How to use the getdist.convolve.convolve2D function in getdist

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cmbant / getdist / getdist / mcsamples.py View on Github

ix1, ix2 = np.mgrid[-winw:winw + 1, -winw:winw + 1]
        Win = np.exp(-(ix1 ** 2 * Cinv[0, 0] + ix2 ** 2 * Cinv[1, 1] + 2 * Cinv[1, 0] * ix1 * ix2) / 2)
        Win /= np.sum(Win)

        logging.debug('time 2D binning and bandwidth: %s ; bins: %s', time.time() - start, fine_bins_2D)
        start = time.time()
        cache = {}
        convolvesize = xsize + 2 * winw + Win.shape[0]
        bins2D = convolve2D(histbins, Win, 'same', largest_size=convolvesize, cache=cache)

        if meanlikes:
            bin2Dlikes = convolve2D(finebinlikes, Win, 'same', largest_size=convolvesize, cache=cache, cache_args=[2])
            if mult_bias_correction_order:
                ix = bin2Dlikes &gt; 0
                finebinlikes[ix] /= bin2Dlikes[ix]
                likes2 = convolve2D(finebinlikes, Win, 'same', largest_size=convolvesize, cache=cache, cache_args=[2])
                likes2[ix] *= bin2Dlikes[ix]
                bin2Dlikes = likes2
            del finebinlikes
            mx = 1e-4 * np.max(bins2D)
            bin2Dlikes[bins2D &gt; mx] /= bins2D[bins2D &gt; mx]
            bin2Dlikes[bins2D &lt;= mx] = 0
        else:
            bin2Dlikes = None

        if has_prior and boundary_correction_order &gt;= 0:
            # Correct for edge effects
            prior_mask = np.ones((ysize + 2 * winw, xsize + 2 * winw))
            self._setEdgeMask2D(parx, pary, prior_mask, winw)
            a00 = convolve2D(prior_mask, Win, 'valid', largest_size=convolvesize, cache=cache)
            ix = a00 * bins2D &gt; np.max(bins2D) * 1e-8
            a00 = a00[ix]

cmbant / getdist / getdist / mcsamples.py View on Github

self._setEdgeMask2D(parx, pary, prior_mask, winw)
            a00 = convolve2D(prior_mask, Win, 'valid', largest_size=convolvesize, cache=cache)
            ix = a00 * bins2D > np.max(bins2D) * 1e-8
            a00 = a00[ix]
            normed = bins2D[ix] / a00
            if boundary_correction_order == 1:
                # linear boundary correction
                indexes = np.arange(-winw, winw + 1)
                y = np.empty(Win.shape)
                for i in range(Win.shape[0]):
                    y[:, i] = indexes
                winx = Win * indexes
                winy = Win * y
                a10 = convolve2D(prior_mask, winx, 'valid', largest_size=convolvesize, cache=cache)[ix]
                a01 = convolve2D(prior_mask, winy, 'valid', largest_size=convolvesize, cache=cache)[ix]
                a20 = convolve2D(prior_mask, winx * indexes, 'valid', largest_size=convolvesize, cache=cache,
                                 cache_args=[1])[ix]
                a02 = convolve2D(prior_mask, winy * y, 'valid', largest_size=convolvesize, cache=cache,
                                 cache_args=[1])[ix]
                a11 = convolve2D(prior_mask, winy * indexes, 'valid', largest_size=convolvesize, cache=cache,
                                 cache_args=[1])[ix]
                xP = convolve2D(histbins, winx, 'same', largest_size=convolvesize, cache=cache)[ix]
                yP = convolve2D(histbins, winy, 'same', largest_size=convolvesize, cache=cache)[ix]
                denom = (a20 * a01 ** 2 + a10 ** 2 * a02 - a00 * a02 * a20 + a11 ** 2 * a00 - 2 * a01 * a10 * a11)
                A = a11 ** 2 - a02 * a20
                Ax = a10 * a02 - a01 * a11
                Ay = a01 * a20 - a10 * a11
                corrected = (bins2D[ix] * A + xP * Ax + yP * Ay) / denom
                bins2D[ix] = normed * np.exp(np.minimum(corrected / normed, 4) - 1)
            elif boundary_correction_order == 0:
                # simple boundary correction by normalization
                bins2D[ix] = normed

cmbant / getdist / getdist / mcsamples.py View on Github

winw = int(round(2.5 * smooth_scale))

