resonances of spin systems that already have a sequential assignment for the assignment of peak dimensions. Spin system to residue assignment is not relevant in this case.',
'Assign all spin systems that have a score of at least a given percentage. 50% or lower is not possible, because than spin systems might have to be assigned to more than 1 residue, which is impossible.',
"As defined in the lower row of buttons in the 'results' tab.",
'One of the single solutions of the annealing.'
]
self.spinSystemTypeSelect = RadioButtons(spinSystemSelectionFrame,
entries=entries,
grid=(0, 0),
select_callback=None,
direction=VERTICAL,
gridSpan=(4, 1),
tipTexts=tipTexts)
tipText = 'The minimal amount of colabelling the different nuclei should have in order to still give rise to a peak.'
self.minScoreEntry = FloatEntry(spinSystemSelectionFrame,
grid=(1, 1),
width=7,
text=str(self.minScore),
returnCallback=self.changeMinScore,
tipText=tipText)
self.minScoreEntry.bind('<Leave>', self.changeMinScore, '+')
self.solutionNumberEntry = IntEntry(spinSystemSelectionFrame,
grid=(3, 1),
width=7,
text=1,
returnCallback=self.solutionUpdate,
tipText=tipText)
self.solutionNumberEntry.bind('<Leave>', self.solutionUpdate, '+')
#self.solutionPullDown = PulldownList(spinSystemSelectionFrame, None, grid=(3,1), sticky='w')
entries = ['all spectra', 'only:']
tipTexts = [
'Assign peaks in all the spectra that where selected before the annealing ran.',
'Only assign peaks in one particular spectrum. You can of course repeat this multiple times for different spectra.'
]
self.spectrumSelect = RadioButtons(spectrumSelectionFrame,
entries=entries,
grid=(0, 0),
select_callback=None,
direction=VERTICAL,
gridSpan=(2, 1),
tipTexts=tipTexts)
self.spectraPullDown = PulldownList(spectrumSelectionFrame,
self.changeSpectrum,
grid=(1, 1),
sticky='w')
entries = [
'skip this residue', 'de-assign old spin system from residue',
'assign, but never merge', 'warn to merge'
]
tipTexts = [
"Don't assign the new spin system to the residue. The residue is not skipped when the old spin system does not contain any resonances",
"De-assign old spin system from residue, unless the old spin system is a spin system without any resonances.",
"Don't merge any spin systems, merging can be performed later if nescesary in the Resonance --> SpinSystems window.",
"Ask to merge individually for each spin system, this might result in clicking on a lot of popups."
]
self.assignedResidueStrategySelect = RadioButtons(assignedResidueFrame,
entries=entries,
grid=(0, 0),
select_callback=None,
direction=VERTICAL,
gridSpan=(2, 1),
tipTexts=tipTexts)
texts = ['Transfer Assignments']
commands = [self.transferAssignments]
self.transferButton = ButtonList(self.frame,
commands=commands,
texts=texts)
self.transferButton.grid(row=5, column=0, sticky='nsew', columnspan=2)
def update(self):
'''Update the nescesary elements in the
tab. Is called when the algorithm
has produced possible assignments.
The only thing that has to be updated
in practice in this tab is the pulldown
with spectra.
'''
self.dataModel = self.guiParent.connector.results
self.updateSpectra()
def setDataModel(self, dataModel):
'''Here the dataModel, which is the dataModel
containing the suggested assignments body
the algorithm, can be set.
'''
self.dataModel = dataModel
self.update()
def updateSpectra(self, *opt):
'''Updates the spectra shown in the spectra
pulldown. These are only the spectra that
were used by the algorithm. All other spectra
in the project are not relevant since for those
no simulated peaks have been matched to real
peaks.
'''
if not self.dataModel:
return
spectrum = self.spectrum
spectra = self.dataModel.getSpectra()
if spectra:
names = [spectrum.name for spectrum in spectra]
index = 0
if self.spectrum not in spectra:
self.spectrum = spectra[0]
else:
index = spectra.index(self.spectrum)
self.spectraPullDown.setup(names, spectra, index)
def changeSpectrum(self, spectrum):
'''Select a spectum to be assigned.'''
self.spectrum = spectrum
def solutionUpdate(self, event=None, value=None):
'''Select a solution. A solution is a
one to one mapping of spin systems
to residues produced by one run of
the algorithm.
args: event: event object, this is
one of the values the number
entry calls his callback
function with.
value: the index of the solution/run.
