<reponame>rnwatanabe/silx<filename>src/silx/gui/plot/tools/profile/core.py<gh_stars>10-100
# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2018-2020 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# ###########################################################################*/
"""This module define core objects for profile tools.
"""

__authors__ = ["<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>"]
__license__ = "MIT"
__date__ = "17/04/2020"

import collections
import numpy
import weakref

from silx.image.bilinear import BilinearImage
from silx.gui import qt


CurveProfileData = collections.namedtuple(
    'CurveProfileData', [
        "coords",
        "profile",
        "title",
        "xLabel",
        "yLabel",
    ])

RgbaProfileData = collections.namedtuple(
    'RgbaProfileData', [
        "coords",
        "profile",
        "profile_r",
        "profile_g",
        "profile_b",
        "profile_a",
        "title",
        "xLabel",
        "yLabel",
    ])

ImageProfileData = collections.namedtuple(
    'ImageProfileData', [
        'coords',
        'profile',
        'title',
        'xLabel',
        'yLabel',
        'colormap',
    ])


class ProfileRoiMixIn:
    """Base mix-in for ROI which can be used to select a profile.

    This mix-in have to be applied to a :class:`~silx.gui.plot.items.roi.RegionOfInterest`
    in order to be usable by a :class:`~silx.gui.plot.tools.profile.manager.ProfileManager`.
    """

    ITEM_KIND = None
    """Define the plot item which can be used with this profile ROI"""

    sigProfilePropertyChanged = qt.Signal()
    """Emitted when a property of this profile have changed"""

    sigPlotItemChanged = qt.Signal()
    """Emitted when the plot item linked to this profile have changed"""

    def __init__(self, parent=None):
        self.__profileWindow = None
        self.__profileManager = None
        self.__plotItem = None
        self.setName("Profile")
        self.setEditable(True)
        self.setSelectable(True)

    def invalidateProfile(self):
        """Must be called by the implementation when the profile have to be
        recomputed."""
        profileManager = self.getProfileManager()
        if profileManager is not None:
            profileManager.requestUpdateProfile(self)

    def invalidateProperties(self):
        """Must be called when a property of the profile have changed."""
        self.sigProfilePropertyChanged.emit()

    def _setPlotItem(self, plotItem):
        """Specify the plot item to use with this profile

        :param `~silx.gui.plot.items.item.Item` plotItem: A plot item
        """
        previousPlotItem = self.getPlotItem()
        if previousPlotItem is plotItem:
            return
        self.__plotItem = weakref.ref(plotItem)
        self.sigPlotItemChanged.emit()

    def getPlotItem(self):
        """Returns the plot item used by this profile

        :rtype: `~silx.gui.plot.items.item.Item`
        """
        if self.__plotItem is None:
            return None
        plotItem = self.__plotItem()
        if plotItem is None:
            self.__plotItem = None
        return plotItem

    def _setProfileManager(self, profileManager):
        self.__profileManager = profileManager

    def getProfileManager(self):
        """
        Returns the profile manager connected to this ROI.

        :rtype: ~silx.gui.plot.tools.profile.manager.ProfileManager
        """
        return self.__profileManager

    def getProfileWindow(self):
        """
        Returns the windows associated to this ROI, else None.

        :rtype: ProfileWindow
        """
        return self.__profileWindow

    def setProfileWindow(self, profileWindow):
        """
        Associate a window to this ROI. Can be None.

        :param ProfileWindow profileWindow: A main window
            to display the profile.
        """
        if profileWindow is self.__profileWindow:
            return
        if self.__profileWindow is not None:
            self.__profileWindow.sigClose.disconnect(self.__profileWindowAboutToClose)
            self.__profileWindow.setRoiProfile(None)
        self.__profileWindow = profileWindow
        if self.__profileWindow is not None:
            self.__profileWindow.sigClose.connect(self.__profileWindowAboutToClose)
            self.__profileWindow.setRoiProfile(self)

    def __profileWindowAboutToClose(self):
        profileManager = self.getProfileManager()
        roiManager = profileManager.getRoiManager()
        try:
            roiManager.removeRoi(self)
        except ValueError:
            pass

    def computeProfile(self, item):
        """
        Compute the profile which will be displayed.

        This method is not called from the main Qt thread, but from a thread
        pool.

