Stefano314

class LinearRegression:
    """
    Simple linear regression using the Ordinary Least Square method:

    Coefs = (x.T * X)^-1 * x.T * y
    
    where Coefs[0] = q, Coefs[1] = m
    """
    def __init__(self, x, y): # Just give X and Y

Stefano314

import networkx as nx
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

class Connectome:
    
    def __init__(self, G, quantities = None, time = None):
        
        self.G = G

Stefano314

import numpy as np
import matplotlib.pyplot as plt

class Grid3D:

    def __init__(self, pointsPosition, boxSubdivision):#, cellSize):

        self._minCorner, self._maxCorner = self._GetCorners(pointsPosition)
        self.cellSize = [0,0,0]#cellSize
        self.boxSubdivision = boxSubdivision

Stefano314

import numpy as np
import trimesh

def array_mask_mesh(A : np.ndarray, radius=1., width=0.05, edges = False, path : str='test.obj', volume='cube'):
    """
    Create a 3D object of a (N,M,L) array mask
    """
    centers = np.argwhere(A)
    radius = [radius]*centers.shape[0]
    

Stefano314

import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as colormap

heat_equation = True
random_walk_diffusion = False

np.random.seed(1)

Stefano314

// READ THE COMMENT FOR ADDITIONAL INFORMATION

#include <iostream>
#include <vector>
#include <fstream>
#include <sstream>
#include <algorithm> // for "erase-remove idiom", unique, remove
#include <set>
#include <random> // for unifrom distribution using mt19937
#include <ctime> // for random seed

Stefano314

def flatten_list(lst : list) -> list:
  """
  Description
  -----------
  Flatten an irregular list of lists (meaning that it can contain also non lists). If there are multiple nested lists
  the function must be used as many times as the nested order.
  """
  
  result = []

Stefano314

from tqdm import tqdm

from pandas.util.testing import assert_frame_equal
import pandas as pd
import numpy as np

def df_equal(df1, df2):
    """
    Description
    -----------

Stefano314

import numpy as np

def fast_combinations(row : list, self_loops = False) -> np.array:
    """
    Description
    -----------
    Fast way to obtain the combination of all the elements inside a list using Numpy.
    Self connections are present.

    Parameters

Stefano314

import pandas as pd
import numpy as np
from tqdm import tqdm

def fast_combinations(row : list, self_loops = False) -> np.array:
    """
    Description
    -----------
    Fast way to obtain the combination of all the elements inside a list using Numpy.
    Self connections are present.