'''
if not self.dataModel:
return
Nsolutions = len(self.dataModel.chain.residues[0].solutions)
if value is None:
value = self.solutionNumberEntry.get()
if value == self.selectedSolution:
return
else:
self.selectedSolution = value
if value < 1:
self.solutionNumberEntry.set(1)
self.selectedSolution = 1
elif value > Nsolutions:
self.selectedSolution = Nsolutions
self.solutionNumberEntry.set(self.selectedSolution)
else:
self.solutionNumberEntry.set(self.selectedSolution)
def fetchOptions(self):
'''Fetches user set options from the gui in
one go and stores them in their corresponding
instance variables.
'''
self.resonanceToDimension = self.peaksCheckButton.get()
self.spinSystemToResidue = self.residuesCheckButton.get()
self.intra = self.intraCheckButton.get()
self.sequential = self.sequentialCheckButton.get()
self.noDiagonal = self.noDiagonalCheckButton.get()
self.spinSystemType = self.spinSystemTypeSelect.getIndex()
self.strategy = ['skip', 'remove', 'noMerge',
None][self.assignedResidueStrategySelect.getIndex()]
self.allSpectra = [True, False][self.spectrumSelect.getIndex()]
def changeMinScore(self, event=None):
'''Set the minimal score for which a spin system
to residue assignment gets transferred to the
ccpn analysis project.
'''
newMinScore = self.minScoreEntry.get()
if self.minScore != newMinScore:
if newMinScore <= 50.0:
self.minScore = 51.0
self.minScoreEntry.set(51.0)
elif newMinScore > 100.0:
self.minScore = 100.0
self.minScoreEntry.set(100.0)
else:
self.minScore = newMinScore
def transferAssignments(self):
'''Transfer assignments to project depending
on the settings from the GUI.
'''
self.fetchOptions()
if not self.dataModel or (not self.resonanceToDimension
and not self.spinSystemToResidue):
return
strategy = self.strategy
lookupSpinSystem = [
self.getAssignedSpinSystem, self.getBestScoringSpinSystem,
self.getUserDefinedSpinSystem, self.getSelectedSolutionSpinSystem
][self.spinSystemType]
residues = self.dataModel.chain.residues
spinSystemSequence = [lookupSpinSystem(res) for res in residues]
ccpnSpinSystems = []
ccpnResidues = []
# if self.spinSystemType == 0 it means that it for sure already
# assigned like this
if self.spinSystemToResidue and not self.spinSystemType == 0:
for spinSys, res in zip(spinSystemSequence, residues):
if spinSys and res:
ccpnSpinSystems.append(spinSys.getCcpnResonanceGroup())
ccpnResidues.append(res.getCcpnResidue())
assignSpinSystemstoResidues(ccpnSpinSystems,
ccpnResidues,
strategy=strategy,
guiParent=self.guiParent)
if self.resonanceToDimension:
allSpectra = self.allSpectra
if self.intra:
for residue, spinSystem in zip(residues, spinSystemSequence):
if not spinSystem:
continue
intraLink = residue.getIntraLink(spinSystem)
for pl in intraLink.getPeakLinks():
peak = pl.getPeak()
if not allSpectra and peak.getSpectrum(
) is not self.spectrum:
continue
if not peak:
continue
resonances = pl.getResonances()
if self.noDiagonal and len(
set(resonances)) < len(resonances):
continue
for resonance, dimension in zip(
resonances, peak.getDimensions()):
ccpnResonance = resonance.getCcpnResonance()
ccpnDimension = dimension.getCcpnDimension()
assignResToDim(ccpnDimension, ccpnResonance)
if self.sequential:
for residue, spinSystemA, spinSystemB in zip(
residues, spinSystemSequence, spinSystemSequence[1:]):
if not spinSystemA or not spinSystemB:
continue
link = residue.getLink(spinSystemA, spinSystemB)
for pl in link.getPeakLinks():
peak = pl.getPeak()
if not allSpectra and peak.getSpectrum(
) is not self.spectrum:
continue
if not peak:
continue
resonances = pl.getResonances()
if self.noDiagonal and len(
set(resonances)) < len(resonances):
continue
for resonance, dimension in zip(
resonances, peak.getDimensions()):
ccpnResonance = resonance.getCcpnResonance()
ccpnDimension = dimension.getCcpnDimension()
assignResToDim(ccpnDimension, ccpnResonance)
self.guiParent.resultsTab.update()
def getAssignedSpinSystem(self, residue):
'''Get the spinSystem that is assigned in the project
to a residue.