        :param ~silx.gui.plot.items.Item item: A plot item
        :rtype: Union[CurveProfileData,ImageProfileData]
        """
        raise NotImplementedError()


def _alignedFullProfile(data, origin, scale, position, roiWidth, axis, method):
    """Get a profile along one axis on a stack of images

    :param numpy.ndarray data: 3D volume (stack of 2D images)
        The first dimension is the image index.
    :param origin: Origin of image in plot (ox, oy)
    :param scale: Scale of image in plot (sx, sy)
    :param float position: Position of profile line in plot coords
                           on the axis orthogonal to the profile direction.
    :param int roiWidth: Width of the profile in image pixels.
    :param int axis: 0 for horizontal profile, 1 for vertical.
    :param str method: method to compute the profile. Can be 'mean' or 'sum' or
        'none'
    :return: profile image + effective ROI area corners in plot coords
    """
    assert axis in (0, 1)
    assert len(data.shape) == 3
    assert method in ('mean', 'sum', 'none')

    # Convert from plot to image coords
    imgPos = int((position - origin[1 - axis]) / scale[1 - axis])

    if axis == 1:  # Vertical profile
        # Transpose image to always do a horizontal profile
        data = numpy.transpose(data, (0, 2, 1))

    nimages, height, width = data.shape

    roiWidth = min(height, roiWidth)  # Clip roi width to image size

    # Get [start, end[ coords of the roi in the data
    start = int(int(imgPos) + 0.5 - roiWidth / 2.)
    start = min(max(0, start), height - roiWidth)
    end = start + roiWidth

    if method == 'none':
        profile = None
    else:
        if start < height and end > 0:
            if method == 'mean':
                fct = numpy.mean
            elif method == 'sum':
                fct = numpy.sum
            else:
                raise ValueError('method not managed')
            profile = fct(data[:, max(0, start):min(end, height), :], axis=1).astype(numpy.float32)
        else:
            profile = numpy.zeros((nimages, width), dtype=numpy.float32)

    # Compute effective ROI in plot coords
    profileBounds = numpy.array(
        (0, width, width, 0),
        dtype=numpy.float32) * scale[axis] + origin[axis]
    roiBounds = numpy.array(
        (start, start, end, end),
        dtype=numpy.float32) * scale[1 - axis] + origin[1 - axis]

    if axis == 0:  # Horizontal profile
        area = profileBounds, roiBounds
    else:  # vertical profile
        area = roiBounds, profileBounds

    return profile, area


def _alignedPartialProfile(data, rowRange, colRange, axis, method):
    """Mean of a rectangular region (ROI) of a stack of images
    along a given axis.

    Returned values and all parameters are in image coordinates.

    :param numpy.ndarray data: 3D volume (stack of 2D images)
        The first dimension is the image index.
    :param rowRange: [min, max[ of ROI rows (upper bound excluded).
    :type rowRange: 2-tuple of int (min, max) with min < max
    :param colRange: [min, max[ of ROI columns (upper bound excluded).
    :type colRange: 2-tuple of int (min, max) with min < max
    :param int axis: The axis along which to take the profile of the ROI.
                     0: Sum rows along columns.
                     1: Sum columns along rows.
    :param str method: method to compute the profile. Can be 'mean' or 'sum'
    :return: Profile image along the ROI as the mean of the intersection
             of the ROI and the image.
    """
    assert axis in (0, 1)
    assert len(data.shape) == 3
    assert rowRange[0] < rowRange[1]
    assert colRange[0] < colRange[1]
    assert method in ('mean', 'sum')

    nimages, height, width = data.shape

    # Range aligned with the integration direction
    profileRange = colRange if axis == 0 else rowRange

    profileLength = abs(profileRange[1] - profileRange[0])

    # Subset of the image to use as intersection of ROI and image
    rowStart = min(max(0, rowRange[0]), height)
    rowEnd = min(max(0, rowRange[1]), height)
    colStart = min(max(0, colRange[0]), width)
    colEnd = min(max(0, colRange[1]), width)

    if method == 'mean':
        _fct = numpy.mean
    elif method == 'sum':
        _fct = numpy.sum
    else:
        raise ValueError('method not managed')

    imgProfile = _fct(data[:, rowStart:rowEnd, colStart:colEnd], axis=axis + 1,
                      dtype=numpy.float32)

    # Profile including out of bound area
    profile = numpy.zeros((nimages, profileLength), dtype=numpy.float32)

    # Place imgProfile in full profile
    offset = - min(0, profileRange[0])
    profile[:, offset:offset + imgProfile.shape[1]] = imgProfile

    return profile


def createProfile(roiInfo, currentData, origin, scale, lineWidth, method):
    """Create the profile line for the the given image.

    :param roiInfo: information about the ROI: start point, end point and
        type ("X", "Y", "D")
    :param numpy.ndarray currentData: the 2D image or the 3D stack of images
        on which we compute the profile.
    :param origin: (ox, oy) the offset from origin
    :type origin: 2-tuple of float
    :param scale: (sx, sy) the scale to use
    :type scale: 2-tuple of float
    :param int lineWidth: width of the profile line
    :param str method: method to compute the profile. Can be 'mean' or 'sum'
        or 'none': to compute everything except the profile
    :return: `coords, profile, area, profileName, xLabel`, where:
        - coords is the X coordinate to use to display the profile
        - profile is a 2D array of the profiles of the stack of images.
          For a single image, the profile is a curve, so this parameter
          has a shape *(1, len(curve))*
        - area is a tuple of two 1D arrays with 4 values each. They represent
          the effective ROI area corners in plot coords.
        - profileName is a string describing the ROI, meant to be used as
          title of the profile plot
        - xLabel the label for X in the profile window

