Python NumericUtils.getMeanAndDeviation方法代码示例

# 需要导入模块: from pyaid.number.NumericUtils import NumericUtils [as 别名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import getMeanAndDeviation [as 别名]
    def _postAnalyze(self):
        """_postAnalyze doc..."""
        self._csv.save()

        meanDiff = NumericUtils.getMeanAndDeviation(self._diffs)
        self.logger.write('Rotation %s' % meanDiff.label)

        self._paths.append(self._makePlot(
            label='Rotation Differences',
            data=self._diffs,
            histRange=[-180, 180]))

        self._paths.append(self._makePlot(
            label='Rotation Differences',
            data=self._diffs,
            histRange=[-180, 180],
            isLog=True))

        circs    = []
        circsUnc = []
        diffs    = []
        diffsUnc = []
        entries  = self.owner.getStage('lengthWidth').entries

        for entry in entries:
            track = entry['track']
            if track.uid not in self.deviations:
                # Skip those tracks with no deviation value (solo tracks)
                continue

            diffDeg = self.deviations[track.uid]
            diffs.append(abs(diffDeg.value))
            diffsUnc.append(diffDeg.uncertainty)

            # Compute the circularity of the track from its aspect ratio. If
            # the aspect is less than or equal to 1.0 use the aspect value
            # directly. However, if the value is greater than one, take the
            # reciprocal so that large and small aspect ratios can be compared
            # equally.
            aspect = entry['aspect']
            if aspect.value > 1.0:
                a = 1.0/aspect.raw
                aspect = NumericUtils.toValueUncertainty(a, a*(aspect.rawUncertainty/aspect.raw))

            circs.append(abs(aspect.value - 1.0))
            circsUnc.append(aspect.uncertainty)

        pl = self.plot
        self.owner.createFigure('circular')
        pl.errorbar(x=circs, y=diffs, xerr=circsUnc, yerr=diffsUnc, fmt='.')
        pl.xlabel('Aspect Circularity')
        pl.ylabel('Rotation Deviation')
        pl.title('Rotation Deviation and Aspect Circularity')
        self._paths.append(self.owner.saveFigure('circular'))

        self.mergePdfs(self._paths)
        self._paths = []

# 需要导入模块: from pyaid.number.NumericUtils import NumericUtils [as 别名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import getMeanAndDeviation [as 别名]
    def _processAspectRatios(self):
        """_processAspectRatios doc..."""
        aspects    = []
        pesAspects = []
        manAspects = []

        for entry in self.entries:
            value = entry['aspect'].value
            aspects.append(value)
            if entry['track'].pes:
                pesAspects.append(value)
            else:
                manAspects.append(value)

        self.logger.write('%s\nASPECT RATIO' % ('='*80))
        self.logger.write('Total: %s' %
            NumericUtils.getMeanAndDeviation(aspects).label)
        self.logger.write('Pes: %s' %
            NumericUtils.getMeanAndDeviation(pesAspects).label)
        self.logger.write('Manus: %s' %
            NumericUtils.getMeanAndDeviation(manAspects).label)

        h = Histogram(data=aspects, color='green')
        h.title = 'Aspect Ratios'
        h.yLabel = 'Count'
        h.xLabel = 'Aspect Ratio (width/length)'
        self._paths.append(h.save(self.getTempFilePath(extension='pdf')))

        h = Histogram(data=pesAspects, color='green')
        h.title = 'Aspect Ratios (Pes)'
        h.yLabel = 'Count'
        h.xLabel = 'Aspect Ratio (width/length)'
        self._paths.append(h.save(self.getTempFilePath(extension='pdf')))

        h = Histogram(data=manAspects, color='green')
        h.title = 'Aspect Ratios (Manus)'
        h.yLabel = 'Count'
        h.xLabel = 'Aspect Ratio (width/length)'
        self._paths.append(h.save(self.getTempFilePath(extension='pdf')))

