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triangles

# Hello World program in Python

print=("angles of a trinagle")
a=float(input("enter angle a in degrees:\n"))
b=float(input("enter angle in b in degrees:\n"))
c=round(180-b-a,1)
print("The missing angle is",c,"degrees")

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print("Angle of triangle")
a = float(input("Enter a in degrees:\n"))
b = float(input("Enter b in degrees:\n"))
c = round(180-a-b,1)
print("The missing angle is" ,c, "degrees")
if a==b or a==c or b==c:
    print("this is a equilateral")
elif a==b or a==c or b==c:
    print("this is a isoseles")
elif a==60 or b==60 or c==60:
    print("this is a right angle triangle")
else :
    print("this is a scalene")
print("")
print("......................................................................")
print("Instrutions")
print ("Type your program code")
print ("Click the edit symbol next to the name at the top")
print ("click execute to run the code")
print ("The results are shown in the white box of what you have done")
print ("Name it")
print("Click ok")
print("and start")
print ("but in order to start the speach type")
print ("PRINT but no capitals")
print ("if it is orange it works if it is not then it is wrong")

Blabla

# Hello World program in Python
    
print ("Hello World!");

cislo = input("Zadaj cislo: ")
print("Dvojnasobok toho cisla je", 2 * cislo)

Python 3 Match Object

#!/usr/bin/python3
import re

line = "Cats are smarter than dogs"

matchObj = re.match( r'(.*) are (.*?) .*', line, re.M|re.I)

if matchObj:
   print ("matchObj.group() : ", matchObj.group())
   print ("matchObj.group(1) : ", matchObj.group(1))
   print ("matchObj.group(2) : ", matchObj.group(2))
   #print ("matchObj.group(3) : ", matchObj.group(3))
else:
   print ("No match!!")

Foundations of predictive analytics in python-Chapter 4

''' Retrieving information from the predictor insight table '''

# Inspect the predictor insight graph table of Country
print(pig_table)

'''
  Country   Size  Incidence
0   India  49849       0.05
1      UK  10057       0.05
2     USA  40094       0.05
'''

# Print the number of UK donors
print(pig_table["Size"][pig_table["Country"]=="UK"])

# Check the target incidence of USA and India donors
print(pig_table["Incidence"][pig_table["Country"]=="USA"])
print(pig_table["Incidence"][pig_table["Country"]=="India"])

# =============================================================================
# Discretization of continuous variables
# =============================================================================

''' Discretization of a certain variable '''

print(basetable)

'''
       Unnamed: 0  target  time_since_last_donation
0               0       1                       808
1               1       1                       977
2               2       1                       641
...           ...     ...                       ...
99997       99997       0                       703
99998       99998       0                       682
99999       99999       0                       666
'''

# Discretize the variable time_since_last_donation in 10 bins
basetable["bins_recency"] = pd.qcut(basetable["time_since_last_donation"],10)

print(basetable["bins_recency"])

'''
0          (738, 833]
1         (933, 1050]
2          (574, 657]
             ...     
99997      (657, 738]
99998      (657, 738]
99999      (657, 738]

Categories (10, object): [[32, 319] < (319, 462] < (462, 574] < (574, 657] ... (833, 933] <
                          (933, 1050] < (1050, 1209] < (1209, 2518]]
'''

# Print the group sizes of the discretized variable
print(basetable.groupby("bins_recency").size())

'''
[32, 319]       10058
(319, 462]       9953
(462, 574]       9999
             ...     
(933, 1050]     10009
(1050, 1209]    10004
(1209, 2518]     9949
'''

''' Discretizing all variables '''

# Get all the variable names except "target"
variables = list(basetable.columns)
variables.remove("target")

# Loop through all the variables and discretize in 10 bins if there are more than 5 different values
for variable in variables:
    if len(basetable.groupby(variable))>5:
        new_variable = "disc_" + variable
        basetable[new_variable] = pd.qcut(basetable[variable], 10)
       
''' Making clean cuts '''

# Discretize the variable 
basetable["disc_number_gift"] = pd.cut(basetable["number_gift"],[0, 5, 10, 20])

# Count the number of observations per group
print(basetable.groupby("disc_number_gift").size())

'''
    (0, 5]      55063
    (5, 10]     41120
    (10, 20]     3817
'''

''' Calculating average incidences '''

