# Copyright 2014 Diamond Light Source Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
.. module:: vo_centering
   :platform: Unix
   :synopsis: A plugin to find the center of rotation per frame
.. moduleauthor:: <NAME>, <NAME>, <NAME> \ 
                    <<EMAIL>>
"""
from savu.plugins.driver.cpu_plugin import CpuPlugin
from savu.plugins.utils import register_plugin
from savu.plugins.filters.base_filter import BaseFilter
from savu.data.plugin_list import CitationInformation
import savu.core.utils as cu

import logging
import numpy as np
import scipy.ndimage as ndi
import pyfftw.interfaces.scipy_fftpack as fft



import sys

@register_plugin
class VoCentering(BaseFilter, CpuPlugin):
    """
    A plugin to calculate the centre of rotation using the Vo Method
    :u*param preview: A slice list of required frames (sinograms) to use in \
    the calulation of the centre of rotation (this will not reduce the data \
    size for subsequent plugins). Default: [].
    :u*param start_pixel: The estimated centre of rotation. If value is None,\
        use the horizontal centre of the image. Default: None.
    :u*param search_area: Search area around the estimated centre of rotation\
        . Default: (-50, 50).
    :u*param ratio: The ratio between the size of object and FOV of \
        the camera. Default: 0.5.
    :param search_radius: Use for fine searching. Default: 6.
    :param step: Step of fine searching. Default: 0.5.
    :param datasets_to_populate: A list of datasets which require this \
        information. Default: [].
    :param out_datasets: The default names\
        . Default: ['cor_preview','cor_broadcast'].
    :param broadcast_method: Method of broadcasting centre values calculated\
        from preview slices to full dataset. Available option: 'median', \
        'mean', 'nearest', 'linear_fit'. Default: 'median'.
    :param row_drop: Drop lines around vertical center of the \
        mask. Default: 20.
    :param average_radius: Averaging sinograms around a required sinogram to\
        improve signal-to-noise ratio. Default: 5.
    """

    def __init__(self):
        super(VoCentering, self).__init__("VoCentering")

    def _create_mask(self, nrow, ncol, radius, drop):
        du = 1.0 / ncol
        dv = (nrow - 1.0) / (nrow * 2.0 * np.pi)
        cen_row = np.int16(np.ceil(nrow / 2.0) - 1)
        cen_col = np.int16(np.ceil(ncol / 2.0) - 1)
        drop = min(drop, np.int16(np.ceil(0.05 * nrow)))
        mask = np.zeros((nrow, ncol), dtype='float32')
        for i in range(nrow):
            pos = np.int16(np.round(((i - cen_row) * dv / radius) / du))
            (pos1, pos2) = np.clip(np.sort(
                (-pos + cen_col, pos + cen_col)), 0, ncol - 1)
            mask[i, pos1:pos2 + 1] = 1.0
        mask[cen_row - drop:cen_row + drop + 1,:] = 0.0
        mask[:, cen_col-1:cen_col+2] = 0.0
        return mask

    def _coarse_search(self, sino, start_cor, stop_cor, ratio, drop):
        """
        Coarse search for finding the rotation center.
        """
        (nrow, ncol) = sino.shape
        start_cor, stop_cor = np.sort((start_cor,stop_cor))
        start_cor = np.int16(np.clip(start_cor, 0, ncol-1))
        stop_cor = np.int16(np.clip(stop_cor, 0, ncol-1))
        cen_fliplr = (ncol - 1.0) / 2.0
        # Flip left-right the [0:Pi ] sinogram to make a full [0;2Pi] sinogram
        flip_sino = np.fliplr(sino)
        # Below image is used for compensating the shift of the [Pi;2Pi] sinogram
        # It helps to avoid local minima.
        comp_sino = np.flipud(sino)
        list_cor = np.arange(start_cor, stop_cor + 1.0)
        list_metric = np.zeros(len(list_cor), dtype=np.float32)
        mask = self._create_mask(2 * nrow, ncol, 0.5 * ratio * ncol, drop)
        sino_sino = np.vstack((sino, flip_sino))
        for i, cor in enumerate(list_cor):
            shift = np.int16(2.0*(cor - cen_fliplr))
            _sino = sino_sino[nrow:]
            _sino[...] = np.roll(flip_sino, shift, axis=1)
            if shift >= 0:
                _sino[:, :shift] = comp_sino[:, :shift]
            else:
                _sino[:, shift:] = comp_sino[:, shift:]
            list_metric[i] = np.mean(
                np.abs(np.fft.fftshift(fft.fft2(sino_sino)))*mask)
        minpos = np.argmin(list_metric)
        if minpos==0:
            self.error_msg_1 = "!!! WARNING !!! Global minimum is out of "\
            "the searching range. Please extend smin"
            logging.warn(self.error_msg_1)
            cu.user_message(self.error_msg_1)
        if minpos==len(list_metric)-1:
            self.error_msg_2 = "!!! WARNING !!! Global minimum is out of "\
             "the searching range. Please extend smax"
            logging.warn(self.error_msg_2)
            cu.user_message(self.error_msg_2)
        rot_centre = list_cor[minpos]
        return rot_centre

