import numpy as np import matplotlib.pyplot as plt from matplotlib import style from scipy.stats import binom style.use('bmh') #Not to show warning messages (to keep the notebook clean) import warnings warnings.filterwarnings('ignore') #FOR TESTING from scipy.stats import poisson

#Array a - Binomial n=100 p=0.3 array1a = np.random.binomial(n,p, 10000) print(array1a)

n_bins100 = np.max(array1a) - np.min(array1a) plt.figure(figsize=(14,5)) plt.hist(array1a, n_bins100, density=True) #plt.hist(samples_mean2,n_bins2,density=True) plt.xlabel('$n$') plt.ylabel('Frequency') plt.title('Binomial distribution') plt.xlim([10, 50]) plt.show()

## n=100 p=0.3 n_arange = np.arange(50) plt.figure(figsize=(14,5)) plt.step(n_arange, binom.pmf(n_arange, n, p), where='pre', label='$n=100, p=0.3$') plt.xlabel('$n$') plt.ylabel('Probability') plt.title('Binomial distribution') #plt.ylim([0, 1]) plt.xlim([10, 50]) plt.grid(True) #grid plt.legend() plt.show()

plt.figure(figsize=(14,5)) plt.hist(array1a, n_bins100, density=True) plt.step(n_arange, binom.pmf(n_arange, n, p), where='pre', label='$n=100, p=0.3$') plt.xlabel('$n$') plt.ylabel('Frequency of $N = n$') plt.title('Binomial distribution') plt.show()

#Array b - Binomial n2=10000 p=0.6 array1b = np.random.binomial(n2,p, 10000) print(array1b)

n_bins10k = np.max(array1b) - np.min(array1b) plt.figure(figsize=(14,5)) plt.hist(array1b, n_bins10k, density=True) #plt.hist(samples_mean2,n_bins2,density=True) plt.xlabel('$n$') plt.ylabel('Frequency') plt.title('Binomial distribution') plt.xlim([5750, 6250]) plt.show()

## n=10000 p=0.6 n_arange2 = np.arange(6250) plt.figure(figsize=(14,5)) plt.step(n_arange2, binom.pmf(n_arange2, n2, p), where='pre', label='$n=10000, p=0.6$') plt.xlabel('$n$') plt.ylabel('Probability') plt.title('Binomial distribution') #plt.ylim([0, 1]) plt.xlim([5750, 6250]) plt.grid(True) #grid plt.legend() plt.show()

plt.figure(figsize=(14,5)) plt.hist(array1b, n_bins10k, density=True) plt.step(n_arange2, binom.pmf(n_arange2, n, p), where='pre', label='$n=100, p=0.3$') plt.xlabel('$n$') plt.ylabel('Frequency of $N = n$') plt.title('Binomial distribution') plt.xlim([5750, 6250]) plt.show()

#Upgrading the library to v2.3 ! pip install --upgrade networkx import networkx as nx #https://networkx.github.io/ import matplotlib.pyplot as plt #https://matplotlib.org/

n = 15 pa = 0.2 era = nx.erdos_renyi_graph(n, pa) nx.draw_networkx(era, node_color='black', font_color='white', edge_color='black', font_weight='bold', node_size=700) plt.axis('off') plt.show()

n = 15 pb = 0.5 erb = nx.erdos_renyi_graph(n, pb) nx.draw_networkx(erb, node_color='black', font_color='white', edge_color='black', font_weight='bold', node_size=700) plt.axis('off') plt.show()

n = 15 pc = 0.8 erc = nx.erdos_renyi_graph(n, pc) nx.draw_networkx(erc, node_color='black', font_color='white', edge_color='black', font_weight='bold', node_size=700) plt.axis('off') plt.show()