# 需要导入模块: from pyaid.number.NumericUtils import NumericUtils [as 别名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import getMeanAndDeviation [as 别名]
    def _process(self):
        """_processDeviations doc..."""
        errors  = []

        for entry in self.entries:
            if 'fractional' in entry:
                errors.append(entry['fractional'])

        res = NumericUtils.getMeanAndDeviation(errors)
        self.logger.write('Fractional Stride Error %s' % res.label)

        label = 'Fractional Stride Errors'
        self._paths.append(self._makePlot(
            label=label,
            data=errors,
            histRange=(-1.0, 1.0) ))
        self._paths.append(self._makePlot(
            label=label,
            data=errors,
            isLog=True,
            histRange=(-1.0, 1.0) ))

        # noinspection PyUnresolvedReferences
        d = np.absolute(np.array(errors))
        self._paths.append(self._makePlot(
            label='Absolute %s' % label,
            data=d, histRange=(0.0, 1.0) ))
        self._paths.append(self._makePlot(
            label='Absolute %s' % label,
            data=d,
            isLog=True,
            histRange=(0.0, 1.0) ))

        highDeviationCount = 0

        for entry in self.entries:
            if 'measured' not in entry:
                # Skip tracks that have no measured stride value for comparison
                continue

            if entry['deviation'] > 2.0:
                highDeviationCount += 1

            track = entry['track']
            delta = NumericUtils.roundToSigFigs(100.0*abs(entry['delta']), 3)

            self._csv.addRow({
                'fingerprint':track.fingerprint,
                'uid':track.uid,
                'measured':entry['measured'].label,
                'entered':entry['entered'].label,
                'dev':entry['deviation'],
                'delta':delta})

        if not self._csv.save():
            self.logger.write(
                '[ERROR]: Failed to save CSV file %s' % self._csv.path)

        percentage = NumericUtils.roundToOrder(
            100.0*float(highDeviationCount)/float(len(self.entries)), -2)
        self.logger.write(
            '%s significant %s (%s%%)' % (
                highDeviationCount,
                label.lower(),
                percentage))

        if percentage > (100.0 - 95.45):
            self.logger.write(
                '[WARNING]: Large deviation count exceeds normal ' +
                'distribution expectations.')

# 需要导入模块: from pyaid.number.NumericUtils import NumericUtils [as 别名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import getMeanAndDeviation [as 别名]
    def _addQuartileEntry(self, label, trackway, data):
        if not data or len(data) < 4:
            return

        if label not in self._quartileStats:
            csv = CsvWriter()
            csv.path = self.getPath(
                '%s-Quartiles.csv' % label.replace(' ', '-'),
                isFile=True)
            csv.autoIndexFieldName = 'Index'
            csv.addFields(
                ('name', 'Name'),

                ('normality', 'Normality'),
                ('unweightedNormality', 'Unweighted Normality'),

                ('unweightedLowerBound', 'Unweighted Lower Bound'),
                ('unweightedLowerQuart', 'Unweighted Lower Quartile'),
                ('unweightedMedian',     'Unweighted Median'),
                ('unweightedUpperQuart', 'Unweighted Upper Quartile'),
                ('unweightedUpperBound', 'Unweighted Upper Bound'),

                ('lowerBound', 'Lower Bound'),
                ('lowerQuart', 'Lower Quartile'),
                ('median',     'Median'),
                ('upperQuart', 'Upper Quartile'),
                ('upperBound', 'Upper Bound'),

                ('diffLowerBound', 'Diff Lower Bound'),
                ('diffLowerQuart', 'Diff Lower Quartile'),
                ('diffMedian',     'Diff Median'),
                ('diffUpperQuart', 'Diff Upper Quartile'),
                ('diffUpperBound', 'Diff Upper Bound') )
            self._quartileStats[label] = csv

        csv = self._quartileStats[label]
        dd = mstats.density.Distribution(data)
        unweighted = mstats.density.boundaries.unweighted_two(dd)
        weighted = mstats.density.boundaries.weighted_two(dd)