    def _fine_search(self, sino, start_cor, search_radius,
                      search_step, ratio, drop):
        """
        Fine search for finding the rotation center.
        """
        # Denoising
        (nrow, ncol) = sino.shape
        flip_sino = np.fliplr(sino)
        search_radius = np.clip(np.abs(search_radius), 1, ncol//10 - 1)
        search_step = np.clip(np.abs(search_step), 0.1, 1.1)
        start_cor = np.clip(start_cor, search_radius, ncol - search_radius - 1)
        cen_fliplr = (ncol - 1.0) / 2.0
        list_cor = start_cor + np.arange(
                -search_radius, search_radius + search_step, search_step)
        comp_sino = np.flipud(sino) # Used to avoid local minima
        list_metric = np.zeros(len(list_cor), dtype = np.float32)
        mask = self._create_mask(2 * nrow, ncol, 0.5 * ratio * ncol, drop)
        for i, cor in enumerate(list_cor):
            shift = 2.0*(cor - cen_fliplr)
            sino_shift = ndi.interpolation.shift(
                flip_sino, (0, shift), order = 3, prefilter = True)
            if shift>=0:
                shift_int = np.int16(np.ceil(shift))
                sino_shift[:,:shift_int] = comp_sino[:,:shift_int]
            else:
                shift_int = np.int16(np.floor(shift))
                sino_shift[:,shift_int:] = comp_sino[:,shift_int:]
            mat1 = np.vstack((sino, sino_shift))
            list_metric[i] = np.mean(
                np.abs(np.fft.fftshift(fft.fft2(mat1)))*mask)
        min_pos = np.argmin(list_metric)
        cor = list_cor[min_pos]
        return cor

    def _downsample(self, image, dsp_fact0, dsp_fact1):
        """
        Downsample an image by averaging.
        ---------
        Parameters: - image: 2D array.
                    - dsp_fact0: downsampling factor along axis 0.
                    - dsp_fact1: downsampling factor along axis 1.
        ---------
        Return:     - Downsampled image.
        """
        (height, width) = image.shape
        dsp_fact0 = np.clip(np.int16(dsp_fact0), 1, height//2)
        dsp_fact1 = np.clip(np.int16(dsp_fact1), 1, width//2)
        height_dsp = height//dsp_fact0
        width_dsp = width//dsp_fact1
        if  (dsp_fact0 == 1) and (dsp_fact1 ==1):
            image_dsp = image
        else:
            image_dsp = image[0:dsp_fact0*height_dsp,0:dsp_fact1*width_dsp]
            image_dsp = image_dsp.reshape(
                height_dsp,dsp_fact0,width_dsp,dsp_fact1).mean(-1).mean(1)
        return image_dsp

    def set_filter_padding(self, in_data, out_data):
        padding = np.int16(self.parameters['average_radius'])
        if padding>0: 
            in_data[0].padding = {'pad_multi_frames': padding}

    def pre_process(self):
        self.drop = np.int16(self.parameters['row_drop'])
        self.smin, self.smax = np.int16(self.parameters['search_area'])
        self.search_radius = np.float32(self.parameters['search_radius'])
        self.search_step = np.float32(self.parameters['step'])
        self.ratio = np.float32(self.parameters['ratio'])
        self.est_cor = self.parameters['start_pixel']
        self.broadcast_method = str(self.parameters['broadcast_method'])
        self.error_msg_1 = ""
        self.error_msg_2 = ""
        self.error_msg_3 = "" 
        if not((self.broadcast_method == 'mean')
                or (self.broadcast_method == 'median')
                 or (self.broadcast_method == 'linear_fit')
                  or (self.broadcast_method == 'nearest')):
            self.error_msg_3 = "!!! WARNING !!! Selected broadcasting "\
             "method is out of the list. Use the default option: 'median'"
            logging.warn(self.error_msg_3)
            cu.user_message(self.error_msg_3)
            self.broadcast_method = 'median'
        in_pData = self.get_plugin_in_datasets()[0]
        data = self.get_in_datasets()[0]
        starts,stops,steps = data.get_preview().get_starts_stops_steps()[0:3]
        start_ind = starts[1]
        stop_ind = stops[1]
        step_ind = steps[1]
        name = data.get_name()
        pre_start = self.exp.meta_data.get(name + '_preview_starts')[1]
        pre_stop = self.exp.meta_data.get(name + '_preview_stops')[1]
        pre_step = self.exp.meta_data.get(name + '_preview_steps')[1]
        self.origin_prev = np.arange(pre_start,pre_stop, pre_step)
        self.plugin_prev = self.origin_prev[start_ind:stop_ind:step_ind]

