Source code for xmipp3.protocols.protocol_movie_dose_analysis

# **************************************************************************
# *
# * Authors:     Carlos Oscar S. Sorzano (
# *              Daniel Marchán Torres (
# *
# * Unidad de  Bioinformatica of Centro Nacional de Biotecnologia , CSIC
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# * This program is free software; you can redistribute it and/or modify
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# * the Free Software Foundation; either version 2 of the License, or
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# * This program is distributed in the hope that it will be useful,
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# * GNU General Public License for more details.
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import matplotlib.pyplot as plt
import numpy as np
import os
from pyworkflow import VERSION_3_0, NEW
from pyworkflow.object import Set
from pyworkflow.protocol import STEPS_PARALLEL
from pyworkflow.protocol.params import (PointerParam, IntParam, FloatParam, LEVEL_ADVANCED)
from import Message
import pyworkflow.protocol.constants as cons
from pwem.emlib.image import ImageHandler
from pwem.objects import SetOfMovies
from pwem.protocols import ProtProcessMovies
from xmipp3.convert import getScipionObj
import statistics as stat
from pyworkflow import BETA, UPDATED, NEW, PROD

OUTPUT_ACCEPTED = 'outputMovies'
OUTPUT_DISCARDED = 'outputMoviesDiscarded'

[docs]class XmippProtMovieDoseAnalysis(ProtProcessMovies): """ Protocol for the dose analysis """ # FIXME: WITH .mrcs IT DOES NOT FILL THE LABELS _devStatus = NEW _label = 'movie dose analysis' _lastUpdateVersion = VERSION_3_0 _devStatus = NEW _possibleOutputs = { OUTPUT_ACCEPTED: SetOfMovies, OUTPUT_DISCARDED: SetOfMovies } finished = False stats = {} estimatedIds = [] meanDoseList = [] medianDoseTemporal = [] medianDifferences = [] meanGlobal = 0 usingExperimental = False def __init__(self, **args): ProtProcessMovies.__init__(self, **args) # -------------------------- DEFINE param functions ---------------------- def _defineParams(self, form): form.addSection(label=Message.LABEL_INPUT) form.addParam('inputMovies', PointerParam, pointerClass='SetOfMovies', label=Message.LABEL_INPUT_MOVS, help='Select one or several movies. A dose analysis ' 'be calculated for each one of them.') form.addParam('percentage_threshold', FloatParam, default=5, label="Maximum percentage difference (%)", help='By default, a difference of 5% against the median dose is used to ' 'assume that the dose has an incorrect value.') form.addParam('n_samples', IntParam, default=20, label="Samples to estimate the median dose", expertLevel=LEVEL_ADVANCED, help='By default, 20 movies are used to ' 'compute the global median.') form.addParam('window', IntParam, default=50, label="Window step (movies)", expertLevel=LEVEL_ADVANCED, help='By default, every 50 movies (window=50) we ' 'compute the percentage of incorrect dose analysis to check if there ' 'is any anomally in the dose.') form.addParam('percentage_window', FloatParam, default=30, label="Windows maximum faulty percentage (%)", expertLevel=LEVEL_ADVANCED, help='By default, if 30% of the movies are discarded' 'it assume that the dose has an incorrect value that endures in time.') # -------------------------- STEPS functions ------------------------------
[docs] def createOutputStep(self): self._closeOutputSet()
def _insertAllSteps(self): # Build the list of all processMovieStep ids by # inserting each of the steps for each movie self.insertedDict = {} self.samplingRate = self.inputMovies.get().getSamplingRate() # Initial steps self.initializeParams() # Gain and Dark conversion step self.convertCIStep = [] convertStepId = self._insertFunctionStep('_convertInputStep', prerequisites=[]) self.convertCIStep.append(convertStepId) self._insertFunctionStep('createOutputStep', prerequisites=[], wait=True)
[docs] def initializeParams(self): self.framesRange = self.inputMovies.get().getFramesRange() self.dims = self.inputMovies.get().getFirstItem().getDim() self.pixelSize = self.inputMovies.get().getFirstItem().getSamplingRate() dosePerFrame = self.inputMovies.get().getFirstItem().getAcquisition().getDosePerFrame() if dosePerFrame != 0 and dosePerFrame != None: self.dosePerFrame = dosePerFrame else: self.usingExperimental = True