        #-----------------------------------------------------------------------
        # PLOT DENSITY
        #   Create a density plot for each value
        p = MultiScatterPlot(
            title='%s %s Density Distribution' % (trackway.name, label),
            xLabel=label,
            yLabel='Probability (AU)')

        x_values = mstats.density.ops.adaptive_range(dd, 10.0)
        y_values = dd.probabilities_at(x_values=x_values)

        p.addPlotSeries(
            line=True,
            markers=False,
            label='Weighted',
            color='blue',
            data=zip(x_values, y_values)
        )

        temp = mstats.density.create_distribution(
            dd.naked_measurement_values(raw=True)
        )
        x_values = mstats.density.ops.adaptive_range(dd, 10.0)
        y_values = dd.probabilities_at(x_values=x_values)

        p.addPlotSeries(
            line=True,
            markers=False,
            label='Unweighted',
            color='red',
            data=zip(x_values, y_values)
        )

        if label not in self._densityPlots:
            self._densityPlots[label] = []
        self._densityPlots[label].append(
            p.save(self.getTempFilePath(extension='pdf')))

        #-----------------------------------------------------------------------
        # NORMALITY
        #       Calculate the normality of the weighted and unweighted
        #       distributions as a test against how well they conform to
        #       the Normal distribution calculated from the unweighted data.
        #
        #       The unweighted Normality test uses a basic bandwidth detection
        #       algorithm to create a uniform Gaussian kernel to populate the
        #       DensityDistribution. It is effectively a density kernel
        #       estimation, but is aggressive in selecting the bandwidth to
        #       prevent over-smoothing multi-modal distributions.
        if len(data) < 8:
            normality = -1.0
            unweightedNormality = -1.0
        else:
            result = NumericUtils.getMeanAndDeviation(data)
            mean = result.raw
            std = result.rawUncertainty
            normality = mstats.density.ops.overlap(
                dd,
                mstats.density.create_distribution([mean], [std])
            )
#.........这里部分代码省略.........

# 需要导入模块: from pyaid.number.NumericUtils import NumericUtils [as 别名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import getMeanAndDeviation [as 别名]
    def _process(
            self, label, widthKey, lengthKey, trackDeviations,
            absoluteOnly =False
    ):
        """_process doc..."""
        pl  = self.plot
        ws  = []
        ls  = []
        w2D = []
        l2D = []

        for entry in self.entries:
            if widthKey in entry:
                ws.append(entry[widthKey])
                if lengthKey in entry:
                    w2D.append(entry[widthKey])

            if lengthKey in entry:
                ls.append(entry[lengthKey])
                if widthKey in entry:
                    l2D.append(entry[lengthKey])

        plotList = [
            ('widths', ws, 'Width', 'b'),
            ('lengths', ls, 'Length', 'r')]

        wRes = NumericUtils.getMeanAndDeviation(ws)
        self.logger.write('Width %ss' % wRes.label)
        lRes = NumericUtils.getMeanAndDeviation(ls)
        self.logger.write('Length %ss' % lRes.label)

        for data in plotList:
            if not absoluteOnly:
                d = data[1]
                self._paths.append(
                    self._makePlot(
                        label, d, data,
                        histRange=(-1.0, 1.0)))
                self._paths.append(
                    self._makePlot(
                        label, d, data,
                        isLog=True,
                        histRange=(-1.0, 1.0)))

            # noinspection PyUnresolvedReferences
            d = np.absolute(np.array(data[1]))
            self._paths.append(
                self._makePlot(
                    'Absolute ' + label, d, data,
                    histRange=(0.0, 1.0)))
            self._paths.append(
                self._makePlot(
                    'Absolute ' + label, d, data,
                    isLog=True,
                    histRange=(0.0, 1.0)))

        self.owner.createFigure('twoD')
        pl.hist2d(w2D, l2D, bins=20, range=([-1, 1], [-1, 1]))
        pl.title('2D %s Distribution' % label)
        pl.xlabel('Width %s' % label)
        pl.ylabel('Length %s' % label)
        pl.xlim(-1.0, 1.0)
        pl.ylim(-1.0, 1.0)
        path = self.getTempPath(
            '%s.pdf' % StringUtils.getRandomString(16),
            isFile=True)
        self.owner.saveFigure('twoD', path)
        self._paths.append(path)

