def __init__(self, **kwargs):
OrderedObject.__init__(self, **kwargs)
self.name = String('default')
self.maxCores = Integer()
self.allowMPI = Boolean()
self.allowThreads = Boolean()
self.maxHours = Integer()
def _createOutputStep(self):
# New Output would be an Integer
boxSize = Integer(10)
if self.iBoxSize.hasValue():
boxSize.set(2 * int(self.iBoxSize.get()))
self._defineOutputs(oBoxSize=boxSize)
def rowToParticle(partRow, **kwargs):
""" Create a Particle from a row of a meta """
img = em.Particle()
# Provide a hook to be used if something is needed to be
# done for special cases before converting image to row
preprocessImageRow = kwargs.get('preprocessImageRow', None)
if preprocessImageRow:
preprocessImageRow(img, partRow)
# Decompose Relion filename
index, filename = relionToLocation(partRow.getValue(md.RLN_IMAGE_NAME))
img.setLocation(index, filename)
if partRow.containsLabel(md.RLN_PARTICLE_CLASS):
img.setClassId(partRow.getValue(md.RLN_PARTICLE_CLASS))
if kwargs.get('readCtf', True):
img.setCTF(rowToCtfModel(partRow))
# alignment is mandatory at this point, it shoud be check
# and detected defaults if not passed at readSetOf.. level
alignType = kwargs.get('alignType')
if alignType != em.ALIGN_NONE:
img.setTransform(rowToAlignment(partRow, alignType))
if kwargs.get('readAcquisition', True):
img.setAcquisition(rowToAcquisition(partRow))
if kwargs.get('magnification', None):
img.getAcquisition().setMagnification(kwargs.get("magnification"))
setObjId(img, partRow)
# Read some extra labels
rowToObject(partRow, img, {},
extraLabels=IMAGE_EXTRA_LABELS + kwargs.get('extraLabels', []))
img.setCoordinate(rowToCoordinate(partRow))
# copy micId if available from row to particle
if partRow.hasLabel(md.RLN_MICROGRAPH_ID):
img.setMicId(partRow.getValue(md.RLN_MICROGRAPH_ID))
# copy particleId if available from row to particle
if partRow.hasLabel(md.RLN_PARTICLE_ID):
img._rlnParticleId = Integer(partRow.getValue(md.RLN_PARTICLE_ID))
# copy particleId if available from row to particle
if partRow.hasLabel(md.RLN_PARTICLE_RANDOM_SUBSET):
img._rln_halfId = Integer(partRow.getValue(md.RLN_PARTICLE_RANDOM_SUBSET))
# Provide a hook to be used if something is needed to be
# done for special cases before converting image to row
postprocessImageRow = kwargs.get('postprocessImageRow', None)
if postprocessImageRow:
postprocessImageRow(img, partRow)
return img
class PKInhalationDissolution(EMObject):
# Hartung2020_MATLAB/functions/get_disol.m
# All implemented dissolution models are based on an adapted version of
# the Noyes - Whitney equation, but differ in whether they
# 1) describe saturable or unsaturable dissolution
# 2) truncate or not the dissolution speed for small particles (for numeric stability)
# 3) truncate or not the dissolution speed for large particles
# (based on the assumption that only a part of the particle surface
# is in contact with the dissolution medium)
UNSAT = 0
TRUNC_UNSAT = 1
SAT = 2 # Only this one is implemented
TRUNC_SAT = 3
TRUNC2_SAT = 4
CAP = 5
XCAP = 6
UNSOL = 7
def __init__(self, **args):
EMObject.__init__(self, **args)
self.type = Integer()
self.type.set(self.SAT)
self.substanceParams = None
def prepare(self, substanceParams, part):
self.substanceParams = substanceParams
substanceData = substanceParams.getData()
if part=="bronchi":
self.kdiss = substanceData['kdiss_br']
self.Cs = substanceData['Cs_br']
else:
self.kdiss = substanceData['kdiss_alv']
self.Cs = substanceData['Cs_alv']
self.rho = substanceData['rho']
print("Substance in %s ==================="%part)
print("Maximum dissolution rate [nmol/(cm*min)]",self.kdiss)
print("Solubility in [nmol/cm3]=[uM]",self.Cs)
print("Density in [nmol/cm3]",self.rho)
D = self.kdiss / self.Cs
K = 4 * math.pi * D / (self.rho * np.power(4.0/3.0 * math.pi, 1.0/3.0))
if self.type.get()==self.SAT:
self.dissol = lambda s, Cf: np.multiply(np.reshape(K*(self.Cs-Cf),(Cf.size,1)),
np.reshape(np.where(s>0, np.power(s,1.0/3.0), 0.0),(1,s.size)))
# [cm^3/min]
def getDissolution(self, s, Cf, h):
return self.dissol(s,Cf)
def _getMetrics(self):
""" Internal method to compute some metrics. """
# mean values of FSC-Q
mtd = md.MetaData()
mtd.read(self._getFileName(MD_MEANS))
mean = mtd.getValue(MDL_VOLUME_SCORE1, 1)
meanA = mtd.getValue(MDL_VOLUME_SCORE2, 1)
# means value for map divided by resolution (FSC-Qr)
mtd2 = md.MetaData()
mtd2.read(self._getFileName(MD2_MEANS))
mean2 = mtd2.getValue(MDL_VOLUME_SCORE1, 1)
meanA2 = mtd2.getValue(MDL_VOLUME_SCORE2, 1)
# statistic from fnal pdb with fsc-q
# Number of atoms greater or less than 0.5
total_atom = 0
fscq_greater = 0
fscq_less = 0
with open(self._getFileName(PDB_VALUE_FILE)) as f:
lines_data = f.readlines()
for j, lin in enumerate(lines_data):
if (lin.startswith('ATOM') or lin.startswith('HETATM')):
total_atom = total_atom + 1
fscq_atom = float(lin[54:60])
if (fscq_atom > 0.5):
fscq_greater = fscq_greater + 1
if (fscq_atom < -0.5):
fscq_less = fscq_less + 1
porc_greater = (fscq_greater * 100) / total_atom
porc_less = (fscq_less * 100) / total_atom
return {
'mean': Float(mean),
'meanA': Float(meanA),
'mean2': Float(mean2),
'meanA2': Float(meanA2),
'total_atom': Integer(total_atom),
'fscq_greater': Integer(fscq_greater),
'fscq_less': Integer(fscq_less),
'porc_greater': Float(porc_greater),
'porc_less': Float(porc_less)
}
def compareClassesStep(self, i1, i2):
set1 = self.inputClasses1.get()
set2 = self.inputClasses2.get()
# Compare each pair of class from set1 and set2
# compute the Jaccard index for each (J = len(intersection) / len(union))
# Create a list will all pairs indexes and the sort them
jaccardList = []
f = open(self._getPath('jaccard.txt'), 'w')
f.write(
'; class1 class2 intersection(i) union(i) jaccard index = len(i)/len(u)\n'
)
for cls1 in set1:
ids1 = cls1.getIdSet()
for cls2 in set2:
ids2 = cls2.getIdSet()
inter = len(ids1.intersection(ids2))
union = len(ids1.union(ids2))
jaccardIndex = float(inter) / union
jaccardTuple = (cls1.getObjId(), cls2.getObjId(), inter, union,
jaccardIndex)
f.write('%d %d %d %d %0.3f\n' % jaccardTuple)
jaccardList.append(jaccardTuple)
f.close()
jaccardList.sort(key=lambda e: e[4], reverse=True)
visitedClasses = set()
outputFn = self._getPath('consensus.sqlite')
cleanPath(outputFn)