        Cinv = np.linalg.inv(np.array([[ry ** 2, rx * ry * corr], [rx * ry * corr, rx ** 2]]))
        ix1, ix2 = np.mgrid[-winw:winw + 1, -winw:winw + 1]
        Win = np.exp(-(ix1 ** 2 * Cinv[0, 0] + ix2 ** 2 * Cinv[1, 1] + 2 * Cinv[1, 0] * ix1 * ix2) / 2)
        Win /= np.sum(Win)

        logging.debug('time 2D binning and bandwidth: %s ; bins: %s', time.time() - start, fine_bins_2D)
        start = time.time()
        cache = {}
        convolvesize = xsize + 2 * winw + Win.shape[0]
        bins2D = convolve2D(histbins, Win, 'same', largest_size=convolvesize, cache=cache)

        if meanlikes:
            bin2Dlikes = convolve2D(finebinlikes, Win, 'same', largest_size=convolvesize, cache=cache, cache_args=[2])
            if mult_bias_correction_order:
                ix = bin2Dlikes &gt; 0
                finebinlikes[ix] /= bin2Dlikes[ix]
                likes2 = convolve2D(finebinlikes, Win, 'same', largest_size=convolvesize, cache=cache, cache_args=[2])
                likes2[ix] *= bin2Dlikes[ix]
                bin2Dlikes = likes2
            del finebinlikes
            mx = 1e-4 * np.max(bins2D)
            bin2Dlikes[bins2D &gt; mx] /= bins2D[bins2D &gt; mx]
            bin2Dlikes[bins2D &lt;= mx] = 0
        else:
            bin2Dlikes = None

        if has_prior and boundary_correction_order &gt;= 0:
            # Correct for edge effects
            prior_mask = np.ones((ysize + 2 * winw, xsize + 2 * winw))

cmbant / getdist / getdist / mcsamples.py View on Github

# Correct for edge effects
            prior_mask = np.ones((ysize + 2 * winw, xsize + 2 * winw))
            self._setEdgeMask2D(parx, pary, prior_mask, winw)
            a00 = convolve2D(prior_mask, Win, 'valid', largest_size=convolvesize, cache=cache)
            ix = a00 * bins2D > np.max(bins2D) * 1e-8
            a00 = a00[ix]
            normed = bins2D[ix] / a00
            if boundary_correction_order == 1:
                # linear boundary correction
                indexes = np.arange(-winw, winw + 1)
                y = np.empty(Win.shape)
                for i in range(Win.shape[0]):
                    y[:, i] = indexes
                winx = Win * indexes
                winy = Win * y
                a10 = convolve2D(prior_mask, winx, 'valid', largest_size=convolvesize, cache=cache)[ix]
                a01 = convolve2D(prior_mask, winy, 'valid', largest_size=convolvesize, cache=cache)[ix]
                a20 = convolve2D(prior_mask, winx * indexes, 'valid', largest_size=convolvesize, cache=cache,
                                 cache_args=[1])[ix]
                a02 = convolve2D(prior_mask, winy * y, 'valid', largest_size=convolvesize, cache=cache,
                                 cache_args=[1])[ix]
                a11 = convolve2D(prior_mask, winy * indexes, 'valid', largest_size=convolvesize, cache=cache,
                                 cache_args=[1])[ix]
                xP = convolve2D(histbins, winx, 'same', largest_size=convolvesize, cache=cache)[ix]
                yP = convolve2D(histbins, winy, 'same', largest_size=convolvesize, cache=cache)[ix]
                denom = (a20 * a01 ** 2 + a10 ** 2 * a02 - a00 * a02 * a20 + a11 ** 2 * a00 - 2 * a01 * a10 * a11)
                A = a11 ** 2 - a02 * a20
                Ax = a10 * a02 - a01 * a11
                Ay = a01 * a20 - a10 * a11
                corrected = (bins2D[ix] * A + xP * Ax + yP * Ay) / denom
                bins2D[ix] = normed * np.exp(np.minimum(corrected / normed, 4) - 1)
            elif boundary_correction_order == 0:

cmbant / getdist / getdist / mcsamples.py View on Github

indexes = np.arange(-winw, winw + 1)
                y = np.empty(Win.shape)
                for i in range(Win.shape[0]):
                    y[:, i] = indexes
                winx = Win * indexes
                winy = Win * y
                a10 = convolve2D(prior_mask, winx, 'valid', largest_size=convolvesize, cache=cache)[ix]
                a01 = convolve2D(prior_mask, winy, 'valid', largest_size=convolvesize, cache=cache)[ix]
                a20 = convolve2D(prior_mask, winx * indexes, 'valid', largest_size=convolvesize, cache=cache,
                                 cache_args=[1])[ix]
                a02 = convolve2D(prior_mask, winy * y, 'valid', largest_size=convolvesize, cache=cache,
                                 cache_args=[1])[ix]
                a11 = convolve2D(prior_mask, winy * indexes, 'valid', largest_size=convolvesize, cache=cache,
                                 cache_args=[1])[ix]
                xP = convolve2D(histbins, winx, 'same', largest_size=convolvesize, cache=cache)[ix]
                yP = convolve2D(histbins, winy, 'same', largest_size=convolvesize, cache=cache)[ix]
                denom = (a20 * a01 ** 2 + a10 ** 2 * a02 - a00 * a02 * a20 + a11 ** 2 * a00 - 2 * a01 * a10 * a11)
                A = a11 ** 2 - a02 * a20
                Ax = a10 * a02 - a01 * a11
                Ay = a01 * a20 - a10 * a11
                corrected = (bins2D[ix] * A + xP * Ax + yP * Ay) / denom
                bins2D[ix] = normed * np.exp(np.minimum(corrected / normed, 4) - 1)
            elif boundary_correction_order == 0:
                # simple boundary correction by normalization
                bins2D[ix] = normed
            else:
                raise SettingError('unknown boundary_correction_order (expected 0 or 1)')

        if mult_bias_correction_order:
            prior_mask = np.ones((ysize + 2 * winw, xsize + 2 * winw))
            self._setEdgeMask2D(parx, pary, prior_mask, winw, alledge=True)
            a00 = convolve2D(prior_mask, Win, 'valid', largest_size=convolvesize, cache=cache, cache_args=[2])

cmbant / getdist / getdist / mcsamples.py View on Github

prior_mask = np.ones((ysize + 2 * winw, xsize + 2 * winw))
            self._setEdgeMask2D(parx, pary, prior_mask, winw)
            a00 = convolve2D(prior_mask, Win, 'valid', largest_size=convolvesize, cache=cache)
            ix = a00 * bins2D > np.max(bins2D) * 1e-8
            a00 = a00[ix]
            normed = bins2D[ix] / a00
            if boundary_correction_order == 1:
                # linear boundary correction
                indexes = np.arange(-winw, winw + 1)
                y = np.empty(Win.shape)
                for i in range(Win.shape[0]):
                    y[:, i] = indexes
                winx = Win * indexes
                winy = Win * y
                a10 = convolve2D(prior_mask, winx, 'valid', largest_size=convolvesize, cache=cache)[ix]
                a01 = convolve2D(prior_mask, winy, 'valid', largest_size=convolvesize, cache=cache)[ix]
                a20 = convolve2D(prior_mask, winx * indexes, 'valid', largest_size=convolvesize, cache=cache,
                                 cache_args=[1])[ix]
                a02 = convolve2D(prior_mask, winy * y, 'valid', largest_size=convolvesize, cache=cache,
                                 cache_args=[1])[ix]
                a11 = convolve2D(prior_mask, winy * indexes, 'valid', largest_size=convolvesize, cache=cache,
                                 cache_args=[1])[ix]
                xP = convolve2D(histbins, winx, 'same', largest_size=convolvesize, cache=cache)[ix]
                yP = convolve2D(histbins, winy, 'same', largest_size=convolvesize, cache=cache)[ix]
                denom = (a20 * a01 ** 2 + a10 ** 2 * a02 - a00 * a02 * a20 + a11 ** 2 * a00 - 2 * a01 * a10 * a11)
                A = a11 ** 2 - a02 * a20
                Ax = a10 * a02 - a01 * a11
                Ay = a01 * a20 - a10 * a11
                corrected = (bins2D[ix] * A + xP * Ax + yP * Ay) / denom
                bins2D[ix] = normed * np.exp(np.minimum(corrected / normed, 4) - 1)
            elif boundary_correction_order == 0:
                # simple boundary correction by normalization