        csv = CsvWriter()
        csv.path = self.getPath(
            '%s-Deviations.csv' % label.replace(' ', '-'),
            isFile=True)
        csv.addFields(
            ('uid', 'UID'),
            ('fingerprint', 'Fingerprint'),
            ('wSigma', 'Width Deviation'),
            ('lSigma', 'Length Deviation') )

        count = 0
        for entry in self.entries:
            widthDevSigma  = NumericUtils.roundToOrder(
                abs(entry.get(widthKey, 0.0)/wRes.uncertainty), -2)
            lengthDevSigma = NumericUtils.roundToOrder(
                abs(entry.get(lengthKey, 0.0)/lRes.uncertainty), -1)
            if widthDevSigma > 2.0 or lengthDevSigma > 2.0:
                count += 1
                track = entry['track']
                data = dict(
                    wSigma=widthDevSigma,
                    lSigma=lengthDevSigma)

                if trackDeviations is not None:
                    trackDeviations[track.uid] = data

                csv.createRow(
                    uid=track.uid,
                    fingerprint=track.fingerprint,
                    **data)

#.........这里部分代码省略.........

# 需要导入模块: from pyaid.number.NumericUtils import NumericUtils [as 别名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import getMeanAndDeviation [as 别名]
    def _postAnalyze(self):
        h = Histogram(
            data=self._uncs,
            binCount=80,
            xLimits=(0, max(*self._uncs)),
            color="r",
            title="Distribution of Rotational Uncertainties",
            xLabel="Uncertainty Value (degrees)",
            yLabel="Frequency",
        )
        p1 = h.save(self.getTempFilePath(extension="pdf"))

        h.isLog = True
        h.title += " (log)"
        p2 = h.save(self.getTempFilePath(extension="pdf"))

        self.mergePdfs([p1, p2], self.getPath("Rotational-Uncertainty-Distribution.pdf"))

        average = NumericUtils.getMeanAndDeviation(self._uncs)
        self.logger.write("Average rotational uncertainty: %s" % average.label)

        # -------------------------------------------------------------------------------------------
        # FIND LARGE UNCERTAINTY TRACKS
        largeUncertaintyCount = 0
        drawing = None
        sitemap = None

        # If track uncertainty is 2x average, add that track to the spreadsheet and map overlay
        for t in self._tracks:

            # if the tracksite has changed, save previous map and make a new one
            if sitemap != t.trackSeries.trackway.sitemap:

                # save the last site map drawing (if there was one)
                if drawing:
                    drawing.save()

                # then start a new drawing for this new site map
                sitemap = t.trackSeries.trackway.sitemap

                fileName = "%s-%s-ROTATION_UNC.svg" % (sitemap.name, sitemap.level)
                path = self.getPath(self.DRAWING_FOLDER_NAME, fileName, isFile=True)
                drawing = CadenceDrawing(path, sitemap)

                # create a group to be instanced for the spreadsheet values
                drawing.createGroup("rect1")
                # create a rectangle of 100x100 cm that is to be scaled by fractional meters
                drawing.rect((0, 0), 100, 100, scene=True, groupId="rect1")

                # create another group to be instanced for the mapped values.
                drawing.createGroup("rect2")
                # create a rectangle of 100x100 cm that is to be scaled by fractional meters
                drawing.rect((0, 0), 100, 100, scene=True, groupId="rect2")

                # and place a grid and the federal coordinates in the drawing file
                drawing.grid()
                drawing.federalCoordinates()

            # now examine the positional uncertainties for this track
            rotation = t.rotationAngle.valueDegrees
            if rotation.uncertainty <= 2.0 * average.uncertainty:

                # then just indicate that this track has low uncertainty
                self._drawLowUncertaintyMarker(drawing, t)

                # label this track green
                # drawing.text(
                #     t.name,
                #     (t.x - 20, t.z),
                #     scene=True,
                #     stroke='green',
                #     stroke_width='0.25',
                #     font_size='8px',
                #     font_family='Arial')
                continue