    :rtype: tuple(ndarray,ndarray,(ndarray,ndarray),str)
    """
    if currentData is None or roiInfo is None or lineWidth is None:
        raise ValueError("createProfile called with invalide arguments")

    # force 3D data (stack of images)
    if len(currentData.shape) == 2:
        currentData3D = currentData.reshape((1,) + currentData.shape)
    elif len(currentData.shape) == 3:
        currentData3D = currentData

    roiWidth = max(1, lineWidth)
    roiStart, roiEnd, lineProjectionMode = roiInfo

    if lineProjectionMode == 'X':  # Horizontal profile on the whole image
        profile, area = _alignedFullProfile(currentData3D,
                                            origin, scale,
                                            roiStart[1], roiWidth,
                                            axis=0,
                                            method=method)

        if method == 'none':
            coords = None
        else:
            coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
            coords = coords * scale[0] + origin[0]

        yMin, yMax = min(area[1]), max(area[1]) - 1
        if roiWidth <= 1:
            profileName = '{ylabel} = %g' % yMin
        else:
            profileName = '{ylabel} = [%g, %g]' % (yMin, yMax)
        xLabel = '{xlabel}'

    elif lineProjectionMode == 'Y':  # Vertical profile on the whole image
        profile, area = _alignedFullProfile(currentData3D,
                                            origin, scale,
                                            roiStart[0], roiWidth,
                                            axis=1,
                                            method=method)

        if method == 'none':
            coords = None
        else:
            coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
            coords = coords * scale[1] + origin[1]

        xMin, xMax = min(area[0]), max(area[0]) - 1
        if roiWidth <= 1:
            profileName = '{xlabel} = %g' % xMin
        else:
            profileName = '{xlabel} = [%g, %g]' % (xMin, xMax)
        xLabel = '{ylabel}'

    else:  # Free line profile

        # Convert start and end points in image coords as (row, col)
        startPt = ((roiStart[1] - origin[1]) / scale[1],
                   (roiStart[0] - origin[0]) / scale[0])
        endPt = ((roiEnd[1] - origin[1]) / scale[1],
                 (roiEnd[0] - origin[0]) / scale[0])

        if (int(startPt[0]) == int(endPt[0]) or
                int(startPt[1]) == int(endPt[1])):
            # Profile is aligned with one of the axes

            # Convert to int
            startPt = int(startPt[0]), int(startPt[1])
            endPt = int(endPt[0]), int(endPt[1])

            # Ensure startPt <= endPt
            if startPt[0] > endPt[0] or startPt[1] > endPt[1]:
                startPt, endPt = endPt, startPt

            if startPt[0] == endPt[0]:  # Row aligned
                rowRange = (int(startPt[0] + 0.5 - 0.5 * roiWidth),
                            int(startPt[0] + 0.5 + 0.5 * roiWidth))
                colRange = startPt[1], endPt[1] + 1
                if method == 'none':
                    profile = None
                else:
                    profile = _alignedPartialProfile(currentData3D,
                                                     rowRange, colRange,
                                                     axis=0,
                                                     method=method)

            else:  # Column aligned
                rowRange = startPt[0], endPt[0] + 1
                colRange = (int(startPt[1] + 0.5 - 0.5 * roiWidth),
                            int(startPt[1] + 0.5 + 0.5 * roiWidth))
                if method == 'none':
                    profile = None
                else:
                    profile = _alignedPartialProfile(currentData3D,
                                                     rowRange, colRange,
                                                     axis=1,
                                                     method=method)
            # Convert ranges to plot coords to draw ROI area
            area = (
                numpy.array(
                    (colRange[0], colRange[1], colRange[1], colRange[0]),
                    dtype=numpy.float32) * scale[0] + origin[0],
                numpy.array(
                    (rowRange[0], rowRange[0], rowRange[1], rowRange[1]),
                    dtype=numpy.float32) * scale[1] + origin[1])

        else:  # General case: use bilinear interpolation

            # Ensure startPt <= endPt
            if (startPt[1] > endPt[1] or (
                    startPt[1] == endPt[1] and startPt[0] > endPt[0])):
                startPt, endPt = endPt, startPt

            if method == 'none':
                profile = None
            else:
                profile = []
                for slice_idx in range(currentData3D.shape[0]):
                    bilinear = BilinearImage(currentData3D[slice_idx, :, :])

                    profile.append(bilinear.profile_line(
                        (startPt[0] - 0.5, startPt[1] - 0.5),
                        (endPt[0] - 0.5, endPt[1] - 0.5),
                        roiWidth,
                        method=method))
                profile = numpy.array(profile)