    def process_frames(self, data):
        if len(data[0].shape)>2:
            sino = np.mean(data[0],axis=1)
        else:
            sino = data[0]
        (nrow, ncol) = sino.shape
        dsp_row = 1
        dsp_col = 1
        if ncol>2000:
            dsp_col = 4
        if nrow>2000:
            dsp_row = 2
        # Denoising
        # There's a critical reason to use different window sizes
        # between coarse and fine search.
        sino_csearch = ndi.gaussian_filter(sino, (3,1), mode='reflect')
        sino_fsearch = ndi.gaussian_filter(sino, (2,2), mode='reflect')
        sino_dsp = self._downsample(sino_csearch, dsp_row, dsp_col)
        fine_srange = max(self.search_radius, dsp_col)
        off_set = 0.5*dsp_col if dsp_col>1 else 0.0
        if self.est_cor is None:
            self.est_cor = (ncol-1.0)/2.0
        else:
            self.est_cor = np.float32(self.est_cor)
        start_cor = np.int16(
            np.floor(1.0 * (self.est_cor + self.smin) / dsp_col))
        stop_cor = np.int16(
            np.ceil(1.0 * (self.est_cor + self.smax) / dsp_col))
        raw_cor = self._coarse_search(sino_dsp, start_cor, stop_cor,
                                       self.ratio, self.drop)
        cor = self._fine_search(
            sino_fsearch, raw_cor*dsp_col + off_set, fine_srange,
             self.search_step, self.ratio, self.drop)
        return [np.array([cor]), np.array([cor])]

    def post_process(self):
        in_datasets, out_datasets = self.get_datasets()
        cor_prev = out_datasets[0].data[...]
        cor_broad = out_datasets[1].data[...]
        cor_broad[:] = np.median(np.squeeze(cor_prev))
        self.cor_for_executive_summary = np.median(cor_broad[:])
        if self.broadcast_method == 'mean':
            cor_broad[:] = np.mean(np.squeeze(cor_prev))
            self.cor_for_executive_summary = np.mean(cor_broad[:])
        if (self.broadcast_method == 'linear_fit') and (len(cor_prev)>1):
            afact, bfact = np.polyfit(self.plugin_prev, cor_prev[:,0], 1)
            list_cor = self.origin_prev*afact + bfact
            cor_broad[:,0] = list_cor
            self.cor_for_executive_summary = cor_broad[:]
        if (self.broadcast_method == 'nearest') and (len(cor_prev)>1):
            for i, pos in enumerate(self.origin_prev):
                minpos = np.argmin(np.abs(pos-self.plugin_prev))
                cor_broad[i,0] = cor_prev[minpos,0]
            self.cor_for_executive_summary = cor_broad[:]
        out_datasets[1].data[:] = cor_broad[:]
        self.populate_meta_data('cor_preview', np.squeeze(cor_prev))
        self.populate_meta_data('centre_of_rotation',
                                out_datasets[1].data[:].squeeze(axis=1))

    def populate_meta_data(self, key, value):
        datasets = self.parameters['datasets_to_populate']
        in_meta_data = self.get_in_meta_data()[0]
        in_meta_data.set(key, value)
        for name in datasets:
            self.exp.index['in_data'][name].meta_data.set(key, value)

    def setup(self):
        self.exp.log(self.name + " Start calculating center of rotation")
        # set up the output dataset that is created by the plugin
        in_dataset, out_dataset = self.get_datasets()
        in_pData, out_pData = self.get_plugin_datasets()
        in_pData[0].plugin_data_setup('SINOGRAM', self.get_max_frames())
        slice_dirs = list(in_dataset[0].get_slice_dimensions())
        self.orig_full_shape = in_dataset[0].get_shape()

        # if preview parameters exist then use these
        # else get the size of the data
        # get n processes and take 4 different sets of 5 from the data if this is feasible based on the data size.
        # calculate the slice list here and determine if it is feasible, else apply to max(n_processes, data_size)

        # reduce the data as per data_subset parameter
        self.set_preview(in_dataset[0], self.parameters['preview'])
        total_frames = \
            self._calc_total_frames(in_dataset[0].get_preview(), slice_dirs)

