from __future__ import division

import copy
import numpy as np
from tigre.algorithms.iterative_recon_alg import IterativeReconAlg
from tigre.algorithms.iterative_recon_alg import decorator
from tigre.utilities.Atb import Atb
from tigre.utilities.Ax import Ax


class MLEM(IterativeReconAlg):  # noqa: D101
    __doc__ = (
        " MLEM solves the CBCT problem using the maximum likelihood expectation maximization\n"
        " algorithm\n"
        " \n"
        "  MLEM(PROJ,GEO,ANGLES,NITER,INIT) solves the reconstruction problem\n"
        "  using the projection data PROJ taken over ALPHA angles, corresponding\n"
        "  to the geometry descrived in GEO, using NITER iterations. INIT specifies\n"
        "  starting image, defaut: None (flat image value=1)"
    ) + IterativeReconAlg.__doc__

    def __init__(self, proj, geo, angles, niter, **kwargs):
        # Don't precompute V and W.
        kwargs.update(dict(W=None, V=None))
        kwargs.update(dict(blocksize=angles.shape[0]))
        IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)

        if self.init is None:
            self.res += 1.0

        self.W = Atb(np.ones(proj.shape, dtype=np.float32), geo, angles, backprojection_type="matched", gpuids=self.gpuids)
        self.W[self.W <= 0.0] = np.inf

    # Overide
    def run_main_iter(self):
        self.res[self.res < 0.0] = 0.0
        for i in range(self.niter):
            if self.Quameasopts is not None:
                res_prev = copy.deepcopy(self.res)
            self._estimate_time_until_completion(i)

            den = Ax(self.res, self.geo, self.angles, "interpolated", gpuids=self.gpuids)
            den[den == 0.0] = np.inf
            auxmlem = self.proj / den

            # update
            img = Atb(auxmlem, self.geo, self.angles, backprojection_type="matched", gpuids=self.gpuids) / self.W  

            self.res = self.res * img
            self.res[self.res < 0.0] = 0.0
            if self.Quameasopts is not None:
                self.error_measurement(res_prev, i)


mlem = decorator(MLEM, name="mlem")

a2e1ee512bd08c0cb41ae8b9b52d7d61177715b8
<mask token>


class MLEM(IterativeReconAlg):
    <mask token>
    <mask token>
    <mask token>


<mask token>

<mask token>


class MLEM(IterativeReconAlg):
    __doc__ = (
        """ MLEM solves the CBCT problem using the maximum likelihood expectation maximization
 algorithm
 
  MLEM(PROJ,GEO,ANGLES,NITER,INIT) solves the reconstruction problem
  using the projection data PROJ taken over ALPHA angles, corresponding
  to the geometry descrived in GEO, using NITER iterations. INIT specifies
  starting image, defaut: None (flat image value=1)"""
         + IterativeReconAlg.__doc__)

    def __init__(self, proj, geo, angles, niter, **kwargs):
        kwargs.update(dict(W=None, V=None))
        kwargs.update(dict(blocksize=angles.shape[0]))
        IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
        if self.init is None:
            self.res += 1.0
        self.W = Atb(np.ones(proj.shape, dtype=np.float32), geo, angles,
            backprojection_type='matched', gpuids=self.gpuids)
        self.W[self.W <= 0.0] = np.inf

    def run_main_iter(self):
        self.res[self.res < 0.0] = 0.0
        for i in range(self.niter):
            if self.Quameasopts is not None:
                res_prev = copy.deepcopy(self.res)
            self._estimate_time_until_completion(i)
            den = Ax(self.res, self.geo, self.angles, 'interpolated',
                gpuids=self.gpuids)
            den[den == 0.0] = np.inf
            auxmlem = self.proj / den
            img = Atb(auxmlem, self.geo, self.angles, backprojection_type=
                'matched', gpuids=self.gpuids) / self.W
            self.res = self.res * img
            self.res[self.res < 0.0] = 0.0
            if self.Quameasopts is not None:
                self.error_measurement(res_prev, i)


<mask token>

<mask token>


class MLEM(IterativeReconAlg):
    __doc__ = (
        """ MLEM solves the CBCT problem using the maximum likelihood expectation maximization
 algorithm
 
