index
int64 | repo_name
string | branch_name
string | path
string | content
string | import_graph
string |
|---|---|---|---|---|---|
38,079
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio11.py
|
# Desafio 11 Curso em Video Python
# Calcula a área de uma parede a apartir da largura e altura informadas, e quantos litros de tinta são necessários para pintá-la.
#By Rafabr
import sys,time
import os
os.system('clear')
print('\033[1;47;30m',end="")
print('\n'+'*'*80)
print('Desafio 11'.center(80))
print('Este programa calcula a área de uma parede a partir da largura e altura'.center(80))
print('e mostra quantos litros de tinta são necessários para pintá-la.'.center(80))
print(80*'*')
print('\033[m',end="")
print('\033[1;36m')
try:
width = float(input('Informe a largura da parede em metros: '))
height = float(input('Informe a altura da parede em metros: '))
print()
except ValueError:
print('\nNao foi digitado um valor numérico!\n')
time.sleep(2)
sys.exit()
area = width*height
print('Uma parede de {:.2f} metros de largura e {:.2f} metros de altura, possui uma área de {:.2f} metros quadrados.\n'.format(width,height,area))
total_litros = area / 2
print('Considerando que um litro de tinta é suficiente para pintar 2 metros quadrados de área:')
print('Serão necessários {:.2f} litros de tinta para a pintura dessa parede.'.format(total_litros))
print('\n---Fim da execução---\n')
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,080
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio34.py
|
# Desafio 34 Curso em Video Python
# Este programa calcula o aumento de salario onde o aumento é maior para que ganha menos.
# By Rafabr
import sys
import time
import os
import random
os.system('clear')
print('\nDesafio 34')
print('Este programa calcula o aumento de salario onde o aumento é maior para que ganha menos.\n\n')
try:
sal = float(input('Informe a salário atual: ').strip())
except ValueError:
os.system('clear')
print('Voçe não digitou um valor válido!')
time.sleep(0.5)
sys.exit()
os.system('clear')
if sal < 0:
os.system('clear')
print('Voçe digitou uma valor de salário negativo!')
time.sleep(0.5)
sys.exit()
print(f'O salário novo será de R$', end="")
print(f'{(sal*1.15 if sal <= 1250 else sal*1.1):.2f}')
print('\n---Fim da execução---\n')
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,081
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio8.py
|
# Desafio 8 Curso em Video Python
# Converte um valor de metros para centímetros e milímetros
#By Rafabr
import sys,time
import os
os.system('clear')
print('\033[1;47;30m',end="")
print('\n'+'*'*80)
print('Desafio 8'.center(80))
print('Este programa converte um valor de metros para centímetros e milímetros!'.center(80))
print(80*'*')
print('\033[m',end="")
print('\033[1;36m')
try:
metros = float(input('Digite a medida em metros - OBS. Somente números - : '))
print()
except ValueError:
print('\nNao foi digitado um valor válido\n')
time.sleep(2)
sys.exit()
print('Escolha para qual medida deseja converter o valor digitado:')
print('(Para sair do programa digite: \'sair\')')
print('1 - Centímetros\n2 - Milímetros')
converter_para = input(':')
while converter_para != '1' and converter_para != '2' and converter_para != 'sair':
print('Escolha para qual medida deseja converter o valor digitado:')
print('(Para sair do programa digite: \'sair\')')
print('1 - Centímetros\n2 - Milímetros')
converter_para = input(':')
if converter_para == 'sair':
print('\nExecução encerrada pelo usuário!\n')
sys.exit()
print()
if converter_para == '1':
print(f'\n{metros} metros é equivalente à {metros*100} centímetros!')
if converter_para == '2':
print(f'\n{metros} metros é equivalente à {metros*1000} milímetros!')
print('\n---Fim da execução---\n')
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,082
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio53.py
|
# Desafio 53 Curso em Video Python
# By Rafabr
from estrutura_modelo import cabecalho, rodape
cabecalho(53, "Verificador de Palíndromo")
text = str(input('Digite a frase a ser análisada: ')).strip().upper()
text_in_list = []
for k in text.replace(' ', ''):
text_in_list.append(k)
text_reversed = text_in_list.copy()
text_reversed.reverse()
print('\nFrase Informada: ', ''.join(text_in_list))
print('Frase Inversa : ', ''.join(text_reversed))
if (text_in_list == text_reversed):
print(f"A frase informada é um palíndromo!")
else:
print(f'A frase informada NÃO é um palíndromo.')
rodape()
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,083
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio50.py
|
# Desafio 50 Curso em Video Python
# By Rafabr
from time import sleep
from sys import exit
from os import system
from estrutura_modelo import cabecalho,rodape
cabecalho(50,"Verificador de Números Pares!")
num = []
try:
print('Digite seis números!')
for k in range(0,6):
num.append(int(input('Informe um número: ')))
except ValueError:
print('Voçe digitou um valor indevido!')
exit()
print()
soma = 0
for j in num:
if (j%2 == 0):
soma += j
print(f'A soma dos números pares digitados é {soma}')
rodape()
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,084
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio60.py
|
# Desafio 60 Curso em Video Python
# By Rafabr
from estrutura_modelo import cabecalho, rodape
cabecalho(60, "Fatorial de um Número!")
while True:
try:
numero = int(
input("Digite um número inteiro: ")
)
except ValueError:
print('Valor inválido inserido! Tente Novamente!')
continue
if numero < 0:
print('O número não pode ser negativo! Tente Novamente')
continue
break
fatorial = 1
fator = numero
while (fator > 0):
fatorial *= fator
fator -= 1
print(f'O fatorial de {numero} é {fatorial}!')
rodape()
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,085
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio62.py
|
# Desafio 62 Curso em Video Python
# By Rafabr
from estrutura_modelo import cabecalho, rodape
cabecalho(62, "Termos de uma Progressão Aritmética - III")
while True:
try:
p0 = float(input('Digite o Termo inicial da PA: '))
r = float(input('Digite a razão da PA: '))
except ValueError:
print('Voçe digitou um valor indevido!\n')
continue
break
n = 1
print()
while (n <= 10):
print(f'Termo {n}:'.ljust(10) + f'{p0 + (n-1)*r}')
n += 1
print()
def maisTermos():
'''Pergunta se deseja-se informar mais termos da PA'''
while True:
try:
termos_adicionais = int(input('Digite quantos termos a mais deseja\
visualizar?\n(Para sair digite 0)\n»»: '))
except ValueError:
print('Valor inválido!')
continue
if (termos_adicionais < 0):
continue
break
return (termos_adicionais)
while True:
n_termos = maisTermos()
if (n_termos == 0):
break
total_termos = n_termos + n
print()
while (n < total_termos):
print(f'Termo {n}:'.ljust(14) + f'{p0 + (n-1)*r}')
n += 1
print()
rodape()
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,086
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio61.py
|
# Desafio 61 Curso em Video Python
# By Rafabr
from estrutura_modelo import cabecalho, rodape
cabecalho(61, "Termos de uma Progressão Aritmética - II")
while True:
try:
p0 = float(input('Digite o Termo inicial da PA: '))
r = float(input('Digite a razão da PA: '))
except ValueError:
print('Voçe digitou um valor indevido!\n')
continue
break
n = 1
print()
while (n <= 10):
print(f'Termo {n}:'.ljust(10) + f'{p0 + (n-1)*r}')
n += 1
rodape()
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,087
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio27.py
|
# Desafio 27 Curso em Video Python
# Este programa ler o nome completo de uma pessoa e mostra o primeiro e o último nome.
# By Rafabr
import os
os.system('clear')
print('\nDesafio 27')
print('Este programa ler o nome completo de uma pessoa e mostra o primeiro e o último nome.\n\n')
nome = str(input('Digite o nome completo de uma pessoa: ')).lower().strip()
print()
print(f"O primeiro nome da pessoa informada é: {nome.split()[0].title()}")
print(f"O último nome da pessoa informada é: {nome.split()[-1].title()}")
print('\n---Fim da execução---\n')
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,088
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio66.py
|
# Desafio 66 Curso em Video Python
# By Rafabr
from estrutura_modelo import cabecalho, rodape
cabecalho(66, "Soma com quantidade indeterminada de números")
soma = 0
while True:
rodape()
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,089
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio26.py
|
# Desafio 26 Curso em Video Python
# Este programa verifica se uma frase digitada tem a letra 'a' e mostras algumas informações.
# By Rafabr
import os
os.system('clear')
print('\nDesafio 26')
print('Este programa verifica se uma frase digitada tem a letra \'a\' e mostras algumas informações.\n\n')
frase = str(input('Digite uma frase: ')).strip().lower()
print()
if frase.count('a'):
print(f"A frase digitada possui {frase.count('a')} letras 'a'.")
print(
f"\nA letra 'a' apareçe pela primeira vez na posição {frase.find('a')+1} da frase")
print(
f"\nA letra 'a' apareçe pela primeira vez na posição {frase.rfind('a')+1} da frase")
else:
print("A frase não possui nenhuma letra 'a'!")
print('\n---Fim da execução---\n')
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,090
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio18.py
|
# Desafio 18 Curso em Video Python
# Calcula o valor do seno, cosseno e tangente de qualquer ângulo
#By Rafabr
import sys,time
import math as doido
print('\nDesafio 18')
print('Este programa calcula o valor do seno, cosseno e tangente de qualquer ângulo\n')
angulo_graus = float(input("Digite o valor do ângulo (EM GRAUS) que deseja calcular as relações trigonométricas básicas\n: "))
angulo_radianos = doido.radians(angulo_graus)
seno = doido.sin(angulo_radianos)
cosseno = doido.cos(angulo_radianos)
tangente = doido.tan(angulo_radianos)
print("Calculando ...")
time.sleep(2)
print("\nAs relações trigonométricas básicas do ângulo informado são as seguintes:")
print("-> sen({}) = {:.2f}".format(angulo_graus,seno))
print("-> cos({}) = {:.2f}".format(angulo_graus,cosseno))
print("-> tg({}) = {:.2f}".format(angulo_graus,tangente))
print('\n---Fim da execução---\n')
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,091
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/aula12-exemplos.py
|
#Aula 12 do Curso Python em Video!
#By Rafabr
import time,sys
import os
from estrutura_modelo import cabecalho,rodape
cabecalho(12,"Esta Aula ira falar sobre Condições Aninhadas!")
escolha = input("Escolha qual opções deseja:\n 1 - Primeira opção\n 2 - Segunda opção\n 3 - Terceira opção\n\t:")
print(f'Voçe escolheu opção {escolha}\n\n')
nome = str(input("Qual é o seu nome: "))
if nome == 'Rafael':
print('Que nome bonito!')
elif nome == "Pedro" or nome == "Maria" or nome == "Paulo":
print("Seu nome é bem popular no Brasil.")
elif nome in "Lya Paula Lyara":
print('Que belo bome feminino.')
else:
print("Seu nome é bem normal.")
print(f"Tenha um bom dia, {nome}")
rodape()
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,092
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio16.py
|
# Desafio 16 Curso em Video Python
# Separa qualquer número informado em uma parte inteira e outra parte fracionária
#By Rafabr
import sys,time
import math as rafael_bruno_paiva
print('\nDesafio 16')
print('Este programa separa qualquer número informado em uma parte inteira e outra parte fracionária.\n')
numero = float(input('Digite um número qualquer (preferencialmente fracionário): '))
num_inteiro = int(rafael_bruno_paiva.trunc(numero))
num_fracionario = numero - num_inteiro
print(F"A parte inteira de {numero} é: {num_inteiro}")
print(F"A parte fracionária de {numero} é: {num_fracionario}")
print('\n---Fim da execução---\n')
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,093
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio24.py
|
# Desafio 24 Curso em Video Python
# Este programa verifica se uma cidade informada começa com a palavra 'santo(a)' ou 'são'.
# By Rafabr
import os
os.system('clear')
print('\nDesafio 24')
print('Este programa verifica se uma cidade informada começa com a palavra \'santo(a)\' ou \'são\'.\n\n')
cidade = input('Digite o nome de uma Cidade: ')
print()
teste_cidade = cidade.split()[0]
if teste_cidade.lower() in ['santo', 'santa', 'são', 'sao']:
print("Voçe digitou uma cidade que começa com nome de santo (santo(a) ou são)!")
else:
print(f"A cidade que voçe digitou - {cidade} - não é nome de santo!")
print('\n---Fim da execução---\n')
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,094
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio58.py
|
# Desafio 58 Curso em Video Python
# By Rafabr
from time import sleep
from os import system
from random import randint
from estrutura_modelo import cabecalho, rodape
cabecalho(58, "Jogo de Adivinha II")
print('O computador irá escolher um número de 0 a 10!')
print('Tente adivinhar qual!')
numero_da_sorte = randint(0, 10)
tentativas = 0
while True:
try:
numero_jogador = int(
input("\nDigite um número maior/igual a 0 e menor/igual a 10: ")
)
except ValueError:
print('\nVoçe digitou um valor inválido!')
sleep(1)
continue
tentativas += 1
print(f'Número de tentativas: {tentativas}')
if numero_jogador > numero_da_sorte:
print('Muito Alto!')
elif numero_jogador < numero_da_sorte:
print('Muito Baixo')
else:
system('clear')
cabecalho(58, "Jogo de Adivinha II")
print('\nParabens!')
print(f'Voçe Acertou o Número Secreto - {numero_da_sorte}')
print(f'Precisou de {tentativas} tentativas!\n')
break
rodape()
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,095
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio41.py
|
# Desafio 41 Curso em Video Python
# By Rafabr
import os,time,sys
from estrutura_modelo import cabecalho,rodape
cabecalho(41,"Categorizar atletas de natação")
try:
nasc = float(input('Digite o ano de nascimento do atleta: '))
print()
except ValueError:
print('Voçe digitou um valor indevido!')
time.sleep(0.5)
sys.exit()
idade = time.localtime().tm_year - nasc
if idade <= 9:
print('O atleta faz parte da categoria \033[1;35mMIRIM\033[m')
elif idade <= 14:
print('O atleta faz parte da categoria \033[1;35mINFANTIL\033[m')
elif idade <= 19:
print('O atleta faz parte da categoria \033[1;35mJUNIOR\033[m')
elif idade <= 25:
print('O atleta faz parte da categoria \033[1;35mSÊNIOR\033[m')
else:
print('O atleta faz parte da categoria \033[1;35mMASTER\033[m')
rodape()
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,096
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio40.py
|
# Desafio 40 Curso em Video Python
# By Rafabr
import os,time,sys,statistics
from estrutura_modelo import cabecalho,rodape
cabecalho(40,"Verificar a média do aluno e sua situação escolar")
try:
n1 = float(input('Digite a primeira nota: '))
n2 = float(input('Digite a segunda nota: '))
print()
except ValueError:
print('Voçe digitou um valor indevido!')
time.sleep(0.5)
sys.exit()
media = float(statistics.mean([n1,n2]))
print(f'\tSua média escolar foi {media}')
if media < 5.0:
print('\tVoçe não conseguiu as notas suficientes para passar de ano')
print(f'\tSITUAÇÃO: \033[1;31mREPROVADO\033[m')
elif media > 6.9:
print('\tPARABÉNS!\n\tVoçe conseguiu as notas suficientes para passar de ano')
print(f'\tSITUAÇÃO: \033[1;36mAPROVADO\033[m')
else:
print('\tVoçe não conseguiu as notas suficientes para passar de ano, porém ainda tem chances')
print(f'\tSITUAÇÃO: \033[1;33mRECUPERAÇÃO\033[m')
rodape()
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,097
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/aula10-exemplos.py
|
#Aula 10 do Curso Python em Video!
#By Rafabr
import time,sys,subprocess
subprocess.run(['clear'])
print('\n'+'*'*80)
print('Aula 10 - Exemplos e Testes'.center(80)+'\n')
print('Questionário sobre carros:')
tem_carro = str(input('Voçe possui carro? (s/n) : ')).strip().lower()
if tem_carro == 's':
pass
else:
if tem_carro == 'n':
print('Voçe não está apto a participar da pesquisa!')
time.sleep(2)
sys.exit()
else:
print('Voçe digitou uma informação inválida!')
time.sleep(2)
sys.exit()
carro = str(input('Informe a marca e modelo do seu carro (Ex. Ford Ká): '))
carro_tempo = int(input('Há quantos anos voçe comprou o carro? '))
ano_compra = int(time.strftime('%Y')) - carro_tempo
print(f"\nVoçe possui o seguinte carro: {carro}")
print(f'Seu Carro tem {carro_tempo} anos, logo voçe comprou ele em {ano_compra}')
if carro_tempo >= 5:
print("Seu carro já tá meio velinho né!")
else:
print('Seu carro ainda é novinho!')
print('Obrigado pelas informações!')
print('\nFim da execução\n')
print('\n'+'*'*80)
time.sleep(2)
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,098
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio20.py
|
# Desafio 20 Curso em Video Python
# Sorteia a ordem de apresentação de um trabalho de quatro alunos informados.
#By Rafabr
import sys,time
import random as ale
print('\nDesafio 20')
print('Este programa sorteia a ordem de apresentação de um trabalho de quatro alunos informados.\n')
a1 = input("Digite o nome do primeiro aluno: ")
a2 = input("Digite o nome do segundo aluno: ")
a3 = input("Digite o nome do terceiro aluno: ")
a4 = input("Digite o nome do quarto aluno: ")
alunos = [a1,a2,a3,a4]
ordem_alunos = ale.sample(alunos,4)
print("\nA ordem de apresenação será:")
print(f"1 - {ordem_alunos[0]}")
print(f"2 - {ordem_alunos[1]}")
print(f"3 - {ordem_alunos[2]}")
print(f"4 - {ordem_alunos[3]}")
print('\n---Fim da execução---\n')
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,099
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio2.py
|
nome = input('Digite seu nome: ')
print(f'Seja bem vindo {nome}!')
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,100
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio30.py
|
# Desafio 30 Curso em Video Python
# Este programa informa se um número informado é par ou ímpar.
# By Rafabr
import sys
import time
import os
import random
os.system('clear')
print('\nDesafio 30')
print('Este programa informa se um número informado é par ou ímpar.\n\n')
try:
numero = int(input('Informe um número inteiro: ').strip())
except ValueError:
os.system('clear')
print('Voçe digitou um valor não reconhecido!')
time.sleep(1)
sys.exit()
if ((numero//1)-numero) != 0:
os.system('clear')
print('Voçe não digitou um número inteiro!')
time.sleep(1)
sys.exit()
os.system('clear')
if (numero % 2) == 0:
print(f'\nO número digitado, {numero}, é um número par!')
else:
print(f'\nO número digitado, {numero}, é um número ímpar!')
print('\n---Fim da execução---\n')
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,101
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio55.py
|
# Desafio 55 Curso em Video Python
# By Rafabr
from time import sleep
from sys import exit
from estrutura_modelo import cabecalho, rodape
cabecalho(55, "Maior e Menor número da sequência.")
print("Digite o peso de várias pessoas separadas por espaço para compararmos!\n")
try:
lista_pesos = list(map(float,input("Digite os pesos (em Kg) das pessoas: ").split()))
except ValueError:
print("Voçe digitou algum valor errado ou indevido!")
sleep(1)
exit()
for peso in lista_pesos:
if peso < 0:
print("Voçe digitou algum peso negativo!")
sleep(1)
exit()
lista_pesos.sort()
print(f"\nForam verificados os pesos de {len(lista_pesos)} pessoas")
print(f"O maior peso é {lista_pesos[-1]}")
print(f"O menor peso é {lista_pesos[0]}")
rodape()
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,102
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio48.py
|
# Desafio 48 Curso em Video Python
# By Rafabr
from time import sleep
from sys import exit
from os import system
from estrutura_modelo import cabecalho,rodape
cabecalho(48,"Soma dos Números Ímpares de um intervalo!")
try:
numeroLimite = int(input("Digite um número positivo inteiro: "))
except ValueError:
print("Valor inválido!")
sleep(1)
exit()
soma = 0
listaNumeros = []
for k in range(0,numeroLimite,3):
if ((k%2)!= 0 ):
soma += k
listaNumeros.append(k)
print(f'\nOs números ímpares e múltiplos de três entre 1 e um {numeroLimite} são: \n{listaNumeros}')
print(f'\n\nA soma dos números acima é {soma}')
rodape()
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,103
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio37.py
|
# Desafio 37 Curso em Video Python
# By Rafabr
import os,time,sys
from estrutura_modelo import cabecalho,rodape
cabecalho(37,"Programa para converter número de base decimal para outras bases númericas")
try:
n_decimal = int(input('Digite um número inteiro: '))
base = int(input('Escolha para base númerica deseja converter:\n 1 - Converter para Binário\n 2 - Converter para Octal\n 3 - Converter para Hexadecimal\n\t:'))
except ValueError:
print('Voçe digitou um valor indevido!')
time.sleep(0.5)
sys.exit()
if base == 1:
print(f'\nO número {n_decimal} convertido para binário é: ',end="")
print(bin(n_decimal)[2:])
elif base == 2:
print(f'\nO número {n_decimal} convertido para octal é: ',end="")
print(oct(n_decimal)[2:])
elif base == 3:
print(f'\nO número {n_decimal} convertido para hexadecimal é: ',end="")
print(hex(n_decimal)[2:])
else:
print('Voçe Escolheu uma opção inexistente!')
time.sleep(0.5)
sys.exit()
rodape()
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,104
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio29.py
|
# Desafio 29 Curso em Video Python
# Este programa retorna mensagem de excesso de velocidade baseado em uma condição usando IF
# By Rafabr
import sys
import time
import os
import random
os.system('clear')
print('\nDesafio 29')
print('Este programa retorna mensagem de excesso de velocidade baseado em uma condição usando IF\n\n')
try:
v_atual = int(
input('Informe a velocidade atual do veículo(Em Km/h): ').strip())
except ValueError:
os.system('clear')
print('Voçe digitou um valor não reconhecido!')
time.sleep(1)
sys.exit()
if v_atual < 0:
os.system('clear')
print('Voçe digitou um número negativo!')
time.sleep(1)
sys.exit()
multa = 0
os.system('clear')
if v_atual > 80:
multa = (v_atual-80)*7
print('O carro ultrapassou o limite de velocidade da via - LIMITE: 80Km/h')
print(f'Velocidade Informada: {v_atual}km/h')
print(f'Devido a infração comentida será aplicada multa de R$ {multa:.2f}')
else:
print(f'Velocidade Informada: {v_atual}km/h')
print('O veiculo está com velocidade dentro do limite da via!\n\n\
---Não Corra, sua vida não vale o risco!---')
print('\n---Fim da execução---\n')
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,105
|
rafarbop/Python-Curso-em-Video
|
refs/heads/master
|
/desafio28.py
|
# Desafio 28 Curso em Video Python
# Este programa escolhe um número aleatório e verifica se o usuário adivinha o número.
# By Rafabr
import sys
import time
import os
import random
os.system('clear')
print('Desafio 28'.center(80))
print('<>'*40)
print('Este programa escolhe um número aleatório e verifica se o usuário adivinha o número.')
print('<-->'*20)
print('\n')
chave = random.choice(range(6))
try:
escolha = int(input('O computador escolheu um número entre 0 e 5.\n\
Voçe consegue adivinhar que número é esse?\nTente acertar: ').strip())
except ValueError:
print('Voçe digitou um valor não númerico!')
time.sleep(1)
sys.exit()
if escolha not in range(6):
os.system('clear')
print('Voçe digitou um valor fora do intervalo solicitado!')
time.sleep(1)
sys.exit()
os.system('clear')
print('Verificando')
time.sleep(0.5)
os.system('clear')
print('Verificando.')
time.sleep(0.5)
os.system('clear')
print('Verificando..')
time.sleep(0.5)
os.system('clear')
print('Verificando...')
time.sleep(0.5)
os.system('clear')
print('Resposta de sua adivinhação!')
if escolha == chave:
print('\v\tPARABEŃS!\nVoçe acertou o número escolhido aleatoriamente pelo computador!')
else:
print(
f"\nNão foi dessa vez!\nO número escolhido pelo computador foi {chave}!")
print('\n---Fim da execução---\n')
|
{"/desafio51.py": ["/estrutura_modelo.py"], "/desafio46.py": ["/estrutura_modelo.py"], "/desafio47.py": ["/estrutura_modelo.py"], "/desafio43.py": ["/estrutura_modelo.py"], "/desafio64.py": ["/estrutura_modelo.py"], "/desafio56.py": ["/estrutura_modelo.py"], "/desafio57.py": ["/estrutura_modelo.py"], "/desafio49.py": ["/estrutura_modelo.py"], "/desafio65.py": ["/estrutura_modelo.py"], "/desafio38.py": ["/estrutura_modelo.py"], "/desafio44.py": ["/estrutura_modelo.py"], "/desafio42.py": ["/estrutura_modelo.py"], "/desafio39.py": ["/estrutura_modelo.py"], "/desafio52.py": ["/estrutura_modelo.py"], "/desafio54.py": ["/estrutura_modelo.py"], "/aula13-exemplos.py": ["/estrutura_modelo.py"], "/desafio36.py": ["/estrutura_modelo.py"], "/desafio59.py": ["/estrutura_modelo.py"], "/desafio45.py": ["/estrutura_modelo.py"], "/desafio63.py": ["/estrutura_modelo.py"], "/desafio53.py": ["/estrutura_modelo.py"], "/desafio50.py": ["/estrutura_modelo.py"], "/desafio60.py": ["/estrutura_modelo.py"], "/desafio62.py": ["/estrutura_modelo.py"], "/desafio61.py": ["/estrutura_modelo.py"], "/aula12-exemplos.py": ["/estrutura_modelo.py"], "/desafio58.py": ["/estrutura_modelo.py"], "/desafio41.py": ["/estrutura_modelo.py"], "/desafio40.py": ["/estrutura_modelo.py"], "/desafio55.py": ["/estrutura_modelo.py"], "/desafio48.py": ["/estrutura_modelo.py"], "/desafio37.py": ["/estrutura_modelo.py"]}
|
38,210
|
gardensgreen/flask-practice-assessment
|
refs/heads/master
|
/app/forms.py
|
from flask_wtf import FlaskForm
from wtforms import StringField, IntegerField, TextAreaField, SubmitField
from wtforms.validators import DataRequired
class SimpleForm(FlaskForm):
name = StringField("Name", validators=[DataRequired()])
age = IntegerField("Age")
bio = TextAreaField("Biography")
submit = SubmitField("Submit")
|
{"/app/__init__.py": ["/app/forms.py"]}
|
38,211
|
gardensgreen/flask-practice-assessment
|
refs/heads/master
|
/app/__init__.py
|
from flask import Flask, render_template, redirect
from flask_migrate import Migrate
from .forms import SimpleForm
from .config import Config
from .models import db, SimplePerson
app = Flask(__name__)
app.config.from_object(Config)
db.init_app(app)
migrate = Migrate(app, db)
@app.route("/")
def main_page():
return render_template("main_page.html")
@app.route("/simple-form")
def simple_form():
form = SimpleForm()
return render_template("simple_form.html", form=form)
@app.route("/simple-form", methods=["POST"])
def simple_form_post():
form = SimpleForm()
if form.validate_on_submit():
person = SimplePerson()
form.populate_obj(person)
db.session.add(person)
db.session.commit()
return redirect("/")
else:
return "Bad Data"
|
{"/app/__init__.py": ["/app/forms.py"]}
|
38,217
|
yassinebelmamoun/graph-trains
|
refs/heads/master
|
/main.py
|
from model.railroad import Railroad
if __name__ == '__main__':
# Create and build the Railroad
railroad = Railroad()
railroad.build('AB5, BC4, CD8, DC8, DE6, AD5, CE2, EB3, AE7')
# Visualise the railroad
railroad.visualise()
# Measure the distance of the routes:
routes = ['A-B-C', 'A-D', 'A-D-C', 'A-E-B-C-D']
print('\n\n*** The distance of:')
for route in routes:
print('\t- The route {} is {} units'.format(route, str(railroad.get_route_distance(route))))
print('\n*** The number of trips starting at C and ending at C with a maximum of 3 stops is: {} routes'.format(railroad.get_count_trips_MaxStop('C', 'C', max_stop=3)))
print('\n*** The number of trips starting at A and ending at C with exactly 4 stops is: {} routes'.format(railroad.get_count_trips_ExactCountStop('A', 'C', count_stop=4)))
print('\n*** The length of the shortest route (in terms of distance to travel) from A to C is: {} units'.format(railroad.get_length_shortest_route('A', 'C')))
print('\n*** The length of the shortest route (in terms of distance to travel) from B to B is: {} units'.format(railroad.get_length_shortest_route('B', 'B')))
print('\n*** The number of different routes from C to C with a distance of less than 30 is: {} routes'.format(railroad.get_count_routes_MaxDistance('C', 'C', 30)))
|
{"/main.py": ["/model/railroad.py"], "/test/test_railroad.py": ["/model/railroad.py"], "/model/railroad.py": ["/model/graph.py", "/exception/railroad_exception.py"], "/test/test_graph.py": ["/model/railroad.py"], "/model/graph.py": ["/exception/graph_exception.py"]}
|
38,218
|
yassinebelmamoun/graph-trains
|
refs/heads/master
|
/test/test_railroad.py
|
import unittest
from model.railroad import Railroad
from exception import graph_exception, railroad_exception
class RailroadTestCase(unittest.TestCase):
def setUp(self):
self.railroad = Railroad()
self.railroad.build('AB5, BC4, CD8, DC8, DE6, AD5, CE2, EB3, AE7')
def test_get_route_distance(self):
# test 1:
self.assertEqual(self.railroad.get_route_distance('A-B-C'), 9)
# test 2:
self.assertEqual(self.railroad.get_route_distance('A-D'), 5)
# test 3:
self.assertEqual(self.railroad.get_route_distance('A-D-C'), 13)
# test 4:
self.assertEqual(self.railroad.get_route_distance('A-E-B-C-D'), 22)
# test 5: (Route does not exist)
self.assertRaises(graph_exception.GraphError, self.railroad.get_route_distance, 'A-E-D')
def test_get_count_trips_MaxStop(self):
# test 6:
# The number of trips starting at C and ending at C with a maximum of 3 stops.
# In the sample data below, there are two such trips: C-D-C (2 stops). and C-E-B-C (3 stops).
self.assertEqual(self.railroad.get_count_trips_MaxStop('C', 'C', max_stop=3), 2)
def test_get_trips_ExactCountStop(self):
# test 7:
# The number of trips starting at A and ending at C with exactly 4 stops.
# In the sample data below, there are three such trips:
# A to C (via B,C,D); A to C (via D,C,D); and A to C (via D,E,B).
self.assertEqual(self.railroad.get_count_trips_ExactCountStop('A', 'C', count_stop=4), 3)
def test_get_length_shortest_route(self):
# test 8:
# The length of the shortest route (in terms of distance to travel) from A to C.
self.assertEqual(self.railroad.get_length_shortest_route('A', 'C'), 9)
# test 9:
# The length of the shortest route (in terms of distance to travel) from B to B.
self.assertEqual(self.railroad.get_length_shortest_route('B', 'B'), 9)
def test_get_count_routes_MaxDistance(self):
# test 10:
# The number of different routes from C to C with a distance of less than 30. In the sample data, the trips are: CDC, CEBC, CEBCDC, CDCEBC, CDEBC, CEBCEBC, CEBCEBCEBC.