        # copy all required information from in_dataset[0]
        fullData = in_dataset[0]
        new_shape = (np.prod(np.array(fullData.get_shape())[slice_dirs]), 1)
        self.orig_shape = \
            (np.prod(np.array(self.orig_full_shape)[slice_dirs]), 1)
        out_dataset[0].create_dataset(shape=new_shape,
                                      axis_labels=['x.pixels', 'y.pixels'],
                                      remove=True,
                                      transport='hdf5')
        out_dataset[0].add_pattern("METADATA", core_dims=(1,), slice_dims=(0,))

        out_dataset[1].create_dataset(shape=self.orig_shape,
                                      axis_labels=['x.pixels', 'y.pixels'],
                                      remove=True,
                                      transport='hdf5')
        out_dataset[1].add_pattern("METADATA", core_dims=(1,), slice_dims=(0,))
        out_pData[0].plugin_data_setup('METADATA', self.get_max_frames())
        out_pData[1].plugin_data_setup('METADATA', self.get_max_frames())
        out_pData[1].meta_data.set('fix_total_frames', total_frames)
        self.exp.log(self.name + " End")

    def _calc_total_frames(self, preview, slice_dims):
        starts, stops, steps, _ = preview.get_starts_stops_steps()
        lengths = [len(np.arange(starts[i], stops[i], steps[i]))
                   for i in range(len(starts))]
        return np.prod([lengths[i] for i in slice_dims])

    def nOutput_datasets(self):
        return 2

    def get_max_frames(self):
        return 'single'

    def fix_transport(self):
        return 'hdf5'

    def get_citation_information(self):
        cite_info = CitationInformation()
        cite_info.description = \
            ("The center of rotation for this reconstruction was calculated " + 
             "automatically using the method described in this work")
        cite_info.bibtex = \
            ("@article{vo2014reliable,\n" + 
             "title={Reliable method for calculating the center of rotation " + 
             "in parallel-beam tomography},\n" + 
             "author={<NAME> and <NAME>, " + 
             "<NAME> and <NAME>},\n" + 
             "journal={Optics Express},\n" + 
             "volume={22},\n" + 
             "number={16},\n" + 
             "pages={19078--19086},\n" + 
             "year={2014},\n" + 
             "publisher={Optical Society of America}\n" + 
             "}")
        cite_info.endnote = \
            ("%0 Journal Article\n" + 
             "%T Reliable method for calculating the center of rotation in " + 
             "parallel-beam tomography\n" + 
             "%A Vo, <NAME>\n" + 
             "%A Drakopoulos, Michael\n" + 
             "%A Atwood, <NAME>\n" + 
             "%A Reinhard, Christina\n" + 
             "%J Optics Express\n" + 
             "%V 22\n" + 
             "%N 16\n" + 
             "%P 19078-19086\n" + 
             "%@ 1094-4087\n" + 
             "%D 2014\n" + 
             "%I Optical Society of America")
        cite_info.doi = "https://doi.org/10.1364/OE.22.019078"
        return cite_info

    def executive_summary(self):
        if ((self.error_msg_1 == "")
             and (self.error_msg_2 == "")):
            msg = "Centre of rotation is : %s" % (str(self.cor_for_executive_summary))
        else:            
            msg = "\n" + self.error_msg_1 + "\n" + self.error_msg_2
            msg2 = "(Not well) estimated centre of rotation is : %s" % (str(
                self.cor_for_executive_summary))
            cu.user_message(msg2)
        return [msg]
# Copyright 2014 Diamond Light Source Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
.. module:: vo_centering
   :platform: Unix
   :synopsis: A plugin to find the center of rotation per frame
.. moduleauthor:: <NAME>, <NAME>, <NAME> \ 
                    <<EMAIL>>
"""
from savu.plugins.driver.cpu_plugin import CpuPlugin
from savu.plugins.utils import register_plugin
from savu.plugins.filters.base_filter import BaseFilter
from savu.data.plugin_list import CitationInformation
import savu.core.utils as cu

import logging
import numpy as np
import scipy.ndimage as ndi
import pyfftw.interfaces.scipy_fftpack as fft