cmbant / getdist / getdist / mcsamples.py View on Github

bins2D[ix] = normed * np.exp(np.minimum(corrected / normed, 4) - 1)
            elif boundary_correction_order == 0:
                # simple boundary correction by normalization
                bins2D[ix] = normed
            else:
                raise SettingError('unknown boundary_correction_order (expected 0 or 1)')

        if mult_bias_correction_order:
            prior_mask = np.ones((ysize + 2 * winw, xsize + 2 * winw))
            self._setEdgeMask2D(parx, pary, prior_mask, winw, alledge=True)
            a00 = convolve2D(prior_mask, Win, 'valid', largest_size=convolvesize, cache=cache, cache_args=[2])
            for _ in range(mult_bias_correction_order):
                box = histbins.copy()
                ix2 = bins2D > np.max(bins2D) * 1e-8
                box[ix2] /= bins2D[ix2]
                bins2D *= convolve2D(box, Win, 'same', largest_size=convolvesize, cache=cache, cache_args=[2])
                bins2D /= a00

        x = np.linspace(xbinmin, xbinmax, xsize)
        y = np.linspace(ybinmin, ybinmax, ysize)
        density = Density2D(x, y, bins2D,
                            view_ranges=[(parx.range_min, parx.range_max), (pary.range_min, pary.range_max)])
        density.normalize('max', in_place=True)
        if get_density:
            return density

        ncontours = len(self.contours)
        if num_plot_contours: ncontours = min(num_plot_contours, ncontours)
        contours = self.contours[:ncontours]

        logging.debug('time 2D convolutions: %s', time.time() - start)

cmbant / getdist / getdist / mcsamples.py View on Github

denom = (a20 * a01 ** 2 + a10 ** 2 * a02 - a00 * a02 * a20 + a11 ** 2 * a00 - 2 * a01 * a10 * a11)
                A = a11 ** 2 - a02 * a20
                Ax = a10 * a02 - a01 * a11
                Ay = a01 * a20 - a10 * a11
                corrected = (bins2D[ix] * A + xP * Ax + yP * Ay) / denom
                bins2D[ix] = normed * np.exp(np.minimum(corrected / normed, 4) - 1)
            elif boundary_correction_order == 0:
                # simple boundary correction by normalization
                bins2D[ix] = normed
            else:
                raise SettingError('unknown boundary_correction_order (expected 0 or 1)')

        if mult_bias_correction_order:
            prior_mask = np.ones((ysize + 2 * winw, xsize + 2 * winw))
            self._setEdgeMask2D(parx, pary, prior_mask, winw, alledge=True)
            a00 = convolve2D(prior_mask, Win, 'valid', largest_size=convolvesize, cache=cache, cache_args=[2])
            for _ in range(mult_bias_correction_order):
                box = histbins.copy()
                ix2 = bins2D > np.max(bins2D) * 1e-8
                box[ix2] /= bins2D[ix2]
                bins2D *= convolve2D(box, Win, 'same', largest_size=convolvesize, cache=cache, cache_args=[2])
                bins2D /= a00

        x = np.linspace(xbinmin, xbinmax, xsize)
        y = np.linspace(ybinmin, ybinmax, ysize)
        density = Density2D(x, y, bins2D,
                            view_ranges=[(parx.range_min, parx.range_max), (pary.range_min, pary.range_max)])
        density.normalize('max', in_place=True)
        if get_density:
            return density

        ncontours = len(self.contours)

How to use the getdist.convolve.convolve2D function in getdist

To help you get started, we’ve selected a few getdist examples, based on popular ways it is used in public projects.

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