  MLEM(PROJ,GEO,ANGLES,NITER,INIT) solves the reconstruction problem
  using the projection data PROJ taken over ALPHA angles, corresponding
  to the geometry descrived in GEO, using NITER iterations. INIT specifies
  starting image, defaut: None (flat image value=1)"""
         + IterativeReconAlg.__doc__)

    def __init__(self, proj, geo, angles, niter, **kwargs):
        kwargs.update(dict(W=None, V=None))
        kwargs.update(dict(blocksize=angles.shape[0]))
        IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
        if self.init is None:
            self.res += 1.0
        self.W = Atb(np.ones(proj.shape, dtype=np.float32), geo, angles,
            backprojection_type='matched', gpuids=self.gpuids)
        self.W[self.W <= 0.0] = np.inf

    def run_main_iter(self):
        self.res[self.res < 0.0] = 0.0
        for i in range(self.niter):
            if self.Quameasopts is not None:
                res_prev = copy.deepcopy(self.res)
            self._estimate_time_until_completion(i)
            den = Ax(self.res, self.geo, self.angles, 'interpolated',
                gpuids=self.gpuids)
            den[den == 0.0] = np.inf
            auxmlem = self.proj / den
            img = Atb(auxmlem, self.geo, self.angles, backprojection_type=
                'matched', gpuids=self.gpuids) / self.W
            self.res = self.res * img
            self.res[self.res < 0.0] = 0.0
            if self.Quameasopts is not None:
                self.error_measurement(res_prev, i)


mlem = decorator(MLEM, name='mlem')

from __future__ import division
import copy
import numpy as np
from tigre.algorithms.iterative_recon_alg import IterativeReconAlg
from tigre.algorithms.iterative_recon_alg import decorator
from tigre.utilities.Atb import Atb
from tigre.utilities.Ax import Ax


class MLEM(IterativeReconAlg):
    __doc__ = (
        """ MLEM solves the CBCT problem using the maximum likelihood expectation maximization
 algorithm
 
  MLEM(PROJ,GEO,ANGLES,NITER,INIT) solves the reconstruction problem
  using the projection data PROJ taken over ALPHA angles, corresponding
  to the geometry descrived in GEO, using NITER iterations. INIT specifies
  starting image, defaut: None (flat image value=1)"""
         + IterativeReconAlg.__doc__)

    def __init__(self, proj, geo, angles, niter, **kwargs):
        kwargs.update(dict(W=None, V=None))
        kwargs.update(dict(blocksize=angles.shape[0]))
        IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
        if self.init is None:
            self.res += 1.0
        self.W = Atb(np.ones(proj.shape, dtype=np.float32), geo, angles,
            backprojection_type='matched', gpuids=self.gpuids)
        self.W[self.W <= 0.0] = np.inf

    def run_main_iter(self):
        self.res[self.res < 0.0] = 0.0
        for i in range(self.niter):
            if self.Quameasopts is not None:
                res_prev = copy.deepcopy(self.res)
            self._estimate_time_until_completion(i)
            den = Ax(self.res, self.geo, self.angles, 'interpolated',
                gpuids=self.gpuids)
            den[den == 0.0] = np.inf
            auxmlem = self.proj / den
            img = Atb(auxmlem, self.geo, self.angles, backprojection_type=
                'matched', gpuids=self.gpuids) / self.W
            self.res = self.res * img
            self.res[self.res < 0.0] = 0.0
            if self.Quameasopts is not None:
                self.error_measurement(res_prev, i)


mlem = decorator(MLEM, name='mlem')

from __future__ import division

import copy
import numpy as np
from tigre.algorithms.iterative_recon_alg import IterativeReconAlg
from tigre.algorithms.iterative_recon_alg import decorator
from tigre.utilities.Atb import Atb
from tigre.utilities.Ax import Ax


class MLEM(IterativeReconAlg):  # noqa: D101
    __doc__ = (
        " MLEM solves the CBCT problem using the maximum likelihood expectation maximization\n"
        " algorithm\n"
        " \n"
        "  MLEM(PROJ,GEO,ANGLES,NITER,INIT) solves the reconstruction problem\n"
        "  using the projection data PROJ taken over ALPHA angles, corresponding\n"
        "  to the geometry descrived in GEO, using NITER iterations. INIT specifies\n"
        "  starting image, defaut: None (flat image value=1)"
    ) + IterativeReconAlg.__doc__

    def __init__(self, proj, geo, angles, niter, **kwargs):
        # Don't precompute V and W.
        kwargs.update(dict(W=None, V=None))
        kwargs.update(dict(blocksize=angles.shape[0]))
        IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)

        if self.init is None:
            self.res += 1.0

        self.W = Atb(np.ones(proj.shape, dtype=np.float32), geo, angles, backprojection_type="matched", gpuids=self.gpuids)
        self.W[self.W <= 0.0] = np.inf

    # Overide
    def run_main_iter(self):
        self.res[self.res < 0.0] = 0.0
        for i in range(self.niter):
            if self.Quameasopts is not None:
                res_prev = copy.deepcopy(self.res)
            self._estimate_time_until_completion(i)

            den = Ax(self.res, self.geo, self.angles, "interpolated", gpuids=self.gpuids)
            den[den == 0.0] = np.inf
            auxmlem = self.proj / den

            # update
            img = Atb(auxmlem, self.geo, self.angles, backprojection_type="matched", gpuids=self.gpuids) / self.W  

            self.res = self.res * img
            self.res[self.res < 0.0] = 0.0
            if self.Quameasopts is not None:
                self.error_measurement(res_prev, i)


mlem = decorator(MLEM, name="mlem")
