<filename>data/transforms/mrirandphasebig.py
import numpy as np
from scipy import signal
from skimage.transform import resize
from scipy.ndimage import gaussian_filter


def concat_zpad(arr1, arr2):
    diff_dims = np.array(arr1.shape) - np.array(arr2.shape)

    for i in range(1, diff_dims):
        pad_size = np.zeros(size=(arr1.ndim, 2))
        pad_size[i, 0] = np.absolute(diff_dims[i]).astype(np.int) // 2
        pad_size[i, 1] = np.absolute(diff_dims[i]) - pad_size[i, 0]

        pad_size = list(pad_size)
        for j, el in enumerate(pad_size):
            pad_size[j] = tuple(el)
        pad_size = tuple(pad_size)

        if diff_dims < 0:
            arr2 = np.pad(arr2, pad_size, mode='constant')
        else:
            arr1 = np.pad(arr1, pad_size, mode='constant')

    return np.concatenate((arr1, arr2))


class MriRandPhaseBig(object):
    """A transform class for applying random phase.

    This is designed for larger arrays (> 256). It generates the phase on a
    256 by 256 grid and then interpolates it to the target grid. This is
    necessary for expedient training.

    This initializes and object that applies random phase to a set of input
    images. The class accomplishes this by generating a random cloud of points
    and convolving the points with a Gaussian filter of fixed size - equivalent
    to summing up many Gaussian basis functions.

    The transform is designed to take as input a "sample" object with "dat"
    and "target" keys. Flags determine operations on the data contained in the
    keys.

    Args:
        dat_op (boolean, default: True): A flag on whether to add random phase
            to the "dat."
        targ_op (boolean, default: False): A flag on whether to add random
            phase to the "target."
        nophase_prob (double, default=0.05): Probability of not applying random
            phase.
        num_bfn_per_size_param (double, default=15): A parameter for a Poisson
            distribution that generates the number of basis functions to
            construct for each basis function size.
        bfn_amp_mean_sig (tuple, default=(0, 30)): A tuple specifying the mean
            and standard deviation for the distribution of basis function
            amplitudes.
        num_bfn_per_size_param (double, default=12): A parameter for a Poisson
            distribution that generates the number of different basis function
            sizes.
        bfn_size_amp_sig_min (tuple, default=(8, 3, 1): Parameters for the
            mean, standard deviation, and minimum value of the radial basis
            function sizes.
    """

    def __init__(self, dat_op=True, targ_op=False, nophase_prob=0.01,
                 num_bfn_per_size_param=50, bfn_amp_mean_sig=(0, 5),
                 num_sizes_param=8, bfn_size_amp_sig_min=(50, 30, 10),
                 pi_noprob=0.5):
        self.dat_op = dat_op
        self.targ_op = targ_op
        self.nophase_prob = nophase_prob
        self.num_bfn_per_size_param = num_bfn_per_size_param
        self.bfn_amp_mean_sig = bfn_amp_mean_sig
        self.num_sizes_param = num_sizes_param
        self.bfn_size_amp_sig_min = bfn_size_amp_sig_min
        self.pi_noprob = pi_noprob
        self.sim_size = 256

    def __call__(self, sample):
        """
        Args:
            sample (dict): a sample with 'target' and 'dat' to receive random
                phase.
        Returns:
            sample (dict): a sample with 'target' and 'dat' arrays with random
                phase.
        """
        target, dat = sample['target'], sample['dat']

        if target.shape[0] < target.shape[1]:
            size1 = 256
            size0 = np.floor(
                target.shape[0]/target.shape[1] * size1).astype(np.int)
        elif target.shape[1] < target.shape[0]:
            size0 = 256
            size1 = np.floor(
                target.shape[1]/target.shape[0] * size0).astype(np.int)
        else:
            size0 = 256
            size1 = 256

        dims = np.flip(target.shape, 0)
        if len(dims) > 2:
            print(
                'mrirandphase.py: Not programmed for more than 2 dimensions! Aborting...')
            return sample

        phase_map = np.zeros(shape=target.shape)
        if np.random.uniform() > self.pi_noprob:
            if np.random.uniform() > 0.5:
                phase_map += 180
            else:
                phase_map -= 180

        if np.random.uniform() > self.nophase_prob:
            num_sizes = np.random.poisson(self.num_sizes_param)
            gauss_map = np.zeros(
                shape=(num_sizes, size0, size1))

            for i in range(num_sizes):
                gauss_dims = gauss_map.shape[1:]

                num_bfns = np.random.poisson(lam=self.num_bfn_per_size_param)

                bfn_size = np.absolute(
                    np.random.normal(
                        loc=self.bfn_size_amp_sig_min[0],
                        scale=self.bfn_size_amp_sig_min[1]
                    )
                )
                bfn_size = np.maximum(bfn_size, self.bfn_size_amp_sig_min[2])

                bfn_amps = np.random.normal(
                    loc=self.bfn_amp_mean_sig[0],
                    scale=self.bfn_amp_mean_sig[1],
                    size=num_bfns
                ) * (bfn_size**2 * 2 * np.pi)

                bfn_size_ind = (i * np.ones(shape=num_bfns)).astype(np.int)
                bfn_x0s = np.floor(np.random.uniform(
                    high=gauss_dims[0], size=num_bfns)).astype(np.int)
                bfn_y0s = np.floor(np.random.uniform(
                    high=gauss_dims[1], size=num_bfns)).astype(np.int)

                gauss_map[tuple([bfn_size_ind, bfn_x0s, bfn_y0s])] = bfn_amps

                gauss_map[i, ...] = gaussian_filter(
                    gauss_map[i, ...], sigma=bfn_size)