self.assertEqual(self.railroad.get_count_routes_MaxDistance('C', 'C', 30), 7)
|
{"/main.py": ["/model/railroad.py"], "/test/test_railroad.py": ["/model/railroad.py"], "/model/railroad.py": ["/model/graph.py", "/exception/railroad_exception.py"], "/test/test_graph.py": ["/model/railroad.py"], "/model/graph.py": ["/exception/graph_exception.py"]}
|
38,219
|
yassinebelmamoun/graph-trains
|
refs/heads/master
|
/model/railroad.py
|
from model.graph import Graph
from exception.railroad_exception import RailroadError
class Railroad(Graph):
"""
Vocabulary:
1. A route/trip is a string (i.e 'ABC') represention:
- Departure from station A
- Stop at station B
- Arrival (last stop) at station C
2. We define the number of stops of a trip as the total number of stations visited minus one:
- For the trip 'ABC', the number of stops is 2
Raildroad is a graph improved to reply to the following questions:
1. Find the number of trips with a maximum number of stops between two stations.
2. Count the number of trips with a maximum number of stops between two stations.
3. Count the exact number of trips with an exact number of stops between two stations.
4. Find the shortest route between two stations.
5. Find the routes between two stations.
"""
def get_trips_MaxStop(self, departure, arrival, max_stop):
# Return a list of all the trips from "departure" to "arrival" with a maximum of stops equal to max_stop
return [route for route in self.generate_all_routes_from_station(departure, max_depth=max_stop) \
if len(route) > 1 and len(route) - 1 <= max_stop and route and route[-1] == arrival]
def get_count_trips_MaxStop(self, departure, arrival, max_stop):
# Return the count of trips from "departure" to "arrival" with a maximum of stops equal to max_stop
return len(self.get_trips_MaxStop(departure, arrival, max_stop))
def get_trips_ExactCountStop(self, departure, arrival, count_stop):
# Return a list of all the trips from "departure" to "arrival" with a number of stops equal to coun_stop
return [route for route in self.generate_all_routes_from_station(departure, max_depth=count_stop) \
if len(route) > 1 and len(route) - 1 == count_stop and route and route[-1] == arrival]
def get_count_trips_ExactCountStop(self, departure, arrival, count_stop):
# Return the count of trips from "departure" to "arrival" with a number of stops equal to count_stop
return len(self.get_trips_ExactCountStop(departure, arrival, count_stop))
def get_length_shortest_route(self, departure, arrival):
# The shortest distance from departure to arrival in terms of distance.
# Testing with small values of max_depth is faster. (This is related to the cycles issues)
max_depth, routes = 3, []
while max_depth <= self.sum_edges_weight and not(routes):
routes = self.get_trips_MaxStop(departure, arrival, max_stop = max_depth)
max_depth = max_depth * 2 if max_depth <= self.sum_edges_weight else self.sum_edges_weight
return min([self.get_route_distance(route) for route in routes])
def get_count_routes_MaxDistance(self, departure, arrival, max_distance):
return len([route for route in self.get_trips_MaxStop(departure, arrival, max_stop=max_distance) \
if self.get_route_distance(route) < max_distance])
|
{"/main.py": ["/model/railroad.py"], "/test/test_railroad.py": ["/model/railroad.py"], "/model/railroad.py": ["/model/graph.py", "/exception/railroad_exception.py"], "/test/test_graph.py": ["/model/railroad.py"], "/model/graph.py": ["/exception/graph_exception.py"]}
|
38,220
|
yassinebelmamoun/graph-trains
|
refs/heads/master
|
/test/test_graph.py
|
import unittest
from model.railroad import Graph
from exception import graph_exception, railroad_exception
class GraphTestCase(unittest.TestCase):
def test_build(self):
# Building graph:
graph = Graph()
self.assertRaises(graph_exception.GraphError, graph.build, 3)
graph = Graph()
self.assertRaises(graph_exception.NodeError, graph.build, 'AA BB CC DD')
graph = Graph()
graph.build('AB5')
self.assertEqual(graph.graph,{'A': {'B':5}})
def test_get_neighbors(self):
graph = Graph()
graph.build('AB3 AC2 BC4')
self.assertEqual(list(graph.get_neighbors('A')), ['B', 'C'])
def test_clean_string_graph(self):
graph = Graph()
self.assertEqual(graph.clean_string_graph('AB4-CD2-JE20'), 'AB4 CD2 JE20')
|
{"/main.py": ["/model/railroad.py"], "/test/test_railroad.py": ["/model/railroad.py"], "/model/railroad.py": ["/model/graph.py", "/exception/railroad_exception.py"], "/test/test_graph.py": ["/model/railroad.py"], "/model/graph.py": ["/exception/graph_exception.py"]}
|
38,221
|
yassinebelmamoun/graph-trains
|
refs/heads/master
|
/exception/railroad_exception.py
|
class Error(Exception):
"""Base class for exceptions in this module."""
class RailroadError(Error):
"""Graph Exception"""
|
{"/main.py": ["/model/railroad.py"], "/test/test_railroad.py": ["/model/railroad.py"], "/model/railroad.py": ["/model/graph.py", "/exception/railroad_exception.py"], "/test/test_graph.py": ["/model/railroad.py"], "/model/graph.py": ["/exception/graph_exception.py"]}
|
38,222
|
yassinebelmamoun/graph-trains
|
refs/heads/master
|
/exception/graph_exception.py
|
class Error(Exception):
"""Base class for exceptions in this module."""
class GraphError(Error):
"""Graph Exception"""
class NodeError(Error):
"""Node Exception"""
|
{"/main.py": ["/model/railroad.py"], "/test/test_railroad.py": ["/model/railroad.py"], "/model/railroad.py": ["/model/graph.py", "/exception/railroad_exception.py"], "/test/test_graph.py": ["/model/railroad.py"], "/model/graph.py": ["/exception/graph_exception.py"]}
|
38,223
|
yassinebelmamoun/graph-trains
|
refs/heads/master
|
/model/graph.py
|
from exception.graph_exception import GraphError, NodeError
class Graph:
"""
The representation of a graph with a string is:
string_graph = 'AB5, AC2, BC3, BD1, CD3, DA5'
We represent a graph with a dictionary:
> railroad = Graph()
> railroad.build(string_graph)
> railroad.graph
[OUTPUT] >> {
'A': {'B': 5, 'C': 2}
'B': {'C': 3, 'D': 1}
'C': {'D': 3}
'D': {'A': 5}
}
1 - Node:
A node is represented by ONE letter (A, B, ..., Z) and is called "station"
2 - Distance between two Nodes:
The distance between two nodes A and B is an integer.
> graph.get_distance(departure='A', arrival='B')
3 - Neighbors:
The neighbors of the node A are represented by a list of nodes.
> graph.get_neighbors('A')
4 - Route distance:
The distance of a route is the sum of the distance between the nodes
> graph.get_route_distance('ABC') = graph.get_distance('A', 'B') + graph.get_distance('B', 'C')
5 - Build of a graph:
The following method are used to build a graph:
- build
- add_node
- clean_string_graph
- parse_node
- check_node_station
- check_node_distance
A graph will be represented by a description. Each key is a node and its value is a dictionary:
> 'A': {'B': 5, 'C': 2}
The node/key A is connected to B and C with a weight of 5 and 2 respectivily.
6 - Graph exploration:
We explore the graph depth by depth starting from a departure station.
We limit the max_depth in case we find a cycle (i.e. ABCDA) which will lead to infinite number of routes ABCDABCDABCD
The following methods are used:
- generate_all_routes_from_station(departure, max_depth):
"departure" is the departure station.
"max_depth" is the maximum depth for the exploration
- create_deeper_routes(routes, max_depth): Recursive function
- create_deeper_route(route)
7 - Graph sum of edges' weight:
Due to the possibility of the existence of cycles, we can proove mathematically than the distance of a cycle is less than the sum of the edges.
"""
def __init__(self):
self.graph = {}
self.sum_edges_weight = 0
def __repr__(self):
return str(self.graph)
def visualise(self):
for departure, arrival_duration_dict in self.graph.items():
print('\n*** The distance between the station {} and:'.format(departure))
for arrival, duration in arrival_duration_dict.items():
print('\t - the station {} is: {} units'.format(arrival, duration))
def build(self, string_graph):
for node in self.clean_string_graph(string_graph).split(): self.add_node(node)
def get_neighbors(self, departure):
return self.graph[departure].keys() if departure in self.graph else []
def get_distance(self, departure, arrival):
if departure in self.graph and arrival in self.graph[departure]:
return self.graph[departure][arrival]
raise GraphError('The connexion from {} to {} does not exist'.format(departure, arrival))
def get_route_distance(self, route):
route = ''.join([c.upper() if c.isalnum() else "" for c in route])
return sum([self.get_distance(departure=route[i],arrival=route[i+1]) for i in range(len(route)-1)])
def create_deeper_route(self, route):
"""
Starting from a route, i.e. 'ABC'
We explore all the possible routes starting from the last station 'C'
If 'B', 'F' and 'L' are neighbors of 'A'.
The output will be ['ABCF', 'ABCL', 'ABCA'].
"""
last_station = route[-1]
neighbors = [neighbor for neighbor in self.get_neighbors(last_station)]
if neighbors:
return [route + neighbor for neighbor in neighbors]
else:
return []
def create_deeper_routes(self, routes, max_depth):
"""
Recursive function for exploring deeper routes
For each route in routes: We create deeper routes
We concatenate all the new (deeper) routes with the initial routes and we continue.
"""
new_routes = []
for route in routes:
new_routes += self.create_deeper_route(route)
if new_routes and max_depth > 0:
return routes + self.create_deeper_routes(new_routes, max_depth=max_depth-1)
else:
return routes
def generate_all_routes_from_station(self, departure, max_depth):
"""
Explore all the routes starting from departure station with a maximum depth of 'max_depth'
"""
return self.create_deeper_routes([departure], max_depth)
def add_node(self, string_graph):
"""
Parse node into departure, arrival and distance
"""
departure, arrival, distance = self.parse_node(string_graph)
if not(departure in self.graph): self.graph[departure] = {}
self.graph[departure][arrival] = distance
self.sum_edges_weight += distance
@classmethod
def parse_node(cls, string_node):
"""
Check the length of the node (at least 3 characters):
- First character(Departure): ONE alpha
- Second character(Arrival): ONE alpha
- The rest of the string are numeric characters
"""
if not(len(string_node) >= 3):
raise NodeError('The following node {} contains {} characters.\n \
The Node should contain exactly 3 characters.'.format(string_node, len(string_node)))
departure = cls.check_node_station(station_type = 'Departure',\
station = string_node[0],\
string_node = string_node)
arrival = cls.check_node_station(station_type = 'Arrival',\
station = string_node[1],\
string_node = string_node)
if departure == arrival: raise NodeError('The arrival and departure stations must be different in the node {}'.format(string_node))
distance = cls.check_node_distance(string_node[2:], string_node)
return (departure, arrival, distance)
@staticmethod
def check_node_station(station_type, station, string_node):
if not(station.isalpha()):
raise NodeError('The {} value {} is not in the right format in the following node "{}" '.format(station_type, station, string_node))
return station
@staticmethod
def check_node_distance(distance, string_node):
try:
distance = int(distance)
except ValueError:
raise NodeError('The Distance value {} is not in the right format in the following node {}.'.format(str(distance), string_node))
else:
return distance
@staticmethod
def clean_string_graph(string_graph):
"""
Clean the string:
- Remove not(alphanumeric) character and replace by whitespace
- Replace lowercase character by uppercase character
"""
if not(isinstance(string_graph, str)): raise GraphError('The graph must be a string.\n i.e. "AB5, AC2, BC3, BD1, CD3, DA5"')
return ''.join([c.upper() if c.isalnum() else " " for c in string_graph])
|
{"/main.py": ["/model/railroad.py"], "/test/test_railroad.py": ["/model/railroad.py"], "/model/railroad.py": ["/model/graph.py", "/exception/railroad_exception.py"], "/test/test_graph.py": ["/model/railroad.py"], "/model/graph.py": ["/exception/graph_exception.py"]}
|
38,248
|
jlane9/SAMpyL
|
refs/heads/master
|
/sampyl/core/__init__.py
|
# -*- coding: utf-8 -*-
"""sampyl.core
.. codeauthor:: John Lane <jlane@fanthreesixty.com>
"""
from sampyl.core import element
from sampyl.core import mixins
from sampyl.core import shortcuts
from sampyl.core import structures
__all__ = ['element', 'mixins', 'shortcuts', 'structures']
|
{"/setup.py": ["/sampyl/__init__.py"], "/sampyl/__init__.py": ["/sampyl/app.py"], "/sampyl/core/structures.py": ["/sampyl/core/element.py"], "/sampyl/app.py": ["/sampyl/core/element.py", "/sampyl/core/structures.py"], "/sampyl/core/element.py": ["/sampyl/core/shortcuts.py"]}
|
38,249
|
jlane9/SAMpyL
|
refs/heads/master
|
/setup.py
|
"""SAMpyL setup.py
"""
from setuptools import setup, find_packages
from sampyl import __author__, __email__, __license__, __version__
setup(
name='sampyl',
version=__version__,
packages=find_packages(),
description='A wrapper for Selenium. This library uses custom data attributes to accelerate '
'testing through the Selenium framework',
author=__author__,
author_email=__email__,
url='https://github.com/jlane9/SAMpyL',
download_url='https://github.com/jlane9/SAMpyL/tarball/%s' % __version__,
keywords='testing selenium qa web automation',
install_requires=['lxml', 'cssselect', 'PyYAML'],
license=__license__,
classifiers=['Development Status :: 3 - Alpha',
'Intended Audience :: Developers',
'Programming Language :: Python :: 2.6',
'Programming Language :: Python :: 2.7',
'License :: OSI Approved :: MIT License',
'Topic :: Software Development :: Quality Assurance',
'Topic :: Software Development :: Testing'])
|
{"/setup.py": ["/sampyl/__init__.py"], "/sampyl/__init__.py": ["/sampyl/app.py"], "/sampyl/core/structures.py": ["/sampyl/core/element.py"], "/sampyl/app.py": ["/sampyl/core/element.py", "/sampyl/core/structures.py"], "/sampyl/core/element.py": ["/sampyl/core/shortcuts.py"]}
|
38,250
|
jlane9/SAMpyL
|
refs/heads/master
|
/sampyl/__init__.py
|
# -*- coding: utf-8 -*-
"""sampyl
.. codeauthor:: John Lane <jlane@fanthreesixty.com>
"""
from sampyl.app import App, Node
__author__ = "John Lane"
__copyright__ = "Copyright 2016, FanThreeSixty"
__credits__ = ["John Lane"]
__license__ = "Apache License v2.0"
__version__ = '0.2.0'
__maintainer__ = "John Lane"
__email__ = "jlane@fanthreesixty.com"
__status__ = "Beta"
__all__ = ['App', 'Node']
|
{"/setup.py": ["/sampyl/__init__.py"], "/sampyl/__init__.py": ["/sampyl/app.py"], "/sampyl/core/structures.py": ["/sampyl/core/element.py"], "/sampyl/app.py": ["/sampyl/core/element.py", "/sampyl/core/structures.py"], "/sampyl/core/element.py": ["/sampyl/core/shortcuts.py"]}
|
38,251
|
jlane9/SAMpyL
|
refs/heads/master
|
/sampyl/core/structures.py
|
# -*- coding: utf-8 -*-
"""sampyl.core.structures
.. codeauthor:: John Lane <jlane@fanthreesixty.com>
"""
# pylint: disable=line-too-long
import inspect
import sys
import warnings
from selenium.webdriver.common.by import By
from sampyl.core.element import Element, join
from sampyl.core.mixins import ClickMixin, InputMixin, SelectMixin, SelectiveMixin, TextMixin
__all__ = ['Button', 'Div', 'Image', 'InputCheckbox', 'InputRadio', 'InputText', 'Link',
'MultiSelect', 'Select', 'Text']
class Button(Element, ClickMixin, TextMixin):
"""The Button implementation
**Example Use:**
Let's take the following example:
.. code-block:: html
<button id="someClassId" class="someClass"
on-click="javascript.function()">Click Me</button>
If the user wants to make the code above recognizable to the testing framework, they
would add the attribute "data-qa-id" with a unique value as well as "data-qa-model"
with a type.
.. code-block:: html
<button data-qa-id="some.identifier" data-qa-model="button" id="someClassId"
class="someClass" on-click="javascript.function()">
Click Me
</button>
An example on how to interact with the element:
.. code-block:: python
from selenium.webdriver import Chrome
from sampyl import App
wd = webdriver.Chrome('/path/to/chromedriver')
app = App(wd, "http://someurl.com/path")
# Example usage
app.page.some.identifier.click()
"""
pass
class Div(Element):
"""The Div implementation
**Example Use:**
Let's take the following example:
.. code-block:: html
<div id="someClassId" class="someClass">
...
</div>
If the user wants to make the code above recognizable to the testing framework, they
would add the attribute "data-qa-id" with a unique value as well as "data-qa-model"
with a type.
.. code-block:: html
<div data-qa-id="some.identifier" data-qa-model="div" id="someClassId"
class="someClass">
...
</div>
An example on how to interact with the element:
.. code-block:: python
from selenium.webdriver import Chrome
from sampyl import App
wd = webdriver.Chrome('/path/to/chromedriver')
app = App(wd, "http://someurl.com/path")
# Returns True
app.page.some.identifier.is_displayed()
"""
pass
class Dropdown(Element, ClickMixin, TextMixin):
"""The Dropdown implementation
.. note:: This structure is specifically for a Bootstrap dropdown
**Example Use:**
.. code-block:: html
<div class="dropdown">
<button class="btn btn-primary dropdown-toggle" type="button" data-toggle="dropdown">Dropdown Example
<span class="caret"></span></button>
<ul class="dropdown-menu">
<li><a href="#">HTML</a></li>
...
</ul>
</div>
If the user wants to make the code above recognizable to the testing framework, they would add the attribute
"data-qa-id" with a unique value as well as "data-qa-model" with a type.
.. code-block:: html
<div class="dropdown" data-qa-id="some.identifier" data-qa-model="dropdown">
<button class="btn btn-primary dropdown-toggle" type="button" data-toggle="dropdown">Dropdown Example
<span class="caret"></span></button>
<ul class="dropdown-menu">
<li><a href="#">HTML</a></li>
...
</ul>
</div>
An example on how to interact with the element:
.. code-block:: python
from selenium.webdriver import Chrome
from sampyl import App
wd = webdriver.Chrome('/path/to/chromedriver')
app = App(wd, "http://someurl.com/path")
# Opens the dropdown
app.page.some.identifier.expand()
"""
_toggle_xpath = (By.XPATH, '/descendant-or-self::*[(contains(@class, "dropdown-toggle") or '
'@ng-mouseover or @ng-click)]')
@property
def _container(self):
"""Dropdown container
:return:
"""
xpath = '/following-sibling::*[(contains(@class, "dropdown-menu") or contains(@class, "tree") or @ng-show) ' \
'and (self::div or self::ul)]'
child = '/descendant-or-self::*[(contains(@class, "dropdown-menu") or contains(@class, "tree") or @ng-show) ' \
'and (self::div or self::ul)]'
xpath_term = join(self.search_term, self._toggle, (By.XPATH, xpath))
child_term = join(self.search_term, self._toggle, (By.XPATH, child))
return Div(self.driver, By.XPATH, '|'.join([xpath_term[1], child_term[1]]))
@property
def _toggle(self):
"""Show/hide toggle button
:return:
"""
return Button(self.driver, *join(self.search_term, self._toggle_xpath))
def expand(self, hover=False):
"""Show dropdown
:return:
"""
if not self._container.is_displayed():
if hover:
self._toggle.hover()
else:
self._toggle.click()
return self._container.wait_until_appears()
def collapse(self, hover=False):
"""Hide dropdown
:return:
"""
if self._container.is_displayed():
if hover:
self._toggle.hover()
else:
self._toggle.click()
return self._container.wait_until_disappears()
class BadgeDropdown(Dropdown):
"""Badge dropdown to capture hover event
"""
def show(self):
"""Show dropdown on mousein of badge
:return:
"""
return self.expand(True)
def hide(self):
"""Hide dropdown on mouseout of badge
:return:
"""
return self.collapse(True)
class Form(Element):
def _get_field(self, field_name):
if not isinstance(field_name, basestring):
raise TypeError
xpath = '/descendant-or-self::*[((self::input and @type="text") or ' \
'self::textarea or self::select) and @name="{}"]'
elements = self.driver.find_elements(*join(self.search_term, (By.XPATH, xpath.format(field_name))))
if elements:
return elements[0]
def get_field(self, field_name):
field = self._get_field(field_name)
if field:
input_xpath = '/descendant-or-self::*[((self::input and @type="text") or self::textarea) and @name="{}"]'
select_xpath = '/descendant-or-self::*[self::select and @name="{}"]'
if field.tag_name == u'input' or field.tag_name == u'textarea':
return InputText(self.driver, *join(self.search_term, (By.XPATH, input_xpath.format(field_name))))
elif field.tag_name == u'select':
return Select(self.driver, *join(self.search_term, (By.XPATH, select_xpath.format(field_name))))
else:
warnings.warn('{} type not currently supported within form'.format(str(field.tag_name)))
class Image(Element):
"""The Image implementation
**Example Use:**
Let's take the following example:
.. code-block:: html
<img id="someClassId" class="someClass" />
If the user wants to make the code above recognizable to the testing framework, they
would add the attribute "data-qa-id" with a unique value as well as "data-qa-model"
with a type.
.. code-block:: html
<img data-qa-id="some.identifier" data-qa-model="image" id="someClassId"
class="someClass" src="http://someSource.net/image.png" />
An example on how to interact with the element:
.. code-block:: python
from selenium.webdriver import Chrome
from sampyl import App
wd = webdriver.Chrome('/path/to/chromedriver')
app = App(wd, "http://someurl.com/path")
# Returns "http://someSource.net/image.png"
app.page.some.identifier.source()
"""
def source(self):
"""Returns image source URL
:return: Image source URL
:rtype: str
"""
return self.src
class InputCheckbox(Element, SelectiveMixin):
"""The InputCheckbox implementation
**Example Use:**
Let's take the following example:
.. code-block:: html
<input id="someClassId" type="checkbox" class="someClass">
If the user wants to make the code above recognizable to the testing framework, they
would add the attribute "data-qa-id" with a unique value as well as "data-qa-model"
with a type.
.. code-block:: html
<input data-qa-id="some.identifier" data-qa-model="inputcheckbox"
id="someClassId" type="checkbox" class="someClass">
An example on how to interact with the element:
.. code-block:: python
from selenium.webdriver import Chrome
from sampyl import App
wd = webdriver.Chrome('/path/to/chromedriver')
app = App(wd, "http://someurl.com/path")
app.page.some.identifier.select()
"""
@property
def label(self):
"""Returns the label for the input item
:return: Returns Text object for label
:rtype: Text
"""
if self.exists():
return Text(self.driver, By.XPATH,
'/descendant-or-self::label[@for="{0}"]'.format(str(self.id))).visible_text() \
if len(self.id) > 0 else ''
class InputRadio(InputCheckbox, SelectiveMixin):
"""The InputRadio implementation
**Example Use:**
Let's take the following example:
.. code-block:: html
<input id="someClassId" type="radio" class="someClass">
If the user wants to make the code above recognizable to the testing framework, they
would add the attribute "data-qa-id" with a unique value as well as "data-qa-model"
with a type.
.. code-block:: html
<input data-qa-id="some.identifier" data-qa-model="inputradio" id="someClassId"
type="radio" class="someClass">
An example on how to interact with the element:
.. code-block:: python
from selenium.webdriver import Chrome
from sampyl import App
wd = webdriver.Chrome('/path/to/chromedriver')
app = App(wd, "http://someurl.com/path")
app.page.some.identifier.select()
"""
pass
class InputText(Element, InputMixin, ClickMixin):
"""The InputText implementation
**Example Use:**
Let's take the following example:
.. code-block:: html
<input id="someClassId" type="text" class="someClass">
If the user wants to make the code above recognizable to the testing framework, they would add the attribute
"data-qa-id" with a unique value as well as "data-qa-model" with a type.
.. code-block:: html
<input data-qa-id="some.identifier" data-qa-model="inputtext" id="someClassId" type="text"
class="someClass">
An example on how to interact with the element:
.. code-block:: python
from selenium.webdriver import Chrome
from sampyl import App
wd = webdriver.Chrome('/path/to/chromedriver')
app = App(wd, "http://someurl.com/path")
app.page.some.identifier.input('Hello World')
"""
@property
def label(self):
"""Returns the label for the input item
:return: Text object for label
:rtype: Text
"""
if self.exists():
return Text(self.driver, By.XPATH,
'/descendant-or-self::label[@for="{0}"]'.format(str(self.id))).visible_text() \
if len(self.id) > 0 else ''
class Link(Element, ClickMixin, TextMixin):
"""The Link implementation
**Example Use:**
Let's take the following example:
.. code-block:: html
<a id="someClassId" class="someClass" href="/some/link/path">Click Me</a>
If the user wants to make the code above recognizable to the testing framework, they
would add the attribute "data-qa-id" with a unique value as well as "data-qa-model"
with a type.
.. code-block:: html
<a data-qa-id="some.identifier" id="someClassId" class="someClass"
href="/some/link/path">Click Me</a>
An example on how to interact with the element:
.. code-block:: python
from selenium.webdriver import Chrome
from sampyl import App
wd = webdriver.Chrome('/path/to/chromedriver')
app = App(wd, "http://someurl.com/path")
app.page.some.identifier.click()
"""
pass
class MultiSelect(Element):
"""The MultiSelect implementation
**Example Use:**
Let's take the following example:
.. code-block:: html
<div id="someClassId" class="someClass" isteven-multi-select input-model="some.model"
output-model="format.model" helper-elements="filter all none">
...
</div>
If the user wants to make the code above recognizable to the testing framework, they would add the attribute
"data-qa-id" with a unique value as well as "data-qa-model" with a type.
.. code-block:: html
<div data-qa-id="some.identifier" data-qa-model="multiselect" id="someClassId" class="someClass"
isteven-multi-select input-model="some.model" output-model="format.model" helper-elements="filter all none">
...
</div>
An example on how to interact with the element:
.. code-block:: python
from selenium.webdriver import Chrome
from sampyl import App
wd = webdriver.Chrome('/path/to/chromedriver')
app = App(wd, "http://someurl.com/path")
# Opens the iSteven dropdown
app.page.some.identifier.expand()
"""
@property
def _container(self):
"""iSteven dropdown container
:return:
"""
xpath = '/descendant-or-self::div[contains(@class, "checkboxLayer")]'
return Div(self.driver, *join(self.search_term, (By.XPATH, xpath)))
@property
def _toggle(self):
"""Show/hide button
:return:
"""
xpath = '/descendant-or-self::button[contains(@ng-click, "toggle")]'
return Button(self.driver, *join(self.search_term, (By.XPATH, xpath)))
@property
def _select_all(self):
"""Select all button
:return:
"""
xpath = '/descendant-or-self::button[contains(@ng-click, "all")]'
return Button(self.driver, *join(self.search_term, (By.XPATH, xpath)))
@property
def _select_none(self):
"""Select none button
:return:
"""
xpath = '/descendant-or-self::button[contains(@ng-click, "none")]'
return Button(self.driver, *join(self.search_term, (By.XPATH, xpath)))
@property
def _reset(self):
"""Reset button
:return:
"""
xpath = '/descendant-or-self::button[contains(@ng-click, "reset")]'
return Button(self.driver, *join(self.search_term, (By.XPATH, xpath)))
@property
def _filter(self):
"""Search field
:return:
"""
xpath = '/descendant-or-self::input[contains(@ng-click, "filter")]'
return InputText(self.driver, *join(self.search_term, (By.XPATH, xpath)))
@property
def _clear(self):
"""Clear search button
:return:
"""
xpath = '/descendant-or-self::button[contains(@ng-click, "clear")]'
return Button(self.driver, *join(self.search_term, (By.XPATH, xpath)))
def _get_index(self, idx):
"""Return item at index 'i'
:param str idx: Index
:return:
"""
if isinstance(idx, int) or isinstance(idx, basestring):
# Convert string to integer
if isinstance(idx, str):
if idx.isdigit():
idx = int(idx)
else:
raise TypeError('Error: Index must be of type int')
if idx in range(0, len(self.options())):
return Button(self.driver, *join(self.search_term,
(By.XPATH, '/descendant-or-self::div[contains(@ng-repeat, '
'"filteredModel")][{}]'.format(idx))))
def _get_text(self, text):
"""Return selection that contains text criteria
:param str text: Text criteria
:return:
"""
if isinstance(text, basestring):
return Button(self.driver, *join(self.search_term,
(By.XPATH, '/descendant-or-self::label[contains(., "{}")]/ancestor::div'
'[contains(@ng-repeat, "filteredModel")]'.format(text))))
def expand(self):
"""Show iSteven dropdown
:return:
"""
if not self._container.is_displayed():
self._toggle.click()
self._container.wait_until_appears()
def collapse(self):
"""Hide iSteven dropdown
:return:
"""
if self._container.is_displayed():
self._toggle.click()
self._container.wait_until_disappears()
def select_all(self):
"""Select all possible selections
:return:
"""
self.expand()
self._select_all.click()
def select_none(self):
"""Deselect all selections
:return:
"""
self.expand()
self._select_none.click()
def reset(self):
"""Reset selection to default state
:return:
"""
self.expand()
self._reset.click()
def search(self, value, clear=True):
"""Filter selections to those matching search criteria
:param str value: Search criteria
:param bool clear: Clear previous search criteria
:return:
"""
self.expand()
self._filter.input(value, clear)
def clear_search(self):
"""Click clear search button
:return:
"""
self.expand()
self._clear.click()
def select_by_index(self, index):
"""Select option at index 'i'
:param str index: Index
:return:
"""
self.expand()
option = self._get_index(index)
if option:
if 'selected' not in option.class_:
option.click()
def select_by_text(self, text):
"""Select option that matches text criteria
:param str text: Text criteria
:return:
"""
self.expand()
option = self._get_text(text)
if option.exists():
option.click()
def deselect_by_index(self, index):
"""Deselect option at index 'i'
:param str index: Index
:return:
"""
self.expand()
option = self._get_index(index)
if option:
if 'selected' in option.class_:
option.click()
def deselect_by_text(self, text):
"""Deselect option that matches text criteria
:param str text: Text criteria
:return:
"""
self.expand()
option = self._get_text(text)
if option.exists():
option.click()
def options(self, include_group=True):
"""Return all available options
:param bool include_group: True, to include groupings
:return: List of options
:rtype: list
"""
if include_group:
xpath = '/descendant-or-self::div[contains(@ng-repeat, "filteredModel")]//label'
else:
xpath = '/descendant-or-self::div[contains(@ng-repeat, "filteredModel") and ' \
'not(contains(@class, "multiSelectGroup"))]//label'
search_term = join(self.search_term, (By.XPATH, xpath))
return [element.get_attribute('textContent').encode('ascii', 'ignore')
for element in self.driver.find_elements(*search_term)]
def selected_options(self):
"""Return all selected options
:return: List of selected options
:rtype: list
"""
search_term = join(self.search_term, (By.XPATH, '/descendant-or-self::div[contains(@ng-repeat, '
'"filteredModel") and contains(@class, "selected")]//label'))
return [element.get_attribute('textContent').encode('ascii', 'ignore')
for element in self.driver.find_elements(*search_term)]
class Select(Element, SelectMixin):
"""The Select implementation
**Example Use:**
Let's take the following example:
.. code-block:: html
<select id="someClassId" class="someClass">
<option value="1">Value 1</option>
<option value="2">Value 2</option>
<option value="3">Value 3</option>
<option value="4">Value 4</option>
</select>
If the user wants to make the code above recognizable to the testing framework, they
would add the attribute "data-qa-id" with a unique value as well as "data-qa-model"
with a type.