            # Extend ROI with half a pixel on each end, and
            # Convert back to plot coords (x, y)
            length = numpy.sqrt((endPt[0] - startPt[0]) ** 2 +
                                (endPt[1] - startPt[1]) ** 2)
            dRow = (endPt[0] - startPt[0]) / length
            dCol = (endPt[1] - startPt[1]) / length

            # Extend ROI with half a pixel on each end
            roiStartPt = startPt[0] - 0.5 * dRow, startPt[1] - 0.5 * dCol
            roiEndPt = endPt[0] + 0.5 * dRow, endPt[1] + 0.5 * dCol

            # Rotate deltas by 90 degrees to apply line width
            dRow, dCol = dCol, -dRow

            area = (
                numpy.array((roiStartPt[1] - 0.5 * roiWidth * dCol,
                             roiStartPt[1] + 0.5 * roiWidth * dCol,
                             roiEndPt[1] + 0.5 * roiWidth * dCol,
                             roiEndPt[1] - 0.5 * roiWidth * dCol),
                            dtype=numpy.float32) * scale[0] + origin[0],
                numpy.array((roiStartPt[0] - 0.5 * roiWidth * dRow,
                             roiStartPt[0] + 0.5 * roiWidth * dRow,
                             roiEndPt[0] + 0.5 * roiWidth * dRow,
                             roiEndPt[0] - 0.5 * roiWidth * dRow),
                            dtype=numpy.float32) * scale[1] + origin[1])

        # Convert start and end points back to plot coords
        y0 = startPt[0] * scale[1] + origin[1]
        x0 = startPt[1] * scale[0] + origin[0]
        y1 = endPt[0] * scale[1] + origin[1]
        x1 = endPt[1] * scale[0] + origin[0]

        if startPt[1] == endPt[1]:
            profileName = '{xlabel} = %g; {ylabel} = [%g, %g]' % (x0, y0, y1)
            if method == 'none':
                coords = None
            else:
                coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
                coords = coords * scale[1] + y0
            xLabel = '{ylabel}'

        elif startPt[0] == endPt[0]:
            profileName = '{ylabel} = %g; {xlabel} = [%g, %g]' % (y0, x0, x1)
            if method == 'none':
                coords = None
            else:
                coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
                coords = coords * scale[0] + x0
            xLabel = '{xlabel}'

        else:
            m = (y1 - y0) / (x1 - x0)
            b = y0 - m * x0
            profileName = '{ylabel} = %g * {xlabel} %+g' % (m, b)
            if method == 'none':
                coords = None
            else:
                coords = numpy.linspace(x0, x1, len(profile[0]),
                                        endpoint=True,
                                        dtype=numpy.float32)
            xLabel = '{xlabel}'

    return coords, profile, area, profileName, xLabel

# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2018-2020 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# ###########################################################################*/
"""This module define core objects for profile tools.
"""

__authors__ = ["<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>"]
__license__ = "MIT"
__date__ = "17/04/2020"

import collections
import numpy
import weakref

from silx.image.bilinear import BilinearImage
from silx.gui import qt


CurveProfileData = collections.namedtuple(
    'CurveProfileData', [
        "coords",
        "profile",
        "title",
        "xLabel",
        "yLabel",
    ])

RgbaProfileData = collections.namedtuple(
    'RgbaProfileData', [
        "coords",
        "profile",
        "profile_r",
        "profile_g",
        "profile_b",
        "profile_a",
        "title",
        "xLabel",
        "yLabel",
    ])

ImageProfileData = collections.namedtuple(
    'ImageProfileData', [
        'coords',
        'profile',
        'title',
        'xLabel',
        'yLabel',
        'colormap',
    ])


class ProfileRoiMixIn:
    """Base mix-in for ROI which can be used to select a profile.

    This mix-in have to be applied to a :class:`~silx.gui.plot.items.roi.RegionOfInterest`
    in order to be usable by a :class:`~silx.gui.plot.tools.profile.manager.ProfileManager`.
    """

    ITEM_KIND = None
    """Define the plot item which can be used with this profile ROI"""

    sigProfilePropertyChanged = qt.Signal()
    """Emitted when a property of this profile have changed"""

    sigPlotItemChanged = qt.Signal()
    """Emitted when the plot item linked to this profile have changed"""

    def __init__(self, parent=None):
        self.__profileWindow = None
        self.__profileManager = None
        self.__plotItem = None
        self.setName("Profile")
        self.setEditable(True)
        self.setSelectable(True)

    def invalidateProfile(self):
        """Must be called by the implementation when the profile have to be
        recomputed."""
        profileManager = self.getProfileManager()
        if profileManager is not None:
            profileManager.requestUpdateProfile(self)

    def invalidateProperties(self):
        """Must be called when a property of the profile have changed."""
        self.sigProfilePropertyChanged.emit()

    def _setPlotItem(self, plotItem):
        """Specify the plot item to use with this profile

        :param `~silx.gui.plot.items.item.Item` plotItem: A plot item
        """
        previousPlotItem = self.getPlotItem()
        if previousPlotItem is plotItem:
            return
        self.__plotItem = weakref.ref(plotItem)
        self.sigPlotItemChanged.emit()

    def getPlotItem(self):
        """Returns the plot item used by this profile

        :rtype: `~silx.gui.plot.items.item.Item`
        """
        if self.__plotItem is None:
            return None
        plotItem = self.__plotItem()
        if plotItem is None:
            self.__plotItem = None
        return plotItem

    def _setProfileManager(self, profileManager):
        self.__profileManager = profileManager

    def getProfileManager(self):
        """
        Returns the profile manager connected to this ROI.

