SaraM92

print('Hello'); print('World!')
x=2; y=3
if x == 1: print(True)
if <Complicated Condition> and <Another Complicated Condition>:
    # do a bunch of things

SaraM92

print('Hello\nWorld!')
x = 2
y = 3
if x == 1:
    print(True)

firstCond = <Complicated Condition>
secCond = <Another Complicated Condition>

if firstCond and secCond:

SaraM92

from random import randint
from collections import Counter
#A function to genrate randome experiments
def gen(x):
    expResults = []
    for i in range(x):
        expResults.append(randint(1,6))
    return expResults
#An experiment with 100 events
event1 = dict(Counter(gen(100)))

SaraM92

#Import Essential Libraries
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stat
#Normal Distribution
n = np.arange(-100, 100)
mean = 0
sd = 15
normalDist = stat.norm.pdf(n, mean, sd)
plt.plot(n, normalDist, color="#8d0801")

SaraM92

#Import Essential Libraries
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stat
#Generate the Binomial Distribution
x = np.arange(0, 25)
noOfTrials = 20 #Number of trials
prob = 0.9 #probability of success
binom = stat.binom.pmf(x, noOfTrials, prob)
plt.plot(x, binom, color="#8d0801")

SaraM92

#Import Essential Libraries
import numpy as np
import matplotlib.pyplot as plt
#Generating Uniform Distribution for a Dice Roll
probs = np.full(6, 1/6)
faces = [1,2,3,4,5,6]
plt.bar(faces, probs, color="#8d0801")
plt.ylabel('Probability', fontsize=15 ,color="#001427")
plt.xlabel('Possible Outcomes', fontsize=15 ,color="#001427")
axes = plt.gca()

SaraM92

#Importing Essential Libraries
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stat
#Genrating the Poisson Distribution
n = np.arange(0, 10)
mu = 2 #average number of events
poisson = stat.poisson.pmf(n, lamda)
plt.plot(n, poisson, '-s', label="Mu = {:f}".format(mu), color="#8d0801")
plt.xlabel('# of Events', fontsize=15)

SaraM92

#Import needed libraries
import matplotlib.pyplot as plt
#Set data
x = list(range(1,6)) #data points
y = [1,1,2,2,4]  #original values  
y_bar = [0.6,1.29,1.99,2.69,3.4]  #predicted values
summation = 0 
n = len(y) 
for i in range(0, n):  
    # finding the difference between observed and predicted value

SaraM92

#Import needed libraries
import matplotlib.pyplot as plt
#Set data
x = list(range(1,6)) #data points
y = [1,1,2,2,4]  #original values  
y_bar = [0.6,1.29,1.99,2.69,3.4]  #predicted values
summation = 0 
n = len(y) 
for i in range(0, n):  
    # finding the difference between observed and predicted value

SaraM92

#Import needed libraries
import numpy as np 
import matplotlib.pyplot as plt 

# Create three images with different features
plain_img = np.array([np.array([100, 100]), np.array([100, 100])]) 
img_with_edge = np.array([np.array([100, 0]), np.array([100, 0])]) 

#Create a kernal to detect vertical edges (sobel, gradient edge detecting kernal)
kernel_vertical = np.array([np.array([2, -2]), np.array([2, -2])])