Python NumericUtils.toValueUncertainty方法代碼示例

# 需要導入模塊: from pyaid.number.NumericUtils import NumericUtils [as 別名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import toValueUncertainty [as 別名]
    def __unicode__(self):
        isPy2 = bool(sys.version < '3')

        return '<%s (%s, %s)>' % (
            self.__class__.__name__,
            NumericUtils.toValueUncertainty(self.x, self.xUnc, asciiLabel=isPy2).label,
            NumericUtils.toValueUncertainty(self.y, self.yUnc, asciiLabel=isPy2).label)

# 需要導入模塊: from pyaid.number.NumericUtils import NumericUtils [as 別名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import toValueUncertainty [as 別名]
    def _calculateDeviation(
            self, track, value, uncertainty, highMeasuredUncertainty, measured,
            prefix, label
    ):
        if not measured:
            return None

        out = dict()

        measuredUncertainty = measured*(
            0.12 if highMeasuredUncertainty else 0.06)

        v = NumericUtils.toValueUncertainty(value, uncertainty)
        mv = NumericUtils.toValueUncertainty(measured, measuredUncertainty)
        unc = math.sqrt(v.uncertainty**2 + mv.uncertainty**2)

        deviation = v.value - mv.value
        out['%sDev' % prefix] = deviation/measured

        try:
            out['%sDelta' % prefix] = abs(deviation)/unc
        except ZeroDivisionError:
            self.logger.write([
                '[ERROR]: Track without %s uncertainty' % label,
                'TRACK: %s (%s)' % (track.fingerprint, track.uid) ])
            raise

        return out

# 需要導入模塊: from pyaid.number.NumericUtils import NumericUtils [as 別名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import toValueUncertainty [as 別名]
    def test_weightedAverage(self):
        """ doc... """
        values = [
            NumericUtils.toValueUncertainty(11.0, 1.0),
            NumericUtils.toValueUncertainty(12.0, 1.0),
            NumericUtils.toValueUncertainty(10.0, 3.0) ]

        result = NumericUtils.weightedAverage(*values)

        self.assertEqual(result.value, 11.4, 'Value Match')
        self.assertEqual(result.uncertainty, 0.7, 'Value Match')

# 需要導入模塊: from pyaid.number.NumericUtils import NumericUtils [as 別名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import toValueUncertainty [as 別名]
    def test_toValueUncertainty(self):
        """test_toValueUncertainty doc..."""
        value = NumericUtils.toValueUncertainty(math.pi, 0.00456)
        self.assertEqual(value.value, 3.142, 'Values do not match %s' % value.label)
        self.assertEqual(value.uncertainty, 0.005, 'Uncertainties do not match %s' % value.label)

        value = NumericUtils.toValueUncertainty(100.0*math.pi, 42.0)
        self.assertEqual(value.value, 310.0, 'Values do not match %s' % value.label)
        self.assertEqual(value.uncertainty, 40.0, 'Uncertainties do not match %s' % value.label)

        value = NumericUtils.toValueUncertainty(0.001*math.pi, 0.000975)
        self.assertEqual(value.value, 0.003, 'Values do not match %s' % value.label)
        self.assertEqual(value.uncertainty, 0.001, 'Uncertainties do not match %s' % value.label)

# 需要導入模塊: from pyaid.number.NumericUtils import NumericUtils [as 別名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import toValueUncertainty [as 別名]
    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 = []

# 需要導入模塊: from pyaid.number.NumericUtils import NumericUtils [as 別名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import toValueUncertainty [as 別名]
    def zValue(self):
        """ Returns the z value as an uncertainty named tuple in units of meters
        """
        r    = math.pi/180.0*float(self.rotation)
        rUnc = math.pi/180.0*float(self.rotationUncertainty)
        wUnc = self.widthUncertainty
        lUnc = self.lengthUncertainty
        zUnc = lUnc*abs(math.cos(r)) + wUnc*abs(math.sin(r)) \
            + rUnc*abs(wUnc*math.cos(r) - lUnc*math.sin(r))

        return NumericUtils.toValueUncertainty(0.01*float(self.z), zUnc)

# 需要導入模塊: from pyaid.number.NumericUtils import NumericUtils [as 別名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import toValueUncertainty [as 別名]
    def slope(self):
        """ Returns the slope of the line as a ValueUncertainty named tuple. """
        s       = self.start
        e       = self.end
        deltaX  = e.x - s.x
        deltaY  = e.y - s.y

        try:
            slope   = deltaY/deltaX
            unc     = abs(1.0/deltaX)*(s.yUnc + e.yUnc) + abs(slope/deltaX)*(s.xUnc + e.xUnc)
            return NumericUtils.toValueUncertainty(slope, unc)
        except Exception:
            return None