            # else, since the uncertainty is high, first write that track in the spreadsheet
            largeUncertaintyCount += 1
            self._largeUncCsv.createRow(uid=t.uid, fingerprint=t.fingerprint, r=rotation.label)

            # if either the measured width or length is 0, mark with a yellow disk with red outline
            if t.rotationMeasured == 0:
                drawing.circle(
                    (t.x, t.z),
                    100 * (t.widthUncertainty + t.lengthUncertainty) / 2.0,
                    scene=True,
                    fill="yellow",
                    stroke="red",
                )

                # drawing.text(
                #     t.name,
                #     (t.x - 20, t.z),
                #     scene=True,
                #     stroke='black',
                #     stroke_width='0.25',
                #     font_size='6px',
                #     font_family='Arial')
                continue

#.........这里部分代码省略.........

# 需要导入模块: from pyaid.number.NumericUtils import NumericUtils [as 别名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import getMeanAndDeviation [as 别名]
    def _postAnalyze(self):
        h = Histogram(
            data=self._uncs,
            binCount=80,
            xLimits=(0, max(*self._uncs)),
            color='r',
            title='Distribution of Spatial (X, Z) Uncertainties',
            xLabel='Uncertainty Value (m)',
            yLabel='Frequency')
        p1 = h.save(self.getTempFilePath(extension='pdf'))

        h.isLog = True
        h.title += ' (log)'
        p2 = h.save(self.getTempFilePath(extension='pdf'))

        self.mergePdfs([p1, p2], self.getPath('Spatial-Uncertainty-Distribution.pdf'))

        average = NumericUtils.getMeanAndDeviation(self._uncs)
        self.logger.write('Average spatial uncertainty: %s' % average.label)

        #-------------------------------------------------------------------------------------------
        # FIND LARGE UNCERTAINTY TRACKS
        largeUncertaintyCount = 0
        drawing = None
        sitemap = None

        # If track uncertainty is 2x average, add that track to the spreadsheet and map overlay
        for t in self._tracks:

            # if the tracksite has changed, save previous map and make a new one
            if sitemap != t.trackSeries.trackway.sitemap:

                # save the last site map drawing (if there was one)
                if drawing:
                    drawing.save()

                # then start a new drawing for this new site map
                sitemap = t.trackSeries.trackway.sitemap

                fileName = sitemap.name + "_" + sitemap.level + '_uncertainty.svg'
                path = self.getPath(self.DRAWING_FOLDER_NAME, fileName, isFile=True)
                drawing = CadenceDrawing(path, sitemap)

                # create a group to be instanced for the spreadsheet values
                drawing.createGroup('rect1')
                # create a rectangle of 100x100 cm that is to be scaled by fractional meters
                drawing.rect((0, 0), 100, 100, scene=True, groupId='rect1')

                # create another group to be instanced for the mapped values.
                drawing.createGroup('rect2')
                # create a rectangle of 100x100 cm that is to be scaled by fractional meters
                drawing.rect((0, 0), 100, 100, scene=True, groupId='rect2')

                # and place a grid and the federal coordinates in the drawing file
                drawing.grid()
                drawing.federalCoordinates()

            # now examine the positional uncertainties for this track
            x = t.xValue
            z = t.zValue

            if x.uncertainty > 0.15 or z.uncertainty > 0.15:
                # s = '%s%s %s%s: %s %s'% (
                #     t.site, t.level, t.trackwayType, t.trackwayNumber, t.name, t.uid)
                # print('%s:  (%s and %s)' % (s, x.uncertainty, z.uncertainty))
                print('%s\t%s' % (t.uid, t.fingerprint))

            if max(x.uncertainty, z.uncertainty) <= 2.0*average.uncertainty:
                # then just indicate that this track has low uncertainty
                self._drawLowUncertaintyMarker(drawing, t)
                # label this track with green
                drawing.text(
                    t.name,
                    (t.x - 20, t.z),
                    scene=True,
                    stroke='green',
                    stroke_width='0.25',
                    font_size='8px',
                    font_family='Arial')
                continue