# Select the income and target columns
basetable_income = basetable[["target","income"]]

print(basetable_income)
'''
       target   income
0           1     high
1           1  average
2           1     high
...       ...      ...
99997       0  average
99998       0      low
99999       0      low
'''

# Group basetable_income by income
groups = basetable_income.groupby("income")

# Calculate the target incidence and print the result
incidence = groups["target"].agg({"Incidence" : np.mean}).reset_index()
print(incidence)
'''
    income  Incidence
0  average   0.049166
1     high   0.061543
2      low   0.043118
'''

# =============================================================================
# Preparing the predictor insight graph table
# =============================================================================

''' Constructing the predictor insight graph table '''

# Function that creates predictor insight graph table
def create_pig_table(basetable, target, variable):
  
    # Create groups for each variable
    groups = basetable[[target,variable]].groupby(variable)
    
    # Calculate size and target incidence for each group
    pig_table = groups["target"].agg({'Incidence' : np.mean, 'Size' : np.size}).reset_index()
    
    # Return the predictor insight graph table
    return pig_table

# Calculate the predictor insight graph table for the variable gender
pig_table_gender = create_pig_table(basetable, "target", "gender")

# Print the result
print(pig_table_gender)
'''
  gender   Size  Incidence
0      F  50033   0.053844
1      M  49967   0.045970
'''

''' Grouping all predictor insight graph tables '''

# Create the list of variables for our predictor insight graph tables
variables = ["income","gender","disc_mean_gift","disc_time_since_last_gift"]

# Create an empty dictionary
pig_tables = {}

# Loop through the variables
for variable in variables:
  
    # Create a predictor insight graph table
    pig_table = create_pig_table(basetable, "target", variable)
    
    # Add the table to the dictionary
    pig_tables[variable] = pig_table

# Print the predictor insight graph table of the variable "disc_time_since_last_gift"
print(pig_tables)
'''
{'disc_time_since_last_gift':
  disc_time_since_last_gift   Size  Incidence
0              (1050, 2518]  19953   0.023255
1                (462, 657]  20069   0.061986
2                (657, 833]  19996   0.050810
3               (833, 1050]  19971   0.033799
4                 [32, 462]  20011   0.079556,

 'disc_mean_gift':
     disc_mean_gift   Size  Incidence
0        (103, 197]  19551   0.103524
1  (78.111, 86.889]  19997   0.029554
2  (86.889, 94.167]  20034   0.040831
3     (94.167, 103]  20405   0.063563
4       [2, 78.111]  20013   0.013042,

 'gender':
  gender   Size  Incidence
0      F  50033   0.053844
1      M  49967   0.045970,

 'income':
    income   Size  Incidence
0  average  62950   0.049166
1     high  16200   0.061543
2      low  20850   0.043118}
'''

# =============================================================================
# Plotting the predictor insight graph
# =============================================================================

''' Plotting the incidences '''

import matplotlib.pyplot as plt
import numpy as np

# The function to plot a predictor insight graph.
def plot_incidence(pig_table,variable):
    
    # Plot the incidence line
    pig_table["incidence"].plot()
    
    # Formatting the predictor insight graph
    plt.xticks(np.arange(len(pig_table)), pig_table[variable])
    plt.xlim([-0.5, len(pig_table) - 0.5])
    plt.ylim([0, max(pig_table["Incidence"] * 2)])
    plt.ylabel("Incidence", rotation=0, rotation_mode="anchor", ha="right")
    plt.xlabel(variable)
    
    # Show the graph
    plt.show()

# Apply the function for the variable "country".
plot_incidence(pig_table, "country")

''' Plotting the group sizes '''

# The function to plot a predictor insight graph
def plot_pig(pig_table, variable):
    
    # Plot formatting
    plt.ylabel("Size", rotation=0, rotation_mode="anchor", ha="right")
    
    # Plot the bars with sizes 
    pig_table["Size"].plot(kind="bar", width=0.5, color="lightgray", edgecolor="none") 
    
    # Plot the incidence line on secondary axis
    pig_table["Incidence"].plot(secondary_y=True)
    
    # Plot formatting
    plt.xticks(np.arange(len(pig_table)), pig_table[variable])
    plt.xlim([-0.5, len(pig_table) - 0.5])
    plt.ylabel("Incidence", rotation=0, rotation_mode="anchor", ha="left")
    
    # Show the graph
    plt.show()
    
# Apply the function for the variable "country"
plot_pig(pig_table, "country")

''' Putting it all together '''