# 需要導入模塊: from pyaid.number.NumericUtils import NumericUtils [as 別名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import toValueUncertainty [as 別名]
    def distanceTo(self, position):
        """distanceBetween doc..."""
        xDelta   = self.x - position.x
        yDelta   = self.y - position.y
        distance = math.sqrt(xDelta*xDelta + yDelta*yDelta)

        # Use the absolute value because the derivatives in error propagation are always
        # absolute values
        xDelta = abs(xDelta)
        yDelta = abs(yDelta)
        try:
            error = (xDelta*(self.xUnc + position.xUnc)
                  + yDelta*(self.yUnc + position.yUnc) )/distance
        except ZeroDivisionError:
            error = 1.0

        return NumericUtils.toValueUncertainty(distance, error)

# 需要導入模塊: from pyaid.number.NumericUtils import NumericUtils [as 別名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import toValueUncertainty [as 別名]
def getDensityDistributionTrace(data, columnName, errorColumnName, **kwargs):
    minVal = (data[columnName] - 3.0*data[errorColumnName]).min()
    maxVal = (data[columnName] + 3.0*data[errorColumnName]).max()

    values = []
    for index, row in data.iterrows():
        values.append(NumericUtils.toValueUncertainty(
            value=row[columnName],
            uncertainty=row[errorColumnName] ))

    dd = DensityDistribution(values=values)
    xValues = np.linspace(minVal, maxVal, 200)
    yValues = dd.createDistribution(xValues)

    return plotlyGraph.Scatter(
        x=xValues,
        y=yValues,
        **kwargs), dd

# 需要導入模塊: from pyaid.number.NumericUtils import NumericUtils [as 別名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import toValueUncertainty [as 別名]
    def _analyzeTrack(self, track, series, trackway, sitemap):
        """ Performs analysis on each track. A dictionary is created to be
            added to the entries list.  That dictionary contains track,
            wDev (the fractional difference in width between that estimated
            from the map and that measured in the field), ldev (the
            corresponding fractional difference in length), and if either of
            those field measurements are missing, the corresponding counter is
            incremented.
        """
        data = dict(track=track)

        result = self._calculateDeviation(
            track=track,
            value=track.width,
            uncertainty=track.widthUncertainty,
            measured=track.widthMeasured,
            highMeasuredUncertainty=track.hasImportFlag(
                ImportFlagsEnum.HIGH_WIDTH_UNCERTAINTY),
            prefix='w',
            label='Width')
        if result:
            data.update(result)

        result = self._calculateDeviation(
            track=track,
            value=track.length,
            uncertainty=track.lengthUncertainty,
            measured=track.lengthMeasured,
            highMeasuredUncertainty=track.hasImportFlag(
                ImportFlagsEnum.HIGH_LENGTH_UNCERTAINTY),
            prefix='l',
            label='Length')
        if result:
            data.update(result)

        aspect         = track.width/track.length
        wErr           = track.widthUncertainty/track.width
        lErr           = track.lengthUncertainty/track.length
        aspectUnc      = abs(aspect)*math.sqrt(wErr*wErr + lErr*lErr)
        value          = NumericUtils.toValueUncertainty(aspect, aspectUnc)
        data['aspect'] = value

        self.entries.append(data)

# 需要導入模塊: from pyaid.number.NumericUtils import NumericUtils [as 別名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import toValueUncertainty [as 別名]
    def _calculateSparseness(cls, spacings, reference):
        """ Calculates the relative sparseness from the series spacings list and the reference
            spacing. """

        out = []
        for data in spacings:
            # For each entry in the tests, normalize that value to the most complete (highest
            # track count) series to create a relative sparseness rating

            diff    = data.value - reference.value
            absDiff = abs(diff)
            dVal    = reference.value
            sign    = 0.0 if absDiff == 0.0 else diff/absDiff
            unc     = abs(data.uncertainty/dVal) + abs(dVal*sign - absDiff)/(dVal*dVal)
            out.append(NumericUtils.toValueUncertainty(
                value=100.0*absDiff/dVal,
                uncertainty=100.0*unc))

        return ListUtils.sortObjectList(out, 'value')