outputSet = EMSet(filename=outputFn)
for clsId1, clsId2, inter, union, jaccardIndex in jaccardList:
if clsId1 not in visitedClasses:
visitedClasses.add(clsId1) # mark as visited
cls1 = set1[clsId1]
cls2 = set2[clsId2]
o = Object()
o.setObjLabel('classes %d - %d' % (clsId1, clsId2))
o.class1 = cls1.clone()
o.class1.id = Integer(clsId1)
o.class2 = cls2.clone()
o.class2.id = Integer(clsId2)
o.jaccard = Float(jaccardIndex)
o.intersection = Integer(inter)
o.union = Integer(union)
outputSet.append(o)
self._defineOutputs(outputConsensus=outputSet)
def createOutputStep(self):
from sklearn.manifold import TSNE
Xdim = self.inputParticles.get().getXDim()
self.Ts = self.inputParticles.get().getSamplingRate()
newTs = self.targetResolution.get() * 1.0 / 3.0
self.newTs = max(self.Ts, newTs)
self.newXdim = int(Xdim * self.Ts / newTs)
fnOut = self._getFileName('fnOut')
mdOut = md.MetaData(fnOut)
coeffMatrix = np.vstack(mdOut.getColumnValues(md.MDL_SPH_COEFFICIENTS))
X_tsne_1d = TSNE(n_components=1).fit_transform(coeffMatrix)
X_tsne_2d = TSNE(n_components=2).fit_transform(coeffMatrix)
newMdOut = md.MetaData()
i = 0
for row in md.iterRows(mdOut):
newRow = row
newRow.setValue(md.MDL_SPH_TSNE_COEFF1D, float(X_tsne_1d[i, 0]))
newRow.setValue(md.MDL_SPH_TSNE_COEFF2D,
[float(X_tsne_2d[i, 0]),
float(X_tsne_2d[i, 1])])
if self.newTs != self.Ts:
coeffs = mdOut.getValue(md.MDL_SPH_COEFFICIENTS,
row.getObjId())
correctionFactor = self.inputVolume.get().getDim(
)[0] / self.newXdim
coeffs = [correctionFactor * coeff for coeff in coeffs]
newRow.setValue(md.MDL_SPH_COEFFICIENTS, coeffs)
newRow.addToMd(newMdOut)
i += 1
newMdOut.write(fnOut)
inputSet = self.inputParticles.get()
partSet = self._createSetOfParticles()
partSet.copyInfo(inputSet)
partSet.setAlignmentProj()
partSet.copyItems(inputSet,
updateItemCallback=self._updateParticle,
itemDataIterator=md.iterRows(
fnOut, sortByLabel=md.MDL_ITEM_ID))
partSet.L1 = Integer(self.l1.get())
partSet.L2 = Integer(self.l2.get())
partSet.Rmax = Integer(self.inputVolume.get().getDim()[0] / 2)
self._defineOutputs(outputParticles=partSet)
self._defineTransformRelation(self.inputParticles, partSet)
def createOutputStep(self):
""" The output is just an Integer. Other protocols can use it in those
IntParam if it has set allowsPointer=True
"""
micSet = self.inputMicrographs.get()
boxSize = Integer(self.particleBoxsize)
self._defineOutputs(boxsize=boxSize)
self._defineSourceRelation(micSet, boxSize)
def _defineParams(self, form):
form.addSection('Input')
form.addParam('inputExperiment',
params.PointerParam,
label="Input experiment",
pointerClass='PKPDExperiment',
help='Select an experiment with samples')
form.addParam(
'protElimination',
params.PointerParam,
label="Elimination rate",
pointerClass='ProtPKPDEliminationRate',
help=
'Select an execution of a protocol estimating the elimination rate'
)
form.addParam(
"absorptionF",
params.FloatParam,
label="Absorption fraction",
default=1,
help="Between 0 (=no absorption) and 1 (=full absorption)")
form.addParam(
'bounds',
params.StringParam,
label="Ka, V, [tlag] bounds",
default="",
expertLevel=LEVEL_ADVANCED,
help=
'Bounds for Ka (absorption constant), V (distribution volume) and optionally tlag.\nExample 1: (0,1e-3);(30,50);(0.1,0.5) -> Ka in (0,1e-3), V in (30,50) and tlag in (0.1,0.5)\n'
)
form.addParam('confidenceInterval',
params.FloatParam,
label="Confidence interval",
default=95,
expertLevel=LEVEL_ADVANCED,
help='Confidence interval for the fitted parameters')
form.addParam(
'includeTlag',
params.BooleanParam,
label="Include tlag",
default=True,
expertLevel=LEVEL_ADVANCED,
help='Calculate the delay between administration and absorption')
self.fitType = Integer() # Logarithmic fit
self.fitType.set(1)
def _updateItem(self, particle, row):
self.reader.setParticleTransform(particle, row)
# FIXME: check if other attrs need saving
particle._rlnImageOriginalName = String(row.rlnImageOriginalName)
particle._rlnRandomSubset = Integer(row.rlnRandomSubset)
newLoc = convert.relionToLocation(row.rlnImageName)
particle.setLocation(newLoc)
def __init__(self, **kwargs):
OrderedObject.__init__(self, **kwargs)
self.name = String()
# Number of cores from which the queue is mandatory
# 0 means no mandatory at all
# 1 will force to launch all jobs through the queue
self.mandatory = Integer()
self.queues = None # List for queue configurations
self.submitCommand = String()
# Allow to change the prefix of submission scripts
# we used by default the ID.job, but in some clusters
# the job script should start by a letter
self.submitPrefix = String()
self.checkCommand = String()
self.cancelCommand = String()
self.submitTemplate = String()
self.jobDoneRegex = String()
def readPartsFromMics(self, micList, outputParts):
""" Read the particles extract for the given list of micrographs
and update the outputParts set with new items.
"""
relionToLocation = relion.convert.relionToLocation
p = Particle()
p._rlnOpticsGroup = Integer()
acq = self.getInputMicrographs().getAcquisition()
# JMRT: Ideally I would like to disable the whole Acquisition for each
# particle row, but the SetOfImages will set it again.
# Another option could be to disable in the set, but then in
# streaming, other protocols might get the wrong optics info
pAcq = Acquisition(magnification=acq.getMagnification(),
voltage=acq.getVoltage(),
amplitudeContrast=acq.getAmplitudeContrast(),
sphericalAberration=acq.getSphericalAberration())
p.setAcquisition(pAcq)
tmp = self._getTmpPath()
extra = self._getExtraPath()
for mic in micList:
posSet = set()
coordDict = {self._getPos(c): c
for c in self.coordDict[mic.getObjId()]}
del self.coordDict[mic.getObjId()]
ogNumber = mic.getAttributeValue('_rlnOpticsGroup', 1)
partsStar = self.__getMicFile(mic, '_extract.star', folder=tmp)
partsTable = relion.convert.Table(fileName=partsStar)
stackFile = self.__getMicFile(mic, '.mrcs', folder=tmp)
endStackFile = self.__getMicFile(mic, '.mrcs', folder=extra)
pwutils.moveFile(stackFile, endStackFile)
for part in partsTable:
pos = (int(float(part.rlnCoordinateX)),
int(float(part.rlnCoordinateY)))
if pos in posSet:
print("Duplicate coordinate at: %s, IGNORED. " % str(pos))
coord = None
else:
coord = coordDict.get(pos, None)
if coord is not None:
# scale the coordinates according to particles dimension.
coord.scale(self.getBoxScale())
p.copyObjId(coord)
idx, fn = relionToLocation(part.rlnImageName)
p.setLocation(idx, endStackFile)
p.setCoordinate(coord)
p.setMicId(mic.getObjId())
p.setCTF(mic.getCTF())
p._rlnOpticsGroup.set(ogNumber)
outputParts.append(p)
posSet.add(pos)
def createOutputStep(self):
posDir = self._getExtraPath()
coordSet = self._createSetOfCoordinates(self.inputMics)
readSetOfCoordinates(posDir, self.inputMics, coordSet)
self._defineOutputs(outputCoordinates=coordSet)
self._defineSourceRelation(self.inputMicrographs, coordSet)
boxSize = Integer(coordSet.getBoxSize())
self._defineOutputs(boxsize=boxSize)
self._defineSourceRelation(self.inputMicrographs.get(), boxSize)
def show(self, form, *params):
patchValues = [Integer(i) for i in range(32, 130, 8)]
# Get a data provider from the patchValues to be used in the tree (dialog)
provider = ListTreeProviderString(patchValues)
dlg = dialog.ListDialog(form.root, "Paych shape values", provider,
"Select one of the size values)")
# Set the chosen value back to the form
form.setVar(PATCH_SHAPE, dlg.values[0].get())
class ProtPKPDExponentialFit(ProtPKPDFitBase):
""" Fit a set of exponentials. The observed measurement is modelled as Y=sum_{i=1}^N c_i exp(-lambda_i * X).\n
Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parameters
are independent, which are not. Use Bootstrap estimates instead.\n
Protocol created by http://www.kinestatpharma.com\n"""
_label = 'fit exponentials'
#--------------------------- DEFINE param functions --------------------------------------------
def _defineParams(self, form, fullForm=True):
self._defineParams1(form,"t","Cp")
if fullForm:
form.addParam('fitType', params.EnumParam, choices=["Linear","Logarithmic","Relative"], label="Fit mode", default=1,
help='Linear: sum (Cobserved-Cpredicted)^2\nLogarithmic: sum(log10(Cobserved)-log10(Cpredicted))^2\n'\
"Relative: sum ((Cobserved-Cpredicted)/Cobserved)^2")
form.addParam('Nexp', params.IntParam, label="Number of exponentials", default=1,
help='Number of exponentials to fit')
else:
self.fitType=Integer()
self.fitType.set(1)
self.Nexp=Integer()
self.Nexp.set(1)
form.addParam('bounds', params.StringParam, label="Amplitude and time constant bounds", default="", expertLevel=LEVEL_ADVANCED,
help='Bounds for the c_i amplitudes and lambdas.\nExample 1: (0,10);(0,1e-2) -> c1 in (0,10), lambda1 in (0,1e-2)\n'\
'Example 2: (0,10);(0,1e-2);(0,1);(0,1e-1) -> c1 in (0,10), lambda1 in (0,1e-2), c2 in (0,1), lambda2 in (0,1e-1)')
form.addParam('confidenceInterval', params.FloatParam, label="Confidence interval=", default=95, expertLevel=LEVEL_ADVANCED,
help='Confidence interval for the fitted parameters')
if fullForm:
form.addParam('reportX', params.StringParam, label="Evaluate at X=", default="", expertLevel=LEVEL_ADVANCED,
help='Evaluate the model at these X values\nExample 1: [0,5,10,20,40,100]\nExample 2: 0:0.55:10, from 0 to 10 in steps of 0.5')
else:
self.reportX=String()
self.reportX.set("")
def getListOfFormDependencies(self):
return [self.predictor.get(), self.predicted.get(), self.fitType.get(), self.bounds.get()]
def createModel(self):
return PKPDExponentialModel()
def setupFromFormParameters(self):
self.model.Nexp=self.Nexp.get()
#--------------------------- INFO functions --------------------------------------------
def _warnings(self):
warnings = []
experiment = self.readExperiment(self.getInputExperiment().fnPKPD,show=False)
incorrectList = experiment.getNonBolusDoses()
if len(incorrectList)>0:
warnings.append("This protocol is meant only for intravenous bolus regimens. Check the doses for %s"%(','.join(incorrectList)))
return warnings
def _validate(self):
errors=ProtPKPDFitBase._validate(self)
if self.Nexp.get()<1:
errors.append("The number of exponentials has to be larger than 0")
return errors
def launchParticlePickGUIStep(self, micFn):
# Launch the particle picking GUI
extraDir = self._getExtraPath()
process = launchSupervisedPickerGUI(micFn, extraDir, self)
process.wait()
# generate the discarded output only if there is a good output
if self.saveDiscarded and exists(self._getPath('coordinates.sqlite')):
self.createDiscardedStep()
coordSet = self.getCoords()
if coordSet:
boxSize = Integer(coordSet.getBoxSize())
self._defineOutputs(boxsize=boxSize)
self._defineSourceRelation(self.inputMicrographs.get(), boxSize)
def initializeRejDict(self):
self.discDict = {'defocus': 0,
'astigmatism': 0,
'singleResolution': 0,
'_xmipp_ctfCritFirstZero': 0,
'_xmipp_ctfCritfirstZeroRatio': 0,
'_xmipp_ctfCritCorr13': 0,
'_xmipp_ctfIceness': 0,
'_xmipp_ctfCritCtfMargin': 0,
'_xmipp_ctfCritNonAstigmaticValidty': 0,
'consensusResolution': 0
}
for k in self.discDict:
setattr(self, "rejBy"+k, Integer(0))
self._store()
def _defineParams(self, form, fullForm=True):
self._defineParams1(form,"t","Cp")
if fullForm:
form.addParam('fitType', params.EnumParam, choices=["Linear","Logarithmic","Relative"], label="Fit mode", default=1,
help='Linear: sum (Cobserved-Cpredicted)^2\nLogarithmic: sum(log10(Cobserved)-log10(Cpredicted))^2\n'\
"Relative: sum ((Cobserved-Cpredicted)/Cobserved)^2")
form.addParam('Nexp', params.IntParam, label="Number of exponentials", default=1,
help='Number of exponentials to fit')
else:
self.fitType=Integer()
self.fitType.set(1)
self.Nexp=Integer()
self.Nexp.set(1)
form.addParam('bounds', params.StringParam, label="Amplitude and time constant bounds", default="", expertLevel=LEVEL_ADVANCED,
help='Bounds for the c_i amplitudes and lambdas.\nExample 1: (0,10);(0,1e-2) -> c1 in (0,10), lambda1 in (0,1e-2)\n'\
'Example 2: (0,10);(0,1e-2);(0,1);(0,1e-1) -> c1 in (0,10), lambda1 in (0,1e-2), c2 in (0,1), lambda2 in (0,1e-1)')
form.addParam('confidenceInterval', params.FloatParam, label="Confidence interval=", default=95, expertLevel=LEVEL_ADVANCED,
help='Confidence interval for the fitted parameters')
if fullForm:
form.addParam('reportX', params.StringParam, label="Evaluate at X=", default="", expertLevel=LEVEL_ADVANCED,
help='Evaluate the model at these X values\nExample 1: [0,5,10,20,40,100]\nExample 2: 0:0.55:10, from 0 to 10 in steps of 0.5')
else:
self.reportX=String()
self.reportX.set("")
def updateItem(item, row):
micName = getMicName(item)
if micName not in micDict:
raise Exception("Micrograph name (aka micName) '%s' was "
"not found in the 'data_micrographs' table of "
"the input star file: %s"
% (micName, inputStar))
ogNumber = micDict[micName]
if not hasattr(item, '_rlnOpticsGroup'):
item._rlnOpticsGroup = Integer()
item._rlnOpticsGroup.set(ogNumber)
def saveConfig(filename):
from pyworkflow.mapper import SqliteMapper
from pyworkflow.object import String, Integer
mapper = SqliteMapper(filename)
o = Config()
for k, v in globals().iteritems():
if k.startswith('cfg'):
if type(v) is str:
value = String(v)
else:
value = Integer(v)
setattr(o, k, value)
mapper.insert(o)
mapper.commit()