            # else, since the uncertainty is high, first write that track in the spreadsheet
            largeUncertaintyCount += 1
            self._largeUncCsv.createRow(
                uid=t.uid,
                fingerprint=t.fingerprint,
                x=x.label,
                z=z.label)

            # if either the measured width or length is 0, mark with a yellow disk with red outline
            if t.widthMeasured == 0 or t.lengthMeasured == 0:
                drawing.circle(
                    (t.x, t.z),
                    100*(t.widthUncertainty + t.lengthUncertainty)/2.0,
                    scene=True,
                    fill='yellow',
                    stroke='red')
                drawing.text(
                    t.name,
                    (t.x - 20, t.z),
#.........这里部分代码省略.........

# 需要导入模块: from pyaid.number.NumericUtils import NumericUtils [as 别名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import getMeanAndDeviation [as 别名]
    def _process(self):
        """_processDeviations doc..."""
        errors  = []

        for entry in self.entries:
            if 'fractional' in entry:
                errors.append(entry['fractional'])

        res = NumericUtils.getMeanAndDeviation(errors)
        self.logger.write('Fractional Pace Error %s' % res.label)

        label = 'Fractional Pace Errors'
        d     = errors
        self._paths.append(self._makePlot(
            label=label,
            data=d,
            histRange=(-1.0, 1.0)))
        self._paths.append(self._makePlot(
            label=label,
            data=d,
            isLog=True,
            histRange=(-1.0, 1.0)))

        # noinspection PyUnresolvedReferences
        d = np.absolute(np.array(d))
        self._paths.append(self._makePlot(
            label='Absolute %s' % label,
            data=d,
            histRange=(0.0, 1.0) ))
        self._paths.append(self._makePlot(
            label='Absolute %s' % label,
            data=d,
            isLog=True,
            histRange=(0.0, 1.0) ))

        highDeviationCount = 0

        for entry in self.entries:
            if 'measured' not in entry:
                # entry['drawFunc']('purple')
                continue

            if entry['deviation'] > 2.0:
                entry['drawFunc']('red')
                highDeviationCount += 1
            else:
                entry['drawFunc'](
                    'black' if abs(entry['deviation']) < 2.0 else '#FFAAAA')

            track = entry['track']
            delta = NumericUtils.roundToSigFigs(100.0*abs(entry['delta']), 3)

            pairTrack = entry.get('pairTrack')
            if pairTrack:
                pairedFingerprint = pairTrack.fingerprint
                pairedUid         = pairTrack.uid
            else:
                pairedFingerprint = ''
                pairedUid         = ''

            self._csv.addRow({
                'fingerprint':track.fingerprint,
                'uid':track.uid,
                'measured':entry['measured'].label,
                'entered':entry['entered'].label,
                'dev':entry['deviation'],
                'delta':delta,
                'pairedUid':pairedUid,
                'pairedFingerprint':pairedFingerprint})

        for sitemap in self.owner.getSitemaps():
            # Remove drawing from the sitemap cache and save the drawing file
            try:
                sitemap.cache.extract('drawing').save()
            except Exception:
                self.logger.write('[WARNING]: No sitemap saved for %s-%s' % (
                    sitemap.name, sitemap.level))

        if not self._csv.save():
            self.logger.write(
                '[ERROR]: Failed to save CSV file %s' % self._csv.path)

        if not self._errorCsv.save():
            self.logger.write(
                '[ERROR]: Failed to save CSV file %s' % self._errorCsv.path)

        percentage = NumericUtils.roundToOrder(
            100.0*float(highDeviationCount)/float(len(self.entries)), -2)
        self.logger.write('%s significant %s (%s%%)' % (
            highDeviationCount,
            label.lower(),
            percentage))
        if percentage > (100.0 - 95.45):
            self.logger.write(
                '[WARNING]: Large deviation count exceeds normal ' +
                'distribution expectations.')