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import pandas
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

data_raw = pandas.read_csv("proctatinium_data.csv")
data_raw = data_raw.astype(float)


def func(t, l):

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import numpy.random as rd
import numpy as np
import matplotlib.pyplot as plt

hist_data = []

def coin_flip_game():
    starting_amount = 250
    bet_amount = 20
    bet = 0 #0=heads, 1=tails

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##Exercise 2

import numpy.random as rd
import matplotlib.pyplot as plt

hist_data = []
for _ in range(100):
    
    p_h = 0.5
    position = 0

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import scipy
from matplotlib import pyplot as plt

class IsingSimulation:

    J = 6.34369e-21  # Interaction constant for iron [Joule]
    kB = 1.38065e-23  # Boltzmann constant [Joule / Kelvin]

    def __init__(self, size, temperature=300):
        '''

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def f(x):
    Z = 24.44321494051954
    if abs(x) > 7:
        return 0
    elif abs(x) > 3:
        return 3 * (1 - (x / 7) ** 2) ** 0.5 / Z
    elif abs(x) > 1:
        return (
        (3 - abs(x)) / 2 -
        3/7 * 10**0.5 * ((3 - x**2 + 2*abs(x))**0.5 - 2)

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import pylab as PL
import random as RD
import scipy as SP
import math
import numpy.random as np

width = 20
height = 20
T = 1

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##Exercise 14

import scipy
import numpy as np
import matplotlib.pyplot as plt

lmbda = np.linspace(0,10,10)
y = scipy.random.exponential(lmbda**(-1))

plt.plot(lmbda,y)

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##random walk 
walk = []
def random_coin_flip_walk():
    location = 0
    for _ in range(30):
        flip = rd.random()
        if flip <= p_h: #gets heads
            location += 1 #go right
            walk.append(1)
        else:

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import matplotlib
matplotlib.use('TkAgg')
from pylab import *
import networkx as nx
import random as rd

def initialize():
    global g
    g = nx.erdos_renyi_graph(200, 0.05)
    g.pos = nx.spring_layout(g)

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import numpy as np
import random as rd
import networkx as nx
g = nx.erdos_renyi_graph(30, 0.05, directed=True, seed=123)
nx.draw(g, pos=nx.kamada_kawai_layout(g))

def random_surfer(a, n, g):
    visit_list = np.zeros(30)
    current_node = rd.choice(list(g.nodes))
    visit_list[current_node] += 1 #mark beginning node