import sys

@register_plugin
class VoCentering(BaseFilter, CpuPlugin):
    """
    A plugin to calculate the centre of rotation using the Vo Method
    :u*param preview: A slice list of required frames (sinograms) to use in \
    the calulation of the centre of rotation (this will not reduce the data \
    size for subsequent plugins). Default: [].
    :u*param start_pixel: The estimated centre of rotation. If value is None,\
        use the horizontal centre of the image. Default: None.
    :u*param search_area: Search area around the estimated centre of rotation\
        . Default: (-50, 50).
    :u*param ratio: The ratio between the size of object and FOV of \
        the camera. Default: 0.5.
    :param search_radius: Use for fine searching. Default: 6.
    :param step: Step of fine searching. Default: 0.5.
    :param datasets_to_populate: A list of datasets which require this \
        information. Default: [].
    :param out_datasets: The default names\
        . Default: ['cor_preview','cor_broadcast'].
    :param broadcast_method: Method of broadcasting centre values calculated\
        from preview slices to full dataset. Available option: 'median', \
        'mean', 'nearest', 'linear_fit'. Default: 'median'.
    :param row_drop: Drop lines around vertical center of the \
        mask. Default: 20.
    :param average_radius: Averaging sinograms around a required sinogram to\
        improve signal-to-noise ratio. Default: 5.
    """

    def __init__(self):
        super(VoCentering, self).__init__("VoCentering")

    def _create_mask(self, nrow, ncol, radius, drop):
        du = 1.0 / ncol
        dv = (nrow - 1.0) / (nrow * 2.0 * np.pi)
        cen_row = np.int16(np.ceil(nrow / 2.0) - 1)
        cen_col = np.int16(np.ceil(ncol / 2.0) - 1)
        drop = min(drop, np.int16(np.ceil(0.05 * nrow)))
        mask = np.zeros((nrow, ncol), dtype='float32')
        for i in range(nrow):
            pos = np.int16(np.round(((i - cen_row) * dv / radius) / du))
            (pos1, pos2) = np.clip(np.sort(
                (-pos + cen_col, pos + cen_col)), 0, ncol - 1)
            mask[i, pos1:pos2 + 1] = 1.0
        mask[cen_row - drop:cen_row + drop + 1,:] = 0.0
        mask[:, cen_col-1:cen_col+2] = 0.0
        return mask

    def _coarse_search(self, sino, start_cor, stop_cor, ratio, drop):
        """
        Coarse search for finding the rotation center.
        """
        (nrow, ncol) = sino.shape
        start_cor, stop_cor = np.sort((start_cor,stop_cor))
        start_cor = np.int16(np.clip(start_cor, 0, ncol-1))
        stop_cor = np.int16(np.clip(stop_cor, 0, ncol-1))
        cen_fliplr = (ncol - 1.0) / 2.0
        # Flip left-right the [0:Pi ] sinogram to make a full [0;2Pi] sinogram
        flip_sino = np.fliplr(sino)
        # Below image is used for compensating the shift of the [Pi;2Pi] sinogram
        # It helps to avoid local minima.
        comp_sino = np.flipud(sino)
        list_cor = np.arange(start_cor, stop_cor + 1.0)
        list_metric = np.zeros(len(list_cor), dtype=np.float32)
        mask = self._create_mask(2 * nrow, ncol, 0.5 * ratio * ncol, drop)
        sino_sino = np.vstack((sino, flip_sino))
        for i, cor in enumerate(list_cor):
            shift = np.int16(2.0*(cor - cen_fliplr))
            _sino = sino_sino[nrow:]
            _sino[...] = np.roll(flip_sino, shift, axis=1)
            if shift >= 0:
                _sino[:, :shift] = comp_sino[:, :shift]
            else:
                _sino[:, shift:] = comp_sino[:, shift:]
            list_metric[i] = np.mean(
                np.abs(np.fft.fftshift(fft.fft2(sino_sino)))*mask)
        minpos = np.argmin(list_metric)
        if minpos==0:
            self.error_msg_1 = "!!! WARNING !!! Global minimum is out of "\
            "the searching range. Please extend smin"
            logging.warn(self.error_msg_1)
            cu.user_message(self.error_msg_1)
        if minpos==len(list_metric)-1:
            self.error_msg_2 = "!!! WARNING !!! Global minimum is out of "\
             "the searching range. Please extend smax"
            logging.warn(self.error_msg_2)
            cu.user_message(self.error_msg_2)
        rot_centre = list_cor[minpos]
        return rot_centre