.. code-block:: html
<select data-qa-id="some.identifier" data-qa-model="select" id="someClassId"
class="someClass">
<option value="1">Value 1</option>
<option value="2">Value 2</option>
<option value="3">Value 3</option>
<option value="4">Value 4</option>
</select>
An example on how to interact with the element:
.. code-block:: python
from selenium.webdriver import Chrome
from sampyl import App
wd = webdriver.Chrome('/path/to/chromedriver')
app = App(wd, "http://someurl.com/path")
# Example usage. Returns ['Value 1', 'Value 2', 'Value 3', 'Value 4']
app.page.some.identifier.options()
"""
pass
class Text(Element, TextMixin, ClickMixin):
"""The Text implementation
**Example Use:**
Let's take the following example:
.. code-block:: html
<p id="someClassId" class="someClass">
...
</p>
If the user wants to make the code above recognizable to the testing framework, they
would add the attribute "data-qa-id" with a unique value.
.. code-block:: html
<p data-qa-id="some.identifier" data-qa-model="text" id="someClassId"
class="someClass">
...
</p>
An example on how to interact with the element:
.. code-block:: python
from selenium.webdriver import Chrome
from sampyl import App
wd = webdriver.Chrome('/path/to/chromedriver')
app = App(wd, "http://someurl.com/path")
# Prints text inside text elements
print app.page.some.identifier
"""
pass
MEMBERS = inspect.getmembers(sys.modules[__name__], predicate=lambda o: inspect.isclass(o) and issubclass(o, Element))
TYPES = {_type[0].lower(): _type[1] for _type in MEMBERS}
|
{"/setup.py": ["/sampyl/__init__.py"], "/sampyl/__init__.py": ["/sampyl/app.py"], "/sampyl/core/structures.py": ["/sampyl/core/element.py"], "/sampyl/app.py": ["/sampyl/core/element.py", "/sampyl/core/structures.py"], "/sampyl/core/element.py": ["/sampyl/core/shortcuts.py"]}
|
38,252
|
jlane9/SAMpyL
|
refs/heads/master
|
/sampyl/app.py
|
# -*- coding: utf-8 -*-
"""sampyl.app
.. codeauthor:: John Lane <jlane@fanthreesixty.com>
"""
# pylint: disable=line-too-long
import keyword
import re
import warnings
from urlparse import urlparse
from sampyl.core.element import SeleniumObject, DEFAULT_NAME_ATTR, DEFAULT_TYPE_ATTR
from sampyl.core.structures import TYPES as T
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as ec
from selenium.common.exceptions import NoSuchElementException
__all__ = ['App', 'Node']
DEFAULT_TYPE = 'text'
def is_legal_variable_name(name):
"""Determines whether the name attribute value is a valid variable name
:param str name: Name attribute
:return:
"""
if isinstance(name, basestring):
if not keyword.iskeyword(name):
return bool(re.compile(r'^[a-zA-Z_][\w]*$').search(name))
return False
def force_legal_variable_name(name):
"""Forces name attribute into a valid variable name
:param str name: Name attribute
:return:
"""
if not is_legal_variable_name(name):
if isinstance(name, basestring):
re_keyword = '|'.join(['(^{}$)'.format(item) for item in keyword.kwlist])
keyword_safe = re.sub('({})'.format(re_keyword), '_\g<1>', name)
return re.sub('^\d|[^\w]', '_', keyword_safe)
return '_'
return name
class App(SeleniumObject):
"""The App implementation
.. TODO:: Add the ability define which types are available
"""
scheme = ""
hostname = ""
def __init__(self, web_driver, url=None):
super(App, self).__init__(web_driver)
full_url = url if isinstance(url, basestring) else ''
path = urlparse(full_url)
self.page = Node(web_driver)
if path.netloc != '':
self.scheme = path.scheme if path.scheme != '' else 'http'
self.hostname = path.netloc
if self.hostname != '':
self.get('%s://%s' % (self.scheme, self.hostname))
def get(self, url):
"""Instruct Selenium to navigate to the following url
:param str url: web url
:return:
"""
if isinstance(url, basestring):
return self.driver.get(url)
raise TypeError('Incorrect type for \'url\', url must be of type \'str\'')
def navigate_to(self, path):
"""Instructs Selenium to navigate to a different path under the hostname
:param str path: Path from hostname
:return:
"""
if isinstance(path, basestring):
full_url = urlparse(path).path
url_path = '/%s' % full_url if not full_url.startswith('/') else full_url
if isinstance(path, basestring):
scheme = self.scheme if self.scheme != '' else 'http'
if self.hostname != '':
return self.get('%s://%s%s' % (scheme, self.hostname, url_path))
raise NotImplementedError('Action cannot be completed because hostname is not set')
raise TypeError('Incorrect type for \'path\', path must be of type \'str\'')
def update(self, name_attr=DEFAULT_NAME_ATTR, type_attr=DEFAULT_TYPE_ATTR):
"""
:param str name_attr:
:param str type_attr:
:return:
"""
if isinstance(name_attr, basestring) and isinstance(type_attr, basestring):
identifiers = [e.get_attribute(name_attr) for e in
self.driver.find_elements(By.XPATH, '/descendant-or-self::*[@{0}]'.format(name_attr))]
duplicates = set(['"{}"'.format(_id) for _id in identifiers if identifiers.count(_id) > 1])
if len(duplicates) > 0:
msg = ' '.join(['UniquenessWarning: There appears to be multiple elements with the'
' same identifier. Please review the following element(s):',
', '.join(duplicates)])
warnings.warn(msg)
self.page = Node(self.driver, name_attr=name_attr, type_attr=type_attr)
self.page.add_children(*set(identifiers))
def wait_until_present(self, path, _by=None, timeout=30):
"""Wait until element with id is present
:param str _by:
:param str path:
:param int timeout: Wait timeout in seconds
:return:
"""
if not _by and isinstance(path, basestring):
_by = 'xpath'
path = '/descendant-or-self::*[contains(@{0}, "{1}")]'.format(self._name_attr, path)
return super(App, self).wait_until_present(_by, path, timeout=timeout)
def wait_until_appears(self, path, _by=None, timeout=30):
"""Wait until the element appears
:param str _by:
:param str path:
:param int timeout: Wait timeout in seconds
:return: True, if the wait does not timeout
:rtype: bool
"""
if not _by and isinstance(path, basestring):
_by = 'xpath'
path = '/descendant-or-self::*[contains(@{0}, "{1}")]'.format(self._name_attr, path)
return super(App, self).wait_until_appears(_by, path, timeout=timeout)
def wait_until_disappears(self, path, _by=None, timeout=30):
"""Wait until the element disappears
:param str _by:
:param str path:
:param int timeout: Wait timeout in seconds
:return: True, if the wait does not timeout
:rtype: bool
"""
if not _by and isinstance(path, basestring):
_by = 'xpath'
path = '/descendant-or-self::*[contains(@{0}, "{1}")]'.format(self._name_attr, path)
return super(App, self).wait_until_disappears(_by, path, timeout=timeout)
class Node(SeleniumObject):
"""The SAMpyL Node implementation
"""
__PATH = '/descendant-or-self::*[@{0}="{1}"]'
DELIMITER = '.'
def __init__(self, web_driver, identifier=None, root=None, **kwargs):
super(Node, self).__init__(web_driver, **kwargs)
self._children = {}
# Sanitize arguments
identifier = identifier if isinstance(identifier, basestring) else ''
root = root if isinstance(root, basestring) else ''
# Get the first attribute from the identifier
cur = identifier.split(self.DELIMITER, 1)
# Assign identifier
if cur[0] != '':
if root != '':
self._identifier = self.DELIMITER.join((root, cur[0]))
else:
self._identifier = cur[0]
else:
self._identifier = ''
# The identifier has "leftover" attributes, recursively create child nodes
if len(cur) > 1:
child = cur[1].split(self.DELIMITER, 1)[0]
if child != '':
self.__setitem__(child, Node(web_driver=web_driver,
identifier=cur[1], root=self._identifier))
def __getattr__(self, item):
if item in self.keys():
return self.__getitem__(item)
else:
element = self.this
if hasattr(element, item):
attr = element.__getattribute__(item)
# SDA method
if hasattr(attr, '__call__'):
def indirect_call_to_this(*args, **kwargs):
"""
:param args:
:param kwargs:
:return:
"""
return attr(*args, **kwargs)
return indirect_call_to_this
# SDA property
else:
return attr
else:
raise AttributeError('%s' % str(item))
def __getitem__(self, item):
if isinstance(item, (basestring, int)):
if str(item) in self.keys():
return self._children[str(item)]
raise KeyError('%s' % str(item))
raise TypeError("%s is not valid, use <type 'str'>" % type(item))
def __setitem__(self, key, value):
if isinstance(value, Node):
self._children[str(key)] = value
setattr(self, force_legal_variable_name(key), value)
return
raise TypeError("value %s is not valid, use value <type 'Node'>" % type(value))
def keys(self):
"""Returns a list of this node's children
:return: List of Node's children
:rtype: list
"""
return self._children.keys()
def add_child(self, child):
"""Create child node from this node
:param str child: Child element identifier
:return:
"""
child = child if isinstance(child, basestring) else ''
cur = child.split(self.DELIMITER, 1)
if cur[0] != '':
# If this Node has not been created
if cur[0] not in self.keys():
self.__setitem__(cur[0], Node(web_driver=self.driver,
identifier=child, root=self._identifier))
# If the Node already exists
elif len(cur) > 1:
try:
self._children[cur[0]].add_child(cur[1])
except KeyError:
raise KeyError('Id %s contains a reserved word.' % child)
def add_children(self, *args):
"""Creates child nodes from this node
:param str args: Child elements' identifiers
:return:
"""
for arg in args:
self.add_child(arg)
@property
def this(self):
"""Returns the sda structure for this node
:return: SDA structure
"""
if self._identifier != '':
return T.get(self.type(), T[DEFAULT_TYPE])(self.driver, by=By.XPATH, path=self.xpath())
def xpath(self):
"""Returns the XPATH selector for this node
:return: XPATH selector
:rtype: str
"""
if self._identifier != '':
return self.__PATH.format(self._name_attr, self._identifier)
return ''
def type(self):
"""Return a node's type
:return: Node type
:rtype: str
"""
try:
element = self.driver.find_element_by_xpath(self.xpath())
except NoSuchElementException:
element = None
_type = element.get_attribute(self._type_attr) if element else None
if _type:
return _type.lower()
else:
return DEFAULT_TYPE
def wait_until_present(self, _by=None, path=None, timeout=30):
"""Wait until the element is available to the DOM
:param str _by: Selector method
:param str path: Selector path
:param timeout: Wait timeout in seconds
:return:
"""
if _by and path:
self._wait_until(ec.presence_of_element_located, _by, path, timeout)
else:
return self._wait_until(ec.presence_of_element_located, 'xpath', self.xpath(), timeout)
def wait_until_appears(self, _by=None, path=None, timeout=30):
"""Wait until the element appears
:param str _by: Selector method
:param str path: Selector path
:param int timeout: Wait timeout in seconds
:return: True, if the wait does not timeout
:rtype: bool
"""
if _by and path:
return self._wait_until(ec.visibility_of_element_located, _by, path, timeout)
else:
return self._wait_until(ec.visibility_of_element_located, 'xpath', self.xpath(), timeout)
def wait_until_disappears(self, _by=None, path=None, timeout=30):
"""Wait until the element disappears
:param str _by: Selector method
:param str path: Selector path
:param int timeout: Wait timeout in seconds
:return: True, if the wait does not timeout
:rtype: bool
"""
if _by and path:
return self._wait_until(ec.invisibility_of_element_located, _by, path, timeout)
else:
return self._wait_until(ec.invisibility_of_element_located, 'xpath', self.xpath(), timeout)
|
{"/setup.py": ["/sampyl/__init__.py"], "/sampyl/__init__.py": ["/sampyl/app.py"], "/sampyl/core/structures.py": ["/sampyl/core/element.py"], "/sampyl/app.py": ["/sampyl/core/element.py", "/sampyl/core/structures.py"], "/sampyl/core/element.py": ["/sampyl/core/shortcuts.py"]}
|
38,253
|
jlane9/SAMpyL
|
refs/heads/master
|
/sampyl/core/shortcuts.py
|
# -*- coding: utf-8 -*-
"""sampyl.core.shortcuts
.. codeauthor:: John Lane <jlane@fanthreesixty.com>
"""
# pylint: disable=line-too-long
__all__ = ['encode_ascii']
def encode_ascii(clean=False):
"""Function returns text as ascii
:param clean: True, to delete trailing spaces
:return:
"""
def encode_ascii_decorator(func):
"""
:param func:
:return:
"""
def func_wrapper(*args, **kwargs):
"""
:param args:
:param kwargs:
:return:
"""
text = func(*args, **kwargs)
# Convert UNICODE to ASCII
if isinstance(text, basestring):
return text.encode('ascii', 'ignore').strip() if clean else text.encode('ascii', 'ignore')
# Iterate list of UNICODE strings to ASCII
elif isinstance(text, (list, tuple)):
if clean:
return [item.encode('ascii', 'ignore').strip() for item in text
if isinstance(item, basestring)]
return [item.encode('ascii', 'ignore') for item in text
if isinstance(item, basestring)]
return ''
return func_wrapper
return encode_ascii_decorator
|
{"/setup.py": ["/sampyl/__init__.py"], "/sampyl/__init__.py": ["/sampyl/app.py"], "/sampyl/core/structures.py": ["/sampyl/core/element.py"], "/sampyl/app.py": ["/sampyl/core/element.py", "/sampyl/core/structures.py"], "/sampyl/core/element.py": ["/sampyl/core/shortcuts.py"]}
|
38,254
|
jlane9/SAMpyL
|
refs/heads/master
|
/sampyl/core/element.py
|
# -*- coding: utf-8 -*-
"""sampyl.core.element
.. codeauthor:: John Lane <jlane@fanthreesixty.com>
"""
# pylint: disable=line-too-long
import keyword
from lxml.cssselect import CSSSelector, SelectorError
from sampyl.core.shortcuts import encode_ascii
from selenium.common.exceptions import WebDriverException
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.common.by import By
from selenium.webdriver.remote.webelement import WebElement
from selenium.webdriver.remote.webdriver import WebDriver
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as ec
from selenium.common.exceptions import InvalidSelectorException, NoSuchElementException, TimeoutException
__all__ = ['Element']
DEFAULT_NAME_ATTR = 'data-qa-id'
DEFAULT_TYPE_ATTR = 'data-qa-model'
def normalize(_by, path, *args, **kwargs):
"""Convert all paths into a xpath selector
:param str _by: Selenium selector
:param str path: Selector value
:param args:
:param kwargs:
:return:
"""
if args or kwargs:
pass
normalizers = dict([('class name', lambda x: '/descendant-or-self::*[contains(@class, "%s")]' % x),
('id', lambda x: '/descendant-or-self::*[@id="%s"]' % x),
('link text', lambda x: '/descendant-or-self::*[contains("input a button", name()) '
'and normalize-space(text()) = "%s"]' % x),
('name', lambda x: '/descendant-or-self::*[@name="%s"]' % x),
('partial link text', lambda x: '/descendant-or-self::*[contains("input a button", name()) '
'and contains(normalize-space(text()), "%s")]' % x),
('tag name', lambda x: '/descendant-or-self::%s' % x),
('xpath', lambda x: x)])
if _by == 'css selector':
try:
return By.XPATH, '/%s' % CSSSelector(str(path)).path
except SelectorError:
return By.XPATH, ''
elif _by == 'element':
if isinstance(path, Element):
return path.search_term
else:
return By.XPATH, normalizers.get(_by, lambda x: '')(str(path))
def join(*args):
"""Join 'x' locator paths into a single path
:param args: Locator path tuples (by, path)
:return: Locator path
:rtype: str
"""
return By.XPATH, ''.join([normalize(*item)[1] for item in args if isinstance(item, (list, tuple))])
class SeleniumObject(object):
"""The SeleniumObject implementation
"""
def __init__(self, web_driver, **kwargs):
self.driver = web_driver if isinstance(web_driver, WebDriver) else None
if not self.driver:
raise TypeError("'web_driver' MUST be a selenium WebDriver element")
if 'name_attr' in kwargs.keys():
self._name_attr = kwargs['name_attr'] if isinstance(kwargs['name_attr'], basestring) else DEFAULT_NAME_ATTR
else:
self._name_attr = DEFAULT_NAME_ATTR
if 'type_attr' in kwargs.keys():
self._name_attr = kwargs['type_attr'] if isinstance(kwargs['type_attr'], basestring) else DEFAULT_TYPE_ATTR
else:
self._type_attr = DEFAULT_TYPE_ATTR
def _wait_until(self, expected_condition, _by, path, timeout=30):
"""Wait until expected condition is fulfilled
:param func expected_condition: Selenium expected condition
:param str _by: Selector method
:param str path: Selector path
:param timeout: Wait timeout in seconds
:return:
"""
wait = WebDriverWait(self.driver, timeout) if isinstance(timeout, int) else WebDriverWait(self.driver, 30)
try:
if _by != 'element':
wait.until(expected_condition((_by, path)))
return True
except TimeoutException:
pass
return False
def wait_until_present(self, _by, path, timeout=30):
"""Wait until the element is available to the DOM
:param str _by: Selector method
:param str path: Selector path
:param timeout: Wait timeout in seconds
:return:
"""
return self._wait_until(ec.presence_of_element_located, _by, path, timeout)
def wait_until_appears(self, _by, path, timeout=30):
"""Wait until the element appears
:param str _by: Selector method
:param str path: Selector path
:param int timeout: Wait timeout in seconds
:return: True, if the wait does not timeout
:rtype: bool
"""
return self._wait_until(ec.visibility_of_element_located, _by, path, timeout)
def wait_until_disappears(self, _by, path, timeout=30):
"""Wait until the element disappears
:param str _by: Selector method
:param str path: Selector path
:param int timeout: Wait timeout in seconds
:return: True, if the wait does not timeout
:rtype: bool
"""
return self._wait_until(ec.invisibility_of_element_located, _by, path, timeout)
def wait_implicitly(self, s):
"""Wait a set amount of time in seconds
:param int s: Seconds to wait
:return:
"""
if isinstance(s, int):
self.driver.implicitly_wait(s)
return True
return False
class Element(SeleniumObject):
"""The Element implementation
An abstract class for interacting with web elements.
"""
def __init__(self, web_driver, _by=By.XPATH, path=None, **kwargs):
"""Basic Selenium element
:param WebDriver web_driver: Selenium webdriver
:param str _by: By selector
:param str path: selection value
:return:
"""
super(Element, self).__init__(web_driver, **kwargs)
# Instantiate selector
self.search_term = normalize(_by=_by, path=path)
# Add any additional attributes
for extra in kwargs:
self.__setattr__(extra, kwargs[extra])
def __contains__(self, attribute):
"""Returns True if element contains attribute
:param str attribute: Element attribute
:return: True, if the element contains that attribute
:rtype: bool
"""
if self.exists() and isinstance(attribute, basestring):
try:
self.driver.find_element(*join(self.search_term, ('xpath', '/self::*[boolean(@{})]'.format(attribute))))
return True
except NoSuchElementException:
pass
return False
@encode_ascii()
def __getattr__(self, attribute):
"""Returns the value of an attribute
.. note:: class and for are both reserved keywords. Prepend/post-pend '_' to reference both.
:param str attribute: Element attribute
:return: Returns the string value
:rtype: str
"""
if self.exists():
if keyword.iskeyword(attribute.replace('_', '')):
attribute = attribute.replace('_', '')
else:
attribute = attribute.replace('_', '-')
return self.element().get_attribute(attribute)
return ''
@encode_ascii()
def __str__(self):
"""Returns HTML representation of the element
:return: HTML representation of the element
:rtype: str
"""
return self.outerHTML if self.exists() else ''
def __repr__(self):
return '<{} name="{}" type="{}">'.format(self.__class__.__name__, *self.search_term)
def angular_scope(self, attribute):
"""Returns an attribute from the angular scope
:param str attribute:
:return:
"""
if self.exists():
try:
return self.driver.execute_script('return angular.element(arguments[0]).scope().'
'{}'.format(str(attribute)), self.element())
except (TypeError, WebDriverException):
pass
def blur(self):
"""Simulate moving the cursor out of focus of this element.
:return:
"""
return self.driver.execute_script('arguments[0].blur();', self.element()) if self.is_displayed() else None
@encode_ascii()
def css_property(self, prop):
"""Return the value of a CSS property for the element
.. warning:: value_of_css_property does not work with Firefox
:param str prop: CSS Property
:return: Value of a CSS property
:rtype: str
"""
return self.element().value_of_css_property(str(prop)) if self.exists() else None
def drag(self, x_offset=0, y_offset=0):
"""Drag element x,y pixels from its center
:param int x_offset: Pixels to move element to
:param int y_offset: Pixels to move element to
:return:
"""
if self.exists() and isinstance(x_offset, int) and isinstance(y_offset, int):
action = ActionChains(self.driver)
action.click_and_hold(self.element()).move_by_offset(x_offset, y_offset).release().perform()
return True
return False
def element(self):
"""Return the selenium webelement object
:return: Selenium WebElement
:rtype: WebElement
"""
# If the search term passed through was an element
if self.search_term[0] == 'element' and isinstance(self.search_term[1], WebElement):
return self.search_term[1]
# If the search term is a valid term
elif self.search_term[0] in ('class name', 'css selector', 'id', 'link text',
'name', 'partial link text', 'tag name', 'xpath'):
try:
# Locate element
element = self.driver.find_elements(*self.search_term)
except InvalidSelectorException:
element = []
if len(element) > 0:
return element[0]
return None
def exists(self):
"""Returns True if element can be located by selenium
:return: Returns True, if the element can be located
:rtype: bool
"""
return True if self.element() else False
def focus(self):
"""Simulate element being in focus
:return:
"""
return self.driver.execute_script('arguments[0].focus();', self.element()) if self.is_displayed() else None
def is_displayed(self):
"""Return True, if the element is visible
:return: True, if element is visible
:rtype: bool
"""
return self.element().is_displayed() if self.exists() else False
def parent(self):
"""Returns the Selenium element for the current element
:return:
"""
xpath = join(self.search_term, ('xpath', '/parent::*'))
return Element(self.driver, xpath[0], xpath[1])
def scroll_to(self):
"""Scroll to the location of the element
:return:
"""
if self.exists():
element = self.element()
script = "var vHeight = Math.max(document.documentElement.clientHeight, window.innerHeight || 0);" \
"var eTop = arguments[0].getBoundingClientRect().top;" \
"window.scrollBy(0, eTop-(vHeight/2));"
# Scroll to Element
self.driver.execute_script(script, element)
@property
@encode_ascii()
def tag_name(self):
"""Returns element tag name
:return: Element tag name
:rtype: str
"""
return self.element().tag_name if self.exists() else ''
def wait_until_present(self, _by=None, path=None, timeout=30):
"""Wait until the element is present
:param str _by: Selector method
:param str path: Selector path
:param timeout: Wait timeout in seconds
:return: True, if the wait does not timeout
:rtype: bool
"""
if _by and path:
return super(Element, self).wait_until_present(_by, path, timeout=timeout)
else:
return super(Element, self).wait_until_present(self.search_term[0], self.search_term[1], timeout=timeout)
def wait_until_appears(self, _by=None, path=None, timeout=30):
"""Wait until the element appears
:param str _by: Selector method
:param str path: Selector path
:param int timeout: Wait timeout in seconds
:return: True, if the wait does not timeout
:rtype: bool
"""
if _by and path:
return super(Element, self).wait_until_appears(_by, path, timeout=timeout)
else:
return super(Element, self).wait_until_appears(self.search_term[0], self.search_term[1], timeout=timeout)
def wait_until_disappears(self, _by=None, path=None, timeout=30):
"""Wait until the element disappears
:param str _by: Selector method
:param str path: Selector path
:param int timeout: Wait timeout in seconds
:return: True, if the wait does not timeout
:rtype: bool
"""
if _by and path:
return super(Element, self).wait_until_disappears(_by, path, timeout=timeout)
else:
return super(Element, self).wait_until_disappears(self.search_term[0], self.search_term[1], timeout=timeout)
|
{"/setup.py": ["/sampyl/__init__.py"], "/sampyl/__init__.py": ["/sampyl/app.py"], "/sampyl/core/structures.py": ["/sampyl/core/element.py"], "/sampyl/app.py": ["/sampyl/core/element.py", "/sampyl/core/structures.py"], "/sampyl/core/element.py": ["/sampyl/core/shortcuts.py"]}
|
38,276
|
yangtseng/Knowledge-Distilling-PyTorch
|
refs/heads/master
|
/train.py
|
import argparse
import torch
import os
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from models import Model
from loss import loss_kd
from dataloader import get_loader
from utils import Params
torch.manual_seed(0)
parser = argparse.ArgumentParser()
parser.add_argument('--image_size', type=int, default=224, help='the height / width of the input image to network')
parser.add_argument('--params_dir', type=str, default="params", help='the directory of hyper parameters')
parser.add_argument('-m', '--model_name', type=str, default='base', help='the name of backbone network')
parser.add_argument('-t', '--teacher_name', type=str, default=None, help='the name of backbone network')
parser.add_argument('--log_path', type=str, default='logs', help="directory to save train log")
parser.add_argument('--epoch', type=int, default=0, help='value of current epoch')
parser.add_argument('--num_epoch', type=int, default=90, help='the number of epoch in train')
parser.add_argument('--decay_epoch', type=int, default=30, help='the number of decay epoch in train')
parser.add_argument('--checkpoint_dir', type=str, default='checkpoints', help="path to saved models (to continue training)")
parser.add_argument('--num_classes', type=int, default=100, help='the number of classes')
parser.add_argument('--dataset', type=str, default='cifar100', help='the name of dataset')
parser.add_argument('--is_distill', type=bool, default=True)
args = parser.parse_args()
if __name__ == '__main__':
student_params = Params(os.path.join(args.params_dir, f'{args.model_name}.json'))
if args.teacher_name is None:
teacher_params = Params(os.path.join(args.params_dir, f'{student_params.teacher_name}.json'))
else:
teacher_params = Params(os.path.join(args.params_dir, f'{args.teacher_name}.json'))
student = Model(args.num_classes, student_params, args.epoch)
student.load_params(os.path.join(args.checkpoint_dir, args.dataset, student_params.model_name, f'{args.epoch-1}.pth'))
teacher = Model(args.num_classes, teacher_params)
teacher.load_params(os.path.join(args.checkpoint_dir, args.dataset, teacher_params.model_name, f'final.pth'))
summary_title = f'{student_params.teacher_name}_teaches_{student_params.model_name} '
if not os.path.exists(os.path.join(args.checkpoint_dir, args.dataset, student_params.model_name)):
os.makedirs(os.path.join(args.checkpoint_dir, args.dataset, student_params.model_name))
if not os.path.exists(args.log_path):
os.makedirs(args.log_path)
writer = SummaryWriter(args.log_path)
criterion = loss_kd
optimizer = torch.optim.Adam(student.parameters(), student_params.lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, args.decay_epoch)
train_loader, validation_loader = get_loader(args.image_size, student_params.batch_size, args.dataset)
scheduler.step(args.epoch)
teacher_train_ans = teacher.fetch_output(train_loader)
teacher_val_ans = teacher.fetch_output(validation_loader)
for iter in range(args.epoch, args.num_epoch):
train_loss, train_acc = student.train_model(train_loader, criterion, optimizer, teacher_train_ans, student_params)
validation_loss, validation_acc = student.validate_model(validation_loader, criterion, teacher_val_ans, student_params)
writer.add_scalars(f'{summary_title}/Loss', {'train': train_loss, 'val': validation_loss}, iter)
writer.add_scalars(f'{summary_title}/Accuracy', {'train': train_acc, 'val': validation_acc}, iter)
torch.save(student.state_dict(), os.path.join(args.checkpoint_dir, args.dataset, student_params.model_name, f'{iter}.pth'))
scheduler.step()
|
{"/train.py": ["/models.py", "/loss.py", "/dataloader.py", "/utils.py"], "/test.py": ["/models.py", "/loss.py", "/dataloader.py", "/utils.py"], "/models.py": ["/utils.py"]}
|
38,277
|
yangtseng/Knowledge-Distilling-PyTorch
|
refs/heads/master
|
/loss.py
|
import torch.nn.functional as F
def loss_kd(preds, labels, teacher_preds, params):
T = params.temperature
alpha = params.alpha
loss = F.kl_div(F.log_softmax(preds / T, dim=1), F.softmax(teacher_preds / T, dim=1),
reduction='batchmean') * T * T * alpha + F.cross_entropy(preds, labels) * (1. - alpha)
return loss
|
{"/train.py": ["/models.py", "/loss.py", "/dataloader.py", "/utils.py"], "/test.py": ["/models.py", "/loss.py", "/dataloader.py", "/utils.py"], "/models.py": ["/utils.py"]}
|
38,278
|
yangtseng/Knowledge-Distilling-PyTorch
|
refs/heads/master
|
/dataloader.py
|
from torchvision.transforms import transforms
from torchvision.datasets.mnist import MNIST
from torchvision.datasets.cifar import CIFAR100, CIFAR10
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
def get_loader(image_size, batch_size, data_set='cifar10'):
transform = transforms.Compose([
transforms.Grayscale(3),
transforms.Resize(image_size),
transforms.ToTensor()
])
if data_set == 'cifar100':
dataset_class = CIFAR100
elif data_set == 'cifar10':
dataset_class = CIFAR10
elif data_set == 'mnist':
dataset_class = MNIST
else:
raise Exception('No matched dataset')
dataset = dataset_class('./dataset', train=True, transform=transform, download=True)
train_length = int(0.9 * len(dataset))
validation_length = len(dataset) - train_length
train_dataset, validation_dataset = random_split(dataset, (train_length, validation_length))
train_loader = DataLoader(train_dataset, batch_size, False)
validation_loader = DataLoader(validation_dataset, batch_size, False)
return train_loader, validation_loader
def get_test_loader(image_size, batch_size, data_set='cifar10'):
transform = transforms.Compose([
transforms.Grayscale(3),
transforms.Resize(image_size),
transforms.ToTensor()
])
if data_set == 'cifar100':
dataset_class = CIFAR100
elif data_set == 'cifar10':
dataset_class = CIFAR10
elif data_set == 'mnist':
dataset_class = MNIST
else:
raise Exception('No matched dataset')
return DataLoader(dataset_class('./dataset', train=False, transform=transform, download=True), batch_size, False)
|
{"/train.py": ["/models.py", "/loss.py", "/dataloader.py", "/utils.py"], "/test.py": ["/models.py", "/loss.py", "/dataloader.py", "/utils.py"], "/models.py": ["/utils.py"]}
|
38,279
|
yangtseng/Knowledge-Distilling-PyTorch
|
refs/heads/master
|
/utils.py
|
import json
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
class Params:
"""Class that loads hyperparameters from a json file.
