示例#1
0
    def __init__(self,
                 phase_obj_3d,
                 wavelength,
                 slice_binning_factor=1,
                 **kwargs):
        """
        Initialization of the class

        phase_obj_3d:           object of the class PhaseObject3D
        wavelength:             wavelength of the light
        slice_binning_factor:   the object is compress in z-direction by this factor
        """
        self.slice_binning_factor = slice_binning_factor
        self._shape_full = phase_obj_3d.shape
        self.shape = phase_obj_3d.shape[0:2] + (int(
            np.ceil(phase_obj_3d.shape[2] / self.slice_binning_factor)), )
        self.RI = phase_obj_3d.RI
        self.wavelength = wavelength
        self.pixel_size = phase_obj_3d.pixel_size
        self.pixel_size_z = phase_obj_3d.pixel_size_z * self.slice_binning_factor
        self.back_scatter = False
        #Broadcasts b to a, size(a) > size(b)
        self.assign_broadcast = lambda a, b: a - a + b

        fxlin, fylin = self._genFrequencyGrid()
        self.fzlin = ((self.RI / self.wavelength)**2 -
                      fxlin * af.conjg(fxlin) - fylin * af.conjg(fylin))**0.5
        self.prop_kernel_phase = 1.0j * 2.0 * np.pi * self.fzlin
示例#2
0
    def adjoint(self, residual, cache):
        V_obj_af, field_layer_in_or_grad,\
        flag_gpu_inout                    = cache

        #back-propagte to volume center
        field_bp = residual

        if self.focus_at_center:
            #propagate to the last layer
            field_bp = self._propagationInplace(field_bp,
                                                self.distance_end_to_center)

        for layer in range(self.shape[2] - 1, -1, -1):
            field_bp_1 = af.ifft2(
                af.fft2(field_bp) *
                af.conjg(self.green_kernel_2d)) * self.pixel_size_z

            if layer > 0:
                field_bp = self._propagationInplace(
                    field_bp, self.slice_separation[layer], adjoint=True)
                field_bp[:, :] += field_bp_1 * af.conjg(V_obj_af[:, :, layer])

            field_layer_in_or_grad[:, :, layer] = field_bp_1 * af.conjg(
                field_layer_in_or_grad[:, :, layer])

        #Unbinning
        grad = self._meanObject(field_layer_in_or_grad, adjoint=True)

        if flag_gpu_inout:
            return {'gradient': grad}
        else:
            return {'gradient': np.array(grad)}
示例#3
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    def __init__(self,
                 shape,
                 pixel_size,
                 wavelength,
                 na,
                 RI_measure=1.0,
                 **kwargs):
        """
        Initialization of the class

        RI_measure: refractive index on the detection side (example: oil immersion objectives)
        """
        super().__init__(shape, pixel_size, na, **kwargs)

        fxlin = genGrid(self.shape[1],
                        1.0 / self.pixel_size / self.shape[1],
                        flag_shift=True)
        fylin = genGrid(self.shape[0],
                        1.0 / self.pixel_size / self.shape[0],
                        flag_shift=True)
        fxlin = af.tile(fxlin.T, self.shape[0], 1)
        fylin = af.tile(fylin, 1, self.shape[1])
        self.pupil_support = genPupil(self.shape, self.pixel_size, self.na,
                                      wavelength)
        self.prop_kernel_phase = 1.0j * 2.0 * np.pi * self.pupil_support * (
            (RI_measure / wavelength)**2 - fxlin * af.conjg(fxlin) -
            fylin * af.conjg(fylin))**0.5
    def _split_fourier_cuda(self, signal: Signal, step):
        '''
            This function is called by split_fourier,and should not be used outside

        :param signal: signal to traverse the span
        :param step: the step of split fourier
        :return: None
        '''
        af.set_backend('cuda')

        freq = fftfreq(len(signal.data_sample[0, :]),
                       (signal.sps * signal.symbol_rate_in_hz)**(-1))

        freq = af.Array(freq.ctypes.data, freq.shape, freq.dtype.char)

        signal_x = np.asarray(signal.data_sample[0, :])
        signal_y = np.asarray(signal.data_sample[1, :])

        signal_x = af.Array(signal_x.ctypes.data,
                            signal_x.shape,
                            dtype=signal_x.dtype.char)
        signal_y = af.Array(signal_y.ctypes.data,
                            signal_x.shape,
                            dtype=signal_y.dtype.char)