args: residue (src.cython.malandro.Residue)
return: spinSystem (src.cython.malandro.SpinSystem)
'''
ccpCode = residue.ccpCode
seqCode = residue.getSeqCode()
spinSystems = self.dataModel.getSpinSystems()[ccpCode]
ccpnResidue = residue.getCcpnResidue()
if ccpnResidue:
assignedResonanceGroups = ccpnResidue.getResonanceGroups()
if len(assignedResonanceGroups) > 1:
print 'There is more than one spin system assigned to residue %s, did not know which one to use to assign peaks. Therefor this residue is skipped.' % (
seqCode)
return
assignedResonanceGroup = ccpnResidue.findFirstResonanceGroup()
if assignedResonanceGroup:
for spinSystem in spinSystems:
if spinSystem.getSerial() == assignedResonanceGroup.serial:
# Just checking to make sure, analysis project could
# have changed
if not self.skipResidue(residue, spinSystem):
return spinSystem
def getBestScoringSpinSystem(self, residue):
'''Get the spinSystem that scores the highest,
i.e. is assigned in most of the runs to the
given residue.
args: residue (src.cython.malandro.Residue)
return: spinSystem (src.cython.malandro.SpinSystem)
'''
solutions = residue.solutions
weigth = 1.0 / len(solutions)
score, bestSpinSystem = max([
(solutions.count(solution) * weigth * 100.0, solution)
for solution in solutions
])
if score >= self.minScore and not bestSpinSystem.getIsJoker(
) and not self.skipResidue(residue, bestSpinSystem):
return bestSpinSystem
return None
def getUserDefinedSpinSystem(self, residue):
'''Get the spinSystem that is defined by the user
(probably in the resultsTab) as the correct
assignment of the given residue.
args: residue (src.cython.malandro.Residue)
return: spinSystem (src.cython.malandro.SpinSystem)
'''
userDefinedSpinSystem = residue.userDefinedSolution
if userDefinedSpinSystem and not userDefinedSpinSystem.getIsJoker(
) and not self.skipResidue(residue, userDefinedSpinSystem):
return userDefinedSpinSystem
return None
def getSelectedSolutionSpinSystem(self, residue):
'''I a solution corresponding to one specific run
of the algorithm is defined, return which spinSystem
in that run got assigned to the given residue.
args: residue (src.cython.malandro.Residue)
return: spinSystem (src.cython.malandro.SpinSystem)
'''
solutions = residue.solutions
spinSystem = solutions[self.selectedSolution - 1]
if not spinSystem.getIsJoker() and not self.skipResidue(
residue, spinSystem):
return spinSystem
return None
def skipResidue(self, residue, spinSystem):
'''One strategy is to skip all residues that
already have a spin system assignment.
If that is the case determine whether to
skip the given residue.
args: residue (src.cython.malandro.Residue)
spinSystem (src.cython.malandro.SpinSystem)
return: boolean, True if residue should be skipped.
'''
if self.strategy == 0:
assignedGroups = residue.getCcpnResidue().getResonanceGroups()
assignedSerials = set(
[spinSys.serial for spinSys in assignedGroups])
if assignedSerials and spinSystem.getSerial(
) not in assignedSerials:
return True
return False
class AnnealingSettingsTab(object):
'''This class describes the tab in the GUI where the user
can change setting that govern the monte carlo / annleaing
procedure. This also includes which information from the ccpn
analysis project is used and which information is
ignored. This includes:
* present sequential assignments
* tentative assignments
* amino acid type information
* whether to include untyped spin systems
* assignments to peak dimensions
ALso the chain can be selected here.
Furthermore the user can set the temperature
regime of the annealing, the amount of times the procedure
is repeated to obtain statistics. The fraction of peaks
that is left out in each run to diversify the results,
the treshhold score for amino acid typing and the treshhold
collabelling for a peak to be expected.
'''
def __init__(self, parent, frame):
'''Init. args: parent: the guiElement that this
tab is part of.
frame: the frame this part of the
GUI lives in.