Example:
```
params = Params(json_path)
print(params.learning_rate)
params.learning_rate = 0.5 # change the value of learning_rate in params
```
"""
def __init__(self, json_path):
with open(json_path) as f:
params = json.load(f)
self.__dict__.update(params)
def save(self, json_path):
with open(json_path, 'w') as f:
json.dump(self.__dict__, f, indent=4)
def update(self, json_path):
"""Loads parameters from json file"""
with open(json_path) as f:
params = json.load(f)
self.__dict__.update(params)
@property
def dict(self):
"""Gives dict-like access to Params instance by `params.dict['learning_rate']"""
return self.__dict__
|
{"/train.py": ["/models.py", "/loss.py", "/dataloader.py", "/utils.py"], "/test.py": ["/models.py", "/loss.py", "/dataloader.py", "/utils.py"], "/models.py": ["/utils.py"]}
|
38,280
|
yangtseng/Knowledge-Distilling-PyTorch
|
refs/heads/master
|
/test.py
|
import argparse
import torch
import os
from torch.utils.tensorboard import SummaryWriter
from models import Model
from loss import loss_kd
from dataloader import get_test_loader
from utils import Params, AverageMeter
from tqdm import tqdm
torch.manual_seed(0)
parser = argparse.ArgumentParser()
parser.add_argument('--image_size', type=int, default=224, help='the height / width of the input image to network')
parser.add_argument('--params_dir', type=str, default="params", help='the directory of hyper parameters')
parser.add_argument('-m', '--model_name', type=str, default='base', help='the name of backbone network')
parser.add_argument('--log_path', type=str, default='logs', help="directory to save train log")
parser.add_argument('--epoch', type=int, default=0, help='value of current epoch')
parser.add_argument('--num_epoch', type=int, default=89, help='the number of epoch in train')
parser.add_argument('--decay_epoch', type=int, default=30, help='the number of decay epoch in train')
parser.add_argument('--checkpoint_dir', type=str, default='checkpoints', help="path to saved models (to continue training)")
parser.add_argument('--num_classes', type=int, default=100, help='the number of classes')
parser.add_argument('--dataset', type=str, default='cifar100', help='the name of dataset')
parser.add_argument('--is_distill', type=bool, default=True)
args = parser.parse_args()
if __name__ == '__main__':
params = Params(os.path.join(args.params_dir, f'{args.model_name}.json'))
acc = AverageMeter()
net = Model(args.num_classes, params)
net.load_params(os.path.join(args.checkpoint_dir, args.dataset, params.model_name, f'80.pth'))
writer = SummaryWriter(args.log_path)
criterion = loss_kd
test_loader = get_test_loader(args.image_size, params.batch_size, args.dataset)
with torch.no_grad():
for images, targets in tqdm(test_loader, desc=f'{params.model_name} Testing...'):
images: torch.Tensor = images.to(net.device)
targets: torch.Tensor = targets.to(net.device)
preds: torch.Tensor = net.predict_image(images)
acc.update((preds == targets).sum().item()/images.shape[0])
print(f'{acc.avg * 100:.4f}%')
|
{"/train.py": ["/models.py", "/loss.py", "/dataloader.py", "/utils.py"], "/test.py": ["/models.py", "/loss.py", "/dataloader.py", "/utils.py"], "/models.py": ["/utils.py"]}
|
38,281
|
yangtseng/Knowledge-Distilling-PyTorch
|
refs/heads/master
|
/models.py
|
import os
import torch
import torch.nn as nn
from torchvision.models import resnet, densenet, vgg
from tqdm import tqdm, tqdm_notebook
from utils import AverageMeter
def conv_block(in_channels, out_channels, batch_norm=True):
block = nn.Sequential(nn.Conv2d(in_channels, out_channels, 3, 1, 1),
nn.ReLU(),
nn.BatchNorm2d(out_channels))
return block
class SimpleModel(nn.Module):
def __init__(self, num_classes):
super().__init__()
self.features = nn.Sequential(conv_block(3, 32),
nn.MaxPool2d(2),
conv_block(32, 64),
conv_block(64, 64),
nn.MaxPool2d(2),
conv_block(64, 128),
conv_block(128, 128),
nn.AdaptiveAvgPool2d(7))
self.classifier = nn.Sequential(nn.Linear(7 * 7 * 128, 1024),
nn.Dropout(),
nn.Linear(1024, num_classes))
def forward(self, x):
x = self.features(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x
class Model(nn.Module):
def __init__(self, num_classes, param, epoch=0):
super(Model, self).__init__()
if param.model_name == "resnet18":
self.net = resnet.resnet18(pretrained=True)
elif param.model_name == "resnet34":
self.net = resnet.resnet34(pretrained=True)
elif param.model_name == "resnet50":
self.net = resnet.resnet50(pretrained=True)
elif param.model_name == "resnet101":
self.net = resnet.resnet101(pretrained=True)
elif param.model_name == "resnet152":
self.net = resnet.resnet152(pretrained=True)
elif param.model_name == "densenet121":
self.net = densenet.densenet121(pretrained=True)
elif param.model_name == "vgg11":
self.net = vgg.vgg11_bn(pretrained=True)
elif param.model_name == "vggb13":
self.net = vgg.vgg13_bn(pretrained=True)
elif param.model_name == "vggb13":
self.net = vgg.vgg19_bn(pretrained=True)
else:
self.net = SimpleModel(num_classes)
if 'resnet' in param.model_name:
in_feature = self.net.fc.in_features
self.net.fc = nn.Linear(in_feature, num_classes)
if 'densenet' in param.model_name:
in_feature = self.net.classifier.in_features
self.net.classifier = nn.Linear(in_feature, num_classes)
if 'vgg11' in param.model_name:
self.net.classifier = nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, num_classes),
)
self.device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
self.param = param
self.epoch = epoch
self.to(self.device)
def forward(self, x):
return self.net(x)
def train_model(self, data_loader, criterion, optimizer, teacher_preds, param):
self.train()
loss_avg = AverageMeter()
acc_avg = AverageMeter()
loss_avg.reset()
acc_avg.reset()
for step, (images, targets) in enumerate(tqdm(data_loader, desc=f'Train Epoch {self.epoch}')):
images: torch.Tensor = images.to(self.device)
targets: torch.Tensor = targets.to(self.device)
preds: torch.Tensor = self.forward(images)
loss: torch.Tensor = criterion(preds, targets, torch.from_numpy(teacher_preds[step]).type_as(preds), param)
optimizer.zero_grad()
loss.backward()
optimizer.step()
preds = preds.argmax(dim=1)
loss_avg.update(loss.mean().item())
acc_avg.update((preds == targets).sum().item() / images.shape[0])
self.epoch += 1
return loss_avg.avg, acc_avg.avg
def validate_model(self, data_loader, criterion, teacher_preds, param):
with torch.no_grad():
self.eval()
loss_avg = AverageMeter()
acc_avg = AverageMeter()
loss_avg.reset()
acc_avg.reset()
for step, (images, targets) in enumerate(tqdm(data_loader, desc=f'Validation Epoch {self.epoch}')):
images: torch.Tensor = images.to(self.device)
targets: torch.Tensor = targets.to(self.device)
preds: torch.Tensor = self.forward(images)
loss: torch.Tensor = criterion(preds, targets, torch.from_numpy(teacher_preds[step]).to(self.device),
param)
preds = preds.argmax(dim=1)
loss_avg.update(loss.mean().item())
acc_avg.update((preds == targets).sum().item() / images.shape[0])
return loss_avg.avg, acc_avg.avg
def predict_image(self, image: torch.Tensor):
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
self.eval()
self.to(device)
pred: torch.Tensor = self.forward(image)
pred = pred.argmax(dim=1)
return pred
def fetch_output(self, data_loader):
self.eval()
results = []
for images, targets in tqdm(data_loader, desc=f'Fetch answer'):
images = images.to(self.device)
results += [self.forward(images).detach().cpu().numpy()]
return results
def load_params(self, path):
if not os.path.exists(path):
print(f"[*] There is no params in {path}")
return
self.load_state_dict(torch.load(path, map_location=self.device))
|
{"/train.py": ["/models.py", "/loss.py", "/dataloader.py", "/utils.py"], "/test.py": ["/models.py", "/loss.py", "/dataloader.py", "/utils.py"], "/models.py": ["/utils.py"]}
|
38,282
|
WeatherGod/NNforZR
|
refs/heads/master
|
/tempyView.py
|
import numpy
import pylab
from scipy import optimize
def ZRModel(coefs, reflects) :
return(((10.0 ** (reflects/10.0))/coefs[0]) ** (1.0/coefs[1]))
def BestZRModel(reflects, rainrate) :
def errFun(coefs) :
return(numpy.mean(numpy.abs(ZRModel(coefs, reflects) - rainrate)))
return(optimize.fmin(errFun, [50, 0.5], maxiter=2000, disp=0))
def ViewPlots(numRows, numCols, years) :
someIndex = 1
for aYear in years :
fullSet = numpy.loadtxt('trainingDir/subsets/'+aYear+'.csv', delimiter=',')
pylab.subplot(numRows, numCols, someIndex)
pylab.scatter(fullSet[:, 5], fullSet[:, 6], color = 'r', s = 1)
finalCoefs = BestZRModel(fullSet[:, 5], fullSet[:, 6])
pylab.scatter(fullSet[:, 5], ZRModel(finalCoefs, fullSet[:, 5]), color = 'b', s = 1)
pylab.xlim((0, 70))
pylab.ylim((0, 200))
pylab.title('%s a=%.1f b=%.3f' % (aYear, finalCoefs[0], finalCoefs[1]))
someIndex += 1
|
{"/filtertraining.py": ["/histtools.py"]}
|
38,283
|
WeatherGod/NNforZR
|
refs/heads/master
|
/histtools.py
|
import numpy
import scipy.stats # for scipy.stats.scoreatpercentile()
# BUG: If the data array has a lot of elements with the same value,
# then .scoreatpercentile() may report the same score for both
# the first and third quartile, which means that the interquartile
# range is zero.
def OptimalBinSize(vals) :
if (len(vals) > 4) :
binSize = 2.0*(scipy.stats.scoreatpercentile(vals, 75)
- scipy.stats.scoreatpercentile(vals, 25)) * (len(vals) ** (-1.0/3.0))
else :
StandDev = std(Values);
binSize = 3.49 * numpy.std(vals) * (len(vals) ** (-1.0/3.0))
# Don't forget to re-adjust the binsize estimate to make sure the
# size will produce equally-spaced bins for the data.
return((max(vals) - min(vals)) / max(numpy.ceil((max(vals) - min(vals)) / binSize), 1))
def OptimalBinCount(vals) :
binSize = OptimalBinSize(vals)
return(max(numpy.ceil((vals.max() - vals.min()) / binSize), 1))
# BUG: The .arange() function isn't quite doing what it claims to do.
# A temporary solution is to put the maximum value at the last
# bin, but this will likely result in a near double-sized bin
def MakeBins(vals, binSize) :
tempHold = numpy.arange(min(vals), max(vals), binSize)
tempHold[-1] = max(vals)
return(tempHold)
def Hist2d(vals1, bins1, vals2, bins2) :
lims1 = [bins1[0:(len(bins1) - 1)], bins1[1:len(bins1)]]
lims1[1][-1] += 100 *numpy.finfo(float).eps
lims2 = [bins2[0:(len(bins2) - 1)], bins2[1:len(bins2)]]
lims2[1][-1] += 100 *numpy.finfo(float).eps
# Returns a two column vector with the index location (j, i) for each value.
binLocs = zip([numpy.nonzero(numpy.logical_and(lims2[0] <= val, lims2[1] > val))[0][0] for val in vals2],
[numpy.nonzero(numpy.logical_and(lims1[0] <= val, lims1[1] > val))[0][0] for val in vals1])
n_2d = numpy.zeros((len(lims2[0]), len(lims1[0])))
for aCoord in binLocs : n_2d[aCoord] += 1
"""
(n_2d, edges1, edges2) = numpy.histogram2d(vals1, vals2, bins=[bins1, bins2])
# Returns a two column vector with the index location (j, i) for each value.
binLocs = zip([numpy.nonzero(numpy.logical_and(edges2[0] <= val, edges2[1] > val))[0][0] for val in vals2],
[numpy.nonzero(numpy.logical_and(edges1[0] <= val, edges1[1] > val))[0][0] for val in vals1])
"""
return(n_2d, binLocs)
|
{"/filtertraining.py": ["/histtools.py"]}
|
38,284
|
WeatherGod/NNforZR
|
refs/heads/master
|
/ProjectTesting.py
|
#!/usr/bin/env python
from optparse import OptionParser # for Command-line parsing
import numpy
import filtertraining as f # for decimate(), jitter(), decimate2d()
from ProjectUtils import * # for ObtainResultInfo(), SaveSubprojectModel()
import os # for mkdir()
import random # for sample()
def MakeARFFHeader(varList, filename) :
arffStream = file(filename, 'w')
arffStream.write("@relation Z-R\n")
arffStream.writelines(["\n@attribute " + varName + " NUMERIC" for varName in varList])
arffStream.write("\n\n@data\n")
arffStream.close()
def PerformTestTrain_zr(dataSet, iterCnt, dirLoc) :
dataLen = dataSet.shape[0]
trainLen = numpy.floor(dataLen * 0.6666)
fileNames = {'training': [], 'testing': [], 'model': [], 'results': []}
for iterIndex in range(iterCnt) :
print "%d of %d iterations" % (iterIndex + 1, iterCnt)
trainSelected = random.sample(range(dataLen), trainLen)
testSelected = numpy.setxor1d(trainSelected, range(dataLen))
trainStem = "%s/trainingData_%dof%d" % (dirLoc, iterIndex + 1, iterCnt)
testStem = "%s/testingData_%dof%d" % (dirLoc, iterIndex + 1, iterCnt)
modelStem = "%s/model_%dof%d" % (dirLoc, iterIndex + 1, iterCnt)
resultsStem = "%s/results_%dof%d" % (dirLoc, iterIndex + 1, iterCnt)
fileNames['training'].append(trainStem + '.csv')
numpy.savetxt(trainStem + '.csv', dataSet[trainSelected, :], fmt="%6.4f", delimiter=',')
fileNames['testing'].append(testStem + '.csv')
numpy.savetxt(testStem + '.csv', dataSet[testSelected, :], fmt="%6.4f", delimiter=',')
trainData = dataSet[trainSelected, :]
testData = dataSet[testSelected, :]
finalCoefs = ZRBest(trainData)
# Perform the model training, saving the resulting model
# I also want the model coefficient info.
fileNames['model'].append(modelStem + '.txt')
numpy.savetxt(modelStem + '.txt', finalCoefs)
# Perform a test of the model using the available test
# data. The results are output to a file for loading
# back into python for analysis
fileNames['results'].append(resultsStem + '.csv')
modelPredicts = ZRModel(finalCoefs, numpy.squeeze(testData[:, 0]))
wholeSet = numpy.append(testData,
modelPredicts.reshape((modelPredicts.shape[0], 1)),
axis = 1)
#print(modelPredicts.shape, testData.shape, wholeSet.shape)
numpy.savetxt(resultsStem + '.csv', wholeSet, delimiter=',')
return(fileNames)
def PerformTestTrain(iterCnt, dirLoc, subProj,
learnRate=0.05, momentumParam=0.3, epochCnt=3000, mlpStruct='4,2') :
mlpTrainCall = "java -Xmx128m weka.classifiers.functions.MultilayerPerceptron -L %f -M %f -N %d -H '%s' -t %s -no-cv -v -d %s > %s"
mlpTestCall = "java -Xmx128m weka.filters.supervised.attribute.AddClassification -serialized %s -classification -i %s -o %s -c last"
for iterIndex in range(iterCnt) :
testStem = "%s/%s/testing_%dof%d" % (dirLoc, subProj, iterIndex + 1, iterCnt)
trainStem = "%s/%s/training_%dof%d" % (dirLoc, subProj, iterIndex + 1, iterCnt)
modelStem = "%s/%s/model_%dof%d" % (dirLoc, subProj, iterIndex + 1, iterCnt)
resultsStem = "%s/%s/results_%dof%d" % (dirLoc, subProj, iterIndex + 1, iterCnt)
os.system(mlpTrainCall % (learnRate, momentumParam, epochCnt, mlpStruct,
trainStem + '.arff', modelStem + '.model', modelStem + '.txt'))
os.system(mlpTestCall % (modelStem + '.model', testStem + '.arff', resultsStem + '.csv'))
def PrepForTestTrain(dataSet, iterCnt, dirLoc, varNames, varIndxs) :
dataLen = dataSet.shape[0]
trainLen = numpy.floor(dataLen * 0.6666)
for iterIndex in range(iterCnt) :
print "%d of %d iterations" % (iterIndex + 1, iterCnt)
# Save a random sample of the data for training, and the rest for testing
trainSelected = random.sample(range(dataLen), trainLen)
testSelected = numpy.setxor1d(trainSelected, range(dataLen))
for subProj in varIndxs.keys() :
arffHeader = dirLoc + '/' + subProj + '/arffHeader.txt'
MakeARFFHeader(varNames[varIndxs[subProj]], arffHeader)
trainStem = "%s/%s/trainingData_%dof%d" % (dirLoc, subProj, iterIndex + 1, iterCnt)
testStem = "%s/testingData_%dof%d" % (dirLoc, subProj, iterIndex + 1, iterCnt)
numpy.savetxt(trainStem + '.csv', dataSet[trainSelected, varIndxs[subProj]], fmt="%6.4f", delimiter=',')
os.system('cat %s %s > %s' % (arffHeader, trainStem + '.csv', trainStem + '.arff'))
numpy.savetxt(testStem + '.csv', dataSet[testSelected, varIndxs[subProj]], fmt="%6.4f", delimiter=',')
os.system('cat %s %s > %s' % (arffHeader, testStem + '.csv', testStem + '.arff'))
return(fileNames)
varNames = numpy.array(['temperature', 'relHumidity', 'pressure',
'u_wind', 'v_wind', 'reflectivity', 'rainrate'])
rrIndex = 6 # column index for the rain rate
uwndIndex = 3 # column index for the U-wnd
vwndIndex = 4 # column index for the V-wnd
reflectIndex = 5 # column index for the Reflectivities
tempIndex = 0
rhIndex = 1
pressIndex = 2
resultLoc = './ModelProject_Retry2/'
parser = OptionParser()
parser.add_option("-n", "--count", dest="count", type="int",
help="Produce N results (training/testing) cycles", metavar="N")
parser.add_option("-t", "--trunc", dest="trunc", type="float",
help="Truncation parameter VAL", metavar="VAL")
parser.add_option("-d", "--data", dest="data", type="string",
help="Using training dataset FILE", metavar="FILE")
parser.add_option("-l", "--loc", dest="dir", type="string",
help="Project to be in DIR", metavar="DIR")
(options, args) = parser.parse_args()
if (options.data == None) :
parser.error("Missing FILE")
dataFile = options.data
print "The dataset file is:", dataFile
if (options.dir != None) :
resultLoc = options.dir
print "Results will be placed in the directory:", resultLoc
if (options.trunc == None) :
parser.error("Missing truncation VAL")
truncVal = options.trunc
if (options.count == None) :
parser.error("Missing N")
resultCnt = options.count
print "There will be %d iterations to perform." % (resultCnt)
dataSet = numpy.loadtxt(dataFile, delimiter=',')
os.makedirs(resultLoc + '/FullSet')
os.makedirs(resultLoc + "/SansWind")
os.makedirs(resultLoc + "/JustWind")
os.makedirs(resultLoc + "/Reflect")
#os.makedirs(resultLoc + "/Shuffled")
os.makedirs(resultLoc + "/ZRBest")
selected = f.decimate2d(dataSet[:, reflectIndex], dataSet[:, rrIndex], truncVal)
truncData = dataSet[selected, :]
numpy.savetxt(resultLoc + '/fullSet_trunc.csv', truncData, fmt='%6.4f', delimiter=',')
fileNames = PrepForTestTrain(truncData, resultCnt, varNames, resultLoc)
############################################################
# performing zr best fits
"""
reflectOnly = numpy.array([reflectIndex, rrIndex])
dirLoc = resultLoc + '/ZRBest/'
filenames['ZRBest'] = PerformTestTrain_zr(truncData[:, reflectOnly], resultCnt, dirLoc)
tempy = [numpy.loadtxt(aFileName, delimiter=',') for aFileName in filenames['ZRBest']['results']]
resultInfo = {'modelPredicts': numpy.array([aRow[:, 2] for aRow in tempy]),
'testObs': numpy.array([aRow[:, 1] for aRow in tempy]),
'reflectObs': numpy.array([aRow[:, 0] for aRow in tempy])}
SaveSubprojectModel(resultInfo, resultLoc, 'ZRBest')
resultInfo['modelPredicts'] = ZRModel([300, 1.4], resultInfo['reflectObs'])
SaveSubprojectModel(resultInfo, resultLoc, "NWSZR")
"""
allVars = [tempIndex, rhIndex, pressIndex, uwndIndex, vwndIndex, reflectIndex, rrIndex]
PerformTestTrain(filenames, resultLoc, 'FullSet', varNames[allVars])
resultInfo = ObtainResultInfo(resultLoc, 'FullSet')
SaveSubprojectModel(resultInfo, resultLoc, 'FullSet')
"""
# Shuffled data
#shuffled = truncData.copy()
#for colIndex in allVars :
# numpy.random.shuffle(shuffled[:, colIndex])
#dirLoc = resultLoc + '/Shuffled/'
#MakeARFFHeader(varNames, dirLoc + '/arffHeader.txt')
#filenames['Shuffled'] = PerformTestTrain(shuffled, resultCnt, dirLoc)
#resultInfo = ObtainResultInfo(resultLoc, 'Shuffled')
#SaveSubprojectModel(resultInfo, resultLoc, 'Shuffled')
# data without wind
sansWind = numpy.array([tempIndex, rhIndex, pressIndex, reflectIndex, rrIndex])
dirLoc = resultLoc + '/SansWind/'
MakeARFFHeader(varNames[sansWind], dirLoc + '/arffHeader.txt')
filenames['SansWind'] = PerformTestTrain(truncData[:, sansWind], resultCnt, dirLoc)
resultInfo = ObtainResultInfo(resultLoc, 'SansWind')
SaveSubprojectModel(resultInfo, resultLoc, 'SansWind')
# data with just wind and reflectivity
justWind = numpy.array([uwndIndex, vwndIndex, reflectIndex, rrIndex])
dirLoc = resultLoc + '/JustWind/'
MakeARFFHeader(varNames[justWind], dirLoc + '/arffHeader.txt')
filenames['JustWind'] = PerformTestTrain(truncData[:, justWind], resultCnt, dirLoc)
resultInfo = ObtainResultInfo(resultLoc, 'JustWind')
SaveSubprojectModel(resultInfo, resultLoc, 'JustWind')
# data without surface info
dirLoc = resultLoc + '/Reflect/'
MakeARFFHeader(varNames[reflectOnly], dirLoc + '/arffHeader.txt')
filenames['Reflect'] = PerformTestTrain(truncData[:, reflectOnly], resultCnt, dirLoc)
resultInfo = ObtainResultInfo(resultLoc, 'Reflect')
SaveSubprojectModel(resultInfo, resultLoc, 'Reflect')
"""
##########################################################
|
{"/filtertraining.py": ["/histtools.py"]}
|
38,285
|
WeatherGod/NNforZR
|
refs/heads/master
|
/filtertraining.py
|
from histtools import * # for Hist2d()
import numpy
def WhereIs(vals, B) :
return([numpy.nonzero(elementVal == B)[0][0] for elementVal in vals])
def decimate(vals, decimation):
B = numpy.unique1d(vals)
N = WhereIs(vals, B)
# n contains the count of the number of elements for each bin
# B contains the index location for each element in the array vals
# NOTE: This histogram function will become obsolete soon by the numpy people. The behavior will change.
(n, B) = numpy.histogram(vals, B, new=False)
return(DataTruncation(n, decimation, len(B), N))
def decimate2d(vals1, vals2, decimation):
bins1 = MakeBins(vals1, OptimalBinSize(vals1))
bins2 = MakeBins(vals2, OptimalBinSize(vals2))
# n contains the count of the number of elements for each bin in a 2-d grid
# binLocs contains the (x,y) location for each element in the parallel arrays vals1 & vals2
(n, binLocs) = Hist2d(vals1, bins1, vals2, bins2)
return(DataTruncation(n, decimation, len(numpy.nonzero(n)[0]), binLocs))
def DataTruncation(n, decimation, binCnt, binLocs):
thresholds = [len(binLocs) * decimation / (binCnt * n[aCoord]) for aCoord in binLocs]
return(numpy.random.random_sample(len(thresholds)) <= thresholds)
def jitter(vals):
totVals = len(vals)
B = numpy.unique(vals)
N = WhereIs(vals, B)
(n, B) = numpy.histogram(vals, B, new=False)
fwdSpace = numpy.ediff1d(B, to_end=0.0)
prevSpace = numpy.flipud(numpy.ediff1d(numpy.flipud(B), to_end=0.0))
baseJit = ((fwdSpace[N] - prevSpace[N]) * numpy.random.rand(totVals)) - prevSpace[N]
baseJit[n[N] <= 1] = 0.0
return(vals + baseJit)
|
{"/filtertraining.py": ["/histtools.py"]}
|
38,286
|
WeatherGod/NNforZR
|
refs/heads/master
|
/boxcox.py
|
#!/usr/bin/env python
import numpy # for std(), sum(), log(), exp() and numpy arrays
from scipy import optimize # for fmin()
def boxcox_opt(lamb, *pargs):
# Don't call this function, it is meant to be
# used by boxcox_auto().
x = numpy.array(pargs)
# Transform data using a particular lambda.
xhat = boxcox(x, lamb)
# The algorithm calls for maximizing the LLF; however, since we have
# only functions that minimize, the LLF is negated so that we can
# minimize the function instead of maximixing it to find the optimum lambda.
return(-(-(len(x)/2.0) * numpy.log(numpy.std(xhat.T)**2) + (lamb - 1.0)*(numpy.sum(numpy.log(x)))))
def boxcox_auto(x):
# Automatically determines the lambda needed to perform a boxcox
# transform of the given vector of data points. Note that it will
# also automatically offset the datapoints so that the minimum value
# is just above 0 to satisfy the criteria for the boxcox transform.
#
# The object returned by this function contains the transformed data
# ('bcData'), the lambda ('lmbda'), and the offset used on the data
# points ('dataOffset'). This object can be fed easily into
# boxcox_inverse() to retrieve the original data values like so:
#
# EXAMPLE:
# >>> bcResults = boxcox_auto(dataVector)
# >>> print bcResults
# {'lmbda': array([ 0.313]), 'bcData': array([ 0.47712018, 1.33916353, 6.66393874, ..., 3.80242394,
# 3.79166974, 0.47712018]), 'dataOffset': 2.2204460492503131e-16}
# >>> reconstit = boxcox_inverse(**bcResults)
# >>> print numpy.mean((dataVector - reconstit) ** 2)
# 5.6965875916e-29
constOffset = -min(x) + numpy.finfo(float).eps
tempX = x + constOffset
bclambda = optimize.fmin(boxcox_opt, 0.0, args=(tempX), maxiter=2000, disp=0)
# Generate the transformed data using the optimal lambda.
return({'bcData': boxcox(tempX, bclambda), 'lmbda': bclambda, 'dataOffset': constOffset})
def boxcox(x, lmbda):
# boxcox() performs the boxcox transformation upon the data vector 'x',
# using the supplied lambda value 'lmbda'.
# Note that this function does not check for minimum value of the data,
# and it will not correct for values being below 0.
#
# The function returns a vector the same size of x containing the
# the transformed values.
if (lmbda != 0.0) :
return(((x ** lmbda) - 1) / lmbda)
else :
return(numpy.log(x))
def boxcox_inverse(bcData, lmbda, dataOffset = 0.0) :
# Performs the inverse operation of the boxcox transform.
# Note that one can use the output of boxcox_auto() to easily
# run boxcox_inverse:
#
# >>> bcResults = boxcox_auto(data)
# >>> reconstitData = boxcox_inverse(**bcResults)
#
# Also can be used directly like so:
# >>> transData = boxcox(origData, lambdaVal)
# >>> transData = DoSomeStuff(transData)
# >>> reconstitData = boxcox_inverse(transData, lambdaVal)
#
if (lmbda != 0.0) :
return((((bcData * lmbda) + 1) ** (1.0/lmbda)) - dataOffset)
else :
return(numpy.exp(bcData) - dataOffset)
|
{"/filtertraining.py": ["/histtools.py"]}
|
38,287
|
WeatherGod/NNforZR
|
refs/heads/master
|
/bootstrap.py
|
"""Bootstrap statistics package.
Given an experimentally determined random distribution, estimates
a desired parameter and its uncertainty.
by Michael J.T. O'Kelly <mokelly@mit.edu>
Version 0.1, 3-08-06"""
import scipy
def fast_bootstrap(function, distribution,
rel_tol = 0.001, abs_tol = 0,
min_samples = 10, max_samples = 1000,
verbose = 0, report_interval=10000):
"""Bootstrap determination of value/error of function applied to
distribution. Distribution must be in format compatible with
numpy. (Any acceptable argument to scipy.array() will work.)
Function should be written to act efficiently on
numpy arrays.
rel_tol gives maximum sufficient std/mean ratio of answer.
abs_tol gives maximum sufficient std of answer.
min_samples is minimum number before completion-checking begins
max_samples gives maximimum # of resamplings of distribution. Set
to <=0 for no limit.
Returns result when any of rel_tol, abs_tol or max_samples reached.
verbose = 1 prints progress info and final state info
Returns a tuple of form ( E(f(D)), std(f(D)) where D is a random
resampling of the distribution argument."""
x_cumulant = 0
x2_cumulant = 0
n_samples = 0
d = scipy.array(distribution, copy = 0) # Don't copy if possible
sample_size = len(d)
while 1:
# Choose #sample_size members of d at random, with replacement
choices = scipy.random.random_integers(0, sample_size-1, sample_size)
sample = d[choices]
# Apply function to sample of random distribution
f_of_d = function(sample)
x_cumulant += f_of_d
x2_cumulant += f_of_d**2
n_samples += 1
if n_samples>=min_samples:
mu = x_cumulant / n_samples # E(f(D))
sigma = ((x2_cumulant*n_samples - x_cumulant**2) /
(n_samples * (n_samples-1)) )**.5 # std(f(d))
error = sigma / n_samples**.5
if (verbose and n_samples % report_interval == 0):
print "Mean", mu, "Std", sigma, "found after", n_samples, "samples"
if ((n_samples >= max_samples and max_samples>0) or
error <= abs_tol or
error/mu <= rel_tol):
if verbose:
print "Mean", mu, "Std", sigma, "found after", n_samples, "samples"
return (mu, sigma)
if __name__ == '__main__':
dist = scipy.randn(40) + 10
print dist
result = fast_bootstrap(scipy.std, dist, verbose = 1, max_samples = 0, rel_tol = .0005)
print "(Estimate, uncertainty)"
print result
print "Actual measured standard deviation:", dist.std()
|
{"/filtertraining.py": ["/histtools.py"]}
|
38,288
|
WeatherGod/NNforZR
|
refs/heads/master
|
/ProjectUtils.py
|
#!/usr/bin/env python
import glob # for filename globbing
import os # for os.sep
import numpy
import scipy.stats as ss # for sem() and other stat funcs
import scipy.stats.stats as sss # for nanmean() and other nan-friendly funcs
import pylab # for plotting
import matplotlib # for colormaps
from filtertraining import * # for MakeBins(), Hist2d()
def decimate2d_ZR(vals1, vals2, decimation):
"""
Don't use this. It is experimental, and really a poor idea!