        Disper = (1j / 2) * self.beta2 * (2 * np.pi * freq)**2 * step + (
            1j / 6) * self.beta3 * (
                (2 * np.pi * freq)**3 * step) - self.alphalin / 2 * step

        dz_Eff = (1 - np.exp(-self.alphalin * step)) / self.alphalin
        step_number = np.ceil(self.length / step)

        for number in range(int(step_number)):
            print(number)
            if number == step_number - 1:
                # dz = step
                dz = self.length - (step_number - 1) * step
                dz_Eff = (1 - np.exp(-self.alphalin * dz)) / self.alphalin
                Disper = (1j / 2) * self.beta2 * (2 * np.pi * freq)**2 * dz + (
                    1j / 6) * self.beta3 * (
                        (2 * np.pi * freq)**3 * dz) - self.alphalin / 2 * step
            signal_x, signal_y = self.linear(signal_x, signal_y, Disper)
            energy = signal_x * af.conjg(signal_x) + signal_y * af.conjg(
                signal_y)
            signal_x, signal_y = self.nonlinear(energy, signal_x, signal_y,
                                                dz_Eff)
            signal_x, signal_y = self.linear(signal_x, signal_y, Disper)

        signal_x_array = np.array(signal_x.to_list())
        signal_y_array = np.array(signal_y.to_list())

        signal_x_array = signal_x_array[:, 0] + 1j * signal_x_array[:, 1]

        signal_y_array = signal_y_array[:, 0] + 1j * signal_y_array[:, 1]

        signal.data_sample[0, :] = signal_x_array
        signal.data_sample[1, :] = signal_y_array
示例#5
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 def func(x0):
     fields              = self._scattering_obj.forward(x0, fx_illu, fy_illu)
     field_scattered     = self._defocus_obj.forward(field_scattered, self.prop_distances)
     field_measure       = self._crop_obj.forward(field_scattered)
     residual            = af.abs(field_measure) - amplitude
     function_value      = af.sum(residual*af.conjg(residual)).real
     return function_value
示例#6
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    def adjoint(self, residual, cache):
        phasecontrast_obj_af, field_layer_conj_or_grad, flag_gpu_inout = cache
        trans_obj_af_conj = af.conjg(phasecontrast_obj_af)
        #back-propagte to volume center
        field_bp = residual
        #propagate to the last layer
        if self.focus_at_center:
            field_bp = self._propagationInplace(field_bp,
                                                self.distance_end_to_center)
        #multi-slice transmittance backward
        for layer in range(self.shape[2] - 1, -1, -1):
            field_layer_conj_or_grad[:, :,
                                     layer] = field_bp * field_layer_conj_or_grad[:, :, layer] * (
                                         -1.0j
                                     ) * self.sigma * trans_obj_af_conj[:, :,
                                                                        layer]
            if layer > 0:
                field_bp[:, :] *= trans_obj_af_conj[:, :, layer]
                field_bp = self._propagationInplace(
                    field_bp, self.slice_separation[layer - 1], adjoint=True)

        #Unbinning
        grad = self._binObject(field_layer_conj_or_grad, adjoint=True)

        phasecontrast_obj_af = None
        if flag_gpu_inout:
            return {'gradient': grad}
        else:
            return {'gradient': np.array(grad)}
示例#7
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 def conj(self):
     if not numpy.issubdtype(self.dtype, numpy.complex):
         return afnumpy.copy(self)
     if(self.d_array is not None):
         s = arrayfire.conjg(self.d_array)
         return ndarray(self.shape, dtype=pu.typemap(s.dtype()), af_array=s)
     else:
         return self.h_array.conj()
示例#8
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 def adjoint(self, field):
     """Adjoint operator for pupil (and estimate pupil if selected)"""
     field_adj_f = af.fft2(field)
     field_pupil_adj = af.ifft2(af.conjg(self.getPupil()) * field_adj_f)
     #Pupil recovery
     if self.flag_update:
         self._update(field_adj_f)
     return field_pupil_adj
示例#9
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 def forward(self, field, **kwargs):
     """Apply pupil"""
     self.field_f = af.fft2(field)
     if self.flag_update:
         if self.update_method == "GaussNewton":
             self.approx_hessian[:, :] += self.field_f * af.conjg(
                 self.field_f)
     field = af.ifft2(self.getPupil() * self.field_f)
     return field
示例#10
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    def __init__(self, phase_obj_3d, wavelength, **kwargs):
        super().__init__(phase_obj_3d, wavelength, **kwargs)
        self.distance_end_to_center = np.sum(
            self.slice_separation) / 2. + phase_obj_3d.slice_separation[-1]
        self.distance_beginning_to_center = np.sum(self.slice_separation) / 2.
        self.slice_separation = np.append(phase_obj_3d.slice_separation,
                                          [phase_obj_3d.slice_separation[-1]])
        self.slice_separation             = np.asarray([sum(self.slice_separation[x:x+self.slice_binning_factor]) \
                                             for x in range(0,len(self.slice_separation),self.slice_binning_factor)])
        if self.slice_separation.shape[0] != self.shape[2]:
            print(
                "Number of slices does not match with number of separations!")