def _readValidationPklFile(self, fileName):
self.SUMMARYFILENAME = self._getTmpPath(self.SUMMARYFILENAME)
command = """import pickle
import collections
import json
def pickleData(file):
with open(file,"r") as f:
return pickle.load(f)
# process file {VALIDATIONCRYOEMPKLFILENAME}"
data = pickleData('{VALIDATIONCRYOEMPKLFILENAME}')
dictSummary = collections.OrderedDict()
dictSummary['Rhama_Outliers'] = data.model.geometry.ramachandran.outliers
dictSummary['Rhama_Favored'] = data.model.geometry.ramachandran.favored
dictSummary['Rota_Outliers'] = data.model.geometry.rotamer.outliers
dictSummary['Cbeta_Outliers_n'] = data.model.geometry.c_beta.cbetadev.n_outliers
dictSummary['Clash_score'] = data.model.geometry.clash.score
dictSummary['MolProbity_score'] = data.model.geometry.molprobity_score
""".format(VALIDATIONCRYOEMPKLFILENAME=fileName)
command += """with open('%s',"w") as f:
f.write(json.dumps(dictSummary))
""" % (self.SUMMARYFILENAME)
pythonFileName = self.SUMMARYFILENAME.replace('.txt', '.py')
# write script file
with open(pythonFileName, "w") as f:
f.write(command)
# execute file with phenix.python
Plugin.runPhenixProgram("", pythonFileName)
# read file in scipion python
with open(self.SUMMARYFILENAME, "r") as f:
dictSummary = f.read()
dictSummary = json.loads(dictSummary,
object_pairs_hook=collections.OrderedDict)
self.ramachandranOutliers = Float(dictSummary['Rhama_Outliers'])
self.ramachandranFavored = Float(dictSummary['Rhama_Favored'])
self.rotamerOutliers = Float(dictSummary['Rota_Outliers'])
self.cbetaOutliers = Integer(dictSummary['Cbeta_Outliers_n'])
self.clashscore = Float(dictSummary['Clash_score'])
self.overallScore = Float(dictSummary['MolProbity_score'])
def _defineParams(self, form):
form.addSection('Input')
form.addParam('inputExperiment', params.PointerParam, label="Input experiment",
pointerClass='PKPDExperiment',
help='Select an experiment with samples')
form.addParam('protElimination', params.PointerParam, label="Elimination rate",
pointerClass='ProtPKPDEliminationRate',
help='Select an execution of a protocol estimating the elimination rate')
form.addParam("absorptionF", params.FloatParam, label="Absorption fraction", default=1,
help="Between 0 (=no absorption) and 1 (=full absorption)")
form.addParam('bounds', params.StringParam, label="Ka, V, [tlag] bounds", default="", expertLevel=LEVEL_ADVANCED,
help='Bounds for Ka (absorption constant), V (distribution volume) and optionally tlag.\nExample 1: (0,1e-3);(30,50);(0.1,0.5) -> Ka in (0,1e-3), V in (30,50) and tlag in (0.1,0.5)\n')
form.addParam('confidenceInterval', params.FloatParam, label="Confidence interval", default=95, expertLevel=LEVEL_ADVANCED,
help='Confidence interval for the fitted parameters')
form.addParam('includeTlag', params.BooleanParam, label="Include tlag", default=True, expertLevel=LEVEL_ADVANCED,
help='Calculate the delay between administration and absorption')
self.fitType=Integer() # Logarithmic fit
self.fitType.set(1)
def _parseFile(self, fileName):
with open(fileName, encoding="ISO-8859-1") as f:
line = f.readline()
while line:
words = line.strip().split()
if len(words) > 1:
if (words[0] == 'Ramachandran' and words[1] == 'outliers'):
self.ramachandranOutliers = Float(words[3])
elif (words[0] == 'favored' and words[1] == '='):
self.ramachandranFavored = Float(words[2])
elif (words[0] == 'Rotamer' and words[1] == 'outliers'):
self.rotamerOutliers = Float(words[3])
elif (words[0] == 'C-beta' and words[1] == 'deviations'):
self.cbetaOutliers = Integer(words[3])
elif (words[0] == 'Clashscore' and words[1] == '='):
self.clashscore = Float(words[2])
elif (words[0] == 'MolProbity' and words[1] == 'score'):
self.overallScore = Float(words[3])
line = f.readline()
class PKCiliarySpeed(EMObject):
# Hartung2020_MATLAB/functions/get_cilspeed.m
# Only 'interp' model is implemented here
EXPON = 0
FIT = 1
INTERP = 2
def __init__(self, **args):
EMObject.__init__(self, **args)
self.type = Integer()
self.type.set(self.INTERP)
self.lungParams = None
def prepare(self, lungParams, ciliarySpeedType):
self.type.set(ciliarySpeedType)
self.lungParams = lungParams
lungData = lungParams.getBronchial()
self.Lx = np.sum(lungData['length_cm'])
self.pos = lungData['pos']
self.diam_cm = lungData['diam_cm']
print("Ciliary speed ================")
print("Total length of the lung [cm]",self.Lx)
print("Location of branches [cm]",self.pos)
print("Diameter of the branches [cm]",self.diam_cm)
if self.type.get()==self.EXPON:
# Exponentially increasing ciliary speed
self.cilspeed = lambda x: np.where(x>self.Lx, 0.0, np.exp(np.log(4e-3) + x * (np.log(50) - np.log(4e-3))))
elif self.type.get()==self.FIT:
# Simple fitted empirical ciliary transport model
self.cilspeed = lambda x: np.where(x<0.0, 0.0, np.exp(5*np.divide(self.Lx-x-3,np.power(3+self.Lx-x,0.92)))/200)
elif self.type.get()==self.INTERP:
# Hofmann - Sturm model for mucociliary transport velocity
# Reference: Hofmann / Sturm (2004) J Aerosol Med, Vol 17(1)
self.v = 0.1 * 1.2553 * np.power(lungData['diam_cm'],2.808) # in [cm / min]
self.cilspeed = interp1d(self.pos, self.v, bounds_error=False, fill_value=(self.v[0], self.v[-1]))
def _updateItem(self, item, row):
""" Implement this function to do some
update actions over each single item
that will be stored in the output Set.
"""
# Add alignment info from corresponding item on inputAlignment
inputAlignment = self.inputAlignment.get()
scale = inputAlignment.getSamplingRate()/self.inputParticles.get().getSamplingRate()
alignedParticle = inputAlignment[item.getObjId()]
# If alignment is found for this particle set the alignment info
# on the output particle, if not do not write that item
if alignedParticle is not None:
alignment = alignedParticle.getTransform()
alignment.scaleShifts(scale, shiftsAppliedBefore=self.shiftsAppliedBefore.get())
item.setTransform(alignment)
if self.assignRandomSubsets:
subset = alignedParticle.getAttributeValue('_rlnRandomSubset', None)
if subset is not None:
item._rlnRandomSubset = Integer(subset)
else:
item._appendItem = False
class QueueConfig(OrderedObject):
def __init__(self, **kwargs):
OrderedObject.__init__(self, **kwargs)
self.name = String('default')
self.maxCores = Integer()
self.allowMPI = Boolean()
self.allowThreads = Boolean()
self.maxHours = Integer()
def getName(self):
return self.name.get()
def getMaxCores(self):
return self.maxCores.get()
def getAllowMPI(self):
return self.allowMPI.get()
def getAllowThreads(self):
return self.allowThreads.get()
def getMaxHours(self):
return self.maxHours.get()
def setName(self, name):
self.name.set(name)
def setMaxCores(self, maxCores):
self.maxCores.set(maxCores)
def setAllowMPI(self, allowMPI):
self.allowMPI.set(allowMPI)
def setAllowThreads(self, allowThreads):
self.allowThreads.set(allowThreads)
def setMaxHours(self, maxHours):
self.maxHours.set(maxHours)
def _updateOutput(self, tsIdList):
""" Update the output set with the finished Tilt-series.