        :rtype: ~silx.gui.plot.tools.profile.manager.ProfileManager
        """
        return self.__profileManager

    def getProfileWindow(self):
        """
        Returns the windows associated to this ROI, else None.

        :rtype: ProfileWindow
        """
        return self.__profileWindow

    def setProfileWindow(self, profileWindow):
        """
        Associate a window to this ROI. Can be None.

        :param ProfileWindow profileWindow: A main window
            to display the profile.
        """
        if profileWindow is self.__profileWindow:
            return
        if self.__profileWindow is not None:
            self.__profileWindow.sigClose.disconnect(self.__profileWindowAboutToClose)
            self.__profileWindow.setRoiProfile(None)
        self.__profileWindow = profileWindow
        if self.__profileWindow is not None:
            self.__profileWindow.sigClose.connect(self.__profileWindowAboutToClose)
            self.__profileWindow.setRoiProfile(self)

    def __profileWindowAboutToClose(self):
        profileManager = self.getProfileManager()
        roiManager = profileManager.getRoiManager()
        try:
            roiManager.removeRoi(self)
        except ValueError:
            pass

    def computeProfile(self, item):
        """
        Compute the profile which will be displayed.

        This method is not called from the main Qt thread, but from a thread
        pool.

        :param ~silx.gui.plot.items.Item item: A plot item
        :rtype: Union[CurveProfileData,ImageProfileData]
        """
        raise NotImplementedError()


def _alignedFullProfile(data, origin, scale, position, roiWidth, axis, method):
    """Get a profile along one axis on a stack of images

    :param numpy.ndarray data: 3D volume (stack of 2D images)
        The first dimension is the image index.
    :param origin: Origin of image in plot (ox, oy)
    :param scale: Scale of image in plot (sx, sy)
    :param float position: Position of profile line in plot coords
                           on the axis orthogonal to the profile direction.
    :param int roiWidth: Width of the profile in image pixels.
    :param int axis: 0 for horizontal profile, 1 for vertical.
    :param str method: method to compute the profile. Can be 'mean' or 'sum' or
        'none'
    :return: profile image + effective ROI area corners in plot coords
    """
    assert axis in (0, 1)
    assert len(data.shape) == 3
    assert method in ('mean', 'sum', 'none')

    # Convert from plot to image coords
    imgPos = int((position - origin[1 - axis]) / scale[1 - axis])

    if axis == 1:  # Vertical profile
        # Transpose image to always do a horizontal profile
        data = numpy.transpose(data, (0, 2, 1))

    nimages, height, width = data.shape

    roiWidth = min(height, roiWidth)  # Clip roi width to image size

    # Get [start, end[ coords of the roi in the data
    start = int(int(imgPos) + 0.5 - roiWidth / 2.)
    start = min(max(0, start), height - roiWidth)
    end = start + roiWidth

    if method == 'none':
        profile = None
    else:
        if start < height and end > 0:
            if method == 'mean':
                fct = numpy.mean
            elif method == 'sum':
                fct = numpy.sum
            else:
                raise ValueError('method not managed')
            profile = fct(data[:, max(0, start):min(end, height), :], axis=1).astype(numpy.float32)
        else:
            profile = numpy.zeros((nimages, width), dtype=numpy.float32)

    # Compute effective ROI in plot coords
    profileBounds = numpy.array(
        (0, width, width, 0),
        dtype=numpy.float32) * scale[axis] + origin[axis]
    roiBounds = numpy.array(
        (start, start, end, end),
        dtype=numpy.float32) * scale[1 - axis] + origin[1 - axis]

    if axis == 0:  # Horizontal profile
        area = profileBounds, roiBounds
    else:  # vertical profile
        area = roiBounds, profileBounds

    return profile, area


def _alignedPartialProfile(data, rowRange, colRange, axis, method):
    """Mean of a rectangular region (ROI) of a stack of images
    along a given axis.

    Returned values and all parameters are in image coordinates.