        # generate green's function convolution kernel
        fxlin, fylin = self._genFrequencyGrid()
        kernel_mask = (self.RI / self.wavelength)**2 > 1.01 * (
            fxlin * af.conjg(fxlin) + fylin * af.conjg(fylin))
        self.green_kernel_2d = -0.25j * af.exp(
            2.0j * np.pi * self.fzlin * self.pixel_size_z) / np.pi / self.fzlin
        self.green_kernel_2d *= kernel_mask
        self.green_kernel_2d[af.isnan(self.green_kernel_2d) == 1] = 0.0
示例#11
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    def forward(self, contrast_obj, fx_illu, fy_illu):
        #compute illumination
        with contexttimer.Timer() as timer:
            field, fx_illu, fy_illu, fz_illu = self._genIllumination(
                fx_illu, fy_illu)
            field[:, :] *= np.exp(1.0j * 2.0 * np.pi * fz_illu *
                                  self.initial_z_position)
            field_layer_conj = af.constant(0.0,
                                           self.shape[0],
                                           self.shape[1],
                                           self.shape[2],
                                           dtype=af_complex_datatype)

            if (type(contrast_obj).__module__ == np.__name__):
                phasecontrast_obj_af = af.to_array(contrast_obj)
                flag_gpu_inout = False
            else:
                phasecontrast_obj_af = contrast_obj
                flag_gpu_inout = True

            #Binning
            obj_af = self._binObject(phasecontrast_obj_af)

            #Potentials to Transmittance
            obj_af = af.exp(1.0j * self.sigma * obj_af)

            for layer in range(self.shape[2]):
                field_layer_conj[:, :, layer] = af.conjg(field)
                field[:, :] *= obj_af[:, :, layer]
                if layer < self.shape[2] - 1:
                    field = self._propagationInplace(
                        field, self.slice_separation[layer])

            #propagate to volume center
            cache = (obj_af, field_layer_conj, flag_gpu_inout)

            if self.focus_at_center:
                #propagate to volume center
                field = self._propagationInplace(field,
                                                 self.distance_end_to_center,
                                                 adjoint=True)
        return {'forward_scattered_field': field, 'cache': cache}
示例#12
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 def _update(self, field_adj_f):
     """function to recover pupil"""
     self.pupil_gradient_phase[:, :] += af.conjg(self.field_f) * field_adj_f
     self.measure_count += 1
     if self.measure_count == self.measurement_num:
         if self.update_method == "gradient":
             self.pupil[:, :] -= self.pupil_step_size * self.pupil_gradient_phase * self.pupil_support
         elif self.update_method == "GaussNewton":
             self.pupil[:, :] -= self.pupil_step_size * af.abs(
                 self.field_f
             ) * self.pupil_gradient_phase * self.pupil_support / (
                 (self.approx_hessian + 1e-3) *
                 af.max(af.abs(self.field_f[:])))
             self.approx_hessian[:, :] = 0.0
         else:
             print("there is no update_method \"%s\"!" %
                   (self.update_method))
             raise
         self.pupil_gradient_phase[:, :] = 0.0
         self.measure_count = 0
示例#13
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    def forward(self, trans_obj, fx_illu, fy_illu):
        if self.slice_binning_factor > 1:
            print(
                "Slicing is not implemented for MultiTransmittance algorithm!")
            raise

        #compute illumination
        field, fx_illu, fy_illu, fz_illu = self._genIllumination(
            fx_illu, fy_illu)
        field[:, :] *= np.exp(1.0j * 2.0 * np.pi * fz_illu *
                              self.initial_z_position)

        #multi-slice transmittance forward propagation
        field_layer_conj = af.constant(0.0,
                                       trans_obj.shape[0],
                                       trans_obj.shape[1],
                                       trans_obj.shape[2],
                                       dtype=af_complex_datatype)
        if (type(trans_obj).__module__ == np.__name__):
            trans_obj_af = af.to_array(trans_obj)
            flag_gpu_inout = False
        else:
            trans_obj_af = trans_obj
            flag_gpu_inout = True

        for layer in range(self.shape[2]):
            field_layer_conj[:, :, layer] = af.conjg(field)
            field[:, :] *= trans_obj_af[:, :, layer]
            if layer < self.shape[2] - 1:
                field = self._propagationInplace(field,
                                                 self.slice_separation[layer])