Params:
:param tsIdList: list of ids of finished tasks.
"""
ts = self._getTiltSeries(tsIdList[0])
tsId = ts.getTsId()
objId = ts.getObjId()
# Flag to check the first time we save output
self._createOutput = getattr(self, '_createOutput', True)
outputSet = self._getOutputSet()
if outputSet is None:
# Special case just to update the outputSet status
# but it only makes sense when there is outputSet
if not tsIdList:
return
outputSet = self._createOutputSet()
else:
outputSet.enableAppend()
self._createOutput = False
newCTFTomoSeries = CTFTomoSeries()
newCTFTomoSeries.copyInfo(ts)
newCTFTomoSeries.setTiltSeries(ts)
newCTFTomoSeries.setTsId(tsId)
newCTFTomoSeries.setObjId(objId)
outputSet.append(newCTFTomoSeries)
index = 1
for ti in self._tsDict.getTiList(tsId):
newCTFTomo = ti._ctfModel
newCTFTomo.setIndex(Integer(index))
index += 1
newCTFTomoSeries.append(newCTFTomo)
newCTFTomoSeries.calculateDefocusUDeviation()
newCTFTomoSeries.calculateDefocusVDeviation()
if not (newCTFTomoSeries.getIsDefocusUDeviationInRange() and
newCTFTomoSeries.getIsDefocusVDeviationInRange()):
newCTFTomoSeries.setEnabled(False)
newCTFTomoSeries.write(properties=False)
outputSet.update(newCTFTomoSeries)
if self._createOutput:
self._defineOutputs(**{self._getOutputName(): outputSet})
self._defineSourceRelation(self._getInputTs(pointer=True),
outputSet)
self._createOutput = False
else:
outputSet.write()
self._store(outputSet)
outputSet.close()
self._store()
if self._tsDict.allDone():
self._coStep.setStatus(STATUS_NEW)
def __init__(self, **args):
ProtRefine3D.__init__(self, **args)
ProtClassify3D.__init__(self, **args)
self.numberOfCtfGroups = Integer(1)
self._lastIter = Integer(0)
class XmippProtProjMatch(ProtRefine3D, ProtClassify3D):
""" 3D reconstruction and classification using multireference projection matching"""
_label = 'projection matching'
FILENAMENUMBERLENGTH = 6
def __init__(self, **args):
ProtRefine3D.__init__(self, **args)
ProtClassify3D.__init__(self, **args)
self.numberOfCtfGroups = Integer(1)
self._lastIter = Integer(0)
def _initialize(self):
""" This function is mean to be called after the
working dir for the protocol have been set. (maybe after recovery from mapper)
"""
self._loadInputInfo()
# Setup the dictionary with filenames templates to
# be used by _getFileName
createFilenameTemplates(self)
# Load the values from several params generating a list
# of values per iteration or references
initializeLists(self)
def _loadInputInfo(self):
from pyworkflow.em.packages.xmipp3 import getImageLocation
reference = self.input3DReferences.get() # Input can be either a single volume or a set of volumes.
if isinstance(reference, Volume): # Treat the case of a single volume
self.referenceFileNames = [getImageLocation(reference)]
else:
self.referenceFileNames = [getImageLocation(vol) for vol in reference]
self.numberOfReferences = len(self.referenceFileNames)
self.resolSam = reference.getSamplingRate()
#--------------------------- DEFINE param functions --------------------------------------------
def _defineParams(self, form):
""" Since the form definition is very very large,
we have do it in a separated function.
"""
_defineProjectionMatchingParams(self, form)
#--------------------------- INSERT steps functions --------------------------------------------
def _insertAllSteps(self):
self._initialize()
# Insert initial steps
self._insertFunctionStep('convertInputStep')
self._insertFunctionStep('executeCtfGroupsStep')
# insertExecuteCtfGroupsStep(self)
# insertInitAngularReferenceFileStep(self)
self._insertFunctionStep('initAngularReferenceFileStep')
# Steps per iteration
self._insertItersSteps()
# Final steps
self._insertFunctionStep('createOutputStep')
def _insertItersSteps(self):
""" Insert several steps needed per iteration. """
for iterN in self.allIters():
dirsStep = self._insertFunctionStep('createIterDirsStep', iterN)
# Insert some steps per reference volume
projMatchSteps = []
for refN in self.allRefs():
# Mask the references in the iteration
insertMaskReferenceStep(self, iterN, refN, prerequisites=[dirsStep])
# Create the library of projections
insertAngularProjectLibraryStep(self, iterN, refN)
# Projection matching steps
projMatchStep = self._insertProjectionMatchingStep(iterN, refN)
projMatchSteps.append(projMatchStep)
# Select the reference that best fits each image
self._insertFunctionStep('assignImagesToReferencesStep', iterN, prerequisites=projMatchSteps)
insertAngularClassAverageStep(self, iterN, refN)
# Reconstruct each reference with new averages
for refN in self.allRefs():
# Create new class averages with images assigned
insertReconstructionStep(self, iterN, refN)
if self.doComputeResolution and self._doSplitReferenceImages[iterN]:
# Reconstruct two halves of the data
insertReconstructionStep(self, iterN, refN, 'Split1')
insertReconstructionStep(self, iterN, refN, 'Split2')
# Compute the resolution
insertComputeResolutionStep(self, iterN, refN)
insertFilterVolumeStep(self, iterN, refN)
# Calculate both angles and shifts devitations for this iteration
self._insertFunctionStep('calculateDeviationsStep', iterN)
def _insertProjectionMatchingStep(self, iterN, refN):
args = getProjectionMatchingArgs(self, iterN)
return self._insertFunctionStep('projectionMatchingStep', iterN, refN, args)
#--------------------------- STEPS functions --------------------------------------------
def convertInputStep(self):
""" Generated the input particles metadata expected
by projection matching. And copy the generated file to be
used as initial docfile for further iterations.
"""
from pyworkflow.em.packages.xmipp3 import writeSetOfParticles
writeSetOfParticles(self.inputParticles.get(), self.selFileName,
blockName=self.blockWithAllExpImages)
#copyFile(self.selFileName, self._getFileName('inputParticlesDoc'))
def createIterDirsStep(self, iterN):
""" Create the necessary directory for a given iteration. """
iterDirs = [self._getFileName(k, iter=iterN) for k in ['iterDir', 'projMatchDirs', 'libraryDirs']]
for d in iterDirs:
makePath(d)
return iterDirs
def volumeConvertStep(self, reconstructedFilteredVolume, maskedFileName):
runVolumeConvertStep(self, reconstructedFilteredVolume, maskedFileName)
def executeCtfGroupsStep(self, **kwargs):
runExecuteCtfGroupsStep(self, **kwargs)
def transformMaskStep(self, args, **kwargs):
runTransformMaskStep(self, args, **kwargs)
def angularProjectLibraryStep(self, iterN, refN, args, stepParams, **kwargs):
runAngularProjectLibraryStep(self, iterN, refN, args, stepParams, **kwargs)
def initAngularReferenceFileStep(self):
runInitAngularReferenceFileStep(self)
def projectionMatchingStep(self, iterN, refN, args):
runProjectionMatching(self, iterN, refN, args)
def assignImagesToReferencesStep(self, iterN):
runAssignImagesToReferences(self, iterN)
def cleanVolumeStep(self, vol1, vol2):
cleanPath(vol1, vol2)
def reconstructionStep(self, iterN, refN, program, method, args, suffix, mpi, threads, **kwargs):
runReconstructionStep(self, iterN, refN, program, method, args, suffix, mpi, threads, **kwargs)
def storeResolutionStep(self, resolIterMd, resolIterMaxMd, sampling):
runStoreResolutionStep(self, resolIterMd, resolIterMaxMd, sampling)
def calculateFscStep(self, iterN, refN, args, constantToAdd, **kwargs):
runCalculateFscStep(self, iterN, refN, args, constantToAdd, **kwargs)
def filterVolumeStep(self, iterN, refN, constantToAddToFiltration, **kwargs):
runFilterVolumeStep(self, iterN, refN, constantToAddToFiltration, **kwargs)
def createOutputStep(self):
runCreateOutputStep(self)
#--------------------------- INFO functions --------------------------------------------
def _validate(self):
errors = []
if self.doCTFCorrection and not self.doAutoCTFGroup and not exists(self.setOfDefocus.get()):
errors.append("Error: for non-automated ctf grouping, please provide a docfile!")