def _processMovie(self, movie): movieId = movie.getObjId() self.estimatedIds.append(movieId) stats = self.estimatePoissonCount(movie) self.stats[movieId] = stats fnMonitorSummary = self._getPath("summaryForMonitor.txt") if not os.path.exists(fnMonitorSummary): fhMonitorSummary = open(fnMonitorSummary, "w") else: fhMonitorSummary = open(fnMonitorSummary, "a") fhMonitorSummary.write("movie_%06d_poisson_count: mean=%f stdev=%f [min=%f,max=%f]\n" % (movieId, stats['mean'], stats['std'], stats['min'], stats['max'])) fhMonitorSummary.close()
[docs] def estimatePoissonCount(self, movie): mean_frames = [] n = movie.getNumberOfFrames() frames = [1, n/2, n] for frame in frames: frame_image = ImageHandler().read("%d@%s" % (frame, movie.getFileName())).getData() mean_dose_per_pixel = np.mean(frame_image) mean_dose_per_angstrom2 = mean_dose_per_pixel/ self.samplingRate**2 mean_frames.append(mean_dose_per_angstrom2) stats = computeStats(np.asarray(mean_frames)) self.meanDoseList.append(stats['mean']) return stats
def _loadOutputSet(self, SetClass, baseName): """ Load the output set if it exists or create a new one. fixSampling: correct the output sampling rate if binning was used, except for the case when the original movies are kept and shifts refers to that one. """ setFile = self._getPath(baseName) if os.path.exists(setFile) and os.path.getsize(setFile) > 0: outputSet = SetClass(filename=setFile) outputSet.loadAllProperties() outputSet.enableAppend() else: outputSet = SetClass(filename=setFile) outputSet.setStreamState(outputSet.STREAM_OPEN) inputMovies = self.inputMovies.get() outputSet.copyInfo(inputMovies) return outputSet def _checkNewOutput(self): if getattr(self, 'finished', False): return # If continue then we need to define again some parameters if self.isContinued(): self.initializeParams() if len(self.meanDoseList) >= self.n_samples.get() and not hasattr(self, 'mu'): medianDoseExperimental = np.median(self.meanDoseList) if hasattr(self, 'dosePerFrame'): refDose = self.dosePerFrame diff = abs((medianDoseExperimental/refDose)-1) * 100 if diff < THRESHOLD: = refDose else: = medianDoseExperimental self.usingExperimental = True"The given dose per frame %f does not match with the experimental one %f" " having a difference of %f percent. " "Therefore, using the experimental as global median." %(refDose, medianDoseExperimental, diff)) else: = medianDoseExperimental if hasattr(self, 'mu'): # Load previously done items (from text file) doneList = self._readDoneList() # Check for newly done items newDone = [m for m in self.listOfMovies if m.getObjId() not in doneList and self._isMovieDone(m)] # Update the file with the newly done movies # or exit from the function if no new done movies self.debug('_checkNewOutput: ') self.debug(' listOfMovies: %s, doneList: %s, newDone: %s' % (len(self.listOfMovies), len(doneList), len(newDone))) allDone = len(doneList) + len(newDone) # We have finished when there is no more input movies # (stream closed) and the number of processed movies is # equal to the number of inputs self.finished = self.streamClosed and allDone == len(self.listOfMovies) streamMode = Set.STREAM_CLOSED if self.finished \ else Set.STREAM_OPEN if newDone: self._writeDoneList(newDone) elif not self.finished: # If we are not finished and no new output have been produced # it does not make sense to proceed and updated the outputs # so we exit from the function here return self.debug(' finished: %s ' % self.finished) self.debug(' self.streamClosed (%s) AND' % self.streamClosed) self.debug(' allDone (%s) == len(self.listOfMovies (%s)' % (allDone, len(self.listOfMovies))) self.debug(' streamMode: %s' % streamMode) # Find the acceptance intervals lower, upper = self.getLimitIntervals()"Acceptance interval: ({lower:.4f}, {upper:.4f})")"Global median: ({})") acceptedMovies = [] discardedMovies = [] for movie in newDone: newMovie = movie.clone() newMovie.setFramesRange(self.framesRange) movieId = newMovie.getObjId() stats = self.stats[movieId] mean = stats['mean'] std = stats['std'] minDose = stats['min'] maxDose = stats['max'] diff_median = ((mean/*100 setAttribute(newMovie, '_DIFF_TO_DOSE_PER_ANGSTROM2', abs(diff_median)) setAttribute(newMovie, '_MEAN_DOSE_PER_ANGSTROM2', mean) setAttribute(newMovie, '_STD_DOSE_PER_ANGSTROM2', std) setAttribute(newMovie, '_MIN_DOSE_PER_FRAME', minDose) setAttribute(newMovie, '_MAX_DOSE_PER_FRAME', maxDose) self.medianDifferences.append(diff_median) self.medianDoseTemporal.append(mean)'Movie with id %d has a mean dose per frame of %f and a diff of %f percent' %(movie.getObjId(), mean, diff_median)) if lower <= mean <= upper:'accepted') acceptedMovies.append(newMovie) else:'discarded') discardedMovies.append(newMovie) if len(self.medianDifferences) % self.window.get() == 0: if self.usingExperimental: # Update the median global = np.median(self.meanDoseList)'Updating median global to %f' windowList = self.medianDoseTemporal[-self.window.get():] percentage = (1 - (len([dose for dose in windowList if lower < dose < upper]) / len(windowList)))*100'The faulty percentage of this window is %f' %percentage) if percentage > self.percentage_window.get(): with open(self._getExtraPath('WARNING.TXT'), 'a') as f:'Percentage of wrong dose in a window surpass the threshold: {}% > {}%' .format(percentage, self.percentage_window.get())) f.write('Percentage of wrong dose in a window surpass the threshold: {}% > {}% \n' .format(percentage, self.percentage_window.get())) f.close() if len(acceptedMovies)>0: moviesSet = self._loadOutputSet(SetOfMovies, 'movies.sqlite') for movie in acceptedMovies: moviesSet.append(movie) self._updateOutputSet(OUTPUT_ACCEPTED, moviesSet, streamMode) if len(discardedMovies)>0: moviesSetDiscarded = self._loadOutputSet(SetOfMovies, 'movies_discarded.sqlite') for movie in discardedMovies: moviesSetDiscarded.append(movie) self._updateOutputSet(OUTPUT_DISCARDED, moviesSetDiscarded, streamMode) plotDoseAnalysis(self.getDosePlot(), self.meanDoseList,, lower, upper) plotDoseAnalysisDiff(self.getDoseDiffPlot(), self.medianDifferences) if self.finished: # Unlock createOutputStep if finished all jobs outputStep = self._getFirstJoinStep() if outputStep and outputStep.isWaiting(): outputStep.setStatus(cons.STATUS_NEW) def _updateOutputSet(self, outputName, outputSet, state=Set.STREAM_OPEN): outputSet.setStreamState(state) if self.hasAttribute(outputName): outputSet.write() # Write to commit changes outputAttr = getattr(self, outputName) # Copy the properties to the object contained in the protocol outputAttr.copy(outputSet, copyId=False) # Persist changes self._store(outputAttr) else: # Here the defineOutputs function will call the write() method self._defineOutputs(**{outputName: outputSet}) self._store(outputSet) # Close set databaset to avoid locking it outputSet.close() # ------------------------- UTILS functions --------------------------------
[docs] def getLimitIntervals(self): """ Funtion to obtain the acceptance interval limits.""" lower = - * (self.percentage_threshold.get()/100) upper = + * (self.percentage_threshold.get()/100) return lower, upper
[docs] def getDosePlot(self): return self._getExtraPath('dose_analysis_plot.png')
[docs] def getDoseDiffPlot(self): return self._getExtraPath('dose_analysis_diff_plot.png')
# --------------------------- INFO functions ------------------------------- def _validate(self): errors = [] if errors: errors.append("") return errors def _summary(self): fnSummary = self._getPath("summary.txt") if not os.path.exists(fnSummary): summary = ["No summary information yet."] else: fhSummary = open(fnSummary, "r") summary = [] for line in fhSummary.readlines(): summary.append(line.rstrip()) fhSummary.close() return summary
# --------------------- WORKERS --------------------------------------
[docs]def setAttribute(obj, label, value): if value is None: return setattr(obj, label, getScipionObj(value))
[docs]def computeStats(mean_frames): mean = np.mean(mean_frames) std = np.std(mean_frames) max_dose = np.max(mean_frames) min_dose = np.min(mean_frames) stats = {'mean': mean, 'std': std, 'max': max_dose, 'min': min_dose, } return stats
[docs]def plotDoseAnalysis(filename, doseValues, medianGlobal, lower, upper): x = np.arange(start=1, stop=len(doseValues)+1, step=1) plt.figure() plt.scatter(x, doseValues,s=10) plt.axhline(y=medianGlobal, color='r', linestyle='-', label='Median dose') plt.axhline(y=upper, color='b', linestyle='-.', label='Upper limit dose') plt.axhline(y=lower, color='g', linestyle='-.', label='Lower limit dose') plt.xlabel("Movies ID") plt.ylabel("Dose (electrons impacts per angstrom**2 )") plt.title('Dose vs time') plt.legend() plt.grid() plt.savefig(filename)
[docs]def plotDoseAnalysisDiff(filename, medianDifferences): medianDiff = np.median(medianDifferences) x = np.arange(start=1+1, stop=len(medianDifferences)+2, step=1) plt.figure() plt.scatter(x, medianDifferences, s=10) plt.axhline(y=medianDiff, color='r', linestyle='-', label='Median dose difference') plt.xlabel("Movies ID") plt.ylabel("Dose differences (%)") plt.title('Dose differences with respect to the global median vs time') plt.legend() plt.grid() plt.savefig(filename)