        #store intermediate variables for adjoint operation
        cache = (trans_obj_af, field_layer_conj, flag_gpu_inout)

        if self.focus_at_center:
            #propagate to volume center
            field = self._propagationInplace(field,
                                             self.distance_end_to_center,
                                             adjoint=True)
        return {'forward_scattered_field': field, 'cache': cache}
示例#14
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def propKernel(shape,
               pixel_size,
               wavelength,
               prop_distance,
               NA=None,
               RI=1.0,
               band_limited=True):
    assert len(shape) == 2, "pupil should be two dimensional!"
    fxlin = genGrid(shape[1], 1 / pixel_size / shape[1], flag_shift=True)
    fylin = genGrid(shape[0], 1 / pixel_size / shape[0], flag_shift=True)
    fxlin = af.tile(fxlin.T, shape[0], 1)
    fylin = af.tile(fylin, 1, shape[1])

    if band_limited:
        assert NA is not None, "need to provide numerical aperture of the system!"
        Pcrop = genPupil(shape, pixel_size, NA, wavelength)
    else:
        Pcrop = 1.0

    prop_kernel  = Pcrop * af.exp(1.0j * 2.0 * np.pi * abs(prop_distance) * Pcrop *\
                                  ((RI/wavelength)**2 - fxlin**2 - fylin**2)**0.5)
    prop_kernel = af.conjg(prop_kernel) if prop_distance < 0 else prop_kernel
    return prop_kernel
示例#15
0
    def adjoint(self, residual, cache):
        trans_obj_af, field_layer_conj_or_grad, flag_gpu_inout = cache

        #back-propagte to volume center
        field_bp = residual
        if self.focus_at_center:
            #propagate to the last layer
            field_bp = self._propagationInplace(field_bp,
                                                self.distance_end_to_center)

        #multi-slice transmittance backward
        for layer in range(self.shape[2] - 1, -1, -1):
            field_layer_conj_or_grad[:, :,
                                     layer] = field_bp * field_layer_conj_or_grad[:, :,
                                                                                  layer]
            if layer > 0:
                field_bp[:, :] *= af.conjg(trans_obj_af[:, :, layer])
                field_bp = self._propagationInplace(
                    field_bp, self.slice_separation[layer - 1], adjoint=True)
        if flag_gpu_inout:
            return {'gradient': field_layer_conj_or_grad}
        else:
            return {'gradient': np.array(field_layer_conj_or_grad)}
示例#16
0
    def _propagationInplace(self,
                            field,
                            propagation_distance,
                            adjoint=False,
                            in_real=True):
        """
        propagation operator that uses angular spectrum to propagate the wave

        field:                  input field
        propagation_distance:   distance to propagate the wave
        adjoint:                boolean variable to perform adjoint operation (i.e. opposite direction)
        """
        if in_real:
            af.fft2_inplace(field)
        if adjoint:
            field[:, :] *= af.conjg(
                af.exp(self.prop_kernel_phase * propagation_distance))
        else:
            field[:, :] *= af.exp(self.prop_kernel_phase *
                                  propagation_distance)
        if in_real:
            af.ifft2_inplace(field)
        return field
示例#17
0
def simple_arith(verbose=False):
    display_func = _util.display_func(verbose)
    print_func = _util.print_func(verbose)

    a = af.randu(3, 3)
    b = af.constant(4, 3, 3)
    display_func(a)
    display_func(b)

    c = a + b
    d = a
    d += b

    display_func(c)
    display_func(d)
    display_func(a + 2)
    display_func(3 + a)

    c = a - b
    d = a
    d -= b

    display_func(c)
    display_func(d)
    display_func(a - 2)
    display_func(3 - a)

    c = a * b
    d = a
    d *= b

    display_func(c * 2)
    display_func(3 * d)
    display_func(a * 2)
    display_func(3 * a)

    c = a / b
    d = a
    d /= b

    display_func(c / 2.0)
    display_func(3.0 / d)
    display_func(a / 2)
    display_func(3 / a)

    c = a % b
    d = a
    d %= b

    display_func(c % 2.0)
    display_func(3.0 % d)
    display_func(a % 2)
    display_func(3 % a)

    c = a**b
    d = a
    d **= b

    display_func(c**2.0)
    display_func(3.0**d)
    display_func(a**2)
    display_func(3**a)