    :param numpy.ndarray data: 3D volume (stack of 2D images)
        The first dimension is the image index.
    :param rowRange: [min, max[ of ROI rows (upper bound excluded).
    :type rowRange: 2-tuple of int (min, max) with min < max
    :param colRange: [min, max[ of ROI columns (upper bound excluded).
    :type colRange: 2-tuple of int (min, max) with min < max
    :param int axis: The axis along which to take the profile of the ROI.
                     0: Sum rows along columns.
                     1: Sum columns along rows.
    :param str method: method to compute the profile. Can be 'mean' or 'sum'
    :return: Profile image along the ROI as the mean of the intersection
             of the ROI and the image.
    """
    assert axis in (0, 1)
    assert len(data.shape) == 3
    assert rowRange[0] < rowRange[1]
    assert colRange[0] < colRange[1]
    assert method in ('mean', 'sum')

    nimages, height, width = data.shape

    # Range aligned with the integration direction
    profileRange = colRange if axis == 0 else rowRange

    profileLength = abs(profileRange[1] - profileRange[0])

    # Subset of the image to use as intersection of ROI and image
    rowStart = min(max(0, rowRange[0]), height)
    rowEnd = min(max(0, rowRange[1]), height)
    colStart = min(max(0, colRange[0]), width)
    colEnd = min(max(0, colRange[1]), width)

    if method == 'mean':
        _fct = numpy.mean
    elif method == 'sum':
        _fct = numpy.sum
    else:
        raise ValueError('method not managed')

    imgProfile = _fct(data[:, rowStart:rowEnd, colStart:colEnd], axis=axis + 1,
                      dtype=numpy.float32)

    # Profile including out of bound area
    profile = numpy.zeros((nimages, profileLength), dtype=numpy.float32)

    # Place imgProfile in full profile
    offset = - min(0, profileRange[0])
    profile[:, offset:offset + imgProfile.shape[1]] = imgProfile

    return profile


def createProfile(roiInfo, currentData, origin, scale, lineWidth, method):
    """Create the profile line for the the given image.

    :param roiInfo: information about the ROI: start point, end point and
        type ("X", "Y", "D")
    :param numpy.ndarray currentData: the 2D image or the 3D stack of images
        on which we compute the profile.
    :param origin: (ox, oy) the offset from origin
    :type origin: 2-tuple of float
    :param scale: (sx, sy) the scale to use
    :type scale: 2-tuple of float
    :param int lineWidth: width of the profile line
    :param str method: method to compute the profile. Can be 'mean' or 'sum'
        or 'none': to compute everything except the profile
    :return: `coords, profile, area, profileName, xLabel`, where:
        - coords is the X coordinate to use to display the profile
        - profile is a 2D array of the profiles of the stack of images.
          For a single image, the profile is a curve, so this parameter
          has a shape *(1, len(curve))*
        - area is a tuple of two 1D arrays with 4 values each. They represent
          the effective ROI area corners in plot coords.
        - profileName is a string describing the ROI, meant to be used as
          title of the profile plot
        - xLabel the label for X in the profile window

    :rtype: tuple(ndarray,ndarray,(ndarray,ndarray),str)
    """
    if currentData is None or roiInfo is None or lineWidth is None:
        raise ValueError("createProfile called with invalide arguments")

    # force 3D data (stack of images)
    if len(currentData.shape) == 2:
        currentData3D = currentData.reshape((1,) + currentData.shape)
    elif len(currentData.shape) == 3:
        currentData3D = currentData

    roiWidth = max(1, lineWidth)
    roiStart, roiEnd, lineProjectionMode = roiInfo

    if lineProjectionMode == 'X':  # Horizontal profile on the whole image
        profile, area = _alignedFullProfile(currentData3D,
                                            origin, scale,
                                            roiStart[1], roiWidth,
                                            axis=0,
                                            method=method)

        if method == 'none':
            coords = None
        else:
            coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
            coords = coords * scale[0] + origin[0]

        yMin, yMax = min(area[1]), max(area[1]) - 1
        if roiWidth <= 1:
            profileName = '{ylabel} = %g' % yMin
        else:
            profileName = '{ylabel} = [%g, %g]' % (yMin, yMax)
        xLabel = '{xlabel}'

    elif lineProjectionMode == 'Y':  # Vertical profile on the whole image
        profile, area = _alignedFullProfile(currentData3D,
                                            origin, scale,
                                            roiStart[0], roiWidth,
                                            axis=1,
                                            method=method)

        if method == 'none':
            coords = None
        else:
            coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
            coords = coords * scale[1] + origin[1]

        xMin, xMax = min(area[0]), max(area[0]) - 1
        if roiWidth <= 1:
            profileName = '{xlabel} = %g' % xMin
        else:
            profileName = '{xlabel} = [%g, %g]' % (xMin, xMax)
        xLabel = '{ylabel}'

    else:  # Free line profile

        # Convert start and end points in image coords as (row, col)
        startPt = ((roiStart[1] - origin[1]) / scale[1],
                   (roiStart[0] - origin[0]) / scale[0])
        endPt = ((roiEnd[1] - origin[1]) / scale[1],
                 (roiEnd[0] - origin[0]) / scale[0])

        if (int(startPt[0]) == int(endPt[0]) or
                int(startPt[1]) == int(endPt[1])):
            # Profile is aligned with one of the axes