'''
self.guiParent = parent
self.frame = frame
self.project = parent.project
self.nmrProject = parent.nmrProject
self.minIsoFrac = 0.1
self.leavePeaksOutFraction = 0.0
self.minTypeScore = 1.0
self.chain = None
self.amountOfRepeats = 10
self.amountOfSteps = 10000
self.acceptanceConstantList = [0.0, 0.01, 0.015, 0.022,
0.033, 0.050, 0.075, 0.113,
0.170, 0.256, 0.384, 0.576,
0.864, 1.297, 1.946, 2.919,
4.378, 6.568, 9.852, 14.77,
22.16, 33.25]
self.energyDataSets = [[]]
self.residues = []
self.body()
def body(self):
'''describes the body of this tab. It bascically consists
of some field to fill out for the user at the top and
a ScrolledGraph that shows the progess of the annealing
procedure a the bottom.
'''
frame = self.frame
# frame.expandGrid(13,0)
frame.expandGrid(15, 1)
row = 0
text = 'Calculate Assignment Suggestions'
command = self.runCalculations
self.startButton = Button(frame, command=command, text=text)
self.startButton.grid(row=row, column=0, sticky='nsew', columnspan=2)
row += 1
Label(frame, text='Amount of runs: ', grid=(row, 0))
tipText = 'The amount of times the whole optimization procedure is performed, each result is safed'
self.repeatEntry = IntEntry(frame, grid=(row, 1), width=7, text=10,
returnCallback=self.updateRepeatEntry,
tipText=tipText, sticky='nsew')
self.repeatEntry.bind('<Leave>', self.updateRepeatEntry, '+')
row += 1
Label(frame, text='Temperature regime: ', grid=(row, 0))
tipText = 'This list of numbers govern the temperature steps during the annealing, every number represents 1/(kb*t), where kb is the Boltzmann constant and t the temperature of one step.'
self.tempEntry = Entry(frame, text=map(str, self.acceptanceConstantList), width=64,
grid=(row, 1), isArray=True, returnCallback=self.updateAcceptanceConstantList,
tipText=tipText, sticky='nsew')
row += 1
Label(frame, text='Amount of attempts per temperature:', grid=(row, 0))
tipText = 'The amount of attempts to switch the position of two spinsystems in the sequence are performed for each temperature point'
self.NAStepEntry = IntEntry(frame, grid=(row, 1), width=7, text=10000,
returnCallback=self.updateStepEntry,
tipText=tipText, sticky='nsew')
self.NAStepEntry.bind('<Leave>', self.updateStepEntry, '+')
row += 1
Label(frame, text='Fraction of peaks to leave out:', grid=(row, 0))
tipText = 'In each run a fraction of the peaks can be left out of the optimization, thereby increasing the variability in the outcome and reducing false negatives. In each run this will be different randomly chosen sub-set of all peaks. 0.1 (10%) can be a good value.'
self.leaveOutPeaksEntry = FloatEntry(frame, grid=(row, 1), width=7, text=0.0,
returnCallback=self.updateLeavePeaksOutEntry,
tipText=tipText, sticky='nsew')
self.leaveOutPeaksEntry.bind(
'<Leave>', self.updateLeavePeaksOutEntry, '+')
row += 1
Label(frame, text='Minmal amino acid typing score:', grid=(row, 0))
tipText = 'If automatic amino acid typing is selected, a cut-off value has to set. Every amino acid type that scores higher than the cut-off is taken as a possible type. This is the same score as can be found under resonance --> spin systems --> predict type. Value should be between 0 and 100'
self.minTypeScoreEntry = FloatEntry(frame, grid=(row, 1), width=7, text=1.0,
returnCallback=self.updateMinTypeScoreEntry,
tipText=tipText, sticky='nsew')
self.minTypeScoreEntry.bind(
'<Leave>', self.updateMinTypeScoreEntry, '+')
row += 1
Label(frame, text='Minimal colabelling fraction:', grid=(row, 0))
tipText = 'The minimal amount of colabelling the different nuclei should have in order to still give rise to a peak.'
self.minLabelEntry = FloatEntry(frame, grid=(row, 1), width=7, text=0.1,
returnCallback=self.updateMinLabelEntry,
tipText=tipText, sticky='nsew')
self.minLabelEntry.bind('<Leave>', self.updateMinLabelEntry, '+')
row += 1
Label(frame, text='Use sequential assignments:', grid=(row, 0))
tipText = 'When this option is select the present sequential assignments will be kept in place'
self.useAssignmentsCheck = CheckButton(
frame, selected=True, tipText=tipText, grid=(row, 1))
row += 1
Label(frame, text='Use tentative assignments:', grid=(row, 0))
tipText = 'If a spin system has tentative assignments this can be used to narrow down the amount of possible sequential assignments.'