    display_func(a < b)
    display_func(a < 0.5)
    display_func(0.5 < a)

    display_func(a <= b)
    display_func(a <= 0.5)
    display_func(0.5 <= a)

    display_func(a > b)
    display_func(a > 0.5)
    display_func(0.5 > a)

    display_func(a >= b)
    display_func(a >= 0.5)
    display_func(0.5 >= a)

    display_func(a != b)
    display_func(a != 0.5)
    display_func(0.5 != a)

    display_func(a == b)
    display_func(a == 0.5)
    display_func(0.5 == a)

    a = af.randu(3, 3, dtype=af.Dtype.u32)
    b = af.constant(4, 3, 3, dtype=af.Dtype.u32)

    display_func(a & b)
    display_func(a & 2)
    c = a
    c &= 2
    display_func(c)

    display_func(a | b)
    display_func(a | 2)
    c = a
    c |= 2
    display_func(c)

    display_func(a >> b)
    display_func(a >> 2)
    c = a
    c >>= 2
    display_func(c)

    display_func(a << b)
    display_func(a << 2)
    c = a
    c <<= 2
    display_func(c)

    display_func(-a)
    display_func(+a)
    display_func(~a)
    display_func(a)

    display_func(af.cast(a, af.Dtype.c32))
    display_func(af.maxof(a, b))
    display_func(af.minof(a, b))
    display_func(af.rem(a, b))

    a = af.randu(3, 3) - 0.5
    b = af.randu(3, 3) - 0.5

    display_func(af.abs(a))
    display_func(af.arg(a))
    display_func(af.sign(a))
    display_func(af.round(a))
    display_func(af.trunc(a))
    display_func(af.floor(a))
    display_func(af.ceil(a))
    display_func(af.hypot(a, b))
    display_func(af.sin(a))
    display_func(af.cos(a))
    display_func(af.tan(a))
    display_func(af.asin(a))
    display_func(af.acos(a))
    display_func(af.atan(a))
    display_func(af.atan2(a, b))

    c = af.cplx(a)
    d = af.cplx(a, b)
    display_func(c)
    display_func(d)
    display_func(af.real(d))
    display_func(af.imag(d))
    display_func(af.conjg(d))

    display_func(af.sinh(a))
    display_func(af.cosh(a))
    display_func(af.tanh(a))
    display_func(af.asinh(a))
    display_func(af.acosh(a))
    display_func(af.atanh(a))

    a = af.abs(a)
    b = af.abs(b)

    display_func(af.root(a, b))
    display_func(af.pow(a, b))
    display_func(af.pow2(a))
    display_func(af.sigmoid(a))
    display_func(af.exp(a))
    display_func(af.expm1(a))
    display_func(af.erf(a))
    display_func(af.erfc(a))
    display_func(af.log(a))
    display_func(af.log1p(a))
    display_func(af.log10(a))
    display_func(af.log2(a))
    display_func(af.sqrt(a))
    display_func(af.cbrt(a))

    a = af.round(5 * af.randu(3, 3) - 1)
    b = af.round(5 * af.randu(3, 3) - 1)

    display_func(af.factorial(a))
    display_func(af.tgamma(a))
    display_func(af.lgamma(a))
    display_func(af.iszero(a))
    display_func(af.isinf(a / b))
    display_func(af.isnan(a / a))

    a = af.randu(5, 1)
    b = af.randu(1, 5)
    c = af.broadcast(lambda x, y: x + y, a, b)
    display_func(a)
    display_func(b)
    display_func(c)

    @af.broadcast
    def test_add(aa, bb):
        return aa + bb

    display_func(test_add(a, b))
示例#18
0
 def conj(self):
     if not numpy.issubdtype(self.dtype, numpy.complex):
         return afnumpy.copy(self)
     s = arrayfire.conjg(self.d_array)
     return ndarray(self.shape, dtype=pu.typemap(s.dtype()), af_array=s)
示例#19
0
文件: aflib.py 项目: bfrosik/pycdi
 def cong(arr):
     return af.conjg(arr)
示例#20
0
af.display(af.hypot(a, b))
af.display(af.sin(a))
af.display(af.cos(a))
af.display(af.tan(a))
af.display(af.asin(a))
af.display(af.acos(a))
af.display(af.atan(a))
af.display(af.atan2(a, b))

c = af.cplx(a)
d = af.cplx(a, b)
af.display(c)
af.display(d)
af.display(af.real(d))
af.display(af.imag(d))
af.display(af.conjg(d))