if self.numberOfMpi<=1:
errors.append("The number of MPI processes has to be larger than 1")
xDimImg = self.inputParticles.get().getXDim()
xDimVol, _, _ = self.input3DReferences.get().getDim()
if xDimImg != xDimVol:
errors.append("The dimensions of the volume(s) and particles must be equal!!!!")
return errors
def _citations(self):
cites = []
return cites
def _summary(self):
summary = []
return summary
def _methods(self):
return self._summary() # summary is quite explicit and serve as methods
#--------------------------- UTILS functions --------------------------------------------
def allIters(self):
""" Iterate over all iterations. """
for i in range(1, self.numberOfIterations.get()+1):
yield i
def allRefs(self):
""" Iterate over all references. """
for i in range(1, self.numberOfReferences+1):
yield i
def allCtfGroups(self):
""" Iterate over all CTF groups. """
for i in range(1, self.numberOfCtfGroups.get() + 1):
yield i
def itersFloatValues(self, attributeName, firstValue=-1):
""" Take the string of a given attribute and
create a list of floats that will be used by
the iteratioins. An special first value will be
added to the list for iteration 0.
"""
valuesStr = self.getAttributeValue(attributeName)
if valuesStr is None:
raise Exception('None value for attribute: %s' % attributeName)
return [firstValue] + getFloatListFromValues(valuesStr, length=self.numberOfIterations.get())
def itersBoolValues(self, attributeName, firstValue=False):
""" Take the string of a given attribute and
create a list of booleans that will be used by
the iteratioins. An special first value will be
added to the list for iteration 0.
"""
valuesStr = self.getAttributeValue(attributeName)
if valuesStr is None:
raise Exception('None value for attribute: %s' % attributeName)
return [firstValue] + getBoolListFromValues(valuesStr, length=self.numberOfIterations.get())
def itersStringValues(self, attributeName, firstValue='c1'):
""" Take the string of a given attribute and
create a list of strings that will be used by
the iteratioins. An special first value will be
added to the list for iteration 0.
"""
valuesStr = self.getAttributeValue(attributeName)
if valuesStr is None:
raise Exception('None value for attribute: %s' % attributeName)
return [firstValue] + getStringListFromValues(valuesStr, length=self.numberOfIterations.get())
def _getBlockFileName(self, blockName, blockNumber, filename, length=None):
l = length or self.FILENAMENUMBERLENGTH
return blockName + str(blockNumber).zfill(l) + '@' + filename
def _getExpImagesFileName(self, filename):
return self.blockWithAllExpImages + '@' + filename
def _getRefBlockFileName(self, ctfBlName, ctfBlNumber, refBlName, refBlNumber, filename, length=None):
l = length or self.FILENAMENUMBERLENGTH
return ctfBlName + str(ctfBlNumber).zfill(l) + '_' + refBlName + str(refBlNumber).zfill(l) + '@' + filename
def _getFourierMaxFrequencyOfInterest(self, iterN, refN):
""" Read the corresponding resolution metadata and return the
desired resolution.
"""
md = xmipp.MetaData(self._getFileName('resolutionXmdMax', iter=iterN, ref=refN))
return md.getValue(xmipp.MDL_RESOLUTION_FREQREAL, md.firstObject())
def calculateDeviationsStep(self, it):
""" Calculate both angles and shifts devitations for all iterations
"""
SL = xmipp.SymList()
mdIter = xmipp.MetaData()
#for it in self.allIters():
mdIter.clear()
SL.readSymmetryFile(self._symmetry[it])
md1 = xmipp.MetaData(self.docFileInputAngles[it])
md2 = xmipp.MetaData(self.docFileInputAngles[it-1])
#ignore disabled,
md1.removeDisabled()
md2.removeDisabled()
#first metadata file may not have shiftx and shifty
if not md2.containsLabel(xmipp.MDL_SHIFT_X):
md2.addLabel(xmipp.MDL_SHIFT_X)
md2.addLabel(xmipp.MDL_SHIFT_Y)
md2.fillConstant(xmipp.MDL_SHIFT_X,0.)
md2.fillConstant(xmipp.MDL_SHIFT_Y,0.)
oldLabels=[xmipp.MDL_ANGLE_ROT,
xmipp.MDL_ANGLE_TILT,
xmipp.MDL_ANGLE_PSI,
xmipp.MDL_SHIFT_X,
xmipp.MDL_SHIFT_Y]
newLabels=[xmipp.MDL_ANGLE_ROT2,
xmipp.MDL_ANGLE_TILT2,
xmipp.MDL_ANGLE_PSI2,
xmipp.MDL_SHIFT_X2,
xmipp.MDL_SHIFT_Y2]
md2.renameColumn(oldLabels,newLabels)
md2.addLabel(xmipp.MDL_SHIFT_X_DIFF)
md2.addLabel(xmipp.MDL_SHIFT_Y_DIFF)
md2.addLabel(xmipp.MDL_SHIFT_DIFF)
mdIter.join1(md1, md2, xmipp.MDL_IMAGE, xmipp.INNER_JOIN)
SL.computeDistance(mdIter,False,False,False)
xmipp.activateMathExtensions()
#operate in sqlite
shiftXLabel = xmipp.label2Str(xmipp.MDL_SHIFT_X)
shiftX2Label = xmipp.label2Str(xmipp.MDL_SHIFT_X2)
shiftXDiff = xmipp.label2Str(xmipp.MDL_SHIFT_X_DIFF)
shiftYLabel = xmipp.label2Str(xmipp.MDL_SHIFT_Y)
shiftY2Label = xmipp.label2Str(xmipp.MDL_SHIFT_Y2)
shiftYDiff = xmipp.label2Str(xmipp.MDL_SHIFT_Y_DIFF)
shiftDiff = xmipp.label2Str(xmipp.MDL_SHIFT_DIFF)
#timeStr = str(dtBegin)
operateString = shiftXDiff+"="+shiftXLabel+"-"+shiftX2Label
operateString += "," + shiftYDiff+"="+shiftYLabel+"-"+shiftY2Label
mdIter.operate(operateString)
operateString = shiftDiff+"=sqrt(" \
+shiftXDiff+"*"+shiftXDiff+"+" \
+shiftYDiff+"*"+shiftYDiff+");"
mdIter.operate(operateString)
iterFile = self._mdDevitationsFn(it)
mdIter.write(iterFile,xmipp.MD_APPEND)
self._setLastIter(it)
def _mdDevitationsFn(self, it):
mdFn = self._getPath('deviations.xmd')
return "iter_%03d@" % it + mdFn
def _setLastIter(self, iterN):
self._lastIter.set(iterN)
self._store(self._lastIter)
def getLastIter(self):
return self._lastIter.get()
def _createItemMatrix(self, item, row):
from pyworkflow.em.packages.xmipp3.convert import createItemMatrix
import pyworkflow.em as em
createItemMatrix(item, row, align=em.ALIGN_PROJ)
class ProtPKPDAbsorptionRate(ProtPKPDFitBase):
""" Estimation of the absorption rate for a non-intravenous route. The estimation is performed after estimating
the elimination rate. The experiment is determined by the\n
Protocol created by http://www.kinestatpharma.com\n.