self.useTentativeCheck = CheckButton(
frame, selected=True, tipText=tipText, grid=(row, 1))
row += 1
Label(frame, text='Use amino acid types:', grid=(row, 0))
tipText = 'Use amino acid types of the spin systems. If this option is not checked the spin systems are re-typed, only resonance names and frequencies are used'
self.useTypeCheck = CheckButton(
frame, selected=True, tipText=tipText, grid=(row, 1))
row += 1
Label(frame, text='Include untyped spin systems:', grid=(row, 0))
tipText = 'Also include spin system that have no type information. Amino acid typing will be done on the fly.'
self.useAlsoUntypedSpinSystemsCheck = CheckButton(
frame, selected=True, tipText=tipText, grid=(row, 1))
row += 1
Label(frame, text='Use dimensional assignments:', grid=(row, 0))
tipText = 'If one or more dimensions of a peak is already assigned, assume that this assignment is the only option. If not the check the program will consider all possibilities for the assignment of the dimension.'
self.useDimensionalAssignmentsCheck = CheckButton(
frame, selected=True, tipText=tipText, grid=(row, 1))
row += 1
Label(frame, text='Chain:', grid=(row, 0))
self.molPulldown = PulldownList(
frame, callback=self.changeMolecule, grid=(row, 1))
self.updateChains()
row += 1
Label(frame, text='Residue ranges: ', grid=(row, 0))
tipText = 'Which residues should be included. Example: "10-35, 62-100, 130".'
self.residueRangeEntry = Entry(frame, text=None, width=64,
grid=(row, 1), isArray=True, returnCallback=self.updateResidueRanges,
tipText=tipText, sticky='nsew')
self.updateResidueRanges(fromChain=True)
row += 1
self.energyPlot = ScrolledGraph(frame, symbolSize=2, width=600,
height=200, title='Annealing',
xLabel='temperature step', yLabel='energy')
self.energyPlot.grid(row=row, column=0, columnspan=2, sticky='nsew')
def runCalculations(self):
'''Run all calculations. Also triggers the disabling of
some buttons and fields.
'''
self.startButton.disable()
self.disableIllegalButtonsAfterPrecalculations()
self.guiParent.connector.runAllCalculations()
self.startButton.configure(text='More runs')
self.startButton.enable()
def disableIllegalButtonsAfterPrecalculations(self):
'''Disable buttons and field the user can not alter
any longer after the model is set up and the
'pre-calculations' have finished.
This is done because this part of the calculation
should only be run once. All settings that would
be changed after this point will not have any influence.
'''
illegalButtons = [self.minTypeScoreEntry, self.minLabelEntry,
self.useAlsoUntypedSpinSystemsCheck, self.useAssignmentsCheck,
self.useTypeCheck, self.useDimensionalAssignmentsCheck,
self.useTentativeCheck]
for illegalButton in illegalButtons:
illegalButton.configure(state='disabled')
self.molPulldown.disable()
def getChainName(self, chain):
'''Get the name for a chain.
args: chain: ccpn analysis
chain object
returns: chain name
'''
return '%s:%s (%s)' % (chain.molSystem.code, chain.code, chain.molecule.molType)
def getChains(self):
'''Get all chains present in the project.
returns: list of ccpn analysis chain objects
'''
chains = []
if self.project:
for molSystem in self.project.sortedMolSystems():
for chain in molSystem.sortedChains():
if chain.residues:
chains.append(chain)
return chains
def updateChains(self, *opt):
'''Updates the list of chains if a new one is added
to or deleted from the project. Updates the
pull down list where a chain can be selected.
'''
index = 0
texts = []
chains = self.getChains()
chain = self.chain
if chains:
if chain not in chains:
chain = chains[0]
texts = [self.getChainName(c) for c in chains]
index = chains.index(chain)
else:
chain = None
self.molPulldown.setup(texts, chains, index)
if chain is not self.chain:
self.chain = chain
def changeMolecule(self, chain):
'''Select a molecular chain.'''
if chain is not self.chain:
self.chain = chain
self.updateResidueRanges(fromChain=True)
def updateStepEntry(self, event=None):
'''Update the value and entry that sets the amount of
steps per temperature point.
