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def sample_dball_uniform(n_points=1000, n_dims=3):
    """Sample uniformly from d-dimensional ball
    
    The code is inspired by this small note:
    https://blogs.sas.com/content/iml/2016/04/06/generate-points-uniformly-in-ball.html
    https://www.sciencedirect.com/science/article/pii/S0047259X10001211
    
    Parameters
    ----------
    n_points : int

sergeyprokudin

def random_number_generator(arg1, arg2):
    """
    Summary line.

    Extended description of function.

    Parameters
    ----------
    arg1 : int
        Description of arg1

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import os

SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__))
PROJECT_DIR = os.path.dirname(SCRIPT_DIR)
LOG_DIR = os.path.join(PROJECT_DIR, 'logs')

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import numpy as np


def count_conv_params_flops(conv_layer, verbose=1):
    # out shape is  n_cells_dim1 * (n_cells_dim2 * n_cells_dim3)
    out_shape = conv_layer.output.shape.as_list()
    n_cells_total = np.prod(out_shape[1:-1])

    n_conv_params_total = conv_layer.count_params()

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import keras.backend as K
import numpy as np


def gaussian_nll(ytrue, ypreds):
    """Keras implmementation of multivariate Gaussian negative loglikelihood loss function. 
    This implementation implies diagonal covariance matrix.
    
    Parameters
    ----------

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#http://www.svcl.ucsd.edu/courses/ece161c/handouts/DCT.pdf

import numpy as np
import matplotlib.pyplot as plt

pi = np.pi

N = 50 # timesteps
k = 10 # n basis functions