"""
def Gaussian(vals, means, stds) :
return(numpy.exp(-((vals - means)**2.0)/(2 * (stds**2.0))) / (numpy.sqrt(2.0 * 3.14) * stds))
bins1 = MakeBins(vals1, OptimalBinSize(vals1))
bins2 = MakeBins(vals2, OptimalBinSize(vals2))
(n, binLocs) = Hist2d(vals1, bins1, vals2, bins2)
[bin1mesh, bin2mesh] = numpy.meshgrid(bins1[0:-1], bins2[0:-1])
weights = Gaussian(bin1mesh, 10.0 * numpy.log10(300.0*bin2mesh**1.4), 6.0) + Gaussian(bin2mesh, ZRModel([300, 1.4], bin1mesh), 6.0)
binCnt = len(numpy.nonzero(n)[0])
baseThresholds = numpy.array([len(binLocs) * decimation / (binCnt * n[aCoord]) for aCoord in binLocs])
scale = baseThresholds.max()/weights.max()
thresholds = baseThresholds * numpy.array([scale * weights[aCoord] for aCoord in binLocs])
return(numpy.random.random_sample(len(thresholds)) <= thresholds)
#################################################################################
# Plotting
#################################################################################
def PlotCorr(obs, estimated, axis=None, **kwargs) :
if axis is None :
axis = pylab.gca()
obs = obs.flatten()
estimated = estimated.flatten()
# pylab.scatter(obs, estimated, s=1, **kwargs)
axis.hexbin(obs, estimated, bins='log', cmap=matplotlib.cm.gray_r, **kwargs)
axis.plot([0.0, obs.max()], [0.0, obs.max()], color='gray', linewidth=2.5)
axis.set_xlabel('Observed Rainfall Rate [mm/hr]', fontsize='large')
axis.set_ylabel('Estimated Rainfall Rate [mm/hr]', fontsize='large')
axis.set_xlim((0.0, obs.max()))
axis.set_ylim((0.0, obs.max()))
def PlotZR(reflects, obs, estimated, axis=None, **kwargs) :
if axis is None :
axis = pylab.gca()
theScale = 1
# Doing some data 'reduction'...
# Starting with precision reduction
reflects = reflects.round(theScale).flatten()
obs = obs.round(theScale).flatten()
estimated = estimated.round(theScale).flatten()
print "Orig Len:", len(obs)
# Now, we feed the data pairs through set() to get unique pairs,
# then rezip that data so that it can be passed as positional arguements
# into scatter
obsZR = zip(*set(zip(reflects, obs)))
modZR = zip(*set(zip(reflects, estimated)))
print "truncated obs len:", len(obsZR[0]), " truncated models len:", len(modZR[0])
axis.scatter(*obsZR, s = 3.0, linewidths = 0, c='grey', rasterized=True)
axis.scatter(*modZR, c='black', s = 0.3, linewidths=0, rasterized=True, **kwargs)
axis.set_xlabel('Reflectivity [dBZ]', fontsize='large')
axis.set_ylabel('Rainfall Rate [mm/hr]', fontsize='large')
axis.set_xlim((reflects.min(), reflects.max()))
axis.set_ylim((obs.min(), obs.max()))
####################################################################################
# Project Analysis
####################################################################################
def AnalyzeResultInfo(modelPredicts, testObs, reflectObs) :
print "FULL SET"
sumInfo = DoSummaryInfo(testObs, modelPredicts)
print "RMSE: %8.4f %8.4f" % (numpy.mean(sumInfo['rmse']), ss.sem(sumInfo['rmse']))
print "MAE : %8.4f %8.4f" % (numpy.mean(sumInfo['mae']), ss.sem(sumInfo['mae']))
print "CORR: %8.4f %8.4f" % (numpy.mean(sumInfo['corr']), ss.sem(sumInfo['corr']))
print "\nZ < 40"
belowCondition = reflectObs < 40
belowSumInfo = DoSummaryInfo(numpy.where(belowCondition, testObs, numpy.NaN),
numpy.where(belowCondition, modelPredicts, numpy.NaN))
print "RMSE: %8.4f %8.4f" % (numpy.mean(belowSumInfo['rmse']), ss.sem(belowSumInfo['rmse']))
print "MAE : %8.4f %8.4f" % (numpy.mean(belowSumInfo['mae']), ss.sem(belowSumInfo['mae']))
print "CORR: %8.4f %8.4f" % (numpy.mean(belowSumInfo['corr']), ss.sem(belowSumInfo['corr']))
print "\nZ >= 40"
aboveSumInfo = DoSummaryInfo(numpy.where(belowCondition, numpy.NaN, testObs),
numpy.where(belowCondition, numpy.NaN, modelPredicts))
print "RMSE: %8.4f %8.4f" % (numpy.mean(aboveSumInfo['rmse']), ss.sem(aboveSumInfo['rmse']))
print "MAE : %8.4f %8.4f" % (numpy.mean(aboveSumInfo['mae']), ss.sem(aboveSumInfo['mae']))
print "CORR: %8.4f %8.4f" % (numpy.mean(aboveSumInfo['corr']), ss.sem(aboveSumInfo['corr']))
def DoSummaryInfo(obs, estimated) :
return({'rmse': numpy.sqrt(sss.nanmean((estimated - obs) ** 2.0, axis = 1)),
'mae': sss.nanmean(numpy.abs(estimated - obs), axis=1),
'corr': numpy.diag(numpy.corrcoef(estimated, obs), k=estimated.shape[0]),
'bias': numpy.mean(estimated - obs, axis=1)})
###############################################################################
# Loading data
###############################################################################
def ProcessModelInfo(filename) :
weights = {}
nodeName = None
for line in open(filename) :
line = line.strip()
if (line.startswith('Linear Node') or line.startswith('Sigmoid Node')) :
nodeName = line.split(' ')[-1].strip()
elif (line.startswith('Threshold')
or line.startswith('Node')
or line.startswith('Attrib')) :
weights["%s-%s" % (nodeName, line.split()[-2])] = float(line.split()[-1])
return(weights)
def ObtainARFFData(filename, columnIndxs, linesToSkip) :
return(numpy.loadtxt(filename, delimiter=',', skiprows=linesToSkip)[:, columnIndxs])
def ObtainResultInfo(dirLoc, subProj) :
resultsList = glob.glob(os.sep.join([dirLoc, subProj, 'results_*.csv']))
resultsList.sort()
skipMap = {'FullSet': 13,
'SansWind': 11,
'JustWind': 10,
'Reflect': 8,
'ZRBest': 0,
'Shuffled': 13,
'NWSZR': 0}
tempy = [ObtainARFFData(filename, numpy.array([-1, -2, -3]), skipMap[subProj]) for filename in resultsList]
return({'modelPredicts': numpy.array([aRow[:, 0] for aRow in tempy]),
'testObs': numpy.array([aRow[:, 1] for aRow in tempy]),
'reflectObs': numpy.array([aRow[:, 2] for aRow in tempy])})
########################################################################
# Saving processed data
########################################################################
def SaveSummaryInfo(resultInfo, dirLoc, subProj) :
"""
resultsList = glob.glob(os.sep.join([dirLoc, subProj, 'results_*.csv']))
resultsList.sort()
summaryInfo = {'rmse': [],
'mae': [],
'corr': []}
skipMap = {'FullSet': 13,
'SansWind': 11,
'JustWind': 10,
'Reflect': 8,
'ZRBest': 0,
'Shuffled': 13,
'NWSZR': 0}
for filename in resultsList :
tempy = ObtainARFFData(filename, numpy.array([-1, -2, -3]), skipMap[subProj])
summaryInfo['rmse'].append(numpy.sqrt(numpy.mean((tempy[:, 0] - tempy[:, 1]) ** 2.0)))
summaryInfo['mae'].append(numpy.mean(numpy.abs(tempy[:, 0] - tempy[:, 1])))
summaryInfo['corr'].append(numpy.diag(numpy.corrcoef(tempy[:, 0], tempy[:, 1]), k=tempy[:, 0].shape[0]))
summaryInfo['sse'].append(numpy.sum((tempy[:, 0] - tempy[:, 1]) ** 2.0))
summaryInfo['sae'].append(numpy.sum(numpy.abs(tempy[:, 0] - tempy[:, 1])))
"""
# PlotCorr(resultInfo['testObs'], resultInfo['modelPredicts'])
# pylab.title('Model/Obs Correlation Plot - Model: ' + subProj)
# pylab.savefig(os.sep.join([dirLoc, "CorrPlot_" + subProj + ".png"]))
# pylab.clf()
# print " Saved Correlation Plot..."
# PlotZR(resultInfo['reflectObs'], resultInfo['testObs'], resultInfo['modelPredicts'])
# pylab.title('Model Comparison - Z-R Plane - ' + subProj)
# pylab.savefig(os.sep.join([dirLoc, "ZRPlot_" + subProj + ".png"]))
# pylab.clf()
# print " Save ZR Plot..."
summaryInfo = DoSummaryInfo(resultInfo['testObs'], resultInfo['modelPredicts'])
statNames = summaryInfo.keys()
for statname in statNames :
numpy.savetxt(os.sep.join([dirLoc, "summary_%s_%s.txt" % (statname, subProj)]), summaryInfo[statname])
print " Saved summary data for", statname, " Mean: ", numpy.mean(summaryInfo[statname]), " StdDev: ", numpy.std(summaryInfo[statname])
def SaveSubprojectModel(dirLoc, subProj) :
resultsList = glob.glob(os.sep.join([dirLoc, subProj, 'results_*.csv']))
resultsList.sort()
summaryInfo = {'rmse': [],
'mae': [],
'corr': [],
'sse': [],
'sae': []}
skipMap = {'FullSet': 13,
'SansWind': 11,
'JustWind': 10,
'Reflect': 8,
'ZRBest': 0,
'Shuffled': 13,
'NWSZR': 0}
resultInfo = ObtainResultInfo(dirLoc, subProj)
PlotCorr(resultInfo['testObs'], resultInfo['modelPredicts'])
pylab.title('Model/Obs Correlation Plot - Model: ' + subProj)
pylab.savefig(os.sep.join([dirLoc, "CorrPlot_" + subProj + ".eps"]))
pylab.clf()
print " Saved Correlation Plot..."
PlotZR(resultInfo['reflectObs'], resultInfo['testObs'], resultInfo['modelPredicts'])
pylab.title('Model Comparison - Z-R Plane - ' + subProj)
pylab.savefig(os.sep.join([dirLoc, "ZRPlot_" + subProj + ".png"]))
pylab.clf()
print " Save ZR Plot..."
|
{"/filtertraining.py": ["/histtools.py"]}
|
38,289
|
WeatherGod/NNforZR
|
refs/heads/master
|
/PaperScript.py
|
#!/usr/bin/env python
import glob # for filename globbing
import os # for os.sep
import matplotlib.pyplot as pyplot
from ProjectUtils import ObtainResultInfo, PlotCorr, PlotZR
# Run this code if this script is executed like a program
# instead of being loaded like a library file.
if __name__ == '__main__':
from optparse import OptionParser # Command-line parsing
parser = OptionParser()
parser.add_option("-r", "--run", dest="projectName", type="string",
help="Use data from PROJECT run", metavar="PROJECT")
parser.add_option("-d", "--dir", dest="dataDir", type="string",
help="Data exists at SRC", metavar="SRC", default=".")
parser.add_option("-m", "--models", dest="models", action="append", type="string",
help="Create images for MODEL", metavar="MODEL", default=[])
parser.add_option("-f", "--format", dest="outputFormat", type="string",
help="Desired FORMAT for the output images", metavar="FORMAT", default="png")
(options, args) = parser.parse_args()
destDir = '.'
if options.projectName is None : parser.error("Missing PROJECT!")
if len(options.models) == 0 : print "WARNING: No models given!"
corrFig = pyplot.figure(figsize=(12.5, 5))
zrFig = pyplot.figure(figsize=(12.5, 5))
for index, model in enumerate(options.models) :
print "Model: ", model
resultInfo = ObtainResultInfo(os.sep.join([options.dataDir, options.projectName]), model)
####### Plot Corr ##########
print "Plotting Corr"
corrAx = corrFig.add_subplot(1, len(options.models), index + 1)
PlotCorr(resultInfo['testObs'], resultInfo['modelPredicts'], axis=corrAx)
corrAx.set_title('Model/Obs Correlation Plot - Model: %s' % model, fontsize = 'large')
#print "Saving..."
#pylab.savefig(os.sep.join([destDir, "Corr%s_Raw.eps" % model]))
#pylab.savefig("%s%sCorr%s_Raw.%s" % (destDir, os.sep, model, options.outputFormat),
# bbox_inches='tight')
#pylab.clf()
####### Plot ZR ###########
print "Plotting ZR"
zrAx = zrFig.add_subplot(1, len(options.models), index + 1)
PlotZR(resultInfo['reflectObs'], resultInfo['testObs'], resultInfo['modelPredicts'], axis=zrAx)
zrAx.set_title('Model Comparison - Z-R Plane - Model: %s' % model, fontsize = 'large')
#print "Saving..."
#pylab.savefig("%s%sZRPlot_%s_Raw.%s" % (destDir, os.sep, model, options.outputFormat),
# transparent=True, bbox_inches='tight')
#pylab.clf()
print "Saving Corr..."
corrFig.savefig("%s%sCorrModels.%s" % (destDir, os.sep, options.outputFormat),
transparent=False, bbox_inches='tight')
print "Saving ZRPlot..."
zrFig.savefig("%s%sZRPlot_Models.%s" % (destDir, os.sep, options.outputFormat),
transparent=False, bbox_inches='tight')
|
{"/filtertraining.py": ["/histtools.py"]}
|
38,290
|
WeatherGod/NNforZR
|
refs/heads/master
|
/Paper_BootstrapFig.py
|
#!/usr/bin/env python
# Author: Benjamin Root
# Copyright (C) 1989, 1991-2009 Free Software Foundation.
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see http://www.gnu.org/licenses/.
import os # for os.sep
import matplotlib.pyplot as pyplot
import numpy
def MakeErrorBars(bootMeans, bootCIs, models, axis) :
axis.errorbar(numpy.arange(len(models)) + 1,
bootMeans, yerr=numpy.array([bootMeans - bootCIs[:, 0],
bootCIs[:, 1] - bootMeans]),
fmt='.', ecolor='k', elinewidth=1.5, capsize=10, markersize=16, color='k')
axis.set_xticks(numpy.arange(len(models)) + 1)
axis.set_xticklabels(models, fontsize='medium')
axis.set_xlim((0.5, len(models) + 0.5))
if __name__ == '__main__':
from optparse import OptionParser # Command-line parsing
parser = OptionParser()
parser.add_option("-r", "--run", dest="projectName", type="string",
help="Use data from PROJECT run", metavar="PROJECT")
parser.add_option("-d", "--dir", dest="dataDir", type="string",
help="Data exists at SRC", metavar="SRC", default=".")
parser.add_option("-m", "--model", dest="models", action="append", type="string",
help="Use MODEL in the images", metavar="MODEL")
parser.add_option("-f", "--format", dest="outputFormat", type="string",
help="Desired FORMAT for the output images", metavar="FORMAT", default="png")
parser.add_option("-s", "--stat", dest="stats", action="append", type="string",
help="Create images for STAT", metavar="STAT")
(options, args) = parser.parse_args()
destDir = '.'
if options.projectName is None : parser.error("Missing PROJECT!")
if len(options.models) == 0 : parser.error("No models given!")
if len(options.stats) == 0 : parser.error("No Stats given!")
statNamesFull = {'Corr': 'Correlation Coefficient',
'RMSE': 'Root Mean Squared Error [mm/hr]',
'MAE': 'Mean Absolute Error [mm/hr]'}
statNamesTitle = {'Corr': 'Correlations',
'RMSE': 'RMSEs',
'MAE': 'MAEs'}
fig = pyplot.figure(figsize=(18.75, 5))
for statIndex, statName in enumerate(options.stats) :
bootCIs = numpy.loadtxt(os.sep.join([options.dataDir, options.projectName, 'bootstrap_CI_%s.txt' % statName]))
bootMeans = numpy.loadtxt(os.sep.join([options.dataDir, options.projectName, 'bootstrap_Mean_%s.txt' % statName]))
print bootCIs
print bootMeans
ax = fig.add_subplot(1, len(options.stats), statIndex + 1)
MakeErrorBars(bootMeans, bootCIs, options.models, ax);
ax.set_ylabel(statNamesFull[statName], fontsize='large');
ax.set_xlabel('Models', fontsize='large');
ax.set_title('Mean Model %s' % statNamesTitle[statName], fontsize='large');
# saveas(gcf, ['Models' statFileStems{statIndex} '_Raw.' outputFormat]);
fig.savefig('%s%sModelPerformances.%s' % (destDir, os.sep, options.outputFormat),
transparent=True, bbox_inches='tight')
|
{"/filtertraining.py": ["/histtools.py"]}
|
38,291
|
WeatherGod/NNforZR
|
refs/heads/master
|
/DisplayResults.py
|
#!/usr/bin/env python
from optparse import OptionParser # Command-line parsing
import pylab
import numpy
parser = OptionParser()
parser.add_option("-t", "--train", dest="train",
help="Display results for TRAIN", metavar="TRAIN")
parser.add_option("-d", "--data", dest="data",
help="Using training dataset FILE", metavar="FILE")
parser.add_option("-r", "--results", dest="test",
help="Using testing RESULTS", metavar="RESULTS")
(options, args) = parser.parse_args()
if (options.train == None) :
parser.error("Missing TRAIN")
trainFile = options.train
print "The training results file is:", trainFile
if (options.data == None) :
parser.error("Missing FILE")
dataFile = options.data
print "The dataset file is:", dataFile
if (options.test == None) :
parser.error("Missing RESULTS")
testFile = options.test
print "The test results file is:", testFile
dataSet = numpy.loadtxt(dataFile, delimiter=',')
testSet = numpy.loadtxt(testFile, delimiter=',')
trainResults = numpy.loadtxt(trainFile)
nwsRainRate = ((10.0 **(dataSet[:, 4]/10.0))/300.0) ** (1/1.4)
nwsMAE = numpy.mean(numpy.abs(dataSet[:, 5] - nwsRainRate))
mlpMAE = numpy.mean(abs(trainResults[:, 3]))
nwsRMSE = numpy.sqrt(numpy.mean((dataSet[:, 5] - nwsRainRate) ** 2))
mlpRMSE = numpy.sqrt(numpy.mean(trainResults[:, 3] ** 2))
nwsSE = numpy.std(dataSet[:, 5] - nwsRainRate)
mlpSE = numpy.std(trainResults[:, 3])
goodPoints = pylab.find(dataSet[:, 4] <= 52.0)
trunc_nwsMAE = numpy.mean(numpy.abs(dataSet[goodPoints, 5] - nwsRainRate[goodPoints]))
trunc_nwsRMSE = numpy.sqrt(numpy.mean((dataSet[goodPoints, 5] - nwsRainRate[goodPoints]) ** 2))
trunc_nwsSE = numpy.std(dataSet[goodPoints, 5] - nwsRainRate[goodPoints])
print ' | NWS | MLP | Improve | NWS Trunc |'
print 'MAE | %7.2f | %7.2f | %7.2f | %8.3f |' % (nwsMAE, mlpMAE, nwsMAE - mlpMAE,
trunc_nwsMAE)
print 'RMSE | %7.2f | %7.2f | %7.2f | %8.3f |' % (nwsRMSE, mlpRMSE, nwsRMSE - mlpRMSE,
trunc_nwsRMSE)
print 'StdDev| %7.2f | %7.2f | | %8.3f |' % (nwsSE, mlpSE, trunc_nwsSE)
pylab.scatter(dataSet[:, 4], dataSet[:, 5], '.r')
pylab.hold(True)
pylab.scatter(testSet[:, 3], testSet[:, 4], '.b')
pylab.hold(False)
pylab.show()
|
{"/filtertraining.py": ["/histtools.py"]}
|
38,292
|
WeatherGod/NNforZR
|
refs/heads/master
|
/PaperErrFig.py
|
#!/usr/bin/env python
import glob # for filename globbing
import os # for os.sep
import matplotlib.pyplot as pyplot
import numpy
import matplotlib.cm
from ProjectUtils import ObtainResultInfo
def CalcErr(resultInfo) :
return numpy.abs(resultInfo['modelPredicts'] - resultInfo['testObs'])
# Run this code if this script is executed like a program
# instead of being loaded like a library file.
if __name__ == '__main__':
from optparse import OptionParser # Command-line parsing
parser = OptionParser()
parser.add_option("-r", "--run", dest="projectName", type="string",
help="Use data from PROJECT run", metavar="PROJECT")
parser.add_option("-d", "--dir", dest="dataDir", type="string",
help="Data exists at SRC", metavar="SRC", default=".")
parser.add_option("-o", "--orig", dest="origModel", type="string",
help="Orig MODEL", metavar="MODEL")
parser.add_option("-n", "--new", dest="newModel", type="string",
help="New MODEL", metavar="MODEL")
parser.add_option("-f", "--format", dest="outputFormat", type="string",
help="Desired FORMAT for the output images", metavar="FORMAT", default="png")
(options, args) = parser.parse_args()
destDir = '.'
if options.projectName is None : parser.error("Missing PROJECT!")
if options.origModel is None : parser.error("Missing original MODEL!")
if options.newModel is None : parser.error("Missing new MODEL!")
resultInfo_Orig = ObtainResultInfo(os.sep.join([options.dataDir, options.projectName]), options.origModel)
resultInfo_New = ObtainResultInfo(os.sep.join([options.dataDir, options.projectName]), options.newModel)
errImprove = CalcErr(resultInfo_Orig) - CalcErr(resultInfo_New)
print "Mean Improve: ", numpy.mean(errImprove.flatten())
fig = pyplot.figure()
ax = fig.gca()
ax.hexbin(resultInfo_Orig['reflectObs'].flatten(), errImprove.flatten(), bins='log', cmap=matplotlib.cm.bone_r)
ax.set_xlabel('Reflectivity [dBZ]', fontsize='large')
ax.set_ylabel('Error Improvement [mm/hr]', fontsize='large')
ax.set_title("%s Error Improvement over %s" % (options.newModel, options.origModel), fontsize='large')
#pylab.savefig(destDir + os.sep + "%s_%s_Improve_Raw.eps" % (options.newModel, options.origModel))
fig.savefig("%s%s%s_%s_Improve.%s" % (destDir, os.sep, options.newModel, options.origModel, options.outputFormat),
transparent=True, bbox_inches='tight')
|
{"/filtertraining.py": ["/histtools.py"]}
|
38,293
|
WeatherGod/NNforZR
|
refs/heads/master
|
/ProjectAnalysis.py
|
#!/usr/bin/env python
from optparse import OptionParser # Command-line parsing
import glob # for filename globbing
import os
import numpy
import scipy.stats as ss # for sem() and other stat funcs
import scipy.stats.stats as sss # for nanmean() and other nan-friendly funcs
import pylab # for plotting
from filtertraining import * # for MakeBins(), Hist2d()
from scipy import optimize
#from arff import arffread
def ZRModel(coefs, reflects) :
return(((10.0 **(reflects/10.0))/coefs[0]) ** (1/coefs[1]))
def ZRBest(trainData) :
def errFun(coefs) :
return(numpy.sqrt(numpy.mean((ZRModel(coefs, trainData[:, 0]) - trainData[:, 1])**2.0)))
return(optimize.fmin(errFun, [300, 1.4], maxiter=2000, disp=0))
def decimate2d_ZR(vals1, vals2, decimation):
def Gaussian(vals, means, stds) :
return(numpy.exp(-((vals - means)**2.0)/(2 * (stds**2.0))) / (numpy.sqrt(2.0 * 3.14) * stds))
bins1 = MakeBins(vals1, OptimalBinSize(vals1))
bins2 = MakeBins(vals2, OptimalBinSize(vals2))
(n, binLocs) = Hist2d(vals1, bins1, vals2, bins2)
[bin1mesh, bin2mesh] = numpy.meshgrid(bins1[0:-1], bins2[0:-1])
weights = Gaussian(bin1mesh, 10.0 * numpy.log10(300.0*bin2mesh**1.4), 6.0) + Gaussian(bin2mesh, ZRModel([300, 1.4], bin1mesh), 6.0)
binCnt = len(numpy.nonzero(n)[0])
baseThresholds = numpy.array([len(binLocs) * decimation / (binCnt * n[aCoord]) for aCoord in binLocs])
scale = baseThresholds.max()/weights.max()
thresholds = baseThresholds * numpy.array([scale * weights[aCoord] for aCoord in binLocs])
return(numpy.random.random_sample(len(thresholds)) <= thresholds)
############################## Plotting #########################################
def PlotCorr(obs, estimated, **kwargs) :
pylab.scatter(obs.flatten(), estimated.flatten(), s=1, **kwargs)
pylab.plot([0.0, obs.max()], [0.0, obs.max()], color='c', hold=True)
pylab.xlabel('Observed Rainfall Rate [mm/hr]')
pylab.ylabel('Estimated Rainfall Rate [mm/hr]')
pylab.xlim((0.0, obs.max()))
pylab.ylim((0.0, obs.max()))
def PlotZR(reflects, obs, estimated, **kwargs) :
pylab.scatter(reflects.flatten(), obs.flatten(), color='r', s = 1)
pylab.scatter(reflects.flatten(), estimated.flatten(), color='b', s = 1, hold = True, **kwargs)
pylab.xlabel('Reflectivity [dBZ]')
pylab.ylabel('Rainfall Rate [mm/hr]')
pylab.xlim((reflects.min(), reflects.max()))
pylab.ylim((obs.min(), obs.max()))
####################################################################################
def ObtainModelInfo(dirLoc, subProj) :
modelList = glob.glob(os.sep.join([dirLoc, subProj, 'model_*.txt']))
modelCoefs = [ProcessModelInfo(filename) for filename in modelList]
coefNames = modelCoefs[0].keys()
coefNames.sort()
vals = []
for weight in modelCoefs :
vals.append([weight[coef] for coef in coefNames])
return((coefNames, numpy.array(vals)))
# print len(tempy), type(tempy[0])
def AnalyzeResultInfo(modelPredicts, testObs, reflectObs) :
print "FULL SET"
sumInfo = DoSummaryInfo(testObs, modelPredicts)
print "RMSE: %8.4f %8.4f" % (numpy.mean(sumInfo['rmse']), ss.sem(sumInfo['rmse']))
print "MAE : %8.4f %8.4f" % (numpy.mean(sumInfo['mae']), ss.sem(sumInfo['mae']))
print "CORR: %8.4f %8.4f" % (numpy.mean(sumInfo['corr']), ss.sem(sumInfo['corr']))
print "\nZ < 40"
belowCondition = reflectObs < 40
belowSumInfo = DoSummaryInfo(numpy.where(belowCondition, testObs, numpy.NaN),
numpy.where(belowCondition, modelPredicts, numpy.NaN))
print "RMSE: %8.4f %8.4f" % (numpy.mean(belowSumInfo['rmse']), ss.sem(belowSumInfo['rmse']))
print "MAE : %8.4f %8.4f" % (numpy.mean(belowSumInfo['mae']), ss.sem(belowSumInfo['mae']))
print "CORR: %8.4f %8.4f" % (numpy.mean(belowSumInfo['corr']), ss.sem(belowSumInfo['corr']))
print "\nZ >= 40"
aboveSumInfo = DoSummaryInfo(numpy.where(belowCondition, numpy.NaN, testObs),
numpy.where(belowCondition, numpy.NaN, modelPredicts))
print "RMSE: %8.4f %8.4f" % (numpy.mean(aboveSumInfo['rmse']), ss.sem(aboveSumInfo['rmse']))
print "MAE : %8.4f %8.4f" % (numpy.mean(aboveSumInfo['mae']), ss.sem(aboveSumInfo['mae']))
print "CORR: %8.4f %8.4f" % (numpy.mean(aboveSumInfo['corr']), ss.sem(aboveSumInfo['corr']))
def DoSummaryInfo(obs, estimated) :
return({'rmse': numpy.sqrt(sss.nanmean((estimated - obs) ** 2.0, axis = 1)),
'mae': sss.nanmean(numpy.abs(estimated - obs), axis=1),
'corr': numpy.diag(numpy.corrcoef(estimated, obs), k=estimated.shape[0]),
'sse': numpy.sum((estimated - obs) ** 2.0, axis = 1)})
def ProcessModelInfo(filename) :
weights = {}
nodeName = None
for line in open(filename) :
line = line.strip()
if (line.startswith('Linear Node') or line.startswith('Sigmoid Node')) :
nodeName = line.split(' ')[-1].strip()
elif (line.startswith('Threshold')
or line.startswith('Node')
or line.startswith('Attrib')) :
weights["%s-%s" % (nodeName, line.split()[-2])] = float(line.split()[-1])
return(weights)
#def ObtainClassifications(filename) :
# f = open(filename)
# (name, sparse, alist, m) = arffread(f)
# f.close()
#
# return(numpy.array([aRow[-1] for aRow in m]))
def ObtainARFFData(filename, columnIndxs, linesToSkip) :
# f = open(filename)
# (name, sparse, alist, m) = arffread(f)
# f.close()
#
# return(numpy.array(m)[:, columnIndxs])
return(numpy.loadtxt(filename, delimiter=',', skiprows=linesToSkip)[:, columnIndxs])
def ObtainResultInfo(dirLoc, subProj) :
resultsList = glob.glob(os.sep.join([dirLoc, subProj, 'results_*.csv']))
resultsList.sort()
skipMap = {'FullSet': 13,
'SansWind': 11,
'JustWind': 10,
'Reflect': 8,
'ZRBest': 0,
'Shuffled': 13,
'NWSZR': 0}
tempy = [ObtainARFFData(filename, numpy.array([-1, -2, -3]), skipMap[subProj]) for filename in resultsList]
return({'modelPredicts': numpy.array([aRow[:, 0] for aRow in tempy]),
'testObs': numpy.array([aRow[:, 1] for aRow in tempy]),
'reflectObs': numpy.array([aRow[:, 2] for aRow in tempy])})
def CalcErrorImprovement(resultInfo1, resultInfo2) :
return(numpy.abs(resultInfo2['modelPredicts'] - resultInfo2['testObs'])
- numpy.abs(resultInfo1['modelPredicts'] - resultInfo1['testObs']))
def SaveSubprojectModel(resultInfo, dirLoc, subProj) :
"""
resultsList = glob.glob(os.sep.join([dirLoc, subProj, 'results_*.csv']))
resultsList.sort()
"""
summaryInfo = {'rmse': [],
'mae': [],
'corr': [],
'sse': [],
'sae': []}
"""
skipMap = {'FullSet': 13,
'SansWind': 11,
'JustWind': 10,
'Reflect': 8,
'ZRBest': 0,
'Shuffled': 13,
'NWSZR': 0}
for filename in resultsList :
tempy = ObtainARFFData(filename, numpy.array([-1, -2, -3]), skipMap[subProj])
summaryInfo['rmse'].append(numpy.sqrt(numpy.mean((tempy[:, 0] - tempy[:, 1]) ** 2.0)))
summaryInfo['mae'].append(numpy.mean(numpy.abs(tempy[:, 0] - tempy[:, 1])))
summaryInfo['corr'].append(numpy.diag(numpy.corrcoef(tempy[:, 0], tempy[:, 1]), k=tempy[:, 0].shape[0]))
summaryInfo['sse'].append(numpy.sum((tempy[:, 0] - tempy[:, 1]) ** 2.0))
summaryInfo['sae'].append(numpy.sum(numpy.abs(tempy[:, 0] - tempy[:, 1])))
"""
# PlotCorr(resultInfo['testObs'], resultInfo['modelPredicts'])
# pylab.title('Model/Obs Correlation Plot - Model: ' + subProj)
# pylab.savefig(os.sep.join([dirLoc, "CorrPlot_" + subProj + ".png"]))
# pylab.clf()
# print " Saved Correlation Plot..."
# PlotZR(resultInfo['reflectObs'], resultInfo['testObs'], resultInfo['modelPredicts'])
# pylab.title('Model Comparison - Z-R Plane - ' + subProj)
# pylab.savefig(os.sep.join([dirLoc, "ZRPlot_" + subProj + ".png"]))
# pylab.clf()
# print " Save ZR Plot..."
summaryInfo = DoSummaryInfo(resultInfo['testObs'], resultInfo['modelPredicts'])
statNames = summaryInfo.keys()
for statname in statNames :
numpy.savetxt(os.sep.join([dirLoc, "summary_%s_%s.txt" % (statname, subProj)]), summaryInfo[statname])
print " Saved summary data for", statname
# Run this code if this script is executed like a program
# instead of being loaded like a library file.
if __name__ == '__main__':
parser = OptionParser()
parser.add_option("-d", "--dir", dest="projLoc",
help="Project located at DIR", metavar="DIR")
(options, args) = parser.parse_args()
if (options.projLoc == None) :
parser.error("Missing DIR")
dirLoc = options.projLoc
print "The project is at:", dirLoc
(pathName, dirNames, filenames) = os.walk(dirLoc).next()
for subProj in dirNames :
print "Subproject:", subProj
SaveSubprojectModel(dirLoc, subProj)
# print "Subproject: NWS ZR"
# resultInfo_nws = ObtainResultInfo(dirLoc, "Reflect")
# resultInfo_nws['modelPredicts'] = ZRModel([300, 1.4], resultInfo_nws['reflectObs'])
# SaveSubprojectModel(resultInfo_nws, dirLoc, "nwszr")
|
{"/filtertraining.py": ["/histtools.py"]}
|
38,329
|
fzgithub2022/jeopardy
|
refs/heads/master
|
/app.py
|
import os
from flask import Flask, request, jsonify, abort
from sqlalchemy import exc
from models import db_drop_and_create_all, setup_db, Questions
import json
app = Flask(__name__)
setup_db(app)
'''
@TODO uncomment the following line to initialize the datbase
!! NOTE THIS WILL DROP ALL RECORDS AND START YOUR DB FROM SCRATCH
!! NOTE THIS MUST BE UNCOMMENTED ON FIRST RUN
'''
# db_drop_and_create_all()
# Decors
@app.route('/')
def jeopardy():
return 'Hello Jeopardy!'