af.display(af.sinh(a))
af.display(af.cosh(a))
af.display(af.tanh(a))
af.display(af.asinh(a))
af.display(af.acosh(a))
af.display(af.atanh(a))

a = af.abs(a)
b = af.abs(b)

af.display(af.root(a, b))
af.display(af.pow(a, b))
af.display(af.pow2(a))
af.display(af.exp(a))
示例#21
0
def cart2Pol(x, y):
    rho = (x * af.conjg(x) + y * af.conjg(y))**0.5
    theta = af.atan2(af.real(y), af.real(x)).as_type(af_complex_datatype)
    return rho, theta
示例#22
0
def vdot(a, b):
    s = arrayfire.dot(arrayfire.conjg(a.flat.d_array), b.flat.d_array)
    return afnumpy.ndarray((), dtype=a.dtype, af_array=s)[()]
示例#23
0
def simple_arith(verbose = False):
    display_func = _util.display_func(verbose)
    print_func   = _util.print_func(verbose)

    a = af.randu(3,3,dtype=af.Dtype.u32)
    b = af.constant(4, 3, 3, dtype=af.Dtype.u32)
    display_func(a)
    display_func(b)

    c = a + b
    d = a
    d += b

    display_func(c)
    display_func(d)
    display_func(a + 2)
    display_func(3 + a)


    c = a - b
    d = a
    d -= b

    display_func(c)
    display_func(d)
    display_func(a - 2)
    display_func(3 - a)

    c = a * b
    d = a
    d *= b

    display_func(c * 2)
    display_func(3 * d)
    display_func(a * 2)
    display_func(3 * a)

    c = a / b
    d = a
    d /= b

    display_func(c / 2.0)
    display_func(3.0 / d)
    display_func(a / 2)
    display_func(3 / a)

    c = a % b
    d = a
    d %= b

    display_func(c % 2.0)
    display_func(3.0 % d)
    display_func(a % 2)
    display_func(3 % a)

    c = a ** b
    d = a
    d **= b

    display_func(c ** 2.0)
    display_func(3.0 ** d)
    display_func(a ** 2)
    display_func(3 ** a)

    display_func(a < b)
    display_func(a < 0.5)
    display_func(0.5 < a)

    display_func(a <= b)
    display_func(a <= 0.5)
    display_func(0.5 <= a)

    display_func(a > b)
    display_func(a > 0.5)
    display_func(0.5 > a)

    display_func(a >= b)
    display_func(a >= 0.5)
    display_func(0.5 >= a)

    display_func(a != b)
    display_func(a != 0.5)
    display_func(0.5 != a)

    display_func(a == b)
    display_func(a == 0.5)
    display_func(0.5 == a)

    display_func(a & b)
    display_func(a & 2)
    c = a
    c &= 2
    display_func(c)

    display_func(a | b)
    display_func(a | 2)
    c = a
    c |= 2
    display_func(c)

    display_func(a >> b)
    display_func(a >> 2)
    c = a
    c >>= 2
    display_func(c)

    display_func(a << b)
    display_func(a << 2)
    c = a
    c <<= 2
    display_func(c)

    display_func(-a)
    display_func(+a)
    display_func(~a)
    display_func(a)

    display_func(af.cast(a, af.Dtype.c32))
    display_func(af.maxof(a,b))
    display_func(af.minof(a,b))
    display_func(af.rem(a,b))

    a = af.randu(3,3) - 0.5
    b = af.randu(3,3) - 0.5

    display_func(af.abs(a))
    display_func(af.arg(a))
    display_func(af.sign(a))
    display_func(af.round(a))
    display_func(af.trunc(a))
    display_func(af.floor(a))
    display_func(af.ceil(a))
    display_func(af.hypot(a, b))
    display_func(af.sin(a))
    display_func(af.cos(a))
    display_func(af.tan(a))
    display_func(af.asin(a))
    display_func(af.acos(a))
    display_func(af.atan(a))
    display_func(af.atan2(a, b))

    c = af.cplx(a)
    d = af.cplx(a,b)
    display_func(c)
    display_func(d)
    display_func(af.real(d))
    display_func(af.imag(d))
    display_func(af.conjg(d))

    display_func(af.sinh(a))
    display_func(af.cosh(a))
    display_func(af.tanh(a))
    display_func(af.asinh(a))
    display_func(af.acosh(a))
    display_func(af.atanh(a))

    a = af.abs(a)
    b = af.abs(b)