    def _fine_search(self, sino, start_cor, search_radius,
                      search_step, ratio, drop):
        """
        Fine search for finding the rotation center.
        """
        # Denoising
        (nrow, ncol) = sino.shape
        flip_sino = np.fliplr(sino)
        search_radius = np.clip(np.abs(search_radius), 1, ncol//10 - 1)
        search_step = np.clip(np.abs(search_step), 0.1, 1.1)
        start_cor = np.clip(start_cor, search_radius, ncol - search_radius - 1)
        cen_fliplr = (ncol - 1.0) / 2.0
        list_cor = start_cor + np.arange(
                -search_radius, search_radius + search_step, search_step)
        comp_sino = np.flipud(sino) # Used to avoid local minima
        list_metric = np.zeros(len(list_cor), dtype = np.float32)
        mask = self._create_mask(2 * nrow, ncol, 0.5 * ratio * ncol, drop)
        for i, cor in enumerate(list_cor):
            shift = 2.0*(cor - cen_fliplr)
            sino_shift = ndi.interpolation.shift(
                flip_sino, (0, shift), order = 3, prefilter = True)
            if shift>=0:
                shift_int = np.int16(np.ceil(shift))
                sino_shift[:,:shift_int] = comp_sino[:,:shift_int]
            else:
                shift_int = np.int16(np.floor(shift))
                sino_shift[:,shift_int:] = comp_sino[:,shift_int:]
            mat1 = np.vstack((sino, sino_shift))
            list_metric[i] = np.mean(
                np.abs(np.fft.fftshift(fft.fft2(mat1)))*mask)
        min_pos = np.argmin(list_metric)
        cor = list_cor[min_pos]
        return cor

    def _downsample(self, image, dsp_fact0, dsp_fact1):
        """
        Downsample an image by averaging.
        ---------
        Parameters: - image: 2D array.
                    - dsp_fact0: downsampling factor along axis 0.
                    - dsp_fact1: downsampling factor along axis 1.
        ---------
        Return:     - Downsampled image.
        """
        (height, width) = image.shape
        dsp_fact0 = np.clip(np.int16(dsp_fact0), 1, height//2)
        dsp_fact1 = np.clip(np.int16(dsp_fact1), 1, width//2)
        height_dsp = height//dsp_fact0
        width_dsp = width//dsp_fact1
        if  (dsp_fact0 == 1) and (dsp_fact1 ==1):
            image_dsp = image
        else:
            image_dsp = image[0:dsp_fact0*height_dsp,0:dsp_fact1*width_dsp]
            image_dsp = image_dsp.reshape(
                height_dsp,dsp_fact0,width_dsp,dsp_fact1).mean(-1).mean(1)
        return image_dsp

    def set_filter_padding(self, in_data, out_data):
        padding = np.int16(self.parameters['average_radius'])
        if padding>0: 
            in_data[0].padding = {'pad_multi_frames': padding}

    def pre_process(self):
        self.drop = np.int16(self.parameters['row_drop'])
        self.smin, self.smax = np.int16(self.parameters['search_area'])
        self.search_radius = np.float32(self.parameters['search_radius'])
        self.search_step = np.float32(self.parameters['step'])
        self.ratio = np.float32(self.parameters['ratio'])
        self.est_cor = self.parameters['start_pixel']
        self.broadcast_method = str(self.parameters['broadcast_method'])
        self.error_msg_1 = ""
        self.error_msg_2 = ""
        self.error_msg_3 = "" 
        if not((self.broadcast_method == 'mean')
                or (self.broadcast_method == 'median')
                 or (self.broadcast_method == 'linear_fit')
                  or (self.broadcast_method == 'nearest')):
            self.error_msg_3 = "!!! WARNING !!! Selected broadcasting "\
             "method is out of the list. Use the default option: 'median'"
            logging.warn(self.error_msg_3)
            cu.user_message(self.error_msg_3)
            self.broadcast_method = 'median'
        in_pData = self.get_plugin_in_datasets()[0]
        data = self.get_in_datasets()[0]
        starts,stops,steps = data.get_preview().get_starts_stops_steps()[0:3]
        start_ind = starts[1]
        stop_ind = stops[1]
        step_ind = steps[1]
        name = data.get_name()
        pre_start = self.exp.meta_data.get(name + '_preview_starts')[1]
        pre_stop = self.exp.meta_data.get(name + '_preview_stops')[1]
        pre_step = self.exp.meta_data.get(name + '_preview_steps')[1]
        self.origin_prev = np.arange(pre_start,pre_stop, pre_step)
        self.plugin_prev = self.origin_prev[start_ind:stop_ind:step_ind]