# 需要導入模塊: from pyaid.number.NumericUtils import NumericUtils [as 別名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import toValueUncertainty [as 別名]
    def _logUnresolvableTrack(self, track, sitemap, message):
        """_logUnresolvableTrack doc..."""
        measured = NumericUtils.toValueUncertainty(
            value=track.snapshotData.get(SnapshotDataEnum.PACE),
            uncertainty=self.MEASURED_UNCERTAINTY)

        self.ignored += 1
        self.logger.write([
            '[ERROR]: %s' % message,
            'TRACK: %s [%s]' % (track.fingerprint, track.uid),
            'PACE[field]: %s' % measured.label ])

        self._errorCsv.addRow({
            'uid':track.uid,
            'fingerprint':track.fingerprint,
            'measured':measured.label })

        sitemap.cache.get('drawing').circle(
            track.positionValue.toMayaTuple(), 10,
            stroke='none', fill='red', fill_opacity=0.5)

# 需要導入模塊: from pyaid.number.NumericUtils import NumericUtils [as 別名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import toValueUncertainty [as 別名]
    def _calculateAverageSpacing(cls, series):
        """ Determines the average spacing of the tracks in the track series for use as a
            comparative measure of sparseness to the other track series in the trackway. If the
            series is not ready or does not have a sufficient number of tracks, this method will
            return None.

            :param: series | TrackSeries
                The series on which to determine the average spacing.

            :return: ValueUncertainty
                A value uncertainty instance that represents the average spacing of the series,
                or None if it's the calculation is aborted. """

        if not series.isReady:
            # Skip trackways with invalid series
            return None

        tracks = series.tracks
        if not tracks or len(tracks) < 2:
            # Ignore series with less than two tracks
            return None

        length = 0.0
        uncs    = []

        for i in ListUtils.range(len(tracks) - 1):
            line = LineSegment2D(
                start=tracks[i].positionValue,
                end=tracks[i + 1].positionValue)
            spacing = line.length
            length += spacing.value
            uncs.append(spacing.uncertainty)

        unc = NumericUtils.sqrtSumOfSquares(*uncs)

        return NumericUtils.toValueUncertainty(
            value=length/float(len(tracks)),
            uncertainty=unc/float(len(tracks)) )

# 需要導入模塊: from pyaid.number.NumericUtils import NumericUtils [as 別名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import toValueUncertainty [as 別名]
    def distanceToPoint(self, point):
        """ Calculates the smallest distance between the specified point and this line segment
            using the standard formulation as described in:
            http://en.wikipedia.org/wiki/Distance_from_a_point_to_a_line#Line_defined_by_two_points """

        length = self.length
        if not length:
            raise ValueError('Cannot calculate point distance. Invalid line segment.')

        s = self.start
        e = self.end
        deltaX = e.x - s.x
        deltaY = e.y - s.y

        if deltaX == 0.0:
            # Vertical Line
            distance = abs(s.x - point.x)
        elif deltaY == 0.0:
            # Horizontal line
            distance = abs(s.y - point.y)
        else:
            distance = abs(deltaY*point.x - deltaX*point.y - s.x*e.y + e.x*s.y)/length.raw

        B       = deltaY*point.x - deltaX*point.y - s.x*e.y + e.x*s.y
        AbsB    = abs(B)
        D       = math.sqrt(deltaX*deltaX + deltaY*deltaY)
        DPrime  = 1.0/math.pow(deltaX*deltaX + deltaY*deltaY, 3.0/2.0)
        bBD     = B/(AbsB*D)

        pointXErr = point.xUnc*abs(deltaY*B/(AbsB*D))
        pointYErr = point.yUnc*abs(deltaX*B/(AbsB*D))
        startXErr = s.xUnc*abs(AbsB*DPrime + bBD*(point.y - e.y))
        startYErr = s.yUnc*abs(AbsB*DPrime + bBD*(e.x - point.x))
        endXErr   = e.xUnc*abs(bBD*(s.y - point.y) - AbsB*DPrime)
        endYErr   = e.yUnc*abs(bBD*(point.x - s.x) - AbsB*DPrime)
        error     = pointXErr + pointYErr + startXErr + startYErr + endXErr + endYErr

        return NumericUtils.toValueUncertainty(distance, error)

# 需要導入模塊: from pyaid.number.NumericUtils import NumericUtils [as 別名]
# 或者: from pyaid.number.NumericUtils.NumericUtils import toValueUncertainty [as 別名]
 def lengthValue(self):
     return NumericUtils.toValueUncertainty(
         self.length, self.lengthUncertainty)