    display_func(af.root(a, b))
    display_func(af.pow(a, b))
    display_func(af.pow2(a))
    display_func(af.exp(a))
    display_func(af.expm1(a))
    display_func(af.erf(a))
    display_func(af.erfc(a))
    display_func(af.log(a))
    display_func(af.log1p(a))
    display_func(af.log10(a))
    display_func(af.log2(a))
    display_func(af.sqrt(a))
    display_func(af.cbrt(a))

    a = af.round(5 * af.randu(3,3) - 1)
    b = af.round(5 * af.randu(3,3) - 1)

    display_func(af.factorial(a))
    display_func(af.tgamma(a))
    display_func(af.lgamma(a))
    display_func(af.iszero(a))
    display_func(af.isinf(a/b))
    display_func(af.isnan(a/a))

    a = af.randu(5, 1)
    b = af.randu(1, 5)
    c = af.broadcast(lambda x,y: x+y, a, b)
    display_func(a)
    display_func(b)
    display_func(c)

    @af.broadcast
    def test_add(aa, bb):
        return aa + bb

    display_func(test_add(a, b))
示例#24
0
    def _solveFirstOrderGradient(self, measurements, verbose, callback=None):
        """
        MAIN part of the solver, runs the FISTA algorithm
        configs:        configs object from class AlgorithmConfigs
        measurements:     all measurements 
                            self.configs.recon_from_field == True: field
                            self.configs.recon_from_field == False: amplitude measurement
        verbose:        boolean variable to print verbosely
        """
        flag_FISTA    = False
        if self.configs.method == "FISTA":
            flag_FISTA = True

        # update multiple angles at a time
        batch_update = False
        if self.configs.fista_global_update or self.configs.batch_size != 1:
            gradient_batch    = af.constant(0.0, self.phase_obj_3d.shape[0],\
                                                 self.phase_obj_3d.shape[1],\
                                                 self.phase_obj_3d.shape[2], dtype = af_complex_datatype)
            batch_update = True
            if self.configs.fista_global_update:
                self.configs.batch_size = 0

        #TODO: what if num_batch is not an integer
        if self.configs.batch_size == 0:
            num_batch = 1
        else:
            num_batch = self.number_illum // self.configs.batch_size
        stepsize      = self.configs.stepsize
        max_iter      = self.configs.max_iter
        reg_term      = self.configs.reg_term
        self.configs.error = []
        obj_gpu       = af.constant(0.0, self.phase_obj_3d.shape[0],\
                                         self.phase_obj_3d.shape[1],\
                                         self.phase_obj_3d.shape[2], dtype = af_complex_datatype)

        #Initialization for FISTA update
        if flag_FISTA:
            restart       = self.configs.restart
            y_k           = self._x.copy()
            t_k           = 1.0

        #Set Callback flag
        if callback is None:
            run_callback = False
        else:
            run_callback = True

        #Start of iterative algorithm
        with contexttimer.Timer() as timer:
            if verbose:
                print("---- Start of the %5s algorithm ----" %(self.scat_model))
            for iteration in range(max_iter):
                illu_counter          = 0
                cost                  = 0.0
                obj_gpu[:]            = af.to_array(self._x)
                if self.configs.random_order:
                    illu_order = np.random.permutation(range(self.number_illum))
                else:
                    illu_order = range(self.number_illum)

                for batch_idx in range(num_batch):
                    if batch_update:
                        gradient_batch[:,:,:] = 0.0

                    if self.configs.batch_size == 0:
                        illu_indices = illu_order
                    else:
                        illu_indices = illu_order[batch_idx * self.configs.batch_size : (batch_idx+1) * self.configs.batch_size]
                    for illu_idx in illu_indices:                                                    
                        #forward scattering
                        fx_illu                       = self.fx_illu_list[illu_idx]
                        fy_illu                       = self.fy_illu_list[illu_idx]
                        fields                        = self._forwardMeasure(fx_illu, fy_illu, obj = obj_gpu)
                        #calculate error
                        measurement             = af.to_array(measurements[:,:,:,illu_idx].astype(np_complex_datatype))

                        if self.configs.recon_from_field:
                            residual                  = fields["forward_scattered_field"] - measurement
                        else:
                            if self.configs.cost_criterion == "intensity":
                                residual          = af.abs(fields["forward_scattered_field"])**2 - measurement**2
                            elif self.configs.cost_criterion == "amplitude":
                                residual          = af.abs(fields["forward_scattered_field"]) - measurement
                        cost                     += af.sum(residual*af.conjg(residual)).real
                        #calculate gradient
                        if batch_update:
                            gradient_batch[:, :, :]  += self._computeGradient(fields, measurement)[0]
                        else:
                            gradient                  = self._computeGradient(fields, measurement)
                            obj_gpu[:, :, :]         -= stepsize * gradient