'''
value = self.NAStepEntry.get()
if value == self.amountOfSteps:
return
if value < 1:
self.NAStepEntry.set(1)
self.amountOfSteps = 1
else:
self.amountOfSteps = value
self.NAStepEntry.set(value)
def updateRepeatEntry(self, event=None):
'''Update the value and entry of that sets
the amount of times the whole annealing
procedure is repeated in order
to obtain statistics.
'''
value = self.repeatEntry.get()
if value == self.amountOfRepeats:
return
if value < 1:
self.repeatEntry.set(1)
self.amountOfRepeats = 1
else:
self.amountOfRepeats = value
self.repeatEntry.set(value)
def updateMinTypeScoreEntry(self, event=None):
'''Updates the value and the entry for the
treshhold value for amino acid typing.
'''
value = self.minTypeScoreEntry.get()
if value == self.minTypeScore:
return
if value < 0:
self.minTypeScoreEntry.set(0.0)
self.minTypeScore = 0.0
elif value > 100:
self.minTypeScoreEntry.set(100.0)
self.minTypeScore = 100.0
else:
self.minTypeScoreEntry.set(value)
self.minTypeScore = value
def updateMinLabelEntry(self, event=None):
'''Updates the minimum colabelling fraction
for which a peak is expected to be present
in the spectra.
'''
value = self.minLabelEntry.get()
if value == self.minIsoFrac:
return
if value < 0:
self.minIsoFrac = 0.0
self.minLabelEntry.set(0.0)
elif value > 1:
self.minIsoFrac = 1.0
self.minLabelEntry.set(1.0)
else:
self.minIsoFrac = value
self.minLabelEntry.set(value)
def updateLeavePeaksOutEntry(self, event=None):
'''Updates the value and entry of the fraction
of peaks that should be left out in each
run in order to diversify the results.
'''
value = self.leaveOutPeaksEntry.get()
if value == self.leavePeaksOutFraction:
return
if value < 0:
self.leavePeaksOutFraction = 0.0
self.leaveOutPeaksEntry.set(0.0)
elif value > 1:
self.leavePeaksOutFraction = 1.0
self.leaveOutPeaksEntry.set(1.0)
else:
self.leavePeaksOutFraction = value
self.leaveOutPeaksEntry.set(value)
def updateAcceptanceConstantList(self, event=None):
'''Updates the list with constants that are used
during the monte carlo procedure to decide whether
a changed is accepted or not.
'''
acList = self.tempEntry.get()
newList = []
for constant in acList:
try:
number = float(constant)
newList.append(number)
except ValueError:
string = constant + \
' in temperature constants is not a number.'
showWarning('Not A Number', string, parent=self.guiParent)
return False
self.acceptanceConstantList = newList
return True
def updateResidueRanges(self, event=None, fromChain=False):
self.residues = set()
subRanges = self.residueRangeEntry.get()
if not subRanges or fromChain:
self.residues = set(self.chain.residues)
residues = self.chain.sortedResidues()
text = '{}-{}'.format(residues[0].seqCode, residues[-1].seqCode)
self.residueRangeEntry.set(text=text)
return
for subRange in subRanges:
indeces = subRange.split('-')
start = int(indeces[0])
stop = int(indeces[-1]) + 1
for seqCode in range(start, stop):
residue = self.chain.findFirstResidue(seqCode=seqCode)
if not residue:
showWarning('Residue out of range.',
'There is no residue at position {}'.format(seqCode),
parent=self.guiParent)
self.residues = set()
return
self.residues.add(residue)
def addEnergyPoint(self, energy, time):
'''Adds a point to the graph that shows the progress
of the annealling procedure.
args: energy: the y-value
time: the x-value
'''
point = (time, energy * -1)
# This means one run has finished
if len(self.energyDataSets[-1]) / (len(self.acceptanceConstantList) + 1):
self.energyDataSets.append([point])
else:
self.energyDataSets[-1].append(point)
colors = colorSeries
Ncolors = len(colors)
NdataSets = len(self.energyDataSets)
colorList = (NdataSets / Ncolors) * colors + \
colors[:NdataSets % Ncolors]
self.energyPlot.update(dataSets=self.energyDataSets,
dataColors=colorList)
# Forcing the graph to draw, eventhough calculations
# are still running. Only do this with high numbers of
# steps, otherwise drawing takes longer than annealling.
if self.amountOfSteps >= 100000:
self.energyPlot.draw()