|
{"/app.py": ["/models.py"]}
|
38,330
|
fzgithub2022/jeopardy
|
refs/heads/master
|
/models.py
|
import os
from sqlalchemy import Column, String, Integer
from flask_sqlalchemy import SQLAlchemy
import json
database_filename = "jeopardy.db"
project_dir = os.path.dirname(os.path.abspath(__file__))
database_path = "sqlite:///{}".format(os.path.join(project_dir, database_filename))
db = SQLAlchemy()
'''
setup_db(app)
binds a flask application and a SQLAlchemy service
'''
def setup_db(app):
app.config["SQLALCHEMY_DATABASE_URI"] = database_path
app.config["SQLALCHEMY_TRACK_MODIFICATIONS"] = False
db.app = app
db.init_app(app)
'''
db_drop_and_create_all()
drops the database tables and starts fresh
can be used to initialize a clean database
!!NOTE you can change the database_filename variable to have multiple verisons of a database
'''
def db_drop_and_create_all():
db.drop_all()
db.create_all()
'''
Question
a persistent drink entity, extends the base SQLAlchemy Model
'''
class Questions(db.Model):
# Attributes
id = Column(Integer().with_variant(Integer, "sqlite"), primary_key=True)
question = Column(String(), unique=True)
answer = Column(Integer().with_variant(Integer, "sqlite"), nullable=False)
value = Column(Integer().with_variant(Integer, "sqlite"), nullable=False)
#Methods
def insert(self):
db.session.add(self)
db.session.commit()
|
{"/app.py": ["/models.py"]}
|
38,331
|
eyeduh/webcrawler
|
refs/heads/master
|
/users/views.py
|
from django.shortcuts import redirect, render, get_object_or_404
from .models import Profile
from django.contrib.auth import login, authenticate
from django.contrib import messages
from .forms import SignUpForm
from django.contrib.auth.forms import AuthenticationForm
# Create your views here.
def user_list(request):
users = Profile.objects.all()
context = {"user": users}
return render(request, 'users/user_list.html', context)
def user_detail(request, user_id):
user = get_object_or_404(Profile, pk=user_id)
context = {'user': user}
return render(request, 'users/user_detail.html', context)
def register_request(request):
if request.method == 'POST':
form = SignUpForm(request.POST)
if form.is_valid():
user = form.save()
user.refresh_from_db()
user.profile.name = form.cleaned_data.get('name')
user.profile.gender = form.cleaned_data.get('gender')
user.profile.age = form.cleaned_data.get('age')
user.profile.avatar = form.cleaned_data.get('avatar')
user.profile.phone_number = form.cleaned_data.get('phone number')
user.profile.bio = form.cleaned_data.get('bio')
user.save()
raw_password = form.cleaned_data.get('password1')
user = authenticate(username=user.username, password=raw_password)
login(request, user)
messages.success(request, "Registration Successful.")
return redirect('/')
else:
messages.error(request, "Unsuccessful Registration. Invalid Information.")
form = SignUpForm()
return render(request, 'users/register.html', context={'form':form})
def login_request(request):
if request.method == 'POST':
form = AuthenticationForm(request, data=request.POST)
if form.is_valid():
username = form.cleaned_data.get('username')
password = form.cleaned_data.get('password')
user = authenticate(username=username, password=password)
if user is not None:
login(request, user)
messages.info(request, f"You are now logged in as {username}.")
return redirect('/')
else:
messages.error(request, "Invalid username or password.")
else:
messages.error(request, "Invalid username or password.")
form = AuthenticationForm()
return render(request, 'users/login.html', context={'form':form})
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,332
|
eyeduh/webcrawler
|
refs/heads/master
|
/webcrawlers/migrations/0013_auto_20210904_1005.py
|
# Generated by Django 3.2.6 on 2021-09-04 10:05
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('webcrawlers', '0012_alter_brand_url'),
]
operations = [
migrations.AlterField(
model_name='product',
name='price',
field=models.CharField(help_text='In Dollars', max_length=50, verbose_name='Product Price'),
),
migrations.AlterField(
model_name='product',
name='url',
field=models.URLField(verbose_name='Product URL'),
),
]
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,333
|
eyeduh/webcrawler
|
refs/heads/master
|
/users/migrations/0004_auto_20210907_0820.py
|
# Generated by Django 3.2.6 on 2021-09-07 08:20
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('users', '0003_alter_profile_age'),
]
operations = [
migrations.AlterField(
model_name='profile',
name='age',
field=models.IntegerField(default=0, verbose_name='Age'),
),
migrations.AlterField(
model_name='profile',
name='phone_number',
field=models.PositiveBigIntegerField(default=0, verbose_name='Phone Number'),
),
]
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,334
|
eyeduh/webcrawler
|
refs/heads/master
|
/scraper/scraper/spiders/product_crawler.py
|
from os import name
from w3lib import url
from webcrawlers.models import Product, Brand
from scraper.scraper.spiders.brand_crawler import BrandsSpider
from urllib import parse
import scrapy
from scrapy.spiders import CrawlSpider, Rule
from scrapy.linkextractors import LinkExtractor
from ..items import ProductScraperItem
class ProductsSpider(CrawlSpider):
name = 'products'
urls = []
with open('brands_list.txt') as filename:
lines = filename.readlines()
for line in lines:
urls.append(line)
start_urls = urls
def parse(self, response):
for product in response.css('a.css-ix8km1'):
name = product.xpath('.//span[@data-at="sku_item_name"]/text()').get()
brand = product.xpath('.//span[@data-at="sku_item_brand"]/text()').get()
url = product.xpath('./@href').get()
img = 'http://www.sephora.com' + product.xpath(('.//img/@src')).get()
price = product.xpath('.//span[@data-at="sku_item_price_list"]/text()').get()
# 'Product Rating' : extract_with_xpath('//div[@data-comp="StarRating"]/@aria-label')
products = ProductScraperItem()
products['name'] = name
products['url'] = url
products['brand'] = brand
products['img'] = img
products['price'] = price
products_objects = Product.objects.create(**products)
yield products
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,335
|
eyeduh/webcrawler
|
refs/heads/master
|
/webcrawlers/views.py
|
from django.shortcuts import render
from django.shortcuts import get_object_or_404
from .models import Category, Brand, Product
# Create your views here.
def index(request):
return render(request, 'index.html')
def list_brands(request):
brands = Brand.objects.all()
context = {'brands' : brands}
return render(request, 'list_brands.html', context)
def list_products(request):
products = Product.objects.all()
context = {'products' : products}
return render(request, 'list_products.html', context)
def brand_products(request):
products = Product.objects.all()
brands = Brand.objects.all()
context = {'products' : products, 'brands' : brands}
return render(request, 'brand-products.html', context)
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,336
|
eyeduh/webcrawler
|
refs/heads/master
|
/webcrawlers/models.py
|
from os import name
from django.db import models
from django.db.models.deletion import PROTECT
# Create your models here.
class Category(models.Model):
name = models.CharField(verbose_name= 'Category Name', max_length=30)
url = models.URLField(verbose_name= 'Category Link', max_length=100)
class Meta:
db_table = 'categories'
verbose_name = 'Category'
verbose_name_plural = 'Categories'
unique_together = ['name', 'url']
class Brand(models.Model):
name = models.CharField(verbose_name= 'Brand Name', max_length=200)
url = models.CharField(verbose_name= 'Brand Link', max_length=100)
new = models.CharField(verbose_name='Is It New?', max_length=10)
class Meta:
db_table = 'brands'
verbose_name = 'Brand'
verbose_name_plural = 'Brands'
unique_together = ['name', 'url', 'new']
def __str__(self):
return self.name
class Product(models.Model):
name = models.CharField(verbose_name= 'Product Name', max_length=100)
brand = models.CharField(verbose_name= 'Product Brand', max_length=50)
url = models.URLField(verbose_name= 'Product URL', max_length=200)
img = models.URLField(verbose_name= 'Product Image', max_length=200)
price = models.CharField(verbose_name= 'Product Price', help_text='In Dollars', max_length=50)
class Meta:
db_table = 'products'
verbose_name = 'Product'
verbose_name_plural = 'Products'
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,337
|
eyeduh/webcrawler
|
refs/heads/master
|
/webcrawlers/migrations/0002_brand.py
|
# Generated by Django 3.2.6 on 2021-09-03 09:46
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('webcrawlers', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Brand',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=30, verbose_name='Category Name')),
('url', models.URLField(max_length=100, verbose_name='Category Link')),
('new', models.CharField(max_length=10, verbose_name='Is It New?')),
],
options={
'verbose_name': 'Brand',
'verbose_name_plural': 'Brands',
'db_table': 'brands',
},
),
]
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,338
|
eyeduh/webcrawler
|
refs/heads/master
|
/webcrawlers/migrations/0006_alter_brand_name.py
|
# Generated by Django 3.2.6 on 2021-09-03 19:58
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('webcrawlers', '0005_alter_product_brand'),
]
operations = [
migrations.AlterField(
model_name='brand',
name='name',
field=models.CharField(max_length=200, verbose_name='Brand Name'),
),
]
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,339
|
eyeduh/webcrawler
|
refs/heads/master
|
/users/forms.py
|
from django.forms import fields
from users.models import Profile
from django import forms
from django.contrib.auth.forms import UserCreationForm
from django.contrib.auth.models import User
class SignUpForm(UserCreationForm):
GENDER_MALE = 1
GENDER_FEMALE = 2
GENDER_CHOICES = [
(GENDER_MALE, 'Male'),
(GENDER_FEMALE, 'Female')
]
gender = forms.ChoiceField(choices=GENDER_CHOICES)
first_name = forms.CharField(max_length=100)
last_name = forms.CharField(max_length=100)
bio = forms.CharField(max_length=10000)
phone_number = forms.IntegerField()
age = forms.IntegerField()
class Meta:
model = User
fields = ("username", "first_name", "last_name", "age", "password1", "password2")
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,340
|
eyeduh/webcrawler
|
refs/heads/master
|
/webcrawlers/migrations/0007_alter_brand_unique_together.py
|
# Generated by Django 3.2.6 on 2021-09-03 20:22
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('webcrawlers', '0006_alter_brand_name'),
]
operations = [
migrations.AlterUniqueTogether(
name='brand',
unique_together={('name', 'url', 'new')},
),
]
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,341
|
eyeduh/webcrawler
|
refs/heads/master
|
/scraper/scraper/spiders/category_crawler.py
|
from webcrawlers.models import Category
import scrapy
from ..items import CategoryScraperItem
class CategorySpider(scrapy.Spider):
name = 'category'
start_urls = ['http://www.sephora.com']
def parse(self, response):
for category in response.css('a.css-hzvn5z'):
name = category.css('a::text').extract()[0]
url = 'http://www.sephora.com' + category.css('a::attr(href)').extract()[0]
categories = CategoryScraperItem()
categories['name'] = name
categories['url'] = url
category_objects = Category.objects.create(**categories)
yield categories
next_page = url
yield response.follow(next_page, callback=self.parse)
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,342
|
eyeduh/webcrawler
|
refs/heads/master
|
/scraper/scraper/spiders/brand_crawler.py
|
from os import name
from webcrawlers.models import Brand
import scrapy
from ..items import BrandScraperItem
class BrandsSpider(scrapy.Spider):
name = 'brands'
start_urls = ['http://www.sephora.com/brands-list']
def parse(self, response):
for brand in response.xpath('//a[@data-at="brand_link"]'):
name = brand.css('a::text').get()
url = 'http://www.sephora.com' + brand.css('a::attr(href)').extract()[0]
new = brand.css('span.css-1yfnlr::text').get(default='Not New')
brands = BrandScraperItem()
brands['name'] = name
brands['url'] = url
brands['new'] = new
brand_objects = Brand.objects.create(**brands)
yield brands
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,343
|
eyeduh/webcrawler
|
refs/heads/master
|
/webcrawlers/migrations/0001_initial.py
|
# Generated by Django 3.2.6 on 2021-09-01 20:22
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Category',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=30, verbose_name='Category Name')),
('url', models.URLField(max_length=100, verbose_name='Category Link')),
],
options={
'verbose_name': 'Category',
'verbose_name_plural': 'Categories',
'db_table': 'categories',
},
),
migrations.CreateModel(
name='Product',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=100, verbose_name='Product Name')),
('brand', models.CharField(max_length=100, verbose_name='Product Brand')),
('url', models.URLField(max_length=100, verbose_name='Product URL')),
('img', models.URLField(verbose_name='Product Image')),
('price', models.IntegerField(help_text='In Dollars', verbose_name='Product Price')),
],
options={
'verbose_name': 'Product',
'verbose_name_plural': 'Products',
'db_table': 'products',
},
),
]
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,344
|
eyeduh/webcrawler
|
refs/heads/master
|
/webcrawlers/admin.py
|
from django.contrib import admin
from .models import Brand, Category, Product
# Register your models here.
class CategoryAdmin(admin.ModelAdmin):
list_display = ['name', 'url']
list_filter = ['name']
admin.site.register(Category, CategoryAdmin)
class BrandAdmin(admin.ModelAdmin):
list_display = ['name', 'url', 'new']
list_filter = ['name']
admin.site.register(Brand, BrandAdmin)
class ProductAdmin(admin.ModelAdmin):
list_display = ['name', 'brand', 'url', 'img', 'price']
list_filter = ['brand']
admin.site.register(Product, ProductAdmin)
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,345
|
eyeduh/webcrawler
|
refs/heads/master
|
/webcrawlers/migrations/0008_alter_brand_unique_together.py
|
# Generated by Django 3.2.6 on 2021-09-03 20:55
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('webcrawlers', '0007_alter_brand_unique_together'),
]
operations = [
migrations.AlterUniqueTogether(
name='brand',
unique_together=set(),
),
]
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,346
|
eyeduh/webcrawler
|
refs/heads/master
|
/webcrawlers/migrations/0005_alter_product_brand.py
|
# Generated by Django 3.2.6 on 2021-09-03 19:31
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('webcrawlers', '0004_alter_product_brand'),
]
operations = [
migrations.AlterField(
model_name='product',
name='brand',
field=models.CharField(max_length=50, verbose_name='Product Brand'),
),
]
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,347
|
eyeduh/webcrawler
|
refs/heads/master
|
/webcrawlers/migrations/0012_alter_brand_url.py
|
# Generated by Django 3.2.6 on 2021-09-04 09:03
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('webcrawlers', '0011_alter_product_unique_together'),
]
operations = [
migrations.AlterField(
model_name='brand',
name='url',
field=models.CharField(max_length=100, verbose_name='Brand Link'),
),
]
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,348
|
eyeduh/webcrawler
|
refs/heads/master
|
/webcrawlers/migrations/0011_alter_product_unique_together.py
|
# Generated by Django 3.2.6 on 2021-09-03 21:05
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('webcrawlers', '0010_alter_brand_unique_together'),
]
operations = [
migrations.AlterUniqueTogether(
name='product',
unique_together={('name', 'url')},
),
]
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,349
|
eyeduh/webcrawler
|
refs/heads/master
|
/scraper/scraper/items.py
|
# Define here the models for your scraped items
#
# See documentation in:
# https://docs.scrapy.org/en/latest/topics/items.html
import scrapy
from scrapy_djangoitem import DjangoItem
from webcrawlers.models import Category, Brand, Product
class CategoryScraperItem(DjangoItem):
django_model = Category
name = scrapy.Field()
url = scrapy.Field()
class BrandScraperItem(DjangoItem):
django_model = Brand
brand = scrapy.Field()
url = scrapy.Field()
new = scrapy.Field()
class ProductScraperItem(DjangoItem):
django_model = Product
name = scrapy.Field()
brand = scrapy.Field()
url = scrapy.Field()
img = scrapy.Field()
price = scrapy.Field()
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,350
|
eyeduh/webcrawler
|
refs/heads/master
|
/webcrawlers/migrations/0004_alter_product_brand.py
|
# Generated by Django 3.2.6 on 2021-09-03 13:22
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('webcrawlers', '0003_auto_20210903_0952'),
]
operations = [
migrations.AlterField(
model_name='product',
name='brand',
field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='webcrawlers.brand'),
),
]
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,351
|
eyeduh/webcrawler
|
refs/heads/master
|
/webcrawlers/urls.py
|
from django.urls import path
from . import views
urlpatterns = [
path('', views.index, name='index'),
path('brands-list/', views.list_brands, name='brands-list'),
path('products-list/', views.list_products, name='products-list'),
path('brands-list/brand-products/', views.brand_products, name='brand-products')
]
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,352
|
eyeduh/webcrawler
|
refs/heads/master
|
/users/models.py
|
from django.db import models
from django.contrib.auth.models import User
from django.db.models.signals import post_save
from django.dispatch import receiver
class Profile(models.Model):
GENDER_MALE = 1
GENDER_FEMALE = 2
GENDER_CHOICES = [
(GENDER_MALE, 'Male'),
(GENDER_FEMALE, 'Female')
]
gender = models.PositiveSmallIntegerField(verbose_name="Gender", choices=GENDER_CHOICES, default=GENDER_FEMALE)
user = models.OneToOneField(User, verbose_name="User", on_delete=models.CASCADE)
age = models.IntegerField(verbose_name="Age", default=0)
avatar = models.ImageField(verbose_name="Avatar", blank=True, upload_to='avatars')
phone_number = models.PositiveBigIntegerField(verbose_name="Phone Number", default=00000000000)
bio = models.TextField(verbose_name="Bio", blank=True)
class Meta:
db_table = 'profiles'
verbose_name = ('Profile')
verbose_name_plural = ('Profiles')
def __str__(self):
return '{}'.format(self.user)
@receiver(post_save, sender=User)
def update_user_profile(sender, instance, created, **kwargs):
if created:
Profile.objects.create(user=instance)
instance.profile.save()
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,353
|
eyeduh/webcrawler
|
refs/heads/master
|
/users/migrations/0002_auto_20210907_0537.py
|
# Generated by Django 3.2.6 on 2021-09-07 05:37
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('users', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='profile',
name='age',
field=models.IntegerField(verbose_name='Age'),
),
migrations.AlterField(
model_name='profile',
name='avatar',
field=models.ImageField(blank=True, upload_to='avatars', verbose_name='Avatar'),
),
migrations.AlterField(
model_name='profile',
name='bio',
field=models.TextField(blank=True, verbose_name='Bio'),
),
migrations.AlterField(
model_name='profile',
name='phone_number',
field=models.PositiveBigIntegerField(unique=True, verbose_name='Phone Number'),
),
migrations.AlterField(
model_name='profile',
name='user',
field=models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='User'),
),
]
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,354
|
eyeduh/webcrawler
|
refs/heads/master
|
/users/urls.py
|
from django.urls import path
from .views import user_list, user_detail, register_request, login_request
app_name = 'users'
urlpatterns = [
path('user-list/', user_list, name='user_list'),
path('user-detail/<int:user_id>/', user_detail, name='user_detail'),
path('register/', register_request, name='register'),
path('login/', login_request, name='login')
]
|
{"/users/views.py": ["/users/models.py", "/users/forms.py"], "/scraper/scraper/spiders/product_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/spiders/brand_crawler.py", "/scraper/scraper/items.py"], "/webcrawlers/views.py": ["/webcrawlers/models.py"], "/users/forms.py": ["/users/models.py"], "/scraper/scraper/spiders/category_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/scraper/scraper/spiders/brand_crawler.py": ["/webcrawlers/models.py", "/scraper/scraper/items.py"], "/webcrawlers/admin.py": ["/webcrawlers/models.py"], "/scraper/scraper/items.py": ["/webcrawlers/models.py"], "/users/urls.py": ["/users/views.py"]}
|
38,355
|
alexnwang/SketchEmbedNet-public
|
refs/heads/master
|
/run_hyper_embedding_experiment.py
|
import os
import traceback
import numpy as np
import cv2
import umap
import tensorflow as tf
import matplotlib.pyplot as plt
import sklearn
from PIL import Image
from absl import app, flags, logging
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from sklearn.cluster import DBSCAN
import models
import configs
import datasets
from models import ClassifierModel, Protonet, DrawerModel, VAE
from util import HParams, scale_and_rasterize, stroke_three_format, scale_and_center_stroke_three, rasterize
from util import log_flags, log_hparams, color_rasterize
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
FLAGS = flags.FLAGS
flags.DEFINE_string("dir", "/h/wangale/project/few-shot-sketch", "Project directory")
flags.DEFINE_string("data_dir", "/h/wangale/data", "Data directory")
flags.DEFINE_string("id", "hyper_state", "training_id")
flags.DEFINE_string("logfile", "comptest", "Logfile name")
flags.DEFINE_integer("random_seed", 1, "Random seed")
def hyper_embed(model: DrawerModel, dataset: tf.data.Dataset, clustering_method, png_dims=(48, 48), min_samples=3):
folder = os.path.join(FLAGS.dir, FLAGS.id, clustering_method.__class__.__name__ + str(clustering_method.eps))
os.makedirs(folder, exist_ok=True)
color_img_folder = os.path.join(folder, "colorimgs")
os.makedirs(color_img_folder, exist_ok=True)
padding = round(min(png_dims) / 10.) * 2
ds_iter = dataset.__iter__()
init = False
batch = next(ds_iter)[2]
embedding = model.embed(batch, training=False)[0]
if not init:
model.decode(embedding, training=False, generation_length=64)
init = True
params, strokes, hyper_states = model.decode(embedding, training=False, generation_length=64, with_hyper_states=True)
hyper_states = hyper_states[:, :, -512:]
stroke_threes = []
cut_hyper_states = []
for i in range(len(hyper_states)):
curr_strokes = strokes[i]
curr_states = hyper_states[i]
curr_strokes = stroke_three_format(curr_strokes)
curr_strokes = scale_and_center_stroke_three(curr_strokes, png_dims, padding)
curr_states = curr_states[:len(curr_strokes)]
stroke_threes.append(curr_strokes)
cut_hyper_states.append(curr_states)
lengths = np.cumsum(np.array([0] + [len(x) for x in stroke_threes]))
cut_hyper_states = np.vstack(cut_hyper_states)
all_cluster_assignments = clustering_method.fit_predict(cut_hyper_states)
all_images = []
for i in range(len(hyper_states)):
curr_strokes = stroke_threes[i]
cluster_assignments = all_cluster_assignments[lengths[i]:lengths[i] + len(curr_strokes)]
orig_strokes = np.copy(curr_strokes)
curr_strokes_abs = np.copy(curr_strokes)
curr_strokes_abs[:, :2] = np.cumsum(curr_strokes_abs[:, :2], axis=0)
clustered_strokes = {cluster_assignments[0]: [curr_strokes[0]]}
for j in range(1, len(cluster_assignments)):
assignment = cluster_assignments[j]
prev_assignment = cluster_assignments[j - 1]
if assignment == prev_assignment:
clustered_strokes[assignment].append(curr_strokes[j])
elif assignment != prev_assignment:
if assignment not in clustered_strokes:
clustered_strokes[assignment] = [curr_strokes_abs[j-1]]
# clustered_strokes[assignment][-1][-1] = 1
elif assignment in clustered_strokes:
last_stroke_in_cluster = np.cumsum(clustered_strokes[assignment], axis=0)[-1]
previous_stroke = curr_strokes_abs[j-1]
clustered_strokes[assignment].append(previous_stroke - last_stroke_in_cluster)
clustered_strokes[assignment][-1][-1] = curr_strokes_abs[j - 1][-1]
clustered_strokes[assignment].append(curr_strokes[j])
if j+1 >= len(cluster_assignments):
continue
else:
next_assignment = cluster_assignments[j+1]
if assignment != next_assignment:
clustered_strokes[assignment].append(np.array([0., 0., 1.]))
clustered_strokes[assignment][-1][-1] = 1
# images = [rasterize(orig_strokes, png_dims)]
# images.append(np.zeros((png_dims[0], 1, 3)))
images = []
color_img = color_rasterize([np.array(x) for x in clustered_strokes.values()], png_dims, stroke_width=1)
images.append(color_img)
for key in range(-1, np.max(all_cluster_assignments)+1):
if key in clustered_strokes:
stroke_cluster = clustered_strokes[key]
stroke_cluster = np.array(stroke_cluster)
images.append(np.zeros((png_dims[0], 1, 3)))
images.append(rasterize(stroke_cluster, png_dims))
else:
images.append(np.zeros((png_dims[0], 1, 3)))
images.append(np.ones(list(png_dims) + [3]) * 255.0)
final_image = np.concatenate(images, axis=1)
all_images.append(final_image)
all_images.append(np.zeros((1, final_image.shape[1], 3)))
Image.fromarray(final_image.astype(np.uint8)).save(os.path.join(folder, "cluster-{}.png".format(i)))
Image.fromarray(color_img.astype(np.uint8)).save(os.path.join(color_img_folder, "cluster-{}.png".format(i)))
all_images = np.concatenate(all_images, axis=0)
Image.fromarray(all_images.astype(np.uint8)).save(os.path.join(folder, "collage.png".format(i)))
def project_plot(clustering_methods, x, axs, title, model, y_image):
for j, method in enumerate(clustering_methods):
x_2d = method.fit_transform(x)
if len(clustering_methods) == 1:
ax = axs
else:
ax = axs[j]
ax.set_xticks([])
ax.set_yticks([])
ax.scatter(x_2d[:, 0], x_2d[:, 1], facecolors='none', edgecolors='none')
for k, img in enumerate(y_image):
ab = AnnotationBbox(OffsetImage(img), (x_2d[k, 0], x_2d[k, 1]), frameon=False)
ax.add_artist(ab)
if title:
ax.set_title("{}".format("SketchEmbedding" if model.__class__.__name__ == "DrawerEncTADAMModel" else model.__class__.__name__),
fontsize=50)
else:
ax.set_title(" ", fontsize=50)
def main(argv):
"""Create directories and configure python settings"""
# Setup Directory
experiment_dir = os.path.join(FLAGS.dir, FLAGS.id)
if not os.path.exists(experiment_dir):
os.makedirs(os.path.join(experiment_dir, "logs"), exist_ok=True)
# Setup Logging
FLAGS.alsologtostderr = True
logging.get_absl_handler().use_absl_log_file(FLAGS.logfile, os.path.join(experiment_dir, "logs"))
# Setup seeds
if FLAGS.random_seed:
np.random.seed(FLAGS.random_seed)
tf.random.set_seed(FLAGS.random_seed)
# Log Flags
log_flags(FLAGS)
drawer_id = "05-22_quickdraw_sweep_pixel_weight_28/drawer_enc_tadam_huge_interval0.05_step10000_maxweight0.5-quickdraw_ST1_msl64_28"
drawer_config: HParams = configs.get_config("drawer/huge")().parse("")
drawer: DrawerModel = models.get_model("drawer_enc_tadam")(FLAGS.dir, drawer_id, drawer_config, training=False)
dataset_config: HParams = configs.get_config("quickdraw")().parse("split=T2_msl64_28,shuffle=True,batch_size={}".format(16))
dataset_proto = datasets.get_dataset('quickdraw')(FLAGS.data_dir, dataset_config)
dataset = dataset_proto.load(repeat=False)[0]
# clustering_methods = [sklearn.manifold.TSNE(n_components=2), sklearn.decomposition.PCA(n_components=2), umap.UMAP()]
clustering_method = DBSCAN(eps=4.2)
try:
hyper_embed(drawer, dataset, clustering_method, min_samples=6)
except:
exception = traceback.format_exc()
logging.info(exception)
logging.info("Complete")
if __name__ == "__main__":
app.run(main)
|
{"/run_hyper_embedding_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/util/utils.py": ["/util/quickdraw_utils.py"], "/configs/sketchy_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/datasets/base/__init__.py": ["/datasets/base/dataset_base.py", "/datasets/base/dataset_episodic.py", "/datasets/base/datasets.py"], "/configs/quickdraw_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/configs/vae_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/configs/drawer_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/models/lr_fs.py": ["/models/__init__.py"], "/util/__init__.py": ["/util/logging.py", "/util/utils.py", "/util/quickdraw_utils.py", "/util/fs_omniglot_utils.py", "/util/sketchy_utils.py", "/util/drawer_utils.py", "/util/write_routines.py", "/util/augmentations.py"], "/configs/base/__init__.py": ["/configs/base/configs.py"], "/util/sketchy_utils.py": ["/util/__init__.py"], "/run_compositionality_exp.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/vae.py": ["/models/base/__init__.py", "/models/subs/conv_block.py", "/util/__init__.py", "/util/write_routines.py"], "/datasets/quickdraw.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/miniimagenet_configs.py": ["/configs/__init__.py", "/util/__init__.py"], "/models/subs/decoders.py": ["/models/subs/cells.py", "/util/__init__.py"], "/run_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/configs/__init__.py": ["/configs/drawer_configs.py", "/configs/vae_configs.py", "/configs/classifier_configs.py", "/configs/quickdraw_configs.py", "/configs/sketchy_configs.py", "/configs/miniimagenet_configs.py", "/configs/base/__init__.py"], "/datasets/__init__.py": ["/datasets/quickdraw.py", "/datasets/fs_omniglot_vinyals.py", "/datasets/sketchy.py", "/datasets/miniimagenet.py", "/datasets/base/__init__.py"], "/util/drawer_utils.py": ["/util/utils.py"], "/models/drawer_enc_block.py": ["/models/__init__.py", "/models/subs/encoders.py"], "/models/subs/encoders.py": ["/models/subs/conv_block.py"], "/prepare_data.py": ["/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/run_full_eval.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/__init__.py": ["/models/drawer.py", "/models/drawer_enc_block.py", "/models/classifier.py", "/models/vae.py", "/models/vae_enc_block.py", "/models/base/__init__.py", "/models/lr_fs.py"], "/datasets/miniimagenet.py": ["/datasets/__init__.py"], "/util/write_routines.py": ["/util/__init__.py"], "/models/classifier.py": ["/models/drawer.py", "/models/vae.py", "/models/base/__init__.py", "/util/__init__.py"], "/datasets/base/dataset_episodic.py": ["/datasets/base/dataset_base.py"], "/datasets/sketchy.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/datasets/fs_omniglot_vinyals.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/classifier_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/models/base/__init__.py": ["/models/base/model_base.py", "/models/base/models.py"], "/models/drawer.py": ["/models/base/__init__.py", "/models/subs/decoders.py", "/models/subs/encoders.py", "/util/__init__.py", "/util/write_routines.py"], "/models/vae_enc_block.py": ["/models/__init__.py", "/models/subs/conv_block.py"]}
|
38,356
|
alexnwang/SketchEmbedNet-public
|
refs/heads/master
|
/util/utils.py
|
import os
import numpy as np
import tensorflow as tf
from multiprocessing import Process, Queue
from PIL import Image
from .quickdraw_utils import stroke_three_format, scale_and_rasterize
def process_write_out(write_fn, fn_args, max_queue_size=5000):
"""
Begins a parallelized writer that runs write_fn. Need to disable cuda devices when beginning Process.