            # Convert to int
            startPt = int(startPt[0]), int(startPt[1])
            endPt = int(endPt[0]), int(endPt[1])

            # Ensure startPt <= endPt
            if startPt[0] > endPt[0] or startPt[1] > endPt[1]:
                startPt, endPt = endPt, startPt

            if startPt[0] == endPt[0]:  # Row aligned
                rowRange = (int(startPt[0] + 0.5 - 0.5 * roiWidth),
                            int(startPt[0] + 0.5 + 0.5 * roiWidth))
                colRange = startPt[1], endPt[1] + 1
                if method == 'none':
                    profile = None
                else:
                    profile = _alignedPartialProfile(currentData3D,
                                                     rowRange, colRange,
                                                     axis=0,
                                                     method=method)

            else:  # Column aligned
                rowRange = startPt[0], endPt[0] + 1
                colRange = (int(startPt[1] + 0.5 - 0.5 * roiWidth),
                            int(startPt[1] + 0.5 + 0.5 * roiWidth))
                if method == 'none':
                    profile = None
                else:
                    profile = _alignedPartialProfile(currentData3D,
                                                     rowRange, colRange,
                                                     axis=1,
                                                     method=method)
            # Convert ranges to plot coords to draw ROI area
            area = (
                numpy.array(
                    (colRange[0], colRange[1], colRange[1], colRange[0]),
                    dtype=numpy.float32) * scale[0] + origin[0],
                numpy.array(
                    (rowRange[0], rowRange[0], rowRange[1], rowRange[1]),
                    dtype=numpy.float32) * scale[1] + origin[1])

        else:  # General case: use bilinear interpolation

            # Ensure startPt <= endPt
            if (startPt[1] > endPt[1] or (
                    startPt[1] == endPt[1] and startPt[0] > endPt[0])):
                startPt, endPt = endPt, startPt

            if method == 'none':
                profile = None
            else:
                profile = []
                for slice_idx in range(currentData3D.shape[0]):
                    bilinear = BilinearImage(currentData3D[slice_idx, :, :])

                    profile.append(bilinear.profile_line(
                        (startPt[0] - 0.5, startPt[1] - 0.5),
                        (endPt[0] - 0.5, endPt[1] - 0.5),
                        roiWidth,
                        method=method))
                profile = numpy.array(profile)

            # Extend ROI with half a pixel on each end, and
            # Convert back to plot coords (x, y)
            length = numpy.sqrt((endPt[0] - startPt[0]) ** 2 +
                                (endPt[1] - startPt[1]) ** 2)
            dRow = (endPt[0] - startPt[0]) / length
            dCol = (endPt[1] - startPt[1]) / length

            # Extend ROI with half a pixel on each end
            roiStartPt = startPt[0] - 0.5 * dRow, startPt[1] - 0.5 * dCol
            roiEndPt = endPt[0] + 0.5 * dRow, endPt[1] + 0.5 * dCol

            # Rotate deltas by 90 degrees to apply line width
            dRow, dCol = dCol, -dRow

            area = (
                numpy.array((roiStartPt[1] - 0.5 * roiWidth * dCol,
                             roiStartPt[1] + 0.5 * roiWidth * dCol,
                             roiEndPt[1] + 0.5 * roiWidth * dCol,
                             roiEndPt[1] - 0.5 * roiWidth * dCol),
                            dtype=numpy.float32) * scale[0] + origin[0],
                numpy.array((roiStartPt[0] - 0.5 * roiWidth * dRow,
                             roiStartPt[0] + 0.5 * roiWidth * dRow,
                             roiEndPt[0] + 0.5 * roiWidth * dRow,
                             roiEndPt[0] - 0.5 * roiWidth * dRow),
                            dtype=numpy.float32) * scale[1] + origin[1])

        # Convert start and end points back to plot coords
        y0 = startPt[0] * scale[1] + origin[1]
        x0 = startPt[1] * scale[0] + origin[0]
        y1 = endPt[0] * scale[1] + origin[1]
        x1 = endPt[1] * scale[0] + origin[0]

        if startPt[1] == endPt[1]:
            profileName = '{xlabel} = %g; {ylabel} = [%g, %g]' % (x0, y0, y1)
            if method == 'none':
                coords = None
            else:
                coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
                coords = coords * scale[1] + y0
            xLabel = '{ylabel}'

        elif startPt[0] == endPt[0]:
            profileName = '{ylabel} = %g; {xlabel} = [%g, %g]' % (y0, x0, x1)
            if method == 'none':
                coords = None
            else:
                coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
                coords = coords * scale[0] + x0
            xLabel = '{xlabel}'

        else:
            m = (y1 - y0) / (x1 - x0)
            b = y0 - m * x0
            profileName = '{ylabel} = %g * {xlabel} %+g' % (m, b)
            if method == 'none':
                coords = None
            else:
                coords = numpy.linspace(x0, x1, len(profile[0]),
                                        endpoint=True,
                                        dtype=numpy.float32)
            xLabel = '{xlabel}'