    def process_frames(self, data):
        if len(data[0].shape)>2:
            sino = np.mean(data[0],axis=1)
        else:
            sino = data[0]
        (nrow, ncol) = sino.shape
        dsp_row = 1
        dsp_col = 1
        if ncol>2000:
            dsp_col = 4
        if nrow>2000:
            dsp_row = 2
        # Denoising
        # There's a critical reason to use different window sizes
        # between coarse and fine search.
        sino_csearch = ndi.gaussian_filter(sino, (3,1), mode='reflect')
        sino_fsearch = ndi.gaussian_filter(sino, (2,2), mode='reflect')
        sino_dsp = self._downsample(sino_csearch, dsp_row, dsp_col)
        fine_srange = max(self.search_radius, dsp_col)
        off_set = 0.5*dsp_col if dsp_col>1 else 0.0
        if self.est_cor is None:
            self.est_cor = (ncol-1.0)/2.0
        else:
            self.est_cor = np.float32(self.est_cor)
        start_cor = np.int16(
            np.floor(1.0 * (self.est_cor + self.smin) / dsp_col))
        stop_cor = np.int16(
            np.ceil(1.0 * (self.est_cor + self.smax) / dsp_col))
        raw_cor = self._coarse_search(sino_dsp, start_cor, stop_cor,
                                       self.ratio, self.drop)
        cor = self._fine_search(
            sino_fsearch, raw_cor*dsp_col + off_set, fine_srange,
             self.search_step, self.ratio, self.drop)
        return [np.array([cor]), np.array([cor])]

    def post_process(self):
        in_datasets, out_datasets = self.get_datasets()
        cor_prev = out_datasets[0].data[...]
        cor_broad = out_datasets[1].data[...]
        cor_broad[:] = np.median(np.squeeze(cor_prev))
        self.cor_for_executive_summary = np.median(cor_broad[:])
        if self.broadcast_method == 'mean':
            cor_broad[:] = np.mean(np.squeeze(cor_prev))
            self.cor_for_executive_summary = np.mean(cor_broad[:])
        if (self.broadcast_method == 'linear_fit') and (len(cor_prev)>1):
            afact, bfact = np.polyfit(self.plugin_prev, cor_prev[:,0], 1)
            list_cor = self.origin_prev*afact + bfact
            cor_broad[:,0] = list_cor
            self.cor_for_executive_summary = cor_broad[:]
        if (self.broadcast_method == 'nearest') and (len(cor_prev)>1):
            for i, pos in enumerate(self.origin_prev):
                minpos = np.argmin(np.abs(pos-self.plugin_prev))
                cor_broad[i,0] = cor_prev[minpos,0]
            self.cor_for_executive_summary = cor_broad[:]
        out_datasets[1].data[:] = cor_broad[:]
        self.populate_meta_data('cor_preview', np.squeeze(cor_prev))
        self.populate_meta_data('centre_of_rotation',
                                out_datasets[1].data[:].squeeze(axis=1))

    def populate_meta_data(self, key, value):
        datasets = self.parameters['datasets_to_populate']
        in_meta_data = self.get_in_meta_data()[0]
        in_meta_data.set(key, value)
        for name in datasets:
            self.exp.index['in_data'][name].meta_data.set(key, value)

    def setup(self):
        self.exp.log(self.name + " Start calculating center of rotation")
        # set up the output dataset that is created by the plugin
        in_dataset, out_dataset = self.get_datasets()
        in_pData, out_pData = self.get_plugin_datasets()
        in_pData[0].plugin_data_setup('SINOGRAM', self.get_max_frames())
        slice_dirs = list(in_dataset[0].get_slice_dimensions())
        self.orig_full_shape = in_dataset[0].get_shape()

        # if preview parameters exist then use these
        # else get the size of the data
        # get n processes and take 4 different sets of 5 from the data if this is feasible based on the data size.
        # calculate the slice list here and determine if it is feasible, else apply to max(n_processes, data_size)

        # reduce the data as per data_subset parameter
        self.set_preview(in_dataset[0], self.parameters['preview'])
        total_frames = \
            self._calc_total_frames(in_dataset[0].get_preview(), slice_dirs)

        # copy all required information from in_dataset[0]
        fullData = in_dataset[0]
        new_shape = (np.prod(np.array(fullData.get_shape())[slice_dirs]), 1)
        self.orig_shape = \
            (np.prod(np.array(self.orig_full_shape)[slice_dirs]), 1)
        out_dataset[0].create_dataset(shape=new_shape,
                                      axis_labels=['x.pixels', 'y.pixels'],
                                      remove=True,
                                      transport='hdf5')
        out_dataset[0].add_pattern("METADATA", core_dims=(1,), slice_dims=(0,))

        out_dataset[1].create_dataset(shape=self.orig_shape,
                                      axis_labels=['x.pixels', 'y.pixels'],
                                      remove=True,
                                      transport='hdf5')
        out_dataset[1].add_pattern("METADATA", core_dims=(1,), slice_dims=(0,))
        out_pData[0].plugin_data_setup('METADATA', self.get_max_frames())
        out_pData[1].plugin_data_setup('METADATA', self.get_max_frames())
        out_pData[1].meta_data.set('fix_total_frames', total_frames)
        self.exp.log(self.name + " End")