:param write_fn:
:param fn_args:
:param max_queue_size:
:return:
"""
os.environ['CUDA_VISIBLE_DEVICES'] = ''
write_queue = Queue(maxsize=max_queue_size)
process = Process(target=write_fn, args=fn_args + (write_queue,))
process.start()
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
return process, write_queue
def gaussFilter(fx, fy, sigma):
"""
Creates a filter with gaussian blurring
:param fx:
:param fy:
:param sigma:
:return:
"""
x = tf.range(-int(fx / 2), int(fx / 2) + 1, 1)
y = x
Y, X = tf.meshgrid(x, y)
sigma = -2 * (sigma ** 2)
z = tf.cast(tf.add(tf.square(X), tf.square(Y)), tf.float32)
k = 2 * tf.exp(tf.divide(z, sigma))
k = tf.divide(k, tf.reduce_sum(k))
return k
def gaussian_blur(image, filtersize, sigma):
"""
Applies gaussian blur to image based on provided parameters
:param image:
:param filtersize:
:param sigma:
:return:
"""
n_channels = image.shape[-1]
fx, fy = filtersize[0], filtersize[1]
filt = gaussFilter(fx, fy, sigma)
filt = tf.stack([filt] * n_channels, axis=2)
filt = tf.expand_dims(filt, 3)
padded_image = tf.pad(image, [[0, 0], [fx, fx], [fy, fy], [0, 0]], constant_values=0.0)
res = tf.nn.depthwise_conv2d(padded_image, filt, strides=[1, 1, 1, 1], padding="SAME")
return res[:, fx:-fx, fy:-fy, :]
def bilinear_interpolate_4_vectors(vectors, interps=10):
"""
Bilinear interplation of 4 vectors in a 2D square
:param vectors:
:param interps:
:return:
"""
#build bilinear interpolation weights
arr = np.zeros((interps, interps, 4))
interval = 1.0/(interps-1)
for i in range(interps):
for j in range(interps):
x_pt, y_pt = i * interval, j * interval
arr[j, i, :] = np.array([(1-x_pt) * (1-y_pt), x_pt * (1-y_pt), (1-x_pt) * y_pt, x_pt * y_pt])
return np.einsum('ija,ak->ijk', arr, vectors)
def interpolate(model, test_dataset, result_name, steps=1, generation_length=64, interps=20):
"""
Used to generate 2D interpolated embeddings.
:param model:
:param test_dataset:
:param result_name:
:param steps:
:param generation_length:
:param interps:
:return:
"""
sampling_dir = os.path.join(model._sampling_dir, result_name)
os.makedirs(sampling_dir)
test_dataset, _ = test_dataset
# Begin Writing Child-Process
for step, entry in enumerate(test_dataset):
if step == steps:
break
if len(entry) == 2:
x_image, class_names = entry
else:
y_sketch_gt, y_sketch_teacher, x_image, class_names = entry[0:4]
z, _, _ = model.embed(x_image, training=False)
for idx in range(0, z.shape[0], 4):
embeddings = z[idx: idx+4].numpy()
classes = class_names[idx: idx+4].numpy()
interpolated_embeddings = bilinear_interpolate_4_vectors(embeddings, interps=interps)
flattened_embeddings = np.reshape(interpolated_embeddings, (-1, z.shape[-1])).astype(np.float32)
_, flattened_strokes = model.decode(flattened_embeddings, training=False, generation_length=generation_length).numpy()
flattened_images = []
for strokes in flattened_strokes:
stroke_three = stroke_three_format(strokes)
flattened_images.append(scale_and_rasterize(stroke_three, (28, 28), 1).astype('uint8'))
flattened_images = np.array(flattened_images, dtype=np.uint8)
interpolated_images = np.reshape(flattened_images, list(interpolated_embeddings.shape[:2]) + list(flattened_images.shape[1:]))
image_rows = []
for row in interpolated_images:
concat_row = np.concatenate(row, axis=1)
image_rows.append(concat_row)
np_image = np.concatenate(image_rows, axis=0)
Image.fromarray(np_image).save(os.path.join(sampling_dir, "{}-{}_{}_{}_{}.png".format(idx//4, *classes)))
|
{"/run_hyper_embedding_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/util/utils.py": ["/util/quickdraw_utils.py"], "/configs/sketchy_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/datasets/base/__init__.py": ["/datasets/base/dataset_base.py", "/datasets/base/dataset_episodic.py", "/datasets/base/datasets.py"], "/configs/quickdraw_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/configs/vae_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/configs/drawer_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/models/lr_fs.py": ["/models/__init__.py"], "/util/__init__.py": ["/util/logging.py", "/util/utils.py", "/util/quickdraw_utils.py", "/util/fs_omniglot_utils.py", "/util/sketchy_utils.py", "/util/drawer_utils.py", "/util/write_routines.py", "/util/augmentations.py"], "/configs/base/__init__.py": ["/configs/base/configs.py"], "/util/sketchy_utils.py": ["/util/__init__.py"], "/run_compositionality_exp.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/vae.py": ["/models/base/__init__.py", "/models/subs/conv_block.py", "/util/__init__.py", "/util/write_routines.py"], "/datasets/quickdraw.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/miniimagenet_configs.py": ["/configs/__init__.py", "/util/__init__.py"], "/models/subs/decoders.py": ["/models/subs/cells.py", "/util/__init__.py"], "/run_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/configs/__init__.py": ["/configs/drawer_configs.py", "/configs/vae_configs.py", "/configs/classifier_configs.py", "/configs/quickdraw_configs.py", "/configs/sketchy_configs.py", "/configs/miniimagenet_configs.py", "/configs/base/__init__.py"], "/datasets/__init__.py": ["/datasets/quickdraw.py", "/datasets/fs_omniglot_vinyals.py", "/datasets/sketchy.py", "/datasets/miniimagenet.py", "/datasets/base/__init__.py"], "/util/drawer_utils.py": ["/util/utils.py"], "/models/drawer_enc_block.py": ["/models/__init__.py", "/models/subs/encoders.py"], "/models/subs/encoders.py": ["/models/subs/conv_block.py"], "/prepare_data.py": ["/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/run_full_eval.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/__init__.py": ["/models/drawer.py", "/models/drawer_enc_block.py", "/models/classifier.py", "/models/vae.py", "/models/vae_enc_block.py", "/models/base/__init__.py", "/models/lr_fs.py"], "/datasets/miniimagenet.py": ["/datasets/__init__.py"], "/util/write_routines.py": ["/util/__init__.py"], "/models/classifier.py": ["/models/drawer.py", "/models/vae.py", "/models/base/__init__.py", "/util/__init__.py"], "/datasets/base/dataset_episodic.py": ["/datasets/base/dataset_base.py"], "/datasets/sketchy.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/datasets/fs_omniglot_vinyals.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/classifier_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/models/base/__init__.py": ["/models/base/model_base.py", "/models/base/models.py"], "/models/drawer.py": ["/models/base/__init__.py", "/models/subs/decoders.py", "/models/subs/encoders.py", "/util/__init__.py", "/util/write_routines.py"], "/models/vae_enc_block.py": ["/models/__init__.py", "/models/subs/conv_block.py"]}
|
38,357
|
alexnwang/SketchEmbedNet-public
|
refs/heads/master
|
/configs/sketchy_configs.py
|
from configs.base import register_config
from util import HParams, teacher_noise_4, rotate_4
@register_config('sketchy')
def sketchy_default():
return HParams(
# ----- Dataset Parameters ----- #
batch_size=256,
split="",
# ----- Loading Parameters ----- #
cycle_length=None,
num_parallel_calls=None,
block_length=1,
buff_size=2,
shuffle=True,
)
@register_config("sketchy/noisy")
def sketchy_noisy(hparam: HParams):
try:
hparam.add_hparam("augmentations", [[teacher_noise_4]])
except:
hparam.set_hparam("augmentations", hparam.augmentations.append(teacher_noise_4))
return hparam
@register_config("sketchy/rotate")
def sketchy_rotate(hparam: HParams):
try:
hparam.add_hparam("augmentations", [[rotate_4]])
except:
hparam.set_hparam("augmentations", hparam.augmentations.append(rotate_4))
return hparam
@register_config('sketchy/batch128')
def sketchy_batch128(hparams: HParams):
hparams = sketchy_default()
hparams.set_hparam("batch_size", 128)
return hparams
@register_config('sketchy/batch64')
def sketchy_batch64(hparams: HParams):
hparams.set_hparam("batch_size", 64)
return hparams
@register_config("sketchy_batch64/rot")
def sketchy_batch64_rot():
hparams = sketchy_batch64()
hparams.add_hparam("augmentations", [[rotate_4]])
return hparams
@register_config("sketchy_batch64/noise_rot")
def sketchy_batch64_noiserot():
hparams = sketchy_batch64()
hparams.add_hparam("augmentations", [[rotate_4, teacher_noise_4]])
return hparams
@register_config("sketchy_batch64/noise")
def sketchy_batch64_noise():
hparams = sketchy_batch64()
hparams.add_hparam("augmentations", [[teacher_noise_4]])
return hparams
|
{"/run_hyper_embedding_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/util/utils.py": ["/util/quickdraw_utils.py"], "/configs/sketchy_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/datasets/base/__init__.py": ["/datasets/base/dataset_base.py", "/datasets/base/dataset_episodic.py", "/datasets/base/datasets.py"], "/configs/quickdraw_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/configs/vae_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/configs/drawer_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/models/lr_fs.py": ["/models/__init__.py"], "/util/__init__.py": ["/util/logging.py", "/util/utils.py", "/util/quickdraw_utils.py", "/util/fs_omniglot_utils.py", "/util/sketchy_utils.py", "/util/drawer_utils.py", "/util/write_routines.py", "/util/augmentations.py"], "/configs/base/__init__.py": ["/configs/base/configs.py"], "/util/sketchy_utils.py": ["/util/__init__.py"], "/run_compositionality_exp.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/vae.py": ["/models/base/__init__.py", "/models/subs/conv_block.py", "/util/__init__.py", "/util/write_routines.py"], "/datasets/quickdraw.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/miniimagenet_configs.py": ["/configs/__init__.py", "/util/__init__.py"], "/models/subs/decoders.py": ["/models/subs/cells.py", "/util/__init__.py"], "/run_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/configs/__init__.py": ["/configs/drawer_configs.py", "/configs/vae_configs.py", "/configs/classifier_configs.py", "/configs/quickdraw_configs.py", "/configs/sketchy_configs.py", "/configs/miniimagenet_configs.py", "/configs/base/__init__.py"], "/datasets/__init__.py": ["/datasets/quickdraw.py", "/datasets/fs_omniglot_vinyals.py", "/datasets/sketchy.py", "/datasets/miniimagenet.py", "/datasets/base/__init__.py"], "/util/drawer_utils.py": ["/util/utils.py"], "/models/drawer_enc_block.py": ["/models/__init__.py", "/models/subs/encoders.py"], "/models/subs/encoders.py": ["/models/subs/conv_block.py"], "/prepare_data.py": ["/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/run_full_eval.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/__init__.py": ["/models/drawer.py", "/models/drawer_enc_block.py", "/models/classifier.py", "/models/vae.py", "/models/vae_enc_block.py", "/models/base/__init__.py", "/models/lr_fs.py"], "/datasets/miniimagenet.py": ["/datasets/__init__.py"], "/util/write_routines.py": ["/util/__init__.py"], "/models/classifier.py": ["/models/drawer.py", "/models/vae.py", "/models/base/__init__.py", "/util/__init__.py"], "/datasets/base/dataset_episodic.py": ["/datasets/base/dataset_base.py"], "/datasets/sketchy.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/datasets/fs_omniglot_vinyals.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/classifier_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/models/base/__init__.py": ["/models/base/model_base.py", "/models/base/models.py"], "/models/drawer.py": ["/models/base/__init__.py", "/models/subs/decoders.py", "/models/subs/encoders.py", "/util/__init__.py", "/util/write_routines.py"], "/models/vae_enc_block.py": ["/models/__init__.py", "/models/subs/conv_block.py"]}
|
38,358
|
alexnwang/SketchEmbedNet-public
|
refs/heads/master
|
/datasets/base/__init__.py
|
from .dataset_base import *
from .dataset_episodic import *
from .datasets import *
|
{"/run_hyper_embedding_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/util/utils.py": ["/util/quickdraw_utils.py"], "/configs/sketchy_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/datasets/base/__init__.py": ["/datasets/base/dataset_base.py", "/datasets/base/dataset_episodic.py", "/datasets/base/datasets.py"], "/configs/quickdraw_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/configs/vae_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/configs/drawer_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/models/lr_fs.py": ["/models/__init__.py"], "/util/__init__.py": ["/util/logging.py", "/util/utils.py", "/util/quickdraw_utils.py", "/util/fs_omniglot_utils.py", "/util/sketchy_utils.py", "/util/drawer_utils.py", "/util/write_routines.py", "/util/augmentations.py"], "/configs/base/__init__.py": ["/configs/base/configs.py"], "/util/sketchy_utils.py": ["/util/__init__.py"], "/run_compositionality_exp.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/vae.py": ["/models/base/__init__.py", "/models/subs/conv_block.py", "/util/__init__.py", "/util/write_routines.py"], "/datasets/quickdraw.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/miniimagenet_configs.py": ["/configs/__init__.py", "/util/__init__.py"], "/models/subs/decoders.py": ["/models/subs/cells.py", "/util/__init__.py"], "/run_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/configs/__init__.py": ["/configs/drawer_configs.py", "/configs/vae_configs.py", "/configs/classifier_configs.py", "/configs/quickdraw_configs.py", "/configs/sketchy_configs.py", "/configs/miniimagenet_configs.py", "/configs/base/__init__.py"], "/datasets/__init__.py": ["/datasets/quickdraw.py", "/datasets/fs_omniglot_vinyals.py", "/datasets/sketchy.py", "/datasets/miniimagenet.py", "/datasets/base/__init__.py"], "/util/drawer_utils.py": ["/util/utils.py"], "/models/drawer_enc_block.py": ["/models/__init__.py", "/models/subs/encoders.py"], "/models/subs/encoders.py": ["/models/subs/conv_block.py"], "/prepare_data.py": ["/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/run_full_eval.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/__init__.py": ["/models/drawer.py", "/models/drawer_enc_block.py", "/models/classifier.py", "/models/vae.py", "/models/vae_enc_block.py", "/models/base/__init__.py", "/models/lr_fs.py"], "/datasets/miniimagenet.py": ["/datasets/__init__.py"], "/util/write_routines.py": ["/util/__init__.py"], "/models/classifier.py": ["/models/drawer.py", "/models/vae.py", "/models/base/__init__.py", "/util/__init__.py"], "/datasets/base/dataset_episodic.py": ["/datasets/base/dataset_base.py"], "/datasets/sketchy.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/datasets/fs_omniglot_vinyals.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/classifier_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/models/base/__init__.py": ["/models/base/model_base.py", "/models/base/models.py"], "/models/drawer.py": ["/models/base/__init__.py", "/models/subs/decoders.py", "/models/subs/encoders.py", "/util/__init__.py", "/util/write_routines.py"], "/models/vae_enc_block.py": ["/models/__init__.py", "/models/subs/conv_block.py"]}
|
38,359
|
alexnwang/SketchEmbedNet-public
|
refs/heads/master
|
/configs/quickdraw_configs.py
|
from configs.base import register_config
from util import HParams, teacher_noise_4, rotate_4
@register_config('quickdraw')
def quickdraw_default():
return HParams(
# ----- Dataset Parameters ----- #
batch_size=256,
split="",
# ----- Loading Parameters ----- #
cycle_length=None,
num_parallel_calls=None,
block_length=1,
buff_size=2,
shuffle=True,
)
@register_config('quickdraw/batch128')
def quickdraw_batch128(hparams: HParams):
hparams.set_hparam("batch_size", 128)
return hparams
@register_config("quickdraw/noisy")
def quickdraw_noisy(hparam: HParams):
try:
hparam.add_hparam("augmentations", [[teacher_noise_4]])
except:
hparam.set_hparam("augmentations", hparam.augmentations.append(teacher_noise_4))
return hparam
@register_config("quickdraw/rotate")
def quickdraw_rotate(hparam: HParams):
try:
hparam.add_hparam("augmentations", [[rotate_4]])
except:
hparam.set_hparam("augmentations", hparam.augmentations.append(rotate_4))
return hparam
return hparams
|
{"/run_hyper_embedding_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/util/utils.py": ["/util/quickdraw_utils.py"], "/configs/sketchy_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/datasets/base/__init__.py": ["/datasets/base/dataset_base.py", "/datasets/base/dataset_episodic.py", "/datasets/base/datasets.py"], "/configs/quickdraw_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/configs/vae_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/configs/drawer_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/models/lr_fs.py": ["/models/__init__.py"], "/util/__init__.py": ["/util/logging.py", "/util/utils.py", "/util/quickdraw_utils.py", "/util/fs_omniglot_utils.py", "/util/sketchy_utils.py", "/util/drawer_utils.py", "/util/write_routines.py", "/util/augmentations.py"], "/configs/base/__init__.py": ["/configs/base/configs.py"], "/util/sketchy_utils.py": ["/util/__init__.py"], "/run_compositionality_exp.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/vae.py": ["/models/base/__init__.py", "/models/subs/conv_block.py", "/util/__init__.py", "/util/write_routines.py"], "/datasets/quickdraw.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/miniimagenet_configs.py": ["/configs/__init__.py", "/util/__init__.py"], "/models/subs/decoders.py": ["/models/subs/cells.py", "/util/__init__.py"], "/run_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/configs/__init__.py": ["/configs/drawer_configs.py", "/configs/vae_configs.py", "/configs/classifier_configs.py", "/configs/quickdraw_configs.py", "/configs/sketchy_configs.py", "/configs/miniimagenet_configs.py", "/configs/base/__init__.py"], "/datasets/__init__.py": ["/datasets/quickdraw.py", "/datasets/fs_omniglot_vinyals.py", "/datasets/sketchy.py", "/datasets/miniimagenet.py", "/datasets/base/__init__.py"], "/util/drawer_utils.py": ["/util/utils.py"], "/models/drawer_enc_block.py": ["/models/__init__.py", "/models/subs/encoders.py"], "/models/subs/encoders.py": ["/models/subs/conv_block.py"], "/prepare_data.py": ["/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/run_full_eval.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/__init__.py": ["/models/drawer.py", "/models/drawer_enc_block.py", "/models/classifier.py", "/models/vae.py", "/models/vae_enc_block.py", "/models/base/__init__.py", "/models/lr_fs.py"], "/datasets/miniimagenet.py": ["/datasets/__init__.py"], "/util/write_routines.py": ["/util/__init__.py"], "/models/classifier.py": ["/models/drawer.py", "/models/vae.py", "/models/base/__init__.py", "/util/__init__.py"], "/datasets/base/dataset_episodic.py": ["/datasets/base/dataset_base.py"], "/datasets/sketchy.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/datasets/fs_omniglot_vinyals.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/classifier_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/models/base/__init__.py": ["/models/base/model_base.py", "/models/base/models.py"], "/models/drawer.py": ["/models/base/__init__.py", "/models/subs/decoders.py", "/models/subs/encoders.py", "/util/__init__.py", "/util/write_routines.py"], "/models/vae_enc_block.py": ["/models/__init__.py", "/models/subs/conv_block.py"]}
|
38,360
|
alexnwang/SketchEmbedNet-public
|
refs/heads/master
|
/util/augmentations.py
|
import numpy as np
def rotate_4(strokes_gt, strokes_teacher, image, class_name, *args):
"""
Rotates input strokes creating 3 new classes 90, 180 and 270 degree rotations
:param strokes_gt: ground truth for loss
:param strokes_teacher: teacher forcing input sequence
:param image: input image
:param class_name: class
:return: origional + 3 augmented images
"""
try:
class_name = class_name.decode('utf-8')
except (UnicodeDecodeError, AttributeError):
pass
rot_90 = np.array([[0., -1., 0., 0., 0.],
[1., 0., 0., 0., 0.],
[0., 0., 1., 0., 0.],
[0., 0., 0., 1., 0.],
[0., 0., 0., 0., 1.]])
flip_y = np.array([[-1., 0., 0., 0., 0.],
[0., -1., 0., 0., 0.],
[0., 0., 1., 0., 0.],
[0., 0., 0., 1., 0.],
[0., 0., 0., 0., 1.]])
return [(strokes_gt, strokes_teacher, image, class_name, *args),
(strokes_gt @ rot_90, strokes_teacher @ rot_90, np.rot90(image, 1), class_name + "-rot90", *args),
(strokes_gt @ flip_y, strokes_teacher @ flip_y, np.rot90(image, 2), class_name + "-rot180", *args),
(strokes_gt @ flip_y @ rot_90, strokes_teacher @ flip_y @ rot_90, np.rot90(image, 3), class_name + "-rot270", *args)]
def teacher_noise_4(strokes_gt, strokes_teacher, image, class_name, *args):
"""
Augments the input (teacher) sequence with 2D Gaussian noise while keeping the ground truth used for the loss computation the same.