# Variables you want to make predictor insight graph tables for
variables = ["income","gender","disc_mean_gift","disc_time_since_last_gift"]

# Loop through the variables
for variable in variables: 
    
    # Create the predictor insight graph table
    pig_table = create_pig_table(basetable, "target", variable)
    
    # Plot the predictor insight graph
    plot_pig(pig_table, variable)

stringscore

"""
C 3 
AB 28
BZ 78

CA 79

"""
BASE_MULT = 26

ALPHA_SCORES = dict(A=1, B=2, C=3, D=4, E=5, F=6, G=7, H=8, I=9, J=10, K=11, L=12, M=13, N=14, O=15, P=16, Q=17, R=18, S=19, T=20, U=21, V=22, W=23, X=24, Y=25, Z=26)

def calcBaseScore(ip_str):
    mult = 0
    for i in range(len(ip_str) - 1):
        mult = mult + ALPHA_SCORES[ip_str[i]] * BASE_MULT
    return mult + ALPHA_SCORES[ip_str[-1]]

print(calcBaseScore('CA'))
print(calcBaseScore('AB'))
print(calcBaseScore('C'))
print(calcBaseScore('BZ'))

BFS PYTHON

# visits all the nodes of a graph (connected component) using BFS
def bfs_connected_component(graph, start):
    # keep track of all visited nodes
    explored = []
    # keep track of nodes to be checked
    queue = [start]

# visits all the nodes of a graph (connected component) using BFS
def bfs_connected_component(graph, start):
    # keep track of all visited nodes
    explored = []
    # keep track of nodes to be checked
    queue = [start]
 
    # keep looping until there are nodes still to be checked
    while queue:
        # pop shallowest node (first node) from queue
        node = queue.pop(0)
        if node not in explored:
            # add node to list of checked nodes
            explored.append(node)
            neighbours = graph[node]
 
            # add neighbours of node to queue
            for neighbour in neighbours:
                queue.append(neighbour)
    return explored
 
 
# sample graph implemented as a dictionary
graph = {'A': ['B', 'C', 'E'],
         'B': ['A','D', 'E'],
         'C': ['A', 'F', 'G'],
         'D': ['B'],
         'E': ['A', 'B','D'],
         'F': ['C'],
         'G': ['C']}
print(bfs_connected_component(graph,'A')) # returns ['A', 'B', 'C', 'E', 'D', 'F', 'G']

TPC2-Muon-Teresa

# Python 3

from random import random
from math   import log, sqrt

# Constantes físicas
m    = 0.106		# massa do muon
tau0 = 2.2e-6		
c    = 3.0e8		# velocidade da luz

# Funções

def rnd_exponencial(a):
    """
    Devolve um número aleatório n distribuído de acordo com exp(-a*n).
    """
    return -1/a*log(random())
    

def prob_deteccao(L, E0, num_iterecoes):
	"""
	Devolve a probabilidade de que um muon de Energia E0 produzido à altura L
	seja detectado na terra.
	"""

	# Contador da quantidade de muões que sobrevivem
	contador=0

	# Ciclo que repete o algoritmo num_iterecoes vezes
	for i in range(num_iterecoes):

		# Gerar aleatoriamente uma energia
		Ek = rnd_exponencial(1/E0)

		# Cálculo de gamma e tau
		gamma = (Ek+m)/m
		tau   = gamma*tau0

		# Geramos um tempo de vida aleatório
		t = rnd_exponencial(1/tau) 

		# Calculamos a distância que este muon pode percorrer
		beta = sqrt( abs(Ek**2 - m**2) ) /Ek
		l    = beta*c*t

		# Determinamos se este muon sobreviveu
		if l>L:
			contador+=1


	# Calculamos a probabilidade
	p = contador/num_iterecoes

	return p


# Código principal

num_iterecoes = 1000

parametros = [ [5000, 2.5], [4000, 3.0], [3000, 1.5], [9000, 2.0], [7500, 1.0] ]

for L, E0 in parametros:
    p=prob_deteccao(L, E0, num_iterecoes)
    print("L=%d E0=%.1f --> p=%0.3f" % (L, E0, p) )

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print("Hello world")
print("   /|")
print("  / |")
print(" /  |")
print("/___|")

print("There once was a man named George,")
print("he was 70 years old.")
print("He really liked the name George,")
print("but didn't like being 70.")







title

a = (int(input()))
print(a)

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