    def _calc_total_frames(self, preview, slice_dims):
        starts, stops, steps, _ = preview.get_starts_stops_steps()
        lengths = [len(np.arange(starts[i], stops[i], steps[i]))
                   for i in range(len(starts))]
        return np.prod([lengths[i] for i in slice_dims])

    def nOutput_datasets(self):
        return 2

    def get_max_frames(self):
        return 'single'

    def fix_transport(self):
        return 'hdf5'

    def get_citation_information(self):
        cite_info = CitationInformation()
        cite_info.description = \
            ("The center of rotation for this reconstruction was calculated " + 
             "automatically using the method described in this work")
        cite_info.bibtex = \
            ("@article{vo2014reliable,\n" + 
             "title={Reliable method for calculating the center of rotation " + 
             "in parallel-beam tomography},\n" + 
             "author={<NAME> and <NAME>, " + 
             "<NAME> and <NAME>},\n" + 
             "journal={Optics Express},\n" + 
             "volume={22},\n" + 
             "number={16},\n" + 
             "pages={19078--19086},\n" + 
             "year={2014},\n" + 
             "publisher={Optical Society of America}\n" + 
             "}")
        cite_info.endnote = \
            ("%0 Journal Article\n" + 
             "%T Reliable method for calculating the center of rotation in " + 
             "parallel-beam tomography\n" + 
             "%A Vo, <NAME>\n" + 
             "%A Drakopoulos, Michael\n" + 
             "%A Atwood, <NAME>\n" + 
             "%A Reinhard, Christina\n" + 
             "%J Optics Express\n" + 
             "%V 22\n" + 
             "%N 16\n" + 
             "%P 19078-19086\n" + 
             "%@ 1094-4087\n" + 
             "%D 2014\n" + 
             "%I Optical Society of America")
        cite_info.doi = "https://doi.org/10.1364/OE.22.019078"
        return cite_info

    def executive_summary(self):
        if ((self.error_msg_1 == "")
             and (self.error_msg_2 == "")):
            msg = "Centre of rotation is : %s" % (str(self.cor_for_executive_summary))
        else:            
            msg = "\n" + self.error_msg_1 + "\n" + self.error_msg_2
            msg2 = "(Not well) estimated centre of rotation is : %s" % (str(
                self.cor_for_executive_summary))
            cu.user_message(msg2)
        return [msg]
# Copyright 2014 Diamond Light Source Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # #     http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. .. module:: vo_centering    :platform: Unix    :synopsis: A plugin to find the center of rotation per frame .. moduleauthor:: <NAME>, <NAME>, <NAME> \                      <<EMAIL>> A plugin to calculate the centre of rotation using the Vo Method     :u*param preview: A slice list of required frames (sinograms) to use in \     the calulation of the centre of rotation (this will not reduce the data \     size for subsequent plugins). Default: [].     :u*param start_pixel: The estimated centre of rotation. If value is None,\         use the horizontal centre of the image. Default: None.     :u*param search_area: Search area around the estimated centre of rotation\         . Default: (-50, 50).     :u*param ratio: The ratio between the size of object and FOV of \         the camera. Default: 0.5.     :param search_radius: Use for fine searching. Default: 6.     :param step: Step of fine searching. Default: 0.5.     :param datasets_to_populate: A list of datasets which require this \         information. Default: [].     :param out_datasets: The default names\         . Default: ['cor_preview','cor_broadcast'].     :param broadcast_method: Method of broadcasting centre values calculated\         from preview slices to full dataset. Available option: 'median', \         'mean', 'nearest', 'linear_fit'. Default: 'median'.     :param row_drop: Drop lines around vertical center of the \         mask. Default: 20.     :param average_radius: Averaging sinograms around a required sinogram to\         improve signal-to-noise ratio. Default: 5. Coarse search for finding the rotation center. # Flip left-right the [0:Pi ] sinogram to make a full [0;2Pi] sinogram # Below image is used for compensating the shift of the [Pi;2Pi] sinogram # It helps to avoid local minima. Fine search for finding the rotation center. # Denoising # Used to avoid local minima Downsample an image by averaging.         ---------         Parameters: - image: 2D array.                     - dsp_fact0: downsampling factor along axis 0.                     - dsp_fact1: downsampling factor along axis 1.         ---------         Return:     - Downsampled image. # Denoising # There's a critical reason to use different window sizes # between coarse and fine search. # set up the output dataset that is created by the plugin # if preview parameters exist then use these # else get the size of the data # get n processes and take 4 different sets of 5 from the data if this is feasible based on the data size. # calculate the slice list here and determine if it is feasible, else apply to max(n_processes, data_size) # reduce the data as per data_subset parameter # copy all required information from in_dataset[0]