                        if verbose:
                            if self.number_illum > 1:
                                print("gradient update of illumination {:03d}/{:03d}.".format(illu_counter, self.number_illum), end="\r")
                                illu_counter += 1
                        fields      = None
                        residual    = None
                        gradient    = None
                        measurement = None
                        pupil       = None
                        af.device_gc()

                    if batch_update:
                        obj_gpu[:, :, :] -= stepsize * gradient_batch

                if np.isnan(obj_gpu).sum() > 0:
                    stepsize     *= 0.1
                    self.configs.time_elapsed = timer.elapsed
                    print("WARNING: Gradient update diverges! Resetting stepsize to %3.2f" %(stepsize))
                    t_k = 1.0
                    continue

                # L2 regularizer
                obj_gpu[:, :, :] -= stepsize * reg_term * obj_gpu

                #record total error
                self.configs.error.append(cost + reg_term * af.sum(obj_gpu*af.conjg(obj_gpu)).real)

                #Prox operators
                af.device_gc()
                obj_gpu = self._regularizer_obj.applyRegularizer(obj_gpu)

                if flag_FISTA:
                    #check convergence
                    if iteration > 0:
                        if self.configs.error[-1] > self.configs.error[-2]:
                            if restart:
                                t_k              = 1.0
                                
                                self._x[:, :, :] = y_k
                                # stepsize        *= 0.8
                                
                                print("WARNING: FISTA Restart! Error: %5.5f" %(np.log10(self.configs.error[-1])))
                                if run_callback:
                                    callback(self._x, self.configs)
                                continue
                            else:
                                print("WARNING: Error increased! Error: %5.5f" %(np.log10(self.configs.error[-1])))

                    #FISTA auxiliary variable
                    y_k1                 = np.array(obj_gpu)
                    if len(y_k1.shape) < 3:
                        y_k1 = y_k1[:,:,np.newaxis]

                    #FISTA update
                    t_k1                 = 0.5*(1.0 + (1.0 + 4.0*t_k**2)**0.5)
                    beta                 = (t_k - 1.0) / t_k1
                    self._x[:, :, :]     = y_k1 + beta * (y_k1 - y_k)
                    t_k                  = t_k1
                    y_k                  = y_k1.copy()
                else:
                    #check convergence
                    temp = np.array(obj_gpu)
                    if len(temp.shape) < 3:
                        temp = temp[:,:,np.newaxis]                    
                    self._x[:, :, :]  = temp
                    if iteration > 0:
                        if self.configs.error[-1] > self.configs.error[-2]:
                            print("WARNING: Error increased! Error: %5.5f" %(np.log10(self.configs.error[-1])))
                            stepsize     *= 0.8
                   
                if verbose:
                    print("iteration: %d/%d, error: %5.5f, elapsed time: %5.2f seconds" %(iteration+1, max_iter, np.log10(self.configs.error[-1]), timer.elapsed))

                if run_callback:
                    callback(self._x, self.configs)
                    
        self.configs.time_elapsed = timer.elapsed
        return self._x
af.display(af.hypot(a, b))
af.display(af.sin(a))
af.display(af.cos(a))
af.display(af.tan(a))
af.display(af.asin(a))
af.display(af.acos(a))
af.display(af.atan(a))
af.display(af.atan2(a, b))

c = af.cplx(a)
d = af.cplx(a,b)
af.display(c)
af.display(d)
af.display(af.real(d))
af.display(af.imag(d))
af.display(af.conjg(d))

af.display(af.sinh(a))
af.display(af.cosh(a))
af.display(af.tanh(a))
af.display(af.asinh(a))
af.display(af.acosh(a))
af.display(af.atanh(a))

a = af.abs(a)
b = af.abs(b)

af.display(af.root(a, b))
af.display(af.pow(a, b))
af.display(af.pow2(a))
af.display(af.exp(a))
示例#26
0
 def conj(self):
     if not numpy.issubdtype(self.dtype, numpy.complex):
         return afnumpy.copy(self)
     s = arrayfire.conjg(self.d_array)
     return ndarray(self.shape, dtype=pu.typemap(s.dtype()), af_array=s)
示例#27
0
def vdot(a, b):
    s = arrayfire.dot(arrayfire.conjg(a.flat.d_array), b.flat.d_array)
    return afnumpy.ndarray((), dtype=a.dtype, af_array=s)[()]