            # update the phase map
            phase_map += resize(
                np.sum(gauss_map, 0),
                phase_map.shape,
                order=3,
                mode='reflect',
            )

        if self.targ_op:
            target = target * np.exp(1j * phase_map * np.pi / 180)
            sample['target'] = target

        if self.dat_op:
            phase_map = np.reshape(phase_map, (1,) + phase_map.shape)
            dat = dat * np.exp(1j * phase_map * np.pi / 180)
            sample['dat'] = dat

        return sample

    def __repr__(self):
        return self.__class__.__name__


def main():
    """No arguments, runs a testing script."""
    print('running test script')
    import matplotlib.pyplot as plt
    import time

    phaseob = MriRandPhaseBig(pi_noprob=1)

    in_im = np.ones(shape=(1024, 1024))

    sample = {
        'dat': in_im,
        'target': in_im
    }

    time_start = time.time()
    out_im = phaseob(sample)['dat'][0, ...]
    time_end = time.time()

    print('sim time: {}'.format(time_end - time_start))

    plt.imshow(np.angle(out_im))
    plt.gray()
    plt.colorbar()
    plt.show()


if __name__ == '__main__':
    main()

<filename>data/transforms/mrirandphasebig.py
import numpy as np
from scipy import signal
from skimage.transform import resize
from scipy.ndimage import gaussian_filter


def concat_zpad(arr1, arr2):
    diff_dims = np.array(arr1.shape) - np.array(arr2.shape)

    for i in range(1, diff_dims):
        pad_size = np.zeros(size=(arr1.ndim, 2))
        pad_size[i, 0] = np.absolute(diff_dims[i]).astype(np.int) // 2
        pad_size[i, 1] = np.absolute(diff_dims[i]) - pad_size[i, 0]

        pad_size = list(pad_size)
        for j, el in enumerate(pad_size):
            pad_size[j] = tuple(el)
        pad_size = tuple(pad_size)

        if diff_dims < 0:
            arr2 = np.pad(arr2, pad_size, mode='constant')
        else:
            arr1 = np.pad(arr1, pad_size, mode='constant')

    return np.concatenate((arr1, arr2))


class MriRandPhaseBig(object):
    """A transform class for applying random phase.

    This is designed for larger arrays (> 256). It generates the phase on a
    256 by 256 grid and then interpolates it to the target grid. This is
    necessary for expedient training.

    This initializes and object that applies random phase to a set of input
    images. The class accomplishes this by generating a random cloud of points
    and convolving the points with a Gaussian filter of fixed size - equivalent
    to summing up many Gaussian basis functions.

    The transform is designed to take as input a "sample" object with "dat"
    and "target" keys. Flags determine operations on the data contained in the
    keys.

    Args:
        dat_op (boolean, default: True): A flag on whether to add random phase
            to the "dat."
        targ_op (boolean, default: False): A flag on whether to add random
            phase to the "target."
        nophase_prob (double, default=0.05): Probability of not applying random
            phase.
        num_bfn_per_size_param (double, default=15): A parameter for a Poisson
            distribution that generates the number of basis functions to
            construct for each basis function size.
        bfn_amp_mean_sig (tuple, default=(0, 30)): A tuple specifying the mean
            and standard deviation for the distribution of basis function
            amplitudes.
        num_bfn_per_size_param (double, default=12): A parameter for a Poisson
            distribution that generates the number of different basis function
            sizes.
        bfn_size_amp_sig_min (tuple, default=(8, 3, 1): Parameters for the
            mean, standard deviation, and minimum value of the radial basis
            function sizes.
    """

    def __init__(self, dat_op=True, targ_op=False, nophase_prob=0.01,
                 num_bfn_per_size_param=50, bfn_amp_mean_sig=(0, 5),
                 num_sizes_param=8, bfn_size_amp_sig_min=(50, 30, 10),
                 pi_noprob=0.5):
        self.dat_op = dat_op
        self.targ_op = targ_op
        self.nophase_prob = nophase_prob
        self.num_bfn_per_size_param = num_bfn_per_size_param
        self.bfn_amp_mean_sig = bfn_amp_mean_sig
        self.num_sizes_param = num_sizes_param
        self.bfn_size_amp_sig_min = bfn_size_amp_sig_min
        self.pi_noprob = pi_noprob
        self.sim_size = 256

    def __call__(self, sample):
        """
        Args:
            sample (dict): a sample with 'target' and 'dat' to receive random
                phase.
        Returns:
            sample (dict): a sample with 'target' and 'dat' arrays with random
                phase.
        """
        target, dat = sample['target'], sample['dat']