:param strokes_gt:
:param strokes_teacher:
:param image:
:param class_name:
:param args:
:return:
"""
try:
class_name = class_name.decode('utf-8')
except (UnicodeDecodeError, AttributeError):
pass
augmentations = 3
noise = np.random.normal(loc=0, scale=0.045, size=(augmentations, strokes_teacher.shape[0]-1, 2))
augmented_teachers = np.repeat(strokes_teacher[np.newaxis, :], repeats=augmentations+1, axis=0)
augmented_teachers[1:, 1:, :2] += noise
return [(strokes_gt, augmented_teachers[0], image, class_name, *args),
(strokes_gt, augmented_teachers[1], image, class_name + "-noise-1", *args),
(strokes_gt, augmented_teachers[2], image, class_name + "-noise-2", *args),
(strokes_gt, augmented_teachers[3], image, class_name + "-noise-3", *args)]
|
{"/run_hyper_embedding_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/util/utils.py": ["/util/quickdraw_utils.py"], "/configs/sketchy_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/datasets/base/__init__.py": ["/datasets/base/dataset_base.py", "/datasets/base/dataset_episodic.py", "/datasets/base/datasets.py"], "/configs/quickdraw_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/configs/vae_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/configs/drawer_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/models/lr_fs.py": ["/models/__init__.py"], "/util/__init__.py": ["/util/logging.py", "/util/utils.py", "/util/quickdraw_utils.py", "/util/fs_omniglot_utils.py", "/util/sketchy_utils.py", "/util/drawer_utils.py", "/util/write_routines.py", "/util/augmentations.py"], "/configs/base/__init__.py": ["/configs/base/configs.py"], "/util/sketchy_utils.py": ["/util/__init__.py"], "/run_compositionality_exp.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/vae.py": ["/models/base/__init__.py", "/models/subs/conv_block.py", "/util/__init__.py", "/util/write_routines.py"], "/datasets/quickdraw.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/miniimagenet_configs.py": ["/configs/__init__.py", "/util/__init__.py"], "/models/subs/decoders.py": ["/models/subs/cells.py", "/util/__init__.py"], "/run_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/configs/__init__.py": ["/configs/drawer_configs.py", "/configs/vae_configs.py", "/configs/classifier_configs.py", "/configs/quickdraw_configs.py", "/configs/sketchy_configs.py", "/configs/miniimagenet_configs.py", "/configs/base/__init__.py"], "/datasets/__init__.py": ["/datasets/quickdraw.py", "/datasets/fs_omniglot_vinyals.py", "/datasets/sketchy.py", "/datasets/miniimagenet.py", "/datasets/base/__init__.py"], "/util/drawer_utils.py": ["/util/utils.py"], "/models/drawer_enc_block.py": ["/models/__init__.py", "/models/subs/encoders.py"], "/models/subs/encoders.py": ["/models/subs/conv_block.py"], "/prepare_data.py": ["/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/run_full_eval.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/__init__.py": ["/models/drawer.py", "/models/drawer_enc_block.py", "/models/classifier.py", "/models/vae.py", "/models/vae_enc_block.py", "/models/base/__init__.py", "/models/lr_fs.py"], "/datasets/miniimagenet.py": ["/datasets/__init__.py"], "/util/write_routines.py": ["/util/__init__.py"], "/models/classifier.py": ["/models/drawer.py", "/models/vae.py", "/models/base/__init__.py", "/util/__init__.py"], "/datasets/base/dataset_episodic.py": ["/datasets/base/dataset_base.py"], "/datasets/sketchy.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/datasets/fs_omniglot_vinyals.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/classifier_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/models/base/__init__.py": ["/models/base/model_base.py", "/models/base/models.py"], "/models/drawer.py": ["/models/base/__init__.py", "/models/subs/decoders.py", "/models/subs/encoders.py", "/util/__init__.py", "/util/write_routines.py"], "/models/vae_enc_block.py": ["/models/__init__.py", "/models/subs/conv_block.py"]}
|
38,361
|
alexnwang/SketchEmbedNet-public
|
refs/heads/master
|
/configs/vae_configs.py
|
from util import HParams
from configs.base import register_config
@register_config("vae")
def vae_default():
return HParams(
# ----- Model Parameters ----- #
latent_size=256,
png_dim=32,
grayscale=False,
kl_weight=1.0,
kl_tolerance=0.0,
filters=[64, 128, 256, 512],
# ----- Model Specific Training Parameters ----- #
lr=0.001,
lr_decay_step=15000,
lr_decay_rate=0.85,
)
@register_config("vae/natural")
def vae_natural(hparams: HParams):
hparams.set_hparam("png_dim", 96)
return hparams
@register_config("vae/ae")
def vae_ae(hparams: HParams):
hparams.set_hparam("png_dim", 96)
return hparams
@register_config("vae/grayscale")
def vae_grayscale(hparams: HParams):
hparams.set_hparam("grayscale", True)
return hparams
|
{"/run_hyper_embedding_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/util/utils.py": ["/util/quickdraw_utils.py"], "/configs/sketchy_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/datasets/base/__init__.py": ["/datasets/base/dataset_base.py", "/datasets/base/dataset_episodic.py", "/datasets/base/datasets.py"], "/configs/quickdraw_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/configs/vae_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/configs/drawer_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/models/lr_fs.py": ["/models/__init__.py"], "/util/__init__.py": ["/util/logging.py", "/util/utils.py", "/util/quickdraw_utils.py", "/util/fs_omniglot_utils.py", "/util/sketchy_utils.py", "/util/drawer_utils.py", "/util/write_routines.py", "/util/augmentations.py"], "/configs/base/__init__.py": ["/configs/base/configs.py"], "/util/sketchy_utils.py": ["/util/__init__.py"], "/run_compositionality_exp.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/vae.py": ["/models/base/__init__.py", "/models/subs/conv_block.py", "/util/__init__.py", "/util/write_routines.py"], "/datasets/quickdraw.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/miniimagenet_configs.py": ["/configs/__init__.py", "/util/__init__.py"], "/models/subs/decoders.py": ["/models/subs/cells.py", "/util/__init__.py"], "/run_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/configs/__init__.py": ["/configs/drawer_configs.py", "/configs/vae_configs.py", "/configs/classifier_configs.py", "/configs/quickdraw_configs.py", "/configs/sketchy_configs.py", "/configs/miniimagenet_configs.py", "/configs/base/__init__.py"], "/datasets/__init__.py": ["/datasets/quickdraw.py", "/datasets/fs_omniglot_vinyals.py", "/datasets/sketchy.py", "/datasets/miniimagenet.py", "/datasets/base/__init__.py"], "/util/drawer_utils.py": ["/util/utils.py"], "/models/drawer_enc_block.py": ["/models/__init__.py", "/models/subs/encoders.py"], "/models/subs/encoders.py": ["/models/subs/conv_block.py"], "/prepare_data.py": ["/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/run_full_eval.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/__init__.py": ["/models/drawer.py", "/models/drawer_enc_block.py", "/models/classifier.py", "/models/vae.py", "/models/vae_enc_block.py", "/models/base/__init__.py", "/models/lr_fs.py"], "/datasets/miniimagenet.py": ["/datasets/__init__.py"], "/util/write_routines.py": ["/util/__init__.py"], "/models/classifier.py": ["/models/drawer.py", "/models/vae.py", "/models/base/__init__.py", "/util/__init__.py"], "/datasets/base/dataset_episodic.py": ["/datasets/base/dataset_base.py"], "/datasets/sketchy.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/datasets/fs_omniglot_vinyals.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/classifier_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/models/base/__init__.py": ["/models/base/model_base.py", "/models/base/models.py"], "/models/drawer.py": ["/models/base/__init__.py", "/models/subs/decoders.py", "/models/subs/encoders.py", "/util/__init__.py", "/util/write_routines.py"], "/models/vae_enc_block.py": ["/models/__init__.py", "/models/subs/conv_block.py"]}
|
38,362
|
alexnwang/SketchEmbedNet-public
|
refs/heads/master
|
/configs/drawer_configs.py
|
from util import HParams, ST1_classes
from configs.base import register_config
@register_config("drawer")
def drawer_default():
return HParams(
# ----- Model Parameters ----- #
rnn_cell="hyper",
rnn_output_size=1024,
z_size=256,
num_mixture=30,
kl_tolerance=0.2,
kl_weight=0.0,
pixel_loss_weight_max=1.0,
pixel_loss_weight_min=0.0,
pixel_loss_weight_interval=0.0,
pixel_loss_step=0,
sigma_decay_start=200000.0,
sigma_decay_rate=1.0,
sigma_decay_freq=10000,
sigma_init=2.0,
cell_configs={"hyper_num_units": 512, "hyper_embedding_size": 64, "use_recurrent_dropout": False, "recurrent_dropout_prob": 0.9},
# ----- Model Specific Training Parameters ----- #
lr=0.001,
lr_decay_freq=15000,
lr_decay_rate=0.85,
gradient_cap=1.0,
# ----- Other Configs ----- #
distributed=False,
)
|
{"/run_hyper_embedding_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/util/utils.py": ["/util/quickdraw_utils.py"], "/configs/sketchy_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/datasets/base/__init__.py": ["/datasets/base/dataset_base.py", "/datasets/base/dataset_episodic.py", "/datasets/base/datasets.py"], "/configs/quickdraw_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/configs/vae_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/configs/drawer_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/models/lr_fs.py": ["/models/__init__.py"], "/util/__init__.py": ["/util/logging.py", "/util/utils.py", "/util/quickdraw_utils.py", "/util/fs_omniglot_utils.py", "/util/sketchy_utils.py", "/util/drawer_utils.py", "/util/write_routines.py", "/util/augmentations.py"], "/configs/base/__init__.py": ["/configs/base/configs.py"], "/util/sketchy_utils.py": ["/util/__init__.py"], "/run_compositionality_exp.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/vae.py": ["/models/base/__init__.py", "/models/subs/conv_block.py", "/util/__init__.py", "/util/write_routines.py"], "/datasets/quickdraw.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/miniimagenet_configs.py": ["/configs/__init__.py", "/util/__init__.py"], "/models/subs/decoders.py": ["/models/subs/cells.py", "/util/__init__.py"], "/run_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/configs/__init__.py": ["/configs/drawer_configs.py", "/configs/vae_configs.py", "/configs/classifier_configs.py", "/configs/quickdraw_configs.py", "/configs/sketchy_configs.py", "/configs/miniimagenet_configs.py", "/configs/base/__init__.py"], "/datasets/__init__.py": ["/datasets/quickdraw.py", "/datasets/fs_omniglot_vinyals.py", "/datasets/sketchy.py", "/datasets/miniimagenet.py", "/datasets/base/__init__.py"], "/util/drawer_utils.py": ["/util/utils.py"], "/models/drawer_enc_block.py": ["/models/__init__.py", "/models/subs/encoders.py"], "/models/subs/encoders.py": ["/models/subs/conv_block.py"], "/prepare_data.py": ["/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/run_full_eval.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/__init__.py": ["/models/drawer.py", "/models/drawer_enc_block.py", "/models/classifier.py", "/models/vae.py", "/models/vae_enc_block.py", "/models/base/__init__.py", "/models/lr_fs.py"], "/datasets/miniimagenet.py": ["/datasets/__init__.py"], "/util/write_routines.py": ["/util/__init__.py"], "/models/classifier.py": ["/models/drawer.py", "/models/vae.py", "/models/base/__init__.py", "/util/__init__.py"], "/datasets/base/dataset_episodic.py": ["/datasets/base/dataset_base.py"], "/datasets/sketchy.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/datasets/fs_omniglot_vinyals.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/classifier_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/models/base/__init__.py": ["/models/base/model_base.py", "/models/base/models.py"], "/models/drawer.py": ["/models/base/__init__.py", "/models/subs/decoders.py", "/models/subs/encoders.py", "/util/__init__.py", "/util/write_routines.py"], "/models/vae_enc_block.py": ["/models/__init__.py", "/models/subs/conv_block.py"]}
|
38,363
|
alexnwang/SketchEmbedNet-public
|
refs/heads/master
|
/datasets/base/dataset_base.py
|
import traceback
import psutil
import tensorflow as tf
import numpy as np
from absl import logging
class DatasetBase(object):
def __init__(self, data_dir, params):
self._data_dir = data_dir
self._split = params.split
if "augmentations" in params:
self._augmentations = params.augmentations
else:
self._augmentations = []
# ----- Dataset Creation Params ----- #
self._batch_size = params.batch_size
self._cycle_length = (params.cycle_length if params.cycle_length else
tf.data.experimental.AUTOTUNE if params.cycle_length == -1 else psutil.cpu_count(logical=False)*2)
self._num_parallel_calls = (params.num_parallel_calls if params.num_parallel_calls else
tf.data.experimental.AUTOTUNE if params.num_parallel_calls == -1 else psutil.cpu_count(logical=False)*2)
self._block_length = params.block_length
self._buff_size = params.buff_size
self._shuffle = params.shuffle
def load(self, repeat=True):
raise NotImplementedError
def prepare(self, *args, **kwargs):
raise NotImplementedError
def _filter_collections(self, files):
"""Determines which stored value are returned and in what order"""
return files
def _apply_augmentations_generator(self, *args, **kwargs):
# Store origional example once
yield args
for augmentation_set in self._augmentations:
examples = [args]
for augmentation in augmentation_set:
augmented = []
for example in examples:
augmented += augmentation(*example)
examples = augmented
# Do not add origional example for every augmentation step
for i in range(len(augmented)):
yield augmented[i]
def _create_dataset_from_filepaths(self, files, repeat):
try:
npz = np.load(files[0], allow_pickle=True, encoding='latin1')
npz_collections = self._filter_collections(npz.files)
shapes = tuple(npz[key][0].shape for key in npz_collections)
types = tuple(tf.as_dtype(npz[key].dtype) for key in npz_collections)
except Exception as e:
logging.error("%s file load unsuccessful from %s \n %s", type(self).__name__, files, str(e))
logging.info(traceback.format_exc())
raise e
shard_dataset = tf.data.Dataset.from_tensor_slices(files)
dataset = shard_dataset.interleave(lambda x: tf.data.Dataset.from_generator(self._make_generator,
args=(x, npz_collections),
output_types=types,
output_shapes=shapes),
cycle_length=self._cycle_length,
block_length=self._block_length,
num_parallel_calls=self._num_parallel_calls)
if self._augmentations:
dataset = dataset.interleave(lambda *args: tf.data.Dataset.from_generator(self._apply_augmentations_generator,
args=args,
output_types=types,
output_shapes=shapes),
cycle_length=self._cycle_length,
num_parallel_calls=self._num_parallel_calls)
dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
if self._shuffle:
dataset = dataset.shuffle(self._buff_size * self._batch_size)
dataset = dataset.batch(self._batch_size, drop_remainder=False)
if repeat:
dataset = dataset.repeat()
return dataset, shard_dataset
def _make_generator(self, filename, npz_collections):
try:
npz = np.load(filename, allow_pickle=True, encoding='latin1')
except FileNotFoundError as error:
logging.fatal("Shard not found when producing generator fn: %s", filename)
raise error
collections = [npz[key.decode('utf-8')] for key in npz_collections]
for idx in range(collections[0].shape[0]):
yield tuple(collection[idx] for collection in collections)
|
{"/run_hyper_embedding_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/util/utils.py": ["/util/quickdraw_utils.py"], "/configs/sketchy_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/datasets/base/__init__.py": ["/datasets/base/dataset_base.py", "/datasets/base/dataset_episodic.py", "/datasets/base/datasets.py"], "/configs/quickdraw_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/configs/vae_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/configs/drawer_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/models/lr_fs.py": ["/models/__init__.py"], "/util/__init__.py": ["/util/logging.py", "/util/utils.py", "/util/quickdraw_utils.py", "/util/fs_omniglot_utils.py", "/util/sketchy_utils.py", "/util/drawer_utils.py", "/util/write_routines.py", "/util/augmentations.py"], "/configs/base/__init__.py": ["/configs/base/configs.py"], "/util/sketchy_utils.py": ["/util/__init__.py"], "/run_compositionality_exp.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/vae.py": ["/models/base/__init__.py", "/models/subs/conv_block.py", "/util/__init__.py", "/util/write_routines.py"], "/datasets/quickdraw.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/miniimagenet_configs.py": ["/configs/__init__.py", "/util/__init__.py"], "/models/subs/decoders.py": ["/models/subs/cells.py", "/util/__init__.py"], "/run_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/configs/__init__.py": ["/configs/drawer_configs.py", "/configs/vae_configs.py", "/configs/classifier_configs.py", "/configs/quickdraw_configs.py", "/configs/sketchy_configs.py", "/configs/miniimagenet_configs.py", "/configs/base/__init__.py"], "/datasets/__init__.py": ["/datasets/quickdraw.py", "/datasets/fs_omniglot_vinyals.py", "/datasets/sketchy.py", "/datasets/miniimagenet.py", "/datasets/base/__init__.py"], "/util/drawer_utils.py": ["/util/utils.py"], "/models/drawer_enc_block.py": ["/models/__init__.py", "/models/subs/encoders.py"], "/models/subs/encoders.py": ["/models/subs/conv_block.py"], "/prepare_data.py": ["/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/run_full_eval.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/__init__.py": ["/models/drawer.py", "/models/drawer_enc_block.py", "/models/classifier.py", "/models/vae.py", "/models/vae_enc_block.py", "/models/base/__init__.py", "/models/lr_fs.py"], "/datasets/miniimagenet.py": ["/datasets/__init__.py"], "/util/write_routines.py": ["/util/__init__.py"], "/models/classifier.py": ["/models/drawer.py", "/models/vae.py", "/models/base/__init__.py", "/util/__init__.py"], "/datasets/base/dataset_episodic.py": ["/datasets/base/dataset_base.py"], "/datasets/sketchy.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/datasets/fs_omniglot_vinyals.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/classifier_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/models/base/__init__.py": ["/models/base/model_base.py", "/models/base/models.py"], "/models/drawer.py": ["/models/base/__init__.py", "/models/subs/decoders.py", "/models/subs/encoders.py", "/util/__init__.py", "/util/write_routines.py"], "/models/vae_enc_block.py": ["/models/__init__.py", "/models/subs/conv_block.py"]}
|
38,364
|
alexnwang/SketchEmbedNet-public
|
refs/heads/master
|
/models/lr_fs.py
|
import tensorflow as tf
from absl import logging
from sklearn.linear_model import LogisticRegression
from models import register_model, BaseModel, np
@register_model("lr_fs")
class LogisticRegressionFewShotModel(BaseModel):
def __init__(self, base_dir, model_id, params):
"""
SKLearn logistic regression head used for meta-test time during few-shot classification.
:param base_dir:
:param model_id:
:param params:
"""
super(LogisticRegressionFewShotModel, self).__init__(base_dir, model_id)
def episode(self, model, dataset, episodes):
way, shot = dataset.way, dataset.shot
running_acc = tf.keras.metrics.Mean()
acc_list = []
episode = 0
for example in dataset.load(repeat=True):
(s_x_image, s_class_ids), (q_x_image, q_class_ids) = example[:len(example) // 2][-2:], example[len(example) // 2:][-2:]
if episodes and episode == episodes:
break
episode += 1
# Splits reduce batch size and memory requirement when running our embedding model.
SPLITS = 2
mu = tf.concat([model.embed(x)[1] for x in tf.split(tf.concat((s_x_image, q_x_image), axis=0), SPLITS, axis=0)], axis=0)
embeddings = mu
embeddings, _ = tf.linalg.normalize(embeddings, axis=1)
support_embeddings = embeddings[:way * shot].numpy()
query_embeddings = embeddings[way * shot:].numpy()
_, indices = np.unique(np.concatenate((s_class_ids.numpy(), q_class_ids.numpy())), return_inverse=True)
support_labels, query_labels = indices[:len(support_embeddings)], indices[len(support_embeddings):]
lr_model = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial')
lr_model.fit(support_embeddings, support_labels)
acc = lr_model.score(query_embeddings, query_labels)
running_acc(acc)
acc_list.append(acc)
if episode % 500 == 0:
logging.info("Episode: %d | Accuracy: %.4f | Running Acc: %.4f", episode, acc, running_acc.result())
logging.info("Final Result | Mean Accuracy: %.4f | Std: %.4f | Var: %.4f | p95: %.4f",
running_acc.result(), np.std(acc_list), np.var(acc_list), 1.96 * np.std(acc_list) / np.sqrt(len(acc_list)))
# logging.info("Few-shot evaluation with Logistic Regression, Complete.")
|
{"/run_hyper_embedding_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/util/utils.py": ["/util/quickdraw_utils.py"], "/configs/sketchy_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/datasets/base/__init__.py": ["/datasets/base/dataset_base.py", "/datasets/base/dataset_episodic.py", "/datasets/base/datasets.py"], "/configs/quickdraw_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/configs/vae_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/configs/drawer_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/models/lr_fs.py": ["/models/__init__.py"], "/util/__init__.py": ["/util/logging.py", "/util/utils.py", "/util/quickdraw_utils.py", "/util/fs_omniglot_utils.py", "/util/sketchy_utils.py", "/util/drawer_utils.py", "/util/write_routines.py", "/util/augmentations.py"], "/configs/base/__init__.py": ["/configs/base/configs.py"], "/util/sketchy_utils.py": ["/util/__init__.py"], "/run_compositionality_exp.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/vae.py": ["/models/base/__init__.py", "/models/subs/conv_block.py", "/util/__init__.py", "/util/write_routines.py"], "/datasets/quickdraw.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/miniimagenet_configs.py": ["/configs/__init__.py", "/util/__init__.py"], "/models/subs/decoders.py": ["/models/subs/cells.py", "/util/__init__.py"], "/run_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/configs/__init__.py": ["/configs/drawer_configs.py", "/configs/vae_configs.py", "/configs/classifier_configs.py", "/configs/quickdraw_configs.py", "/configs/sketchy_configs.py", "/configs/miniimagenet_configs.py", "/configs/base/__init__.py"], "/datasets/__init__.py": ["/datasets/quickdraw.py", "/datasets/fs_omniglot_vinyals.py", "/datasets/sketchy.py", "/datasets/miniimagenet.py", "/datasets/base/__init__.py"], "/util/drawer_utils.py": ["/util/utils.py"], "/models/drawer_enc_block.py": ["/models/__init__.py", "/models/subs/encoders.py"], "/models/subs/encoders.py": ["/models/subs/conv_block.py"], "/prepare_data.py": ["/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/run_full_eval.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/__init__.py": ["/models/drawer.py", "/models/drawer_enc_block.py", "/models/classifier.py", "/models/vae.py", "/models/vae_enc_block.py", "/models/base/__init__.py", "/models/lr_fs.py"], "/datasets/miniimagenet.py": ["/datasets/__init__.py"], "/util/write_routines.py": ["/util/__init__.py"], "/models/classifier.py": ["/models/drawer.py", "/models/vae.py", "/models/base/__init__.py", "/util/__init__.py"], "/datasets/base/dataset_episodic.py": ["/datasets/base/dataset_base.py"], "/datasets/sketchy.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/datasets/fs_omniglot_vinyals.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/classifier_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/models/base/__init__.py": ["/models/base/model_base.py", "/models/base/models.py"], "/models/drawer.py": ["/models/base/__init__.py", "/models/subs/decoders.py", "/models/subs/encoders.py", "/util/__init__.py", "/util/write_routines.py"], "/models/vae_enc_block.py": ["/models/__init__.py", "/models/subs/conv_block.py"]}
|
38,365
|
alexnwang/SketchEmbedNet-public
|
refs/heads/master
|
/util/__init__.py
|
from .logging import *
from .utils import *
from .hparams import *
from .quickdraw_utils import *
from .fs_omniglot_utils import *
from .sketchy_utils import *
from .drawer_utils import *
from .write_routines import *
from .augmentations import *
from .class_lists import *
|
{"/run_hyper_embedding_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/util/utils.py": ["/util/quickdraw_utils.py"], "/configs/sketchy_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/datasets/base/__init__.py": ["/datasets/base/dataset_base.py", "/datasets/base/dataset_episodic.py", "/datasets/base/datasets.py"], "/configs/quickdraw_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/configs/vae_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/configs/drawer_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/models/lr_fs.py": ["/models/__init__.py"], "/util/__init__.py": ["/util/logging.py", "/util/utils.py", "/util/quickdraw_utils.py", "/util/fs_omniglot_utils.py", "/util/sketchy_utils.py", "/util/drawer_utils.py", "/util/write_routines.py", "/util/augmentations.py"], "/configs/base/__init__.py": ["/configs/base/configs.py"], "/util/sketchy_utils.py": ["/util/__init__.py"], "/run_compositionality_exp.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/vae.py": ["/models/base/__init__.py", "/models/subs/conv_block.py", "/util/__init__.py", "/util/write_routines.py"], "/datasets/quickdraw.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/miniimagenet_configs.py": ["/configs/__init__.py", "/util/__init__.py"], "/models/subs/decoders.py": ["/models/subs/cells.py", "/util/__init__.py"], "/run_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/configs/__init__.py": ["/configs/drawer_configs.py", "/configs/vae_configs.py", "/configs/classifier_configs.py", "/configs/quickdraw_configs.py", "/configs/sketchy_configs.py", "/configs/miniimagenet_configs.py", "/configs/base/__init__.py"], "/datasets/__init__.py": ["/datasets/quickdraw.py", "/datasets/fs_omniglot_vinyals.py", "/datasets/sketchy.py", "/datasets/miniimagenet.py", "/datasets/base/__init__.py"], "/util/drawer_utils.py": ["/util/utils.py"], "/models/drawer_enc_block.py": ["/models/__init__.py", "/models/subs/encoders.py"], "/models/subs/encoders.py": ["/models/subs/conv_block.py"], "/prepare_data.py": ["/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/run_full_eval.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/__init__.py": ["/models/drawer.py", "/models/drawer_enc_block.py", "/models/classifier.py", "/models/vae.py", "/models/vae_enc_block.py", "/models/base/__init__.py", "/models/lr_fs.py"], "/datasets/miniimagenet.py": ["/datasets/__init__.py"], "/util/write_routines.py": ["/util/__init__.py"], "/models/classifier.py": ["/models/drawer.py", "/models/vae.py", "/models/base/__init__.py", "/util/__init__.py"], "/datasets/base/dataset_episodic.py": ["/datasets/base/dataset_base.py"], "/datasets/sketchy.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/datasets/fs_omniglot_vinyals.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/classifier_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/models/base/__init__.py": ["/models/base/model_base.py", "/models/base/models.py"], "/models/drawer.py": ["/models/base/__init__.py", "/models/subs/decoders.py", "/models/subs/encoders.py", "/util/__init__.py", "/util/write_routines.py"], "/models/vae_enc_block.py": ["/models/__init__.py", "/models/subs/conv_block.py"]}
|
38,366
|
alexnwang/SketchEmbedNet-public
|
refs/heads/master
|
/configs/base/__init__.py
|
from .configs import *
|
{"/run_hyper_embedding_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/util/utils.py": ["/util/quickdraw_utils.py"], "/configs/sketchy_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/datasets/base/__init__.py": ["/datasets/base/dataset_base.py", "/datasets/base/dataset_episodic.py", "/datasets/base/datasets.py"], "/configs/quickdraw_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/configs/vae_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/configs/drawer_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/models/lr_fs.py": ["/models/__init__.py"], "/util/__init__.py": ["/util/logging.py", "/util/utils.py", "/util/quickdraw_utils.py", "/util/fs_omniglot_utils.py", "/util/sketchy_utils.py", "/util/drawer_utils.py", "/util/write_routines.py", "/util/augmentations.py"], "/configs/base/__init__.py": ["/configs/base/configs.py"], "/util/sketchy_utils.py": ["/util/__init__.py"], "/run_compositionality_exp.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/vae.py": ["/models/base/__init__.py", "/models/subs/conv_block.py", "/util/__init__.py", "/util/write_routines.py"], "/datasets/quickdraw.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/miniimagenet_configs.py": ["/configs/__init__.py", "/util/__init__.py"], "/models/subs/decoders.py": ["/models/subs/cells.py", "/util/__init__.py"], "/run_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/configs/__init__.py": ["/configs/drawer_configs.py", "/configs/vae_configs.py", "/configs/classifier_configs.py", "/configs/quickdraw_configs.py", "/configs/sketchy_configs.py", "/configs/miniimagenet_configs.py", "/configs/base/__init__.py"], "/datasets/__init__.py": ["/datasets/quickdraw.py", "/datasets/fs_omniglot_vinyals.py", "/datasets/sketchy.py", "/datasets/miniimagenet.py", "/datasets/base/__init__.py"], "/util/drawer_utils.py": ["/util/utils.py"], "/models/drawer_enc_block.py": ["/models/__init__.py", "/models/subs/encoders.py"], "/models/subs/encoders.py": ["/models/subs/conv_block.py"], "/prepare_data.py": ["/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/run_full_eval.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/__init__.py": ["/models/drawer.py", "/models/drawer_enc_block.py", "/models/classifier.py", "/models/vae.py", "/models/vae_enc_block.py", "/models/base/__init__.py", "/models/lr_fs.py"], "/datasets/miniimagenet.py": ["/datasets/__init__.py"], "/util/write_routines.py": ["/util/__init__.py"], "/models/classifier.py": ["/models/drawer.py", "/models/vae.py", "/models/base/__init__.py", "/util/__init__.py"], "/datasets/base/dataset_episodic.py": ["/datasets/base/dataset_base.py"], "/datasets/sketchy.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/datasets/fs_omniglot_vinyals.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/classifier_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/models/base/__init__.py": ["/models/base/model_base.py", "/models/base/models.py"], "/models/drawer.py": ["/models/base/__init__.py", "/models/subs/decoders.py", "/models/subs/encoders.py", "/util/__init__.py", "/util/write_routines.py"], "/models/vae_enc_block.py": ["/models/__init__.py", "/models/subs/conv_block.py"]}
|
38,367
|
alexnwang/SketchEmbedNet-public
|
refs/heads/master
|
/util/sketchy_utils.py
|
import os
import numpy as np
from PIL import Image
from absl import logging
from util import string_to_strokes, apply_rdp, strokes_to_stroke_three, scale_and_center_stroke_three, \
stroke_five_format_centered, rasterize, stroke_three_format_centered, stroke_five_format
def svg_to_stroke_three(batch_data, epsilon, flip_x, flip_y):
"""
Converts sketch SVG format into stroke-3 format.
:param batch_data:
:param epsilon:
:param flip_x:
:param flip_y:
:return:
"""
for idx in range(len(batch_data)):
svg = batch_data[idx, 0]
if svg:
stroke_str = "START\n"
max_length = max([path.length() for path in svg])
for path in svg:
if path.length() < 0.1 * max_length:
continue
for curve in path:
curve_length = curve.length()
if curve_length == 0.0:
continue
for d in np.linspace(0, curve_length, max(int(curve_length // 20), 3)) / curve_length:
point = curve.point(d)
x, y = np.real(point), np.imag(point)
stroke_str += "{},{}\n".format(x, y)
stroke_str += "BREAK\n"
stroke_str = stroke_str[:-1]
strokes = string_to_strokes(stroke_str, flip=False)
strokes = apply_rdp(strokes, epsilon=epsilon)
stroke_three = strokes_to_stroke_three(strokes)
if flip_x:
stroke_three[:, 0] = -stroke_three[:, 0]
if flip_y:
stroke_three[:, 1] = -stroke_three[:, 1]
batch_data[idx, 0] = stroke_three
return batch_data
def sketch_process(batch_data, padding, max_seq_len, png_dims, normalizing_scale_factor, save_path, sample_path):
accumulate = {"natural_image": [], "sketch_path": [], "strokes": [], "rasterized_strokes": [], "imagenet_id": [], "sketch_id": []}
for natural_image, sketch_path, stroke_three, sketch_id, bbx, width_height in batch_data:
imagenet_id = sketch_id.split("-")[0]
image: Image = natural_image
crop_box = [bbx[0], bbx[1], bbx[0] + bbx[2], bbx[1] + bbx[3]]
crop = image.resize(width_height).crop(crop_box)
crop.save(os.path.join(sample_path + "_test.png"))
scale = min(png_dims[0] / bbx[2], png_dims[1] / bbx[3])
resize = crop.resize([int(x * scale) for x in bbx[2:]])
img_w, img_h = resize.size
pasted_image = Image.new("RGB", png_dims, (0, 0, 0))
pasted_image.paste(resize, ((png_dims[0] - img_w) // 2, (png_dims[1] - img_h) // 2))
processed_natural_image = pasted_image
if stroke_three is not None:
stroke_three[:, 0:2] /= normalizing_scale_factor
stroke_three_scaled_and_centered = scale_and_center_stroke_three(np.copy(stroke_three), png_dimensions=png_dims, padding=padding)
try:
stroke_five = stroke_five_format(stroke_three, max_seq_len)
except:
logging.info("Stroke limit exceeds 65 for example: %s | length: %s", sketch_id, stroke_three.shape[0])
stroke_three = None
rasterized_strokes = rasterize(stroke_three_scaled_and_centered, png_dims)
accumulate["natural_image"].append(np.array(processed_natural_image, dtype=np.float32))
accumulate["sketch_path"].append(sketch_path)
accumulate["rasterized_strokes"].append(np.array(rasterized_strokes, dtype=np.float32))
accumulate["strokes"].append(stroke_five.astype(np.float32))
accumulate["imagenet_id"].append(imagenet_id)
accumulate["sketch_id"].append(sketch_id)
rand_idx = np.random.randint(0, len(accumulate["natural_image"]) - 1)
im = Image.fromarray(accumulate['natural_image'][rand_idx].astype('uint8'))
im.save(os.path.join(sample_path + "_{}_gt.png".format(accumulate['sketch_id'][rand_idx])))
im_raster = Image.fromarray(accumulate['rasterized_strokes'][rand_idx].astype('uint8'))
stroke_three_string = "\n".join([str(x) for x in stroke_three_format_centered(accumulate['strokes'][rand_idx])])
im_raster.save(os.path.join(sample_path + "_{}_raster.png".format(accumulate['sketch_id'][rand_idx])))
with open(os.path.join(sample_path + "_{}_strokes.txt".format(accumulate['sketch_id'][rand_idx])), 'w') as f:
f.write(stroke_three_string)
try:
sketch = Image.open(accumulate["sketch_path"][rand_idx])
sketch.save(os.path.join(sample_path + "_{}_sketch.png".format(accumulate['sketch_id'][rand_idx])))
except:
logging.info("Sketch not found: %s", accumulate["sketch_path"][rand_idx])
del accumulate['sketch_path']
np.savez(save_path, **accumulate)
return len(accumulate["sketch_id"])
|
{"/run_hyper_embedding_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/util/utils.py": ["/util/quickdraw_utils.py"], "/configs/sketchy_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/datasets/base/__init__.py": ["/datasets/base/dataset_base.py", "/datasets/base/dataset_episodic.py", "/datasets/base/datasets.py"], "/configs/quickdraw_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/configs/vae_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/configs/drawer_configs.py": ["/util/__init__.py", "/configs/base/__init__.py"], "/models/lr_fs.py": ["/models/__init__.py"], "/util/__init__.py": ["/util/logging.py", "/util/utils.py", "/util/quickdraw_utils.py", "/util/fs_omniglot_utils.py", "/util/sketchy_utils.py", "/util/drawer_utils.py", "/util/write_routines.py", "/util/augmentations.py"], "/configs/base/__init__.py": ["/configs/base/configs.py"], "/util/sketchy_utils.py": ["/util/__init__.py"], "/run_compositionality_exp.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/vae.py": ["/models/base/__init__.py", "/models/subs/conv_block.py", "/util/__init__.py", "/util/write_routines.py"], "/datasets/quickdraw.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/miniimagenet_configs.py": ["/configs/__init__.py", "/util/__init__.py"], "/models/subs/decoders.py": ["/models/subs/cells.py", "/util/__init__.py"], "/run_experiment.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/configs/__init__.py": ["/configs/drawer_configs.py", "/configs/vae_configs.py", "/configs/classifier_configs.py", "/configs/quickdraw_configs.py", "/configs/sketchy_configs.py", "/configs/miniimagenet_configs.py", "/configs/base/__init__.py"], "/datasets/__init__.py": ["/datasets/quickdraw.py", "/datasets/fs_omniglot_vinyals.py", "/datasets/sketchy.py", "/datasets/miniimagenet.py", "/datasets/base/__init__.py"], "/util/drawer_utils.py": ["/util/utils.py"], "/models/drawer_enc_block.py": ["/models/__init__.py", "/models/subs/encoders.py"], "/models/subs/encoders.py": ["/models/subs/conv_block.py"], "/prepare_data.py": ["/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/run_full_eval.py": ["/models/__init__.py", "/configs/__init__.py", "/datasets/__init__.py", "/util/__init__.py"], "/models/__init__.py": ["/models/drawer.py", "/models/drawer_enc_block.py", "/models/classifier.py", "/models/vae.py", "/models/vae_enc_block.py", "/models/base/__init__.py", "/models/lr_fs.py"], "/datasets/miniimagenet.py": ["/datasets/__init__.py"], "/util/write_routines.py": ["/util/__init__.py"], "/models/classifier.py": ["/models/drawer.py", "/models/vae.py", "/models/base/__init__.py", "/util/__init__.py"], "/datasets/base/dataset_episodic.py": ["/datasets/base/dataset_base.py"], "/datasets/sketchy.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/datasets/fs_omniglot_vinyals.py": ["/datasets/base/__init__.py", "/util/__init__.py"], "/configs/classifier_configs.py": ["/configs/base/__init__.py", "/util/__init__.py"], "/models/base/__init__.py": ["/models/base/model_base.py", "/models/base/models.py"], "/models/drawer.py": ["/models/base/__init__.py", "/models/subs/decoders.py", "/models/subs/encoders.py", "/util/__init__.py", "/util/write_routines.py"], "/models/vae_enc_block.py": ["/models/__init__.py", "/models/subs/conv_block.py"]}
|
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