    return coords, profile, area, profileName, xLabel
# coding: utf-8 # /*########################################################################## # # Copyright (c) 2018-2020 European Synchrotron Radiation Facility # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # # ###########################################################################*/ This module define core objects for profile tools. Base mix-in for ROI which can be used to select a profile.      This mix-in have to be applied to a :class:`~silx.gui.plot.items.roi.RegionOfInterest`     in order to be usable by a :class:`~silx.gui.plot.tools.profile.manager.ProfileManager`. Define the plot item which can be used with this profile ROI Emitted when a property of this profile have changed Emitted when the plot item linked to this profile have changed Must be called by the implementation when the profile have to be         recomputed. Must be called when a property of the profile have changed. Specify the plot item to use with this profile          :param `~silx.gui.plot.items.item.Item` plotItem: A plot item Returns the plot item used by this profile          :rtype: `~silx.gui.plot.items.item.Item` Returns the profile manager connected to this ROI.          :rtype: ~silx.gui.plot.tools.profile.manager.ProfileManager Returns the windows associated to this ROI, else None.          :rtype: ProfileWindow Associate a window to this ROI. Can be None.          :param ProfileWindow profileWindow: A main window             to display the profile. Compute the profile which will be displayed.          This method is not called from the main Qt thread, but from a thread         pool.          :param ~silx.gui.plot.items.Item item: A plot item         :rtype: Union[CurveProfileData,ImageProfileData] Get a profile along one axis on a stack of images      :param numpy.ndarray data: 3D volume (stack of 2D images)         The first dimension is the image index.     :param origin: Origin of image in plot (ox, oy)     :param scale: Scale of image in plot (sx, sy)     :param float position: Position of profile line in plot coords                            on the axis orthogonal to the profile direction.     :param int roiWidth: Width of the profile in image pixels.     :param int axis: 0 for horizontal profile, 1 for vertical.     :param str method: method to compute the profile. Can be 'mean' or 'sum' or         'none'     :return: profile image + effective ROI area corners in plot coords # Convert from plot to image coords # Vertical profile # Transpose image to always do a horizontal profile # Clip roi width to image size # Get [start, end[ coords of the roi in the data # Compute effective ROI in plot coords # Horizontal profile # vertical profile Mean of a rectangular region (ROI) of a stack of images     along a given axis.      Returned values and all parameters are in image coordinates.      :param numpy.ndarray data: 3D volume (stack of 2D images)         The first dimension is the image index.     :param rowRange: [min, max[ of ROI rows (upper bound excluded).     :type rowRange: 2-tuple of int (min, max) with min < max     :param colRange: [min, max[ of ROI columns (upper bound excluded).     :type colRange: 2-tuple of int (min, max) with min < max     :param int axis: The axis along which to take the profile of the ROI.                      0: Sum rows along columns.                      1: Sum columns along rows.     :param str method: method to compute the profile. Can be 'mean' or 'sum'     :return: Profile image along the ROI as the mean of the intersection              of the ROI and the image. # Range aligned with the integration direction # Subset of the image to use as intersection of ROI and image # Profile including out of bound area # Place imgProfile in full profile Create the profile line for the the given image.      :param roiInfo: information about the ROI: start point, end point and         type ("X", "Y", "D")     :param numpy.ndarray currentData: the 2D image or the 3D stack of images         on which we compute the profile.     :param origin: (ox, oy) the offset from origin     :type origin: 2-tuple of float     :param scale: (sx, sy) the scale to use     :type scale: 2-tuple of float     :param int lineWidth: width of the profile line     :param str method: method to compute the profile. Can be 'mean' or 'sum'         or 'none': to compute everything except the profile     :return: `coords, profile, area, profileName, xLabel`, where:         - coords is the X coordinate to use to display the profile         - profile is a 2D array of the profiles of the stack of images.           For a single image, the profile is a curve, so this parameter           has a shape *(1, len(curve))*         - area is a tuple of two 1D arrays with 4 values each. They represent           the effective ROI area corners in plot coords.         - profileName is a string describing the ROI, meant to be used as           title of the profile plot         - xLabel the label for X in the profile window      :rtype: tuple(ndarray,ndarray,(ndarray,ndarray),str) # force 3D data (stack of images) # Horizontal profile on the whole image # Vertical profile on the whole image # Free line profile # Convert start and end points in image coords as (row, col) # Profile is aligned with one of the axes # Convert to int # Ensure startPt <= endPt # Row aligned # Column aligned # Convert ranges to plot coords to draw ROI area # General case: use bilinear interpolation # Ensure startPt <= endPt # Extend ROI with half a pixel on each end, and # Convert back to plot coords (x, y) # Extend ROI with half a pixel on each end # Rotate deltas by 90 degrees to apply line width # Convert start and end points back to plot coords