        if target.shape[0] < target.shape[1]:
            size1 = 256
            size0 = np.floor(
                target.shape[0]/target.shape[1] * size1).astype(np.int)
        elif target.shape[1] < target.shape[0]:
            size0 = 256
            size1 = np.floor(
                target.shape[1]/target.shape[0] * size0).astype(np.int)
        else:
            size0 = 256
            size1 = 256

        dims = np.flip(target.shape, 0)
        if len(dims) > 2:
            print(
                'mrirandphase.py: Not programmed for more than 2 dimensions! Aborting...')
            return sample

        phase_map = np.zeros(shape=target.shape)
        if np.random.uniform() > self.pi_noprob:
            if np.random.uniform() > 0.5:
                phase_map += 180
            else:
                phase_map -= 180

        if np.random.uniform() > self.nophase_prob:
            num_sizes = np.random.poisson(self.num_sizes_param)
            gauss_map = np.zeros(
                shape=(num_sizes, size0, size1))

            for i in range(num_sizes):
                gauss_dims = gauss_map.shape[1:]

                num_bfns = np.random.poisson(lam=self.num_bfn_per_size_param)

                bfn_size = np.absolute(
                    np.random.normal(
                        loc=self.bfn_size_amp_sig_min[0],
                        scale=self.bfn_size_amp_sig_min[1]
                    )
                )
                bfn_size = np.maximum(bfn_size, self.bfn_size_amp_sig_min[2])

                bfn_amps = np.random.normal(
                    loc=self.bfn_amp_mean_sig[0],
                    scale=self.bfn_amp_mean_sig[1],
                    size=num_bfns
                ) * (bfn_size**2 * 2 * np.pi)

                bfn_size_ind = (i * np.ones(shape=num_bfns)).astype(np.int)
                bfn_x0s = np.floor(np.random.uniform(
                    high=gauss_dims[0], size=num_bfns)).astype(np.int)
                bfn_y0s = np.floor(np.random.uniform(
                    high=gauss_dims[1], size=num_bfns)).astype(np.int)

                gauss_map[tuple([bfn_size_ind, bfn_x0s, bfn_y0s])] = bfn_amps

                gauss_map[i, ...] = gaussian_filter(
                    gauss_map[i, ...], sigma=bfn_size)

            # update the phase map
            phase_map += resize(
                np.sum(gauss_map, 0),
                phase_map.shape,
                order=3,
                mode='reflect',
            )

        if self.targ_op:
            target = target * np.exp(1j * phase_map * np.pi / 180)
            sample['target'] = target

        if self.dat_op:
            phase_map = np.reshape(phase_map, (1,) + phase_map.shape)
            dat = dat * np.exp(1j * phase_map * np.pi / 180)
            sample['dat'] = dat

        return sample

    def __repr__(self):
        return self.__class__.__name__


def main():
    """No arguments, runs a testing script."""
    print('running test script')
    import matplotlib.pyplot as plt
    import time

    phaseob = MriRandPhaseBig(pi_noprob=1)

    in_im = np.ones(shape=(1024, 1024))

    sample = {
        'dat': in_im,
        'target': in_im
    }

    time_start = time.time()
    out_im = phaseob(sample)['dat'][0, ...]
    time_end = time.time()

    print('sim time: {}'.format(time_end - time_start))

    plt.imshow(np.angle(out_im))
    plt.gray()
    plt.colorbar()
    plt.show()


if __name__ == '__main__':
    main()

A transform class for applying random phase.      This is designed for larger arrays (> 256). It generates the phase on a     256 by 256 grid and then interpolates it to the target grid. This is     necessary for expedient training.      This initializes and object that applies random phase to a set of input     images. The class accomplishes this by generating a random cloud of points     and convolving the points with a Gaussian filter of fixed size - equivalent     to summing up many Gaussian basis functions.      The transform is designed to take as input a "sample" object with "dat"     and "target" keys. Flags determine operations on the data contained in the     keys.      Args:         dat_op (boolean, default: True): A flag on whether to add random phase             to the "dat."         targ_op (boolean, default: False): A flag on whether to add random             phase to the "target."         nophase_prob (double, default=0.05): Probability of not applying random             phase.         num_bfn_per_size_param (double, default=15): A parameter for a Poisson             distribution that generates the number of basis functions to             construct for each basis function size.         bfn_amp_mean_sig (tuple, default=(0, 30)): A tuple specifying the mean             and standard deviation for the distribution of basis function             amplitudes.         num_bfn_per_size_param (double, default=12): A parameter for a Poisson             distribution that generates the number of different basis function             sizes.         bfn_size_amp_sig_min (tuple, default=(8, 3, 1): Parameters for the             mean, standard deviation, and minimum value of the radial basis             function sizes. Args:             sample (dict): a sample with 'target' and 'dat' to receive random                 phase.         Returns:             sample (dict): a sample with 'target' and 'dat' arrays with random                 phase. # update the phase map No arguments, runs a testing script.