phydev

  subroutine grid_output(this, filename, other)

    class(mesh) :: this
    class(cell), optional, intent(in) :: other
    character(len=20), intent(in) :: filename
    integer :: tag, ip, L(3), nodes, s(3), icom(3), ip_new, gcom(3), lp(3), i

    tag = 33423
    L = this%L
    nodes = this%nodes

phydev

"""
creates a decorator that allows to call functions parameterless without empty brackets.
@author: phydev.github.io
date: Sunday, 20 october 2019 02:20
"""

def without_brackets(function):
  """
  function that will be used as decorator for calling functions without using brackets
  :param function: any function without arguments

phydev

builtin_print(PyObject *self, PyObject *const *args, Py_ssize_t nargs, PyObject *kwnames)
{
    static const char * const _keywords[] = {"sep", "end", "file", "flush", 0};
    static struct _PyArg_Parser _parser = {"|OOOp:print", _keywords, 0};
    PyObject *sep = NULL, *end = NULL, *file = NULL;
    int flush = 0;
    int i, err;

    if (kwnames != NULL &&
            !_PyArg_ParseStackAndKeywords(args + nargs, 0, kwnames, &_parser,

phydev

"""
    allen-cahn.jl
    integration of the allen-cahn equation from the phase-field formalism using convolutions
    @author: phydev.github.io
    License: GNU GPL v3
"""

# importing external packages
using DSP  # digital signal processing package 
using Plots

phydev

import trajpy.trajpy as tj
import trajpy.traj_generator as tjg

trajectory = 'anomalous'
r = np.zeros((250, 4))
n_steps = 250
n_samples = 5000
dt = 1.0
D = 100.

phydev

import numpy as np

def center_of_mass(rho, L, ndim=2):
    """
    :param rho: array N x M
    :param L: array with the grid dimensions np.array([Lx, Ly])
    :param ndim: number of dimensions
    :return com: center of mass
    """
    com, volume = np.zeros((ndim)), 0.0

phydev

""" 
  Convert sklearn dataset into pandas DataFrame object.
  Source: https://stackoverflow.com/a/46379878/5059616

"""

import numpy as np
import pandas as pd
from sklearn import datasets

phydev

"""
    Computes the set of features for each .dat file in PATH,
    then loads a random forest classifier and predicts if the
    trajectory is confined, normal, anomalous or superdiffusive.

    The output is given inside PATH as a yyyy-mm-dd.csv file.

    @phydev 
"""
import os

phydev

def train_test_split_tensors(X, y, **options):
    """
    encapsulation for the sklearn.model_selection.train_test_split function
    in order to split tensors objects and return tensors as output

    :param X: tensorflow.Tensor object
    :param y: tensorflow.Tensor object
    :dict **options: typical sklearn options are available, such as test_size and train_size
    """

phydev

"""
    LASSO regression (L1 regularization) with gradient descent
    TODO: estimate intercept
    phydev.github.io
"""

def predict(X, beta):
    """
        predict the regression
    """