See the theory at http://www.pharmpress.com/files/docs/Basic%20Pharmacokinetics%20sample.pdf"""
_label = 'absorption rate'
#--------------------------- DEFINE param functions --------------------------------------------
def _defineParams(self, form):
form.addSection('Input')
form.addParam('inputExperiment', params.PointerParam, label="Input experiment",
pointerClass='PKPDExperiment',
help='Select an experiment with samples')
form.addParam('protElimination', params.PointerParam, label="Elimination rate",
pointerClass='ProtPKPDEliminationRate',
help='Select an execution of a protocol estimating the elimination rate')
form.addParam("absorptionF", params.FloatParam, label="Absorption fraction", default=1,
help="Between 0 (=no absorption) and 1 (=full absorption)")
form.addParam('bounds', params.StringParam, label="Ka, V, [tlag] bounds", default="", expertLevel=LEVEL_ADVANCED,
help='Bounds for Ka (absorption constant), V (distribution volume) and optionally tlag.\nExample 1: (0,1e-3);(30,50);(0.1,0.5) -> Ka in (0,1e-3), V in (30,50) and tlag in (0.1,0.5)\n')
form.addParam('confidenceInterval', params.FloatParam, label="Confidence interval", default=95, expertLevel=LEVEL_ADVANCED,
help='Confidence interval for the fitted parameters')
form.addParam('includeTlag', params.BooleanParam, label="Include tlag", default=True, expertLevel=LEVEL_ADVANCED,
help='Calculate the delay between administration and absorption')
self.fitType=Integer() # Logarithmic fit
self.fitType.set(1)
def getListOfFormDependencies(self):
return [self.protElimination.get().getObjId(), self.absorptionF.get(), self.bounds.get(), self.confidenceInterval.get()]
#--------------------------- STEPS functions --------------------------------------------
def setupFromFormParameters(self):
self.eliminationFitting = self.readFitting(self.protElimination.get().outputFitting.fnFitting.get())
self.model.F = self.absorptionF.get()
def getXYvars(self):
self.varNameX = self.protElimination.get().predictor.get()
self.varNameY = self.protElimination.get().predicted.get()
def createModel(self):
return PKPDSimpleEVModel(self.includeTlag.get())
def prepareForSampleAnalysis(self, sampleName):
sample = self.experiment.samples[sampleName]
sampleFit = self.eliminationFitting.getSampleFit(sampleName)
sample.interpretDose()
self.model.D = sample.getDoseAt(0.0)
self.model.Dunits = sample.getDoseUnits()
if sampleFit == None:
print(" Cannot process %s because its elimination rate cannot be found\n\n"%sampleName)
return False
self.model.C0 = sampleFit.parameters[0]
self.model.C0units = self.eliminationFitting.modelParameterUnits[0]
self.model.Ke = sampleFit.parameters[1]
self.model.KeUnits = self.eliminationFitting.modelParameterUnits[1]
print("Concentration at t=0 = %f [%s]"%(self.model.C0,strUnit(self.model.C0units)))
print("Elimination rate = %f [%s]"%(self.model.Ke,strUnit(self.model.KeUnits)))
self.experiment.addParameterToSample(sampleName, "Ke", self.model.KeUnits, "Automatically estimated elimination rate", self.model.Ke)
return True
def postSampleAnalysis(self, sampleName):
xunits = self.experiment.getVarUnits(self.varNameX)
Cunits = self.experiment.getVarUnits(self.varNameY)
Ka = self.model.parameters[0]
Ke = self.model.Ke
tmax = math.log(Ka/Ke)/(Ka-Ke)
self.experiment.addParameterToSample(sampleName, "tmax", xunits, "Estimated time of the Maximum of the non-iv peak", tmax)
Cmax = self.model.forwardModel(self.model.parameters,x=[np.atleast_1d(np.array(tmax))])[0][0]
self.experiment.addParameterToSample(sampleName, "Cmax", Cunits, "Estimated concentration of the Maximum of the non-iv peak", Cmax)
print("tmax = %f [%s]"%(tmax,strUnit(xunits)))
print("Cmax = %f [%s]"%(Cmax,strUnit(Cunits)))
#--------------------------- INFO functions --------------------------------------------
def _summary(self):
msg=[]
msg.append("Non-compartmental analysis for the observations of the variable %s"%self.protElimination.get().predicted.get())
return msg
def _warnings(self):
msg = []
experiment = self.readExperiment(self.getInputExperiment().fnPKPD,show=False)
incorrectList = experiment.getNonBolusDoses()
if len(incorrectList)!=0:
msg.append("This protocol is meant only for bolus regimens. Check the doses for %s"%(','.join(incorrectList)))
return msg
class ProtPKPDAbsorptionRate(ProtPKPDFitBase):
""" Estimation of the absorption rate for a non-intravenous route. The estimation is performed after estimating
the elimination rate. The experiment is determined by the\n
Protocol created by http://www.kinestatpharma.com\n.
See the theory at http://www.pharmpress.com/files/docs/Basic%20Pharmacokinetics%20sample.pdf"""
_label = 'absorption rate'
#--------------------------- DEFINE param functions --------------------------------------------
def _defineParams(self, form):
form.addSection('Input')
form.addParam('inputExperiment',
params.PointerParam,
label="Input experiment",
pointerClass='PKPDExperiment',
help='Select an experiment with samples')
form.addParam(
'protElimination',
params.PointerParam,
label="Elimination rate",
pointerClass='ProtPKPDEliminationRate',
help=
'Select an execution of a protocol estimating the elimination rate'
)
form.addParam(
"absorptionF",
params.FloatParam,
label="Absorption fraction",
default=1,
help="Between 0 (=no absorption) and 1 (=full absorption)")
form.addParam(
'bounds',
params.StringParam,
label="Ka, V, [tlag] bounds",
default="",
expertLevel=LEVEL_ADVANCED,
help=
'Bounds for Ka (absorption constant), V (distribution volume) and optionally tlag.\nExample 1: (0,1e-3);(30,50);(0.1,0.5) -> Ka in (0,1e-3), V in (30,50) and tlag in (0.1,0.5)\n'
)
form.addParam('confidenceInterval',
params.FloatParam,
label="Confidence interval",
default=95,
expertLevel=LEVEL_ADVANCED,
help='Confidence interval for the fitted parameters')
form.addParam(
'includeTlag',
params.BooleanParam,
label="Include tlag",
default=True,
expertLevel=LEVEL_ADVANCED,
help='Calculate the delay between administration and absorption')
self.fitType = Integer() # Logarithmic fit
self.fitType.set(1)
def getListOfFormDependencies(self):
return [
self.protElimination.get().getObjId(),
self.absorptionF.get(),
self.bounds.get(),
self.confidenceInterval.get()
]
#--------------------------- STEPS functions --------------------------------------------
def setupFromFormParameters(self):
self.eliminationFitting = self.readFitting(
self.protElimination.get().outputFitting.fnFitting.get())
self.model.F = self.absorptionF.get()
def getXYvars(self):
self.varNameX = self.protElimination.get().predictor.get()
self.varNameY = self.protElimination.get().predicted.get()
def createModel(self):
return PKPDSimpleEVModel(self.includeTlag.get())
def prepareForSampleAnalysis(self, sampleName):
sample = self.experiment.samples[sampleName]
sampleFit = self.eliminationFitting.getSampleFit(sampleName)
sample.interpretDose()
self.model.D = sample.getDoseAt(0.0)
self.model.Dunits = sample.getDoseUnits()
if sampleFit == None:
print(
" Cannot process %s because its elimination rate cannot be found\n\n"
% sampleName)
return False
self.model.C0 = sampleFit.parameters[0]
self.model.C0units = self.eliminationFitting.modelParameterUnits[0]
self.model.Ke = sampleFit.parameters[1]
self.model.KeUnits = self.eliminationFitting.modelParameterUnits[1]
print("Concentration at t=0 = %f [%s]" %
(self.model.C0, strUnit(self.model.C0units)))
print("Elimination rate = %f [%s]" %
(self.model.Ke, strUnit(self.model.KeUnits)))
self.experiment.addParameterToSample(
sampleName, "Ke", self.model.KeUnits,
"Automatically estimated elimination rate", self.model.Ke)
return True
def postSampleAnalysis(self, sampleName):
xunits = self.experiment.getVarUnits(self.varNameX)
Cunits = self.experiment.getVarUnits(self.varNameY)
Ka = self.model.parameters[0]
Ke = self.model.Ke
tmax = math.log(Ka / Ke) / (Ka - Ke)
self.experiment.addParameterToSample(
sampleName, "tmax", xunits,
"Estimated time of the Maximum of the non-iv peak", tmax)
Cmax = self.model.forwardModel(self.model.parameters,
x=[np.atleast_1d(np.array(tmax))])[0][0]
self.experiment.addParameterToSample(
sampleName, "Cmax", Cunits,
"Estimated concentration of the Maximum of the non-iv peak", Cmax)
print("tmax = %f [%s]" % (tmax, strUnit(xunits)))
print("Cmax = %f [%s]" % (Cmax, strUnit(Cunits)))
#--------------------------- INFO functions --------------------------------------------
def _summary(self):
msg = []
msg.append(
"Non-compartmental analysis for the observations of the variable %s"
% self.protElimination.get().predicted.get())
return msg
def _warnings(self):
msg = []
experiment = self.readExperiment(self.getInputExperiment().fnPKPD,
show=False)
incorrectList = experiment.getNonBolusDoses()
if len(incorrectList) != 0:
msg.append(
"This protocol is meant only for bolus regimens. Check the doses for %s"
% (','.join(incorrectList)))
return msg
def __init__(self, **args):
EMObject.__init__(self, **args)
self.type = Integer()
self.type.set(self.INTERP)
self.lungParams = None
def operateStep(self):
outputSet = self.inputSet.get().create(self._getPath())
if self.operation.get() == 0:
# Filter columns
referenceValue = self.filterValue.get()
value = self.inputSet.get().getFirstItem().getAttributeValue(
self.filterColumn.get())
if isinstance(value, float):
referenceValue = float(referenceValue)
elif isinstance(value, int):
referenceValue = int(referenceValue)
filterOp = self.filterOp.get()
if filterOp == 6:
referenceValue2 = self.filterValue2.get()
if isinstance(value, float):
referenceValue2 = float(referenceValue2)
elif isinstance(value, int):
referenceValue2 = int(referenceValue2)
for oldEntry in self.inputSet.get():
value = oldEntry.getAttributeValue(self.filterColumn.get())
if isinstance(value, Float):
value = float(value)
elif isinstance(value, Integer):
value = int(value)
add = False
if filterOp == 0: # ==
add = value == referenceValue
elif filterOp == 1: # >
add = value > referenceValue
elif filterOp == 2: # >=
add = value > referenceValue
elif filterOp == 3: # <
add = value < referenceValue
elif filterOp == 4: # <=
add = value <= referenceValue
elif filterOp == 5: # !=
add = value != referenceValue
elif filterOp == 6: # between
add = (value <= referenceValue
and value >= referenceValue2)
elif filterOp == 7: #startswith
add = value.startswith(referenceValue)
elif filterOp == 8: # endswith
add = value.endswith(referenceValue)
elif filterOp == 9: # contains
add = referenceValue in value
elif filterOp == 10: # does not startswith
add = not (value.startswith(referenceValue))
elif filterOp == 11: # does not endswith
add = not (value.endswith(referenceValue))
elif filterOp == 12: # does not contains
add = not (referenceValue in value)
if add:
newEntry = self.inputSet.get().ITEM_TYPE()
newEntry.copy(oldEntry)
outputSet.append(newEntry)
elif self.operation.get() == 1:
# Keep columns
keepList = [x.strip() for x in self.keepColumns.get().split()]
ignoreList = []
for name, _ in self.inputSet.get().getFirstItem().getAttributes():
if not name in keepList:
ignoreList.append(name)
for oldEntry in self.inputSet.get():
newEntry = self.inputSet.get().ITEM_TYPE()
newEntry.copy(oldEntry, ignoreAttrs=ignoreList)
outputSet.append(newEntry)
elif self.operation.get() == 2:
# Unique
found = {}
for oldEntry in self.inputSet.get():
value = oldEntry.getAttributeValue(self.filterColumn.get())
if not value in found:
found[value] = True
newEntry = self.inputSet.get().ITEM_TYPE()
newEntry.copy(oldEntry)
outputSet.append(newEntry)
elif self.operation.get() >= 3 and self.operation.get() <= 6:
# Top N, Bottom N,Top %, Bottom %
V = []
for entry in self.inputSet.get():
V.append(entry.getAttributeValue(self.filterColumn.get()))
V.sort()
op = self.operation.get()
if op == 3:
threshold = V[-self.N.get()]
elif op == 4:
threshold = V[self.N.get() - 1]
elif op == 5:
threshold = V[-ceil(self.percentile.get() / 100 * len(V))]
elif op == 6:
threshold = V[ceil(self.percentile.get() / 100 * len(V)) - 1]
for oldEntry in self.inputSet.get():
value = oldEntry.getAttributeValue(self.filterColumn.get())
if (op == 3 or op == 5) and value >= threshold:
newEntry = self.inputSet.get().ITEM_TYPE()
newEntry.copy(oldEntry)
outputSet.append(newEntry)
elif (op == 4 or op == 6) and value <= threshold:
newEntry = self.inputSet.get().ITEM_TYPE()
newEntry.copy(oldEntry)
outputSet.append(newEntry)
elif self.operation.get() == 7:
# Count the number of entries that are the same
count = {}
for oldEntry in self.inputSet.get():
value = oldEntry.getAttributeValue(self.filterColumn.get())
if not value in count:
count[value] = 0
count[value] += 1
for oldEntry in self.inputSet.get():
value = oldEntry.getAttributeValue(self.filterColumn.get())
newEntry = self.inputSet.get().ITEM_TYPE()
newEntry.copy(oldEntry)
newEntry.count = Integer(count[value])
outputSet.append(newEntry)
elif self.operation.get() == 8:
# Intersection between 2 Sets
secondSet = {}
for entry in self.secondSet.get():
value = entry.getAttributeValue(self.filterColumn.get())
if not value in secondSet:
secondSet[value] = True
for oldEntry in self.inputSet.get():
value = oldEntry.getAttributeValue(self.filterColumn.get())
if value in secondSet:
newEntry = self.inputSet.get().ITEM_TYPE()
newEntry.copy(oldEntry)
outputSet.append(newEntry)
elif self.operation.get() == 9:
# Sort
V = []
newEntries = []
for entry in self.inputSet.get():
V.append(entry.getAttributeValue(self.filterColumn.get()))
newEntry = self.inputSet.get().ITEM_TYPE()
newEntry.copy(entry)
newEntry.cleanObjId()
newEntries.append(newEntry)
if self.direction.get() == 0:
idxSort = np.argsort(V)
else:
idxSort = np.argsort(-np.asarray(V))
for idx in idxSort:
outputSet.append(newEntries[idx])
if len(outputSet) > 0:
self._defineOutputs(output=outputSet)
self._defineSourceRelation(self.inputSet, outputSet)
def __init__(self, **args):
EMObject.__init__(self, **args)
self.type = Integer()
self.type.set(self.SAT)
self.substanceParams = None
